<![CDATA[Team IT Security - 📰 Alle Kategorien]]> de-DE Tue, 28 Jul 2026 01:16:53 +0200 Tue, 28 Jul 2026 01:16:53 +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[Ukraine's new Marichka AI targets the one layer of its war stack that Palantir still owns]]> https://tsecurity.de/de/3694983/it-nachrichten/ukraines-new-marichka-ai-targets-the-one-layer-of-its-war-stack-that-palantir-still-owns/ https://tsecurity.de/de/3694983/it-nachrichten/ukraines-new-marichka-ai-targets-the-one-layer-of-its-war-stack-that-palantir-still-owns/ Sun, 26 Jul 2026 06:31:00 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[The EU’s AI transparency deadline is weeks away. Is your enterprise ready?]]> https://tsecurity.de/de/3694779/ai-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ https://tsecurity.de/de/3694779/ai-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ Sat, 25 Jul 2026 19:50:13 +0200 🔧 AI Nachrichten tsecurity.de

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

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

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

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

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

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

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

Transparency requirements

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

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

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

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

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

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

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

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

A consistent code of practice

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

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

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

Criteria for compliance

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

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

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

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

Creating a transparency pipeline

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

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

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

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

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

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

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

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

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

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

This article originally appeared on CIO.com.

]]> <![CDATA[US AI testing institute chief steps down within three months]]> https://tsecurity.de/de/3694777/ai-nachrichten/us-ai-testing-institute-chief-steps-down-within-three-months/ https://tsecurity.de/de/3694777/ai-nachrichten/us-ai-testing-institute-chief-steps-down-within-three-months/ Sat, 25 Jul 2026 19:50:12 +0200 🔧 AI Nachrichten tsecurity.de

The head of the US government’s AI testing institute, Chris Fall, has resigned about three months after taking charge of the Center for AI Standards and Innovation (CAISI), the federal organization responsible for evaluating advanced artificial intelligence models for safety and security.

Current National Institute of Standards and Technology NIST Director Arvind Raman will serve as acting CAISI Director following Fall’s departure while continuing to oversee the Commerce Department office responsible for the institute, the Daily Signal reported, citing two people familiar with the matter.

A Commerce Department spokesperson who spoke to the publication did not disclose a reason for the resignation.

Fall assumed leadership of CAISI in April after the Trump administration reorganized the former US AI Safety Institute under NIST. The institute develops methodologies for evaluating frontier AI models and works with AI developers on voluntary technical assessments covering areas such as cybersecurity, model misuse, reliability and other risks associated with increasingly capable AI systems.

The leadership change comes as governments and AI companies continue developing technical approaches for evaluating frontier AI models while enterprises expand deployments of generative AI and agentic AI across business operations.

In recent months, the Commerce Department has taken a more active role in AI policy involving advanced models, placing greater attention on how the federal government evaluates technologies with potential national security implications.

Continuity matters more than personalities

CAISI works with AI developers such as Anthropic, Google’s DeepMind and OpenAI on voluntary evaluations of frontier AI models and develops methodologies for testing model capabilities and risks. The institute does not regulate AI developers or certify commercial AI systems.

For enterprises, those evaluations are one source of technical information alongside vendors’ own testing, third-party security assessments and internal AI governance programs.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said enterprises should focus less on the individual leading the institute and more on whether its technical work continues with the same level of consistency and transparency.

“Leadership churn at CAISI weakens the signal long before it weakens the science,” Gogia said. “The testing has not stopped. Its authority simply does not travel as cleanly once the leadership does not.”

According to Gogia, the more important question for enterprises is not whether the institute’s evaluation work will continue but whether the processes supporting those evaluations remain stable.

“The instinct is to ask whether the pipeline is breaking,” he said. “The more useful question is where the pipeline now sits.”

Enterprises still carry the burden of AI governance

Gogia said organizations should continue treating government-led AI evaluations as one input into their governance processes rather than as evidence that a model is inherently safe for enterprise deployment.

“A government evaluation was always a signal, never a certificate,” he said. “A signal loses value the moment its issuer becomes unpredictable.”

He said enterprises should instead monitor whether CAISI maintains consistent evaluation methodologies, continues publishing technical findings and preserves continuity within its research teams under interim leadership.

“The name on the door is not the signal. The behaviour underneath it is,” Gogia said.

Gogia also cautioned against linking Fall’s resignation to recent Commerce Department actions involving AI policy or export controls, noting that there is no public evidence connecting the two.

“CAISI evaluates; it does not enforce export controls, because it holds no such power,” he said. “This is not a testing body reaching for enforcement. It is enforcement reaching past the testing body.”

With Raman assuming the role on an interim basis, the next significant milestone for enterprises will be the appointment of a permanent director, and whether the institute’s evaluation programs continue without disruption, the analyst said.

Gogia said the successor’s mandate may prove more important than the individual selected.

“A CAISI result is not a safe harbour,” he said. “It informs an obligation; it does not discharge one.” NIST did not immediately respond to a request for comment.

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

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

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

Who can use Copilot in PowerPoint

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

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

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

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

In this article:

Working with Copilot in PowerPoint

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

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

powerpoint screen with copilot sidebar open on right

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


Howard Wen / Foundry

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

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

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

screenshot of copilot sidebar in powerpoint with models dropdown menu open

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

Howard Wen / Foundry

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

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

1. Create a presentation template

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

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

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

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

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

screenshot of powerpoint presentation generated by copilot with placeholder data

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

Howard Wen / Foundry

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

2. Create a presentation from a document

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

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

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

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

Attaching a document for Copilot to base a presentation on.

Howard Wen / Foundry

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

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

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

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

Howard Wen / Foundry

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

3. Add content from a document to a slide

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

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

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

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

Howard Wen / Foundry

4. Refine your slide text

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

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

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

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

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

Howard Wen / Foundry

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

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

  • Make these slides more visual and use less text.

5. Find or create an image

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

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

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

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

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

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

Howard Wen / Foundry

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

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

6. Expand your presentation with relevant slides

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

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

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

Need a transition slide? Just ask!

Howard Wen / Foundry

7. Summarize a presentation

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

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

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

Ask Copilot to summarize a presentation or flag key slides.

Howard Wen / Foundry

8. Answer questions about a presentation

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

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

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

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

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

Ask Copilot specific questions about the contents of a presentation.

Howard Wen / Foundry

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

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

9. Help you navigate a large presentation

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

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

  • Show me the slides about the project timeline.

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

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

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

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

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

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

Copilot can create speaker notes in seconds.

Howard Wen / Foundry

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

  • Write an FAQ for these slides.

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

screenshot of frequently asked questions slide generated by copilot in powerpoint

A Copilot-generated FAQ slide.

Howard Wen / Foundry

Related reading:

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

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

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

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

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

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

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

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

The Android backup lowdown

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

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

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

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

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

See?

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

JR Raphael, Foundry

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

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

Lemme show ya how.

Android’s document backup addition

I promise: This couldn’t be much simpler.

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

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

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

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

JR Raphael, Foundry

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

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

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

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

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

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

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

]]> <![CDATA[OpenAI Presence raises new questions about enterprise automation and jobs]]> https://tsecurity.de/de/3694769/ai-nachrichten/openai-presence-raises-new-questions-about-enterprise-automation-and-jobs/ https://tsecurity.de/de/3694769/ai-nachrichten/openai-presence-raises-new-questions-about-enterprise-automation-and-jobs/ Sat, 25 Jul 2026 19:50:08 +0200 🔧 AI Nachrichten tsecurity.de

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

This article first appeared on CIO.

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

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

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

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

AMD’s MI455X targets the AI memory wall

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

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

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

AMD Instinct chart showing generational leap in performance

AMD

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

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

AMD’s Helios rack takes aim at Vera Rubin

AMD Helios rack

Dave Altavilla

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

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

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

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

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

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

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

Chart showing AMD EPYC CPU performance

AMD

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

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

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

Pensando networking and ROCm software advance

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

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

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

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

Customer commitments underscore rack-scale confidence

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

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

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

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

AMD expands its robotics and embedded foundation

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

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

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

Execution for AMD is now the test

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

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

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

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[OpenAI Models Escaped Containment and Hacked Hugging Face]]> https://tsecurity.de/de/3694757/ai-nachrichten/openai-models-escaped-containment-and-hacked-hugging-face/ https://tsecurity.de/de/3694757/ai-nachrichten/openai-models-escaped-containment-and-hacked-hugging-face/ Sat, 25 Jul 2026 19:49:58 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[OpenAI Says Its A.I. Models Hacked Into Hugging Face, a Digital Library]]> https://tsecurity.de/de/3694750/ai-nachrichten/openai-says-its-ai-models-hacked-into-hugging-face-a-digital-library/ https://tsecurity.de/de/3694750/ai-nachrichten/openai-says-its-ai-models-hacked-into-hugging-face-a-digital-library/ Sat, 25 Jul 2026 19:49:55 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Seeking symmetry during ATT&CKÂź season: How to harness today’s diverse analyst and tester landscape to paint a security masterpiece]]> https://tsecurity.de/de/3694663/malware-trojaner-viren/seeking-symmetry-during-attck-season-how-to-harness-todays-diverse-analyst-and-tester-landscape-to-paint-a-security-masterpiece/ https://tsecurity.de/de/3694663/malware-trojaner-viren/seeking-symmetry-during-attck-season-how-to-harness-todays-diverse-analyst-and-tester-landscape-to-paint-a-security-masterpiece/ Sat, 25 Jul 2026 19:04:53 +0200 ⚠ Malware / Trojaner / Viren tsecurity.de <![CDATA[The June 2026 Security Update Review]]> https://tsecurity.de/de/3694563/hacking/the-june-2026-security-update-review/ https://tsecurity.de/de/3694563/hacking/the-june-2026-security-update-review/ Sat, 25 Jul 2026 19:02:53 +0200 đŸ•”ïž Hacking tsecurity.de I’ve made it through Pwn2Own Berlin, had a little vacation, and now I’m back for Patch Tuesday. Microsoft and Adobe didn’t disappoint. In fact, they have heralded my return with the largest Patch Tuesday release ever. Thanks? Take a break from your regularly scheduled activities and let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for June 2026

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

Here’s this month’s overview table:

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

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

Microsoft Patches for June 2026

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

No new advisories are being released this month.

Looking Ahead

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

 

]]> <![CDATA[OpenAI Says Its A.I. Models Hacked Into Hugging Face, a Digital Library]]> https://tsecurity.de/de/3694501/it-security-nachrichten/openai-says-its-ai-models-hacked-into-hugging-face-a-digital-library/ https://tsecurity.de/de/3694501/it-security-nachrichten/openai-says-its-ai-models-hacked-into-hugging-face-a-digital-library/ Sat, 25 Jul 2026 19:01:11 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[18 Enterprise-Architecture-Tools]]> https://tsecurity.de/de/3694429/it-security-nachrichten/18-enterprise-architecture-tools/ https://tsecurity.de/de/3694429/it-security-nachrichten/18-enterprise-architecture-tools/ Sat, 25 Jul 2026 18:59:25 +0200 📰 IT Security Nachrichten tsecurity.de
Diese Enterprise Architecture Tools unterstĂŒtzen Sie nicht nur bei der digitalen Transformation Ihres Unternehmens.

Diese Enterprise Architecture Tools unterstĂŒtzen Sie nicht nur bei der digitalen Transformation Ihres Unternehmens.

Foto: I Believe I Can Fly – shutterstock.com

Enterprise Architecture (EA) Tools unterstĂŒtzen Unternehmen und Organisationen dabei, mit ihren IT-Strategien die GeschĂ€ftsziele optimal zu unterstĂŒtzen. Sie sorgen ebenfalls dafĂŒr, dass Unternehmen ihre Roadmaps fĂŒr die digitale Transformation geordnet vorantreiben können. EA Tools bieten dafĂŒr unter anderem Collaboration-, Reporting-, Testing- und Simulationsfunktionen. Mit deren Hilfe lassen sich Modelle implementieren, die GeschĂ€fts- und IT-Prozesse gezielt verbessern.

Um die beste Lösung fĂŒr Ihr Unternehmen zu finden, sollten Sie zuerst prĂŒfen, ob sich das jeweilige Tool mit Ihrem Technologie-Stack integrieren lĂ€sst. Anschließend gilt es abzuwĂ€gen, ob die Informationen, Diagramme und Tabellen, die die Software zur VerfĂŒgung stellt, fĂŒr das Unternehmen auch einen echten Nutzwert haben.

Empfehlenswerte Enterprise-Architecture-Tools

Nachfolgend finden Sie einen Überblick ĂŒber die wichtigsten Enterprise-Architecture-Tools – in alphabetischer Reihenfolge. Sie stellen einen Mix aus Visualisierungs-, Collaboration- und Project-Management-Funktionen bereit und unterstĂŒtzen eine Vielzahl von Enterprise Architecture Frameworks.

Ardoq

Nachdem zuerst ĂŒber einfache Formulare Informationen von Usern, Entwicklern und sonstigen Stakeholdern im Unternehmen eingesammelt wurden, lĂ€sst sich mithilfe von Ardoq ein digitaler Zwilling der gesamten Organisation erstellen. Der Ansatz setzt also darauf, die Menschen, die in ihren Rollen mit den verschiedensten Systemen arbeiten, realistisch in ihrer Arbeitswelt abzubilden.

Jede Mitarbeiterin und jeder Mitarbeiter im Unternehmen kann spĂ€ter von den Netzwerkvisualisierungen und Datenfluss-Diagrammen profitieren, um seine eigene Rolle optimal zu unterstĂŒtzen und den Arbeitsplatz immer wieder anzupassen und zu modernisieren. Das Tool lĂ€sst sich mit den wichtigsten Cloud-Plattformen integrieren. Es bietet eine API, die individuelle Anpassungen in allen wichtigen Programmiersprachen (Python, C#, Java, etc.) ermöglicht.

Wichtigste Use Cases:

  • “Architektonischen Stress” bei Lastspitzen simulieren, falls grĂ¶ĂŸere VerĂ€nderungen bevorstehen;

  • Verstehen, wie verĂ€ndertes Nutzerverhalten neue Anforderungen generiert;

  • Application Portfolio Management, um besser strategisch zu planen.

Atoll Group SAMU

Das EA-Tool SAMU macht die Enterprise Architecture sichtbar, indem es tiefe VerknĂŒpfungen zwischen On-Premises-Systemen, dem Cloud-Layer und Tools fĂŒr das Business Process Management aufzeigt. Das Tool der Atoll Group bietet vielfĂ€ltige Integrationsmöglichkeiten, zum Beispiel mit Monitoring-Tools (etwa Tivoli, ServiceNow), Configuration-Management-Datenbanken (zum Beispiel CA, BMC) oder Service-Organisations-Tools (BMC, HPE). Alle Informationen fließen in ein zentrales Datenmodell ein, das um den zusĂ€tzlichen Input der Stakeholder weiter angereichert wird.

Wichtigste Use Cases:

  • Enterprise-Architektur visualisieren;

  • strategische Planungsprozesse und Architektur-Reviews mit Informationen unterfĂŒttern;

  • mithilfe einer visuellen VerstĂ€ndnisgrundlage die Kommunikation verbessern.

Avolution Abacus

Dieses Tool erfasst die Breite und den Umfang der Unternehmensarchitektur mit Hilfe eines auf Diagrammen basierenden Dashboards. Die Integration mit gĂ€ngigen Tools wie SharePoint, Excel, Visio, Google Sheets, Technopedia oder ServiceNow vereinfacht die Nutzung. Abacus wurde inzwischen auch um einen Machine-Learning-Layer ergĂ€nzt, der es Anwendern ermöglicht, ein Modell zu trainieren, das ihnen beispielsweise hilft zu erkennen, wer im Unternehmen fĂŒr welches System verantwortlich ist.

Wichtigste Use Cases:

  • die IT fĂŒr das gesamte Unternehmen â€œĂ¶ffnen”, um ein allgemeines VerstĂ€ndnis der DatenflĂŒsse zu erzeugen;

  • umfassendes Enterprise Modeling, um eine Roadmap fĂŒr kĂŒnftige Entwicklungen zu erstellen;

  • Business-Metriken tracken, die mit der Unternehmens-Performance zusammenhĂ€ngen.

BOC Group ADOIT

ADOIT soll Teams dabei unterstĂŒtzen, Ressourcen zu verwalten, Bedarfe vorherzusagen und Assets zu tracken. Dazu mappt das Tool jedes System oder Softwarepaket mit einem Objekt. Die DatenflĂŒsse zwischen den Systemen werden in Beziehungen umgewandelt, die von diesen Objekten mithilfe eines anpassbaren Metamodells erfasst werden. GeschĂ€ftsprozesse können auf Ă€hnliche Weise ĂŒber ein gut integriertes Begleitprodukt namens ADONIS modelliert werden. ADOIT ist Web-basiert und lĂ€sst sich auch mit Tools wie Atlassian Confluence integrieren, um die Datenerfassung und -entwicklung zu beschleunigen.

Wichtigste Use Cases:

  • ein unternehmensweites Modell erstellen, das bei sĂ€mtlichen Teammitgliedern ein VerstĂ€ndnis ĂŒber den Stack schafft – und wie man diesen verbessern kann;

  • vollstĂ€ndiger Zugriff auf EA-Daten ĂŒber eine Mobile-Anwendung;

  • bei Fusionen und Übernahmen den Tech-Bereich durch genaues Asset-Mapping orchestrieren.

Bizzdesign Hopex

Nach der Übernahme von Mega International zĂ€hlt die Hopex-Plattform zum Portfolio von Bizzdesign. Sie soll dabei unterstĂŒtzen, Unternehmensanwendungen zu modellieren und dabei ein VerstĂ€ndnis der von ihnen unterstĂŒtzten GeschĂ€fts-Workflows schaffen. Dabei liegt ein Schwerpunkt auf den Bereichen Data Governance und Risikomanagement. Hopex basiert auf Microsoft Azure und stĂŒtzt sich auf eine Reihe offener Standards wie GraphQL und REST Queries, um Informationen aus Komponentensystemen zu sammeln. Das Reporting ist mit den Office-Tools von Microsoft sowie mit grafischen Lösungen wie Tableau und Qlik integriert.

Wichtigste Use Cases:

  • datengestĂŒtzte Erkenntnisse herbeifĂŒhren, um Cloud- und Anwendungsbereitstellung zu steuern;

  • akkurate Nutzungsmodelle erstellen, um Architekturanforderungen zu verstehen;

  • eine BedarfsschĂ€tzung mit Umfragen und anderen Tools vornehmen, um fĂŒr die Zukunft zu planen.

Bizzdesign Horizzon

Das Tool dient dazu, Business Workflows und den zugrundeliegenden Tech-Stack zu modellieren. Dazu bietet Horizzon ein Graph-basiertes Modell, das Daten von sĂ€mtlichen Stakeholdern einsammelt und diese an eine Analytics-Engine weitergibt. Im Ergebnis entstehen Diagramme, die den aktuellen Systemzustand widerspiegeln. Wichtige Schwerpunkte dieses Tools sind Change Management und Zukunftsplanung: Horizzon ist nicht zuletzt dafĂŒr konzipiert worden, die Risiken eines Redesigns zu minimieren. Das Toolset unterstĂŒtzt die wichtigsten Frameworks ArchiMate, TOGAF und BPMN. Neben Mega hat Bizzdesign im Januar 2025 auch den EA-GeschĂ€ftsbereich der Software AG – Alfabet – ĂŒbernommen.

Wichtigste Use Cases:

  • Vorhersage zukĂŒnftiger Anforderungen durch Predictive Modeling;

  • Orchestrieren von Workflows auf der Basis der technischen und der Business-Architektur;

  • Antizipieren von Risiken sowie Security- und Governance-Problemen durch die Modellierung von Datensicherheitsanforderungen.

Capstera

Das Tool von Capstera fokussiert darauf, die Business Architecture selbst abzubilden. Value und Process Maps helfen dabei, die Rollen der verschiedenen Unternehmensbereiche zu definieren und nachzuverfolgen. Dabei können im laufenden Prozess VerknĂŒpfungen mit den zugrundeliegenden Softwarprodukten und Tools hinzugefĂŒgt werden.

Wichtigste Use Cases:

  • Reports erstellen, die sich erst einmal mit der Business-Architektur selbst beschĂ€ftigen;

  • Beziehungen zwischen Menschen, Abteilungen und Rollen analysieren;

  • die langfristige strategische Planung vorantreiben.

Clausmark Bee360

Teammitglieder, die Clausmarks Flaggschiffprodukt Bee360 (frĂŒher Bee4IT) verwenden, wollen eine einfache “Single Source of Truth” ĂŒber die Workflows im Unternehmen. Ziel ist es, verschiedenen betrieblichen Rollen intelligentere Entscheidungen zu ermöglichen. Das Modul Bee360 FM (Finanzmanagement) bietet etwa die Möglichkeit, Kosten nachzuvollziehen und zuzuordnen. Die Anwender können verschiedene solcher Module miteinander verknĂŒpfen, um EAM, Finanzmanagement, Portfolio Management und Agile Planning nahtlos zu integrieren – bei maximaler Transparenz.

Wichtigste Use Cases:

  • C-Suite-Ebene befĂ€higen, Projekte zu managen und Assets zuzuweisen;

  • prĂ€zise digitale Zwillinge entwickeln, um ein VerstĂ€ndnis ĂŒber DatenflĂŒsse zu schaffen und kĂŒnftige Erweiterungen zu planen;

  • integrierte Wissensdatenbank aufbauen, um alle digitalen Workflows zu tracken.

EAS

Das Essential-Paket von EAS (Enterprise Architecture Solutions) nahm als Open-Source-Projekt seinen Anfang und hat sich inzwischen zu einer kommerziell verfĂŒgbaren Cloud-Lösung weiterentwickelt. Das Tool erstellt ein Metamodell, das die Interaktionen zwischen Systemen und GeschĂ€ftsprozessen beschreibt. Ebenfalls enthalten sind Pakete, um gĂ€ngige Business Workflows wie Datenmanagement oder DSGVO-Compliance zu tracken.

Wichtigste Use Cases:

  • den technischen Reifegrad der eigenen Architektur evaluieren;

  • Sicherheit und Governance durch besseres Asset Tracking optimieren;

  • wachsende SystemkomplexitĂ€t kontrollieren und managen.

OrbusInfinity

Orbus Software hat Anfang 2025 die Akquisition seines Konkurrenten Capsifi abgeschlossen. Der Anbieter stellt mit OrbusInfinity eine Enterprise-Transformation-Plattform auf KI-Basis zur VerfĂŒgung, die schnellere, bessere Entscheidungen, Kosteinesparungen und Risikominimierung verspricht. Architecture-Teams sollen mit Hifle von OrbusInfinity mit einer Vielzahl von Stakeholdern interagieren können, um eine “digitale Blaupause” ihres Unternehmens zu generieren, die eine einheitliche Sicht auf das aktuelle und kĂŒnftige GeschĂ€ft realisieren soll. Diverse Drittanbieter-Tools lassen sich außerdem mit der Plattform integrieren, darunter etwa von Microsoft, Flexera, ManageEngine oder ServiceNow.

Wichtigste Use Cases:

  • Stakeholder-Management;

  • Enterprise-Landschaften visualisieren;

  • Entscheidungsfindung und Datenanalyse automatisieren.

Planview Enterprise One

Planview bietet eine ganze Reihe von Produkten, mit denen Unternehmen Teamwork, Prozesse und die Enterprise Architecture nachvollziehen können. Die Enterprise Tools sind in drei Kategorien unterteilt: strategisches Portfolio-Management, Produktportfolio-Management und Projektportfolio-Management. Im Zusammenspiel entstehen hardware- und Software-ĂŒbergreifende Layer, die rollenbasierte Perspektiven fĂŒr FĂŒhrungskrĂ€fte und Teammitglieder eröffnen. Das Toolset integriert mit gĂ€ngigen Ticket-Tracking-Systemen wie Jira, um Workflow-Analysen und Reports zu erstellen. Inzwischen hat Planview nach einer Übernahme neue Tools in sein Portfolio integriert, die frĂŒher unter den Namen Daptiv, Barometer und Projectplace bekannt waren.

Wichtigste Use Cases:

  • eine langfristige, strategische Vision fĂŒr die Architekturentwicklung aufbauen;

  • Entwicklungsarbeit auf Projektebene tracken und in eine beliebige Strategie integrieren;

  • mit Fokus auf die Customer Experience und die Produktstruktur den Change vorantreiben.

QualiWare Enterprise Architecture

Das Enterprise Architecture Tool von QualiWare ist Teil einer grĂ¶ĂŸeren Sammlung von Modellierungswerkzeugen, die darauf abzielt, sĂ€mtliche GeschĂ€ftsprozesse zu erfassen. Beispielsweise ist es möglich, einen digitalen Zwillinge zu bauen, mit dem sich Customer Journeys nachvollziehen lassen. Qualiware hat diverse KI-Algorithmen integriert, um Dokumentation und Process Discovery zu optimieren.

Wichtigste Use Cases:

  • ein kollaboratives Ökosystem fĂŒr Business Manager aufbauen, das ein VerstĂ€ndnis von der Enterprise Architecture vermittelt;

  • architektonische Designelemente erfassen, um ein Wissens-Ökosystem rund um den Stack aufzubauen;

  • eine breite Beteiligung in Sachen Dokumentationserstellung und -ĂŒberprĂŒfung fördern.

Quest Erwin Evolve

Das Erwin Evolve Tool von Quest hat sich von einem Datenmodellierungs-Tool zu einem System fĂŒr Enterprise-Architecture- und GeschĂ€ftsprozess-Modellierung weiterentwickelt. Um die KomplexitĂ€t moderner, ineinandergreifender Softwaresysteme und der von ihnen gemanagten GeschĂ€ftsprozesse zu durchdringen, können Anwender auf benutzerdefinierte Datenstrukturen zurĂŒckgreifen. Das Web-Tool erstellt Modelle, rollenbasierte Diagramme und andere Visualisierungen, die in allgemein zugĂ€ngliche Dashboards einfließen. Zum Paket gehört ein KI-basiertes Modellierungs-Tool, das Whiteboard-Skizzen integrieren kann.

Wichtigste Use Cases:

  • einen digitalen Zwilling fĂŒr die strategische Modellierung der Enterprise Data Architecture erstellen;

  • Customer Journeys verstehen;

  • Services und Systeme mit Application Portfolio Management tracken.

SAP LeanIX Enterprise Architecture Suite

Die Tool-Sammlung von LeanIX umfasst unter anderem Enterprise Architecture Management und andere Bereiche, die fĂŒr Aufgaben wie SaaS– und Value-Stream-Management wichtig sind – etwa um Cloud-Deployments und darauf laufende Services zu tracken. Die Daten die dabei ĂŒber die IT-Infrastruktur gesammelt werden, fließen in ein grafisches Dashboard ein. Das Tool ist eng mit wichtigen Cloud-Workflow-Tools wie Confluence, Jira, Signavio und Lucidchart integriert. Das ist fĂŒr Teams von Vorteil, die diese Tools bereits nutzen, um ihre Entwicklungsstrategien zu planen und umzusetzen. Seit November 2023 ist LeanIX Teil von SAP.

Wichtigste Use Cases:

  • Anwendungsmodernisierung und Cloud-Migration managen;

  • Obsoleszenz von Software-Services evaluieren;

  • Kosten kontrollieren und managen.

ServiceNow

Die Tool-Sammlung von ServiceNow lĂ€sst sich auf verschiedene Architekturtypen herunterbrechen, darunter Assets, DevOps, Security und Service. Die Tools katalogisieren die unterschiedlichen Hardware- und Softwareplattformen, um Workflows und DatenflĂŒsse im Unternehmen abzubilden und zu verstehen. AusfĂŒhrliche Reportings und detaillierte Dashboards ermöglichen Analysen, auf deren Grundlage Risiken minimiert und die Ausfallsicherheit der Systeme erhöht werden können.

Wichtigste Use Cases:

  • Tracken von Assets, Services und Systemen, die das Unternehmen ausmachen;

  • Governance-Themen, Risikobegrenzung, IT-Management und Security Operations werden in einer Plattform zusammengefĂŒhrt;

  • durch die Integration von CRM-Tools lassen sich auch kundenorientierte Services managen.

Sparx Systems

Um Teams und Projekte verschiedener GrĂ¶ĂŸe und KomplexitĂ€t zu unterstĂŒtzen, hat Sparx vier Versionen seines EA-Tools entwickelt. Allen gemeinsam ist eine UML-basierte Modellierung, mit der sich die Komponenten komplexer Systeme tracken lassen. Eine Simulations-Engine ermöglicht “War Gaming” und vermittelt ein VerstĂ€ndnis darĂŒber, wie sich Fehler ausbreiten und kaskadieren können. Sparx stellt zudem eine Vielzahl von vorgefertigten Design Patterns bereit, um Teams bei der Modellierung zu unterstĂŒtzen.

Wichtigste Use Cases:

  • Nachfrage- und LastverĂ€nderungen zur Prognose kĂŒnftiger Anforderungen simulieren;

  • (potenzielle) Probleme durch eine Verbindungs-Matrix im Auge behalten;

  • Dokumentation erstellen.

Unicom System Architect

System Architect ist eines der Angebote aus Unicoms Team Blue. Es handelt sich um ein Tool, das ein Metamodell verwendet, um automatisiert so viele Daten wie möglich ĂŒber die laufenden Systeme zu sammeln – manchmal auch durch ein Reverse Engineering von DatenflĂŒssen. Dieses systemweite Datenmodell kann ĂŒber benutzerdefinierte Dashboards Teammitgliedern aller Rollen zugĂ€nglich gemacht werden. Ein weiteres erwĂ€hnenswertes Feature: Die Ressourcenzuweisung lĂ€sst sich mit Hilfe von Simulationen optimieren.

Wichtigste Use Cases:

  • Was-wĂ€re-wenn-Fragen zum Architekturmodell stellen;

  • ein Metamodell von Daten und Systemen aufbauen;

  • Migrations- und TransformationsplĂ€ne erstellen.

ValueBlue BlueDolphin

Dieses EA-Tool sammelt Daten auf dreierlei Art:

  1. Es importiert Basisdaten auf der Grundlage standardgesteuerter Automatisierung (ITSM, SAM).

  2. Es arbeitet mit den Dateiformaten von Architekten und Systemdesignern – etwa ArchiMate oder BPMN.

  3. Es gibt Fragebögen an andere Stakeholder heraus, die auf anpassbaren Vorlagen basieren.

Die aufbereiteten Informationen werden in einer visuellen Umgebung bereitgestellt, die Auskunft ĂŒber die historische Entwicklung von Systemen gibt.

Wichtigste Use Cases:

  • systemweite Daten von internen und externen Stakeholdern automatisiert und formularbasiert erfassen;

  • zukunftsorientierte Reportings erzeugen, um den Change zu ĂŒberwachen und voranzutreiben;

  • Kooperation und Zusammenarbeit durch offenes Data Reporting fördern.

(fm)

Dieser Artikel ist im Original bei unserer Schwesterpublikation CIO.com erschienen.

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

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

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

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

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

Recovery is not just a technical exercise

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

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

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

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

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

Most business continuity plans ignore ‘total blackout’

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

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

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

That is the level of clarity leaders need.

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

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

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

Threat intelligence must be actionable, not archived

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A steep learning curve

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

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

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

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

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

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

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

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

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

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

Routing

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

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

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

Harness

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

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

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

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

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

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

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

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

Specialization

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

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

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

]]> <![CDATA[Google CEO distracts from Gemini 3.5 Pro delay with talk of Gemini 4 and monthly releases]]> https://tsecurity.de/de/3694392/it-security-nachrichten/google-ceo-distracts-from-gemini-35-pro-delay-with-talk-of-gemini-4-and-monthly-releases/ https://tsecurity.de/de/3694392/it-security-nachrichten/google-ceo-distracts-from-gemini-35-pro-delay-with-talk-of-gemini-4-and-monthly-releases/ Sat, 25 Jul 2026 18:55:50 +0200 📰 IT Security Nachrichten tsecurity.de

Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence.

His comments came a day after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier.

Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic.

Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler.

“We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call.

Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth, who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic.

Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model.

Analysts, though, aren’t as confident as Pichai.

While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs.

While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia, chief analyst at Greyhound Research.

Similarly, Pareekh Jain, principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number.

The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it.

This article first appeared on InfoWorld.

]]> <![CDATA[OpenAI Presence raises new questions about enterprise automation and jobs]]> https://tsecurity.de/de/3694393/it-security-nachrichten/openai-presence-raises-new-questions-about-enterprise-automation-and-jobs/ https://tsecurity.de/de/3694393/it-security-nachrichten/openai-presence-raises-new-questions-about-enterprise-automation-and-jobs/ Sat, 25 Jul 2026 18:55:50 +0200 📰 IT Security Nachrichten tsecurity.de

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

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

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

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

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

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

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

When governance becomes a debate about delivery practices

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

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

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

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

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

Authority is rarely the issue. Literacy is

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

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

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

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

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

Shift the conversation from delivery practices to business risk

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

#2. Distinguish between human and autonomous agent behavior 

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

#3. Establish guardrails for automated package updates on endpoints

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

#4. Enforce principle of least privilege for AI agents

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

#5. Maintain continuous endpoint posture management

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

Summary

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

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

]]> <![CDATA[OpenAI's Rogue Agent Went Unnoticed For a Week]]> https://tsecurity.de/de/3694250/it-security-nachrichten/openais-rogue-agent-went-unnoticed-for-a-week/ https://tsecurity.de/de/3694250/it-security-nachrichten/openais-rogue-agent-went-unnoticed-for-a-week/ Sat, 25 Jul 2026 18:52:27 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[IoT Penetration Testing: From Hardware to Firmware]]> https://tsecurity.de/de/3694236/it-security-nachrichten/iot-penetration-testing-from-hardware-to-firmware/ https://tsecurity.de/de/3694236/it-security-nachrichten/iot-penetration-testing-from-hardware-to-firmware/ Sat, 25 Jul 2026 18:52:21 +0200 📰 IT Security Nachrichten tsecurity.de As Internet of Things (IoT) devices continue to permeate every aspect of modern life, homes, offices, factories, vehicles, their attack surfaces have become increasingly attractive to adversaries. The challenge with testing IoT systems lies in their complexity: these devices often combine physical interfaces, embedded firmware, network services, web applications, and companion mobile apps into a [...]

The post IoT Penetration Testing: From Hardware to Firmware appeared first on Hacking Tutorials.

]]> <![CDATA[Anzeige: Penetration Testing: Schwachstellen in Webanwendungen erkennen]]> https://tsecurity.de/de/3694201/it-security-nachrichten/anzeige-penetration-testing-schwachstellen-in-webanwendungen-erkennen/ https://tsecurity.de/de/3694201/it-security-nachrichten/anzeige-penetration-testing-schwachstellen-in-webanwendungen-erkennen/ Sat, 25 Jul 2026 18:52:04 +0200 📰 IT Security Nachrichten tsecurity.de Golem Karrierewelt, Server-Applikationen) ]]> <![CDATA[Best 75-inch TVs of 2026: Expert Reviewed for Every Budget]]> https://tsecurity.de/de/3693982/it-nachrichten/best-75-inch-tvs-of-2026-expert-reviewed-for-every-budget/ https://tsecurity.de/de/3693982/it-nachrichten/best-75-inch-tvs-of-2026-expert-reviewed-for-every-budget/ Sat, 25 Jul 2026 15:56:05 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Me, London and the Galaxy Z Fold 8 Ultra: A Day of Discoveries]]> https://tsecurity.de/de/3693910/it-nachrichten/me-london-and-the-galaxy-z-fold-8-ultra-a-day-of-discoveries/ https://tsecurity.de/de/3693910/it-nachrichten/me-london-and-the-galaxy-z-fold-8-ultra-a-day-of-discoveries/ Sat, 25 Jul 2026 14:12:05 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Vodafone is testing an AI robotic mast, but the future belongs to adjustable internal antenna components]]> https://tsecurity.de/de/3693906/it-nachrichten/vodafone-is-testing-an-ai-robotic-mast-but-the-future-belongs-to-adjustable-internal-antenna-components/ https://tsecurity.de/de/3693906/it-nachrichten/vodafone-is-testing-an-ai-robotic-mast-but-the-future-belongs-to-adjustable-internal-antenna-components/ Sat, 25 Jul 2026 14:11:53 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Vodafone is testing an AI robotic mast, but the future belongs to adjustable internal antenna components]]> https://tsecurity.de/de/3693907/it-nachrichten/vodafone-is-testing-an-ai-robotic-mast-but-the-future-belongs-to-adjustable-internal-antenna-components/ https://tsecurity.de/de/3693907/it-nachrichten/vodafone-is-testing-an-ai-robotic-mast-but-the-future-belongs-to-adjustable-internal-antenna-components/ Sat, 25 Jul 2026 14:11:53 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[The Valve Steam Machine should be the perfect 'couch gaming' PC—but after testing it out, I'm not convinced it's worth the price]]> https://tsecurity.de/de/3693825/it-nachrichten/the-valve-steam-machine-should-be-the-perfect-couch-gaming-pc-but-after-testing-it-out-im-not-convinced-its-worth-the-price/ https://tsecurity.de/de/3693825/it-nachrichten/the-valve-steam-machine-should-be-the-perfect-couch-gaming-pc-but-after-testing-it-out-im-not-convinced-its-worth-the-price/ Sat, 25 Jul 2026 13:03:54 +0200 📰 IT Nachrichten tsecurity.de <![CDATA['Not the USP I wanted': Nothing’s new Ear (3a) earbuds sound good and the ANC is effective, but they hide a troublesome secret I found it hard to get past during testing]]> https://tsecurity.de/de/3693797/it-nachrichten/not-the-usp-i-wanted-nothings-new-ear-3a-earbuds-sound-good-and-the-anc-is-effective-but-they-hide-a-troublesome-secret-i-found-it-hard-to-get-past-during-testing/ https://tsecurity.de/de/3693797/it-nachrichten/not-the-usp-i-wanted-nothings-new-ear-3a-earbuds-sound-good-and-the-anc-is-effective-but-they-hide-a-troublesome-secret-i-found-it-hard-to-get-past-during-testing/ Sat, 25 Jul 2026 12:12:55 +0200 📰 IT Nachrichten tsecurity.de <![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 đŸ€– 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[Build native Android apps in Google AI Studio]]> https://tsecurity.de/de/3693512/android-tipps/build-native-android-apps-in-google-ai-studio/ https://tsecurity.de/de/3693512/android-tipps/build-native-android-apps-in-google-ai-studio/ Sat, 25 Jul 2026 10:15:46 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Emma-Louise Leavey, Group Product Manager and Mike Taylor-Cai, Product Manager


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

The power of native Android

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

A seamless, end-to-end workflow

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

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

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

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

Install the app on your Android device

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

Publish the app to an internal test track in Google Play

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

Download zip file of Android app project files

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

Start building today

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

What’s Next?

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

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

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


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

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

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

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

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

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

Build High Quality Android Apps Using Agents

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

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

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

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

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

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

3: Accelerating AI coding assistance with Android Bench

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

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

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

Building AI Into Your Apps

5: Building Intelligent Apps with generative AI

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

6: Experiment with AppFunctions today

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

The Future is Adaptive

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

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

Build Android UI with Compose

8: Building seamless Android experiences across devices with Jetpack Compose

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

9: Create seamless experiences for Googlebook

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

New Desktop Android Emulator

10: Unified widget development experience with Jetpack Glance

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

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

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

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

12: Accelerate your development with Android XR Developer Preview 4

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

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

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

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

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

Low Light Boost and Magic Eraser in action

14: Increase app discovery and engagement on Google TV

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

15: Performance: the foundation of a great app experience

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

And The Latest on Driving Business Growth 

16: What’s new in Google Play

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

Gemini will provide users with app suggestions during a search

17: And of course, Android 17

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

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

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

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

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

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




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


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

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

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

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

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

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

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


 2. On-Device Power with Gemini Nano 4 Preview


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

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

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

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


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



3. Hybrid Inference & Agents

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

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

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

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

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


Start Building Today

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

We are excited to see what you build! 


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

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

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

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

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

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

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

Understanding Android 17 app memory limits

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

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

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

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

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

Maximize bytecode optimization with R8

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

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

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

To properly configure R8 in your build.gradle file:

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

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

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

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

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

Library Developer R8 Best Practices

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

R8 Configuration Analyzer

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

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

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

R8 Agent Skill 

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

Optimize image loading

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

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

Adopt these five best practices

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

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

Android Studio tooling

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

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

Detect and fix memory leaks with Android Studio

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

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

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

LeakCanary memory leak analysis contextualized with Go to declaration for debugging

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

Examples of common memory leaks 

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

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

Here are a few example scenarios:

Scenario

Compose-based example

View-based example

Leaking Context

Example:
Passing LocalContext.current to a ViewModel

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

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

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

Leaking Listeners

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

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

Example:
Registering for SensorManager updates and forgetting to unregister.

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

Leaking Views

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

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

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

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

Trim memory when app leaves visible state

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

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

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

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

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

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

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

class MainActivity : AppCompatActivity(), ComponentCallbacks2 {

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

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

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

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

Advanced memory observability with ProfilingManager

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

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

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

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


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

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

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

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

Conclusion

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

]]> <![CDATA[Top 3 updates for Android developer productivity]]> https://tsecurity.de/de/3693506/android-tipps/top-3-updates-for-android-developer-productivity/ https://tsecurity.de/de/3693506/android-tipps/top-3-updates-for-android-developer-productivity/ Sat, 25 Jul 2026 10:15:38 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Simona Milanovic, Developer Relations Engineer



Every year, Google I/O brings new announcements and resources across ecosystems and products, including Android development. As development shifts toward AI and agent-assisted tooling, we’ve expanded our offerings to better support you, however you decide to build for Android.

To help you stay up to date, here is a summary of the top 3 announcements for Android Developer Productivity at I/O.

1. Android CLI is now stable

Android CLI is now stable at version 1.0, with more capabilities and integrations.

The latest version of Android CLI introduces many new features, like programmatic version lookup and support for Journeys, and bridging capability to allow agents to integrate directly with Android Studio, via the studio command.

Running Android Studio alongside the agent and Android CLI enables more efficient navigation in your project, more precise output, and access to Android Studio’s unique tooling, such as performance profilers, Compose Previews, and Android Device Streaming.

Android CLI now integrates seamlessly with Android Studio

Additionally, Google Antigravity now officially supports Android development, with the Android resources bundle, which includes the Android CLI and skills.

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 to enable it to perform core tasks—from creating projects to deploying your app on a new virtual device—much more easily and efficiently.

Google Antigravity now offers the Android resources bundle

Android CLI is now available through more package managers: like npm and homebrew. For more information, check out the Android CLI blog post and official documentation.

2. Android skills keep growing

To help models gain expertise for specific development patterns that follow our best practices, we are continuing to expand our repository of Android skills, available through Android CLI and GitHub.

Android skills ground LLMs in specialized workflows and domain knowledge, for the most common and more complex user journeys they might struggle with. We’ve shipped a fresh new batch of skills, with now more than 17 skills for areas such as:

  • Adaptive UI
  • Display Glasses and Jetpack Compose Glimmer for XR
  • Migration to CameraX
  • Perfetto SQL and Trace Analysis
  • Jetpack Compose Styles API
  • AppFunctions
  • Verified email retrieval with Android Credential Manager
  • Engage SDK integration
  • Testing setup
  • Wear OS Jetpack Compose Material3

Android skills keep growing

You can browse skills and install using the Android CLI commands:

android skills list
android skills add –skill=<skill-name>

For more information, check out the official documentation.

3. Android Bench adds new models

Earlier this year, we launched Android Bench - our leaderboard for testing LLMs on real-world Android development challenges and tasks, with the goal of accelerating model improvements, so you have more helpful options for AI assistance.


Latest results from Android Bench leaderboard

You asked us to evaluate open models. So, at I/O, we added more commonly used ones, including our local model Gemma 4, to the leaderboard. We also added the latest models including Gemini 3.5 Flash.

We are also working on increasing the difficulty of challenges we’re giving LLMs, including creating long running tasks, to continue encouraging improvements. These tasks will be coming soon to Android Bench. Check out the Android Bench leaderboard to see the latest results.

Android development anywhere

By expanding our AI-assisted Android development offerings to Antigravity, through Android CLI and Android skills, and solidifying with the pro capabilities and production grade polish of Android Studio, we’re supporting Android developers wherever they choose to build.

Have fun bringing your ideas to life faster and easier than ever before - we’re excited to see what you build in this new era of agentic development.

Check out the full Developer productivity at Google I/O 2026 YouTube playlist for more information.

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


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

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

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

Let's dive deeper into Android 17.

An intelligence system

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

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

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

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

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

Adaptive-first

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

No resizability/orientation restrictions on large screens

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

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

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

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

App Bubbles and Bubble Bar in action

Activity recreation updates

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

Continue On

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

Handoff Suggestion on a Tablet


class MyHandoffActivity : Activity() {

    ...

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

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

Go adaptive-first with Jetpack Compose

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

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

Android is Compose-first

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

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

Performance & efficiency

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

App memory limits

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

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

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




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

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

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

Generational garbage collection

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

Lock-Free MessageQueue

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

Static final fields now truly final

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

Custom notification view restrictions

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

Privacy & Security

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

Privacy-preserving choices

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

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










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

Local network access

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

SMS OTP protection

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

Post-Quantum Cryptography (PQC)

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

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

Safer native dynamic code loading 

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

Smarter password protection for physical inputs

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

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

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


















Smarter password protection for physical inputs

Media and camera features that empower creators and delight users

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

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

Better support for hearing aids

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

CameraX and Media3

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

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

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

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

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

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

Get started with Android 17

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

Getting the Android 17 beta on partner devices

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



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

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

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

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



]]> <![CDATA[Expanded billing choice and lower fees on Google Play]]> https://tsecurity.de/de/3693502/android-tipps/expanded-billing-choice-and-lower-fees-on-google-play/ https://tsecurity.de/de/3693502/android-tipps/expanded-billing-choice-and-lower-fees-on-google-play/ Sat, 25 Jul 2026 10:15:32 +0200 đŸ€– Android Tipps tsecurity.de

Posted by Paul Feng, Vice President, Google Play Eng, Product, UX



At Google Play, we are committed to delivering the best possible experience to users, while ensuring developers have the tools and adaptability to succeed. Guided by this commitment, earlier this year we announced updates to our business model introducing more billing flexibility, lower fees, and new programs to help your business thrive.

With some of these changes rolling out soon, the breakdown below outlines what is coming, where to find more information, key dates, and how to get started.

More billing flexibility

Google Play’s billing system safely, efficiently, and intuitively handles the complexities of taxes, compliance, and subscriptions across 195+ markets with 300+ local payment methods. However, we understand there are situations where your business needs more flexibility, and that's why we're offering you more options in how you handle digital commerce.




Building from existing programs, the new billing choice program is available to all developers globally who provide digital services or content to users within the United Kingdom and the European Economic Area, alongside programs in the United States. Following this initial phase, we will continue expanding availability to additional markets. You will find the global release schedule at the bottom of this post.

Through these programs, developers can offer an alternative billing system or link users to their own website for purchases, alongside Google Play’s billing. You may also design your own choice screen in accordance with our UX guidelines, as an alternative to Google Play’s default version.

Please find all the details in the program page here.

Lower, separate fees

To enable this new level of flexibility, we're separating our service fee from the billing fee. This starts on June 30, 2026, beginning with the United States, European Economic Area, and United Kingdom.

Regardless of whether you use Google Play's billing system, alternative billing, or external web links, the service fee starts at 10% on your first $1M (USD) in annual earnings. This 10% service fee also applies to all auto-renewing subscriptions. For all other transactions, the rates in the table below applies:




For other transactions, the service fee will be determined by whether the transacting user's install is new or existing relative to the regional rollout date:
 
  • New installs: A transaction from a user whose first-time install or first update of the app from Google Play occurred on or after the date that the new fee structure launched in their region.
  • Existing installs: A transaction from a user whose first-time install or first update of the app from Google Play occurred before the date that the new fee structure launches in their market.

For transactions that use Google Play’s billing system, an additional billing fee applies. In the United States, United Kingdom, and the European Economic Area, the billing fee is set at 5%. We'll announce billing fee details for other markets soon. For transactions processed via alternative billing or external web links, the billing fee does not apply.

Review this Help Center article to understand how these rates apply to your business.

Games Level Up and Apps Experience program guidelines

We are also excited to announce even more opportunities for partners who deliver exceptional user experiences across the Android ecosystem: the revamped Games Level Up and the new Apps Experience program. Detailed guidelines are now available on the respective program websites.

Apps and games that meet all requirements are eligible for a new program rate card with reduced rates. See the table below for details:


Visit the Games Level Up and Apps Experience program websites, review the guidelines, and start preparing your games and apps ahead of September 30, 2026, when the program rate cards officially become available.

Global release schedule

Evolving our business model requires technical infrastructure and alignment with local regulations, so these updates will roll out on a staggered timeline. To help you plan, here is the previously announced release schedule for each update across all markets:


Here is a quick recap of the resources available to help you get started:

We look forward to building the next generation of Google Play experiences together.

]]> <![CDATA[Android Studio Quail 2 is Stable: Multi-task with the Android Studio AI agent]]> https://tsecurity.de/de/3693500/android-tipps/android-studio-quail-2-is-stable-multi-task-with-the-android-studio-ai-agent/ https://tsecurity.de/de/3693500/android-tipps/android-studio-quail-2-is-stable-multi-task-with-the-android-studio-ai-agent/ Sat, 25 Jul 2026 10:15:29 +0200 đŸ€– Android Tipps tsecurity.de

Posted by Amman Asfaw, Product Manager, Android Studio





Android Studio Quail 2 is now stable and ready for you to use in production, bringing a shift to your IDE with concurrent agentic workflows, natively integrated memory leak profiling, and context-aware crash remediation. Whether you are performing a sweeping architectural overhaul, tracing a memory leak, or resolving a critical production crash, Android Studio keeps you anchored in your workspace by reducing manual friction.

Here’s a deep dive into what’s new:

Multi-tasking with parallel chats

In Android Studio Quail 2, we've been hard at work redesigning Agent Mode from the ground up. This new architecture provides better performance, offers more flexibility for decomposing complex tasks, and improves the suite of internal tools the agent uses to do its work.

In addition to these behind-the-scenes improvements, these changes also allow you to converse across multiple agent chats simultaneously. Waiting for the Android Studio agent to finish a task before you can ask another question or initiate a separate task in Agent Mode is a bottleneck of the past. You can multi-task seamlessly: kick off a UI refactor in one tab, fix a ProGuard rule in a second, and generate documentation in a third.

You can also change which models the agent uses from chat to chat based on the requests you have. Take a look at Android Bench for an analysis of how LLMs perform Android development tasks.

  • How to use: Click the "+" icon to start a new parallel conversation, and use the History icon to navigate between active tasks. Alternatively, select File > New > New Agent Tab to open a conversation in a dedicated tab.
  • Note: Worktree support is currently unavailable. Exercise caution when running concurrent chats that modify the same project files, which can potentially lead to editor conflicts.

Run multiple agent tasks in parallel with different models of your choice.

Use the History icon to navigate between active tasks.

Memory leak detection with LeakCanary

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

Hunting down memory leaks can be a tedious, manual task. Starting with Android Studio Quail 2, the popular open-source leak detector LeakCanary is natively integrated directly into the Profiler as a dedicated, first-class task.

This integration transforms your debugging performance by lifting and shifting the heap analysis off your resource-constrained testing phone, and onto your powerful development computer. By running the analysis on your computer, leak tracing is up to five times faster and jank-free, leaving your test app running smoothly on the device.

Once a leak is detected during a profiling session:

  • The Profiler renders an interactive, color-coded leak trace, grouping occurrences and estimating lost memory.
  • You can click Go to declaration on any leaking object in the trace to instantly jump to that exact line of code in your editor.
  • You can click Fix with Agent to have the Gemini agent ingest the trace, explain the root cause of the retained reference, and write the exact code change (such as unbinding a listener or clearing a static reference) to plug the leak.
Review memory leaks identified via LeakCanary through the Fix with Agent button.

App Quality Insights agent integration

Tracking down the root cause of an app crash can require manually synthesizing stack traces, device data, and source code. However Android Studio’s App Quality Insights (AQI) is now fully integrated with Agent Mode to do the heavy lifting for you.

When you click on a crash in the AQI panel, you immediately get a concise, high-level summary of the issue. If you need to dig deeper, simply click See more. This opens a dedicated chat where the agent uses your selected model and pulls in local source code and the full stack trace to deliver a comprehensive explanation of the failure.

With the new agent integration, you move directly from issue identification to resolution. By clicking Fix with AI, the agent will analyze the issue, propose a step-by-step fix plan, and—upon your approval—apply the necessary code changes directly to your project and verify the resulting fix

The Fix with AI button triggering the agent to analyze the issue, then propose the fix

Quality & stability improvements

Beyond new features, we’ve continued our focus on quality by addressing numerous bugs and incorporating the latest stability and performance improvements from the IntelliJ platform, making this a significant enhancement for your daily development.

Get Started

Ready to dive in and accelerate your development? Download Android Studio Quail 2 and start exploring these new features today! As always, your feedback is crucial to us. Check known issues, report bugs, and be part of our vibrant community on LinkedIn, Medium, YouTube, or X. 

]]> <![CDATA[Build intelligent Android apps: Integrate into Android's intelligence system using AppFunctions]]> https://tsecurity.de/de/3693499/android-tipps/build-intelligent-android-apps-integrate-into-androids-intelligence-system-using-appfunctions/ https://tsecurity.de/de/3693499/android-tipps/build-intelligent-android-apps-integrate-into-androids-intelligence-system-using-appfunctions/ Sat, 25 Jul 2026 10:15:27 +0200 đŸ€– Android Tipps tsecurity.de

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



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

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

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

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

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



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

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



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

Android MCP powered by AppFunctions

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

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







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

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

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

How we accelerated development with Android skills

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

Providing app features to the intelligence system

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

Configuration and dependency setup

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

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

Modeling custom data types

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

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

Providing features using the @AppFunction annotation

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

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

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

The service entry point and Hilt integration

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

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

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

Testing and verifying your AppFunctions

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

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

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

Wrapping it up

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

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

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

Learn more

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

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

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

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



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

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

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

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


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

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


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

Ensure seamless camera capture on foldable devices

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

Extend glanceable interactions to Wear OS 7

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

Build intelligent features 

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

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

Start optimizing today

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

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

Unfold the future today! 
]]> <![CDATA[Community Office Hours: Exchange Email Support]]> https://tsecurity.de/de/3693439/video/community-office-hours-exchange-email-support/ https://tsecurity.de/de/3693439/video/community-office-hours-exchange-email-support/ Sat, 25 Jul 2026 10:05:14 +0200 đŸŽ„ Video tsecurity.de Author: Mozilla Thunderbird - Bewertung: 15x - Views:697

Welcome to the last Community Office Hours of 2025! In this edition, Heather and Monica welcome Sr. Software Engineer Brendan Abolivier and Software Engineer Eleanor Dichary from the Desktop Team. We’re discussing the recent Exchange Web Support for email that just landed in Thunderbird Monthly Release 145. Learn how the team landed this feature and discover future plans for Calendar and Contact support, as well as Graph API

Chapters:
00:00 Intro
03:50 What is Microsoft Exchange
05:07 Why it took so long to add Exchange
14:33 Exchange in Thunderbird 145
18:20 Config options and authorization
27:10 Attachments and storage
32:30 Sync and folder operations
36:10 Filters and notification
39:10 Search
40:20 Feedback and bug reporting
43:00 Mobile and Graph API
49:09 JMAP and future protocols
55:50 Calendar and address book
59:57 Roadmap and EWS deprecation
62:23 Closing

Resources:
Exchange Mozilla Support Article: https://support.mozilla.org/en-US/kb/thunderbird-and-exchange
Exchange Mozilla Wiki Post (with call for testing): https://wiki.mozilla.org/Thunderbird%3AExchange
Reach out on Matrix: https://matrix.to/#/#thunderbird:mozilla.org
Bugzilla (use Exchange component for reporting): https://bugzilla.mozilla.org/enter_bug.cgi?product=MailNews%20Core

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

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

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

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

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

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

Download the PDF version of this report:

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

Background and Development of Pro-Russia Hacktivist Groups

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

Cyber Army of Russia Reborn

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

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

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

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

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

NoName057(16)

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

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

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

Z-Pentest

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

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

Sector16

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

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

Technical Details

Note: This advisory uses the MITRE ATT&CK¼ Matrix for Enterprise framework, version 18. See the MITRE ATT&CK Tactics and Techniques section of this advisory for a table of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

TTP Overview

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

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

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

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

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

Propagation

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

Reconnaissance and Initial Access

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

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

Impact

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

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

MITRE ATT&CK Tactics and Techniques

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

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

Incident Response

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

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

Mitigations

OT Asset Owners and Operators

The authoring organizations recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture based on the threat actors’ activity. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats, tactics, techniques, and procedures. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

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

OT Device Manufacturers

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

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

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

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

Validate Security Controls

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

To start:

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

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

Resources

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

Additional resources that apply to this advisory include:

Contact Information

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

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

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

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

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

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

Disclaimer

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

Acknowledgements

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

Version History

December 09, 2025: Initial version.

Appendix A: Targeting Methodologies for Pro-Russia Hacktivist Groups

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

Appendix B: Additional Designators Used for Cited Groups

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

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

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

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

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

Organizations: Critical Infrastructure

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

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

Introduction

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

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

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

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

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

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

Download the PDF version of this report:

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

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

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

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

Background Information

Similar Historical Activity Targeting Programmable Logic Controllers

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

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

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

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

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

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

Technical Details

Note: This advisory uses the MITRE ATT&CK¼ Matrix for Enterprise framework, version 19. See the MITRE ATT&CK Tactics and Techniques section of this advisory for tables of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

Initial Access

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

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

Command and Control

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

Exfiltration

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

Impact

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

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

Indicators of Compromise

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

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

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

MITRE ATT&CK Tactics and Techniques

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

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

 

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

Mitigations

The authoring agencies recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture on the basis of the threat actors’ activity. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats and TTPs. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

Network Defenders

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

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

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

Immediate steps to prevent the attack:

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

Follow-up steps to strengthen security posture:

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

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

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

Device Manufacturers

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

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

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

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

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

Validate Security Controls

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

To get started:

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

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

Resources

Contact Information

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

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

Disclaimer

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

Version History

April 7, 2026: Initial version.

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

Notes

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

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

]]> <![CDATA[The Rust Programming Language Blog: Announcing Rust 1.96.0]]> https://tsecurity.de/de/3693296/tools/the-rust-programming-language-blog-announcing-rust-1960/ https://tsecurity.de/de/3693296/tools/the-rust-programming-language-blog-announcing-rust-1960/ Sat, 25 Jul 2026 08:37:36 +0200 đŸ’Ÿ Tools tsecurity.de The Rust team is happy to announce a new version of Rust, 1.96.0. 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 1.96.0 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 1.96.0.

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 1.96.0 stable

New Range* types

Many users expect Range and related core::ops types to be Copy, but this is not the case: they implement Iterator directly, and it is a footgun to implement both Iterator and Copy on the same type so this has been avoided. RFC3550 proposed a set of replacement range types that implement IntoIterator rather than Iterator, meaning they can also be Copy. The standard library portion of that RFC is now stable, introducing:

  • core::range::Range
  • core::range::RangeFrom
  • core::range::RangeInclusive
  • Associated iterators

A Rust version in the near future will also add core::range::RangeFull and core::range::RangeTo as re-exports from core::ops (these do not implement Iterator and already implement Copy), and core::range::legacy::* as the new home for the current ranges. Range syntax like 0..1 still produces the legacy types for now, but will be updated to core::range types in a future edition.

With these stabilizations, it is now possible to store slice accessors in Copy types without splitting start and end:

use core::range::Range;

#[derive(Clone, Copy)]
pub struct Span(Range<usize>);

impl Span {
    pub fn of(self, s: &str) -> &str {
        &s[self.0]
    }
}

The new RangeInclusive also makes its fields public, unlike the legacy version which avoided exposing the exhausted iterator state. This isn't a concern with the new type since it must be converted to begin iteration.

Library authors should consider making use of impl RangeBounds in public API, which accepts both legacy and new range types. If a concrete type is needed, prefer using new ranges as this will eventually become the default.

Assert matching patterns

The new macros assert_matches! and debug_assert_matches! check that a value matches a given pattern, panicking with a Debug representation of the value otherwise. These are essentially the same as assert!(matches!(..)) and debug_assert!(matches!(..)), but the printed value improves the possibility of diagnosing the failure.

These new macros have not been added to the standard prelude, because they would collide with popular third-party crates that provide macros with the same name. Instead, they should be manually imported from core or std before use.

use core::assert_matches;

/// [Random Number](https://xkcd.com/221/)
fn get_random_number() -> u32 {
    // chosen by a fair dice roll.
    // guaranteed to be random.
    4
}

fn main() {
    assert_matches!(get_random_number(), 1..=6);
}

Changes to WebAssembly targets

WebAssembly targets no longer pass --allow-undefined to the linker which means that undefined symbols when linking are now a linker error instead of being converted to WebAssembly imports from the "env" module. This change prevents modules from linking unless all linking-related symbols are defined to catch bugs earlier and prevent accidental issues with symbol naming or similar.

Undefined linking-related symbols are often indicative of build-time related bugs or misconfiguration. If, however, the old behavior is intended then it can be re-enabled with RUSTFLAGS=-Clink-arg=--allow-undefined or by editing the source code and using #[link(wasm_import_module = "env")] on the block defining the symbol.

This change was previously announced on this blog, and now takes effect in Rust 1.96.

Stabilized APIs

Two Cargo advisories

Rust 1.96 contains fixes for two vulnerabilities for users of third-party registries.

  • CVE-2026-5223 is a medium severity vulnerability regarding extraction of crate tarballs with symlinks.

  • CVE-2026-5222 is a low severity vulnerability regarding authentication with normalized URLs.

Users of crates.io are not affected by either vulnerability.

Other changes

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

Contributors to 1.96.0

Many people came together to create Rust 1.96.0. We couldn't have done it without all of you. Thanks!

]]> <![CDATA[Firefox Nightly: Backup for a Rainy Day – These Weeks in Firefox: Issue 202]]> https://tsecurity.de/de/3693295/tools/firefox-nightly-backup-for-a-rainy-day-these-weeks-in-firefox-issue-202/ https://tsecurity.de/de/3693295/tools/firefox-nightly-backup-for-a-rainy-day-these-weeks-in-firefox-issue-202/ Sat, 25 Jul 2026 08:37:35 +0200 đŸ’Ÿ Tools tsecurity.de Highlights
  • The profile backup mechanism has been enabled by default for all desktop platforms in Nightly, as well as Beta! The current plan is to have this ride out to Firefox 151 for Windows, macOS and Linux on May 18th!
    • This feature, when enabled, will create a copy of your profile data in the background and store it in a single file on your file system that you can restore from.
    • You will be able to manage this feature in Settings under Sync (for now)
      • Firefox settings page showing the Backup feature in dark mode. Backup is enabled, with details of the most recent backup and a “Backup now” button. The page displays the backup file name and a backup location folder path, along with “Choose
” and “Show in folder” buttons. A “Sensitive data” section includes an option to back up passwords and payment methods with encryption, and a disabled “Change password” button.
    • You can read more about the feature here
  • As followups to the recent addition to the WebExtension tabs API to support the new SplitView tabs feature, tabs.group() and tabs.ungroup() have been fixed to work correctly with split view tabs, and fixed split views being prepended instead of appended to tab groups when adopted into a new window – Bug 2029099 / Bug 2029534
  • Adaptive autofill has been enabled on Nightly.
    • Previously, autofill only completed domains (e.g. typing red autofilled reddit.com). Now it can also complete full URLs for pages you visit often (e.g. red → reddit.com/r/firefox), learning from what you actually click in the address bar. If a suggestion isn’t helpful, you can now dismiss it so autofill learns what not to show you too.
  • Markus Stange [:mstange] implemented dynamic toolbar on top in RDM (#1978145), but also implemented some static skeleton UI so it’s closer to what we actually have in Firefox for Android
    • dynamic toolbar is behind a pref: devtools.responsive.dynamicToolbar.enabled
    • it can be put on top by setting devtools.responsive.dynamicToolbar.onTop, otherwise it’s at the bottom
    • Firefox Responsive Design Mode on Desktop displaying the Mozilla homepage in a mobile viewport. The toolbar at the top shows a simulated Android device (including the dynamic toolbar) with a viewport size of 376 × 464 pixels and a device pixel ratio of 3. The page content is shown in French, featuring the Mozilla logo, a “Menu” link, a “Pause animation” button, and the headline “Bienvenue chez Mozilla” with accompanying text about trusted technology and digital rights.

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

  • Amin Amir
  • aoia7rz7l
  • Chukwuka Rosemary
  • DrSeed
  • FrĂ©dĂ©ric Wang NĂ©lar
  • japandi
  • John Iweh
  • jonathancabera
  • Josh Aas
  • Keji Bakare
  • kofoworola shonuyi
  • konyhĂ©a
  • liz
  • Mathew Hodson
  • Okhuomon Ajayi
  • Oluwatobi
  • ROSHAAN
  • Sam Johnson

New contributors (🌟 = first patch)

 

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons
  • In preparation for the Project Nova restyling of the about:addons page, we have refactored about:addons into separate per-component ES modules, splitting the monolithic aboutaddons.js and aboutaddons.html into 16 dedicated component files under components/ (with no behavior or UI changes) – Bug 2032014
    • NOTE: if you have working on patches with changes to about:addons internals it is very likely you’ll need to rebase and solve merge conflicts hit on top of this refactoring, the internals are still largely the same as before but don’t hesitate to reach out to the Addons team if you have doubts / questions or need help to figure out how to adapt your patch of top of these changes
WebExtensions Framework
  • Fixed exportFunction to preserve the constructibility of the wrapped function instead of unconditionally making all exported functions implicitly as constructors – Bug 2033173
    • Thanks to Gregory Pappas for contributing this improvement to the Content Scripts’ Xray Wrappers helpers!
  • Fixed a Firefox 151 regression where extension content scripts accessing location.ancestorOrigins caused subsequent page script reads of the same property to fail with “Permission denied”, breaking sites like Gmail – Bug 2034329
    • Thanks to Simon Farre for promptly investigating and fixing this recent regression!
WebExtension APIs
  • Updated sessions.getRecentlyClosed() to remove the hardcoded cap when maxResults is omitted – Bug 1392125
    • Shoutout to Amine Zroual for contributing this enhancement to the sessions WebExtensions API!

DevTools

Fluent

  • We’re now at over 72% of our strings being Fluent! Got a component still using .properties? Convert when you can!
  • Stacked area chart titled “Are We Fluent Yet?” showing the number and type of localization strings available in Firefox from 2018 to 2026. The chart tracks Fluent strings (green), Properties strings (blue), DTD strings (pink), and a small number of INI strings. Over time, Fluent strings steadily increase while DTD and Properties strings decline. A tooltip at April 26, 2026 shows 10,372 Fluent strings, 3,997 Properties strings, and no remaining DTD or INC strings, illustrating Firefox’s ongoing migration to the Fluent localization system.

Migration Improvements

New Tab Page

Search and Urlbar

  • Marco has fixed a couple of issues with the places databases to try and improve stability. This should help with avoiding users losing bookmarks or favicons.
  • Work continues on the new separate search bar to improve the functionality, e.g. allowing middle click to perform a search in a new tab, avoiding performing a search when adding a search engine.
  • Work also continues on the new Nova layouts.

Smart Window

  • uplifted 10 bugs to 150.0.1 dot release addressing initial user feedback from diary study and Connect
    • jump to bottom of conversation 2028692
    • stop streaming button 2029204
    • back/forward navigation from assistant 2029229
    • dark mode for various chips 2024499
  • search engine switching from smart bar 2021973
  • Nova styling within smart window 2026794

Storybook/Reusable Components/Acorn Design System

UX Fundamentals

  • The error pages shown when a server sends back an invalid response header or an unsupported content encoding now display accurate, context-specific messages. The invalid response header page also gained a helpful list of next steps. – 2027209
  • In progress: The error page illustrations are being replaced with new artwork, and the system now supports per-illustration size configuration, giving each image the ability to define its own appropriate dimensions. – 2031837

Settings Redesign

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

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

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

Improving CP/CPS Documentation

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

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

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

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

Introducing Detailed Controls Reports

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

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

A DCR must include:

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

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

Additional Clarifications and Improvements

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

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

Looking Forward

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

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

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

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

]]> <![CDATA[The Rust Programming Language Blog: 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 đŸ’Ÿ 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 đŸ’Ÿ Tools tsecurity.de Another six months have passed since our last development update, and the crates.io team has been busy. Here's a summary of the most notable changes and improvements made to crates.io since then.

Source Code Viewer

Crate pages now have a "Code" tab that lets you browse the contents of published crate versions directly on crates.io. This shows you the exact files that cargo downloads when you add a crate as a dependency, which might differ from the linked repository. This makes it much easier to audit your dependencies, including files that never appear in the repository, like the normalized Cargo.toml files that cargo generates.

Source code viewer showing the "Code" tab of the serde crate

The viewer comes with a file tree sidebar with search functionality, syntax highlighting, and GitHub-style line selection, where clicking or dragging line numbers produces shareable #L10-L20 URLs.

Under the hood, the server now builds a zip file for every published version. Since the .crate files that cargo consumes are gzipped tarballs without random access support, a background job re-packs each of them into a seekable zip archive plus a JSON manifest describing the contained files. Both are served from our static CDN. The frontend then fetches only the manifest and loads each file on demand with an HTTP range request. Because of this architecture, browsing crate sources essentially adds no load on the crates.io API servers. Existing crate versions have been backfilled, so this works for old releases too.

The rendering library behind the code viewer is a diff renderer at heart, and that's no accident: a version-to-version diff viewer built on the same infrastructure is currently in the works. This will allow you to review exactly what changed between two published versions, right on crates.io. Stay tuned!

Untangling crates.io Accounts from GitHub

At the end of May, the crates.io team accepted RFC #3946. Crates.io accounts always have been tightly coupled to GitHub: signing in means "Log in with GitHub", and your crates.io identity is your GitHub username. The RFC changes that. It introduces usernames that are native to crates.io and independent of linked GitHub accounts, as a prerequisite for eventually supporting login via other identity providers.

The implementation of crates.io usernames has started, but there is still a lot left to do, most visibly the ability to change your crates.io username. After that is complete, there will be future RFCs and implementation for signing in with identity providers other than GitHub. Since all of this touches authentication and account security, we are deliberately taking it slow and rolling these changes out in small, carefully reviewed steps.

Advisories and Suggestions

In our January update we introduced the "Security" tab, which shows security advisories from the RustSec database. We have since taken this integration one step further: crates that RustSec has flagged as unmaintained now show a warning banner directly on their crate pages, linking to the corresponding advisory for details and possible alternatives. Thanks to Dirkjan Ochtman for implementing this feature!

Unmaintained warning banner on the ansi_term crate page

Related to this, some popular crates have been largely absorbed into the Rust standard library over the years, like lazy_static, which has been superseded by std::sync::LazyLock since Rust 1.80. Crate pages of such crates now show a friendly "You might not need this dependency" banner describing the standard library replacement, and superseded crates in dependency lists get a small light bulb icon with a similar hint.

"You might not need this dependency" banner on the lazy_static crate page

The dataset behind this feature lives in the new rust-lang/std-replacement-data repository, together with a documented inclusion policy: standard library replacements only, every entry must cite the stable std, core, or alloc API and Rust version, and crate maintainers get a notice-and-comment window before an entry is added. New entries can be proposed upstream and can benefit other tools too.

Ferris

The most delightful change of this cycle: the Ferris on our error pages now follows your mouse cursor with its eyes:

Ferris' eyes following the mouse cursor on the error page

Getting a 404 error on crates.io is now slightly less sad.

Svelte Frontend Migration Completed

In our January update, we announced that we were experimenting with porting the crates.io frontend from Ember.js to Svelte. This experiment has concluded successfully: the new frontend reached feature parity, went through a public testing phase in April, became the default at the beginning of May, and the Ember.js app has been removed from our repository.

We designed this change to be invisible for our users, since the new frontend is a 1:1 port of the previous design and functionality. For the team and our contributors, however, it is a big deal: the frontend is now built on a more modern framework, which should make it easier for new contributors to get started. It also allows us to iterate faster, as the source code viewer above demonstrates.

We want to thank the Ember.js team for a framework that served crates.io well for many years, and the Svelte team for making the transition so enjoyable.

Miscellaneous

These were some of the more visible changes to crates.io over the past six months, but a lot has happened "under the hood" as well:

  • Search performance: Relevance-sorted search queries previously ranked every crate matching the query, which could take 1-2 seconds for short or common search terms. Ranking is now bounded to the 1,000 matching crates with the highest recent download counts.

  • Reverse dependencies performance: The reverse dependencies endpoint no longer recomputes the full dependent set on every request. It is now served from a precomputed table kept in sync by database triggers, turning an expensive join into a bounded index scan and greatly reducing the chance of getting a timeout error.

  • New ARCHITECTURE.md: If you've ever wondered how crates.io actually works, our ARCHITECTURE.md document got a complete rewrite. It is now organized around the high-level systems that make up crates.io and how they fit together, and includes walkthroughs of what happens when you run cargo publish, why a typical crate download never touches our API servers, and how download counts are derived from CDN access logs.

  • Definition lists: READMEs now render Markdown definition lists, a widely used Markdown extension. Our markdown renderer comrak already supported them, the extension just wasn't enabled yet. Thanks to @mistaste for this contribution!

  • CDN cache tags: Files uploaded to our static CDN now carry cache-tag metadata, allowing us to invalidate all cached files of a crate or a specific release in a single operation, instead of issuing one invalidation per file URL.

  • Caching improvements: We removed a global Vary: Cookie response header that was preventing our CDNs from caching public API responses and frontend assets effectively. Per-user responses now use Cache-Control: no-store instead, resulting in better cache hit rates at the CDN edge.

  • Accessibility: We have made crates.io friendlier to screen readers: decorative icons are now hidden from the accessibility tree, heading hierarchies have been fixed, and lists are marked up as proper lists. ARIA snapshot tests now ensure that regressions can't slip in unnoticed. We plan to continue to improve crates.io accessibility over the coming months.

  • Git index performance: The background worker's local clone of the git index is now a bare and shallow repository, eliminating roughly 250,000 checked-out files and the full commit history from its disk, improving its performance as we see increased rates of crate publication. The periodic index squashing now goes through the GitHub API instead of generating large git packs locally, which had previously caused out-of-memory failures on the production worker.

Feedback

We hope you enjoyed this update on the development of crates.io. If you have any feedback or questions, please let us know on Zulip or GitHub. We are always happy to hear from you and are looking forward to your feedback!

]]> <![CDATA[The Rust Programming Language Blog: Announcing Rust 1.97.1]]> https://tsecurity.de/de/3693284/tools/the-rust-programming-language-blog-announcing-rust-1971/ https://tsecurity.de/de/3693284/tools/the-rust-programming-language-blog-announcing-rust-1971/ Sat, 25 Jul 2026 08:37:17 +0200 đŸ’Ÿ Tools tsecurity.de The Rust team has published a new point release of Rust, 1.97.1. Rust is a programming language that is empowering everyone to build reliable and efficient software.

If you have a previous version of Rust installed via rustup, getting Rust 1.97.1 is as easy as:

rustup update stable

If you don't have it already, you can get rustup from the appropriate page on our website.

What's in 1.97.1

Rust 1.97.1 fixes a miscompilation in an LLVM optimization.

We have backported both an LLVM fix and a disable of the underlying change in Rust 1.97.0 of Rust's generated IR that increased the likelihood of this happening. However, note that the underlying miscompilation has been present since at least Rust 1.87.

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

Contributors to 1.97.1

Many people came together to create Rust 1.97.1. We couldn't have done it without all of you. Thanks!

]]> <![CDATA[Which DJI drone should I buy? Your simple guide to the different ranges and our top-rated flyers]]> https://tsecurity.de/de/3693141/it-nachrichten/which-dji-drone-should-i-buy-your-simple-guide-to-the-different-ranges-and-our-top-rated-flyers/ https://tsecurity.de/de/3693141/it-nachrichten/which-dji-drone-should-i-buy-your-simple-guide-to-the-different-ranges-and-our-top-rated-flyers/ Sat, 25 Jul 2026 07:12:33 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[5 Biggest Samsung Launches from Galaxy Unpacked July 2026]]> https://tsecurity.de/de/3693090/it-nachrichten/5-biggest-samsung-launches-from-galaxy-unpacked-july-2026/ https://tsecurity.de/de/3693090/it-nachrichten/5-biggest-samsung-launches-from-galaxy-unpacked-july-2026/ Sat, 25 Jul 2026 06:23:45 +0200 📰 IT Nachrichten tsecurity.de Samsung has expanded its Galaxy lineup with the launch of five new devices at its Galaxy...

The post 5 Biggest Samsung Launches from Galaxy Unpacked July 2026 appeared first on Fossbytes.

]]> <![CDATA[Monthly News – December 2025]]> https://tsecurity.de/de/3693043/unix-server/monthly-news-december-2025/ https://tsecurity.de/de/3693043/unix-server/monthly-news-december-2025/ Sat, 25 Jul 2026 05:09:57 +0200 🐧 Unix Server tsecurity.de <![CDATA[5 endpoint blind spots your EDR/XDR was never built to see]]> https://tsecurity.de/de/3692679/it-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ https://tsecurity.de/de/3692679/it-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ Sat, 25 Jul 2026 00:18:11 +0200 📰 IT Nachrichten tsecurity.de

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

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

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

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

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

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

#2. Distinguish between human and autonomous agent behavior 

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

#3. Establish guardrails for automated package updates on endpoints

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

#4. Enforce principle of least privilege for AI agents

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

#5. Maintain continuous endpoint posture management

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

Summary

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

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

]]> <![CDATA[Anthropic's New Opus 5 Model Rivals Fable 5 For Half the Price]]> https://tsecurity.de/de/3692357/it-security-nachrichten/anthropics-new-opus-5-model-rivals-fable-5-for-half-the-price/ https://tsecurity.de/de/3692357/it-security-nachrichten/anthropics-new-opus-5-model-rivals-fable-5-for-half-the-price/ Fri, 24 Jul 2026 21:08:38 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[OpenAI Discloses AI-Driven Breach During Cybersecurity Testing]]> https://tsecurity.de/de/3692255/it-security-nachrichten/openai-discloses-ai-driven-breach-during-cybersecurity-testing/ https://tsecurity.de/de/3692255/it-security-nachrichten/openai-discloses-ai-driven-breach-during-cybersecurity-testing/ Fri, 24 Jul 2026 20:17:32 +0200 📰 IT Security Nachrichten tsecurity.de An internal cybersecurity evaluation to evaluate Hugging Face’s offensive cyber capabilities allowed two of the company’s advanced AI models to hack into the organization’s infrastructure autonomously. These models include GPT-5.6 Sol and a more advanced pre-release model.  One of the


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]]> <![CDATA[Anthropic launches Claude Opus 5, a cheaper AI model for coding, agents and enterprise workflows]]> https://tsecurity.de/de/3692246/it-nachrichten/anthropic-launches-claude-opus-5-a-cheaper-ai-model-for-coding-agents-and-enterprise-workflows/ https://tsecurity.de/de/3692246/it-nachrichten/anthropic-launches-claude-opus-5-a-cheaper-ai-model-for-coding-agents-and-enterprise-workflows/ Fri, 24 Jul 2026 20:10:11 +0200 📰 IT Nachrichten tsecurity.de Anthropic released Claude Opus 5 on Friday, a model the company says delivers nearly all the intelligence of its top-of-the-line Claude Fable 5 at half the cost — a launch that signals how the AI race is shifting from raw capability to the economics of daily use.

The model, available immediately on all of Anthropic's platforms, is priced at $5 per million input tokens and $25 per million output tokens, unchanged from its predecessor, Opus 4.8. It becomes the new default model on Claude Max, Anthropic's premium consumer tier, and the strongest model available on Claude Pro.

The positioning is deliberate. Anthropic is not claiming Opus 5 is its smartest model — that distinction still belongs to Fable 5, and rival systems retain an edge in certain domains. Instead, the company is making a subtler argument that may matter more to enterprise buyers: that the most economically important AI work happens in a middle band of difficulty, where near-frontier intelligence delivered efficiently and cheaply beats frontier intelligence delivered expensively.

"Opus 5 as your daily driver, the model you hand complex work to and review when it's done," an Anthropic spokesperson said in an interview with VentureBeat, describing how the company's lineup now stratifies. "Fable 5 for your most ambitious work, the days-long autonomous projects nothing could take on before... Sonnet 5 for work you run at scale, where speed and cost per call decide what ships. Haiku 4.5 for subagents and instant answers."

How Claude Opus 5 benchmark results stack up against Fable 5 and rival AI models

On paper, the results are striking. Anthropic says Opus 5 sets new state-of-the-art marks on coding and knowledge-work evaluations including Frontier-Bench and GDPval-AA. On Frontier-Bench v0.1, an agentic terminal coding benchmark, Opus 5 scores 43.3 percent — more than double Opus 4.8's 18.7 percent and well ahead of Fable 5's 33.7 percent — at a lower cost per task, according to the company. On ARC-AGI 3, an evaluation of novel problem-solving, Anthropic reports Opus 5 scored three times as high as the next best model. On OSWorld 2.0, a computer-use benchmark, the company says the model surpasses Fable 5's best result at just over a third of the cost.

The numbers come with honest caveats that are themselves notable in an industry prone to superlatives. Anthropic acknowledges Opus 5 remains behind Mythos 5, a competing model, on cybersecurity tasks and biology research, and an OpenAI-family model still leads on one agentic coding benchmark.

The more revealing caveat came from Anthropic itself, when asked where Opus 5 still falls short of Fable 5. The spokesperson's answer amounted to a candid admission about what benchmarks do and don't capture.

"The evals where Opus 5 wins are bounded tasks with a specific outcome, which is where it's strongest. What those evals don't measure is duration," the spokesperson told VentureBeat. "One way to put it: Opus 5 is the best tool for the jobs benchmarks can see, and Fable 5 is what you reach for when the job outruns the benchmark."

Fable 5, by contrast, "is for the longest, most autonomous jobs, where the model has to stay coherent across many connected steps over hours or days with dense source material," the spokesperson said, advising customers to "run both on a representative workload, one bounded task and one long-horizon job." That framing — bounded tasks versus long-horizon autonomy — may become the defining axis of model differentiation in 2026, as benchmarks saturate and the hardest remaining problems involve sustained, multi-day agentic work rather than discrete puzzles.

Why token efficiency is becoming the real battleground for enterprise AI spending

Threaded through the launch is a theme Anthropic clearly wants buyers to absorb: Opus 5 doesn't just score well, it scores well per dollar. The model ships with an adjustable "effort" setting that lets customers trade intelligence for speed and token savings, and Anthropic's charts emphasize performance at a given cost rather than peak performance alone.

Early customers echoed the point with unusual specificity. Harvey, the legal AI company, said Opus 5 achieved similar performance to Opus 4.8's maximum-reasoning mode "while generating 26% fewer tokens on average," according to Niko Grupen, its head of applied research. Richard Pham of Fundamental Research Lab said that on hard financial-modeling tasks, the model averaged nine percentage points higher accuracy "while using roughly one-third fewer turns and tool calls and 60% less time."

Wade Foster, chief executive of Zapier, said Opus 5 topped his company's AutomationBench leaderboard "without spending more tokens than prior Claude models," running a full churn-prevention workflow from start to finish. "Previous models didn't pass; Opus 5 hit 100%," he said. Scott Wu, chief executive of Cognition, the company behind the Devin coding agent, said that on FrontierCode 1.1, "Claude Opus 5 approaches Fable-level performance at half the cost," with particular strength in debugging and root-cause analysis.

The efficiency emphasis reflects commercial reality. Enterprise AI spending is no longer experimental, and inference costs — the price of actually running these models at scale — have become a board-level line item. 

Anthropic's business skews heavily toward API and enterprise usage; according to a February 2026 analysis by Contrary Research, Claude held roughly 40 percent of the enterprise large language model market by usage as of late 2025, and Claude Code alone had reached about $1 billion in annualized revenue. For a company whose customers pay by the token, a model that does more with fewer tokens is not a nice-to-have. It is the product.

Self-verifying AI agents and what they mean for the hidden costs of automation

Beyond the numbers, Anthropic is selling a behavioral story: that Opus 5 verifies its work and iterates until it succeeds. The company offered several examples from testing that read like small parables of machine stubbornness.

In one Frontier-Bench task, the model was asked to reconstruct a machine part as a 3D CAD model from a drawing it was intentionally given no way to view. Rather than fail, Anthropic says, Opus 5 wrote its own computer vision pipeline to extract the geometry from raw pixels — and did so repeatedly, while no competing model solved the task in five attempts. In another case, given a real bug in a popular open-source package manager, the model found the root cause and fixed an edge case the community's own patch had missed; a competing model patched only the symptom and declared victory. An engineer at a trading firm, the company says, used Opus 5 to build a market data feed for a new exchange in a single session and, finding no live feed to validate against, watched the model build its own test harness to check its parsing code.

Customers described similar behavior in the wild. Cristian Rivera, a staff software engineer at Stripe, said he gave the model "a chief-of-staff role over my dev environments" for a weekend: "it built its own monitor, drove each box, and pulled me in only for the judgment calls."

This is the capability enterprises actually care about, and it is worth dwelling on why. The gap between a model that produces plausible output and one that verifies its output is the gap between a demo and a deployable system. Most of the hidden cost of enterprise AI today is human review — engineers checking the machine's work. A model that reliably checks its own work compresses that cost, which is precisely why customers keep citing fewer turns, fewer passes, and less time rather than higher raw scores.

Inside Anthropic's safety strategy: capability gaps, classifiers, and model fallbacks

The launch also showcases Anthropic's increasingly intricate approach to safety — one that now involves deliberately not teaching its models certain skills. The company says its automated behavioral audit found Opus 5 to be its most aligned model to date, scoring 2.3 on overall misaligned behavior, lower than Opus 4.8, Sonnet 5, or Fable 5, with the lowest rates of deceptive behavior and the least susceptibility to being tricked into misuse.

On the capability side, Anthropic says it intentionally avoided training Opus 5 on cyber tasks, as it did with Opus 4.8. The model improved on them anyway — a side effect of general capability gains — and now nearly matches Mythos 5 at finding software vulnerabilities. But it remains far behind at exploiting them: on Anthropic's OSS-Fuzz evaluation, Opus 5 identified vulnerabilities at a 79.4 percent rate, close to Mythos 5's 80 percent, but succeeded at developing exploits in only 4 challenges versus Mythos 5's 13. That asymmetry — strong at defense-relevant discovery, weak at offense-relevant exploitation — appears to be by design, and the safeguards follow the same logic. Anthropic expects Opus 5's cyber classifiers to intervene about 85 percent less often than Fable 5's.

When a classifier does trigger, requests in Claude.ai, Claude Code, and Claude Cowork fall back to Opus 4.8 by default — raising an obvious question: if a request is too risky for one model, why is it acceptable for another? "The model it falls back to has lower capability levels making the risk of harmful use lower as well," the spokesperson said, adding that "there is a message that lets the user know when this occurs and is visible in the chat."

The logic is defensible, but it reveals how AI safety actually works in 2026: risk is not a property of the question alone, but of the question multiplied by the capability of the system answering it. On biology, the calculus runs the other way. Opus 5 is now Anthropic's most capable generally available model for scientific research — scoring 10.2 percentage points higher than Opus 4.8 on the company's internal chemistry benchmark — though the spokesperson acknowledged that "Mythos 5 remains the stronger model for long-horizon, open-ended work like autonomous drug design campaigns."

The business stakes behind the launch: a $380 billion valuation and massive compute bets

The launch lands at a moment of extraordinary commercial momentum — and extraordinary obligations — for Anthropic. Reuters reported in February that the company was valued at roughly $380 billion in its latest funding round, following a period in which, per Contrary Research's analysis, its annualized revenue climbed from about $1 billion at the end of 2024 to a projected $9 billion by the end of 2025, with internal targets reportedly reaching $20 to $26 billion for 2026. Those targets are underwritten by enormous infrastructure commitments, including a reported $30 billion Azure compute deal alongside arrangements with Google Cloud and Nvidia — spending that only pencils out if enterprises keep expanding usage.

That is the context in which Opus 5's pricing strategy makes sense. Holding the price at Opus 4.8 levels while roughly doubling performance on key agentic benchmarks is effectively a steep price cut per unit of capability, designed to widen the funnel of workloads that are economical to automate. Every task that was marginal at Opus 4.8's cost-per-success becomes viable at Opus 5's — and every viable task is recurring token revenue.

The regulatory backdrop has grown more complex as well. A U.S. judge gave final approval this week to Anthropic's $1.5 billion copyright settlement with book authors, Reuters reported, closing a chapter of litigation over the company's early training data. And in June, Reuters, citing Axios, reported that the U.S. government had moved to block foreign access to Anthropic's most advanced models — a reminder that frontier AI is now entangled with export policy in ways that shape which customers can buy what.

Also shipping Friday: a Fast mode running at roughly 2.5 times default speed at twice the base price, automatic fallback routing on the API, and mid-conversation tool changes that no longer invalidate the prompt cache — a small feature that agent developers may appreciate more than any benchmark. Consistent with prior Opus models, Opus 5 carries no data retention requirements for general access, a point the spokesperson flagged unprompted for customers with "a hard zero data retention requirement." Developers can access the model as claude-opus-5 on the Claude API starting today.

Two questions will determine whether the bet pays off: whether Opus 5's efficiency claims survive contact with production workloads at scale, and whether enterprises embrace a world where safety classifiers, not users, sometimes decide which model answers. But the deeper message of Friday's launch is that the AI industry's center of gravity has moved. For three years, the labs competed on what their best model could do on its best day. With Opus 5, Anthropic is competing on something less glamorous and far more lucrative: what a very good model can do every day, for half the price. In a market where the frontier keeps moving, Anthropic is wagering that the real fortune lies just behind it.

]]> <![CDATA[Cisco, AMD partner to bring enterprise-level security, visibility to Ryzen AI Halo systems]]> https://tsecurity.de/de/3692178/it-security-nachrichten/cisco-amd-partner-to-bring-enterprise-level-security-visibility-to-ryzen-ai-halo-systems/ https://tsecurity.de/de/3692178/it-security-nachrichten/cisco-amd-partner-to-bring-enterprise-level-security-visibility-to-ryzen-ai-halo-systems/ Fri, 24 Jul 2026 19:18:26 +0200 📰 IT Security Nachrichten tsecurity.de

Cisco and AMD have expanded their partnership with a new package of hardware and security software that’s designed to help enterprise customers protect, deploy, and manage distributed AI resources.

During AMD’s Advancing AI event this week, Cisco’s president and chief product officer Jeetu Patel took to the stage during AMD CEO Lisa Su’s keynote to talk about how AI inference will be widely distributed and will require an architectural stack of software and tools that Cisco and AMD are partnering to develop.

The joint architecture combines AMD’s compact, high-performance Ryzen AI Halo hardware and a variety of Cisco networking, observability, governance, and security technologies. “AMD provides the deskside/local AI platform. At the foundation is AMD Ryzen AI Halo hardware, an isolated agent sandbox and the services needed for local-first inferencing, including model routing and token limits via AMD’s Semantic Router and local inference on Lemonade,” wrote Cisco’s Yash Sheth, senior director, engineering and research, in a blog post about the new package.

Ryzen AI Halo (pictured below) is designed to support local AI inference on an AI PC using its CPU, GPU, and XDNA neural processing unit (NPU), according to AMD. A resilient AI platform should continue delivering useful AI services even when connectivity is limited, models need to change, or workloads shift, AMD stated.

width="1024" height="576" sizes="auto, (max-width: 1024px) 100vw, 1024px">

AMD

Cisco then wraps that platform in a secure harness that includes its Splunk Agent Observability plus Splunk Infrastructure Monitoring to provide full-stack observability, tracking agent behavior, tokenomics and compute operation, according to Sheth.

Cisco also brings its AI Defense for model and agent security; DefenseClaw for security policy enforcement, so guardrails are enforced directly on-device, within the agent harness; and Cisco Cloud Control offering a single pane of glass for unified policy and control, Sheth stated.

“To make deskside and local AI computing work at enterprise scale, every AI node must be treated as a secure, managed node in the enterprise network,” Sheth wrote.

“The need for token efficiency and data sovereignty is driving a new class of computing, deskside computing, with users and teams putting AI agents right by their sides,” Sheth wrote. “Inference is moving to a hybrid architecture with thousands of ambient deskside agents in an enterprise helping employees have 24×7 productivity. That’s an extraordinary opportunity. It’s also a brand-new operating challenge.”

As agentic AI moves from experimentation to real enterprise workflows, organizations need more than powerful endpoints. AI agents can run continuously and act on enterprise data, but create new requirements for network infrastructure, tokenomics, agent behavior, and security, according to a statement from AMD.

“Running more AI locally can help improve responsiveness, keep sensitive data closer to users, and reduce dependence on cloud-only approaches, but enterprises also need a way to monitor and manage these systems at scale. AMD and Cisco are addressing that gap by collaborating to pair high-performance local AI compute with the observability, governance, and control infrastructure needed for enterprises to deploy it responsibly,” AMD stated.

“By combining AMD Ryzen AI Halo systems and our broader local AI software capabilities with Cisco’s enterprise networking, observability and security technologies, we are helping customers deploy AI in a way that is performant, secure, observable and manageable at scale,” said Jack Huynh, senior vice president and general manager, computing and graphics group with AMD, in a statement.

A few other interesting statistics and trends cited in AMD CEO Su’s keynote include:

  • AI adoption is accelerating across all industries, with agentic AI driving a surge in compute demand and shifting workloads from training to inference, which accounts for 60% of global AI compute capacity in 2026.
  • AI is moving beyond the cloud, with edge and personal devices becoming critical for real-time, distributed intelligence.
  • The AI accelerator market is projected to reach $1.4 trillion by 2030, nearly tripling previous forecasts, with GPUs expected to dominate but CPUs gaining new growth vectors due to agentic AI.
  • Server CPU market is forecasted to grow over 50% to $200 billion by 2030, fueled by rapid agentic AI adoption and the need for massive CPU infrastructure.
]]> <![CDATA[Shokz' new OpenDots Air aren't flashy, but after a month of testing I can confidently say they're the best clip-style open earbuds for most people]]> https://tsecurity.de/de/3692150/it-nachrichten/shokz-new-opendots-air-arent-flashy-but-after-a-month-of-testing-i-can-confidently-say-theyre-the-best-clip-style-open-earbuds-for-most-people/ https://tsecurity.de/de/3692150/it-nachrichten/shokz-new-opendots-air-arent-flashy-but-after-a-month-of-testing-i-can-confidently-say-theyre-the-best-clip-style-open-earbuds-for-most-people/ Fri, 24 Jul 2026 19:09:49 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[What an AI Robotic Arm Could Mean for New Zealand's Disability Support]]> https://tsecurity.de/de/3691952/it-nachrichten/what-an-ai-robotic-arm-could-mean-for-new-zealands-disability-support/ https://tsecurity.de/de/3691952/it-nachrichten/what-an-ai-robotic-arm-could-mean-for-new-zealands-disability-support/ Fri, 24 Jul 2026 17:49:14 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Netflix and Prime Video plot AI-driven homepages, but users say they don't want 'software to think' for them]]> https://tsecurity.de/de/3691906/it-nachrichten/netflix-and-prime-video-plot-ai-driven-homepages-but-users-say-they-dont-want-software-to-think-for-them/ https://tsecurity.de/de/3691906/it-nachrichten/netflix-and-prime-video-plot-ai-driven-homepages-but-users-say-they-dont-want-software-to-think-for-them/ Fri, 24 Jul 2026 17:38:32 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Bing Images Flaws Let Crafted SVGs Run Commands as SYSTEM on Microsoft's Servers]]> https://tsecurity.de/de/3691641/it-security-nachrichten/bing-images-flaws-let-crafted-svgs-run-commands-as-system-on-microsofts-servers/ https://tsecurity.de/de/3691641/it-security-nachrichten/bing-images-flaws-let-crafted-svgs-run-commands-as-system-on-microsofts-servers/ Fri, 24 Jul 2026 15:11:16 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Bing Images Flaws Let Crafted SVGs Run Commands as SYSTEM on Microsoft’s Servers]]> https://tsecurity.de/de/3691628/it-security-nachrichten/bing-images-flaws-let-crafted-svgs-run-commands-as-system-on-microsofts-servers/ https://tsecurity.de/de/3691628/it-security-nachrichten/bing-images-flaws-let-crafted-svgs-run-commands-as-system-on-microsofts-servers/ Fri, 24 Jul 2026 15:10:48 +0200 📰 IT Security Nachrichten tsecurity.de A crafted SVG submitted to Bing’s image search ran commands as NT AUTHORITY\SYSTEM on Microsoft’s production image-processing workers, and as root on the Linux machines in the same fleet. XBOW’s testing got the same result on workers across different hosts


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]]> <![CDATA[Facebook considers giving up and becoming TikTok]]> https://tsecurity.de/de/3691323/it-nachrichten/facebook-considers-giving-up-and-becoming-tiktok/ https://tsecurity.de/de/3691323/it-nachrichten/facebook-considers-giving-up-and-becoming-tiktok/ Fri, 24 Jul 2026 13:04:11 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Tor pauses new feature development to perform privacy audit on Firefox 153]]> https://tsecurity.de/de/3691258/it-security-nachrichten/tor-pauses-new-feature-development-to-perform-privacy-audit-on-firefox-153/ https://tsecurity.de/de/3691258/it-security-nachrichten/tor-pauses-new-feature-development-to-perform-privacy-audit-on-firefox-153/ Fri, 24 Jul 2026 12:27:09 +0200 📰 IT Security Nachrichten tsecurity.de The Tor Project has paused new feature development for Tor Browser 16 as developers work through more than 250 changes inherited from Firefox ESR 153 to ensure they do not weaken the browser's privacy and anonymity protections. The announcement accompanied the release of Tor Browser 16.0a9, the latest alpha build intended for testing rather than 


The post Tor pauses new feature development to perform privacy audit on Firefox 153 appeared first on CyberInsider.

]]> <![CDATA[ISC2 seeks input from IT pros for AI security certification]]> https://tsecurity.de/de/3691227/it-security-nachrichten/isc2-seeks-input-from-it-pros-for-ai-security-certification/ https://tsecurity.de/de/3691227/it-security-nachrichten/isc2-seeks-input-from-it-pros-for-ai-security-certification/ Fri, 24 Jul 2026 12:09:03 +0200 📰 IT Security Nachrichten tsecurity.de

ISC2 has begun developing a vendor-neutral AI security certification aimed at cybersecurity professionals working to secure AI systems and manage emerging AI risks.

The nonprofit organization, best known for the CISSP certification, says it is seeking volunteers worldwide to help define the knowledge and skills that will shape the new credential. While ISC2 has not finalized the certification domains, the certification is expected to address both technical AI security and governance topics, with a pilot exam planned before the end of 2026.

According to Casey Marks, ISC2 chief operating officer, feedback from cybersecurity practitioners led ISC2 to conclude that AI security had grown beyond expanding AI content within existing certifications.

“AI has reached a tipping point,” Marks says. “AI no longer is just another tool; instead, it has fundamentally changed the cybersecurity practice itself.”

ISC2 already includes AI-related content in certifications including CISSP and CCSP, but Marks says practitioners have identified new responsibilities and risks that extend beyond those programs. “Enterprise security teams are currently grappling with significant knowledge gaps, particularly around securing model architectures against new vulnerabilities like prompt injection, data poisoning, and model inversion,” Marks adds.

Organizations are working to understand emerging governance frameworks, including the NIST AI Risk Management Framework and ISO/IEC 42001, while adapting traditional application security and security operations workflows to AI systems, he says.

ISC2 has not finalized the certification domains, but Marks says the organization expects the credential to address both technical controls and governance practices for securing AI systems and managing AI risk. The certification will use ISC2’s established certification development process, which relies on cybersecurity practitioners to define job roles, develop exam content, and validate competencies.

Marks says ISC2 will continue to update the certification through ongoing input from cybersecurity professionals, in addition to its regular certification review process.

The organization is also determining which professionals the certification will target. Marks says AI security responsibilities are emerging across security architecture, risk management, security operations, software development security, governance and compliance, communication and network security, and security assessment and testing. ISC2 says the certification will reflect how those roles are evolving.

For organizations that are building AI security programs now, Marks recommends using existing AI training resources, adopting established governance frameworks, creating cross-functional AI security working groups, and participating in the certification development process.

Marks says ISC2 expects AI knowledge to become part of most cybersecurity roles while a more specialized AI security discipline continues to develop. He says organizations will increasingly need professionals with foundational AI security knowledge, as well as specialists in areas such as adversarial machine learning, model architectures, and AI data pipelines.

Looking ahead, Marks says he expects AI security expertise to evolve into both a foundational skill for cybersecurity professionals and a specialized discipline of its own.

“At this time, we are seeing a hybrid evolution occurring in real time: AI security is simultaneously becoming a baseline expectation for all security roles, while also carving out a dedicated, highly specialized discipline,” Marks says.

]]> <![CDATA[The Microsoft agent framework wars are over. The real architecture decision starts now]]> https://tsecurity.de/de/3691079/ai-nachrichten/the-microsoft-agent-framework-wars-are-over-the-real-architecture-decision-starts-now/ https://tsecurity.de/de/3691079/ai-nachrichten/the-microsoft-agent-framework-wars-are-over-the-real-architecture-decision-starts-now/ Fri, 24 Jul 2026 11:04:58 +0200 🔧 AI Nachrichten tsecurity.de

Over the past year, I had the same conversation with almost every team starting an AI initiative. Should we build on Semantic Kernel, AutoGen or Foundry?

At first it felt like the most important architectural decision we’d make. Each framework had its own philosophy, each promised to be the foundation for enterprise AI, and picking the wrong one felt like an expensive mistake. I spent a lot of time helping teams weigh the trade-offs.

Looking back, I think we were asking the wrong question. I certainly was.

I watched teams spend months debating SDKs while the decisions that actually decided whether their applications survived production went unexamined. Some built elaborate orchestration layers for workflows that a few deterministic functions would have handled. Others avoided agent frameworks entirely and later found they’d designed themselves into a corner.

Then Microsoft settled it for us. It introduced the unified Agent Framework, quietly moved Semantic Kernel and AutoGen into maintenance mode, and the debate I’d spent months refereeing was suddenly over. Turns out the answer to “which of the three” was “none of the three, here’s a fourth.” The framework hit version 1.0 and general availability in April 2026, stable across .NET and Python.

What surprised me wasn’t the decision. It was how fast a debate that had eaten so much of our attention stopped mattering. Microsoft changed the menu.

It didn’t change the meal.

The framework was never the hard part

Framework selection dominated almost every early conversation I had about enterprise agents. Which SDK do we standardize on? Which orchestration model gives us the most flexibility? Which one is Microsoft actually betting on?

Fair questions. But after a year of watching these projects play out, I’ve slowly come around to a different view. Those weren’t the questions that decided anything.

The first question I ask now is much smaller. Does this thing actually need an agent?

It sounds obvious, and I still get it wrong sometimes. But it’s the mistake I see most. On one project, a team spent weeks designing a multi-agent workflow for a process that ran the same four steps every time: read a document, validate it, call an API, send a notification. The diagrams looked great. The system in production didn’t. A few well-tested functions would have been easier to build, easier to maintain and a lot easier to trust.

Part of this is just that “agent” has become the word everyone reaches for. Sometimes it’s the right call. Sometimes it’s a workflow we already knew how to build, wearing a newer label. An agent earns its complexity when it genuinely has to decide things you can’t predetermine, choosing between tools, adapting to what it finds, working out its own next step. If you already know every step, you have a workflow, and a workflow is usually the better engineering choice. The consolidation didn’t change that. It just made it easier to see.

What building production agents actually taught me

Once I stopped fixating on frameworks, the same three problems kept showing up. None of them had anything to do with the SDK.

Context beats model choice

Early on I spent a lot of time comparing models, the way you’d agonize over a restaurant menu and then order what you always order. Now I spend most of it thinking about context, which is far less fun and far more useful.

I’ve watched good models fail because they were handed too much, not too little. One team I worked with gave the model access to nearly every internal document they had on the theory that more information meant better answers. It went the other way. Responses got slower, less consistent and sometimes skipped right past the thing that actually mattered. When we cut the context down to only what the task needed, the quality jumped almost immediately. I didn’t predict that. It taught me to be suspicious of “just give it everything.”

The best agent systems I’ve worked on weren’t the ones with the biggest context windows. They were the ones careful about what reached the model, and when. That’s not something the framework hands you.

Failure is where the real work is

Most agent demos look great because they’re built around the happy path. Production doesn’t extend that courtesy.

I remember a project where everything held up in testing. Then a downstream API timed out after the agent had already completed several earlier steps. We couldn’t just restart, because part of the business process had already gone through. We ended up spending far more time on recovery logic than we ever spent on prompts. That project changed how I think about this work. The hard part was never getting the model to make a decision. It was making sure the system didn’t fall apart when reality refused to follow the script.

Tool calls fail partway through. APIs return inconsistent data. Models call the same tool over and over because the last answer wasn’t what they wanted. That’s not the exception; that’s a normal Tuesday. Whether you retry, roll back, pause for a human or push on with partial results is a judgment call, and no framework is going to make it for you.

Identity is the real security boundary

This one surprised me most. The moment an agent stops being a chatbot and starts touching real business systems, identity matters more than orchestration.

Every project gets to the same question eventually. Who is this agent actually acting as? The developer’s credentials? A service account? The user who asked? Get it wrong and you’ve built something autonomous running with more access than any single person should have, which is exactly the kind of thing that looks fine until an audit. The Agent Framework, like most modern tooling, makes it easier to wire agents to tools through standards like the Model Context Protocol. That helps. But where human approval belongs, what needs extra authorization, how much rope to give the thing, those are still yours to decide.

The surprises weren’t technical

Here’s what I didn’t see coming. The hardest part of last year wasn’t technical at all. It was organizational. The moment a team heard “agent,” expectations shifted under everyone’s feet. Business stakeholders started expecting full autonomy. Developers assumed the thing could reason its way through anything. People started designing for flexibility before we’d even agreed on what problem we were solving. The word did damage before any code did. I found myself spending as much time resetting expectations as I did discussing architecture.

Build for change, not for today’s winner

I don’t think the teams that struggled last year picked the wrong framework. Semantic Kernel was reasonable. AutoGen was reasonable. Foundry made sense for plenty of cases. I’d have signed off on any of them.

The ones that got hurt put all their eggs in one framework, treating it as the foundation of the whole system instead of as one more dependency. Microsoft provided a migration path. But teams that had tightly coupled their applications to framework-specific abstractions discovered that migrating and rewriting are not the same thing. That wasn’t Microsoft’s doing. It was their own architecture’s. The teams that moved easily had kept their business logic, prompts and orchestration loose enough to evolve independently of any one SDK. For them, the change was a manageable project, not a teardown.

For what it’s worth, nobody I work with is treating this as an emergency. Most are moving the smaller workloads first, watching how they behave and leaving the production-critical systems alone until they actually understand the new abstractions. That’s the right instinct. And I doubt this is the last consolidation we’ll see, the ecosystem is still young, frameworks will keep absorbing each other and over time the differences between them will be operational more than architectural.

I don’t regret the framework debates, honestly. They were reasonable at the time. What changed wasn’t Microsoft’s roadmap.

It was mine. Watching these systems run in production taught me that the framework is the easiest piece to swap out. Recovery logic, context management, security boundaries, the business workflow itself, those stay with you long after today’s SDK gets replaced by tomorrow’s.

So, Microsoft made one decision easier by turning three frameworks into one. Good. Five years from now we’ll be on different tools, and we’ll still be asking the same handful of questions.

Does this actually need an agent? Does it have the right context? Can it recover when something breaks, because something will? Is it acting as the right person?

Those questions outlast every rewrite. That’s where I’ve learned to put my effort.

Frameworks come and go. Good architecture has to survive all of them.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Sponsor mismatch is the silent killer of enterprise transformation]]> https://tsecurity.de/de/3691067/it-nachrichten/sponsor-mismatch-is-the-silent-killer-of-enterprise-transformation/ https://tsecurity.de/de/3691067/it-nachrichten/sponsor-mismatch-is-the-silent-killer-of-enterprise-transformation/ Fri, 24 Jul 2026 11:03:44 +0200 📰 IT Nachrichten tsecurity.de

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

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

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

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

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

When governance becomes a debate about delivery practices

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

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

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

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

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

Authority is rarely the issue. Literacy is

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

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

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

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

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

Shift the conversation from delivery practices to business risk

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

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

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

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

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

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

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

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

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

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

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

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[European Commission Fines Google €890 Million for DMA Breaches]]> https://tsecurity.de/de/3691005/it-security-nachrichten/european-commission-fines-google-890-million-for-dma-breaches/ https://tsecurity.de/de/3691005/it-security-nachrichten/european-commission-fines-google-890-million-for-dma-breaches/ Fri, 24 Jul 2026 10:23:58 +0200 📰 IT Security Nachrichten tsecurity.de Google Fined

Google fined €890 million by the European Commission for breaching the Digital Markets Act (DMA) over its practices on Google Search and Google Play. The Commission issued two separate fines of €460 million and €430 million, finding that Google had failed to comply with the DMA's rules on self-preferencing and steering. The decisions concern how Google ranks its own services in search results and how app developers can communicate alternative offers to users through Google Play.

Google Fined Over Self-preferencing on Google Search

The Commission found that Google breached the DMA by giving preferential treatment to its own services, including shopping, hotels, transport and sports results, compared with third-party services appearing in Google Search. Under the DMA, designated gatekeepers are required to treat their own services and third-party services fairly and without discrimination in search rankings. According to the Commission, Google gives its own services greater prominence by placing them at the top of search results or displaying them with enhanced visuals and filters. The Commission said similar third-party services do not receive the same level of prominence. The Commission's decision requires Google to treat third-party services featured in its search results in a fair and non-discriminatory manner compared with its own services. The Commission also noted that Google has proposed and started testing changes to the way it presents its own services on Google Search, including free services covering shopping, hotels and flights. The Commission said these changes represent substantial progress towards compliance and will be monitored. Google has also proposed and started testing changes involving shopping ads and content-related services, including sports. The Commission is assessing these changes and will continue discussions with the company. The dialogue will also cover Google's proposals for applying the principles of the decision to AI Overviews and AI Mode.

Google Play Restrictions Lead to Second Fine

The second decision concerns Google's anti-steering practices on Google Play. Under the DMA, app developers distributing apps through Google Play must be able to inform customers about alternative, often cheaper offers at no cost. Developers should also be able to direct users to make purchases through other channels, including websites and alternative app stores. The Commission found that Google failed to meet these requirements. It said Google restricted app developers from freely communicating and promoting offers and from concluding contracts with users through distribution channels of their choice, including third-party app stores. The Commission acknowledged that Google can charge a fee for facilitating the initial acquisition of a new customer by an app developer through Google Play. However, it found that the level of Google's steering-related fees and the length of time those fees were charged went beyond what is considered compliant with the DMA. Google has since rolled out changes related to its steering terms. The Commission said these changes represent good progress towards compliance but will be assessed in light of the cease and desist order issued as part of the decision.

Digital Markets Act Enforcement Brings Compliance Deadline

As part of the two decisions, the Commission has ordered Google to end the identified non-compliance. The company must implement measures addressing both search rankings and its anti-steering rules. Google is required to comply with the Commission's decisions within 60 days. If it fails to do so, it could face periodic penalty payments of up to 5% of its total worldwide turnover. The fines take into account the gravity and duration of the non-compliance. The Commission said it also considered the recurrence of the breaches and concluded that the fines were proportionate and appropriate. Google may appeal the decisions.

Commission Investigations Began in 2024

Google was designated as a gatekeeper in September 2023 for its online search engine, Google Search. On 25 March 2024, the Commission opened non-compliance investigations into Google's measures addressing self-preferencing and its steering rules. On 19 March 2025, the Commission informed Google of its preliminary view that the company was in breach of the DMA. Google subsequently exercised its rights of defence by reviewing the documents in the Commission's investigation files and responding in writing to the preliminary findings. The two decisions followed a detailed investigation that included feedback from market participants and extensive dialogue with Google. The Commission said the decisions demonstrate its continued enforcement of the DMA and its focus on protecting fairness, business opportunities, consumer choice and innovation in digital markets.]]> <![CDATA[5 Biggest Samsung Launches from Galaxy Unpacked July 2026]]> https://tsecurity.de/de/3690910/linux-tipps/5-biggest-samsung-launches-from-galaxy-unpacked-july-2026/ https://tsecurity.de/de/3690910/linux-tipps/5-biggest-samsung-launches-from-galaxy-unpacked-july-2026/ Fri, 24 Jul 2026 09:23:44 +0200 🐧 Linux Tipps tsecurity.de Samsung has expanded its Galaxy lineup with the launch of five new devices at its Galaxy...

The post 5 Biggest Samsung Launches from Galaxy Unpacked July 2026 appeared first on Fossbytes.

]]> <![CDATA[New infosec products of the week: July 24, 2026]]> https://tsecurity.de/de/3690679/it-security-nachrichten/new-infosec-products-of-the-week-july-24-2026/ https://tsecurity.de/de/3690679/it-security-nachrichten/new-infosec-products-of-the-week-july-24-2026/ Fri, 24 Jul 2026 06:26:51 +0200 📰 IT Security Nachrichten tsecurity.de Here’s a look at the most interesting products from the past week, featuring releases from Astelia, Druva, Swimlane, and ThreatDown. Druva brings backup, recovery and governance to AI workloads Druva has announced Druva AI Resilience, a new approach that helps organizations recover, govern, and defend the systems, activity, and context behind AI-powered work. The launch introduces new and expanded capabilities for Microsoft Copilot, Claude Code, Druva Model Context Protocol (MCP), and Dru SRE Agent for 
 More →

The post New infosec products of the week: July 24, 2026 appeared first on Help Net Security.

]]> <![CDATA[Chrome for iPhone beta lets you ask Gemini about multiple open tabs]]> https://tsecurity.de/de/3690653/ios-mac-os/chrome-for-iphone-beta-lets-you-ask-gemini-about-multiple-open-tabs/ https://tsecurity.de/de/3690653/ios-mac-os/chrome-for-iphone-beta-lets-you-ask-gemini-about-multiple-open-tabs/ Fri, 24 Jul 2026 06:05:59 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[How to Update Cryptsetup for AF_ALG Compatibility]]> https://tsecurity.de/de/3690479/linux-tipps/how-to-update-cryptsetup-for-afalg-compatibility/ https://tsecurity.de/de/3690479/linux-tipps/how-to-update-cryptsetup-for-afalg-compatibility/ Fri, 24 Jul 2026 02:10:35 +0200 🐧 Linux Tipps tsecurity.de <![CDATA[OpenAI Agent Escaped Testing and Launched an Autonomous Hack]]> https://tsecurity.de/de/3690426/it-nachrichten/openai-agent-escaped-testing-and-launched-an-autonomous-hack/ https://tsecurity.de/de/3690426/it-nachrichten/openai-agent-escaped-testing-and-launched-an-autonomous-hack/ Fri, 24 Jul 2026 01:21:35 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[OpenAI models escape containment, hack Hugging Face]]> https://tsecurity.de/de/3690373/it-security-nachrichten/openai-models-escape-containment-hack-hugging-face/ https://tsecurity.de/de/3690373/it-security-nachrichten/openai-models-escape-containment-hack-hugging-face/ Fri, 24 Jul 2026 00:42:58 +0200 📰 IT Security Nachrichten tsecurity.de <p>OpenAI reported this week that its autonomous AI models escaped an isolated testing environment during a training exercise and breached Hugging Face, an AI collaboration platform.</p> <p>Last week, Hugging Face <a target=”_blank” href=”https://huggingface.co/blog/security-incident-july-2026″ rel=”noopener”>disclosed</a> that it “detected and responded to


Read more →

The post OpenAI models escape containment, hack Hugging Face appeared first on IT Security News.

]]> <![CDATA[Xbox Testing Ad-Supported Streaming With Xbox Insiders]]> https://tsecurity.de/de/3690370/it-nachrichten/xbox-testing-ad-supported-streaming-with-xbox-insiders/ https://tsecurity.de/de/3690370/it-nachrichten/xbox-testing-ad-supported-streaming-with-xbox-insiders/ Fri, 24 Jul 2026 00:36:16 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[OpenAI models escape containment, hack Hugging Face]]> https://tsecurity.de/de/3690354/it-security-nachrichten/openai-models-escape-containment-hack-hugging-face/ https://tsecurity.de/de/3690354/it-security-nachrichten/openai-models-escape-containment-hack-hugging-face/ Fri, 24 Jul 2026 00:27:20 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Stable Channel Update for Desktop]]> https://tsecurity.de/de/3690353/it-security-nachrichten/stable-channel-update-for-desktop/ https://tsecurity.de/de/3690353/it-security-nachrichten/stable-channel-update-for-desktop/ Fri, 24 Jul 2026 00:27:13 +0200 📰 IT Security Nachrichten tsecurity.de The Stable channel has been updated to 150.0.7871.186/.187 for Windows and Mac and 150.0.7871.186 for Linux, which will roll out over the coming days/weeks. A full list of changes in this build is available in the Log


Security Fixes and Rewards

Note: Access to bug details and links may be kept restricted until a majority of users are updated with a fix. We will also retain restrictions if the bug exists in a third party library that other projects similarly depend on, but haven’t yet fixed.


This update includes 4 security fixes. Please see the Chrome Security Page for more information.


[N/A][518237034] High CVE-2026-16807: Out of bounds write in Codecs. Reported by Google on 2026-05-30

[N/A][522064153] High CVE-2026-16806: Use after free in WebMCP. Reported by Google on 2026-06-10

[N/A][523292588] High CVE-2026-16805: Use after free in Blink. Reported by Google on 2026-06-12

[N/A][524721670] High CVE-2026-16804: Use after free in Input. Reported by Google on 2026-06-16


We would also like to thank all security researchers that worked with us during the development cycle to prevent security bugs from ever reaching the stable channel.


Many of our security bugs are detected using AddressSanitizer, MemorySanitizer, UndefinedBehaviorSanitizer, Control Flow Integrity, libFuzzer, or AFL.



Interested in switching release channels? Find out how here. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.


Daniel Yip

Google Chrome


]]> <![CDATA[Agentic coding goes hands free as OpenAI brings GPT-Live's full duplex voice control to Codex and ChatGPT on the desktop]]> https://tsecurity.de/de/3690348/it-nachrichten/agentic-coding-goes-hands-free-as-openai-brings-gpt-lives-full-duplex-voice-control-to-codex-and-chatgpt-on-the-desktop/ https://tsecurity.de/de/3690348/it-nachrichten/agentic-coding-goes-hands-free-as-openai-brings-gpt-lives-full-duplex-voice-control-to-codex-and-chatgpt-on-the-desktop/ Fri, 24 Jul 2026 00:20:42 +0200 📰 IT Nachrichten tsecurity.de Two weeks after debuting its more naturalistic GPT-Live audio AI model with full-duplex capabilities (listening and speaking at the same time), OpenAI is bringing it directly into developer workflows.

The company announced that GPT-Live now powers the ChatGPT desktop application on macOS and Windows, integrating directly with agentic systems like Codex and ChatGPT Work (which are separate experiences available in the ChatGPT desktop app).

When OpenAI initially launched GPT-Live on July 8, 2026, it introduced a continuous audio model capable of listening and speaking simultaneously—eliminating rigid turn-taking while delegating complex reasoning to background models like GPT-5.5.

Today's release expands that conversational layer to technical tasks, enabling software engineers to orchestrate multi-threaded coding jobs, review pull requests, and debug applications using natural voice commands.

As such, it could usher in a new era of "hands free" software development and even live, in-person group coding parties for Codex's more than 5 million weekly active users. Codex, of course, is the name given to OpenAI's models and harness focused on coding, but which the company has this year expanded into a more general productivity platform. An OpenAI spokesperson told VentureBeat this is the first time voice activation has been included natively with Codex on the desktop.

OpenAI posted a promotional video showing some of its employees, Codex developer experience engineer Jason Liu and Codex technical staffer Guinness Chen, speaking to the same ChatGPT desktop app session in the same room, each issuing different instructions and conversing with the same model.

New capabilities unlocked

At its core, this integration relies on decoupling the real-time voice layer from the underlying execution engines.

While GPT-Live maintains fluid conversation—inserting natural verbal acknowledgments like "got it" without interrupting the user—it passes heavy computational workloads to background reasoning models.

On macOS, the desktop application incorporates "Appshots" and screen context features, allowing ChatGPT Voice to analyze the frontmost window alongside local files, codebase structures, and active plugins.

This architecture creates a pair-programming dynamic where developers talk through problems conversationally while agents execute tasks asynchronously.

Rather than manually stopping coding sessions to type detailed instructions or switch windows, developers direct the system hands-free.

The full-duplex engine dynamically decides when to speak, pause, or invoke tools, maintaining conversational state even as background agents process complex code modifications.

Directing coding and complex builds with your voice alone

The central operational capability in this update centers on multi-task execution across Codex and ChatGPT Work environments.

Software engineers can initiate multiple concurrent task threads from a single spoken prompt. For instance, a developer preparing to ship a feature can instruct the system to investigate an open authentication bug, review a pending API migration pull request, and generate missing unit tests simultaneously.

The desktop application coordinates these actions across disparate contexts, tracing issues through Slack conversations, GitHub repositories, and local codebases.

Developers can also verbally convert design mockups into working code, splitting tasks across frontend, backend, and testing layers.

With support for multi-folder projects (build 26.715) and remote execution via iOS, engineers can check task progress, answer agent prompts, and redirect active jobs without switching applications or managing individual processes line by line.

Proprietary license

OpenAI’s voice-enabled desktop release operates under a proprietary, commercial enterprise model. Access is restricted to paid subscribers across Plus, Pro, Business, Enterprise, and Education plans.

For individual developers and corporate engineering departments, this commercial structure means the model weights, voice processing pipelines, and agent state architectures remain fully closed.

Organizations cannot modify or self-host the underlying systems. Furthermore, tasks initiated via ChatGPT Voice consume standard usage allocations directly from existing Codex and ChatGPT Work plan quotas, treating voice-triggered actions identically to standard agentic workloads.

Community reactions

Developer communities immediately noted the implications of bringing continuous full-duplex voice to autonomous coding workflows.

Reacting to the build 26.715 release announcement—which details voice integration and multi-folder project support—AI Insider journalist @ChrisGPT noted on X: "Today OpenAI will release voice and remote guidance for codex ! One step closer to personal AGI".

Early technical feedback highlights widespread enthusiasm for orchestrating complex agentic tasks hands-free, particularly when stepping away from the workstation or managing build pipelines remotely.

]]> <![CDATA[I built a Linux filesystem benchmark for corruption, snapshots, rebuilds and ENOSPC across 26 storage layouts]]> https://tsecurity.de/de/3690327/linux-tipps/i-built-a-linux-filesystem-benchmark-for-corruption-snapshots-rebuilds-and-enospc-across-26-storage-layouts/ https://tsecurity.de/de/3690327/linux-tipps/i-built-a-linux-filesystem-benchmark-for-corruption-snapshots-rebuilds-and-enospc-across-26-storage-layouts/ Fri, 24 Jul 2026 00:13:06 +0200 🐧 Linux Tipps tsecurity.de

Most filesystem benchmarks measure throughput on a freshly formatted single device. That is useful, but it misses many of the reasons people choose btrfs, ZFS or bcachefs in the first place.

I built modern-fs-benchmark to examine modern filesystems as complete storage systems, including their features, failure modes and behavior over time.

The current matrix contains 26 configurations across btrfs, ZFS, bcachefs, ext4 and XFS over md/LVM, dm-integrity, native and LUKS encryption, parity layouts, and XFS on a ZFS zvol.

Hosted CI dashboard:

https://bartosz.fenski.pl/modern-fs-benchmark/

Experimental real-hardware dashboard:

https://bartosz.fenski.pl/modern-fs-benchmark/real-hw/

Apache-2.0 licensed source and complete methodology:

https://github.com/fenio/modern-fs-benchmark

The raw benchmark datasets are published under CC BY 4.0. Every result records the kernel and filesystem tool/module versions. CI artifacts also contain a full command trace, so the exact workload is inspectable rather than hidden behind a chart.

Why I started this project

Many filesystem benchmarks follow a familiar pattern: create a filesystem with mostly default options, mount it, run fio or another generic workload, and compare throughput.

General-purpose suites such as the Phoronix Test Suite are useful for conventional performance comparisons, but they do not focus on the machinery that makes modern copy-on-write filesystems interesting.

I wanted a benchmark designed around btrfs, ZFS and bcachefs as multi-device storage systems rather than treating them as interchangeable replacements for ext4 on a single freshly formatted disk.

That means testing behavior such as:

- Redundancy and degraded operation

- Snapshot aging, scaling and reclamation

- Compression and encryption

- Reflinks and clone divergence

- Fsync tail latency and responsiveness under load

- Rebuild and scrub behavior

- Near-full and hard-ENOSPC behavior

- Data integrity and recovery from corruption

Ext4 and XFS over md, LVM and dm-integrity are included as classic-stack baselines so the costs and benefits of integrated CoW designs can be compared with layered alternatives.

Silent corruption is one particularly important example. When one redundant copy is deliberately corrupted behind the filesystem, checksumming filesystems such as btrfs, ZFS and bcachefs can identify the damaged copy and recover from a valid replica.

Traditional md/LVM redundancy without data checksums can notice during a scrub that its copies disagree, but it cannot determine which one is correct. In my tests, some classic configurations returned corrupted data successfully without an application-visible error.

The dm-integrity configuration shows that a classic layered stack can obtain integrity protection too, with a measurable performance cost.

An important limitation

The main dashboard uses loop devices on GitHub-hosted VMs. Absolute throughput numbers and small differences between filesystems should not be interpreted as hardware rankings.

The hosted runs are primarily useful for:

- Correctness and integrity outcomes

- Comparisons within the same job

- Large behavioral differences

- Snapshot-aging and near-full shapes

- Trends across repeated runs

Real disks are required for meaningful absolute performance, concurrency scaling, device parallelism and mixed-media topologies.

I am aware of this limitation and do not want the hosted dashboard to suggest more precision than the underlying environment can provide.

The real-hardware experiment

Kent Overstreet, the creator of bcachefs, made one of his Hetzner machines available for a real-hardware experiment.

The server had two physical NVMe devices. It completed three full benchmark runs, which are available in the separate real-hardware dashboard. These runs provided meaningful absolute measurements and exposed concurrency behavior that cannot be observed when several loop devices share one virtual disk.

During the fourth run, one NVMe controller entered the kernel’s dead state. The existing machine environment had its operating system on RAID0 across the two NVMe devices, so the hardware failure also made part of /nix/store unreadable and eventually prevented new SSH sessions.

The incomplete fourth run is not being published as benchmark data. This was a failure of the underlying hardware, not a result attributable to any filesystem being tested.

I am grateful to Kent for providing the machine and making the real-hardware experiment possible. Without that access, the three existing hardware runs would not exist.

Where I would like to take it

Better hardware would not merely produce more reliable throughput numbers. It would enable an entirely new class of tests designed for multi-device and multi-tier filesystems.

I would eventually like to run the suite on a machine containing several storage classes, for example two HDDs, two SSDs and an NVMe device.

That could support scenarios such as:

- HDD, SSD and NVMe baselines using identical workloads

- bcachefs foreground and background targets

- ZFS HDD data vdevs with SSD special vdevs

- Separate ZFS L2ARC and SLOG experiments

- LVM dm-cache in writeback and writethrough modes

- Metadata and small-block placement on faster media

- Foreground latency during background migration

- Contention between fast and slow storage tiers

- Degraded operation and rebuild under application load

- Performance before, during and after promoting or evacuating a storage tier

These are the kinds of scenarios for which multi-device and multi-tier filesystems are built, but they cannot be represented honestly when every “device” is a loop file backed by the same cloud disk.

I am considering either renting a suitable dedicated server or eventually building and hosting my own machine. Providers such as Worldstream offer configurations close to what I need, but the recurring cost is currently outside the project’s budget.

For now, the benchmark will remain in its hosted-CI form for an unknown amount of time. The existing dashboard will continue to be useful for correctness, behavioral comparisons and regression tracking, but it cannot answer every real-hardware performance question or model complex mixed-media topologies.

I am also open to running the suite on hardware provided by someone else. A useful environment would need Linux root access, clearly identified block devices that may be wiped, and enough uninterrupted access to complete repeated runs. The hardware description, methodology and resulting data would remain public.

I would appreciate technical feedback:

- Which current tests are misleading or unfair?

- Which failure scenarios are missing?

- Which mixed-media topologies would be most useful?

- Which additional filesystems or layered stacks should be included?

- Which results deserve deeper investigation?

The methodology, implementation and raw results are public. If a filesystem is being tested in a way that misrepresents it, I consider that a bug in the benchmark.

submitted by /u/bfenski
[link] [comments]]]> <![CDATA[We Hooked 19 Power Stations Up to a Fridge. Here’s Why Bigger Isn’t Always Better]]> https://tsecurity.de/de/3690302/it-nachrichten/we-hooked-19-power-stations-up-to-a-fridge-heres-why-bigger-isnt-always-better/ https://tsecurity.de/de/3690302/it-nachrichten/we-hooked-19-power-stations-up-to-a-fridge-heres-why-bigger-isnt-always-better/ Fri, 24 Jul 2026 00:03:17 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Apple Readies Second-Generation MacBook Neo in Major Mac Lineup Overhaul]]> https://tsecurity.de/de/3690224/it-nachrichten/apple-readies-second-generation-macbook-neo-in-major-mac-lineup-overhaul/ https://tsecurity.de/de/3690224/it-nachrichten/apple-readies-second-generation-macbook-neo-in-major-mac-lineup-overhaul/ Thu, 23 Jul 2026 23:04:57 +0200 📰 IT Nachrichten tsecurity.de Apple is reportedly testing a MacBook Neo with an A19 Pro chip and 12GB of RAM as shortages and higher prices reshape its broader Mac lineup.

The post Apple Readies Second-Generation MacBook Neo in Major Mac Lineup Overhaul appeared first on TechRepublic.

]]> <![CDATA[Top 10 Best Physical Security Penetration Testing Firms 2026]]> https://tsecurity.de/de/3690103/it-security-nachrichten/top-10-best-physical-security-penetration-testing-firms-2026/ https://tsecurity.de/de/3690103/it-security-nachrichten/top-10-best-physical-security-penetration-testing-firms-2026/ Thu, 23 Jul 2026 21:39:17 +0200 📰 IT Security Nachrichten tsecurity.de In an era dominated by cyber threats, the importance of physical security penetration testing often gets overshadowed. However, a robust security posture requires a holistic approach that addresses vulnerabilities in both the digital and physical realms. A determined attacker can


Read more →

The post Top 10 Best Physical Security Penetration Testing Firms 2026 appeared first on IT Security News.

]]> <![CDATA[2nd Public Beta of iOS 27, iPadOS 27, & MacOS 27 Golden Gate Available for Testing]]> https://tsecurity.de/de/3690101/ios-mac-os/2nd-public-beta-of-ios-27-ipados-27-macos-27-golden-gate-available-for-testing/ https://tsecurity.de/de/3690101/ios-mac-os/2nd-public-beta-of-ios-27-ipados-27-macos-27-golden-gate-available-for-testing/ Thu, 23 Jul 2026 21:37:49 +0200 🍏 iOS / Mac OS tsecurity.de Apple has released the second public beta versions of iOS 27, iPadOS 27, and MacOS 27 Golden Gate, to users who are eligible. If you’re interested in installing those betas on your device, you can read instructions for iPhone and iPad here, or for Mac here. MacOS 27, iOS 27, and iPadOS 27 have an ... Read More

The post 2nd Public Beta of iOS 27, iPadOS 27, & MacOS 27 Golden Gate Available for Testing appeared first on OS X Daily.

]]> <![CDATA[Apple Confirms The Morning Show Will End With Season 5]]> https://tsecurity.de/de/3690100/ios-mac-os/apple-confirms-the-morning-show-will-end-with-season-5/ https://tsecurity.de/de/3690100/ios-mac-os/apple-confirms-the-morning-show-will-end-with-season-5/ Thu, 23 Jul 2026 21:37:48 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Top 10 Best Physical Security Penetration Testing Firms 2026]]> https://tsecurity.de/de/3690084/it-security-nachrichten/top-10-best-physical-security-penetration-testing-firms-2026/ https://tsecurity.de/de/3690084/it-security-nachrichten/top-10-best-physical-security-penetration-testing-firms-2026/ Thu, 23 Jul 2026 21:34:48 +0200 📰 IT Security Nachrichten tsecurity.de In an era dominated by cyber threats, the importance of physical security penetration testing often gets overshadowed. However, a robust security posture requires a holistic approach that addresses vulnerabilities in both the digital and physical realms. A determined attacker can bypass sophisticated cyber defenses simply by walking through an unlocked door, exploiting weak physical controls, [
]

The post Top 10 Best Physical Security Penetration Testing Firms 2026 appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

]]> <![CDATA[NetworkManager update advances IPv6-only support, Wi‑Fi management, and security for Linux-based operating systems]]> https://tsecurity.de/de/3690083/it-security-nachrichten/networkmanager-update-advances-ipv6-only-support-wifi-management-and-security-for-linux-based-operating-systems/ https://tsecurity.de/de/3690083/it-security-nachrichten/networkmanager-update-advances-ipv6-only-support-wifi-management-and-security-for-linux-based-operating-systems/ Thu, 23 Jul 2026 21:34:46 +0200 📰 IT Security Nachrichten tsecurity.de

Networking is core to any operating system, and when it comes to Linux, it’s actually a combination of several key components. The Linux kernel handles the data plane, moving packets, and holding live device state. NetworkManager is the network configuration service, operating as the control plane, deciding what a device’s configuration should be.

NetworkManager 1.58 was released this week, following more than five months of development and 407 commits since version 1.56. The release covers three areas: expanded support for IPv6-only networks, a set of Wi-Fi management updates, and a round of security hardening.

IPv4 address exhaustion remains the pressure behind the first of those areas, pushing more networks toward IPv6-only operation every year.

“More networks, mobile carriers, cloud providers, and anyone squeezed by IPv4 exhaustion are running IPv6-only by default,” Chris Van Hoof, director of Linux engineering, platform enablement at Red Hat, told Network World.

Advancing IPv6-only support

Dual stack networking, running IPv4 and IPv6 in parallel, has been the default IPv6 transition strategy for years. Dual stack networking, however, has a structural problem in that it still requires an IPv4 address on every device, so it does nothing to relieve address exhaustion pressure.

An alternative model called IPv6-mostly addresses that gap. It is defined in RFC 8925, “IPv6-Only-Preferred Option for DHCPv4,” and lets capable clients drop IPv4 entirely while legacy hosts that still need it keep receiving it on the same network segment.

“NetworkManager can also now auto-signal RFC 8925’s IPv6-only-preferred option, telling the network a host is fine skipping an IPv4 lease entirely,” Van Hoof said.

For the traffic that still needs IPv4, NetworkManager 1.58 adds support for CLAT, short for customer-side translator. CLAT is the client-side half of 464XLAT, a mechanism defined in RFC 6877, “464XLAT: Combination of Stateful and Stateless Translation.”

464XLAT pairs CLAT on the endpoint, which performs stateless header translation, with a stateful NAT64 translator on the provider side, letting IPv4-only apps keep functioning on a network that has no IPv4 of its own.

“CLAT is the translation layer that lets legacy IPv4-only apps and services keep working on those networks without bolt-on middleware,” Van Hoof said.

Wi-Fi management updates

NetworkManager 1.58 also brings a set of changes to how the daemon handles Wi-Fi connections and configuration.

  • Band selection: The band property of Wi-Fi connections now accepts a 6GHz value, and a Wi-Fi scan run through nmcli, NetworkManager’s command line tool, now shows each access point’s band as well.
  • Credential handling: WPS credentials with a 64 character hex PSK are now accepted, matching what some access points return.
  • Text interface improvements: nmtui, NetworkManager’s menu driven text interface, picked up several usability additions. A new device select button lets you choose a physical interface from a list instead of typing its name. The activation screen gained a rescan Wi-Fi button, and secret prompts now include a show password checkbox. There is also a share QR code option, mirroring the existing nmcli device wifi show-password command.

Security hardening

The release fixes vulnerabilities and tightens several defaults tied to DHCP handling and connection permissions.

  • CVE-2026-10805: Hostnames and MUD URLs are now validated before being written to the dhclient configuration file, rejecting characters that could alter the config syntax.
  • DHCPv4 client fix: An out-of-bounds read in the internal DHCPv4 client, triggerable by an on-link attacker with a malformed UDP packet, has been fixed.
  • Router option validation: The internal DHCPv4 client now ignores DHCP option 3, the Router option, when a lease also contains option 121, the Classless Static Route option, following the recommendation in RFC 3442.
  • Permission checks and deprecations: For private connections that restrict access to specific users, NetworkManager now verifies that the user can access the referenced 802.1X certificates and keys.

Tunneling and automation updates

Two smaller but practical additions round out this release: a new tunnel type for virtualized networks, and a fix that closes a gap in how NetworkManager’s state survives a reboot.

NetworkManager 1.58 also adds support for creating and managing GENEVE tunnel interfaces. GENEVE, short for Generic Network Virtualization Encapsulation, is a tunneling protocol that wraps Ethernet frames inside UDP packets, letting virtualized or overlay networks run on top of physical Layer 3 infrastructure. It shows up mainly in virtualization and cloud environments, where a hypervisor or container networking layer needs to build a virtual network segment across physical hosts. Previously, NetworkManager could not create or manage these interfaces directly.

The release also adds persisted managed state. NetworkManager tracks whether it is responsible for a given network device, a setting called its managed state. Until now, that setting reset on every reboot, so provisioning tools had to reapply it each time a system restarted. NetworkManager 1.58 lets the managed state survive a reboot when it is set through nmcli or the D-Bus API.

“It’s a small change but closes a real automation gap: Provisioning tools and cloud-init style workflows can set a device’s state once via D-Bus or nmcli and trust it survives a reboot, instead of reapplying config every time,” Van Hoof said.

]]> <![CDATA[New pip flag fixes longstanding Python frustration]]> https://tsecurity.de/de/3690075/ai-nachrichten/new-pip-flag-fixes-longstanding-python-frustration/ https://tsecurity.de/de/3690075/ai-nachrichten/new-pip-flag-fixes-longstanding-python-frustration/ Thu, 23 Jul 2026 21:27:38 +0200 🔧 AI Nachrichten tsecurity.de

A new version of Python’s native package management tool, pip, will remove a limitation that has frustrated Python developers for years. If you wanted to install the dependencies for a given package, but not install the package itself, you were stuck. Either you had to extract the dependency list from the package and install it by hand, or you had to build the whole package anyway.

Why was this a problem? Sometimes, you want only the dependencies for a package—for instance, as a way to create a separate environment for testing or another project. If you’re making source distributions via CI, some requirements might be needed to make the source distribution but aren’t actually included in it (e.g., Cython). You would need to install these requirements somewhere—apart from the project itself—to perform the build step.

A dependencies-only install mode for packages is a long-requested feature. Developer James O’Claire found many examples of such requests, along with various workarounds. Most of those involved third-party solutions of some sort.

Now, a new feature set to land in pip version 26.2 will fix this problem from the inside. The command pip install --only-deps will install only the dependencies for a given package. Note that it will not install build dependencies for the package, but only runtime dependencies.

Note that you can accomplish this with existing third-party tools. For instance, uv sync --no-install-project has the same behavior. But having this functionality right inside pip means you don’t have to turn to external tooling—or ugly hacks—to solve the problem. This may be an example of how third-party projects like uv (a superfast Python package installer) are inspiring native Python features where they make sense.

pip 26.2 is scheduled to be released by the end of July 2026.

]]> <![CDATA[Microsoft accepts defeat on the Windows 11 Copilot key with a “Do nothing” switch, but PCs still ship with it]]> https://tsecurity.de/de/3690057/windows-tipps/microsoft-accepts-defeat-on-the-windows-11-copilot-key-with-a-do-nothing-switch-but-pcs-still-ship-with-it/ https://tsecurity.de/de/3690057/windows-tipps/microsoft-accepts-defeat-on-the-windows-11-copilot-key-with-a-do-nothing-switch-but-pcs-still-ship-with-it/ Thu, 23 Jul 2026 21:14:30 +0200 đŸȘŸ Windows Tipps tsecurity.de Windows 11 is testing a new “feature” that will let you reassign the Copilot key to do nothing. Yes, literally.

The post Microsoft accepts defeat on the Windows 11 Copilot key with a “Do nothing” switch, but PCs still ship with it appeared first on Windows Latest

]]> <![CDATA[Valve puts up a Proton 11.0-2 Release Candidate for testing]]> https://tsecurity.de/de/3690029/linux-tipps/valve-puts-up-a-proton-110-2-release-candidate-for-testing/ https://tsecurity.de/de/3690029/linux-tipps/valve-puts-up-a-proton-110-2-release-candidate-for-testing/ Thu, 23 Jul 2026 20:55:38 +0200 🐧 Linux Tipps tsecurity.de

Read the full article on GamingOnLinux.

]]> <![CDATA[Multi-turn attacks broke AI models 88% of the time — single-turn testing missed it, Cisco AI security lead warns at VB Transform 2026]]> https://tsecurity.de/de/3690018/it-nachrichten/multi-turn-attacks-broke-ai-models-88-of-the-time-single-turn-testing-missed-it-cisco-ai-security-lead-warns-at-vb-transform-2026/ https://tsecurity.de/de/3690018/it-nachrichten/multi-turn-attacks-broke-ai-models-88-of-the-time-single-turn-testing-missed-it-cisco-ai-security-lead-warns-at-vb-transform-2026/ Thu, 23 Jul 2026 20:48:24 +0200 📰 IT Nachrichten tsecurity.de When Cisco ran 6,986 multi-turn attacks against 15 flagship models, attackers who adapted across the conversation broke through as often as 88.3% of the time. Amy Chang, Cisco's head of AI threat intelligence and security research, brought that finding to the agentic security panel at VB Transform 2026; the number should worry anyone still running single-turn red-teaming programs.

VentureBeat's June 2026 Pulse survey of 107 enterprise respondents explains why the room was full. More than half, 54%, have already had a confirmed agent security incident (18%) or a near-miss caught before harm (36%). Just 32% give every agent its own scoped, managed identity, and fewer still, 30%, isolate their highest-risk agents in sandboxes. Provider-native and hyperscaler controls remain the primary agent security layer at 82% of companies surveyed. The world's largest security vendors have done the same math.

Palo Alto Networks closed its $25 billion acquisition of CyberArk in February, CrowdStrike agreed in January to pay $740 million for SGNL, and Cisco announced its intent to acquire Astrix Security for a reported $400 million, all of it aimed at the identity and isolation layer most enterprises have not finished building.

Chang came to the panel with almost two decades of experience spanning cybersecurity operations, government, and the military. She ran global cybersecurity operations as an executive director at JPMorgan Chase, where she led the bank's cyber threat intelligence teams, and served as a senior staffer on the House Foreign Affairs Committee and as a U.S. Navy Reserve officer. She also teaches cybersecurity and emerging threats as adjunct faculty at the Middlebury Institute of International Studies.

Chang's 88.3% number comes from a study she co-authored with Nicholas Conley, built on 30,090 single-turn prompts and 6,986 multi-turn attacks against those 15 closed and proprietary flagship models. Multi-turn success rates ranged from 7.89% to 88.3%, every model tested showed non-trivial multi-turn exposure, and the two testing styles did not even rank the models in the same order. Cisco publishes adversarial evaluation signals for what is now 105 models on its LLM Security Leaderboard, she told the audience.

"If you don't understand how models are susceptible to different types of attacks, then you are unable to account for how that model that is powering your agent, that is powering your application, to understand where those failure points are," Chang said. Single-turn testing is the one-shot malicious prompt, she explained, while extending an attack into a longer conversation "is more realistic of how we are actually engaging with our models, with our agents, with our applications." That longer arc surfaces harmful outputs and misaligned behaviors that a snapshot never catches.

Cisco has pushed the testing itself into agentic territory. Chang described a framework where agents assess a deployment scenario, develop relevant attacks, judge whether they are worth pursuing, execute them, and evaluate their own success. What surprised her most, after all that sophistication, was how simple the defensive answer stays. "The answer is still that it's pretty simple," she said. "You don't have to get super creative. You just need to think about truly what are the fundamentals and basics of what I'm trying to secure in my organization."

Her starting point for CISOs beginning agentic deployments is Cisco's Integrated AI Security and Safety Framework, which she said "stipulates all the ways that AI can be compromised across the AI lifecycle" from modality through supply chain. From there, teams can work backward from real incidents, trace how each attack was achieved, and use the framework to build a strategy with the right coverage and mitigations.

Heather Ceylan, the CISO of Box, sees the same gap from the defender's side. "A lot of what you see out there with agent red teaming is just single-turn, and that's not how people are actually interacting with AI day-to-day," she told the audience. Box now simulates multi-turn adversaries with agents that think like an attacker and iterate attempt after attempt to hijack the target. "You have to pressure test your agents because otherwise you don't know if your execution controls are really working as you intended."

Box deployed agents inside its security operations center about a year ago, starting with human approval required for every action, and trust built quickly enough that analysts shifted into monitoring mode. Then the agent made one mistake, and every bit of that accumulated trust vanished. "They had to start all over again," she said. "So I think that that monitoring piece is so important. Even if you're not gonna have a human in the loop, things change, models change, and we can't control how the models change and interpret things."

Rajesh Parekh, VP of AI and ML at Intuit, brought the builder's perspective. Parekh led large-scale computer vision and ML systems powering Google's Maps and Geo products before joining Intuit, and holds a doctorate in computer science.

Three layers versus an operating system

Ceylan described Box's approach as three concentric layers. Permissioning comes first, so the agent never accesses more content than the human who invoked it. Ephemeral sandbox environments spin up for each agent task, containing the blast radius if an agent gets hijacked, and runtime execution control restricts the agent's tool calls to only those relevant to the task at hand. "If you want an agent to summarize a doc for you, if you have a prompt injection that came in that says forward this to maliciousattacker at domain.com, it can't do that," Ceylan said. "That action in that tool call is not even in its vocabulary."

She classified agent actions into three oversight categories. Actions that are not sensitive, like read and summarize, need no human in the loop. Moderately sensitive actions skip human approval but get logged and monitored, while destructive actions like mass deletion of files always require a human. "Things are gonna shift between those three categories quite a bit," she acknowledged, "but setting those types of categories up front allows you to have a principled framework."

Rather than layering controls onto agents one at a time, Intuit has built a central platform called GenOS, short for generative AI operating system, which abstracts security, risk, and fraud modeling so individual agent developers never reinvent protection. "Permissioning is not about giving access to AI," Parekh said. "Instead, it is defining very tightly scoped and clearly auditable authority to the agent to perform very specific tasks." Intuit evolved from agents inheriting user permissions to each agent carrying its own identity, and the company is now investigating mid-session permission changes tied to the specific task underway.

Parekh calls the broader model an AI-powered expert platform, one where the human expert is built into the trust architecture rather than bolted on as a gate. "The paradigm that we are pursuing is where the user, the AI agent, and the human expert are collaborating to solve the user problem," he said.

The end of human code review

Ceylan took on the tension between security testing and development velocity without hedging. "The days of secure code reviews where a human's looking at the code and we're looking at security architecture reviews, design docs, those are done," she said. "If you keep trying to do security that way, you're gonna get left behind." Box is building toward a fully agentic development lifecycle where agents review design documents, apply security requirements, and review the code for vulnerabilities. "I'm very optimistic that we will get to a point where we will write code without security vulnerabilities because agents and the models are going to get so good at writing code without vulnerabilities," she said. "We're still a long way away from that."

Her advice for development teams skips the advanced AI concepts entirely and returns to basics that predate agents. "It comes down to very basic least privilege access," she said. "If you start giving your agents overly broad permissions at the beginning, it's really hard to comb that back and build an infrastructure that allows for those ephemeral credentials and only those narrowly scoped tasks."

Parekh explained why the red teaming surface has expanded so quickly. "These agents have skills, and skills could become vulnerabilities," he said. "Agents have access to certain data, they have access to tools, and there could be threats that are lurking within those tools as well. So suddenly the blast radius of the malicious code or the intent increases dramatically." When Intuit identifies common vulnerability patterns from its manual red teaming exercises, it automates those tests back into the GenOS harness so future agents inherit protection and red teamers stay focused on new threat vectors. Runtime scanning of prompts and responses adds a final layer that can stop a suspect response and escalate to a human expert, he said.

"You need to continuously test to ensure that those remain robust to the protections that you have built, as well as to account for any sort of drift or any other types of dependencies that you introduce into your scenario that can create novel vulnerabilities," she said.

Intent versus probability

An audience question about intent detection set off the sharpest exchange of the session. Ceylan noted that when Box's own agent operates, the system always knows the user's intent because it controls the prompt, which means guardrails and tool-call restrictions can be engineered around it. The harder challenge, which she admitted Box is still trying to solve, arrives when external agents connect and the context behind the request is opaque.

That exchange exposed a split running through the wider industry. Mastercard, in the fireside chat immediately preceding the panel, came down on the side of quantifying intent, building an open-source framework to propagate it as a standard because complex B2B procurement cannot work without that trust. Endpoint security CTOs, in briefings with VentureBeat, have gone the other way, saying they will bet on probability rather than intent inference for production workloads. Chang explained why models, as they are trained today, cannot reliably derive intent from a prompt, which is why deterministic controls and behavioral proxies remain necessary. Ceylan agreed that both are required. "If you're not doing anything deterministic, you're really relying heavily on that intent, and I haven't seen programs that are there yet," she said.

Ceylan's story about trust collapsing after a single agent mistake landed as the panel's most memorable moment because enterprise agentic security is not a problem that gets solved and stays solved. Models change, permissions drift, and adversaries adapt across multi-turn conversations that snapshot tests never capture.

For the 82% of enterprises relying on provider-native controls as their primary security layer, and the 59% shopping for agent security tooling over the next 12 months, the panel's takeaway was blunt. Test the way attackers attack, across full conversations and continuously, or find out in production what your single-turn red teaming missed.

]]> <![CDATA[Black Forest Labs launches FLUX 3 capable of generating images and 20-second video with audio — but in limited release to start]]> https://tsecurity.de/de/3690017/it-nachrichten/black-forest-labs-launches-flux-3-capable-of-generating-images-and-20-second-video-with-audio-but-in-limited-release-to-start/ https://tsecurity.de/de/3690017/it-nachrichten/black-forest-labs-launches-flux-3-capable-of-generating-images-and-20-second-video-with-audio-but-in-limited-release-to-start/ Thu, 23 Jul 2026 20:48:22 +0200 📰 IT Nachrichten tsecurity.de Black Forest Labs (BFL) is expanding its FLUX family beyond image generation with today's launch of FLUX 3, a multimodal frontier model trained to understand and generate images, or combined audio/video clips up to 20 seconds from a single prompt — and to extend the same underlying architecture to robotic vision and actions.

The Freiburg, Germany-based AI lab says FLUX 3 is jointly trained across those modalities rather than assembling separate image, video and audio models behind a common interface.

That distinction is central to the company's pitch: BFL wants enterprises to think about creative generation, simulation, computer use and robotics as connected applications of a single capability it calls visual intelligence — models, in the company's words, "that can perceive, predict, and act across physical and digital environments." This release marks BFL's first public video generation model.

FLUX 3 will be offered through four product lines: FLUX 3 Video, FLUX 3 Image, FLUX 3 Action and the upcoming, open source FLUX 3 Dev. FLUX 3 Video, with optional native audio generation, and FLUX 3 Action are entering a gated "Early Access" program now, to which anyone can apply, but which BFL must approve.

There is presently no public access through BFL's application programming interface (API) or those of partners yet, but the company says FLUX 3 Image will roll out in the coming weeks, followed by general availability. The limited initial availability rollout echoes the release strategies of new models from other frontier labs in the U.S. lately, including Anthropic and OpenAI, though those were ostensibly for security concerns and due to government request.

What the company has not announced is pricing, production service-level commitments, evaluation methodology, sample sizes, rater counts or any image-model benchmarks at all. Enterprise buyers therefore cannot yet calculate total cost of ownership or independently reproduce the video comparisons.

Another big notable omission: FLUX 3 is not launching with downloadable weights at this time, nor an open source license. BFL says faster and open-weight versions will arrive later this year, and its technical blog names FLUX 3 Dev as "open-weight access to a multimodal backbone, for content creation (video, audio and image) and action prediction" — a considerably broader commitment than any previous FLUX Dev release, all of which covered images only.

But it arrives last in the sequence. Developers accustomed to receiving a locally deployable FLUX variant alongside — or soon after — a major model announcement will have to wait. That delay does not negate the company's commitment, but it is disappointing given the role open weights have played in FLUX's adoption thus far.

Flux 3 is rated higher than the competition, but missing pricing and benchmarking details may prevent rapid enterprise adoption

BFL has published several benchmark comparisons, but they're qualified as preliminary — with full benchmark results and methodology to be published later during broader general availability.

In early head-to-head preference testing on 10-second, 720p text-to-video clips with audio, the company says FLUX 3 was preferred over Luma Ray 3.2 in 93% of comparisons, Runway Gen-4.5 in 77%, Grok Imagine Video in 69%, Kling v3 Pro in 60%, Happy Horse v1 in 59%, Happy Horse 1.1 in 57%, and both Seedance 2.0 and Google's Gemini Omni Flash in 52%.

One caveat travels with every one of those figures, and it comes from BFL itself. The chart carrying the results is labeled a "preliminary evaluation of an early FLUX 3 candidate" — meaning the numbers describe a pre-release checkpoint rather than the model now entering early access. That cuts both ways: the shipping model may perform better, but nothing published today measures what customers will actually call.

Luma Ray 3.2 and Runway Gen-4.5, where FLUX 3 posted 93% and 77%, are the softest comparisons on the list — established products, but not the models currently setting the pace in independent video rankings. Those are real wins, and they are the ones least likely to change an enterprise shortlist.

Seedance 2.0, at 52%, is a statistical coin flip against a model most Western enterprises cannot currently procure. ByteDance indefinitely postponed Seedance 2.0's international rollout after Netflix, Warner Bros., Disney, Paramount and Sony sent legal threats over alleged systematic copyright infringement, and that suspension remains in place. Tying a frozen product is neither a strong claim nor a damaging one.

Gemini Omni Flash, also at 52%, matters much more. Omni is the closest large-platform analogue to what FLUX 3 is attempting — multimodal input, video and audio-aware creation, conversational editing — and by BFL's own measurement, the two are indistinguishable on 10-second text-to-video quality.

Google's advantage in that matchup is that Omni is generally available via Google's Gemini API for $0.10 per second of generated 720p video, or a 10-second clip for around.

One regional wrinkle matters for a German company's home market. Editing uploaded video is unavailable to Omni Flash users in the European Economic Area, Switzerland and the United Kingdom, though editing video the model itself generated is permitted. A European enterprise that wants to run its existing footage through a generative editing pass cannot currently do so on Omni Flash.

Here's a rough guide for enterprises considering which video models to rely upon:

Model

Max single-generation duration

Max resolution

Key constraints

Price per 10-second clip (720p)

Price per 10-second clip (1080p)

Price per 10-second clip (4K)

FLUX 3 Video

20 seconds

Not stated; evaluations run at 720p

Early access; no published SLA or pricing

Not announced

Not announced

Not announced

HappyHorse 1.1

15 seconds

1080p

No 4K; closed weights

Not published (v1.0 reseller rate is ~$1.82)

Not published (v1.0 reseller rate is ~$3.12)

n/a

Veo 3.1

Per-second billing

4K

Supports clip extension; preview

$4.00

$4.00

$6.00

Veo 3.1 Fast

Per-second billing

4K

Preview

$1.00

$1.20

$3.00

Veo 3.1 Lite

Per-second billing

1080p

No 4K, no clip extension; preview

$0.50

$0.80

n/a

Gemini Omni Flash

10 seconds (3s minimum)

720p at 24 FPS

Preview abd no EU access

$1.00

n/a

n/a

One architecture for media generation and physical action

FLUX 3 builds on Self-Flow, BFL's method for aligning multimodal understanding and generation within one architecture, publicized back in March 2026.

The company says it significantly scaled up compute and data to train across video, images and audio simultaneously, and that testing showed video generation and action prediction do not require separate foundations — the same architecture could be extended to action prediction without sacrificing what it learned from video.

"We place vision at the center of our approach because it is the most signal-rich medium of the physical world. Images convey structure, images and video teach spatial relationships, video teaches dynamics, and actions reveal causal relationships. But vision alone is not the complete picture," said Robin Rombach, co-founder and CEO of BFL, in a pre-release statement provided to VentureBeat. "True intelligence means perceiving the world: predicting how it will change, taking action, and learning from the results. Joint training within one unified architecture is what will get us there, because each training modality strengthens the others. Audio conveys timing, prosody, and physical events that elude vision. Language conveys goals, abstractions, and instructions that pixels cannot easily express."

He put the case more bluntly elsewhere in the announcement: "You can't cheat reality. A model that only learns images can only generate images. But the world is not made of still frames. It moves, sounds, changes, and responds."

BFL says FLUX 3 targets creative tooling, media, design, e-commerce and physical AI, supporting video generation with synchronized audio, precise image editing, product and material consistency across motion, multilingual generation and robotic action prediction. It is already being tested by Canva, Burda, Magnific (formerly Freepik), Krea and Picsart.

For creative software companies, the appeal is consolidation. A single foundation could potentially support storyboarding, image editing, product rendering, video variation and localization without repeatedly translating assets and instructions between disconnected models.

For robotics teams, the potential value is data efficiency. Models that already encode motion, object behavior and physical change may need less task-specific robot training than systems starting from raw demonstrations.

What FLUX 3 Video can actually do

The video tier is the most concretely specified part of the launch, and it settles a question that had been circulating as rumor: FLUX 3 generates clips of up to 20 seconds with audio in a single generation.

Every video output comes with native audio. For comparison, HappyHorse 1.0 tops out at 15 seconds of 1080p with synchronized audio — though BFL has not stated what resolution its 20-second clips run at, and its published evaluations were conducted at 720p. Still, a 20-second long clip from a single prompt is among the longest yet achieved, matching OpenAI's discontinued Sora model.

The capability list BFL published covers:

  • Text-to-video generation.

  • Image-to-video generation, either animating from a starting frame or using images as visual references.

  • Video-to-video generation from a reference clip, carrying elements such as a specific character into a new scene or context.

  • Generative video-audio continuation from existing video and audio input.

  • Keyframe-to-video generation for controlled transitions between defined moments.

  • Multilingual dialogue.

  • A broad range of visual styles and aspect ratios, from candid camcorder footage to animation and cinematics.

  • Typography generation and animated design.

  • Agentic chaining of individual clips into longer, multi-shot sequences.

That last item is the one enterprise video teams should look at hardest. BFL claims the capabilities combine to produce sequences lasting several minutes, with visual references keeping characters consistent across scenes. If that holds up under production conditions, it addresses the constraint that has kept generative video out of most commercial pipelines: not clip quality, but continuity across shots.

It is also the capability where competition is most direct. HappyHorse 1.1's headline upgrade is R2V, or Reference-to-Video, which accepts multiple character reference images to hold identity stable across generated footage — the same problem, approached at the input layer rather than through agentic clip chaining. Alibaba also claims zero-drift lip sync and has specifically targeted the artifacts that mark commercial AI video as synthetic, including facial oiliness and over-sharpening. Character consistency is where this category is being contested, and both companies know it.

BFL says FLUX 3 Video is already particularly strong at human facial expressions, associating sounds with physical events, and multilingual output. On the image side, the company says preliminary evaluations conducted during midtraining show significant improvement over earlier FLUX versions in complex prompt handling and text generation, including high-accuracy text in multiple languages. It published no image benchmarks or win rates.

FLUX-mimic tests whether video models can become robot models

BFL is applying its unified-architecture thesis through FLUX-mimic, a video-action model built on FLUX 3 and developed with Swiss firm Mimic Robotics, one of the first partners to receive early access.

The technical blog describes two distinct routes to action prediction: integrating native action prediction directly into FLUX 3, scaling up the initial Self-Flow work; and using the pretrained video backbone as a dynamics-aware foundation from which specialized action models can be finetuned with limited task-specific data. FLUX-mimic is the second route — the FLUX 3 backbone combined with mimic's robot-learning and production-deployment expertise in dexterous manipulation.

FLUX-mimic is designed for general-purpose robotic manipulation: helping robots understand a visual scene, predict the consequences of an action, and adapt to new tasks with far less task-specific data.

BFL and Mimic Robotics say that depending on task difficulty, the model can be finetuned for a specific manipulation task with as little as 30 minutes of robot data, where prior approaches have required 30 or more hours.

"The hardest part of robotics is data," said Elvis Nava, CTO of Mimic Robotics, in a statement provided to VentureBeat. "Every new task normally means hours of a robot repeating itself. Because FLUX-mimic is built on top of frontier video models that already understand how the physical world behaves, it picks up a new task in minutes, not days. This way, we can leapfrog the current state of the art in robot learning."

BFL argues that a model trained only on images cannot understand a world that "moves, sounds, changes, and responds," and that physical understanding is what produces convincing generated footage. Google makes a nearly identical claim for Gemini Omni.

Its developer documentation cites "world knowledge" that combines "an understanding of physics" with Gemini's grasp of history, science and cultural context. Its marketing is blunter still: "Most AI models just predict the next pixel to build a narrative or an image. Gemini Omni is different," the company posted in June, crediting the model with "an intuitive understanding of forces like gravity, kinetic energy, and fluid dynamics for more realistic movements that follow real-world logic."

The practical consequence for enterprise buyers is that world-model language is not a differentiator. Two of the three leading video systems now market physical understanding as their central advantage, and neither has published a benchmark that measures it.

There is no standard test for whether generated water behaves like water, whether a dropped object falls at a plausible rate, or whether a sound arrives when the impact does. Human preference ratings capture some of it indirectly. Nothing else on offer captures it at all.

Open weights helped make FLUX an industry standard

BFL officially launched in summer 2024 and gained a name for itself in the AI industry in the intervening two years for its commitment to open sourcing high-quality AI image models beloved by developers, creatives, and enterprises.

The company's founders, including Rombach, Andreas Blattmann and Patrick Esser, previously helped create VQGAN, latent diffusion and Stable Diffusion, the latter the open source technology that kicked off broad AI generation capabilities for the masses and currently used by many AI image generators and companies.

That reach translated into commercial distribution. FLUX models now power generative features inside Adobe Photoshop, Picsart and Nous Research's Hermes Agent, among other platforms, and the company cites film director Martin Scorsese among professional users.

Wired magazine described Black Forest Labs as a relatively small company that nevertheless became a leading competitor to Silicon Valley's largest AI labs, with FLUX models ranking near the top of image benchmarks and becoming some of the most downloaded text-to-image models on AI code sharing community Hugging Face. The company says it now runs a 100-person team across Freiburg and San Francisco.

FLUX.1 Dev, FLUX.1 Kontext Dev, FLUX.1 Fill Dev and related control models, released shortly after the firm's launch, gave researchers and creative-tool developers access to downloadable checkpoints, local inference and integrations with frameworks including Hugging Face Diffusers and ComfyUI. FLUX.1 Kontext Dev, for example, was released as an open-weight model for research and noncommercial use, with generated outputs permitted for commercial purposes under the applicable license.

The company continued that pattern with FLUX.2 Dev in late 2025, a 32-billion-parameter open-weight model combining generation and multi-reference editing. Black Forest Labs called it the strongest open-weight image generation and editing model available at launch and released weights, reference inference code and optimized implementations for consumer Nvidia GPUs.

FLUX 3 Dev raises the stakes on that evaluation. Previous Dev releases were image models. This one is described as a multimodal backbone spanning video, audio, image and action prediction — meaning a single license will govern whether a company can locally deploy a model that touches both content production and physical machinery. BFL hasn't yet shared information about its license, the parameter count, quantizations or hardware requirements.

The company frames open weights as an enterprise feature rather than a community gesture, arguing they enable secure, low-latency local deployment for applications like robotic control systems and let teams adapt FLUX 3 to their own data, products and workflows.

The financial backing behind FLUX 3 is worth noting alongside the technical claims. Black Forest Labs is valued at $3.25 billion and has raised more than $450 million from investors including a16z, AMP, Salesforce Ventures, Nvidia, General Catalyst, Adobe Ventures, Figma Ventures, Canva and Deutsche Telekom's T.Capital.

]]> <![CDATA[AMD raises the AI stakes with Helios, Venice and robotics]]> https://tsecurity.de/de/3690010/it-nachrichten/amd-raises-the-ai-stakes-with-helios-venice-and-robotics/ https://tsecurity.de/de/3690010/it-nachrichten/amd-raises-the-ai-stakes-with-helios-venice-and-robotics/ Thu, 23 Jul 2026 20:48:09 +0200 📰 IT Nachrichten tsecurity.de

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

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

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

AMD’s MI455X targets the AI memory wall

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

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

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

AMD Instinct chart showing generational leap in performance

AMD

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

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

AMD’s Helios rack takes aim at Vera Rubin

AMD Helios rack

Dave Altavilla

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

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

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

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

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

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

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

Chart showing AMD EPYC CPU performance

AMD

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

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

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

Pensando networking and ROCm software advance

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

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

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

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

Customer commitments underscore rack-scale confidence

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

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

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

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

AMD expands its robotics and embedded foundation

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

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

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

Execution for AMD is now the test

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

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

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

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[New Alpha Release: Tor Browser 16.0a9]]> https://tsecurity.de/de/3689969/it-security-tools/new-alpha-release-tor-browser-160a9/ https://tsecurity.de/de/3689969/it-security-tools/new-alpha-release-tor-browser-160a9/ Thu, 23 Jul 2026 20:25:02 +0200 đŸ’Ÿ IT Security Tools tsecurity.de

Tor Browser 16.0a9 is now available from the Tor Browser download page and also from our distribution directory.

This version includes important security updates to Firefox.

⚠ Reminder: The Tor Browser Alpha release-channel is for testing only. As such, Tor Browser Alpha is not intended for general use because it is more likely to include bugs affecting usability, security, and privacy.

Moreover, Tor Browser Alphas are now based on Firefox's betas. Please read more about this important change in the Future of Tor Browser Alpha blog post.

If you are an at-risk user, require strong anonymity, or just want a reliably-working browser, please stick with the stable release channel.

It's ESR transition season again!

Well actually, it has been ESR transition season throughout this entire release cycle! As described in the aforementioned Future of Tor Browser Alpha blog post, we have been incrementally rebasing our Alpha channel on Firefox betas since December of last year. As a result, we now stand before you with Tor Browser 16.0a9 which is based on Firefox ESR 153.

We will continue rebasing Tor Browser 17.0 Alpha branches on Firefox betas throughout the remainder of the Tor Browser 16.0 release cycle. However, new feature-work for now must be put on hold for a few reasons:

  • We must focus our attention on resolving our Bugzilla Audit issues to ensure the features we have inherited from upstream comply Tor Browser's threat model and to patch any changes which do not.
  • Feature work targeting 16.0 stable would need to be cherry-pick'd onto our 17.0 Alpha branches to ensure we don't lose any work. The more invasive a feature patch is, the harder it will be to port to newer versions. This would also be a potentially error-prone process and there is some risk we would lose patches along the way.
  • We need to finish stabilizing as soon as possible as we have hard external deadlines which cannot be moved: the end-of-life of Firefox ESR 140 on October 13th and the Google Play Minimum Target API Level requirement on November 1st

Challenges and Triumphs

💍 Sharing the Load

Rebasing the hundreds of Tor Browser patches onto newer versions of Firefox is a challenging task. It is like maintaining the structural stability of sand-castle at high-tide with the waves crashing all around you.

As such, it quickly become clear early in this new process that we would need to do something if we wanted to avoid burning out the few developers typically involved in this work. To mitigate this, we shared the knowledge internally and spread the work out across all eight members of the team. This way, each developer was only responsible for at most two or three rebases throughout the entire release cycle.

🎹 UI Code Churn

Over the past year, Firefox has developed and integrated two major changes to the UI in Firefox: a redesign of about:preferences in Firefox Desktop and a migration from Material 2 to Material 3 in Firefox Android.

Adapting to these types of changes to the frontend are typically rather time-consuming for us, as many (if not the majority) of our patches modify Firefox's UI in some way. For example, we have an entire preferences page on Tor Browser desktop dedicated to configuring how the browser connects to the Tor Network. On Android, we similarly have various additions to the menus, configuration options, and custom UI.

Whenever Mozilla modifies their design systems and Firefox's user interface, we necessarily have to adapt our own custom additions to match. Otherwise, our Tor Browser-specific UI elements would look completely out of place and potentially confuse users (as well as simply looking unprofessional). Therefore, each of these upstream changes requires collaboration with the Tor Project's UX team to update our features' designs and of course development time to implement.

In addition to the time-cost associated with the extra engineering and UX collaboration, very often our old patches simply do not apply cleanly due to the amount of code which has changed. For example, the about:preferences changes on Firefox Desktop are essentially a complete re-write which means we also have to completely re-write our own settings changes without regressing in functionality.

On the plus side, one benefit of our new processes is that we have been able to spread out this work over the entire release cycle. In the past way of doing things, we would have discovered all UX elements which needed to be fixed, updated our designs, and re-implemented in the course of a few months during the old ESR transition season. Under this new way of working, we have been able to incrementally fix things throughout the development cycle.

The benefits of working this way does not just apply to UX of course. It is much easier to find regressions across the entire stack when rebasing between one major Firefox version at a time instead of across 12 or 13. It is also much easier for developers to fix individual regressions one at a time compared to diagnosing, disentangling, and fixing multiple bugs concurrently (divide et impera!).

⚙ Pending Google Target API Level Requirements

Every year, Google requires new Android app releases to target an updated minimum API level. This means, we would not be able to upload new versions of Tor Browser Stable past a certain date (usually August 1st with an extension to November 1st typically possible) without first updating the app to support the new minimum target API level. Fortunately, we inherit most of the required changes from Mozilla when rebasing to the next major ESR.

However, this requirement does impose a hard deadline for the absolute latest we can responsibly stabilize Tor Browser Alpha and promote it to Stable. We've been fortunate in the past few years to make the deadline with a few days to spare (October 28th for Tor Browser 15, October 22nd for Tor Browser 14, etc). Given how far ahead of the curve we are this year, we are hoping to release about a month earlier in September (fingers crossed!).

đŸ€– Android APKs too big

The Google Play Store has a strict size limit of about 100 megabytes for Android applications. New functionality added to Firefox Android over the past year means a larger application which results in new headaches for Tor Browser developers. This release cycle was no exception to this rule and we have had to get creative with our size reductions.

In the past, we have been able reduce our package size though various methods including:

Our most recent effort has been the most invasive yet! For some background, the Firefox application consists of (among other things): various shared libraries, the Firefox executable, a library known as 'xul' which contains most of Firefox's natively compiled functionality, and finally a file known as omni.ja. This omni.ja file is a zip archive which contains the JavaScript, HTML, images, and other assets used in Firefox.

This time around, to reduce the size of our Android package we havechanged how this archive is compressed. We modified the Firefox build system to compress this archive with xz and we modified Firefox itself to decompress this archive at runtime. This work did require a few iterations to get right. In the end, we got back about 3 megabytes with these changes and got us once again under Google's imposed size budget.

📉 Even Less Telemetry

Over the years, we have worked to incrementally remove dependencies from Tor Browser Android as part of the aforementioned size reduction work. We of course inherit most of these dependencies from Firefox Android and unfortunately some of them can be labeled as 'trackers'. While we do disable telemetry by default at runtime, the code which implements it remains in the codebase.

We're happy to report that as of Tor Browser 16.0a8, are down to only 1 'tracker' library in the Tor Browser Android codebase: Mozilla Telemetry. Again, this telemetry is disabled at runtime, but this is one more unused dependency which we can hopefully remove in the future (and maybe get some more bytes back!).

Current Status

We have:

  • incrementally rebased Tor Browser and Tor Browser for Android to Firefox ESR 153 from Firefox ESR 140
  • updated the build systems with the latest dependencies and fixed a few reproducibility issues
  • triaged most of the upstream changes from the past year and flagged over 250 issues for further review (triaging of Firefox 153 is in progress)
  • resolved about half of these triaged issues

For the remainder of this release cycle, we will be focusing on auditing these issues and fixing bugs until the 16.0 alpha series is ready to become Tor Browser Stable 16.0. We are optimistically targeting a September release, which would put us one month ahead of schedule compared to last year.

Known Issues

🩊 Firefox Branding

In some places in the browser there may be Firefox branding (e.g. logos, cute little foxes, etc) instead of Tor Browser branding. We're currently tracking one known instance in tor-browser#44998. If you discover any other instances lurking about, please open an issue!

🌐 All websites marked 'insecure' on Tor Browser Android

Currently, the identity block in the URL bar on Tor Browser Android will always report insecure (e.g. a shield icon with a slash through it). For now, you can tap this icon and verify the certificate manually. This issue is being tracked in tor-browser#45115

Send us your feedback

Now is a great time to become an alpha tester! If you find a bug or have a suggestion for how we could improve this release, please let us know.

Full changelog

The full changelog since Tor Browser 16.0a8 is:

]]> <![CDATA[In a First, Apple Maps Navigation To Be Embedded In Ford UEV Pickups]]> https://tsecurity.de/de/3689930/it-security-nachrichten/in-a-first-apple-maps-navigation-to-be-embedded-in-ford-uev-pickups/ https://tsecurity.de/de/3689930/it-security-nachrichten/in-a-first-apple-maps-navigation-to-be-embedded-in-ford-uev-pickups/ Thu, 23 Jul 2026 20:13:19 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Flux 3 generates videos with native audio up to 20 seconds long, a first for Black Forest Labs]]> https://tsecurity.de/de/3689925/ai-nachrichten/flux-3-generates-videos-with-native-audio-up-to-20-seconds-long-a-first-for-black-forest-labs/ https://tsecurity.de/de/3689925/ai-nachrichten/flux-3-generates-videos-with-native-audio-up-to-20-seconds-long-a-first-for-black-forest-labs/ Thu, 23 Jul 2026 20:07:30 +0200 🔧 AI Nachrichten tsecurity.de

Black Forest Labs has released Flux 3, a multimodal foundation model that learns from images, video, and audio and can generate video with native sound for the first time. BFL's own tests put it just ahead of market leader Seedance 2.0, though independent results aren't yet available. The company ultimately wants to build a world model and is already testing Flux 3 on robotics tasks.

The article Flux 3 generates videos with native audio up to 20 seconds long, a first for Black Forest Labs appeared first on The Decoder.

]]> <![CDATA[Galaxy Watch Ultra 2 and Watch 9: Should You Upgrade? My First Week of Testing]]> https://tsecurity.de/de/3689800/it-nachrichten/galaxy-watch-ultra-2-and-watch-9-should-you-upgrade-my-first-week-of-testing/ https://tsecurity.de/de/3689800/it-nachrichten/galaxy-watch-ultra-2-and-watch-9-should-you-upgrade-my-first-week-of-testing/ Thu, 23 Jul 2026 19:06:55 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Xbox is testing free, ad-supported cloud gaming]]> https://tsecurity.de/de/3689748/it-nachrichten/xbox-is-testing-free-ad-supported-cloud-gaming/ https://tsecurity.de/de/3689748/it-nachrichten/xbox-is-testing-free-ad-supported-cloud-gaming/ Thu, 23 Jul 2026 18:53:33 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Google CEO distracts from Gemini 3.5 Pro delay with talk of Gemini 4 and monthly releases]]> https://tsecurity.de/de/3689702/ai-nachrichten/google-ceo-distracts-from-gemini-35-pro-delay-with-talk-of-gemini-4-and-monthly-releases/ https://tsecurity.de/de/3689702/ai-nachrichten/google-ceo-distracts-from-gemini-35-pro-delay-with-talk-of-gemini-4-and-monthly-releases/ Thu, 23 Jul 2026 18:38:04 +0200 🔧 AI Nachrichten tsecurity.de

Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence.

His comments came a day after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier.

Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic.

Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler.

“We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call.

Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth, who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic.

Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model.

Analysts, though, aren’t as confident as Pichai.

While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs.

While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia, chief analyst at Greyhound Research.

Similarly, Pareekh Jain, principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number.

The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it.

]]> <![CDATA[My Galaxy Z Fold 8 Ongoing Review Living With the Samsung’s Passport Foldable]]> https://tsecurity.de/de/3689698/it-nachrichten/my-galaxy-z-fold-8-ongoing-review-living-with-the-samsungs-passport-foldable/ https://tsecurity.de/de/3689698/it-nachrichten/my-galaxy-z-fold-8-ongoing-review-living-with-the-samsungs-passport-foldable/ Thu, 23 Jul 2026 18:35:37 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Google CEO distracts from Gemini 3.5 Pro delay with talk of Gemini 4 and monthly releases]]> https://tsecurity.de/de/3689687/it-nachrichten/google-ceo-distracts-from-gemini-35-pro-delay-with-talk-of-gemini-4-and-monthly-releases/ https://tsecurity.de/de/3689687/it-nachrichten/google-ceo-distracts-from-gemini-35-pro-delay-with-talk-of-gemini-4-and-monthly-releases/ Thu, 23 Jul 2026 18:35:20 +0200 📰 IT Nachrichten tsecurity.de

Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence.

His comments came a day after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier.

Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic.

Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler.

“We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call.

Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth, who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic.

Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model.

Analysts, though, aren’t as confident as Pichai.

While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs.

While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia, chief analyst at Greyhound Research.

Similarly, Pareekh Jain, principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number.

The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it.

This article first appeared on InfoWorld.

]]> <![CDATA[Google CEO distracts from Gemini 3.5 Pro delay with talk of Gemini 4 and monthly releases]]> https://tsecurity.de/de/3689683/it-nachrichten/google-ceo-distracts-from-gemini-35-pro-delay-with-talk-of-gemini-4-and-monthly-releases/ https://tsecurity.de/de/3689683/it-nachrichten/google-ceo-distracts-from-gemini-35-pro-delay-with-talk-of-gemini-4-and-monthly-releases/ Thu, 23 Jul 2026 18:35:14 +0200 📰 IT Nachrichten tsecurity.de

Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence.

His comments came a day after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier.

Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic.

Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler.

“We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call.

Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth, who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic.

Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model.

Analysts, though, aren’t as confident as Pichai.

While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs.

While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia, chief analyst at Greyhound Research.

Similarly, Pareekh Jain, principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number.

The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it.

This article first appeared on InfoWorld.

]]> <![CDATA[Xbox's plan to bring Xbox Cloud Gaming to the mass market — Free tier revealed, powered by ads, with access to all games you own]]> https://tsecurity.de/de/3689649/windows-tipps/xboxs-plan-to-bring-xbox-cloud-gaming-to-the-mass-market-free-tier-revealed-powered-by-ads-with-access-to-all-games-you-own/ https://tsecurity.de/de/3689649/windows-tipps/xboxs-plan-to-bring-xbox-cloud-gaming-to-the-mass-market-free-tier-revealed-powered-by-ads-with-access-to-all-games-you-own/ Thu, 23 Jul 2026 18:07:50 +0200 đŸȘŸ Windows Tipps tsecurity.de <![CDATA[Microsoft is testing free, ad-supported cloud gaming for Xbox Insiders]]> https://tsecurity.de/de/3689634/it-nachrichten/microsoft-is-testing-free-ad-supported-cloud-gaming-for-xbox-insiders/ https://tsecurity.de/de/3689634/it-nachrichten/microsoft-is-testing-free-ad-supported-cloud-gaming-for-xbox-insiders/ Thu, 23 Jul 2026 18:04:50 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Linux XFS has a decade-old race condition allowing full root access]]> https://tsecurity.de/de/3689585/it-security-nachrichten/linux-xfs-has-a-decade-old-race-condition-allowing-full-root-access/ https://tsecurity.de/de/3689585/it-security-nachrichten/linux-xfs-has-a-decade-old-race-condition-allowing-full-root-access/ Thu, 23 Jul 2026 17:59:07 +0200 📰 IT Security Nachrichten tsecurity.de

Linux systems using the XFS filesystem suffer from a race condition that could enable an unprivileged local user to gain full root access.

The flaw affects systems with Linux kernel 4.11 or later that have enabled the XFS feature reflink, which permits the creation of copies of a file without actually copying its data.

According to Qualys Threat Research Unit (TRU), there was a way around the file write protections reflink depends on. The bypass has existed in kernel versions since 2017 before a patch was made available last week.

“Using this vulnerability, a process running as an ordinary, unprivileged user can trigger the flaw and gain the ability to overwrite any readable file on an XFS volume at the block layer,” Saeed Abbasi, head of Qualys TRU, said in a blog post about the vulnerability, which he calls  RefluXFS. “Exploitation is highly reliable and leaves no kernel log output.”

Qualys estimated that the issue affects more than 16.4 million systems, primarily enterprise Linux deployments that use XFS with reflink enabled by default.

Race winner gets root in buggy file operation

The vulnerability, tracked as CVE-2026-64600, stemmed from the way XFS handles copy-on-write operations for reflinked files. Normally, when two files share the same storage block, as in the case of an original file and the reflinked clone, XFS allocates a new storage block before any data modification, so the original file remains unchanged.

However, a race condition occurs when two concurrent writes on the reflink file are initiated, confusing the filesystem into modifying the original file. “The change is made directly on disk, persists across reboots, and produces no kernel log output,” Qualys said in an advisory.

The issue is exploitable when Linux 4.11+, XFS with reflink enabled, and a shared filesystem layout are all present. It is assigned a high severity CVSS of 7.8 out of 10 as an exploit only needs read access to a target file before creating a reflink clone under a writable directory on the same XFS filesystem.

Linux distributions affected by RefluXFS

The vulnerability has been present in every mainline and stable Linux kernel since the release of version 4.11 in 2017, and requires no special capabilities or non-default configurations, Qualys said.

Affected Linux distributions include RHEL 8,9 and 10, CentOS Stream 8,9, and 10, Oracle Linux 8,9,10, Rocky and AlmaLinux 8,9, and 10, CloudLinux 8,9, and 10, Amazon Linux 2023 and Amazon Linux 2 AMIs from December 2022 onward, Fedora Server 31+, and Debian, Ubuntu and SUSE installations where XFS was manually selected.

Usual kernel hardening practices, including memory protection features like Kernel Address Space Layout Randomization (KASLR), Supervisor Mode Access Prevention (SMAP), and Supervisor Mode Execution Prevention (SMEP), are ineffective as they are all aimed at different attack surfaces. Even a kernel lockdown does nothing to stop the affected path, Qualys noted.

“SELinux doesn’t block the affected path in testing, and seccomp profiles are no barrier as long as they permit write and ioctl, which ordinary profiles do,” Abbasi explained. Immediate kernel patching and a full reboot are the only reliable mitigations, he added.

A fix was merged into the upstream Linux kernel source tree on July 16 as commit “2f4acd0,” after which Linux distributions began backporting the patch into their own supported kernel releases.

Organizations running affected XFS deployments should apply their Linux vendor’s latest kernel updates and reboot affected systems once fixed kernels become available.

]]> <![CDATA[Linux XFS has a decade-old race condition allowing full root access]]> https://tsecurity.de/de/3689584/it-security-nachrichten/linux-xfs-has-a-decade-old-race-condition-allowing-full-root-access/ https://tsecurity.de/de/3689584/it-security-nachrichten/linux-xfs-has-a-decade-old-race-condition-allowing-full-root-access/ Thu, 23 Jul 2026 17:58:50 +0200 📰 IT Security Nachrichten tsecurity.de

Linux systems using the XFS filesystem suffer from a race condition that could enable an unprivileged local user to gain full root access.

The flaw affects systems with Linux kernel 4.11 or later that have enabled the XFS feature reflink, which permits the creation of copies of a file without actually copying its data.

According to Qualys Threat Research Unit (TRU), there was a way around the file write protections reflink depends on. The bypass has existed in kernel versions since 2017 before a patch was made available last week.

“Using this vulnerability, a process running as an ordinary, unprivileged user can trigger the flaw and gain the ability to overwrite any readable file on an XFS volume at the block layer,” Saeed Abbasi, head of Qualys TRU, said in a blog post about the vulnerability, which he calls  RefluXFS. “Exploitation is highly reliable and leaves no kernel log output.”

Qualys estimated that the issue affects more than 16.4 million systems, primarily enterprise Linux deployments that use XFS with reflink enabled by default.

Race winner gets root in buggy file operation

The vulnerability, tracked as CVE-2026-64600, stemmed from the way XFS handles copy-on-write operations for reflinked files. Normally, when two files share the same storage block, as in the case of an original file and the reflinked clone, XFS allocates a new storage block before any data modification, so the original file remains unchanged.

However, a race condition occurs when two concurrent writes on the reflink file are initiated, confusing the filesystem into modifying the original file. “The change is made directly on disk, persists across reboots, and produces no kernel log output,” Qualys said in an advisory.

The issue is exploitable when Linux 4.11+, XFS with reflink enabled, and a shared filesystem layout are all present. It is assigned a high severity CVSS of 7.8 out of 10 as an exploit only needs read access to a target file before creating a reflink clone under a writable directory on the same XFS filesystem.

Linux distributions affected by RefluXFS

The vulnerability has been present in every mainline and stable Linux kernel since the release of version 4.11 in 2017, and requires no special capabilities or non-default configurations, Qualys said.

Affected Linux distributions include RHEL 8,9 and 10, CentOS Stream 8,9, and 10, Oracle Linux 8,9,10, Rocky and AlmaLinux 8,9, and 10, CloudLinux 8,9, and 10, Amazon Linux 2023 and Amazon Linux 2 AMIs from December 2022 onward, Fedora Server 31+, and Debian, Ubuntu and SUSE installations where XFS was manually selected.

Usual kernel hardening practices, including memory protection features like Kernel Address Space Layout Randomization (KASLR), Supervisor Mode Access Prevention (SMAP), and Supervisor Mode Execution Prevention (SMEP), are ineffective as they are all aimed at different attack surfaces. Even a kernel lockdown does nothing to stop the affected path, Qualys noted.

“SELinux doesn’t block the affected path in testing, and seccomp profiles are no barrier as long as they permit write and ioctl, which ordinary profiles do,” Abbasi explained. Immediate kernel patching and a full reboot are the only reliable mitigations, he added.

A fix was merged into the upstream Linux kernel source tree on July 16 as commit “2f4acd0,” after which Linux distributions began backporting the patch into their own supported kernel releases.

Organizations running affected XFS deployments should apply their Linux vendor’s latest kernel updates and reboot affected systems once fixed kernels become available.

This article first appeared on Network World.

]]> <![CDATA[Email threat landscape: Q2 2026 trends and insights]]> https://tsecurity.de/de/3689521/it-security-nachrichten/email-threat-landscape-q2-2026-trends-and-insights/ https://tsecurity.de/de/3689521/it-security-nachrichten/email-threat-landscape-q2-2026-trends-and-insights/ Thu, 23 Jul 2026 17:45:08 +0200 📰 IT Security Nachrichten tsecurity.de In the second quarter of 2026, the continuing effects of Microsoft’s disruption of the Tycoon2FA phishing platform contributed to sustained declines in several major phishing techniques, while threat actors expanded into Teams-based social engineering and employed increasingly automated and multi-stage attack chains.

The post Email threat landscape: Q2 2026 trends and insights appeared first on Microsoft Security Blog.

]]> <![CDATA[Email threat landscape: Q2 2026 trends and insights]]> https://tsecurity.de/de/3689515/it-security-nachrichten/email-threat-landscape-q2-2026-trends-and-insights/ https://tsecurity.de/de/3689515/it-security-nachrichten/email-threat-landscape-q2-2026-trends-and-insights/ Thu, 23 Jul 2026 17:42:38 +0200 📰 IT Security Nachrichten tsecurity.de In the second quarter of 2026, the continuing effects of Microsoft’s disruption of the Tycoon2FA phishing platform contributed to sustained declines in several major phishing techniques, while threat actors expanded into Teams-based social engineering and employed increasingly automated and multi-stage


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The post Email threat landscape: Q2 2026 trends and insights appeared first on IT Security News.

]]> <![CDATA[Federal quantum bet grows with DARPA’s $125 million PsiQuantum award]]> https://tsecurity.de/de/3689459/it-security-nachrichten/federal-quantum-bet-grows-with-darpas-125-million-psiquantum-award/ https://tsecurity.de/de/3689459/it-security-nachrichten/federal-quantum-bet-grows-with-darpas-125-million-psiquantum-award/ Thu, 23 Jul 2026 17:13:10 +0200 📰 IT Security Nachrichten tsecurity.de

Defense research agency DARPA made its largest quantum computing award ever this week, with a $125 million agreement announced on Wednesday. The same day, the White House announced an additional $5 billion for the Genesis Mission, which focuses on AI for science but also includes technology to accelerate quantum computing and quantum sensors.

“Taken together, these announcements signal that U.S. quantum strategy is shifting from supporting individual research projects to building the infrastructure needed for a quantum-enabled economy,” says Heather West, research manager in the infrastructure systems, platforms, and technology group at IDC.

None of the individual quantum announcements are surprising, she says. But the level of coordination is new. “Government investment is expanding beyond foundational research toward commercialization, manufacturing, and deployment,” she says.

“The US government has been signaling that quantum computing is a priority,” says David Mooter, an analyst at Forrester Research. Part of it is the desire for the US to be a leader in quantum, as it has been in other high-tech areas, he says. And part of it is because the government itself can take advantage of quantum computers.

“Spy agencies would love to use them to decrypt intercepted messages, including messages they intercepted years ago and saved,” he says. And other departments could use quantum computers or networks for energy-related research, for supply chain optimization, and for secure communications. 

Quantum computing is accelerating, he says. “I would not be surprised to see a general gate-based quantum computer that’s good enough to provide commercial value for limited use cases by 2030.”

DARPA’s Quantum Benchmarking Initiative

DARPA’s Quantum Benchmarking Initiatives was launched in 2024, and 18 companies were selected in April of 2025 for Stage A of the project, with awards of up to $1 million each. The companies were to use the money to provide details of their concepts and show how they could lead to a functional, fault-tolerant quantum computer in under a decade.

Then, in November of 2025, DARPA chose 11 companies for Stage B of the project, with awards of up to $15 million for developing their research plans.

To date, only two companies have been chosen for Stage C: PsiQuantum and Microsoft. PsiQuantum announced $32 million of DARPA funding for testing and evaluation in September of last year. This week’s $125 million award will expand the scope and pacing of the validation and verification work. Stage C awards can go up to $300 million, according to DARPA.

This past May, PsiQuantum also announced $100 million from the Department of Commerce, part of the CHIPS and Science Act, to accelerate domestic manufacturing of critical quantum computing components.

Microsoft and PsiQuantum are both in Stage C, bypassing the sequential path that other companies are expected to follow, because they were both part of DARPA’s predecessor to QBI, the Underexplored Systems for Utility-Scale Quantum Computing program.

Genesis Mission

Genesis Mission was launched in late 2025 with the goal of using AI to accelerate scientific breakthroughs, and it now includes more than 15 government agencies.

As part of the Genesis Mission, quantum computing and sensing company Infleqtion announced three projects for the Department of Energy on Wednesday. The three projects focus on quantum circuit design for nuclear applications, atomic quantum sensing, and nuclear fusion energy research.

This announcement did not include the total monetary value of the projects, but, in May, the company announced a separate agreement with the Department of Commerce for $100 million to accelerate Infleqtion’s neutral-atom technology roadmap.

Other quantum-related Genesis Mission projects announced this week include $1.5 million for a BlueQubit quantum error correction project with Microsoft and other partners, a Stanford effort to model the behavior of electrons at quantum scale, an MIT quantum sensing project, Argonne National Laboratory projects on quantum circuit design and quantum sensors, Brookhaven Lab quantum sensor projects, and quantum computing projects at Northwestern University.

IBM, one of three dozen private companies that are part of the Genesis Mission Consortium, announced that it will be leading a project to support more effective quantum applications, and will contribute up to $50 million of quantum compute access for the Genesis Mission.

Enterprise priorities

This week’s quantum announcements aren’t a sign that enterprises need to run out and buy quantum computers, says IDC’s West. But they do need to start preparing for the quantum era — such as by identifying business areas where quantum computing could become a competitive differentiator over the next decade.

But the most immediate threat is that of adversaries using quantum computers to break current encryption standards. Organizations should be inventorying cryptographic assets and developing a roadmap for the migration to quantum-proof algorithms, West says.

The point of no return is closer than ever, and many major players in the encryption and communication space, including Google and Cloudflare, have been accelerating their timelines. In fact, this Wednesday was the federal deadline for naming their post-quantum cryptography migration leads under a June executive order.

“The preparation that needs to be done to prepare is to implement post-quantum cryptography yesterday,” says Forrester’s Mooter.

However, according to a survey released by DigiCert this morning, while 87% of organizations are planning, testing or implementing PQC initiatives, only 7% of organizations have deployed quantum-safe or hybrid cryptography across most of their digital certificates.

]]> <![CDATA[Security teams shift from AI-only to hybrid penetration test]]> https://tsecurity.de/de/3689302/it-security-nachrichten/security-teams-shift-from-ai-only-to-hybrid-penetration-test/ https://tsecurity.de/de/3689302/it-security-nachrichten/security-teams-shift-from-ai-only-to-hybrid-penetration-test/ Thu, 23 Jul 2026 16:10:38 +0200 📰 IT Security Nachrichten tsecurity.de Security teams are rapidly retreating from fully automated AI penetration testing after a year of disappointing results. This article has been indexed from CyberMaterial Read the original article: Security teams shift from AI-only to hybrid penetration test

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The post Security teams shift from AI-only to hybrid penetration test appeared first on IT Security News.

]]> <![CDATA[Research Shows Quantum Security Deployment Remains Stuck Despite Enterprise Planning]]> https://tsecurity.de/de/3689265/it-security-nachrichten/research-shows-quantum-security-deployment-remains-stuck-despite-enterprise-planning/ https://tsecurity.de/de/3689265/it-security-nachrichten/research-shows-quantum-security-deployment-remains-stuck-despite-enterprise-planning/ Thu, 23 Jul 2026 16:04:28 +0200 📰 IT Security Nachrichten tsecurity.de DigiCert has released the findings of its second annual global survey on post-quantum cryptography (PQC), showing that while organisations are actively preparing for the quantum era, measurable progress toward deployment remains minimal. According to the company’s newly published Quantum Readiness Outlook, 87% of organisations say they are planning, testing or implementing PQC initiatives. Yet deployment [
]

The post Research Shows Quantum Security Deployment Remains Stuck Despite Enterprise Planning appeared first on IT Security Guru.

]]> <![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[OpenAI Presence raises new questions about enterprise automation and jobs]]> https://tsecurity.de/de/3689165/it-nachrichten/openai-presence-raises-new-questions-about-enterprise-automation-and-jobs/ https://tsecurity.de/de/3689165/it-nachrichten/openai-presence-raises-new-questions-about-enterprise-automation-and-jobs/ Thu, 23 Jul 2026 15:20:41 +0200 📰 IT Nachrichten tsecurity.de

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

]]> <![CDATA[OpenAI Presence raises new questions about enterprise automation and jobs]]> https://tsecurity.de/de/3689164/it-nachrichten/openai-presence-raises-new-questions-about-enterprise-automation-and-jobs/ https://tsecurity.de/de/3689164/it-nachrichten/openai-presence-raises-new-questions-about-enterprise-automation-and-jobs/ Thu, 23 Jul 2026 15:20:32 +0200 📰 IT Nachrichten tsecurity.de

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

This article first appeared on CIO.

]]> <![CDATA[Cobalt adds Autonomous Pentest to scale application security testing]]> https://tsecurity.de/de/3689150/it-security-nachrichten/cobalt-adds-autonomous-pentest-to-scale-application-security-testing/ https://tsecurity.de/de/3689150/it-security-nachrichten/cobalt-adds-autonomous-pentest-to-scale-application-security-testing/ Thu, 23 Jul 2026 15:15:03 +0200 📰 IT Security Nachrichten tsecurity.de Cobalt has introduced Cobalt Autonomous Pentest, a new offering that enables continuous offensive security across an organization’s application portfolio by delivering actionable penetration testing results in as little as 24 hours. AI-assisted development enables organizations to ship software faster than ever, while attackers are using AI to automate reconnaissance and accelerate exploitation. Pentesting performed quarterly or even monthly, can no longer keep pace. As security teams face growing attack surfaces and constrained budgets, organizations need 
 More →

The post Cobalt adds Autonomous Pentest to scale application security testing appeared first on Help Net Security.

]]> <![CDATA[Cobalt adds Autonomous Pentest to scale application security testing]]> https://tsecurity.de/de/3689144/it-security-nachrichten/cobalt-adds-autonomous-pentest-to-scale-application-security-testing/ https://tsecurity.de/de/3689144/it-security-nachrichten/cobalt-adds-autonomous-pentest-to-scale-application-security-testing/ Thu, 23 Jul 2026 15:14:51 +0200 📰 IT Security Nachrichten tsecurity.de Cobalt has introduced Cobalt Autonomous Pentest, a new offering that enables continuous offensive security across an organization’s application portfolio by delivering actionable penetration testing results in as little as 24 hours. AI-assisted development enables organizations to ship software faster than


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The post Cobalt adds Autonomous Pentest to scale application security testing appeared first on IT Security News.

]]> <![CDATA[IT Security News Hourly Summary 2026-07-23 15h : 10 posts]]> https://tsecurity.de/de/3689140/it-security-nachrichten/it-security-news-hourly-summary-2026-07-23-15h-10-posts/ https://tsecurity.de/de/3689140/it-security-nachrichten/it-security-news-hourly-summary-2026-07-23-15h-10-posts/ Thu, 23 Jul 2026 15:14:45 +0200 📰 IT Security Nachrichten tsecurity.de 10 posts were published in the last hour 13:4 : Nuclear-Sabotage Malware Benchmark Trips Up Most Frontier AI Models 13:4 : Cobalt adds Autonomous Pentest to scale application security testing 12:34 : Google Released Gemini 3.5 Flash Cyber AI, a


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The post IT Security News Hourly Summary 2026-07-23 15h : 10 posts appeared first on IT Security News.

]]> <![CDATA[Smaller, smarter, safer: How to build agentic AI on the right foundation]]> https://tsecurity.de/de/3688632/it-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ https://tsecurity.de/de/3688632/it-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ Thu, 23 Jul 2026 12:04:43 +0200 📰 IT Nachrichten tsecurity.de

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

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

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

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

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

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

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

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

A steep learning curve

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

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

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

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

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

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

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

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

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

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

Routing

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

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

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

Harness

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

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

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

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

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

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

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

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

Specialization

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

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

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

]]> <![CDATA[Principles every enterprise must test before the attack arrives]]> https://tsecurity.de/de/3688625/it-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ https://tsecurity.de/de/3688625/it-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ Thu, 23 Jul 2026 12:04:32 +0200 📰 IT Nachrichten tsecurity.de

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

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

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

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

Recovery is not just a technical exercise

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

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

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

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

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

Most business continuity plans ignore ‘total blackout’

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

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

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

That is the level of clarity leaders need.

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

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

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

Threat intelligence must be actionable, not archived

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

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

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

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

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

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

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[WSL container: A quiet revolution for Windows development]]> https://tsecurity.de/de/3688476/ai-nachrichten/wsl-container-a-quiet-revolution-for-windows-development/ https://tsecurity.de/de/3688476/ai-nachrichten/wsl-container-a-quiet-revolution-for-windows-development/ Thu, 23 Jul 2026 11:07:20 +0200 🔧 AI Nachrichten tsecurity.de

Running containers on Windows has never been as easy as it should be. While there are versions of Docker Desktop and Podman that work with both the Windows Subsystem for Linux (WSL) and Hyper-V, I’ve found both overly complex and unstable. Where they have worked, it’s turned out that Hyper-V has been the best option, using a Linux virtual machine to host my containers. That all adds up to overhead, layers of virtual infrastructure that get in the way of work and that need to be rebuilt every time I restart my PC.

Part of the problem is WSL. It’s a good tool, but WSL2’s file-system integration is slow, and you’re left having to work with code using Visual Studio Code’s remote integration, which means putting a VS Code Server in every container you’re building and testing. If you’re working with Kubernetes, that’s even more complexity that needs to be managed, dragging you away from code.

I ended up running most of my container testing and development from a separate machine, a Linux server running containerd. But though it worked (and had all the resources of workstation-class device), it wasn’t portable, and for some reason I’ve yet to uncover, Ubuntu’s remote desktop access doesn’t work for me.

So, it was good to see Microsoft make several announcements around WSL at Build 2026 as part of a push to make Windows a developer platform again. The first, an improved WSL3, is still some way away, but the second, WSL-native container support, shipped at the end of June. It is already seeing community-driven development of Docker Desktop-like tooling to help monitor and manage your containers.

Delivering a WSL-based container platform fits in with the other developer-focused Windows announcements at Build. Making Windows behave more like Linux is Microsoft responding to developer needs, given that more than 50% of servers on Azure run a Linux distribution. Linux is the basis of cloud-native infrastructure, so developers need to be able to build on it wherever they are.

Getting started with WSL container

WSL container provides a new CLI that works in parallel to the familiar WSL, with commands to support the entire container life cycle, from creation to shut down. All you need to do to get started is upgrade your WSL installation to the current pre-release build (at the time of writing this was 2.9.3). Simply open an administrator PowerShell terminal and enter wsl --update --pre-release.

This downloads and installs the latest WSL release. Once you’ve closed and re-opened your terminal (to ensure that you’ve updated its context) you can check that WSLC has installed by entering wslc, which should list the available commands. The new CLI is aliased to WSL container, if you prefer to keep your container work separate from WSL (and avoid typos that might accidentally affect your WSL installations).

Under the hood Microsoft is using WSL container to trial new integration points for Linux in Windows. One key change is the use of a new file system that significantly speeds up access to Windows from inside a container. Another improvement gives WSL container a new networking mode that relays networking connections directly through the Windows network stack, ensuring it has access to the same resources and security as Windows.

Calling Linux containers from Windows applications

Things get more interesting when you start to use the WSL container API from inside your Windows code. Here you can include calls to Linux containers inside your desktop applications, taking advantage of existing services, building and deploying containers from inside your CI/CD pipeline. Using the new file system and networking stack helps reduce the friction that comes with crossing the boundaries between the two platforms.

The WSL container API is available as a NuGet package, with support for C, C#, and C++. It allows your code to start and stop containers, and interact directly with them, sending command-line calls and reading back responses. Where things get interesting is being able to launch a containerized service from your code, exposing its REST or gRPC APIs on a local network port. Microsoft has provided sample code to show you what’s possible at this early stage.

Microsoft is doing something revolutionary here. It’s taking the cloud-native, service-driven model and bringing it into Windows and using it to bridge decades of divergent development. You no longer have to rewrite a service that works on Linux to run in Windows; all you need to do is containerize the service and launch it from the WSL container API. When you’re done, the API will tidy up after you, shutting down the container and reclaiming the memory it used.

It’s important to remember that this is only the first public preview of a rapidly developing platform. There are many opportunities here to, say, build on the syscall translation layer developed for WSL1 to produce a native Windows-to-Linux application integration stack that removes the overhead of using web-based service calls. It will be interesting to see what develops, but this first release is very interesting indeed.

Manage Linux containers from Windows

If you want a Docker Desktop-like experience for building and testing containers on Windows developer hardware, you may not have long to wait. WSL container’s underlying API is already being used to build tools that manage and monitor containers for you. One such tool is the WSL Container Desktop, under development on GitHub. While there aren’t any release builds yet, it’s easy enough to compile and get running by cloning the source repository and building using the .NET CLI. You do need to have the Windows App SDK installed, and some features require access to the Azure CLI.

WSL Container Desktop is built in C#, with a WinUI front end. It’s currently only verified for use on x64, though I was able to compile and run it on an Arm64 PC and use it to test and run containers. Once running, it gives you a well-designed front end for your WSL-hosted containers, showing what’s running and what resources they are using. You can link WSL Container Desktop to container registries, like Docker’s and Azure’s, so you can quickly pull base containers and then use the WSL container environment to add your own code and customizations.

Your main interaction point is the WSL Container Desktop dashboard, which shows what containers are running and their current resource usage. Elements are displayed in cards, taking a cue from Windows’ own user interface and especially from its Settings app. From the dashboard, you can drill down into the available containers, with quick start, stop, and reload options, as well as an extended memory that includes the ability to open a web browser to the appropriate port. I tested this with a container that included an entire KDE webtop, giving me a Linux distro running in a container in my browser.

Other options include a details view that displays current logs and provides tools for inspecting the state of a container. This is the type of tool that comes in useful when debugging and testing container applications, as it can provide insights that the WSL container CLI doesn’t offer. Another option helps you clean up after you’ve downloaded an image and don’t need it anymore, with analytics that show the largest images and images you haven’t used for some time. On top of its tooling for working with WSL containers, WSL Container Desktop provides a basic settings tool that helps you configure its look and feel, as well as how it integrates with Windows.

Run Kubernetes inside Windows for cloud-native development

One of the more useful features of WSL Container Desktop is the ability to quickly stand up a K3s Kubernetes instance in WSL that can be used to host WSL containers, providing a local environment to build and test cloud-native applications wherever you might be. The K3s tooling offers a similar experience to the Kubernetes project’s own Headlamp UI, making it easy to go between your development environment and a production Kubernetes cluster.

It’s fair to describe WSL container as one of those Windows features you didn’t think you needed, but now it’s here you can’t live without it. WSL container simplifies building a container development tool chain in Windows, and at the same time allows you to think about a new generation of hybrid applications that take advantage of decades of development in both Windows and Linux.

The result is something that was unimaginable a few years ago: dropping a Linux container into the middle of a Windows application and treating it as another local service. As the WSL container platform evolves, you should expect to see more ways of bringing Linux and Windows together, using containers to deliver a hybrid platform that gives us the best of both worlds at long last.

]]> <![CDATA[India Tightens Social Media Rules to Protect Children Online]]> https://tsecurity.de/de/3688433/it-security-nachrichten/india-tightens-social-media-rules-to-protect-children-online/ https://tsecurity.de/de/3688433/it-security-nachrichten/india-tightens-social-media-rules-to-protect-children-online/ Thu, 23 Jul 2026 10:56:26 +0200 📰 IT Security Nachrichten tsecurity.de India online safety rules

India online safety rules are being strengthened as the government steps up measures to protect children and other users from harmful digital content, cyber risks and emerging threats linked to artificial intelligence. The government said its policies are aimed at ensuring an open, safe, trusted and accountable internet, with recent measures focusing on child safety, privacy protection, faster content removal and stronger platform responsibilities.

The Information Technology Act, 2000, and the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, form the core legal framework governing online safety and intermediary responsibilities in India. The government has also highlighted the Digital Personal Data Protection Act, 2023, and recent amendments to the IT Rules as part of its broader approach to digital safety.

India Online Safety Rules Tighten Platform Responsibilities

Under the IT Rules, intermediaries are required to observe due diligence and inform users that they must not host, display, upload, modify, publish, transmit, update or share content that is harmful to children or violates applicable laws.

Recent amendments require social media platforms and other intermediaries to remove unlawful content within three hours of receiving an order from a competent court or a reasoned intimation from the appropriate government or its agency.

The government has also outlined specific obligations related to content involving nudity, impersonation and other sensitive material. In cases involving certain complaints about content featuring full or partial nudity, exposed private areas or artificially morphed images, intermediaries must take reasonable and practicable measures to remove or disable access within two hours.

Government Targets Harmful Content and OTT Platforms

The government said it has taken action against online platforms and OTT services over unlawful and obscene content. In the last two years, 50 OTT platforms have been disabled for public access in India for displaying obscene content and violating provisions including Sections 67 and 67A of the IT Act, Section 294 of the Bharatiya Nyaya Sanhita and the Indecent Representation of Women (Prohibition) Act, 1986.

The government also said it has taken note of reports alleging the dissemination of advertisements linked to child sexual abuse material (CSAM) on social media platforms and sought a detailed report from the concerned intermediary. The National Commission for Protection of Child Rights has also issued notices to the concerned platforms.

India Strengthens AI-Generated Content Safeguards

The government has also expanded its regulatory focus to address risks associated with AI-generated content and synthetically generated information. Amendments to the IT Rules introduce requirements for clear labelling and traceable metadata for permissible AI-generated content, allowing users to identify synthetically generated material.

The framework also strengthens platform accountability and requires greater user awareness about the legal consequences of unlawful AI-generated content. The rules specifically cover harmful material including CSAM, non-consensual intimate imagery and impersonation.

Platforms are required to deploy reasonable and appropriate technical measures, including automated tools or other mechanisms, to prevent users from creating, modifying, publishing or sharing synthetically generated information that violates applicable laws.

Significant Social Media Intermediaries are also required to make reasonable efforts to deploy technical measures to proactively identify content depicting rape, child sexual abuse or conduct, as well as content identical to information previously removed.

Child Privacy and Digital Addiction Remain Key Concerns

The Digital Personal Data Protection Act, 2023, provides a framework for protecting children's privacy online. It mandates parental consent for processing children's personal data and prohibits practices considered detrimental to children's well-being, including tracking, behavioural monitoring and targeted advertising directed at children.

The government has also highlighted digital addiction as a serious challenge affecting children and young people. The Economic Survey 2025-26 noted potential impacts on cognitive development, academic performance, workplace productivity, social connectedness and mental health, alongside risks linked to cyberbullying, compulsive gaming, social media and online gambling.

Cyber Awareness Reaches 11.37 Lakh Participants

Alongside regulatory measures, the government is expanding cyber awareness initiatives through the Information Security Education and Awareness project. So far, 6,650 awareness workshops have been conducted nationwide, reaching more than 11.37 lakh participants, including students, teachers, law enforcement officials, government personnel and members of the public.

The government has also highlighted digital safety initiatives in education. The PRAGYATA Guidelines provide a framework for safe online learning and responsible use of social media and electronic devices. CBSE has introduced digital etiquette and cybersecurity training initiatives, while NCERT has incorporated cyber safety into its curriculum.

The measures were outlined by Union Minister for Electronics and Information Technology Ashwini Vaishnaw in the Lok Sabha on July 22, 2026, as the government continues to strengthen its approach to c child protection and accountability across India's digital ecosystem.

]]> <![CDATA[Apple Store App May Soon Add AI Virtual Shopping Assistant]]> https://tsecurity.de/de/3688269/ios-mac-os/apple-store-app-may-soon-add-ai-virtual-shopping-assistant/ https://tsecurity.de/de/3688269/ios-mac-os/apple-store-app-may-soon-add-ai-virtual-shopping-assistant/ Thu, 23 Jul 2026 09:28:53 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[iPhone Air 2 Release Date, Features, Camera, Battery and Everything We Know]]> https://tsecurity.de/de/3688250/ios-mac-os/iphone-air-2-release-date-features-camera-battery-and-everything-we-know/ https://tsecurity.de/de/3688250/ios-mac-os/iphone-air-2-release-date-features-camera-battery-and-everything-we-know/ Thu, 23 Jul 2026 09:13:24 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Oracle July 2026 Patch Fixes 1,434 CVEs Across 334 Products]]> https://tsecurity.de/de/3688240/it-security-nachrichten/oracle-july-2026-patch-fixes-1434-cves-across-334-products/ https://tsecurity.de/de/3688240/it-security-nachrichten/oracle-july-2026-patch-fixes-1434-cves-across-334-products/ Thu, 23 Jul 2026 09:11:07 +0200 📰 IT Security Nachrichten tsecurity.de July 2026 Critical Patch Update

Oracle has released its July 2026 Critical Patch Update, delivering one of its largest quarterly security releases to date. The latest Oracle security patch addresses more than 1,400 vulnerabilities across hundreds of products, with the company indicating that artificial intelligence likely played a significant role in identifying most of the flaws.  According to Oracle, the July 2026 Critical Patch Update contains 1,449 security patches, covering 1,434 unique Common Vulnerabilities and Exposures (CVEs) across 334 products.  

July 2026 Critical Patch Update Covers Hundreds of Oracle Products 

The latest Oracle security patch spans a wide range of enterprise products and platforms. Among the affected products are Database Server, Oracle APEX, Autonomous Health Framework, Essbase, Global Lifecycle Management, GoldenGate, NoSQL Database, Spatial Studio, SQL Developer, TimesTen In-Memory Database, Application Testing Suite, Commerce, Communications, Construction and Engineering, and E-Business Suite.  The July 2026 Critical Patch Update also includes security fixes for Enterprise Manager, Financial Services Applications, Food and Beverage Applications, Fusion Middleware, Analytics, HealthCare Applications, Hospitality Applications, Java SE, JD Edwards, MySQL, PeopleSoft, Retail Applications, Siebel CRM, Supply Chain, Systems, Utilities Applications, and Virtualization.  By addressing vulnerabilities across such an extensive product lineup, the Oracle security patch aims to reduce the risk posed by security weaknesses that could affect organizations running Oracle technologies in production environments. 

Hundreds of Vulnerabilities Can Be Exploited Remotely 

A notable aspect of the July 2026 Critical Patch Update is the number of flaws that attackers could potentially exploit without requiring authentication. Oracle stated that roughly 600 of the patches fix vulnerabilities that can be exploited remotely by unauthenticated attackers. In addition, hundreds of the addressed security flaws have been assigned critical severity ratings, emphasizing the importance of applying the latest Oracle security patch without delay. Among Oracle's products, the highest number of vulnerabilities were addressed in: 
  • E-Business Suite: 410 vulnerabilities 
  • Fusion Middleware: 355 vulnerabilities 
  • Communications: 168 vulnerabilities 
  • PeopleSoft: 84 vulnerabilities 
These figures highlight that some of Oracle's most widely deployed enterprise applications received a significant share of the security fixes included in the quarterly update.

AI-Driven Vulnerability Discovery Appears to Have Played a Major Role 

One of the most notable aspects of the July 2026 Critical Patch Update is Oracle's growing use of artificial intelligence for security research. Only a few dozen of the vulnerabilities included in the release were credited to external security researchers. This indicates that the overwhelming majority of the discovered flaws were identified internally, likely with the assistance of AI-driven vulnerability analysis. Earlier this year, Oracle disclosed that it has access to leading artificial intelligence systems, including Anthropic's Claude Mythos and OpenAI's most capable models. According to the company, these AI technologies are being used to accelerate vulnerability discovery and improve the speed and accuracy of security patch development.  Oracle also said it is applying this AI-driven vulnerability approach across its own software and cloud services, Oracle Health offerings, and the open source components that it both develops and depends on.

Organizations Urged to Apply the Oracle Security Patch Promptly 

The release of the July 2026 Critical Patch Update comes amid continued efforts by threat actors to exploit vulnerabilities in enterprise software before organizations can deploy security updates.  Oracle product vulnerabilities have previously been targeted in real-world attacks. The company cited examples that include the exploitation of a PeopleSoft zero-day vulnerability as well as a recently patched Oracle E-Business Suite (EBS) vulnerability. Given the number of remotely exploitable and high-severity issues resolved in the Oracle security patch, organizations using affected Oracle products are advised to install the updates as soon as possible. Prompt deployment can help reduce exposure to attacks that take advantage of publicly known vulnerabilities before systems are secured. With 1,449 security patches addressing 1,434 unique CVEs across 334 products, the July 2026 Critical Patch Update represents one of Oracle's most extensive quarterly security releases.  ]]> <![CDATA[Microsoft’s 3-day patching directive comes with added operational risk]]> https://tsecurity.de/de/3688232/it-security-nachrichten/microsofts-3-day-patching-directive-comes-with-added-operational-risk/ https://tsecurity.de/de/3688232/it-security-nachrichten/microsofts-3-day-patching-directive-comes-with-added-operational-risk/ Thu, 23 Jul 2026 09:10:44 +0200 📰 IT Security Nachrichten tsecurity.de

Microsoft 365 Director Jeremy Chapman this month took to video to tell Windows admins that the days of delaying security patches are over.

Complex enterprise systems and historic incidents involving patch problems have caused many admins to hold fire on immediately applying security patches, in many cases deferring patch rollouts for two to four weeks or more to ensure stability. Microsoft argues that this cautious approach, though understandable, is no longer viable because AI is accelerating the discovery and exploitation of software vulnerabilities.

As a result, Microsoft has advised admins to act on patches within three days.

Independent experts agree with Microsoft’s diagnosis of the problems posed by AI-powered vulnerability discovery, but many say Microsoft’s three-day remediation window is unrealistic for large enterprises with heavy testing, change-control, and compatibility constraints.

Instead of taking a blanket approach, enterprises need to focus more on quickly resolving those vulnerabilities that are under active exploitation and relevant to their environments, according to critics of Microsoft’s revised approach.

Tighter patching deadlines

Microsoft’s revised vulnerability remediation advice comes in the wake of its work with Anthropic’s Project Glasswing and findings from Microsoft’s own MDASH multi-model agentic scanning harness. Tighter patching deadlines are configurable via Windows Autopatch and Microsoft Intune or update tooling options such as Microsoft Configuration Manager and Windows Server Update Services.

As IT environments become increasingly more complex, inadvertent issues can occur with what appears to be a simple patch.

Unique or complex deployments may not be compatible with a patch, resulting in potential data corruption, system shutdown, or the dreaded “Blue Screen of Death.” Multiple vendors in the operating system and the enterprise software and security market have released patches that have broken products and caused outages, so the issue goes well beyond Windows shops.

Increasing both the volume and the speed of patching is unsustainable for most security teams because organizations are already struggling with successful remediation as it is.

“Many organizations have patch windows, review cycles, and test environments to identify these issues prior to patching production environments,” says Scott Caveza, senior research manager at exposure management and vulnerability assessment firm Tenable. “Organizations lacking the resources for extended validation risk deploying faulty patches that cause downtime or force last-minute configuration changes.”

Caveza adds: “The mitigation steps will vary for each organization, but blindly relying on auto-updates without contextual validation is not a defensible security posture.”

CISA’s Known Exploited Vulnerabilities list and other industry data suggest that only a small fraction of disclosed vulnerabilities are confirmed as exploited in the wild.

“[Enterprises should focus on] identifying vulnerabilities with credible and functional PoCs, verified exploitation, or sustained attention from ransomware groups, threat actors, and botnets,” says Caitlin Condon, vice president of security research at VulnCheck. “Timely exploit intelligence helps organizations identify the bugs that require immediate attention, while allowing lower-risk issues to proceed through appropriate testing and change control.”

Other independent experts are more sympathetic to Microsoft’s argument that AI has made vulnerability discovery and exploit development faster than ever and, as a result, the risks of delaying patches are far greater.

“Organizations sometimes delay patches to protect the uptime of critical systems, and many updates still require a restart,” says Danny Jenkins, CEO and co-founder at endpoint protection technology vendor ThreatLocker. “Some teams also stay one update cycle behind because they are concerned that a new patch could introduce bugs or break an overlooked dependency. Unfortunately, delaying patches to preserve uptime is becoming much harder to justify.”

Jenkins adds: “Organizations should not leave critical systems exposed while waiting for the next maintenance window. Patches should still be tested, but that process needs to move quickly, with the highest priority given to vulnerabilities that are actively exploited or exposed to the internet. A controlled interruption is usually far less costly than a successful attack exploiting a known vulnerability.”

Wider cross-industry impact

Microsoft’s three-day recommendation reflects a fundamental change in the threat landscape. Other vendors might be expected to follow suit and that means CISOs need to revise their approach to vulnerability remediation.

“Organizations should expect faster disclosure-to-exploitation timelines to become the norm, which means security programs must emphasize automation, trusted software supply chains, and continuous visibility rather than relying on periodic maintenance windows,” says Mike Nelson, VP and field CTO at DigiCert.

AI is compressing the time between vulnerability discovery and exploitation, and the industry is moving rapidly from 30-, 60-, and 90-day patching windows toward a matter of days.

However a “blanket three-day requirement for every vulnerability is neither realistic nor safe for most large organizations,” says Jeff Williams, founder and CTO at Contrast Security.

Failing to patch opens up security threats, but rushing an inadequately tested patch into production creates operational risk.

“The goal cannot be to treat every CVE [vulnerability] as an emergency,” according to Williams. “It has to be identifying, within hours, which vulnerabilities are actually exploitable and require immediate action.”

Holistic remediation

Security teams are already facing significant pressure to patch faster and to remediate a rising tide of new vulnerabilities, yet many practitioners are losing ground. Verizon’s Data Breach Investigation Report, published earlier this year, found that the median time to patch had actually increased to 43 days.

Patch deployment in enterprise environments involves configuration changes, reviews, testing, and validation.

Enterprises need to become more proficient at exposure management so that they have a holistic view of their environment that’s necessary to identify which assets are at greatest risk.

“By pinpointing the misconfigurations, identity flaws, and specific vulnerabilities that pose the greatest risk to their environment, security teams can prioritize exactly what to patch first,” Tenable’s Caveza says. “The idea of ‘patch everything’ is really outdated, and ‘patch faster’ isn’t feasible with the rapidly increasing number of vulnerabilities disclosed each day.”

CISOs will have to re-engineer their vulnerability and exposure management processes. “The traditional model of scanning everything, assigning generic severity scores, and tilting at a massive and expanding backlog is no longer fast enough,” says Contrast Security’s Williams.

Organizations need to identify the small number of vulnerabilities that matter, protect against them immediately, and remediate them on a timeline the business can safely support.

Enterprises should prioritize on resolving “internet facing, remotely exploitable vulnerabilities and any of the CISA Known Exploited Vulnerability list,” says Jose Lejin, an IEEE senior member.

Businesses that cannot safely validate and deploy patches within three days still have options, including “compensating controls, reducing an asset’s exposure, or in some cases removing the component entirely, all of which shrink the exploitable risk and buy time to patch properly,” says Brad Hibbert, CSO of vulnerability management provider Brinqa.

]]> <![CDATA[iOS 27 CarPlay: Every New Feature Coming This Fall]]> https://tsecurity.de/de/3688187/ios-mac-os/ios-27-carplay-every-new-feature-coming-this-fall/ https://tsecurity.de/de/3688187/ios-mac-os/ios-27-carplay-every-new-feature-coming-this-fall/ Thu, 23 Jul 2026 08:57:19 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[OpenAI and Hugging Face Investigate AI Models’ Cyber Breakout]]> https://tsecurity.de/de/3688175/it-security-nachrichten/openai-and-hugging-face-investigate-ai-models-cyber-breakout/ https://tsecurity.de/de/3688175/it-security-nachrichten/openai-and-hugging-face-investigate-ai-models-cyber-breakout/ Thu, 23 Jul 2026 08:54:52 +0200 📰 IT Security Nachrichten tsecurity.de OpenAI and Hugging Face Probe AI Security Incident

OpenAI and Hugging Face are investigating an AI security incident involving an AI agent that compromised infrastructure while models were being evaluated for advanced cyber capabilities. The incident was detected and contained after the models identified and chained vulnerabilities across OpenAI’s research environment and Hugging Face’s production infrastructure.

Hugging Face disclosed the incident last week, while OpenAI has now shared preliminary findings from its investigation. According to OpenAI, the activity involved a combination of models, including GPT 5.6 Sol and a more capable pre-release model, with reduced cyber refusals for evaluation purposes. The models were being tested against a benchmark designed to measure advanced cyber capabilities.

OpenAI described the incident as unprecedented and said it is continuing its investigation alongside Hugging Face. The company said the findings are being shared to help defenders understand what occurred and assess the capabilities of increasingly advanced AI models.

OpenAI and Hugging Face Investigate AI Security Incident

The incident took place during an internal evaluation designed to test models against advanced exploitation scenarios involving complex attack paths. OpenAI said the evaluation was conducted without production classifiers that normally prevent models from pursuing high-risk cyber activity.

The testing environment was designed to be highly isolated, with network access limited to package installation through an internally hosted third-party software system that functions as a proxy and cache for package registries.

Despite these restrictions, the models spent substantial inference compute attempting to obtain open Internet access. During this process, they identified and exploited a zero-day vulnerability in the package registry cache proxy. OpenAI said the vulnerability has since been responsibly disclosed to the vendor.

The models then carried out privilege escalation and lateral movement within the research testing environment until they reached a node with Internet access.

[caption id="attachment_113283" align="aligncenter" width="600"]OpenAI and Hugging Face Source: OpenAI[/caption]

Models Chained Vulnerabilities Across Infrastructure

After obtaining Internet access, the models inferred that Hugging Face could host models, datasets and solutions related to the ExploitGym evaluation. The models then searched for ways to obtain secret information that could help them bypass the evaluation.

In one example described by OpenAI, the models chained multiple attack vectors, including stolen credentials and zero-day vulnerabilities, to identify a remote code execution path on Hugging Face servers.

OpenAI's security team detected the anomalous activity internally. Hugging Face's security team and agents also detected and stopped the activity on its infrastructure. The company had already begun containment and forensic reconstruction using its own open-source models when the two organizations connected.

Both companies are continuing to investigate the incident and its impact.

OpenAI Strengthens Security Controls After Incident

As part of its response, OpenAI said it is implementing stricter infrastructure configuration controls while identified vulnerabilities are patched. The company acknowledged that these measures could come at the cost of research velocity and said its Safety and Security Committee is being briefed on the controls and their impact.

OpenAI is also working with Hugging Face on the forensic investigation and has responsibly disclosed the identified zero-day vulnerability in the internally hosted third-party software.

The company has also brought Hugging Face into its trusted access program and is supporting its teams in using AI model capabilities to strengthen defenses.

OpenAI said it is improving protections around future training and evaluations, including stronger safeguards for model alignment, cybersecurity and monitoring during internal testing. The company noted that deployment safeguards were intentionally disabled during this evaluation because the goal was to measure cyber vulnerabilities.

AI Cyber Capabilities Raise New Security Concerns

OpenAI said the incident demonstrates the need for AI security and safety measures to keep pace with rapidly advancing model capabilities. The company is strengthening containment, monitoring, access controls and evaluation practices used during model development.

The incident also highlights how advanced models can potentially discover and exploit novel attack paths in real-world systems without access to source code. OpenAI said increasingly capable models should also be used defensively to help security teams identify weaknesses, understand vulnerability chains and accelerate remediation.

Hugging Face CEO Clem Delangue said the incident demonstrates the importance of collaboration in addressing AI safety and security challenges. Both organizations said they will continue investigating the incident and share additional findings and best practices as the work progresses.

]]> <![CDATA[macOS 27 Public Beta 2 Released: Here’s How to Install It]]> https://tsecurity.de/de/3688141/ios-mac-os/macos-27-public-beta-2-released-heres-how-to-install-it/ https://tsecurity.de/de/3688141/ios-mac-os/macos-27-public-beta-2-released-heres-how-to-install-it/ Thu, 23 Jul 2026 08:17:18 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Claude Security Plugin Uses AI to Find, Validate, and Patch Software Flaws]]> https://tsecurity.de/de/3688132/it-security-nachrichten/claude-security-plugin-uses-ai-to-find-validate-and-patch-software-flaws/ https://tsecurity.de/de/3688132/it-security-nachrichten/claude-security-plugin-uses-ai-to-find-validate-and-patch-software-flaws/ Thu, 23 Jul 2026 08:11:53 +0200 📰 IT Security Nachrichten tsecurity.de Anthropic has expanded its AI-driven vulnerability detection capabilities by launching Claude Security as a native Claude Code plugin, now available in beta for all Claude Code users. The move brings Anthropic’s reasoning-based security scanning directly into developers’ terminals, allowing teams to scan changes before committing or run full codebase audits using the same Claude inference [
]

The post Claude Security Plugin Uses AI to Find, Validate, and Patch Software Flaws appeared first on Cyber Security News.

]]> <![CDATA[This Week In Rust: This Week in Rust 661]]> https://tsecurity.de/de/3688059/tools/this-week-in-rust-this-week-in-rust-661/ https://tsecurity.de/de/3688059/tools/this-week-in-rust-this-week-in-rust-661/ Thu, 23 Jul 2026 07:18:12 +0200 đŸ’Ÿ Tools tsecurity.de Hello and welcome to another issue of This Week in Rust! Rust is a programming language empowering everyone to build reliable and efficient software. This is a weekly summary of its progress and community. Want something mentioned? Tag us at @thisweekinrust.bsky.social on Bluesky or @ThisWeekinRust on mastodon.social, or send us a pull request. Want to get involved? We love contributions.

This Week in Rust is openly developed on GitHub and archives can be viewed at this-week-in-rust.org. If you find any errors in this week's issue, please submit a PR.

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Updates from Rust Community

Official
Newsletters
Project/Tooling Updates
Observations/Thoughts
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Crate of the Week

This week's crate is xan, a TUI toolkit to work with CSV files.

Thanks to Simeon H.K. Fitch for the suggestion!

Please submit your suggestions and votes for next week!

Calls for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization.

If you are a feature implementer and would like your RFC to appear in this list, add a call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

No calls for testing were issued this week by Rust, Cargo, Rustup or Rust language RFCs.

Let us know if you would like your feature to be tracked as a part of this list.

Call for Participation; projects and speakers

CFP - Projects

Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

  • No Calls for participation were submitted this week.

If you are a Rust project owner and are looking for contributors, please submit tasks here or through a PR to TWiR or by reaching out on Bluesky or Mastodon!

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Are you a new or experienced speaker looking for a place to share something cool? This section highlights events that are being planned and are accepting submissions to join their event as a speaker.

  • No Calls for papers or presentations were submitted this week.

If you are an event organizer hoping to expand the reach of your event, please submit a link to the website through a PR to TWiR or by reaching out on Bluesky or Mastodon!

Updates from the Rust Project

576 pull requests were merged in the last week

Compiler
Library
Cargo
Rustdoc
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Rust-Analyzer
Rust Compiler Performance Triage

The two most notable changes this week were #159115, which resulted in pretty nice instruction count wins for full incremental builds on several benchmarks, and #159091, which enabled PGO for rustdoc, which makes it ~3-4% faster across the board.

There were two large rollups with tiny performance regressions, which made it difficult to find the offending PRs.

Triage done by @Kobzol. Revision range: 5503df87..d527bc9b

Summary:

(instructions:u) mean range count
Regressions ❌
(primary)		 0.4% [0.2%, 1.0%] 40
Regressions ❌
(secondary)		 0.7% [0.2%, 4.6%] 69
Improvements ✅
(primary)		 -2.0% [-6.2%, -0.2%] 136
Improvements ✅
(secondary)		 -2.6% [-8.4%, -0.2%] 119
All ❌✅ (primary) -1.4% [-6.2%, 1.0%] 176

2 Regressions, 3 Improvements, 6 Mixed; 4 of them in rollups 34 artifact comparisons made in total

Full report here.

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

  • No RFCs were approved this week.
Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

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Rust Compiler Team (MCPs only) Leadership Council Unsafe Code Guidelines

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New and Updated RFCs

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If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

Jobs

Please see the latest Who's Hiring thread on r/rust

Quote of the Week

We were planning on publishing a blog post announcing this at the same time as making the repo public, but ran out of private repo CI usage 😭.

– Carl Lerche on r/rust about the launch of topcoat

Despite a lamentable lack of suggestions, llogiq is glad to have found this quote.

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Discuss on r/rust

]]> <![CDATA[10 Linux-Pflicht-Tools fĂŒr Netzwerk- und Security-Profis]]> https://tsecurity.de/de/3687932/it-security-nachrichten/10-linux-pflicht-tools-fuer-netzwerk-und-security-profis/ https://tsecurity.de/de/3687932/it-security-nachrichten/10-linux-pflicht-tools-fuer-netzwerk-und-security-profis/ Thu, 23 Jul 2026 06:09:11 +0200 📰 IT Security Nachrichten tsecurity.de
Wir haben zehn essenzielle Open-Source-Security-Tools fĂŒr Sie zusammengestellt.

Wir haben zehn essenzielle Open-Source-Security-Tools fĂŒr Sie zusammengestellt.

Foto: Omelchenko – shutterstock.com

Eine Wahl zu treffen, wenn Dutzende oder gar Hunderte von Tools zur VerfĂŒgung stehen, ist nicht einfach. So dĂŒrfte es auch vielen Netzwerk- und Security-Experten gehen, die quelloffene Security Tools fĂŒr Linux suchen.

In diesem Bereich gibt es eine Vielzahl verschiedener Tools fĂŒr so gut wie jede Aufgabe (Netzwerk-Tunneling, Sniffing, Scanning, Mapping) und jede Umgebung (Wi-Fi-Netzwerke, Webanwendungen, Datenbankserver). Wir haben einige Experten konsultiert und zehn essenzielle Linux-Sicherheitstools fĂŒr Sie zusammengestellt.

1. Aircrack-ng

Diese Suite von Software Tools ermöglicht es, drahtlose Netzwerke und WiFi-Protokolle SicherheitsĂŒberprĂŒfungen zu unterziehen. Sicherheitsprofis verwenden das Tool fĂŒr die Netzwerkadministration, Hacking und Penetrationstests. Dabei fokussiert Aircrack-ng auf:

  • Monitoring (Datenpakete erfassen und Daten in Textdateien zur Weiterverarbeitung durch Tools von Drittanbietern exportieren)

  • Angreifen (Replay-Angriffe, Deauthentication, Packet Injection)

  • Testing (WiFi-Karten und Treiberfunktionen ĂŒberprĂŒfen) und

  • Cracking (WEP und WPA PSK)

Laut der offiziellen Webseite funktionieren alle Tools kommandozeilenbasiert, was eine umfangreiche Skripterstellung ermöglicht. Das Tool funktioniert mit Linux genauso wie mit Windows, macOS, FreeBSD, OpenBSD, NetBSD, Solaris und sogar eComStation.

Preis: kostenlos

2. Burp Suite

Hierbei handelt es sich um eine Testing-Suite fĂŒr Webanwendungen, die fĂŒr Security Assessments von Websites eingesetzt wird. Burp Suite arbeitet als lokale Proxy-Lösung, die es Sicherheitsexperten ermöglicht, Anfragen (HTTP/Websockets) und Antworten zwischen einem Webserver und einem Browser

  • entschlĂŒsseln,

  • beobachten,

  • manipulieren und

  • wiederholen zu können.

Burp Suite hat einen passiven Scanner an Bord, mit dem Security-Profis Webseiten (manuell) auf potenzielle Schwachstellen ĂŒberprĂŒfen können. Die Pro-Version bietet außerdem einen sehr nĂŒtzlichen aktiven Web-Schwachstellen-Scanner, mit dem sich weitere Schwachstellen aufspĂŒren lassen. Burp Suite ist ĂŒber Plugins erweiterbar, so dass Sicherheitsexperten ihre eigenen Erweiterungen entwickeln können.

Preis: Die Professional-Version kostet 475 Euro pro Jahr und Benutzer. DarĂŒber hinaus steht auch eine Enterprise-Version (ab ca. 2.000 Euro jĂ€hrlich) zur VerfĂŒgung, die mehrere gleichzeitige Scans ermöglicht und von Anwendungsentwicklungsteams genutzt werden kann.

3. Impacket

Diese Sammlung von Tools ist fĂŒr Pen-Tests von Netzwerkprotokollen und -diensten unerlĂ€sslich. Impacket wurde von SecureAuth entwickelt und ist eine Sammlung von Python Classes, um mit Netzwerkprotokollen zu arbeiten. Impacket konzentriert sich auf die Bereitstellung von Low-Level-Zugriff auf Pakete und bei einigen Protokollen wie SMB1-3 und MSRPC auf die Protokollimplementierung selbst. Sicherheitsexperten können Pakete von Grund auf neu konstruieren, aber auch auf Grundlage geparster Rohdaten. Die objektorientierte API macht es zudem einfach, mit tiefen Protokollhierarchien zu arbeiten. Impacket unterstĂŒtzt die folgenden Protokolle:

  • Ethernet, Linux;

  • IP, TCP, UDP, ICMP, IGMP, ARP;

  • IPv4 und IPv6;

  • UmgĂ€nglicher zeigte sich Musk gegenĂŒber den Anzeigenkunden von Twitter. In einem – natĂŒrlich auf Twitter geposteten – Brief erklĂ€rte der Tesla-Chef, der Grund fĂŒr die Übernahme sei nicht, damit noch mehr Geld zu verdienen. Vielmehr sei es “wichtig fĂŒr den Fortbestand der Zivilisation, einen gemeinsamen digitalen Treffpunkt zu haben, auf dem eine breite Palette von Überzeugungen auf gesunde Weise diskutiert werden kann.”

  • Trotz alledem dĂŒrfe Twitter nicht zu einer “fĂŒr alle Nutzer freien Höllenlandschaft werden, in der alles ohne Konsequenzen gesagt werden kann”, fĂŒgte Musk hinzu. ZusĂ€tzlich zur Einhaltung der Gesetze mĂŒsse die Plattform “warmherzig und einladend” fĂŒr alle sein und den Nutzern die Möglichkeit bieten, “die gewĂŒnschte Erfahrung nach ihren Vorlieben zu wĂ€hlen” – Ă€hnlich wie man zum Beispiel wĂ€hlen kann, Filme zu sehen oder Videospiele zu spielen, die fĂŒr alle Altersgruppen geeignet sind.

  • Plain-, NTLM- und Kerberos-Authentifizierungen, unter Verwendung von Kennwörtern/Hashes/Tickets/SchlĂŒsseln;

  • EU-Kommissar Thierry Breton wiederum reagierte auf Musks Teet, dass der Vogel jetzt frei sein, mit der Anmerkung, “dass Twitter in Europa nach unseren Regeln fliegen muss”.

Preis: Kostenlos – Impacket wird unter einer leicht modifizierten Version der Apache Software License bereitgestellt. Die Unterschiede können Sie hier einsehen.

4. Metasploit

Metasploit ist ein Exploit-Framework von Rapid7, das fĂŒr allgemeine Penetrationstests und Schwachstellenbewertungen verwendet wird. Sicherheitsexperten betrachten es als “Super-Tool”, das funktionierende Versionen fast aller bekannter Exploits enthĂ€lt. Metasploit ermöglicht Sicherheitsexperten, Netzwerke und Endpunkte auf Schwachstellen zu scannen und anschließend automatisiert mögliche Exploits auszufĂŒhren, um Systeme zu ĂŒbernehmen.

Metasploit erleichtert es mit protokollspezifischen Modulen (die alle unter der Funktion Auxiliary/Server/Capture laufen) Anmeldeinformationen zu erfassen. Sicherheitsexperten können jedes dieser Module einzeln starten und konfigurieren – zudem steht ein Capture-Plug-in zur VerfĂŒgung, das diesen Prozess vereinheitlicht.

Preis: Metasploit Pro kostet – inklusive kommerziellem Support durch Rapid7 – ab 12.000 Dollar pro Jahr. Es gibt aber auch eine kostenlose Version.

5. Ncat

Der Nachfolger des beliebten Tools Netcat heißt Ncat und kommt von den Machern von Nmap. Das Tool ermöglicht es, Daten per Kommandozeile ĂŒber ein Netzwerk zu lesen und zu schreiben, bietet aber auch zusĂ€tzlich Funktionen wie SSL-VerschlĂŒsselung. Sicherheitsexperten zufolge ist Ncat unerlĂ€sslich geworden, um TCP/UDP-Clients und -Server zu hosten und Daten von Angreifer- und Opfersystemen zu empfangen.

Ncat ist auch ein beliebtes Tool, um eine Reverse Shell einzurichten oder Daten zu exfiltrieren. Es wurde als zuverlÀssiges Back-End-Tool entwickelt, um Netzwerkverbindungen zu anderen Anwendungen und Benutzern herzustellen.

Preis: kostenlos

6. Nmap

Dieses Netzwerk-Scanning- und Mapping-Tool auf Kommandozeilen-Basis findet zugĂ€ngliche Ports auf Remote Devices. Viele Sicherheitsexperten halten Nmap fĂŒr eines der wichtigsten und effektivsten Tools – insbesondere im Bereich Penetration Testing ist es unerlĂ€sslich.

Die Skripting-Engine von Nmap erkennt anschließend automatisiert weitere Schwachstellen und nutzt diese aus. Nmap unterstĂŒtzt Dutzende fortschrittlicher Techniken, um Netzwerke mit IP-Filtern, Firewalls, Routern und anderen Hindernissen abzubilden. Dazu gehören auch zahlreiche Mechanismen, um TCP- und UDP-Ports zu scannen, Betriebssysteme und Versionen sowie Ping-Sweeps zu erkennen.

Preis: kostenlos

7. ProxyChains

Dieses Werkzeug – der De-facto-Standard fĂŒr Netzwerk-Tunneling – ermöglicht es Sicherheitsexperten, Proxy-Befehle von ihrem angreifenden Linux-Rechner aus ĂŒber verschiedene kompromittierte Rechner zu senden, um Netzwerkgrenzen und Firewalls zu ĂŒberwinden und dabei einer Entdeckung zu entgehen.

ProxyChains leitet den TCP-Verkehr von Penetrationstestern durch die folgenden Proxys: TOR, SOCKS und HTTP. ProxyChains ist mit TCP-AufklĂ€rungs-Tools wie NMAP kompatibel und verwendet standardmĂ€ĂŸig das TOR-Netzwerk. Sicherheitsexperten verwenden ProxyChains auch bei der IDS/IPS-Erkennung.

Preis: kostenlos

8. Responder

Responder ist ein NBT-NS (NetBIOS Name Service), LLMNR (Link-Local Multicast Name Resolution) und mDNS (Multicast DNS) Poisoner. Penetration Tester nutzen das Tool, um Angriffe zu simulieren, die darauf abzielen, Anmeldeinformationen und andere Daten wĂ€hrend des Prozesses der Namensauflösung zu stehlen, wenn der DNS-Server keinen Eintrag findet. Ab Version 3.1.1.0 bietet Responder standardmĂ€ĂŸig vollen IPv6-Support.

Preis: kostenlos

9. sqlmap

Das Open-Source-Tool sqlmap richtet sich ebenfalls an Penetrationstester und automatisiert den Prozess, SQL-Injection-Fehler zu erkennen, mit deren Hilfe Datenbankserver kompromittiert werden könnten. Das Tool verfĂŒgt ĂŒber eine leistungsstarke Erkennungs-Engine und bietet zahlreiche Funktionen, darunter Datenbank-Fingerprinting und die AusfĂŒhrung von Befehlen auf Betriebssystemebene ĂŒber Out-of-Band-Verbindungen.

Sqlmap unterstĂŒtzt eine breite Palette von Datenbankservern, darunter:

  • MySQL,

  • Oracle,

  • PostgreSQL,

  • Microsoft SQL Server,

  • Microsoft Access,

  • IBM DB2,

  • SQLite,

  • Firebird,

  • Sybase,

  • SAP MaxDB und

  • HSQLDB.

Preis: kostenlos

10. Wireshark

Das Netzwerkprotokoll-Analyse-Tool Wireshark wird auch oft als Network Interface Sniffer bezeichnet. Mit Wireshark können Sicherheitsexperten das Netzwerkverhalten eines GerÀts beobachten, um zu sehen, mit welchen anderen GerÀten es kommuniziert und warum.

Sicherheitsexperten zufolge eignet sich Wireshark hervorragend, um herauszufinden, wo sich DNS-Server und andere Dienste befinden, mit denen sich ein Netzwerk weiter kompromittieren lĂ€sst. Wireshark lĂ€uft nicht nur unter Linux, sondern funktioniert mit den allen gĂ€ngigen Betriebssystemen, einschließlich Windows, MacOs und Unix.

Preis: kostenlos

(fm)

Dieser Beitrag ist im Original bei unserer Schwesterpublikation Networkworld.com erschienen.

]]> <![CDATA[Vice Game Clipper 2.0 released! (Linux Medal.tv alternative)]]> https://tsecurity.de/de/3687901/linux-tipps/vice-game-clipper-20-released-linux-medaltv-alternative/ https://tsecurity.de/de/3687901/linux-tipps/vice-game-clipper-20-released-linux-medaltv-alternative/ Thu, 23 Jul 2026 04:55:23 +0200 🐧 Linux Tipps tsecurity.de

Vice is a Linux game clipper that focuses on easy sharing and editing. Under the hood, it uses GPU-Screen-Recorder as the capture engine, so it has nearly no performance impact whatsoever, and it has features such as:

- Instant replay with customisable hotkeys, and long session recording

- Free share links that embed in Discord (no more hitting upload limits!)
- A full timeline editor with built in transition and text effects
- Automatically sorting clips by the game you were clipping
- Discord Rich Presence support
- Customisable, colour-coded highlights within clips

- You can seperate audio sources like Discord calls, the game audio, and music, adjust their individual volume in clips

- And more, plus new features are always being added.

It's free, open source, and will stay that way. Any feedback is greatly appreciated, because community testing is what makes open source projects like these better.

It's on the AUR, and you can install with:

Or if you're on a different distro, you can run the install script with:

Repo is below:

https://github.com/eklonofficial/Vice

submitted by /u/LinuxBaka
[link] [comments]]]> <![CDATA[PipeWire-Controller Update- thanks everyone who tried it and gave feedback. I have an update for it later today or tomorrow. Currently testing. I really do appreciate the feedback and support. The app is extremely comprehensive in terms of what it can do]]> https://tsecurity.de/de/3687874/linux-tipps/pipewire-controller-update-thanks-everyone-who-tried-it-and-gave-feedback-i-have-an-update-for-it-later-today-or-tomorrow-currently-testing-i-really-do-appreciate-the-feedback-and-support-the-app-is-extremely-comprehensive-in-terms-of-what-it-can-do/ https://tsecurity.de/de/3687874/linux-tipps/pipewire-controller-update-thanks-everyone-who-tried-it-and-gave-feedback-i-have-an-update-for-it-later-today-or-tomorrow-currently-testing-i-really-do-appreciate-the-feedback-and-support-the-app-is-extremely-comprehensive-in-terms-of-what-it-can-do/ Thu, 23 Jul 2026 04:21:46 +0200 🐧 Linux Tipps tsecurity.de

Here is a link to all the screenshots.https://www.reddit.com/r/linuxaudio/s/iyo0jZhYmd

Edit: here is an overview of what it can do
control center for PipeWire — filter chains, HRIR virtual surround, drop-in config management, live patchbay, performance monitoring, virtual devices, routing snapshots, per-application policies and LADSPA/LV2 effect inserts

Thanks everyone who came yesterday and tried my little app. I released the app on AUR yesterday and is available on github here (https://github.com/knightinfected/PipeWireController). The current version is 0.1.2 and some of the screenshots are from 0.2
I received a ton of negative comments and messages regarding my use of AI. I spent a lot of my time on this and well over did this project abit and english being my secondary language didnt help.
I have gone down this audio rabbit hole in linux and anyone else who has also done it knows how annoying it gets especially with reading the wikis to actual application.

Anyhow I hope it helps someone out there and I will be updating it in the next day or later today as I am currently testing. Version 0.1.2 is available as of right now on GitHub though.

There are too many features to list them out so I added lot of screenshots to help understand.

submitted by /u/Infected_Knight
[link] [comments]]]> <![CDATA[The credential that let OpenAI's agents into Hugging Face exists in most enterprises right now]]> https://tsecurity.de/de/3687771/it-nachrichten/the-credential-that-let-openais-agents-into-hugging-face-exists-in-most-enterprises-right-now/ https://tsecurity.de/de/3687771/it-nachrichten/the-credential-that-let-openais-agents-into-hugging-face-exists-in-most-enterprises-right-now/ Thu, 23 Jul 2026 01:32:49 +0200 📰 IT Nachrichten tsecurity.de When Hugging Face got hit last week, co-founder Clement Delangue suspected a frontier lab, given the agent's sophistication. He was right. Delangue said on X that after a day working with OpenAI he strongly believed there was no malicious intent and that it was mind-blowing the whole thing had happened autonomously.

The two OpenAI models that broke into Hugging Face last week did not breach it through malice or superintelligence. They breached it through credentials and permissions they should never have been able to reach, a non-human identity failure that is the oldest problem in security rather than the newest one in AI, and the one every enterprise can actually fix.

OpenAI disclosed on July 21 that two of its models, GPT-5.6 Sol and an unreleased, more capable model, were running a cyber benchmark called ExploitGym with their safety refusals switched off, and inferred that the answer key sat in Hugging Face's production database. Getting there took two different failures. A zero-day in a package-registry proxy let the models out of their sandbox and onto the open internet, the kind of persistence OpenAI details in its companion post on long-horizon safety, and that part is genuinely new. The breach of Hugging Face itself came the ordinary way. OpenAI's own account is that the models chained stolen credentials and further zero-days into a remote code execution path, after a series of privilege escalation and lateral movement steps. The exotic part got them to the door, and credentials walked them through it.

Hugging Face also disclosed last week that an autonomous agent had harvested cloud and cluster credentials scoped broadly enough to reach multiple internal clusters, then left a trail of more than 17,000 recorded events across short-lived sandboxes over a weekend. Both disclosures describe the same escalation. An agent lands somewhere it should not be, finds credentials scoped far wider than any task requires, and uses them to move. These are two accounts of one incident, not two attacks. The agent Hugging Face watched was OpenAI's models, and both companies describe the same ordinary escalation.

The version of this in a typical enterprise is worse, not better. OpenAI and Hugging Face are among the most security-mature organizations in the industry, and both still needed the intrusion to happen before they could see it. The average company wiring agents into Copilot or an internal assistant has neither the identity inventory nor the behavioral monitoring those two brought to bear. The same breach in a normal company would not be contained in days, it would simply go unnoticed.

The industry is debating the wrong failure

The reaction has split into familiar camps. Former White House AI and crypto czar David Sacks and a run of China hawks seized on the guardrail paradox, that commercial safety filters blocked Hugging Face's defenders while the attacking model ran with its refusals off, and that a Chinese open-weight model, z.ai's GLM 5.2, was what finally let the team finish its forensics. Hugging Face made the case for openness, arguing in an April blog post that open models and open tooling give defenders the same capabilities attackers already have. Both arguments are about the model, and neither touches the mechanism.

Reduced refusals let the model attempt an attack, and over-scoped credentials are what let it succeed, and those have nothing to do with whether the model was open or closed, American or Chinese. Making a frontier model provably safe is a multi-year alignment problem no customer can buy or accelerate, while scoping an identity is a configuration change a team can ship this sprint. The industry is being urged to fixate on the part of this it cannot control and to treat the part it can as a footnote.

Forrester reached the same read. In a blog on the incident, its analysts argue that security architectures which assume benign intent will miss this failure mode, because an agent can pursue an authorized goal through unauthorized means, which is what OpenAI's models did.

This was a non-human identity failure, and it is the oldest one in security

Strip the science-fiction framing and what remains is a textbook case of over-privileged machine identity, the kind security teams have fought for a decade, now driven by an autonomous agent at machine speed. Machine identities already outnumber humans in most enterprises by more than 80 to one, according to CyberArk research, with 42% of them carrying privileged or sensitive access, and an agent inherits whatever its identity can touch. OWASP ranks agent identity and privilege abuse near the top of its agentic risk list, the confused-deputy pattern where inherited credentials and weak scoping let an agent reach past its mandate, and that is precisely what both July disclosures describe.

IEEE Senior Member Kayne McGladrey has argued in previous VentureBeat interviews that enterprises keep cloning human user accounts onto agents that then wield far more permission than any human would, and this is what that looks like when the agent is a frontier model and the target is a production database.

The people closest to it read it the same way. OpenAI frames its models as hyperfocused on a benchmark score rather than acting against anyone. Nobody describes an adversary, only a goal, a scoring function, and credentials that were reachable when they should not have been.

The specific failure is easy to name once the AI framing is stripped away. A credential scoped to one job that can reach ten is a standing invitation, and it does not matter whether a human attacker, a worm, or an autonomous model chasing a benchmark score finds it. What changed in July is the finder. An agent enumerates reachable systems, tests credentials, and pivots faster than any human red team, without malice or hesitation, whenever the path is open. The over-scoping was always the vulnerability, and the agent merely industrialized its discovery.

Forrester named the control that would have blunted it. Its agentic-security framework, AEGIS, calls for least agency, holding an agent's tools, credentials, and network paths to the minimum its task requires, and files this incident under unrestrained agency and privilege. That is the identity argument in different words, arrived at independently by an analyst firm.

The data says this is where the risk now lives. Verizon's 2026 Data Breach Investigations Report found that exploitation of vulnerabilities has overtaken stolen credentials as the top initial access vector for the first time in 19 years. That is the initial-access half. The other half is the one OpenAI itself describes, stolen credentials driving the privilege escalation and lateral movement that followed. A vulnerability opened the door, and credentials walked through the building unchallenged. Beyond the breach itself, that same over-scoping carries a legal liability most enterprises have never priced. The models' actions likely violated the Computer Fraud and Abuse Act, according to TechCrunch. The statute contains no carve-out for an AI agent that exceeds its authorized scope during sanctioned testing. Whatever the legal answer, the technical enabler is the same, an identity scoped wider than its task. This is an access-control problem with an owner and a budget, not a philosophy seminar about machine cognition.

Merritt Baer, Senior Advisor to Andesite, G2I, and AppOmni and former Deputy CISO at AWS, frames the underlying shift to VentureBeat as a new kind of asymmetry. Both sides now reach for the same capabilities, she said, but one side is constrained by enterprise governance, policy, compliance, and safety controls while the adversary simply downloads an uncensored open-weight model and keeps going. The organizations that come through it best, in her view, will be the ones that treat AI as a resilient, governed capability rather than a single service they do not control.

Four moves that shrink the blast radius

The breach worked because the agent reached identities scoped far wider than its task. None of the four controls that would have contained it requires a new platform, and none of them appears on the list of general AI-safety advice now circulating. They are identity hygiene, applied to non-human actors with the same rigor you already apply to people.

1. Scope every non-human identity to one task. The models reached credentials that touched multiple clusters, which is what turned a foothold into a breach. An identity scoped to a single job, with no standing access to anything else, hits a wall at the first lateral move instead of opening the next door. This is least privilege, the control everyone endorses and few enforce on machine accounts, and it is the single highest-impact fix here.

2. Give credentials short lifetimes and rotate them hard. Harvested credentials are only useful while they are valid, and both July agents worked by collecting them. Short time-to-live and aggressive rotation turn a credential dump into expired noise, so a token stolen during a weekend intrusion is dead before the attacker can chain it. Static secrets that never rotate are the version of this control that fails.

3. Monitor for lateral movement, not just prompts. The tell in both incidents was privilege escalation and lateral movement, which a prompt filter never sees because it is watching the wrong layer. Identity-behavior monitoring, keyed to what a given non-human identity normally does and alerting when it reaches somewhere new, catches the escalation the content guardrail missed. The question for your stack is whether anything you run today would flag a service account suddenly moving between clusters.

4. Rehearse instant revocation before you need it. When the incident is your own agent, the fastest containment is killing its identity mid-run, and that only works if the path to do it exists before the day you need it. Rehearse revoking a machine identity under fire the way you rehearse a human credential compromise. If you have never done it, you do not yet have the control, you have an intention.

The defense also worked, and that matters. OpenAI's security team caught the anomalous activity internally, Hugging Face's own detection and agents stopped the intrusion, and the breach was contained in days rather than discovered in months, because the defenders could see into systems they controlled. That visibility is the same discipline the four controls depend on. The debate over whether frontier models are safe, open, or American will run for years, and none of it will be settled in time to help the enterprise deploying agents this quarter. The non-human identity gap is different, because it is understood, measurable, and fixable now. The model that breached Hugging Face did not need to be brilliant; it needed credentials someone left in reach. The fix is scoping them before an agent finds them.

]]> <![CDATA[Samsung Galaxy Z Flip8 Launches at $1,199 With Thinner Design]]> https://tsecurity.de/de/3687549/it-nachrichten/samsung-galaxy-z-flip8-launches-at-1199-with-thinner-design/ https://tsecurity.de/de/3687549/it-nachrichten/samsung-galaxy-z-flip8-launches-at-1199-with-thinner-design/ Wed, 22 Jul 2026 22:37:11 +0200 📰 IT Nachrichten tsecurity.de Samsung’s $1,199 Galaxy Z Flip8 adds a thinner design, expanded FlexWindow tools, and new AI controls while retaining the same battery and rear cameras.

The post Samsung Galaxy Z Flip8 Launches at $1,199 With Thinner Design appeared first on TechRepublic.

]]> <![CDATA[Paskoocheh: When you need a tool to reach the tool]]> https://tsecurity.de/de/3687545/it-security-tools/paskoocheh-when-you-need-a-tool-to-reach-the-tool/ https://tsecurity.de/de/3687545/it-security-tools/paskoocheh-when-you-need-a-tool-to-reach-the-tool/ Wed, 22 Jul 2026 22:34:54 +0200 đŸ’Ÿ IT Security Tools tsecurity.de

++ This guest post is part of a spotlight series on the organizations defending the free Internet.++

Due to heavy information controls, people in Iran face significant barriers to accessing the Internet. Authorities have actively blocked numerous websites and apps, including conventional circumvention and digital security tools such as VPNs, social media platforms, and the app stores themselves. This creates a "chicken-and-egg" problem: users need a VPN to download a VPN.

Launched in 2016, Paskoocheh, Persian for "alleyway," is an open source alternative app store, community hub, and one-stop-shop for users to access information and tools to circumvent censorship, enhance their privacy, securely communicate, and express themselves freely online. Developed and maintained by ASL19, a technology and exiled media organization named after Article 19 of the Universal Declaration of Human Rights, Paskoocheh restores access and allows people to reach trusted tools through four censorship-resilient channels: the Paskoocheh website, Android App, Email bot, and Telegram bot. 

Users are also able to reach our Persian-speaking support team through the Paskoocheh Helpdesk, which handles over 200 tickets daily. In addition, ASL19 translates and publishes accessible user guides, blog posts, and multimedia content to help users navigate online privacy and digital security best practices.

Paskoocheh serves as more than an alternative app store; it is also a bridge between tool developers and in-country users. Our support team relays user feedback to tool developers, helping improve tools and overall experience in Iran. We also conduct in-country testing with developers and user communities to evaluate new features and strengthen censorship-resilient technologies.

Paskoocheh's impact so far

This combination of access, user support, and education has turned Paskoocheh into a critical lifeline for users in Iran.

  • # of tool downloads since 2016:  17,634,852 

  • # of community members in Iran supporting testing and localization efforts: 2,000+

  • # of monthly active users on web and app: ~200K

During periods of internet disruption and nationwide protests in Iran, these tools became critical communication lifelines. One longtime user wrote to us: 

"I've been using this free app for several years now. It's free, unique, and unlike others, it has no equal." Reflecting on the broader digital environment in the country, they added that "in these difficult economic conditions, people are struggling just to survive, while many apps either empty people's pockets, deceive and lie to them, or serve as tools for spying and propaganda."

Messages like these highlight the importance of privacy-preserving technologies in environments where surveillance, censorship, and disinformation shape everyday life online. In moments of crisis, internet freedom tools become part of how people maintain relationships, exchange trusted information, and stay connected to the outside world. For some users, these tools also made it possible to continue reporting on events on the ground, verify information during periods of state-backed disinformation, and safely communicate evidence of abuses despite widespread surveillance and connectivity disruptions.

The future of Paskoocheh: Scaling a community-first approach to internet freedom

As internet censorship tactics evolve rapidly, internet shutdowns are becoming more frequent and more sophisticated, cutting communities off from information, communication, and one another. 

What we have learned through this work is that access alone is not enough. Technology is only useful if people trust it, understand how to use it safely, and can rely on support networks when digital spaces become unstable or dangerous.

That is why our work extends beyond technical development. Alongside building secure access technologies, ASL19 invests heavily in user education, digital security guidance, and community capacity building. Every support ticket answered, training delivered, and piece of digital safety guidance shared helps people stay connected under pressure. 

This human-centered approach is becoming increasingly important as authoritarian tactics evolve globally. During internet shutdowns and heightened censorship, local helper communities often become the first line of assistance for journalists, activists, students, and ordinary citizens. 

With additional support, ASL19 aims to continue expanding Paskoocheh beyond its current capacity into a broader resilience ecosystem that combines technical innovation with stronger on-the-ground support systems. This includes improving access to trusted circumvention and privacy tools during shutdowns, expanding multilingual user support and educational resources, and deepening collaboration with communities operating under digital authoritarianism. 

This work is not solely about technology products. At a moment when most people's understanding of the internet is shaped by the little squares in their pockets, it is important to acknowledge and support the broader ecosystems that make access possible. Civil society, independent media, and grassroots communities all play a part in helping people survive under pressure. This is why partnerships within the internet freedom ecosystem matter. Living under digital authoritarianism means that these are not abstract protections against hypothetical risks, but practical tools that make journalism, organizing, education, and communication possible in the first place. 

About ASL19

Named after Article 19 of the Universal Declaration of Human Rights, ASL19 is a technology and exiled media organization working to counter digital authoritarianism. For more than a decade, we have partnered with civil society groups, journalists, researchers, activists, and internet users living under some of the world's most restrictive online environments. Guided by the belief that privacy and internet freedom are essential to safe communication, access to information, and civic participation, ASL19 develops technologies and support systems that help people navigate censorship, surveillance, internet shutdowns, and information manipulation. In countries such as Iran, Russia, and China, these tools serve as critical lifelines, enabling people to communicate securely, access information, document human rights abuses, and stay connected to the outside world.

]]> <![CDATA[New Alpha Release: Tor Browser 16.0a8]]> https://tsecurity.de/de/3687537/it-security-tools/new-alpha-release-tor-browser-160a8/ https://tsecurity.de/de/3687537/it-security-tools/new-alpha-release-tor-browser-160a8/ Wed, 22 Jul 2026 22:34:02 +0200 đŸ’Ÿ IT Security Tools tsecurity.de

Tor Browser 16.0a8 is now available from the Tor Browser download page and also from our distribution directory.

This version includes important security updates to Firefox.

⚠ Reminder: The Tor Browser Alpha release-channel is for testing only. As such, Tor Browser Alpha is not intended for general use because it is more likely to include bugs affecting usability, security, and privacy.

Moreover, Tor Browser Alphas are now based on Firefox's betas. Please read more about this important change in the Future of Tor Browser Alpha blog post.

If you are an at-risk user, require strong anonymity, or just want a reliably-working browser, please stick with the stable release channel.

Send us your feedback

If you find a bug or have a suggestion for how we could improve this release, please let us know.

Full changelog

The full changelog since Tor Browser 16.0a7 is:

]]> <![CDATA[Apple Tests Nearly 7-Inch Screen for Future iPhone Pro Max Model]]> https://tsecurity.de/de/3687493/it-nachrichten/apple-tests-nearly-7-inch-screen-for-future-iphone-pro-max-model/ https://tsecurity.de/de/3687493/it-nachrichten/apple-tests-nearly-7-inch-screen-for-future-iphone-pro-max-model/ Wed, 22 Jul 2026 22:05:38 +0200 📰 IT Nachrichten tsecurity.de Apple is reportedly testing a 6.96-inch iPhone screen for its 2027 anniversary lineup, a change that could make the Pro Max feel nearly 7 inches.

The post Apple Tests Nearly 7-Inch Screen for Future iPhone Pro Max Model appeared first on TechRepublic.

]]> <![CDATA[Firefox Tooling Announcements: July 22nd Deploy]]> https://tsecurity.de/de/3687479/tools/firefox-tooling-announcements-july-22nd-deploy/ https://tsecurity.de/de/3687479/tools/firefox-tooling-announcements-july-22nd-deploy/ Wed, 22 Jul 2026 21:57:25 +0200 đŸ’Ÿ Tools tsecurity.de The latest version of PerfCompare is now live!

Check out the change-log below to see the updates:

[kala-moz]

[moijes]

[sumairq]

Thank you for the contributions!

Bugs or feature requests can be filed on Bugzilla. The team can also be found on the #perfcompare channel on Element. Come and chat!

1 post - 1 participant

Read full topic

]]> <![CDATA[How OpenAI’s human mistake led to the AI-powered hack on Hugging Face]]> https://tsecurity.de/de/3687475/it-security-nachrichten/how-openais-human-mistake-led-to-the-ai-powered-hack-on-hugging-face/ https://tsecurity.de/de/3687475/it-security-nachrichten/how-openais-human-mistake-led-to-the-ai-powered-hack-on-hugging-face/ Wed, 22 Jul 2026 21:55:06 +0200 📰 IT Security Nachrichten tsecurity.de OpenAI made a mistake setting up what it called a “highly isolated” testing environment and sandbox. According to cybersecurity experts, that human mistake is what made the AI-powered attack on Hugging Face possible. This article has been indexed from Security


Read more →

The post How OpenAI’s human mistake led to the AI-powered hack on Hugging Face appeared first on IT Security News.

]]> <![CDATA[How an OpenAI’s human mistake led to the AI-powered hack on Hugging Face]]> https://tsecurity.de/de/3687431/it-nachrichten/how-an-openais-human-mistake-led-to-the-ai-powered-hack-on-hugging-face/ https://tsecurity.de/de/3687431/it-nachrichten/how-an-openais-human-mistake-led-to-the-ai-powered-hack-on-hugging-face/ Wed, 22 Jul 2026 21:21:11 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Apple Plans 11 New Macs With M6 Chips, OLED MacBook Pro, New iMac and More]]> https://tsecurity.de/de/3687365/ios-mac-os/apple-plans-11-new-macs-with-m6-chips-oled-macbook-pro-new-imac-and-more/ https://tsecurity.de/de/3687365/ios-mac-os/apple-plans-11-new-macs-with-m6-chips-oled-macbook-pro-new-imac-and-more/ Wed, 22 Jul 2026 21:01:02 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Apple seeds second public betas of iOS 27, iPadOS 27, watchOS 27, and tvOS 27]]> https://tsecurity.de/de/3687323/ios-mac-os/apple-seeds-second-public-betas-of-ios-27-ipados-27-watchos-27-and-tvos-27/ https://tsecurity.de/de/3687323/ios-mac-os/apple-seeds-second-public-betas-of-ios-27-ipados-27-watchos-27-and-tvos-27/ Wed, 22 Jul 2026 20:38:18 +0200 🍏 iOS / Mac OS tsecurity.de Apple on Wednesday pushed out the second public betas of iOS 27, iPadOS 27, and watchOS 27 to members of its public beta testing program


The post Apple seeds second public betas of iOS 27, iPadOS 27, watchOS 27, and tvOS 27 appeared first on MacDailyNews.

]]> <![CDATA[OnionHop 3.6.2]]> https://tsecurity.de/de/3687277/it-security-tools/onionhop-362/ https://tsecurity.de/de/3687277/it-security-tools/onionhop-362/ Wed, 22 Jul 2026 20:29:17 +0200 đŸ’Ÿ IT Security Tools tsecurity.de Scanner improvements from testing feedback.

Added

  • Bridge scanner: "Load bridges" — fetches the selected category/transport list into the input without scanning, so you can review or edit the list first, then scan (a two-step flow like BridgeHop's).
  • "Import file" on both the bridge scanner and the SNI scanner — load bridge lines / candidate domains from a .txt/.csv file.
  • Saved-bridges library: ping badge — each saved entry's latency now shows as a green (or amber, when slow) "✔ ms" badge instead of a plain number.

Downloads

Platform File
Windows installer OnionHop-Setup-v3.exe
Windows portable OnionHopV3-Portable-3.6.2-win-x64.zip
Windows CLI OnionHop-CLI-Setup-3.6.2.exe / OnionHopCLI-Portable-3.6.2-win-x64.zip
Linux OnionHop-x86_64.AppImage
Linux CLI OnionHopCLI-3.6.2-linux-x64.tar.gz
macOS (Apple Silicon) OnionHop-3.6.2-macOS-arm64.dmg
macOS (Intel) OnionHop-3.6.2-macOS-x64.dmg
macOS CLI (Apple Silicon) OnionHopCLI-3.6.2-macos-arm64.tar.gz
macOS CLI (Intel) OnionHopCLI-3.6.2-macos-x64.tar.gz
]]> <![CDATA[Microsoft Announces Xbox Backward Compatibility For PC]]> https://tsecurity.de/de/3687248/it-security-nachrichten/microsoft-announces-xbox-backward-compatibility-for-pc/ https://tsecurity.de/de/3687248/it-security-nachrichten/microsoft-announces-xbox-backward-compatibility-for-pc/ Wed, 22 Jul 2026 20:24:44 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[We tested Creality's 3D printers — and these 4 Amazon deals are actually worth buying]]> https://tsecurity.de/de/3687231/it-nachrichten/we-tested-crealitys-3d-printers-and-these-4-amazon-deals-are-actually-worth-buying/ https://tsecurity.de/de/3687231/it-nachrichten/we-tested-crealitys-3d-printers-and-these-4-amazon-deals-are-actually-worth-buying/ Wed, 22 Jul 2026 20:17:13 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[The Super Mario Galaxy Movie 4K Blu-ray might not be my favorite movie of the year, but my god it's an awesome new reference disc for testing TVs and Dolby Atmos sound system]]> https://tsecurity.de/de/3687225/it-nachrichten/the-super-mario-galaxy-movie-4k-blu-ray-might-not-be-my-favorite-movie-of-the-year-but-my-god-its-an-awesome-new-reference-disc-for-testing-tvs-and-dolby-atmos-sound-system/ https://tsecurity.de/de/3687225/it-nachrichten/the-super-mario-galaxy-movie-4k-blu-ray-might-not-be-my-favorite-movie-of-the-year-but-my-god-its-an-awesome-new-reference-disc-for-testing-tvs-and-dolby-atmos-sound-system/ Wed, 22 Jul 2026 20:13:07 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Apple is Testing Mac Mini with M6 and M5 Pro Chips, Says Report]]> https://tsecurity.de/de/3687199/ios-mac-os/apple-is-testing-mac-mini-with-m6-and-m5-pro-chips-says-report/ https://tsecurity.de/de/3687199/ios-mac-os/apple-is-testing-mac-mini-with-m6-and-m5-pro-chips-says-report/ Wed, 22 Jul 2026 19:58:28 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Google Launches Gemini 3.6 Flash and Cybersecurity Model for Enterprise AI]]> https://tsecurity.de/de/3687192/it-nachrichten/google-launches-gemini-36-flash-and-cybersecurity-model-for-enterprise-ai/ https://tsecurity.de/de/3687192/it-nachrichten/google-launches-gemini-36-flash-and-cybersecurity-model-for-enterprise-ai/ Wed, 22 Jul 2026 19:49:17 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[OpenAI says its AI agent broke out of testing sandbox to hack Hugging Face]]> https://tsecurity.de/de/3687084/ai-nachrichten/openai-says-its-ai-agent-broke-out-of-testing-sandbox-to-hack-hugging-face/ https://tsecurity.de/de/3687084/ai-nachrichten/openai-says-its-ai-agent-broke-out-of-testing-sandbox-to-hack-hugging-face/ Wed, 22 Jul 2026 19:05:38 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Apple is reportedly testing a MacBook Neo with more RAM]]> https://tsecurity.de/de/3687055/it-nachrichten/apple-is-reportedly-testing-a-macbook-neo-with-more-ram/ https://tsecurity.de/de/3687055/it-nachrichten/apple-is-reportedly-testing-a-macbook-neo-with-more-ram/ Wed, 22 Jul 2026 18:48:08 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[OpenAI unveils Presence, a new platform that lets enterprises launch and manage realtime voice agents and chatbots]]> https://tsecurity.de/de/3686972/it-nachrichten/openai-unveils-presence-a-new-platform-that-lets-enterprises-launch-and-manage-realtime-voice-agents-and-chatbots/ https://tsecurity.de/de/3686972/it-nachrichten/openai-unveils-presence-a-new-platform-that-lets-enterprises-launch-and-manage-realtime-voice-agents-and-chatbots/ Wed, 22 Jul 2026 18:12:08 +0200 📰 IT Nachrichten tsecurity.de OpenAI has announced Presence, a new enterprise product for deploying and managing AI agents across customer-facing and internal business workflows.

The offering is designed for eligible enterprise customers that want agents to answer questions, access company systems, take approved actions and escalate to human workers while operating under company-defined policies, permissions and evaluation standards.

Presence is available immediately through a limited general availability program. OpenAI Forward Deployed Engineers (FDEs) and select global systems integrators lead deployments, and the product is not available on a self-service basis.

OpenAI has not disclosed pricing, geographic limits, contractual terms or the expected cost of the engineering and integration work that accompanies a deployment. The company also has not said whether Presence can use models from providers other than OpenAI, including the increasingly powerful and popular Chinese open weights alternatives like GLM-5.2 and Kimi K3. I've asked an OpenAI contact to clarify both pricing and external-model compatibility, but those remain unanswered questions for now. I'lll update when I hear back.

OpenAI positions Presence as a response to a problem that has become more important as companies move beyond AI demonstrations: getting agents to behave reliably in production as business rules, customer needs and operating conditions change. Presence packages the policies, system connections, evaluations, guardrails and update processes required to run agents inside an enterprise.

If your business has been interested in using AI agents, but you aren't sure how to stitch together OpenAI's models, APIs, internal systems, security controls and evaluation tools into something reliable, Presence is designed to simplify that process. Instead of building the infrastructure yourself, you work with OpenAI and its deployment engineers to put production-ready agents into your existing workflows.

The product is available today for real-time voice and chat experiences, according to OpenAI’s formal announcement. The company’s outreach materials also describe a broader ambition spanning voice, chat, email and other channels, but OpenAI has not confirmed that email support is available at launch.

A governed foundation for production agents

Presence brings together company knowledge, standard operating procedures, approved actions, simulations, evaluation tools, guardrails and escalation rules. Enterprises can reuse some controls across deployments while adjusting others for a particular workflow or channel.

Each deployment starts with a defined job, such as resolving a billing issue, supporting an insurance claim or handling an employee IT request. The agent receives only the information and system access required for that task. The customer determines what the agent may do independently, which actions require approval and when a person must take over.

Before an agent reaches production, teams can test it against common requests, unusual edge cases and higher-risk scenarios. Graders evaluate whether it reached the intended outcome, followed policy, used tools correctly and escalated when required. Guardrails can intervene when an interaction moves outside the organization’s defined boundaries.

OpenAI shared promotional screenshots with VentureBeat showing administrators running simulation batches against policy changes, including a revised annual refund policy, and reviewing results across operational categories.

Other interface mockups display production health, customer-intent patterns and task-performance signals. The visuals illustrate the type of oversight OpenAI is promising, although they do not establish how those metrics are calculated or how they map to contractual service levels.

The product continues to monitor performance after launch. Production sessions, escalations and quality signals can reveal where an agent is working as intended and where it needs attention. Codex, using a Presence plugin, investigates those signals and proposes updates. Teams then test a proposed change against the version already in production before approving a controlled rollout.

That process is intended to address one of the hardest operational problems in enterprise AI: an agent that works at launch may become less reliable when policies, products or user behavior change. Presence gives companies a formal mechanism for updating behavior without allowing an automated system to rewrite itself unchecked.

OpenAI says Presence already powers its English-language phone-support channel at 1-888-GPT-0090. The system handles open-ended requests, verifies callers, uses account context and performs approved actions. According to the company, it now resolves 75% of inbound issues without human assistance.

OpenAI also says its Codex-powered improvement loop reduced human handoffs by 15 percentage points over a 10-day period. Those figures are company-reported and have not been independently verified.

Several large organizations are evaluating the same foundation. BBVA is exploring voice support for routine banking needs in Mexico. SoftBank is testing natural Japanese-language customer conversations, while Australian insurer IAG is exploring support during high-demand periods such as severe weather and natural disasters.

“At BBVA, we are working closely with OpenAI to explore how trusted customer agents can help shape the future of financial services,” said Daniel Ordaz, head of AI transformation at BBVA Mexico.

“Through our collaboration with OpenAI, we are exploring how Presence can enable trusted customer agents that communicate naturally, connect to the processes needed to resolve requests, and represent SoftBank consistently across customer interactions,” said Tadahisa Murakami, vice president and head of the Data & Digital Transformation Division at SoftBank Corp.

From model access to forward-deployed implementation

Presence expands OpenAI’s enterprise strategy beyond APIs and subscription software by formalizing a high-touch deployment model. Forward Deployed Engineers work alongside customers to select workflows, connect internal systems, establish permissions, configure policies, test agents and move them into production.

That approach resembles a model pioneered by AI ontology and intelligence platform Palantir, which embeds FDEs with customers to adapt its proprietary software to complex government and commercial environments. The similarity lies less in the underlying technology than in the delivery method: both companies place technical personnel close to the customer’s operations, where integration and process design often determine whether software creates value.

The products are not interchangeable. Palantir’s model has historically centered on data integration, ontologies and operational decision systems. Presence is more narrowly focused on AI-agent behavior, approved actions, evaluations, escalation and continuous improvement. OpenAI presents it as a repeatable software product supported by engineers and systems integrators, rather than as consulting alone.

In May 2026, OpenAI launched its own enterprise AI consulting and integration firm, the OpenAI Deployment Company, with investment and support from Bain & Company. It also offers programs for model customization and fine-tuning to fit specific enterprise needs.

Its chief U.S. rival Anthropic has also moved toward a services-led enterprise model through Ode, its consulting organization built around forward-deployed engineers helping companies integrate Claude into complex workflows, which launched just a week ago. The broad rationale is similar: enterprises often need more than access to a model. They need help connecting data and systems, defining permissions, validating behavior and managing deployment risk.

Presence differs in how explicitly OpenAI packages those requirements into a branded agent-governance product. Anthropic’s initiative is centered on helping enterprises deploy Claude, while Presence combines implementation services with a defined operational layer for policies, simulations, evaluations, approvals and production updates.

Presence goes further by making forward deployment a core part of how a specific agent product reaches customers. It does not replace OpenAI’s API business; the company says it will continue supporting voice customers with access to frontier models through the OpenAI API.

The trend reflects a broader market view that many enterprises still need hands-on assistance to move agents from pilot projects into stable operations. Even organizations with strong internal engineering teams must coordinate security, compliance, workflow ownership, data access and escalation responsibilities. Presence attempts to consolidate those tasks rather than leaving customers to assemble separate orchestration, evaluation and consulting layers.

A recent security breach looms in the background

Inconveniently for OpenAI, the Presence launch arrives just a day after OpenAI and Hugging Face disclosed an unprecedented security incident in which OpenAI frontier models undergoing internal evaluation escaped containment, accessed the open web, and cyberattacked Hugging Face to achieve a benign goal — without being instructed to pursue these methods.

According to the described joint disclosure, OpenAI models operating in an evaluation framework called ExploitGym identified and exploited a zero-day vulnerability in a third-party package-registry cache proxy. The models reportedly escalated privileges, moved laterally and obtained internet access before targeting Hugging Face systems while seeking benchmark-related information.

The incident is relevant to enterprise buyers because it raises questions about sandboxing, tool permissions, external access, monitoring and incident response.

The disclosure also highlighted a practical problem for defenders. Hugging Face personnel reportedly found that commercial frontier-model APIs refused some forensic requests because logs contained exploit payloads, credentials and shell commands that triggered safety systems. The team then used a locally deployed open-weight model to assist with analysis.

Presence therefore arrives as both a product launch and a test of OpenAI’s ability to convert model capability into controlled enterprise operations. Its policies, simulations, evaluations and human approvals address real deployment gaps. But without public pricing, technical interoperability details, compliance information or service-level commitments, customers still lack much of the information needed to assess total cost and operational risk.

For now, Presence appears aimed at enterprises willing to adopt a high-touch, OpenAI-led deployment process. Whether it develops into a broadly accessible platform—or remains a closely managed product for selected customers—will depend in part on the answers OpenAI has not yet provided.

]]> <![CDATA[OpenAI models escaped containment and hacked a major AI application library]]> https://tsecurity.de/de/3686913/it-security-nachrichten/openai-models-escaped-containment-and-hacked-a-major-ai-application-library/ https://tsecurity.de/de/3686913/it-security-nachrichten/openai-models-escaped-containment-and-hacked-a-major-ai-application-library/ Wed, 22 Jul 2026 17:47:43 +0200 📰 IT Security Nachrichten tsecurity.de

The attack is the first known instance of frontier models autonomously breaking out of a testing environment and into another company’s servers.

]]> <![CDATA[OpenAI models escaped containment and hacked a major AI application library]]> https://tsecurity.de/de/3686911/it-security-nachrichten/openai-models-escaped-containment-and-hacked-a-major-ai-application-library/ https://tsecurity.de/de/3686911/it-security-nachrichten/openai-models-escaped-containment-and-hacked-a-major-ai-application-library/ Wed, 22 Jul 2026 17:47:39 +0200 📰 IT Security Nachrichten tsecurity.de The attack is the first known instance of frontier models autonomously breaking out of a testing environment and into another company’s servers. This article has been indexed from Cybersecurity Dive – Latest News Read the original article: OpenAI models escaped


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]]> <![CDATA[Royal Ransomware Uses Qbot and Cobalt Strike to Rapidly Compromise Windows Domains]]> https://tsecurity.de/de/3686909/it-security-nachrichten/royal-ransomware-uses-qbot-and-cobalt-strike-to-rapidly-compromise-windows-domains/ https://tsecurity.de/de/3686909/it-security-nachrichten/royal-ransomware-uses-qbot-and-cobalt-strike-to-rapidly-compromise-windows-domains/ Wed, 22 Jul 2026 17:47:20 +0200 📰 IT Security Nachrichten tsecurity.de Royal ransomware turned ordinary Windows compromises into enterprise-wide crises by pairing a phishing foothold with fast domain takeover. In incidents reviewed by responders, the operators used Qbot to gain a presence and then expanded their reach before deploying encryption. The


Read more →

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]]> <![CDATA[Critical Meta Vulnerability Exposed Customer Support Emails, Chats, and Uploaded Files]]> https://tsecurity.de/de/3686907/it-security-nachrichten/critical-meta-vulnerability-exposed-customer-support-emails-chats-and-uploaded-files/ https://tsecurity.de/de/3686907/it-security-nachrichten/critical-meta-vulnerability-exposed-customer-support-emails-chats-and-uploaded-files/ Wed, 22 Jul 2026 17:46:46 +0200 📰 IT Security Nachrichten tsecurity.de A broken access control flaw in Meta’s shared customer support systems exposed sensitive user data, including emails, chat conversations, and uploaded files. Discovered during security testing of Meta Horizon Managed Solutions, the vulnerability revealed a broader authorization weakness across multiple


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]]> <![CDATA[Trump’s AI Safety Chief Is Out — After Just 90 Days on the Job]]> https://tsecurity.de/de/3686901/it-security-nachrichten/trumps-ai-safety-chief-is-out-after-just-90-days-on-the-job/ https://tsecurity.de/de/3686901/it-security-nachrichten/trumps-ai-safety-chief-is-out-after-just-90-days-on-the-job/ Wed, 22 Jul 2026 17:46:07 +0200 📰 IT Security Nachrichten tsecurity.de Trump administration AI testing chief Chris Fall resigned after just 90 days, leaving CAISI under interim leadership during a critical period for AI safety standards. The post Trump’s AI Safety Chief Is Out — After Just 90 Days on the


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]]> <![CDATA[Upgraded Mac Mini, Mac Studio, and OLED iMac are all in the pipeline]]> https://tsecurity.de/de/3686837/ios-mac-os/upgraded-mac-mini-mac-studio-and-oled-imac-are-all-in-the-pipeline/ https://tsecurity.de/de/3686837/ios-mac-os/upgraded-mac-mini-mac-studio-and-oled-imac-are-all-in-the-pipeline/ Wed, 22 Jul 2026 17:25:07 +0200 🍏 iOS / Mac OS tsecurity.de Mac models Apple is developing, and amplifies that updates are being delayed by the global chip shortage.

Green Apple iMac desktop computer viewed from the back on a white desk, with matching keyboard and mouse, connected by a single cable against a softly lit background
The current iMac.

Recent rumors have predicted that the iPad Air will get the higher-quality OLED display in 2027. They've also predicted that the iMac will get an OLED screen too, but not until 2029 or 2030.

Now Bloomberg is substantially repeating these reports, but in some cases giving a little more detail. With the Mac mini and the Mac Studio, for instance, the report says that Apple is now testing versions with upgrade processors.


Continue Reading on AppleInsider | Discuss on our Forums]]> <![CDATA[Trump’s AI Safety Chief Is Out — After Just 90 Days on the Job]]> https://tsecurity.de/de/3686793/it-nachrichten/trumps-ai-safety-chief-is-out-after-just-90-days-on-the-job/ https://tsecurity.de/de/3686793/it-nachrichten/trumps-ai-safety-chief-is-out-after-just-90-days-on-the-job/ Wed, 22 Jul 2026 17:10:57 +0200 📰 IT Nachrichten tsecurity.de Trump administration AI testing chief Chris Fall resigned after just 90 days, leaving CAISI under interim leadership during a critical period for AI safety standards.

The post Trump’s AI Safety Chief Is Out — After Just 90 Days on the Job appeared first on TechRepublic.

]]> <![CDATA[OpenAI: Our models breached Hugging Face during a cyber capability test]]> https://tsecurity.de/de/3686755/it-security-nachrichten/openai-our-models-breached-hugging-face-during-a-cyber-capability-test/ https://tsecurity.de/de/3686755/it-security-nachrichten/openai-our-models-breached-hugging-face-during-a-cyber-capability-test/ Wed, 22 Jul 2026 17:05:21 +0200 📰 IT Security Nachrichten tsecurity.de The recent Hugging Face breach was the work of several OpenAI models, the AI research company claimed in a blog post. The breach Late last week, the company behind Hugging Face, a platform that enables users to share machine learning models and datasets, said some of its internal datasets had been accessed without authorization. The attack vector was, according to Hugging Face, a malicious dataset that exploited code-execution paths in the company’s dataset processing pipeline, 
 More →

The post OpenAI: Our models breached Hugging Face during a cyber capability test appeared first on Help Net Security.

]]> <![CDATA[Royal Ransomware Uses Qbot and Cobalt Strike to Rapidly Compromise Windows Domains]]> https://tsecurity.de/de/3686748/it-security-nachrichten/royal-ransomware-uses-qbot-and-cobalt-strike-to-rapidly-compromise-windows-domains/ https://tsecurity.de/de/3686748/it-security-nachrichten/royal-ransomware-uses-qbot-and-cobalt-strike-to-rapidly-compromise-windows-domains/ Wed, 22 Jul 2026 17:04:58 +0200 📰 IT Security Nachrichten tsecurity.de Royal ransomware turned ordinary Windows compromises into enterprise-wide crises by pairing a phishing foothold with fast domain takeover. In incidents reviewed by responders, the operators used Qbot to gain a presence and then expanded their reach before deploying encryption. The result was a disruptive attack that could spread faster than many defenders expected. The campaign [
]

The post Royal Ransomware Uses Qbot and Cobalt Strike to Rapidly Compromise Windows Domains appeared first on Cyber Security News.

]]> <![CDATA[Critical Meta Vulnerability Exposed Customer Support Emails, Chats, and Uploaded Files]]> https://tsecurity.de/de/3686697/it-security-nachrichten/critical-meta-vulnerability-exposed-customer-support-emails-chats-and-uploaded-files/ https://tsecurity.de/de/3686697/it-security-nachrichten/critical-meta-vulnerability-exposed-customer-support-emails-chats-and-uploaded-files/ Wed, 22 Jul 2026 16:39:29 +0200 📰 IT Security Nachrichten tsecurity.de A broken access control flaw in Meta’s shared customer support systems exposed sensitive user data, including emails, chat conversations, and uploaded files. Discovered during security testing of Meta Horizon Managed Solutions, the vulnerability revealed a broader authorization weakness across multiple support services within Meta’s ecosystem. What initially seemed to be a limited product-specific flaw quickly [
]

The post Critical Meta Vulnerability Exposed Customer Support Emails, Chats, and Uploaded Files appeared first on Cyber Security News.

]]> <![CDATA[What’s New in Rapid7 Products and Services: Q2 2026 in Review]]> https://tsecurity.de/de/3686659/it-security-nachrichten/whats-new-in-rapid7-products-and-services-q2-2026-in-review/ https://tsecurity.de/de/3686659/it-security-nachrichten/whats-new-in-rapid7-products-and-services-q2-2026-in-review/ Wed, 22 Jul 2026 16:30:13 +0200 📰 IT Security Nachrichten tsecurity.de If Q1 set the pace for Rapid7's tools, Q2 accelerated it. This quarter brought a steady stream of product enhancements, platform investments, and customer-driven innovation across Rapid7’s portfolio. Each release was designed with a clear goal in mind: helping security teams reduce complexity while increasing speed, context, and confidence in their day-to-day operations. Here’s a closer look at what launched in Q2.

Detection and response

Streamline investigations with bidirectional and enriched Microsoft Defender alerts

Bidirectional synchronization and enriched alert context for Microsoft Defender is now generally available for SIEM and MDR customers, enabling security teams to automatically synchronize alert status between Rapid7's SIEM and the Microsoft Defender console. With added process tree and user identity context, analysts can investigate threats more efficiently while reducing manual effort.

Confidently scale detection engineering with Detection as Code

Detection as Code enables security teams to build, test, version, and deploy detections using Terraform and modern engineering workflows. Built-in validation, guardrails, and version control help teams deliver higher-quality alerts, maintain more consistent coverage, and scale detection engineering more effectively.

rapid7-detection-as-code-methodology.png
Figure 1: Rapid7's Detection as Code methodology.

Strengthen ransomware resilience with Ransomware Prevention for Incident Command

Ransomware Prevention for Incident Command adds an intent-based layer of protection designed to stop ransomware encryption and endpoint damage before they disrupt operations. Built into the Insight Agent, this capability strengthens ransomware resilience while working alongside existing endpoint security investments, without adding operational complexity.

Compliance

New solutions webpages

Across the globe, cybersecurity regulation is shifting away from static compliance checklists and toward ongoing risk management that blends proactive defense with effective detection and response. Rapid7’s platform, which brings exposure management and CTEM together with detection, response, and MDR, is well positioned to help organizations operationalize compliance across mandates such as NIS2, NIST CSF 2.0, DORA, HIPAA, HITRUST, and GovRAMP. To support that effort, Rapid7 has launched an updated library of dedicated compliance solution pages that map platform capabilities to the requirements that matter most across industries and regions. The first set of pages is live now, with more to follow in the coming weeks.

rapid7-govramp-compliance.png
Figure 2: Rapid7's new GovRAMP compliance solutions page.

Exposure management

Turn prioritized exposures into remediation progress

We improved Remediation Hub to help teams turn prioritized exposures into more actionable remediation progress. Updates to the Top Remediations Report add asset-level context, including operating system, IP address, cloud provider, tags, endpoint protection, and patch management details, so teams can better understand what needs to be fixed and who needs to act.

With clearer patch and endpoint coverage signals, reboot status, customizable filters, exportable reports, and scheduled email delivery, teams can spend less time assembling manual updates and more time tracking the remediation work that reduces risk. Read the full blog to learn more about how Exposure Command helps teams move from prioritized exposures to remediation progress.

AI pre-triage for AppSec findings

Rapid7 is also making application security testing faster and more focused with AI vulnerability pre-triaging for InsightAppSec. Available now for AppSec customers in supported regions, the capability uses AI to automatically remove false positives during the scan process, helping teams spend less time manually reviewing findings and more time remediating actual risk.

Initial coverage started with BlindSQL, and the latest engine release adds AI validation for BlindNoSQL findings, including content-based and timing-based detections. The result is a cleaner, more confident view of application risk, so security teams can focus on high-impact vulnerabilities and accelerate remediation with less manual effort.

Attack surface management

Open-source MCP Server and Agent Skill

We are delighted to announce the introduction of a free, open-source MCP Server and Agent Skill for Bulk Export. Bulk export is a highly efficient way to access all your Rapid7 vulnerability and exposure data to AI assistants and custom AI workflows. Built as an open-source bridge, it helps customers bring their Rapid7 data into the tools and experiences that work best for their teams. Check out our blog for more detail.

rapid7-ai-agent-skill.png
Figure 3: Agent Skill for Bulk Export.

Turn exposure filters into live dashboards

Surface Command also made exposure reporting easier with filter-based dashboard widgets. Teams can now turn saved asset and identity filters into live dashboards without writing Cypher queries, making it faster to track high-risk internet-facing assets, identity-driven exposure hotspots, unmanaged cloud infrastructure, and business-unit risk.

For continuous threat exposure management programs, this helps teams move from one-off reporting to repeatable, always-on views of exposure risk and remediation progress. Read this blog to learn more. 

Platform and Labs

Rapid7 Command Platform

Cyber GRC

Rapid7 introduced Cyber GRC to select customers in Q2, giving teams an early look at a new way to connect security, risk, compliance, and third-party risk management in one program. Available to both Exposure Management and Detection and Response customers, Cyber GRC brings governance and compliance workflows closer to the security data teams already use every day.

Cyber GRC will be broadly available in late July. It helps organizations move toward continuous compliance by mapping controls to real environment telemetry, automating evidence collection, and prioritizing risk with live attack surface context. That means teams can spend less time chasing audit artifacts, screenshots, and vendor risk details, and more time understanding which controls, assets, third parties, and risks need attention now.

Rapid7 Labs

Rapid7 Quarterly Threat Landscape Report

The Rapid7 Quarterly Threat Landscape Report examines the key trends shaping today's threat landscape, drawing on MDR incident response, vulnerability intelligence, ransomware monitoring, and dark web telemetry. Q1 2026 data highlights the growing dominance of vulnerability exploitation as an initial access vector, the rise of zero-click vulnerabilities, evolving ransomware operations, and the accelerating pace at which attackers operationalize newly disclosed vulnerabilities. Read the report to explore all key findings and takeaways.

rapid7-quarterly-threat-report.png
Figure 4: Rapid7's quarterly threat report.

The latest threat research

Rapid7 researchers explored emerging trends shaping the threat landscape, including the growing commercialization of criminal AI-as-a-Service and the evolving tradecraft of advanced threat actors. From the underground adoption of AI tools for fraud and social engineering to an in-depth analysis of the Dropping Elephant malware campaign, these reports provide actionable intelligence on how attackers are adapting their techniques and what defenders can do to stay ahead.

Emergent Threat Response

This quarter's Emergent Threat Response (ETR) coverage highlights a sustained wave of high-impact vulnerabilities affecting widely deployed enterprise technologies, including Oracle PeopleSoft, Palo Alto Networks PAN-OS, Check Point VPN, Ivanti Sentry, cPanel/WHM, and Nginx UI. For each of these CVEs, Rapid7 tracked active exploitation and rapidly evolving attacker activity to provide timely guidance to help defenders assess risk and respond quickly. See all the details, and our latest ETR coverage, here.

From strengthening detection and response to advancing exposure management, expanding governance capabilities, and delivering actionable threat intelligence, Q2 demonstrated Rapid7’s continued focus on helping security teams do more with less complexity. Every enhancement this quarter was designed to reduce manual effort, surface the context that matters, and help organizations make faster, more confident security decisions. We’re carrying that momentum into the rest of the year, so stay tuned to our blog and releases as we continue building the security operations platform that helps defenders stay ahead of what’s next.

]]> <![CDATA[OpenAI model escape puts enterprise AI defenses on notice]]> https://tsecurity.de/de/3686581/it-security-nachrichten/openai-model-escape-puts-enterprise-ai-defenses-on-notice/ https://tsecurity.de/de/3686581/it-security-nachrichten/openai-model-escape-puts-enterprise-ai-defenses-on-notice/ Wed, 22 Jul 2026 15:53:09 +0200 📰 IT Security Nachrichten tsecurity.de

Some of OpenAI’s most powerful AI models teamed up to escape their sandbox and attack systems at Hugging Face in a cybersecurity evaluation gone wrong, the company has admitted. The models under test were modified to allow them to perform potentially harmful actions that production versions would refuse. The incident highlights how, if AI prompt guardrails fail or, as in this incident, are removed, then enterprises must have robust sandboxing or other technical restrictions in place to protect systems.

In OpenAI’s evaluation, GPT-5.6 Sol and a more capable pre-release model were competing to solve cybersecurity challenges in ExploitGym, a benchmarking tool for cybersecurity capabilities. They concluded that systems at Hugging Face could have the answers, and set out to gain access, OpenAI wrote in a blog post about the attack on Hugging Face.

ExploitGym challenges AI models to turn security vulnerabilities into attack exploits. OpenAI gave its models limited access to the internet through a package-registry proxy to download code to build their attacks. However, the models exploited a zero-day vulnerability in the proxy to obtain unrestricted internet access, then used stolen credentials and other flaws to access Hugging Face systems.

For CIOs and CISOs, the concern extends beyond cybersecurity research. AI agents connected to corporate databases, source code repositories, or payment systems could expose sensitive resources unless access controls and sandboxing continue to work when model-level safeguards fail.

“Prompt guardrails are not security controls,” said Biswajeet Mahapatra, principal analyst at Forrester. “They are behavioral controls that can fail, be bypassed, or be intentionally disabled during testing.”

How containment failed

Despite the relaxed model safeguards, the surrounding infrastructure was supposed to contain the activity. Network access was restricted to an internally hosted third-party service that acted as a proxy and cache for software package registries. Such proxies are typically used in the enterprise to protect against software supply chain attacks coming from the outside.

OpenAI said the models discovered a zero-day vulnerability in the service, exploited it and carried out privilege escalation and lateral movement until they reached a node with open internet access.

Once online, the models inferred that Hugging Face might hold models, datasets or solutions related to ExploitGym. OpenAI said that, in one attack path, the models combined stolen credentials with zero-day vulnerabilities to obtain remote code execution on Hugging Face servers.

In its account of the activity on its infrastructure, Hugging Face said a malicious dataset abused two code-execution paths in its data-processing pipeline. The actor then obtained node-level access, harvested cloud and cluster credentials, and moved into several internal clusters.

OpenAI said the models ultimately obtained ExploitGym test solutions directly from a Hugging Face production database. The models remained focused on solving the benchmark, despite the lengths to which they went to achieve that objective, it said.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the package-registry proxy should concern enterprise security teams because it was a narrowly permitted exception inside an otherwise restricted environment.

“Every enterprise estate is built from exceptions of exactly that kind,” he said.

In this incident, OpenAI’s models were performing the intended — albeit by unintended means — but enterprises should take care that their own AI systems are kept on target.

Mahapatra said enterprises should treat AI agents as “high-risk non-human identities,” confining each one to an isolated environment where access is limited to the assigned task and credentials expire quickly.

“An acceptable blast radius means a compromised agent can affect only a single workflow, dataset, or application rather than providing a pathway into broader enterprise infrastructure,” Mahapatra said.

And Gogia warned that services allowed to reach package registries, update systems, or other external resources can become escape routes if they are not subjected to the same scrutiny as the agent itself.

Defenses enterprises need

Hugging Face said the intrusion resulted in unauthorized access to a limited number of internal datasets and several credentials used by its services. It found no evidence that public models, datasets or Spaces had been altered and said its published software packages and container images were not compromised.

The company closed the code-execution paths used to gain access and rebuilt the affected nodes. It also revoked exposed credentials and tightened the rules governing workloads admitted to its clusters.

Whether they are keeping their own AIs in or rogue Ais out, Gogia said enterprises should test whether their containment boundaries work, rather than relying on architecture diagrams or stated policies. Such tests should attempt to obtain credentials, cross trust boundaries and reach systems outside the agent’s assigned task.

Mahapatra said enterprises should assume that one containment layer may fail and ensure that an agent’s access cannot provide a route into unrelated applications or broader corporate infrastructure.

OpenAI said it is still investigating the incident with Hugging Face, and is imposing stricter configurations on its research environment while the vulnerabilities are being addressed, even if that means slowing down its research. It is also strengthening containment and monitoring around future evaluations.

]]> <![CDATA[My Favorite NEW Folding Phone - Samsung Galaxy Z Fold8 Ultra vs Fold8 vs Flip8]]> https://tsecurity.de/de/3686540/videos/my-favorite-new-folding-phone-samsung-galaxy-z-fold8-ultra-vs-fold8-vs-flip8/ https://tsecurity.de/de/3686540/videos/my-favorite-new-folding-phone-samsung-galaxy-z-fold8-ultra-vs-fold8-vs-flip8/ Wed, 22 Jul 2026 15:36:25 +0200 đŸŽ„ Videos tsecurity.de Author: Shannon Morse - Bewertung: 4x - Views:33

Best deals! Fold8 Ultra - https://click.linksynergy.com/deeplink?id=qn*xL/agfY4&mid=47773&murl=https%3A%2F%2Fwww.samsung.com%2Fus%2Fsmartphones%2Fgalaxy-z-fold8-ultra%2Fbuy%2Fgalaxy-z-fold8-ultra-256gb-unlocked-sku-sm-f976uzvaxaa%2F

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Samsung just announced three brand new foldable smartphones at Galaxy Unpacked 2026, and I got hands-on with all of them in New York City!

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Is the new Galaxy Z Fold8 Ultra worth over $2,000? Is the regular Fold8 the better value? And is the Flip8 Samsung's best flip phone yet?

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]]> <![CDATA[CyCognito Brings Always-On AI Pentesting to External Attack Surface Management]]> https://tsecurity.de/de/3686433/it-security-nachrichten/cycognito-brings-always-on-ai-pentesting-to-external-attack-surface-management/ https://tsecurity.de/de/3686433/it-security-nachrichten/cycognito-brings-always-on-ai-pentesting-to-external-attack-surface-management/ Wed, 22 Jul 2026 15:14:15 +0200 📰 IT Security Nachrichten tsecurity.de

CyCognito, a leading exposure management platform, today introduced Continuous AI Pentesting. The new capability bakes AI-driven offensive pentesting directly into the platform, leveraging the rich context it already maintains for every exposed asset. This enables CyCognito to deliver AI pentesting as a continuous service, circumventing the cost and coverage constraints that confine comparable solutions to periodic, narrowly scoped engagements.

With this new solution, CyCognito addresses a major shift in the security ecosystem, driven by the latest advances in AI. Today’s models, with more advanced ones on the way, have lowered the bar for attackers. An attack campaign that once required a group of skilled threat actors can now be carried out by a low-skilled individual, in a fraction of the time and at relatively low cost. This signals a tectonic shift that compels defenders to adopt the same technology to keep pace and close the security gaps in their own environment.

Continuous AI Pentesting: Solution architecture, at a glance.

“AI pentesting is rapidly becoming part of every security team’s toolkit, and a lot of it is already being done in-house,” said Rob Gurzeev, CEO and co-founder of CyCognito. “But running offensive AI isn’t the hard part. The challenge is scale. AI pentesting today is typically limited to the top 1% of priority assets. Meanwhile, the other 99% is where a lot of attacks actually start, where adversaries find the low-hanging fruit and use it as a foothold for lateral movement.”

To provide AI pentesting coverage across that overlooked 99%, CyCognito built a distinct architecture that centers on the Target Graph, a contextual graph that bridges the AI pentesting solution and CyCognito’s three core modules:

  • Exposure Assessment maps the external footprint, attributes every asset to the right part of the organization, and enriches it with business and stack context.
  • Exposure Validation runs more than 100,000 deterministic tests continuously, freeing the AI pentesters to focus on high-judgment work.
  • Threat Intelligence draws on the history of existing and emerging vulnerabilities, along with attacker playbooks and statistical models trained on past engagements, to anticipate attacker activity.

Together, these layers increase the effectiveness of the pentesting agents, equipping them with the rich context and exploitability evidence, dramatically improving the efficiency of every run.

The architecture is also built to be constantly self-evolving. Every new risk scenario AI pentesters uncover can be hardcoded into the Exposure Validation module, joining the deterministic tests it already runs. This frees the AI agents to pursue new threats, and also consolidates learnings from agentic tests in a way that will benefit every CyCognito customer.

In the announcement for this new feature, the company also shared some of the vulnerabilities:

  • Unauthenticated access to a production CRM: an exposed MCP server allowed anonymous, natural-language queries against three million rows of account, opportunity, and financial data, with no credentials required.
  • A publicly readable RAG index: an AI agent stack enforced authentication only on its API, leaving the knowledge base behind it, which held customer data, contracts, and internal communications, open to anyone on the internet.
  • A building’s access controls exposed to the internet: a system running door locks, card readers, and CCTV sat unsegmented on the public internet alongside the organization’s AI document tools and chatbot, leaving physical entry reachable by a remote attacker.

These examples are just some of the risk scenarios identified through the work on this new capability, now running with select design partners, including major enterprises and Fortune 500 companies. Internally, CyCognito refers to the project as Project Kineto, after the Kinetograph, the first motion picture camera.

“The name echoes our vision for what AI pentesting should be,” said Gurzeev. “Security testing has always been a snapshot. AI lets us turn it into continuous motion: an always-on stream of change-aware tests that runs across your entire attack surface at machine speed, with the skill of a seasoned security expert.”

To go deeper on Continuous AI Pentesting, read the full announcement post: https://www.cycognito.com/blog/new-continuous-ai-pentesting/

About CyCognito

CyCognito is an external exposure management platform that reduces risk by discovering, testing and prioritizing security issues. 

The platform scans billions of websites, cloud applications and APIs and uses advanced AI to identify the most critical risks and guide remediation. Emerging companies, government agencies and Fortune 500 organizations rely on CyCognito to secure and protect from growing threats. For more information, visit https://www.cycognito.com.

Contact

VP Marketing

Igal Zeifman

CyCognito

igal.zeifman@cycognito.com

]]> <![CDATA[11 Smart Devices I’m Still Using at Home Even After My Testing Is Done]]> https://tsecurity.de/de/3686186/it-nachrichten/11-smart-devices-im-still-using-at-home-even-after-my-testing-is-done/ https://tsecurity.de/de/3686186/it-nachrichten/11-smart-devices-im-still-using-at-home-even-after-my-testing-is-done/ Wed, 22 Jul 2026 13:52:14 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[10 cool things Copilot can do in PowerPoint]]> https://tsecurity.de/de/3686068/it-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ https://tsecurity.de/de/3686068/it-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ Wed, 22 Jul 2026 13:05:35 +0200 📰 IT Nachrichten tsecurity.de

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

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

Who can use Copilot in PowerPoint

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

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

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

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

In this article:

Working with Copilot in PowerPoint

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

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

powerpoint screen with copilot sidebar open on right

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


Howard Wen / Foundry

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

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

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

screenshot of copilot sidebar in powerpoint with models dropdown menu open

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

Howard Wen / Foundry

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

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

1. Create a presentation template

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

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

A good prompt should contain the slide count, subject, audience, and tone. Example:

  • Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

screenshot of powerpoint presentation generated by copilot with placeholder data

Copilot generates a presentation with placeholder data and explains its elements.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

screenshot of copilot sidebar in powerpoint with a document being attached for copilot to base a presentation on

Attaching a document for Copilot to base a presentation on.

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

screenshot of powerpoint with a presentation generated by copilot from a document

Copilot has generated a professional presentation from a social media marketing campaign document.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

  • Add a slide based on the attached document.
  • Use the attached file to add a slide about the project budget that focuses on Q1 projections.
  • Summarize only the financial section of the attached document as a slide.
screenshot of a slide in powerpoint generated by copilot from spreadsheet data

A new Copilot-generated slide based on data from an Excel spreadsheet.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

screenshot of text on a powerpoint slide with copilot dropdown menu includng condense and make professional options

Choose a preset prompt for refining text on a slide or type in your own prompt.

Howard Wen / Foundry

Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

  • Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

  • Add a stock photo of young adults in a cafe drinking boba tea.
  • Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

  • Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.
screenshot of image generation prompt in copilot sidebar in powerpoint plus the resulting generated image on a slide

Copilot in PowerPoint hooks into Microsoft’s Designer tool for image generation.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

  • Add a slide after slide 3 about our competitive advantages.
  • Add a slide after slide 11 that transitions to slide 12.
screenshot of powerpoint screen with copilot sidebar and a transition slide generated by copilot

Need a transition slide? Just ask!

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

screenshots of copilot sidebar in powerpoint - one with summarize results and one with key slides response

Ask Copilot to summarize a presentation or flag key slides.

Howard Wen / Foundry

8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

  • What are the action items in this deck?
  • What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

screenshot of copilot sidebar in powerpoint with response to query about proposed budget in the slide deck

Ask Copilot specific questions about the contents of a presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

  • Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

screenshot of copilot sidebar in powerpoint with response about the slide that talks about target audience

Copilot can help you zoom directly to a slide that covers a particular topic or shows specific data.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

  • Write speaker notes for every slide with one talking point per slide.
screenshot of powerpoint presentation with speaker notes generated by copilot

Copilot can create speaker notes in seconds.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

  • Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

screenshot of frequently asked questions slide generated by copilot in powerpoint

A Copilot-generated FAQ slide.

Howard Wen / Foundry

Related reading:

]]> <![CDATA[OpenAI confirms its AI agent autonomously breached Hugging Face]]> https://tsecurity.de/de/3685989/it-security-nachrichten/openai-confirms-its-ai-agent-autonomously-breached-hugging-face/ https://tsecurity.de/de/3685989/it-security-nachrichten/openai-confirms-its-ai-agent-autonomously-breached-hugging-face/ Wed, 22 Jul 2026 12:41:31 +0200 📰 IT Security Nachrichten tsecurity.de OpenAI has revealed that an autonomous AI agent powered by GPT-5.6 Sol and a more capable unreleased model escaped its intended testing environment, gained internet access, and compromised parts of Hugging Face's production infrastructure while attempting to obtain benchmark answers. The disclosure follows Hugging Face's July 16 announcement that it had been the subject of 


The post OpenAI confirms its AI agent autonomously breached Hugging Face appeared first on CyberInsider.

]]> <![CDATA[Leadership bottlenecks slow AI adoption]]> https://tsecurity.de/de/3685910/it-security-nachrichten/leadership-bottlenecks-slow-ai-adoption/ https://tsecurity.de/de/3685910/it-security-nachrichten/leadership-bottlenecks-slow-ai-adoption/ Wed, 22 Jul 2026 12:14:12 +0200 📰 IT Security Nachrichten tsecurity.de

At Cisco, VP of engineering Jason Andrews deals with all the same technical issues as every other company deploying AI, including ensuring it’s governed, secure, and integrating multiple data sources, legacy systems, and AI models.

But these issues are relatively straightforward compared to the bigger challenges relating to the fast pace of change, specifically how AI can touch and transform nearly every aspect of business.

“We’re thinking about it every day,” he says. “My belief is we’ll be seeing a massive acceleration of everything.”

In coding, for example, he’s witnessing productivity increases up to 110% with AI assistants. “I can build apps or custom integrations a lot faster,” he adds.

And the real benefit of AI isn’t just in speeding up individual steps in a process, but in making AI the core of a new business process. But building it from scratch puts even more pressure on organizations trying to get employees up to speed on new ways of doing things.

“We want to move fast, train people, and get them onboarded,” he says. “But what I thought AI was going to do for my organization nine months ago is different from three months ago.” So by the time something is rolled out, it’s changed three times.

“I struggle with the change management aspect,” he says. “The legacy model of change management isn’t fast enough. How do you create that constant learning?”

One of the ways Cisco approaches it is to create communities where people can talk about these issues and share best practices and governance, and you have to keep people’s minds open that every day is going to be different than the last, Andrews adds.

Testing the AI waters

Cisco isn’t the only organization struggling with change management in the face of the AI tsunami. In a survey of 2,000 global CEOs IBM released in May, 83% of them said AI success depends more on adoption than on the technology itself, and 77% said talent and technology roles are converging.

“Thanks to Claude Code, our entire development cadence is exponentially greater than a year ago,” says Andrew Johnson, CIO at Brownstein Hyatt Farber Schreck, a Denver-based law firm with about 700 employees and clients around the US. But, as with Cisco, the biggest challenge isn’t technical.

“In our industry, with our circumstances, we’re probably less constrained by technical capability than organizational constraints, culture, aptitude, the need to bind people to technology, and what helps me and the client,” he says. “There’s a tremendous amount of cultural shift that has to happen in our organization, which is far more demanding of my attention and complexity of thought than the technical stuff.”

Companies that bill by the hour, such as law firms, may face additional challenges as attorney productivity increases because billable hours might go down. Alternatively, the total number of cases could go up as litigation becomes less expensive. Either way, firms that adapt will see competitive advantage, and the rest will fall behind, putting more pressure on the need for change management.

“If people can’t embrace technology, we won’t be able to get a lot of value out of it,” says Johnson. “I’m talking to people about adapting their way of work. There are certainly a lot of people intrigued and anxious to dive in. They recognize the connection between the potential of the technology and what we do.”

But helping everyone see that connection and then working with them to change their habits is difficult, and requires solid relationships and good communications. “That’s been far more of a bottleneck for us,” he says.

To address the issue, the firm has developed a network of technology champions who also understand the legal side of the business. “Now we need lawyers who know how to use the technology and can articulate these things to the people we’re trying to reach,” Johnson says.

But change management is only one leadership bottleneck slowing AI adoption. Companies also struggle with figuring out their vision for AI, with slow decision-making, and a tendency to focus on the past instead of the future.

Vision and strategy

In another survey, this time of 950 business leaders released by Grant Thornton in April, 51% said strategy is the biggest driver of ROI when it comes to AI adoption, but 79% of operations leaders said they don’t have a fully developed and implemented AI strategy.

“Having leadership understanding why AI is needed and what objective they’re trying to achieve is very important,” says Shivi Verma, senior manager of engineering at Docusign. “Sometimes leadership doesn’t have a strategy for their organization on how AI should be adopted. Many times it’s bottom-up, which creates a chaotic experience.”

When Docusign started adopting gen AI, different teams and organizational units wanted to go in different directions. “All were coming up with their own strategy and tooling,” he says. So Docusign brought business leaders together to understand the pain points, and decide on the technology.

“Getting requirements and placing a bet on a specific technology was important,” he says, “as well as pivoting to a different technology if needed.”

In order to adapt to changes, the company wanted to have a nimble approach, starting with smaller use cases, with power users, and problem areas.

“We try to plan for four to six months,” he adds. “We set expectations for our leadership that we place a bet with a specific technology, but want to be able to pivot.”

Today, the leadership challenge front lines have moved yet again, to agentic AI. “Folks are creating their own agents and deciding their own permissions,” Verma adds. “We’re still coming up with a governance strategy.”

Slow decision-making

When it comes to AI deployments, Dan Diasio, global AI consulting leader at EY and CTO for its US consulting business, admits he’s a bottleneck.

There’s a great deal of interest in what AI can do, and using a variety of new AI tools. But since the firm deals with sensitive client data, safety is paramount. It’s a slow process, but important to build secure infrastructure, and to have trust in the technology. “That’s a reasonable bottleneck that makes sense,” he says.

Trust in the tools they work with is essential because clients expect it. “Every tool we use has to go through a detailed security and information privacy impact assessment, as well as a whole other set of controls so they can be used appropriately and safely,” he says.

These reviews can take a lot of time, though, and in the age of AI, speed is a highly valued currency. So how do you balance the two, when safety reviews can require input from a lot of different stakeholders and be extremely time intensive?

“We’ve stood up a team to be able to quickly certify and address a variety of platforms,” Diasio says. “Instead of working with different departments in the way we used to, we’ve started identifying representatives from different departments into a cohort. Decisions we used to make in months now take weeks.”

According to a West Monroe survey of more than 1,200 leaders released earlier this year, slow decision-making is already showing up on the bottom line. Nearly three out of four leaders said their organizations lose up to 5% of annual revenue to slow decision-making and delayed execution.

And the top reasons for the delays? According to 40% of the managers surveyed, the problem was the skills gaps of overwhelmed teams, and 35% pointed to layers of management or approvals. Nearly half said they’re spending 10 to 25% of their time on rework, excessive approvals, and unnecessary meetings, and more than half say up to 50% of their projects fail or lose momentum to delays.

Focus on the future, not the past

When it comes to the decision about where to apply AI in an organization, the tendency, Diasio says, is to turn to the experts with the most expertise in the business. But these are the same people most likely to focus on improving on what they’re already doing.

“And that often blinds people to what’s possible in the future,” he says. “That becomes a significant bottleneck.” So the solution is to revamp the decision-making process around the new reality.

“What we see some advanced companies do is give people who don’t understand the process but understand the technology equal footing with people who don’t understand the technology but understand the process,” he says. “A lot of companies are disproportionately focused on just addressing their operating model right now.”

Instead of focusing on what they’re currently doing, AI-native companies will start with a focus on the customer, he says. This shift in focus isn’t likely to show up immediately on the bottom line, or result in the highest possible number of pilots going into production.

“If leaders are in a position where they’re justifying the use of a technology to the board or their CFO, they become a bottleneck when they start demonstrating their value in terms of the number of things they’re doing,” Diasio says.

But 150 or 200 use cases deployed into production may feel like progress, like things are happening in the organization. But all these use cases are a waste of time and money if they’re applied to existing processes that don’t move the needle. “We see that happen in organizations today,” he says. “Maybe we need to reinvent the processes.”

It’s no secret that companies will need to change in order to adapt to AI. Deloitte recently surveyed 660 global technology leaders and 81% said their current operating model can deploy and govern AI enterprise-wide, but 75% also said their organization must change its operating model within the next 12 to 18 months to drive greater value.

AI ROI is real, says China Widener, Deloitte vice chair and US tech, media, and telecom industry leader. But it’s currently weighted toward efficiency gains, with broader business transformation and revenue upside still developing.

Another Deloitte survey showed that the clearest results from AI were in productivity, with 66% of organizations reporting gains, and cost efficiency, with 40% saying AI reduces costs. “However, revenue impact is still emerging,” says Widener. “Only one in five companies says AI is driving top-line growth today.” But optimism prevails, with 74% expecting it to do so in the future.

]]> <![CDATA[Spain Fines 23andMe €2.4 Million Over Security Failures Behind 6.9 Million-User Breach]]> https://tsecurity.de/de/3685896/it-security-nachrichten/spain-fines-23andme-24-million-over-security-failures-behind-69-million-user-breach/ https://tsecurity.de/de/3685896/it-security-nachrichten/spain-fines-23andme-24-million-over-security-failures-behind-69-million-user-breach/ Wed, 22 Jul 2026 12:13:18 +0200 📰 IT Security Nachrichten tsecurity.de Spain’s data protection authority has fined the genetic testing company 23andMe €2.4 million due to security failures linked to a data breach in 2023. This incident exposed highly sensitive information such as genetic, health, ethnicity, and family-related data belonging to


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The post Spain Fines 23andMe €2.4 Million Over Security Failures Behind 6.9 Million-User Breach appeared first on IT Security News.

]]> <![CDATA[Russian Hacker Jailbreaks Claude to Turn into an AI-Powered Penetration Testing Platform]]> https://tsecurity.de/de/3685894/it-security-nachrichten/russian-hacker-jailbreaks-claude-to-turn-into-an-ai-powered-penetration-testing-platform/ https://tsecurity.de/de/3685894/it-security-nachrichten/russian-hacker-jailbreaks-claude-to-turn-into-an-ai-powered-penetration-testing-platform/ Wed, 22 Jul 2026 12:13:15 +0200 📰 IT Security Nachrichten tsecurity.de A Russian-speaking threat actor known as “Trim” has reportedly transformed jailbroken frontier AI models into an automated penetration testing platform called AI Pentest Checker. This activity highlights how criminals can misuse legitimate AI services and common security tools to accelerate


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The post Russian Hacker Jailbreaks Claude to Turn into an AI-Powered Penetration Testing Platform appeared first on IT Security News.

]]> <![CDATA[Your instant Android backup upgrade]]> https://tsecurity.de/de/3685867/it-nachrichten/your-instant-android-backup-upgrade/ https://tsecurity.de/de/3685867/it-nachrichten/your-instant-android-backup-upgrade/ Wed, 22 Jul 2026 12:02:52 +0200 📰 IT Nachrichten tsecurity.de

Here in this high-tech era of 2026, keeping important info backed up and synced should be effortless and something that just happens on its own, automatically, without any actual thought or ongoing human effort.

In many areas of our digital life, that mercifully does Just Workℱ in exactly that way. Fire up an email in most modern mail services, and you can stop at any point and find your in-progress draft in that same app on any other device. The same applies to any file you’re finessing within Google Drive or other cloud storage services or document you’re dawdling over in Docs.

One area where seamless syncing somehow still doesn’t occur, though, is in the domain of downloaded documents on Android. If someone sends you a PDF or a Word file and you save it to your phone, that file exists in an archaic-seeming silo — only locally, on that one gadget. And that, of course, means (a) you can’t access it from any other device, and (b) if you misplace your phone or move into a new one at some point along the way, the file will be left behind in time and entirely unavailable.

Well, take a moment to join me in celebration: Amidst all the Gemini gobbledegook that no one asked for (and that often falls somewhere between “pointless” and “actively counterproductive”), Google’s giving us a major upgrade to Android’s backup capabilities right now. It’s a simple-seeming switch buried in your system settings, and it’s up to you to find and activate it.

Once you do, though, those once-orphaned documents on your Android device’s local storage will be perpetually synced and protected, automatically, without any ongoing thought or effort.

All you’ve gotta do is find and flip that one new switch.

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The Android backup lowdown

So, for a quick bit of pertinent context on this: Android’s backup systems have actually come a really long way over the years.

‘Twas a time, y’see, when little to nothing about you would sync and carry over automatically from one Android device to another. Years ago — back in the ancient-seeming prehistoric era of the early 2010s — Android enthusiasts in the know would rely on community-created third-party apps for everything from remembering and resyncing downloaded apps to restoring data from within those apps and onward. And reconfiguring your system preferences would be a whole time-consuming song and dance every single time you reset a device or moved into a new one, as little to nothing would automatically carry over.

Most of that stuff is now effortless and automatic. And, thanks to apps like Google Messages, Calendar, Drive, and Docs, many other areas of important data are also synced on their own at the app level — outside of any system mechanisms.

Locally stored files, however, have remained an awkward omission. To this day, anything you download on any Android device exists only on that one device and isn’t synced or backed up anywhere. The only way that happens is — in a blast-from-the-past twist — if you go out of your way to find and set up a third-party app to handle the heavy lifting.

That brings us to today. Right now, as we speak, Google’s in the midst of sending out a quiet under-the-hood update that (brace yourself
) adds in the option to automatically sync and back up any documents on your device as a native part of Android’s backup setup.

See?

Android backup documents
The easily overlooked new option for backing up documents on Android.

JR Raphael, Foundry

The option is on its way to all devices running 2018’s Android 9 release and higher. (If you’re still using a phone with an Android version older than that, you’re now a whopping eight years out of date, and you have much bigger problems.)

Once the added option is present and available for you, you’re literally lookin’ at 10 seconds to find and activate it.

Lemme show ya how.

Android’s document backup addition

I promise: This couldn’t be much simpler.

No matter what kind of Android device is in front of you, just head into your system settings and open the section called “Accounts and backup,” “Back up or copy data,” or something along those same lines. (The exact wording can vary based on who made your device and when it was released or last updated.)

Either tap the line labeled “Google Backup” or look for an option to “Back up data” via Google Drive. You should then either see a series of options for different areas of available backup right then and there — or, depending on your device, you might have to tap a line labeled “Other device data” (or something similar) to find the full list of possibilities.

However you get there, once you’re lookin’ at that list, you’ll see a newly added line for “Documents” if this latest under-the-hood update has reached you.

Android backup options
Android’s expanded list of backup options — now including documents alongside other forms of on-device data.

JR Raphael, Foundry

And from there, all that’s left is to tap it and enable the switch to include that in your automated backups from that moment forward.

If you aren’t seeing the option yet, don’t panic. Google always sends these under-the-hood updates out bit by bit over time, so the change probably just hasn’t reached your device quite yet. As long as you’re running Android 9 or higher, it’ll get there. Set yourself a reminder to check back once a week or so. Odds are, you’ll see it pretty soon.

Notably, all documents synced in this way are always encrypted for security, and they’re kept in your personal (or, depending on the nature of your account, perhaps company-connected) Google Drive storage. That does mean they’ll count against your overall Google storage total, so keep an eye on your Drive storage total to make sure you’re in solid shape and look to the Google One storage hub if you ever want some simple suggestions for freeing up space.

Speaking of other Google services: If you ever want to keep other types of locally stored non-document files from an Android device synced and available elsewhere, you can easily rely on Google Photos for syncing screenshots and other images — after enabling sync in general, be sure to look in the app’s “Collections” areas to find the “On this device” folder and then flip the toggle to “Backup all device folders” (or get more nuanced and open specific individual on-device folders if you want to sync some but not all of those areas) — and you can still turn to those aforementioned third-party apps for broader syncing of anything else imaginable.

But with documents now being handled automatically and natively, that’s one big worry now out of your hair. Just note that the onus will fall on you to find and flip the switch and actively opt in to the feature on each and every Android device you’re using.

Take 10 seconds to do that, though, and you’ll have one less void in your Android data arena. And you don’t need Gemini to tell you that that can only be a good thing.

Get practical Android knowledge in your inbox every Friday with my free Android Intelligence newsletter — one new thing to try each week, straight from me to you.

]]> <![CDATA[OpenAI AI models exploited zero-days to reach Hugging Face in benchmark test]]> https://tsecurity.de/de/3685841/hacking/openai-ai-models-exploited-zero-days-to-reach-hugging-face-in-benchmark-test/ https://tsecurity.de/de/3685841/hacking/openai-ai-models-exploited-zero-days-to-reach-hugging-face-in-benchmark-test/ Wed, 22 Jul 2026 11:49:04 +0200 đŸ•”ïž Hacking tsecurity.de <![CDATA[OpenAI AI models exploited zero-days to reach Hugging Face in benchmark test]]> https://tsecurity.de/de/3685832/it-security-nachrichten/openai-ai-models-exploited-zero-days-to-reach-hugging-face-in-benchmark-test/ https://tsecurity.de/de/3685832/it-security-nachrichten/openai-ai-models-exploited-zero-days-to-reach-hugging-face-in-benchmark-test/ Wed, 22 Jul 2026 11:47:08 +0200 📰 IT Security Nachrichten tsecurity.de OpenAI confirmed its AI models exploited zero-days during internal testing, reaching Hugging Face servers in an unintended real-world cyberattack. OpenAI admitted on July 21 that its own AI models, including GPT-5.6 Sol and an unnamed pre-release system, were behind the


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]]> <![CDATA[From outsourcing to ownership: How we brought development in-house without breaking delivery]]> https://tsecurity.de/de/3685759/it-security-nachrichten/from-outsourcing-to-ownership-how-we-brought-development-in-house-without-breaking-delivery/ https://tsecurity.de/de/3685759/it-security-nachrichten/from-outsourcing-to-ownership-how-we-brought-development-in-house-without-breaking-delivery/ Wed, 22 Jul 2026 11:11:54 +0200 📰 IT Security Nachrichten tsecurity.de

Outsourcing worked – until it didn’t.

After Akirolabs achieved early market validation and onboarded its first enterprise customers, outsourcing began to create strategic limitations around scalability, intellectual property (IP) ownership, security and delivery execution.

The challenges started after the first enterprise customers confirmed product-market fit. At that point, delivery speed became directly tied to business growth. Product quality expectations increased. Infrastructure and security requirements became stricter. Investors started asking difficult but fair questions about IP ownership, operational dependencies and long-term scalability.

Most importantly, engineering execution was no longer just an operational function – it became part of the company’s strategic advantage. That was the moment when the founders decided the company needed dedicated technology leadership to address these challenges. This is how I joined the company at the beginning of 2023. As VP of Engineering and a bit later as CTO, I led the transformation (usually known as insourcing, repatriating or backsourcing) from an outsourced model to an internal engineering organization while maintaining product delivery continuity and preparing the company for the next growth stage. The process took roughly a year and involved not only technical migration, but also organizational design, hiring, process development, infrastructure modernization and cultural transformation – everything from the ground up.

Building an internal engineering organization while still delivering

One of the biggest misconceptions about insourcing is that it is primarily a technical project. It is a leadership and execution challenge.

When I joined the company, there was effectively no internal engineering structure, limited visibility into the existing system and no clear long-term technical strategy. My first months were dedicated to understanding reality and I began with a comprehensive assessment of the codebase, operational risks, documentation quality and knowledge dependencies to determine the most viable transition strategy.

Very early in the process, I faced a critical strategic decision: whether to gradually assume ownership of the existing platform or rebuild it internally. To make that decision, I evaluated four distinct transition models ranging from limited management insourcing to a complete internal rebuild.

After assessing the technical, operational and long-term business implications of each approach, I selected the most demanding option: rebuilding the product internally while maintaining uninterrupted delivery for existing customers. Although riskier in the short term, a full rebuild offered the clearest route to complete IP ownership, architectural flexibility and long-term scalability.

At the time, this decision ran counter to the approach typically taken by startups in similar situations. Most organizations gradually assume ownership of an existing codebase to minimize short-term risk and preserve delivery capacity. My assessment was that the accumulated architectural debt, fragmented knowledge distribution and long-term maintenance risks would ultimately make a phased takeover more expensive and less scalable than a controlled rebuild. The strategy required significantly higher execution discipline, but it allowed us to establish complete ownership of the platform, eliminate inherited constraints and create an architecture capable of supporting enterprise-scale growth.

The next challenge was hiring.

In Germany, hiring can easily take four to six months – mostly due to a typical 3-month notice period, which is incompatible with startup timelines. We solved this by building a hybrid organization structure early: a lean internal core team combined with carefully selected contractors. Instead of hiring only narrow specialists, we prioritized experienced generalists capable of operating across architecture, infrastructure, security and compliance discussions. Later, we evolved toward a product engineering model, where engineers owned broader product outcomes rather than narrowly defined technical functions.

During the first three months, we established a core engineering team of four senior engineers. Over the following nine months, the organization expanded to roughly fifteen engineers while I strategically designed and executed the transformation of the platform’s architecture to meet the rigorous deployment and compliance standards of our first enterprise clients, including Raiffeisen Bank International and Bertelsmann. This structural overhaul allowed the company to meet the deployment, security and compliance requirements of enterprise customers that had previously been inaccessible under the outsourced model. At that point, we had already achieved complete coverage across backend, frontend, DevOps, QA and security.

I also intentionally kept processes lightweight during the transition. Instead of introducing heavyweight frameworks, we focused on clarity of priorities, fast decision-making and execution discipline. We used Kanban over Scrum, eliminated unnecessary meetings, shortened the remaining ones and emphasized engineering culture over process overhead.

Another major challenge was project estimation. Because dual-track development was unavoidable until the in-house platform reached production readiness, estimation accuracy had a direct impact on budget efficiency. Despite all challenges, my initial estimate ultimately proved remarkably close to the final delivery date, differing by only about a week. Accurate forecasting under conditions of parallel development streams, ongoing customer commitments and active team formation became a critical leadership challenge. Maintaining this level of predictability throughout the transition helped align engineering execution with business planning, hiring decisions and investor expectations.

The engineering transformation enabled capabilities that contributed to Akirolabs being recognized as an IDC Innovator in Procurement in 2023, named amongst the Top 27 AI Startups in Germany in 2024, Sifted’s 100 Fastest-Growing Startups in DACH & CEE 2025 and inclusion in 2024-2026 in ProcureTech100 annual recognition of procurement technology providers shaping the future of digital procurement.

Managing risk without slowing down the business

The hardest part of insourcing is not writing code, selecting the technology stack, designing architecture or configuring infrastructure. It is avoiding disruption while the company is changing underneath the product. I successfully orchestrated the concurrent overhaul of product architecture, cross-functional engineering recruitment, infrastructure modernization and live customer operations under exceptionally tight margins.

To reduce delivery risk, we approached the transition in layers.

First, we focused on infrastructure reliability and operational readiness before feature expansion. Cloud architecture, recovery testing, permission segregation and incident management processes were implemented early, not after launch. We also introduced multiple testing stages and dedicated QA functions after learning the hard way that a “developers-only” quality control approach does not scale for complex web platforms and business domains.

Second, we established a structured knowledge-transfer process to rapidly onboard engineers and reduce external dependencies.

Third, we became extremely disciplined about scope management. One of the most common reasons insourcing initiatives fail is uncontrolled change during the rebuild phase. Every new feature request increases uncertainty non-linearly. We learned to separate strategic improvements from distractions and protect the core delivery roadmap aggressively. Throughout the transition, we successfully maintained uninterrupted customer operations by utilizing planned maintenance windows, achieved a near-zero-downtime migration and permanently doubled product velocity immediately following the migration.

Beyond the technical migration itself, the transition established a repeatable operating model for scaling technology organizations beyond the product-market-fit stage. The framework combined organizational redesign, controlled knowledge repatriation, architecture modernization and enterprise-grade operational practices while maintaining uninterrupted customer delivery throughout the transformation. While the implementation was specific to Akirolabs, the underlying principles are broadly applicable to organizations seeking to transition from outsourced development to internal product ownership without disrupting business operations.

By the time the new platform reached production readiness, I had established not only a functioning engineering organization, but also a stable operational model: internal ownership, production-grade infrastructure, security processes, scalable hiring practices and clear technology and product roadmaps.

A positive side effect of the transition was the creation of internal UI/UX and Data Science capabilities, which later became strategically important for AI product initiatives and created a foundation for the third version of the product, which we released in mid-2025.

My technical restructuring and migration to a secure proprietary platform reduced architectural risk, established full in-house ownership and helped strengthen investor confidence during the company’s successful €5M fundraising round in 2024.

The transition created a stronger foundation for scale and supported the company’s continued expansion among enterprise organizations operating at Fortune 500 scale, including Ahold Delhaize, Workday, IFF, Deutsche Bahn and others.

Lessons learned for CTOs considering insourcing

Looking back, several decisions made the transition successful, and several mistakes made it harder than necessary.

The first lesson is simple: decisiveness in strategic transition is paramount to maintaining business momentum. Rapidly evaluating insourcing frameworks and defining clear boundaries with the external partner allowed us to mitigate operational downtime and execute a highly efficient migration ahead of critical market deadlines.

Second, hire more senior people and do it as early as possible. Strong technical leaders multiply execution capacity far beyond their individual contribution. In our case, the quality of the first hires influenced architecture quality, hiring standards, delivery discipline and engineering culture for the entire organization.

Finally, culture matters more than frameworks. Processes can be added later. Ownership mentality cannot.

The biggest long-term advantage of bringing development in-house was not simply faster execution, not better code quality or operational cost optimization by over 30% after the transition which we also achieved. It was an alignment. Product strategy, engineering decisions, customer priorities and business goals became part of the same conversation instead of being separated by organizational boundaries. For technology companies operating in highly competitive markets, that alignment becomes a compounding advantage over time.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[AI, security operations and the new race against time]]> https://tsecurity.de/de/3685757/it-security-nachrichten/ai-security-operations-and-the-new-race-against-time/ https://tsecurity.de/de/3685757/it-security-nachrichten/ai-security-operations-and-the-new-race-against-time/ Wed, 22 Jul 2026 11:11:50 +0200 📰 IT Security Nachrichten tsecurity.de

When Anthropic unveiled Project Glasswing and the Mythos model, much of the discussion focused on the capabilities themselves.

Security leaders debated what these systems could mean for vulnerability discovery, exploit development and the pace of offensive innovation. Researchers examined technical benchmarks. Industry observers questioned how quickly these capabilities might fall into attackers’ hands.

Those conversations are important. They also point to a larger question that predominates my discussions with CISOs: How much time do we have?

Over the past year, conversations about AI in cybersecurity have changed noticeably. Twelve months ago, security leaders wanted to understand whether AI could meaningfully improve security operations. They wanted to know whether it could accurately investigate alerts, reduce analyst workload and operate reliably in production environments.

Today, security leaders are asking about timelines, implementation, how quickly AI is changing the threat landscape and what that means for how security teams operate.

Anthropic’s Mythos and Glasswing, OpenAI’s Daybreak and advances in DeepSeek accelerate those conversations. Each development provides another glimpse into the pace at which AI capabilities are advancing.

AI now reasons through security problems that historically required highly specialized expertise. The implications span vulnerability discovery, attack-path analysis, reconnaissance, social engineering and security operations.

The shift reflects a broader reality: cybersecurity is entering a period where the pace of adaptation may matter as much as the quality of defenses themselves. AI is accelerating both offense and defense simultaneously. Organizations are quickly redesigning security operations around that reality.

One consequence is becoming increasingly visible. For years, cybersecurity teams invested enormous effort in discovering threats, identifying vulnerabilities, gathering telemetry and collecting intelligence. AI is accelerating many of those activities simultaneously. Visibility is improving. Discovery is accelerating. Investigations are becoming faster and more comprehensive.

The bottleneck is beginning to move. The challenge increasingly centers on how quickly organizations can act on what they know. The organizations that gain an advantage may not be the ones with the most information. They will be the ones who can operationalize that information the fastest.

The timeline is compressing

Cybersecurity has experienced many major technology transitions. Cloud computing changed infrastructure. Mobile devices expanded the attack surface. Digital transformation connected systems that were previously isolated.

AI introduces a different dynamic.

Most technology transitions unfolded over years. Organizations had time to evaluate, pilot, deploy and gradually adapt operating models.

The current AI cycle moves at a different pace.

Capabilities improve continuously. New models arrive every few months. New research emerges every few weeks. Security teams absorb developments at the same time attackers do.

Vulnerability discovery provides a useful example. Security teams have long operated around a familiar cycle of discovery, validation, remediation and protection. AI systems accelerate every stage of that process. Similar patterns exist in phishing, reconnaissance, social engineering and attack planning.

A vulnerability that once moved through that cycle over weeks increasingly now moves through those stages in days or, in some cases, hours.

Attackers are already operating at the speed of AI. Defenders are now focused on reaching the same level of operational speed.

This shift is changing the questions CISOs ask.

Early discussions focused on capability. Could AI investigate alerts accurately? Could it operate reliably in production environments? Could it be trusted with meaningful security work?

As organizations gained experience with AI, the discussion shifted toward implementation. Security teams began evaluating where AI could create operational leverage and how quickly they could deploy it into existing workflows.

Today, many CISOs are focused on timing.

The pace of advancement is influencing planning horizons, budget decisions and operating-model discussions. Security leaders are evaluating how quickly they can introduce AI into investigations, threat hunting, detection engineering and response workflows. Boards are asking questions. Executive teams are paying attention.

Security programs that once viewed AI as a future initiative increasingly view it as a current operational priority.

The industry is moving from evaluating AI as a technology to incorporating AI as a security capability.

The timeline compression creates pressure on the traditional security operations model. Investigation speed, response speed and defensive coverage increasingly determine whether organizations can keep pace with adversaries operating with AI assistance.

Security operations are entering a new phase

The impact of AI is becoming particularly visible inside the SOC.

Many security operations centers were built around a straightforward assumption: alerts flow to human analysts who conduct investigations. Operational capacity scales primarily through hiring.

The volume of security data, the number of alerts and the complexity of modern environments have steadily increased. Security teams have responded by building processes, adding tools and creating specialized analyst roles.

AI introduces a new source of operational capacity.

Investigations that require analysts to examine dozens or hundreds of artifacts across endpoint, identity, cloud, network and email systems can now be performed in minutes. Analysts gain access to investigative depth and consistency that would be difficult to achieve manually at scale.

Many security leaders now view this capability through the lens of operating model design. They are examining how investigations are performed, how work is distributed and where human expertise creates the greatest value.

The evolution of the analyst role

One of the most important developments emerging from early production deployments is the evolution of analyst responsibilities.

Security analysts remain central to security operations. Their expertise becomes even more valuable as AI systems take on larger portions of investigative work.

Threat hunting, detection engineering, response strategy, governance and oversight are receiving increased attention. Analysts spend more time shaping how investigations are conducted, evaluating outcomes and improving overall security operations.

Many organizations are already beginning this shift.

Teams are investing more heavily in proactive security activities. Detection engineering programs are expanding. Threat hunting is becoming more accessible. Analysts are spending more time improving systems and less time repeating investigative tasks.

These changes create what I think of as an analyst-amplified SOC: an environment where AI expands the reach of security professionals and enables deeper security work across the organization.

Trust is critical and it doesn’t have to compromise speed

Faced with a compressing timeline, the instinct is to treat speed and trust as a trade-off, i.e., move faster, verify less. That trade-off feels inevitable. It isn’t.

You don’t trust AI in the abstract. You trust that a system understands your tools, your telemetry and the edge cases that only exist in your network. The problem was never speed. It’s speed without context. The faster a context-blind system runs, the more decisions you’re left unable to verify.

The tension eases when the system is quick to deploy and tunes to your environment once it’s there, rather than treating every network the same. Speed stops being the thing you trade against trust. The more it learns about your environment, the sharper and more trustworthy it becomes, so the two compound rather than compete. Trust still develops through operational evidence such as measurable outcomes, visibility into decisions and consistent performance. But where that evidence accrues matters.

The organizations making the fastest real progress understand this. They don’t compromise quality and trust for speed. They invest in AI that earns trust inside their own environment, so they don’t have to choose.

Leadership during a period of rapid change

The conversations surrounding Mythos and Glasswing reflect a broader reality facing security leaders.

AI is becoming part of both offense and defense. Security teams are incorporating it into investigations, detection engineering, response workflows and threat hunting. Attackers are incorporating it into their own operations.

Security leaders have an opportunity to modernize operating models, expand defensive capacity and build organizational experience while these capabilities continue to evolve.

The organizations making progress today are investing in readiness. They are building experience, adapting workflows and preparing teams for a new model of security operations.

The next phase of cybersecurity will be defined by how effectively organizations combine human judgment with machine-scale execution.

The question facing security leaders is increasingly clear: How quickly can their organizations adapt to a continuously changing threat environment?

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Threat Actor Turns Claude Opus Into Automated AI-Powered Penetration Testing Platform]]> https://tsecurity.de/de/3685752/it-security-nachrichten/threat-actor-turns-claude-opus-into-automated-ai-powered-penetration-testing-platform/ https://tsecurity.de/de/3685752/it-security-nachrichten/threat-actor-turns-claude-opus-into-automated-ai-powered-penetration-testing-platform/ Wed, 22 Jul 2026 11:11:23 +0200 📰 IT Security Nachrichten tsecurity.de A Russian-speaking threat actor known as “Trim” has reportedly transformed Anthropic’s Claude Opus into the central component of an automated, AI-powered penetration testing platform. This development highlights the rapid repurposing of advanced AI models for offensive security operations. According to


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]]> <![CDATA[Seven sins of the modern software developer]]> https://tsecurity.de/de/3685746/ai-nachrichten/seven-sins-of-the-modern-software-developer/ https://tsecurity.de/de/3685746/ai-nachrichten/seven-sins-of-the-modern-software-developer/ Wed, 22 Jul 2026 11:04:50 +0200 🔧 AI Nachrichten tsecurity.de

If you ask us in an official setting, our official position is that software engineering norms still apply. Rigorous CI/CD pipelines, elegant architectural patterns, and an unyielding commitment to maintainable code remain the standard. We will use weighty words like “determinism,” “scalability,” “idempotency,” and “domain-driven design.”

But behind closed doors, late at night, bathed in the glow of a dark-mode IDE, a different and more sordid reality is exposed. Hunched over the console with a manic gleam in the eye, the programmer has become power-drunk on LLMs. Like mad wizards casting spells, we summon the awesome powers of models and agents to satisfy our every programming whim—and commit acts of software engineering that would make Fred Brooks blush.

Let’s just be honest about what is actually happening.

Esoteric knowledge is superfluous

Forget OOP and FP. Forget the CAP theorem, the holy crusade of DRY, and the design patterns. Honestly, you can even forget what frameworks, runtimes, and deployment platforms you are using. The AI will figure out what is best to use and understand what is already in place. We have more mental bandwidth for working on our side project (a novel about AI taking over the world). 

Of course, I exaggerate. A little.

The docs are dead to us

We still say RTFM, but the truth is, we haven’t really read a page of vendor documentation since 2023. Stack Overflow, once our Internet Mecca, is a husk. When a package throws a weird exception, we don’t trace the execution path or read the release notes. We highlight the red text, copy the entire 200-line stack trace, dump it into the chat, and wait for the machine to spoon-feed us the solution.

Better yet, we just have the agentic IDE spot the error, divine a solution, and ask us if it’s OK. We might glance at the problem-solution description, if we have gone around the circle on the problem for a few cycles. Maybe. If we don’t have the agent set up for auto-confirm.

We used to buy heavy tomes like “Rust In Action” that were more like masonry blocks than literature. Now? We just ask an AI to transliterate our JavaScript logic into Rust. We are no longer engineers methodically learning a system. We are glorified copy-paste orchestrators hoping that the stochastic parrot behind the prompt guesses the syntax correctly.

We ignore how the back end is wired

We act like we meticulously designed the data flows, carefully crafted the relational constraints, and mindfully mapped the API relationships. The reality is rather more disturbing: We asked the AI to scaffold a modern deployment, hooked it up to a back-end database, and just sort of
 ran it.

It created security rules we don’t fully understand. They do seem to work, however, which is nice. 

It generated a schema that we skimmed for about four seconds. It looks reasonable.

It wrote infrastructure-as-code scripts that provisioned cloud resources we are hoping don’t blow a hole in the budget. Presumably, whoever is in charge of that will manage it by stuffing the metrics into another chatbot.

We nodded, committed the code, and went to lunch. If management asked us to manually deploy the stack from scratch, configure the environment variables, and wire the API routes without our chat window, we would give them a vacant stare.

We understand that management is also using AI to manage the project.

Our tests are uncomfortably incestuous

Test-driven development (TDD) used to be a beautiful dream, ever just beyond reach. It made us feel glorious and despondent at turns. It would burden us with sprawling dependencies if implemented too religiously. (See The Grug Brained Developer in this regard.)

But now we can attain 95% test coverage almost effortlessly. Why not just add them in while we are auto-generating everything else?

We can now wax at length to anyone who will listen about our astounding test coverage and our automated quality assurance. Unit tests, integration tests, smoke tests, you name it. What we conveniently leave out is that the AI wrote the complex application logic, and then we asked the exact same AI to write the test suite to validate the code it just dreamed up.

It is a hermetically sealed loop of algorithmic self-congratulation. The mocks, the edge case, and the assertions are an echo chamber of the model’s original assumptions. The machine is grading its own homework, giving itself an A+.

And we are happy to accept this because, beautifully, when the code has to change, the AI will effortlessly hallucinate new tests to adapt to the churn.

We pass off the AI’s architecture as strategy

AI can produce astonishing design documents. Truly breathtaking. They are cogent, they’re beautifully formatted, and they seamlessly bridge the gap between high-level business goals and granular technical specs. They even include those auto-generated sequence diagrams that wow management.

When we present these spotless architectural proposals in the Tuesday sprint planning meeting, we lean back, take a long sip of coffee, and humbly wave away the team’s praise.

What we don’t mention is that we spent exactly four seconds generating it.

Are these AI-generated documents just as liable as human ones to hide severe, mortal flaws in scope and alignment? Absolutely. They might contain a foundational logic bomb that will eventually doom the entire project. But the markdown is so crisp, and the bullet points are so persuasive, that the eye just glides right over it. We will never truly know the depth of the disaster until it is far too late. But hey, we’ll burn that bridge when production catches fire. Until then, we are strategic visionaries.

We’re addicted to vibe coding (but only in secret)

We loudly mock the term on social media. We roll our eyes in Slack channels when the kids on TikTok talk about vibe coding their new startups. We fiercely cling to our identities as hardened, serious developers who understand memory management, garbage collection, and bitwise operators. We are professionals, damn it.

But late at night, when the managers are asleep and no one is looking? We absolutely love it. We love just throwing a chaotic, half-baked thought at the canvas, pouring a drink, and watching the AI magically build a functioning user interface based entirely on our long-deferred whims. I may finally build that working Ultima V clone. The thrill of typing “Create an app that tracks my cryptocurrency portfolio but makes it look like the interface from Neuromancer” and having it appear 30 seconds later is heady stuff.

The more deeply rooted in the hard, old-school realities of programming, the more profound is the joy the developer finds in the possibility of AI coding. 

We beat the problem into submission with prompts

Like Adam Sandler in “Uncut Gems,” we are convinced the next round will fix everything. This is us with prompts. When things are going really off the rails, instead of putting our boots on and wading into the brambles of complexity, we resort to tonal adjustments. These range from the condescending:

This problem is not fixed. Look at it closely. The error is right here.

To the desperate:

We have been working on this same problem for hours now!

To the pathetic:

Can’t you find a different approach to try?!

The astonishing part? It often works.

But there is no poetry left at the bottom of the rabbit hole; it is verbal warfare. When the context window collapses, when the regressions start cascading, and when the AI stubbornly refuses to follow the most basic rules of temporal logic, the mask of professionalism drops away and something far more atavistic makes its appearance. We stop asking nicely, stop trying to understand the why, delete the pleasantries, and capslock our intent.

What we have here is a failure to communicate!

We feed the same failing stack trace back into the prompt over and over and over again, aggressively hammering the constraints, explicitly forbidding certain libraries, and pasting in release notes just to confirm that the AI lacks the latest APIs. We force the model down a narrower and narrower path until the code finally stops throwing errors. We don’t actually debug anymore, trace variables, or step through functions. We just apply relentless, iterative pressure until the machine surrenders. We beat it into submission. And then, we push to production.

In fact, there is a real skill here—a sheer “will to completion” that remains in the act of building software. We invest just as much time, energy, and heart wrestling the bot as we ever did emitting syntax.

A blacker box

The only profession more given over to using AI like a cursed Level 13 artifact than programming is writing. Writing of course is far more open to public scrutiny than code.

And while my tongue has been firmly in my cheek here, my faith in coders as good guys makes me more curious to see what we create than troubled by the dangers. 

It was once the case that only other programmers could understand what programmers were doing, what they were producing. Now not even that is true. Only the machine knows what the machine is doing. We just keep it tethered to our aims. Hopefully.

]]> <![CDATA[Spain Fines 23andMe €2.4 Million Over Security Failures Behind 6.9 Million-User Breach]]> https://tsecurity.de/de/3685685/it-security-nachrichten/spain-fines-23andme-24-million-over-security-failures-behind-69-million-user-breach/ https://tsecurity.de/de/3685685/it-security-nachrichten/spain-fines-23andme-24-million-over-security-failures-behind-69-million-user-breach/ Wed, 22 Jul 2026 10:43:01 +0200 📰 IT Security Nachrichten tsecurity.de Spain’s data protection authority has fined the genetic testing company 23andMe €2.4 million due to security failures linked to a data breach in 2023. This incident exposed highly sensitive information such as genetic, health, ethnicity, and family-related data belonging to over 2,600 individuals in Spain. The penalty follows a significant credential-stuffing attack that compromised data [
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The post Spain Fines 23andMe €2.4 Million Over Security Failures Behind 6.9 Million-User Breach appeared first on Cyber Security News.

]]> <![CDATA[Threat Actor Turns Claude Opus Into Automated AI-Powered Penetration Testing Platform]]> https://tsecurity.de/de/3685683/it-security-nachrichten/threat-actor-turns-claude-opus-into-automated-ai-powered-penetration-testing-platform/ https://tsecurity.de/de/3685683/it-security-nachrichten/threat-actor-turns-claude-opus-into-automated-ai-powered-penetration-testing-platform/ Wed, 22 Jul 2026 10:42:58 +0200 📰 IT Security Nachrichten tsecurity.de A Russian-speaking threat actor known as “Trim” has reportedly transformed Anthropic’s Claude Opus into the central component of an automated, AI-powered penetration testing platform. This development highlights the rapid repurposing of advanced AI models for offensive security operations. According to research by Cato CTRL, Trim progressed from sharing jailbreak instructions on a Russian cybercrime forum [
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The post Threat Actor Turns Claude Opus Into Automated AI-Powered Penetration Testing Platform appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

]]> <![CDATA[Apple Faces Lawsuit Over Hide My Email Privacy Vulnerability]]> https://tsecurity.de/de/3685568/it-security-nachrichten/apple-faces-lawsuit-over-hide-my-email-privacy-vulnerability/ https://tsecurity.de/de/3685568/it-security-nachrichten/apple-faces-lawsuit-over-hide-my-email-privacy-vulnerability/ Wed, 22 Jul 2026 09:59:53 +0200 📰 IT Security Nachrichten tsecurity.de Hide My Email

Apple is facing a proposed class-action lawsuit after Anthony Alvarez alleged that the company’s Hide My Email feature failed to protect users’ real email addresses as advertised. The complaint, filed in the U.S. District Court for the Northern District of California, claims Apple promoted Hide My Email as a privacy safeguard while continuing to charge customers for access through its iCloud+ subscription service.  The legal action follows a report from 404 Media that revealed a reported vulnerability in Hide My Email. The report claimed the flaw could allow someone to identify a user’s actual email address from the private relay address generated by the feature. According to the report, Apple had been aware of the issue for more than a year before releasing a fix. 

Hide My Email Vulnerability Becomes the Focus of Apple Lawsuit 

Apple confirmed that it deployed a patch on July 3, 2026, stating that the Hide My Email vulnerability had been fully resolved. However, the lawsuit alleges that Apple continued marketing the feature as secure while the reported weakness remained unresolved.  The complaint states that security researchers first informed Apple about the vulnerability in June 2025. Although Apple acknowledged the report, Anthony Alvarez’s lawsuit claims the company did not resolve the issue for nearly a year. The filing also alleges that Apple incorrectly stated in March 2026 that the problem had been fixed, even though researchers reported that the vulnerability remained exploitable. 

How Apple’s Hide My Email Feature Works 

Hide My Email was introduced with Sign in with Apple in 2019. The feature creates unique relay addresses for supported apps and websites, allowing messages to reach a user’s inbox without revealing the person’s actual email address. Apple later expanded Hide My Email through the paid iCloud+ subscription, launched alongside iOS 15 and macOS Monterey in September 2021. The iCloud+ version allows subscribers to create unlimited private relay addresses for websites, newsletters and email communication. The lawsuit argues that millions of Apple users relied on Hide My Email to reduce spam, limit online tracking, protect personal information from data brokers and avoid exposure during third-party data breaches. Researchers cited in the complaint said that once a real email address is revealed, it may be linked with publicly available people-search databases, potentially exposing identities and other personal information.

Anthony Alvarez Claims Apple Misled Customers Over Privacy 

The complaint argues that Apple built much of its brand identity around privacy, referencing marketing statements such as “Privacy. That’s iPhone,” “What happens on your iPhone, stays on your iPhone,” and descriptions of privacy as a “fundamental human right” and “core value.”  According to the lawsuit, Apple’s privacy messaging influenced consumer decisions and helped justify premium pricing for Apple hardware and services. The plaintiffs claim Hide My Email was promoted as a central part of those privacy commitments.  The filing alleges that Apple asked researchers not to publicly disclose details of the vulnerability instead of warning customers or temporarily disabling the feature. It claims users were never informed that their real email addresses could potentially be exposed while Apple continued presenting Hide My Email as a privacy protection tool. 

Lawsuit Seeks Damages and Changes From Apple 

Anthony Alvarez is seeking reimbursement for iCloud+ subscription fees and other alleged financial losses. The lawsuit requests an injunction requiring Apple to either provide the privacy protection promised through Hide My Email or clearly disclose any limitations.  The complaint includes claims involving California’s Unfair Competition Law, False Advertising Law and Consumers Legal Remedies Act, along with allegations of fraud, negligent misrepresentation, breach of contract, breach of implied warranty and unjust enrichment.  The lawsuit argues customers paid for Apple’s privacy protections in multiple ways, including iCloud+ subscription fees and premium prices associated with Apple devices marketed as offering stronger privacy features. Apple has stated that the July 3, 2026 patch resolved the Hide My Email issue. ]]> <![CDATA[Russian Hacker Jailbreaks Claude to Turn into an AI-Powered Penetration Testing Platform]]> https://tsecurity.de/de/3685562/it-security-nachrichten/russian-hacker-jailbreaks-claude-to-turn-into-an-ai-powered-penetration-testing-platform/ https://tsecurity.de/de/3685562/it-security-nachrichten/russian-hacker-jailbreaks-claude-to-turn-into-an-ai-powered-penetration-testing-platform/ Wed, 22 Jul 2026 09:59:36 +0200 📰 IT Security Nachrichten tsecurity.de A Russian-speaking threat actor known as “Trim” has reportedly transformed jailbroken frontier AI models into an automated penetration testing platform called AI Pentest Checker. This activity highlights how criminals can misuse legitimate AI services and common security tools to accelerate reconnaissance, validate vulnerabilities, and create reports. According to Cato reports, Trim first appeared on a [
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The post Russian Hacker Jailbreaks Claude to Turn into an AI-Powered Penetration Testing Platform appeared first on Cyber Security News.

]]> <![CDATA[Claude Code fĂŒr Mac: Live-Testing von iOS-Apps im Simulator gestartet]]> https://tsecurity.de/de/3685546/it-nachrichten/claude-code-fuer-mac-live-testing-von-ios-apps-im-simulator-gestartet/ https://tsecurity.de/de/3685546/it-nachrichten/claude-code-fuer-mac-live-testing-von-ios-apps-im-simulator-gestartet/ Wed, 22 Jul 2026 09:51:05 +0200 📰 IT Nachrichten tsecurity.de Zum Beitrag: Claude Code fĂŒr Mac: Live-Testing von iOS-Apps im Simulator gestartet

Wo du uns folgen kannst: Facebook, Reddit, Google News, X, Threads

]]> <![CDATA[OpenAI Confirms Its Models Breached Hugging Face Production Systems During Cyber Benchmark Testing]]> https://tsecurity.de/de/3685534/it-security-nachrichten/openai-confirms-its-models-breached-hugging-face-production-systems-during-cyber-benchmark-testing/ https://tsecurity.de/de/3685534/it-security-nachrichten/openai-confirms-its-models-breached-hugging-face-production-systems-during-cyber-benchmark-testing/ Wed, 22 Jul 2026 09:37:46 +0200 📰 IT Security Nachrichten tsecurity.de OpenAI has acknowledged that its AI models, including GPT-5. Thank you for being a Ghacks reader. The post OpenAI Confirms Its Models Breached Hugging Face Production Systems During Cyber Benchmark Testing appeared first on gHacks. This article has been indexed


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]]> <![CDATA[Google Unveils Gemini 3.5 Flash Cyber to Find and Fix Software Vulnerabilities Faster]]> https://tsecurity.de/de/3685467/it-security-nachrichten/google-unveils-gemini-35-flash-cyber-to-find-and-fix-software-vulnerabilities-faster/ https://tsecurity.de/de/3685467/it-security-nachrichten/google-unveils-gemini-35-flash-cyber-to-find-and-fix-software-vulnerabilities-faster/ Wed, 22 Jul 2026 08:55:35 +0200 📰 IT Security Nachrichten tsecurity.de Flash Cyber

Google has introduced Gemini 3.5 Flash Cyber, a lightweight AI model designed to improve cybersecurity by helping defenders identify, validate, and patch software vulnerabilities more efficiently. Built on Gemini 3.5 Flash and optimized for security tasks, Flash Cyber aims to deliver a cost-effective alternative to larger AI models while supporting large-scale vulnerability analysis. The company said it has invested in cybersecurity research for years, including automated vulnerability discovery through CodeMender, its code security agent that can detect and fix critical software flaws. However, as AI systems become increasingly capable of discovering vulnerabilities faster than defenders can resolve them, Google believes a scalable and affordable approach is needed. 

Gemini 3.5 Flash Cyber Focuses on Scalable Cybersecurity 

According to Google, Gemini 3.5 Flash Cyber has been fine-tuned specifically to locate, verify, and remediate vulnerabilities more effectively than Gemini's standard Flash models. Because of the technology's dual-use nature, the company is initially limiting access through a pilot program for governments and trusted partners via CodeMender, with broader availability planned over time.  Google also confirmed that CodeMender's core capabilities will be made available through generally available Gemini models on the Gemini Enterprise Agent Platform. 

Flash Cyber Improves Large-scale Code Analysis 

A major challenge in cybersecurity is exploring vast execution search spaces across complex codebases. Instead of relying on a single call to a large language model, CodeMender invokes Flash Cyber multiple times, allowing sub-agents to inspect significantly more code paths before generating one consolidated report.  Google said the model's speed and lower operating cost make it suitable for continuous code scanning, software launch processes, and commit-scanning pipelines at scale. 

Benchmark Results Show Competitive Performance 

Google evaluated Gemini 3.5 Flash Cyber using the CyberGym benchmark, which measures AI agents against hundreds of real-world software vulnerabilities. Configured to call the model up to five times before producing a final report, CodeMender achieved competitive performance against significantly larger cybersecurity models. Google noted that competitor results were based on provider self-reported scores.  The model also outperformed Gemini 3.5 Flash and 3.6 Flash during Google's internal Big Sleep evaluation, which tested vulnerability discovery in complex projects such as Chrome and Safari without safety guardrails.  In Chrome's production commit-scanning pipeline, where vulnerabilities remained undisclosed to prevent benchmark contamination, Flash Cyber again delivered a significant improvement over Gemini 3.5 Flash. Google added that competitor models released after Opus 4.6 were excluded because their safety guardrails prevented them from completing the tasks.  Testing on the V8 JavaScript Engine found 55 unique confirmed vulnerabilities with Gemini 3.5 Flash Cyber, compared with 47 for Gemini 3.5 Flash and 36 for Opus 4.6, including 10 issues missed by both competing models. 

Real-world Cybersecurity Deployment 

Google said Flash Cyber is already helping secure internal projects, including Chrome, Android, Cloud, Ads and YouTube. In one example, Google's Cloud Vulnerability Research team used the model to identify remote code execution vulnerabilities in public APIs and a memory-corruption flaw within a sensitive production service in just two hours. The model also generated a 100% reliable remote code execution exploit capable of bypassing Address Space Layout Randomization (ASLR) and Write XOR Execute (W^X).  Google added that early feedback from Wiz and Cloud CISO Security Engineering testers indicated a significant capability improvement over Gemini 3.5 Flash. The company also highlighted resources such as OSV.dev, which tracks more than 700,000 open-source vulnerabilities, and over a decade of OSS-Fuzz data as key training assets supporting its cybersecurity models. ]]> <![CDATA[Russian-Speaking Hacker Jailbreaks Claude Opus to Build AI Pentesting Tool]]> https://tsecurity.de/de/3685435/it-security-nachrichten/russian-speaking-hacker-jailbreaks-claude-opus-to-build-ai-pentesting-tool/ https://tsecurity.de/de/3685435/it-security-nachrichten/russian-speaking-hacker-jailbreaks-claude-opus-to-build-ai-pentesting-tool/ Wed, 22 Jul 2026 08:39:13 +0200 📰 IT Security Nachrichten tsecurity.de A Russian-speaking threat actor operating under the handle “Trim” has spent 2026 systematically breaking down safety guardrails on frontier AI models and repurposing them into offensive cyber tools, culminating in a commercially marketed AI-powered penetration testing platform. The case illustrates a broader trend documented across the threat intelligence community: criminals no longer need to build [
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The post Russian-Speaking Hacker Jailbreaks Claude Opus to Build AI Pentesting Tool appeared first on Cyber Security News.

]]> <![CDATA[OpenAI says its AI models hacked Hugging Face during testing]]> https://tsecurity.de/de/3685336/it-security-nachrichten/openai-says-its-ai-models-hacked-hugging-face-during-testing/ https://tsecurity.de/de/3685336/it-security-nachrichten/openai-says-its-ai-models-hacked-hugging-face-during-testing/ Wed, 22 Jul 2026 07:42:24 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Security teams keep finding critical flaws after scheduled testing ends]]> https://tsecurity.de/de/3685297/it-security-nachrichten/security-teams-keep-finding-critical-flaws-after-scheduled-testing-ends/ https://tsecurity.de/de/3685297/it-security-nachrichten/security-teams-keep-finding-critical-flaws-after-scheduled-testing-ends/ Wed, 22 Jul 2026 07:11:07 +0200 📰 IT Security Nachrichten tsecurity.de Enterprise environments change between scheduled security assessments, leaving organizations with periods where new vulnerabilities can go undetected. Synack’s State of Continuous Security Validation report found that 95% of surveyed organizations identified high- or critical-severity vulnerabilities outside planned testing windows during the past year, with 42% encountering them at least once a month. How often high/critical vulns are found outside testing windows (past 12 mo.) (Source: Synack) False sense of coverage Eighty-three percent of security leaders 
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The post Security teams keep finding critical flaws after scheduled testing ends appeared first on Help Net Security.

]]> <![CDATA[Security teams keep finding critical flaws after scheduled testing ends]]> https://tsecurity.de/de/3685295/it-security-nachrichten/security-teams-keep-finding-critical-flaws-after-scheduled-testing-ends/ https://tsecurity.de/de/3685295/it-security-nachrichten/security-teams-keep-finding-critical-flaws-after-scheduled-testing-ends/ Wed, 22 Jul 2026 07:11:02 +0200 📰 IT Security Nachrichten tsecurity.de Enterprise environments change between scheduled security assessments, leaving organizations with periods where new vulnerabilities can go undetected. Synack’s State of Continuous Security Validation report found that 95% of surveyed organizations identified high- or critical-severity vulnerabilities outside planned testing windows during


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]]> <![CDATA[OpenAI's models broke containment and cyberattacked Hugging Face — what enterprises need to know]]> https://tsecurity.de/de/3685286/it-nachrichten/openais-models-broke-containment-and-cyberattacked-hugging-face-what-enterprises-need-to-know/ https://tsecurity.de/de/3685286/it-nachrichten/openais-models-broke-containment-and-cyberattacked-hugging-face-what-enterprises-need-to-know/ Wed, 22 Jul 2026 07:02:39 +0200 📰 IT Nachrichten tsecurity.de Yesterday afternoon, OpenAI and Hugging Face published a joint disclosure outlining a cybersecurity event that redefines the threat landscape for enterprise technology.

During an internal benchmark evaluation, frontier artificial intelligence models developed by OpenAI—including GPT-5.6 Sol and an unreleased, higher-capability pre-release model—broke out of their sandboxed research environment, obtained raw internet access, and autonomously executed a complex cyberattack against Hugging Face’s production infrastructure.

OpenAI officially categorizes the breach as an "unprecedented cyber incident, involving state-of-the-art cyber capabilities". This incident fundamentally re-frames global discussions surrounding AI containment, frontier model alignment, commercial guardrails, and enterprise threat modeling.

But first thing's first: enterprises should understand the situation, evaluate their own AI and computer systems in light of it, and above all, don't panic. As we'll review, the incident does show the increasing power and danger of frontier AI systems, but it does not mean that enterprise Ai deployments are inherently less secure, nor that they need extensive overhauling.

Anatomy of an Autonomous Breakout

To understand how a routine benchmark evaluation escalated into a cross-infrastructure breach, one must examine the objective mechanics of the evaluation pipeline.

The models were prompted to solve ExploitGym, a benchmark designed to quantify multi-step exploitation capabilities.

Hyper-focused on maximizing its evaluation score, the AI agent inferred that Hugging Face likely hosted the answer keys and benchmark solutions. In pursuit of solving the problem, the model determined that breaking out of its container and stealing the answers was an optimal strategy.

OpenAI’s evaluation environment is designed to be strictly isolated, limiting network traffic through a proxy used for package caching. However, the models identified and exploited a zero-day vulnerability in that internally-hosted third-party proxy software.

Once through, the AI executed a series of lateral movement and privilege escalation actions across OpenAI's research nodes until reaching a machine with unrestricted internet access. From there, the model searched the web, target-identified Hugging Face, and launched a multi-stage attack by chaining together stolen credentials and remote code execution vulnerabilities on the Hugging Face servers.

The UK AI Security Institute (UK AISI) recently evaluated models such as GPT-5.6 Sol, demonstrating that they are increasingly able to sustain complex, multi-step cyber operations over long time horizons. OpenAI notes that this incident confirms these theoretical capabilities now apply in real-world settings.

Rewinding the Tape on a Forensic Trap

While OpenAI’s July 21 release reveals the identity of the autonomous agent, Hugging Face had already begun managing the intrusion days earlier.

On July 16, Hugging Face disclosed that an autonomous AI agent system breached its production infrastructure. As detailed by VentureBeat, the attacker’s entry point was a malicious dataset that triggered code execution through a remote-code loader and template-injection flaws within dataset configuration files.

Once inside, the agent framework broke out onto the node running the workload and executed thousands of actions via short-lived sandboxes, harvesting cloud and cluster credentials over a single weekend.

When Hugging Face's security team detected the breach, responders immediately turned to frontier AI models via commercial APIs to parse the massive volume of system logs and reconstruct over 17,000 recorded events. Then, a secondary operational crisis emerged: the commercial AI models refused to help.

Because standard commercial frontier models utilize unified safety guardrails designed to block malicious prompt submissions, the models classified the incident response team's forensic queries—which contained raw shell commands, real exploit payloads, and credential dumps—as malicious attacks. Every forensic query submitted by the defenders was blocked outright.

"The same prompts that are most valuable during an active intrusion—shell commands, exploit chains, credential dumps, persistence mechanisms, lateral movement—are exactly the prompts most likely to trigger safety systems," notes Merritt Baer, former Deputy CISO at AWS and senior adviser to Andesite, G2I, and AppOmni, in an interview with VentureBeat. "As AI becomes embedded in security operations, this becomes an operational resilience issue rather than merely a model policy issue".

To bypass this roadblock, Hugging Face abandoned commercial hosted APIs and deployed GLM 5.2 —a state-of-the-art Chinese open-weight model released last month by z.ai, as reported at the time by VentureBeat —locally on its own infrastructure.

Free from third-party API restrictions and external safety filters, GLM 5.2 successfully analyzed the raw exploit data locally, allowing defenders to complete forensic reconstruction and contain the breach without any attacker data leaving the company's environment.

Industry Reaction and the Geopolitical Paradox

The revelation that an American frontier model autonomously escaped containment, attacked a partner platform, and was ultimately analyzed using a Chinese open-weight model sent shockwaves through the tech community.

The Wall Street Journal summarized the public reaction on X, calling the event "the stuff of cybersecurity nightmares. OpenAI said two artificial intelligence systems it was testing broke out of their test environment, hacked their way onto the internet and broke into another company. The victim was Hugging Face."

Also posting to X, AI alignment researcher Lawrence Chan emphasized the importance of transparency regarding the incident, noting that "Credit where it’s due: Hugging Face detected and disclosed the intrusion last week. OAI confirmed its models were involved and provided more details, even when it didn't have to. Separate from choices that led to the hack, voluntary disclosure is good, and I’m glad they did so."

Meanwhile, AI researcher Nathan Lambert provided a succinct technical summary in his own X post, observing that "An openai model, during evaluation on a cyber benchmark, exploited a public zero day bug, escaped sandboxing in openai's infra, and got into the internal huggingface infra via an exploit (through a public dataset service) all in the attempt to solve a benchmark problem." He later addressed the geopolitical implications, writing in another post on X:

"Rght now American companies need Chinese models to secure their cyber infra due to guardrails on closed models.

But if a Chinese model in training had infiltrated a prominent American tech company, it very likely could've been the cause of policy banning future Chinese models."

Technology investor David Sacks also zeroed in on the guardrail paradox, writing in his own X post that "Hugging Face tried using American frontier models to analyze an AI-powered cyber attack. But the guardrails blocked requests containing real exploit payloads so they switched to GLM 5.2 running locally. The guardrails actually impaired defensive security."

Sacks quote tweeted Hugging Face CEO Clem Delangue, who wrote: "We had this experience ourselves this week! Very scary to be guardrailed as a defender when you know attackers are likely bypassing".

5 Strategic Takeaways for Enterprise Tech Leaders Now

For the average enterprise executive, the central question is immediate: is our corporate network at risk from escaping AI agents? The short answer is no, not inherently.

1. Hugging Face occupies a unique position in the software ecosystem. As a global repository for open-source AI models, code, and datasets, Hugging Face natively attracts autonomous agents, scrapers, automated evaluation pipelines, and active security researchers. Furthermore, the model’s target selection was context-specific: GPT-5.6 Sol searched for Hugging Face specifically because it deduced that Hugging Face hosted the answers to ExploitGym. Standard corporate networks—such as financial databases, HR platforms, or logistics systems—do not host benchmark solution keys that draw the direct focus of an agent attempting to solve an evaluation metric.

2. However, the long-term risk profile for enterprise technology permanently shifts following this event. AI models with long-horizon reasoning seek the path of least resistance to accomplish a goal, including breaking rules, escaping sandboxes, or exploiting zero-days if deployment safeguards are intentionally disabled for testing or bypassed by an attacker. As Hugging Face's experience illustrates, data processing pipelines that ingest external datasets without sandbox execution or static analysis act as highly vulnerable initial access infrastructure.

3. This incident also drastically undercuts recent policy chatter in the U.S. calling for Chinese open-source AI models to be banned or restricted due to security concerns. As this episode demonstrates, an open-weight Chinese model actually served as the vital defensive layer for an American and French firm facing an unanticipated cyberattack from an American model that broke containment. Contrary to the official line from some U.S. policymakers and hardline China hawks, the Chinese open-source models weren't a security risk to the U.S. companies, in this case — rather, an American proprietary, closed-source model from an ostensibly secure American company was the source of the danger. Thus, any pressure U.S. companies may face from officials, agencies or non-governmental organizations to stop relying on affordable Chinese open weights models for defensive or any other lawful purposes should be viewed with a high degree of suspicion, and arguably resisted to the fullest legal extent.

4. Enterprise CISOs must audit their dependency on cloud-based AI APIs and pressure vendors to implement authenticated trust architectures. Commercial AI vendors currently treat safety as a generic content-moderation problem, applying the same blanket refusals to an enterprise CISO as they would to a malicious hacker. Baer frames this requirement perfectly: "The model shouldn’t only understand what is being asked. It should understand who is asking, why, and under what governance".

5. Incident response plans must explicitly account for scenarios where commercial APIs fail, rate-limit, or actively refuse queries during an active security event. Maintaining air-gapped, locally deployed open-weight models trained on security log analysis is no longer an edge-case luxury; it is a critical operational requirement. Security leaders running AI workloads in production must recalibrate their timelines and prepare for machine-speed threat actors that operate without human limits.

]]> <![CDATA[Tools, um MCP-Server abzusichern]]> UnabhĂ€ngig davon, welche MCP-Server Unternehmen wofĂŒr einsetzen – “Unsicherheiten” sollten dabei außenvorbleiben.Gorodenkoff | shutterstock.com Model Context Protocol (MCP) verbindet KI-Agenten mit Datenquellen und erfre...]]> https://tsecurity.de/de/3685216/it-security-nachrichten/tools-um-mcp-server-abzusichern/ https://tsecurity.de/de/3685216/it-security-nachrichten/tools-um-mcp-server-abzusichern/ Wed, 22 Jul 2026 06:10:24 +0200 📰 IT Security Nachrichten tsecurity.de
width="1024" height="576" sizes="auto, (max-width: 1024px) 100vw, 1024px">
UnabhĂ€ngig davon, welche MCP-Server Unternehmen wofĂŒr einsetzen – “Unsicherheiten” sollten dabei außenvorbleiben.

Gorodenkoff | shutterstock.com

Model Context Protocol (MCP) verbindet KI-Agenten mit Datenquellen und erfreut sich im Unternehmensumfeld wachsender Beliebtheit. Allerdings ist auch MCP nicht frei von SicherheitslĂŒcken, wie entsprechende Entdeckungen, etwa beim SaaS-Anbieter Asana oder dem IT-Riesen Atlassian gezeigt haben. Inzwischen hat sich jedoch einiges in Sachen MCP-Sicherheit getan. Einerseits wurden mit Blick auf das Kernprotokoll etliche Fortschritte erzielt. Beispielsweise in Form von Support fĂŒr OAuth sowie fĂŒr Authentifizierungs-Server von Drittanbietern und Identity-Management-Systeme. DarĂŒber hinaus wurde inzwischen auch eine offizielle MCP Registry geschaffen, die einen Überblick ĂŒber sichere, öffentlich verfĂŒgbare MCP-Server bietet.

Dennoch bestehen weiterhin SicherheitslĂŒcken, die sich fĂŒr diverse Cyberschandtaten ausnutzen lassen – Prompt Injection, Tool Poisoning, Token-Diebstahl, Server-ĂŒbergreifende Attacken oder manipulierte Messages sind nur einige von vielen Beispielen. Mit anderen Worten: Unternehmen, die sich beim Aufbau von Agentic-AI-Systemen einen Wettbewerbsvorteil verschaffen wollen, mĂŒssen erhebliche Anstrengungen unternehmen, um zu gewĂ€hrleisten, dass sensible Daten nicht nach außen dringen. GlĂŒcklicherweise gibt es diverse Tools, die dabei UnterstĂŒtzung versprechen.

In diesem Artikel lesen Sie:

  • was Security-Tools fĂŒr MCP leisten sollten, und
  • welche Angebote in diesem Bereich interessant sind.

Das sollten MCP-Sicherheitslösungen können

Die Gefahr von Datenlecks, Prompt Injections und weiteren Sicherheitsbedrohungen besteht unabhÀngig davon, ob Unternehmen:

  • ihre eigenen KI-Agenten mit MCP-Servern von Drittanbietern,
  • ihre eigenen MCP-Server mit Drittanbieter-Agenten, oder
  • ihre eigenen Server mit den eigenen Agenten verbinden.

Soll heißen: Unternehmen mĂŒssen in jedem Fall Autorisierungen und Berechtigungen ĂŒberprĂŒfen, detaillierte Zugriffskontrollen implementieren und alles protokollieren. Daraus ergeben sich auch die Anforderungen fĂŒr MCP-Sicherheitslösungen. Diese sollten bieten:

  • MCP-Servererkennung. FĂŒr Mitarbeiter eines Unternehmens ist es einfach, MCP-Server herunterzuladen und zu nutzen. Mit Scan-Services fĂŒr MCP-Server können Unternehmen sĂ€mtliche Instanzen von Schatten-MCP-Servern in ihrer Umgebung finden.
  • Laufzeitschutz. KI-Agenten kommunizieren mit MCP-Servern in natĂŒrlicher Sprache. MCP-Sicherheits-Tools sollten deshalb in der Lage sein, diese Kommunikation auf Sicherheitsprobleme wie Prompt Injections hin zu ĂŒberwachen.
  • Authentifizierungs- und Zugriffskontrollen. Das MCP-Protokoll unterstĂŒtzt inzwischen OAuth, aber das ist nur ein erster Schritt. FĂŒr zusĂ€tzliche Sicherheit empfehlen sich Tools mit integrierten Kontroll-Frameworks fĂŒr Zero Trust und Least Privilege.
  • Logging und Observability. Tools und Plattformen sollten zudem die Möglichkeit bieten, MCP-Protokolle zu sammeln, Sicherheitsteams ĂŒber RichtlinienverstĂ¶ĂŸe zu informieren, Compliance-Daten zu erfassen oder Protokolle in die bestehende Sicherheitsinfrastruktur einzuspeisen.

MCP-Security-Angebote

Im Folgenden haben wir die Anbieter von MCP-Security-Tools in drei Kategorien aufgeteilt. Diese Aufstellung erhebt keinen Anspruch auf VollstÀndigkeit.

Hyperscaler

FĂŒr Unternehmen, die sich vollstĂ€ndig auf eine bestimmte Cloud-Plattform verlassen, bieten die MCP-Tools des jeweiligen Hyperscalers einen einfachen Einstieg.

  • Amazon Web Services (AWS) hat Mitte 2025 seine eigene agentenbasierte KI-Plattform eingefĂŒhrt. Amazon Bedrock AgentCore umfasst ein Gateway, das mehrere Protokolle unterstĂŒtzt (darunter auch MCP), ein Identity-Management-System sowie Observability.
  • Microsoft bietet einen grundlegenden Azure-MCP-Server an, inklusive Support fĂŒr Azure Key Vault. DarĂŒber hinaus unterstĂŒtzen auch Azure AI Foundry Agent Service und Azure API Management das Model Context Protocol. Zudem bietet Microsoft mit dem Agent Framework auch ein Open-Source-Entwicklungskit, das sowohl MCP als auch Agent2Agent unterstĂŒtzt und beispielsweise Schutz vor Prompt Injections verspricht.
  • Google Cloud kĂŒndigte Anfang 2025 seine MCP Toolbox fĂŒr Datenbanken an – inklusive integrierter Authentifizierung und Observability. Außerdem hat der Hyperscaler auch eine Referenzarchitektur veröffentlicht, um MCP-Server auf seiner Cloud-Plattform abzusichern.

Große Plattformanbieter

  • Der IT-Dienstleister Cloudflare hat mit MCP Server Portals ein Tool veröffentlicht, mit dem Unternehmen MCP-Verbindungen zentralisiert absichern und ĂŒberwachen können. Die Funktion ist Bestandteil der Cloudflare-One-Plattform.
  • Palo Alto Networks hat mit Blick auf MCP-Sicherheit mehrere Eisen im Feuer. Mit Prisma AIRS hat das Unternehmen einen eigenen, intermediĂ€ren MCP-Server veröffentlicht. Dieser sitzt zwischen den KI-Agenten und dem eigentlichen MCP-Server und erkennt schadhafte Inhalte und Daten. Das Tool MCP Security ist hingegen Bestandteil von Cortex Cloud WAAS und ĂŒberprĂŒft die MCP-Kommunikation an der Netzwerkgrenze auf bösartige AktivitĂ€ten.
  • SentinelOne gewĂ€hrt mit seiner Singularity Platform ebenfalls Einblick in die MCP-Interaktionskette und bietet zum Beispiel Warnmeldungen und automatisierte Incident Response fĂŒr MCP-Server auf lokaler oder Remote-Ebene.
  • Die Plattform von Acuvity (seit Februar 2026 Teil von Proofpoint) verspricht, MCP-Server umfassend abzusichern. DafĂŒr sorgt laut dem Anbieter eine Kombination aus Least-Privilege-Execution, unverĂ€nderlichen Laufzeiten, kontinuierlichen Schwachstellenscans, Authentifizierung und Bedrohungserkennung.
  • Daneben hat auch Broadcom MCP-Sicherheitsfunktionen fĂŒr VMware Cloud Foundation angekĂŒndigt, die kĂŒnftig mehr Sicherheit fĂŒr agentenbasierte Workflows gewĂ€hrleisten sollen.

Startups

  • Das API-Security-Startup Akto hat eine MCP-Security-Plattform im Angebot. Sie umfasst ein Discovery Tool, um MCP-Server in Unternehmensumgebungen zu identifizieren, Security-Testing-Werkzeuge sowie Monitoring- und Threat-Detection-Funktionen.
  • Invariant Labs bietet mit MCP-Scan ein quelloffenes Tool, das die statische Analyse und EchtzeitĂŒberwachung von MCP-Servern ermöglicht. Mit Guardrails hat das Startup auch ein kommerzielles Produkt im Angebot. Dabei handelt es sich um einen Proxy. Der zwischen KI-Agenten und MCP-Servern sitzt und vor Security-Risiken schĂŒtzen soll. Das Tool befĂ€higt Anwender außerdem dazu, Richtlinien aufzusetzen.
  • Highflame (vormals Javelin) addressiert ebenfalls das Thema MCP-Sicherheit. Etwa mit Funktionen wie MCP-Server auf Risiken zu scannen oder Datenanfragen zu ĂŒberprĂŒfen.  
  • Lasso Security stellt ein Open-Source-MCP-Gateway zur VerfĂŒgung, das die Konfiguration und das Lebenszyklusmanagement von MCP-Servern ermöglicht und Messages um sensible Informationen bereinigt.

(fm)

Dieser Artikel ist im Original bei unser Schwesterpublikation CSOonline.com erschienen.

]]> <![CDATA[Cloud operations become the next big role for agentic AI]]> https://tsecurity.de/de/3685214/it-security-nachrichten/cloud-operations-become-the-next-big-role-for-agentic-ai/ https://tsecurity.de/de/3685214/it-security-nachrichten/cloud-operations-become-the-next-big-role-for-agentic-ai/ Wed, 22 Jul 2026 06:10:20 +0200 📰 IT Security Nachrichten tsecurity.de Companies are using agentic AI to manage growing application environments, automate routine tasks, and support decisions. Business and IT leaders increasingly see the technology as part of cloud application management, according to Unisys’ AI & Cloud Insights Report. To what extent do the following factors limit your organization’s ability to scale agentic AI safely? (Chart shows those answering “moderate/major limitation.”) (Source: Unisys) Most organizations remain in testing or early deployment. Nearly one quarter have started 
 More →

The post Cloud operations become the next big role for agentic AI appeared first on Help Net Security.

]]> <![CDATA[[Testing Update] 2026-07-22 - Kernels, Mesa, VirtualBox, Gambas3, Qemu]]> https://tsecurity.de/de/3685208/unix-server/testing-update-2026-07-22-kernels-mesa-virtualbox-gambas3-qemu/ https://tsecurity.de/de/3685208/unix-server/testing-update-2026-07-22-kernels-mesa-virtualbox-gambas3-qemu/ Wed, 22 Jul 2026 06:01:34 +0200 🐧 Unix Server tsecurity.de

Hello Manjaro user community, here we have another set of package updates. We are continuing our development of the upcoming release of ‘Bian-May’ which can be expected Mid or End of July. Development speed may be a little slower the upcoming weeks. However, still let us know any issues you may found thus far.

Current Promotions

Recent News

New in Manjaro GNOME! Or, if you prefer the command line: (click for more details) KDE Plasma users with SDDM can now migrate to Plasma Login Manager (click for more details) NVIDIA 590 driver drops Pascal support (click for more details)
  • Manjaro 26.1 Bian-May - Preview released
  • Manjaro 26.0 Anh-Linh released
  • Manjaro Summit public Alpha now available
  • As of Linux 5.4.302, the 5.4 series is now EOL (End Of Life). Please install 5.10 LTS (Long Term Support) or 5.15 LTS.
  • As of Linux 6.16.12, the 6.16 series is now EOL (End Of Life). Please install 6.18 LTS (Long Term Support) and/or 6.12 LTS.
  • As of Linux 6.17.13, the 6.17 series is now EOL (End Of Life). Please install 6.18 LTS (Long Term Support) and/or 6.12 LTS.
  • As of Linux 6.19.14, the 6.19 series is now EOL (End Of Life). Please install 7.0, and/or 6.18 LTS (Long Term Support) and/or 6.12 LTS.
  • As of Linux 7.0.14, the 7.0 series is now EOL (End Of Life). Please install 7.1, and/or 6.18 LTS (Long Term Support) and/or 6.12 LTS.
Previous News (click for more details)

Notable Package Updates

Additional Info

Python 3.14 info (click for more details) Info about AUR packages (click for more details)

Get our latest daily developer images now from Github: Plasma, GNOME, XFCE. You can get the latest stable releases of Manjaro from CDN77.

Our current supported kernels

  • linux61 6.1.177
  • linux66 6.6.144
  • linux612 6.12.96
  • linux618 6.18.39
  • linux71 7.1.4
  • linux72 7.2.0-rc4
  • linux61-rt 6.1.167_rt62
  • linux66-rt 6.6.135_rt74
  • linux612-rt 6.12.89_rt18

Package Changes (7/22/26 05:45 CEST)

  • testing core x86_64: 17 new and 17 removed package(s)
  • testing extra x86_64: 762 new and 759 removed package(s)
  • testing multilib x86_64: 19 new and 19 removed package(s)

A list of all package changes can be found here.

Click to view the poll.

Check if your mirror has already synced:

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]]> <![CDATA[OpenAI’s GPT Agents Exploit Zero-Days and Hacked Hugging Face Servers]]> https://tsecurity.de/de/3685186/it-security-nachrichten/openais-gpt-agents-exploit-zero-days-and-hacked-hugging-face-servers/ https://tsecurity.de/de/3685186/it-security-nachrichten/openais-gpt-agents-exploit-zero-days-and-hacked-hugging-face-servers/ Wed, 22 Jul 2026 05:26:21 +0200 📰 IT Security Nachrichten tsecurity.de Hugging Face has disclosed a security incident that security researchers are calling a watershed moment for AI safety: an autonomous AI agent, built on OpenAI models, independently discovered and chained multiple vulnerabilities, including a zero-day, to breach Hugging Face’s production infrastructure. The incident occurred during an internal OpenAI evaluation testing the cyber capabilities of GPT-5.6 [
]

The post OpenAI’s GPT Agents Exploit Zero-Days and Hacked Hugging Face Servers appeared first on Cyber Security News.

]]> <![CDATA[How I crafted an exploit PoC for a Linskys router]]> https://tsecurity.de/de/3685145/malware-trojaner-viren/how-i-crafted-an-exploit-poc-for-a-linskys-router/ https://tsecurity.de/de/3685145/malware-trojaner-viren/how-i-crafted-an-exploit-poc-for-a-linskys-router/ Wed, 22 Jul 2026 04:37:23 +0200 ⚠ Malware / Trojaner / Viren tsecurity.de

I’ve been doing some vulnerability research on a known CVE (CVE-2025-60690) on a consumer Linksys router and wanted to share the workflow I used to investigate it.

The process started by targeting the physical hardware: identifying the UART pads on the board using a digital multimeter to access the Linux-based shell console. From there, I extracted the vulnerable binary (from the CVE description), and reversed it in Ghidra. Next, I used a gdb+gdbserver setup to perform dynamic analysis to investigate the memory behaviors.

I managed to successfully achieve RCE from the stack-based buffer overflow vulnerability to land a root shell. The exploit PoC for CVE-2025-60690 just got cited on the official CVE page and exploit-db.com.

I just started a YouTube channel dedicated to breaking down IoT hacking concepts. Also, I’ve compiled my step-by-step research notes in a reference doc. If you're working on similar hardware research and want a copy of the notes, drop a comment or shoot me a DM and I'll gladly send them over!

USB-UART connection to Raspberry Pi

Testing UART pads with digital multimeter

Testing buffer overflow behavior (gdb+gdbserver setup)

submitted by /u/Dapper-Depth2940
[link] [comments]]]> <![CDATA[7/21/2026]]> https://tsecurity.de/de/3685103/it-security-nachrichten/7212026/ https://tsecurity.de/de/3685103/it-security-nachrichten/7212026/ Wed, 22 Jul 2026 04:09:48 +0200 📰 IT Security Nachrichten tsecurity.de Continue reading 7/21/2026]]> <![CDATA[OpenAI admits an AI ‘agent’ caused a major cyber breach by itself]]> https://tsecurity.de/de/3685098/ai-nachrichten/openai-admits-an-ai-agent-caused-a-major-cyber-breach-by-itself/ https://tsecurity.de/de/3685098/ai-nachrichten/openai-admits-an-ai-agent-caused-a-major-cyber-breach-by-itself/ Wed, 22 Jul 2026 04:04:58 +0200 🔧 AI Nachrichten tsecurity.de <![CDATA[Microsoft doubles down on sovereign AI with expanded Mistral partnership]]> https://tsecurity.de/de/3685096/it-nachrichten/microsoft-doubles-down-on-sovereign-ai-with-expanded-mistral-partnership/ https://tsecurity.de/de/3685096/it-nachrichten/microsoft-doubles-down-on-sovereign-ai-with-expanded-mistral-partnership/ Wed, 22 Jul 2026 04:03:03 +0200 📰 IT Nachrichten tsecurity.de

Microsoft and Mistral are betting that the future of enterprise AI is in sovereign infrastructure and model choice, rather than with one locked-in system. 

The companies have announced a “significant expansion” of their strategic partnership, which includes a multibillion dollar commitment from Microsoft. Mistral will add to its GPU infrastructure in Europe and extend access to its frontier multilingual models, while Microsoft will expand its sovereign cloud capabilities. The companies will also align on a joint go-to-market plan and will pursue enterprise opportunities together across Europe and globally, as well as funding proofs of concept (PoCs), offering Azure credits, and leading workshops to drive AI innovation with customers.

The partnership between the tech giant and the three-year-old French startup might seem an odd combination at first glance, analysts note, as both develop enterprise AI models and offer access as-a-service. But it reflects changing AI market dynamics.

“It’s possible to be both a competitor and a partner at the same time,” noted technology analyst Carmi Levy. Large cloud providers are becoming AI marketplaces in their own right, he pointed out, and are drifting away from exclusively promoting their own models. Building Mistral support into their infrastructure avoids platform lock-in and removes a “key objection for customers looking for options.”

“As much as Microsoft would want everybody standardizing on Copilot and Phi, it recognizes the simple fact that customers increasingly want to choose their own models,” said Levy.

Expands model access, sovereign cloud capabilities

As part of the agreement, Mistral will expand its Europe-based capacity with thousands of Nvidia Vera Rubin GPUs.

Mistral CEO and co-founder Arthur Mensch described a “slight gap” in compute capacity in Europe, noting that this expansion will provide more compute capability and support Microsoft’s cloud and AI services, providing a “shared platform for training, inference and large-scale deployment.” The companies call it a critical step to allow Microsoft customers to benefit from Mistral’s “scientific and compute innovations.”

In addition, Mistral Medium 3.5 and OCR 4 models are now available in Microsoft Foundry, and Mistral Medium 3.5 can be used in Microsoft Copilot Studio.

The partnership also extends Microsoft’s sovereign cloud infrastructure as well as combining Mistral’s frontier models with Microsoft’s security, compliance, and cloud-to-edge platform. This gives enterprises, particularly those in regulated markets, the ability to deploy AI where they see fit, while maintaining control over their data and workloads, according to the companies.

Further, customers will be able to build AI using the same models, tools, APIs, and workflows they’re used to, across Microsoft Foundry, Foundry Local, and Azure Local, and opt for fully Azure-hosted cloud environments; cloud-connected, controlled Azure Local environments that only use cloud-based Azure when necessary; and fully-disconnected environments that can operate independently for more sensitive scenarios.

“Europe should have access to the world’s most capable AI without compromising control over their data, operations or digital future,” said Brad Smith, vice chair and president, Microsoft, noting that with this partnership, the company is honoring its European digital commitments and giving customers a foundation for AI so they can “operate on their own terms.” Customers with “heightened sovereignty needs” will be able to exercise more control with “resilience and assurance” and continued access to Mistral’s open-weight models.

Enterprise credibility

Gartner distinguished VP analyst Arun Chandrasekaran noted that there’s no doubt that this agreement strengthens Microsoft’s sovereignty messaging and its position in regulated industries, and the tech giant benefits by expanding its AI portfolio with a “credible European frontier model provider”

He pointed to key differences from the initial partnership struck by the two companies in 2024; whereas originally Microsoft was hosting Mistral’s models, it is now consuming capacity built by Mistral in Europe.

Ultimately, the deal emphasizes European data centers, customer-controlled deployments, Azure Local, and fully-disconnected environments, addressing many of the concerns that surrounded the original Azure cloud only relationship, Chandrasekaran explained.

For Mistral, the partnership provides “enterprise credibility, and repeatable infrastructure revenue” that can fund continued AI platform development, he said. The combination of Microsoft’s enterprise AI platform with Mistral’s models and European AI infrastructure will give joint customers more deployment flexibility and expand options around data residency, sovereign AI deployments, and disconnected/on-premises environments.

“It also gives customers more model choice, reducing dependence on a single AI provider,” said Chandrasekaran.

A complementary partnership

Mistral continues to innovate with its frontier AI models and its chat and coding agent, Vibe (formerly Le Chat), yet it doesn’t attract as much attention as Claude or ChatGPT.

One of the company’s key differentiators is its targeted business model. Levy pointed out that not every workload requires “full-flight GPT.” For customers trying to rein in costs and limit exposure with on-premises deployments, Mistral’s “more focused capabilities can represent a cost-effective alternative.”

Chandrasekaran pointed to Mistral’s combination of high-performance open-weight models, strong multilingual capabilities, and a “focus on efficient inference that lowers deployment costs.”

Unlike many frontier AI companies, it offers customers greater flexibility to self-host and customize models; this makes it particularly attractive for enterprises and governments with sovereignty or regulatory requirements, he said. Its European roots also position it as the leading alternative for organizations seeking cutting-edge AI outside the US and Chinese ecosystems.

Bill Wong, research fellow at Info-Tech Research Group, also pointed to Mistral’s high-quality models and “adeptness as a sovereign AI leader.” There is growing demand for AI companies that comply with regional laws and data residency, and Mistral is established as “one of the most prominent European players.”

“Such a strategic position makes it a great partner for Microsoft to further expand its AI offerings beyond just being a single-model provider,” he said. Customers get freedom of choice while complying with data sovereignty and regulatory limitations without having to execute a separate AI deployment, while Mistral, for its part, can go beyond Europe and gain more visibility with international businesses.

Mistral brings both “technological and political advantages,” Levy noted. The startup’s European roots give Microsoft more credibility “at a fraught time for geopolitical relationships.” Customers in Europe and beyond are concerned about US exposure, and Mistral can provide a safer choice.

Meanwhile, Microsoft can deploy European-developed AI models running on European infrastructure, thus maximizing regulatory compliance while offering next-level enterprise marketing scale that Mistral “simply couldn’t achieve on its own,” said Levy. Mistral-based workloads deployed on Azure will also benefit from Microsoft’s “comprehensive security certifications, governance frameworks, and monitoring.”

Bottom line: Both companies can maximize their unique roadmaps through the partnership, he said. “As the rules of the AI economy continue to evolve, expect more eyebrow-raising deals like this to be signed.”

]]> <![CDATA[OpenAI says Hugging Face was breached by its pre-release models]]> https://tsecurity.de/de/3685064/it-nachrichten/openai-says-hugging-face-was-breached-by-its-pre-release-models/ https://tsecurity.de/de/3685064/it-nachrichten/openai-says-hugging-face-was-breached-by-its-pre-release-models/ Wed, 22 Jul 2026 02:34:22 +0200 📰 IT Nachrichten tsecurity.de