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Azizcan Dastan and Ozlem Ozan discovered a local privilege escalation vulnerability in udisks2 involving the Filesystem.Mount D-Bus method. Using the 'as-user' option, an unprivileged local user can in some cases influence the mount execution path so that a filesystem mount is performed in a privileged/root context without the expected PolicyKit authorization behavior.

https://security-tracker.debian.org/tracker/DSA-6414-1

Release 0.147.0

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Dieser Beitrag ThreatsDay: KI-Agenten, Repo-Vorab-Code und npm-Malware zeigen neue Angriffswege erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

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Check Point brings open research, objective benchmarks and customer-controlled runtime protection to the industry initiative introduced by NVIDIA.  AI is rapidly changing how organizations build, operate, and protect their digital environments. As a leader in the global cyber community, we believe in the power of collective intelligence. We are proud to join the Open Secure AI Alliance as an inaugural member, working alongside leaders from across cyber security, cloud computing, enterprise software, AI, and the open-source community.  Introduced by NVIDIA, the alliance is creating a collaborative foundation for developing and sharing open technologies that advance AI safety and security, and will help organizations identify, remediate, and responsibly disclose […]

The post Check Point Joins the Open Secure AI Alliance to Advance Open, Measurable and Enterprise-Ready AI Security appeared first on Check Point Blog.

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Read the original post: Storytime Comes to Gemini on Google Home Devices

A practical framework for securing every identity in the modern workforce, human or not.

Your organization already has a rigorous process for governing human identities. New employees go through onboarding. They get a role, a set of entitlements, and a named manager accountable for their access. When they leave, their credentials are revoked and access is terminated. It’s a well known IT process: every workforce identity that can access your systems needs to be known, scoped, and accountable from the moment they enter your world, to the moment they are off-boarded.

AI agents are now operating inside those same systems. They access Salesforce, create tickets in Jira, provision infrastructure, process financial transactions, and communicate on behalf of your teams. In every meaningful sense they are members of your workforce, except that in most organizations they were never onboarded, have no named owner, and have no offboarding process when their purpose expires.

JumpCloud’s Q3 2026 research found that non-human identities now outnumber human users in 83% of organizations, and only 21% have implemented governance controls specifically for them. The framework below is designed to close that gap.

Stage 1: Discover every agent operating in your environment

Governance starts with an accurate inventory, and most organizations are working with an incomplete one. AI agents are being deployed by product teams, operations leaders, and individual contributors who have both the tools and the motivation to move fast. IT inherits the governance responsibility after the fact, often without knowing the full scope of what has been deployed.

Shadow AI is the practical consequence: agents operating across production environments with no formal record, no defined owner, and no systematic way to stop them if something goes wrong. Discovering your agent population is an ongoing practice, not a one-time audit. Build an inventory across every environment where agents could be running: cloud platforms, managed devices, SaaS integrations, and on-premise systems. For each agent, document what it can access, what workflows it influences, and what triggers its actions. That inventory is the foundation everything else in this framework depends on.

Stage 2: Register every agent as a formal identity with a named owner

Every agent that operates in your environment should exist as a formal identity in your directory, with the same basic attributes you assign to any employee: a defined purpose, a scope of authorized action, and a named human owner who is accountable for its behavior.

This is the architectural decision that separates organizations that can govern their agents from those that cannot. Agents registered as proper identities can be assigned entitlements, subjected to conditional access policies, and included in access reviews. Agents that exist only as service account workarounds or API keys in environment variables are ungovernable by any systematic means.

Registration is also the mechanism for addressing Zombie Agents: agents that outlived their original purpose but kept running, kept accessing systems, and kept accumulating permissions. When every agent has a named owner responsible for its renewal, agents without active ownership naturally lose their access when that ownership lapses. The offboarding happens as a consequence of process rather than as a reactive cleanup after something breaks.

Stage 3: Manage agent access with least privilege and zero standing credentials

Registered agents need access to do their jobs. The governing principle for that access is least privilege: each agent should have entitlements scoped precisely to what its defined purpose requires, with access that is time-bounded wherever possible and revocable immediately if the agent’s behavior changes.

Standing credentials in environment variables are a persistent liability. Static API keys that never rotate are a persistent liability. In practice, managing agent access securely means issuing just-in-time credentials for privileged operations, building approval workflows that require human sign-off before agents reach sensitive systems, and maintaining emergency shutdown mechanisms that work at the speed the situation requires.

For agents that need access to privileged web applications, SSH servers, or databases, credential shielding is an additional requirement: the agent should be able to complete its task without the underlying credentials ever being exposed to the model running it. Every privileged session should be recorded and available for audit.

Stage 4: Govern agent behavior continuously, not just at deployment

The first three stages establish the controls. Governance is what keeps them current. It is the ongoing practice of verifying that what agents are actually doing matches what they are authorized to do, and course-correcting when those diverge.

Every agent action should be logged. Access reviews should happen on a regular cadence, evaluating whether each agent’s entitlements remain appropriate for its current purpose. When an agent’s behavior deviates from its defined scope, the anomaly should be detectable before it becomes an incident. When an agent’s purpose ends, access revocation should be a procedural step, not a reactive measure triggered by something going wrong.

Governance also means maintaining the audit trail needed to answer accountability questions: what did this agent access, what actions did it take, who authorized it, and what was the outcome? Organizations that cannot reconstruct that chain for any given agent are not governing their agents in any meaningful sense. They have deployed them and hoped for the best.

The foundation underneath all four stages

Each stage of this framework becomes significantly harder to execute when the underlying IT environment is fragmented. Identity, access, device management, and security controls spread across disconnected systems create the gaps where agent governance falls through, and organizations end up applying different policies in different places rather than consistent governance everywhere.

JumpCloud’s research found that organizations operating in fully unified IT environments are five times more likely to deploy agents in business-critical workflows than those running fragmented stacks. Whether the control layer is coherent enough to apply consistent policies across humans, devices, and agents simultaneously is what determines whether governance scales with AI adoption or lags behind it.

This is the core premise of Agentic IAM: that governing humans, devices, and agents through a single coherent control layer is what makes the framework above executable at scale rather than aspirational.

Securing every identity, human or not, is the operational foundation that makes AI safe to scale. Organizations that build it now will not just reduce risk. They will expand AI into more workflows, move faster, and do it with the confidence that comes from knowing every identity in their environment is known, governed, and accountable.

JumpCloud’s Q3 2026 IT Trends Research report (n=800 IT leaders, US + UK) is available here. The Agentic IAM lifecycle framework referenced in this article was developed by JumpCloud and is available here.

Greg Keller is CTO and Co-founder at JumpCloud.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Both results are real and defensible. The gap between them is about token and time budgets, and that matters because those figures aren’t usually headline numbers. Alibaba's footnotes give its coding numbers a five-hour timeout, and up to 12 hours per run on PaperBench. The independent harness, VulcanBench, allowed between 45 and 60 minutes of wall clock time. A time budget between five and 16 times larger on Alibaba’s side explains the huge difference in results.

It’s time to do two things to start accounting for these differences when choosing models. First, the metric to use is cost per successful task: total spend, including everything you spent on attempts that failed, divided by the tasks that actually passed your acceptance check. Second, you need to make time or token budgets an explicit part of your acceptance criteria, not a hidden detail.

Price per token has stopped predicting the bill

The comparison everyone published in Qwen 3.8-Max's first week was a price comparison, because that was the only data available. It is not a cheap model. DeepSeek-V4-Flash-0731, which entered public API beta on July 31, lists at 14 cents per million input tokens and 28 cents output. Qwen 3.8-Max lists at $2 and $6. Kimi K3 sits at $3 and $15.

Those prices tell you less than they used to, for a reason specific to reasoning models like Qwen: getting to a result costs thinking tokens. A model that spends most of its token allowance on reasoning can reach a token cap before it writes the answer, giving you an empty result indistinguishable from a total failure at the cost of a full run.

Artificial Analysis has the cleanest published measurement of how this can affect real agent spend: running its Intelligence Index on DeepSeek-V4-Flash at maximum effort took 210 million output tokens against a class median of 100 million. Absolute cost stayed low anyway, because the tokens were so cheap. But verbosity costs time, not just money, and depending on your use case that can sink you.

What you need is a number that counts everything you spent, including the attempts that came back empty, against the tasks that actually got done in the time and token budget you specified. This is what a cost-per-success metric helps you see.

Your failure rate is partly a configuration setting

A run that produces a wrong answer and a run that runs out of budget are different events with different fixes. Almost no harness distinguishes them, and almost no leaderboard reports the split. I hit this building an agent benchmark of my own: the harness logged a failure and nothing about why, and I had to add the distinction myself. When you do separate them, budget exhaustion turns out to dominate.

Long-Horizon-Terminal-Bench, published in July, ran 17 frontier models across 46 tasks through a shared harness with one 90-minute attempt each. Timeouts accounted for 79% of unresolved runs, against 19% for agents that stopped on their own and 3% for harness errors. The authors are careful about what that does and does not mean: the timed-out runs were not close to finishing, with mean reward between 0.10 and 0.35, so you cannot assume more time would have resulted in success. But the lesson is: benchmarks are implicitly measuring time efficiency, whether or not they shout about that.

The clearest published example of the mechanism comes from VulcanBench, the same open-source harness behind the Qwen chart. In a report dated July 26, Claude Opus 5's lowest-effort setting was its best, solving 20 of 23 tasks against 18 at high effort. The extra reasoning wasn’t useless: high effort returned the fewest wrong answers of any setting, one against three. It ran out of clock instead, and a timeout scores zero. Two of its three regressions were cutoffs on tasks that low effort solves, and given unlimited time on both it only ties its cheapest setting, at 3.1 times the cost.

That has a direct consequence for anyone building a routing ladder. The standard design escalates to more reasoning when a cheap attempt fails, on the assumption that the next rung is better and merely costs more. For a meaningful share of model and task combinations that assumption is wrong, and you pay the higher rung's price to escalate into a timeout or hitting a cap.

Who is already measuring this

Several groups have landed on cost per successful task independently in the last few months, which is the strongest signal it's becoming standard.

VulcanBench reports dollars per solved task as a headline column and has since its earliest reports. Long-Horizon-Terminal-Bench publishes per-task cost next to accuracy, and its most instructive row is GPT-5.4 at roughly $26 per task with a much lower pass rate than Grok 4.5 at about $11. TestEvo-Bench runs agents under a cost cap, and Claude Code's test-generation score falls from 71% to 44% at the tighter cap.

Vendors are already on board with the idea of measuring per successful task. HubSpot moved its Breeze Customer Agent in April to 50 cents per resolved conversation, down from $1 per handled conversation. Zendesk bills per automated resolution. Fin charges 99 cents per outcome and bills only on end-to-end resolution.

What to change this week

• Emit a failure reason on every agent run as a required field, with budget exhaustion, verifier failure and harness error as distinct values rather than one failure flag. Until you can separate a timeout from a wrong answer, your pass rate is measuring two things at once and you cannot tell which one to fix.

• Compute cost per successful task per effort level, not just per model. Total spend including failed attempts, divided by tasks that passed your acceptance check. The ranking will not match the rate card, and the cheapest setting may well win.

• Cap on tokens rather than wall clock unless latency is genuinely in your service level objective. A wall-clock cap scores your provider's serving speed as model quality.

• Check the default effort setting on everything you have deployed. Qwen 3.8-Max runs at its highest reasoning setting when the effort field is unset, and its highest setting was its worst performer in independent testing. A team that never touches that parameter is running the configuration that costs the most per solved task.

Every other Tuesday, AJ Daulerio wakes up at 5.30am and meditates for five to 20 minutes before beginning his multistep daily journaling protocol: reading a selection from a poetry anthology, then a passage from one of his daily wisdom readers (his go-to is a heavily annotated copy of Leo Tolstoy’s A Calendar of Wisdom); writing his Fear of the Day and gratitude list, then sharing his spiritual takeaway in a note to his three kids.

After a banana and coffee, Daulerio trains Brazilian jujitsu before hitting a 9.45am meditation at Besant Lodge – a community center just below the Hollywood sign that once served as a meeting place for mid-century mystics – immediately followed by him leading a recovery meeting of his own through Zoom.

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Während die Preise für RTX-GPUs weiter steigen, verkauft Nvidia auf der angelaufenen QuakeCon 2026 Founders-Edition-Karten der GeForce RTX 5090, 5080 und 5070 zur UVP über „Verified Priority Access IRL“. Die Verfügbarkeit ist jedoch begrenzt und richtet sich nur an Teilnehmer, die in Texas vor Ort sind.

Ex-Google Deepmind CEO Demis Hassabis has reportedly stepped back from day-to-day operations for about a year, as he sees himself more as a scientist than a manager. Researchers are also complaining about limited access to Google’s own TPU chips, while external customers like Anthropic can purchase the same hardware through Google Cloud.

The article Deepmind's talent drain likely comes down to chip shortages, a conflict of interest, and Google's bureaucracy appeared first on The Decoder.

OpenAI has updated GPT-5.6 Sol with more focused responses and a reasoning slider that lets users adjust how deeply the model thinks. Free users will get unlimited text chats with the smaller GPT-5.6 Luna starting next week, plus a button that lets Luna reason longer. But the smaller model still falls well short of its bigger siblings.

The article OpenAI improves GPT-5.6 Sol in ChatGPT and restricts free users to its weakest model appeared first on The Decoder.

Scientists have made the first viruses designed by artificial intelligence in a milestone that raises hopes for new medicines but also concerns over how to ensure the technology remains safe.

The viruses are specific kinds known as bacteriophages, which only infect bacteria and are used around the world to treat patients with persistent infections. In lab tests, a cocktail of the AI-designed viruses killed E coli bugs that were resistant to natural bacteriophages.

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Apple raised trade-in values again. See how much you can get from that older iPhone, iPad, Mac or Apple Watch.

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What's Changed

• Capture user defined errors separately from UNHANDLED_EXCEPTION by @dylanjew in #5402
• Skip incompatible jobs in variant_task.utask_preprocess by @dylanjew in #5403
• Short-circuit duplicate/deleted testcases when processing/scheduling tasks by @dylanjew in #5405
• Gracefully handle InvalidTestcaseError in variant and minimize tasks by @dylanjew in #5406

Full Changelog: v2.36.0...v2.36.1

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