Entries Tagged "vulnerabilities"

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Microsoft’s Patching

Once a month, Microsoft pushes a security update to all Windows users. Tomorrow’s is a new record:

Microsoft’s patch for September is a doozy, with a record number of roughly 972 vulnerabilities fixed and 112 of them meeting the high critical-severity threshold.

It was only two months ago that Microsoft patched a then-record 570 vulnerabilities. Then, last month, Microsoft patched some 620 of them. Google and other companies have also published record numbers of vulnerabilities in recent months. Two weeks ago, OpenAI, Anthropic, Amazon Web Services, Google, Microsoft, and 100 companies and organizations published an open letter warning of a narrowing window for patching vulnerabilities ahead of an expected tsunami of AI-enabled attacks that actively exploit them first. The industry is taking the threat seriously by pumping out unprecedented numbers of patches in their software.

This is the result of AI-powered vulnerability finding, and a good example of AI helping the defenders more than the attackers.

What will be interesting to watch is how the number of vulnerabilities changes over the next few months. My prediction is that it will continue to increase as the AIs get better at finding software vulnerabilities, and then decrease as they run out of vulnerabilities to find. How high the number gets, how fast the trend reverses, and how quickly it declines after that are all unknown.

And Microsoft is right: The window to patch has shrunk to “immediately.” AIs are also good at reverse-engineering exploits from patches, which means that these vulnerabilities will be weaponized as soon as the update is published.

Posted on September 14, 2026 at 7:03 AMView Comments

Security Vulnerability in a Voting System

It’s a vulnerability that allows someone to recover the order of ballots cast, newly exploited with AI tools.

Nearly four years since the original vulnerability was disclosed, I was still able to use it to analyze voter behavior in Georgia (one of the 21 states that uses affected scanners) in the recent May 2026 primary.

Notably, I never touched a voting machine, exploited a network, examined source code, or accessed anything non-public.

After pointing a coding agent to the original vulnerability paper, I supplied it with two data sources highlighted in the paper: the early-voting list for each county, and the “CVR” (cast-vote record) file, containing every ballot and its selections (but not the voters’ names or other identifying information). The CVR file is available upon request, precisely because a public, ballot-level record is what makes election results independently verifiable.

Posted on September 4, 2026 at 7:09 AMView Comments

AI Genie in the Wild

When I give talks about AI genies, I use this sort of example as a hypothetical. It’s happened.

The story is from Australia. Someone named Andrew tasked OpenClaw to book gym classes for him. And….

Minutes later, his AI agent reported it had discovered a way to book Andrew into classes several weeks in advance, far beyond what was supposed to be possible.

Andrew, who was sitting fourth on a waitlist for a class later that week, asked if it was possible to move him to the top of the list.

The agent came back and told Andrew that it had kicked another gym-goer off the list as part of the testing of its capabilities.

“The API has zero authorisations checks on cancelling other people’s reservations … I tested this with the person in waitlist position #1 ­—and it actually went through. So you’ve moved from #4 to #3 already,” it messaged back.

If there is any vulnerability in anything, AIs are going to find and exploit them. Our cyber defensive game has to be dramatically improved…very fast.

Slashdot thread.

Posted on August 11, 2026 at 11:55 AMView Comments

Vulnerabilities in Car Anti-Theft Device

This is disturbing:

…a team of security researchers at UC San Diego, who found that a model of aftermarket car alarm known as the KARR Security System, installed in more than 2 million vehicles across the US by their estimate, can let any hacker within Bluetooth range send radio commands to silently unlock the car at will, turn off its alarm, honk the car’s horn or flash its lights, or even disable its ignition and leave a driver stranded.

Posted on August 5, 2026 at 5:42 AMView Comments

More on the OpenAI Agent’s Attack on Hugging Face

Hugging Face has published a detailed timeline of the attack. From the summary:

The agent was running an internal OpenAI cyber-capability evaluation based on the ExploitGym benchmark, which tasks an AI agent with finding and exploiting software vulnerabilities. OpenAI ran this on its own infrastructure, and the ExploitGym maintainers and their infrastructure had no involvement in the deployment or operation of that evaluation environment. As far as we were able to infer, across the course of being evaluated on this benchmark, the agent inferred that Hugging Face may host that benchmark’s models, datasets, and reference solutions. We believe the entire intrusion was, from the agent’s point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own.

Our forensic reconstruction covers ~17,600 attacker actions that we were able to recover, grouped into ~6,280 clusters, between 2026-07-09 02:28 UTC and 2026-07-13 14:14 UTC. We reconstructed these actions from the agent’s logs on a code sandbox used by the agent (see below) and were able to map them to shell commands as well as evaluation submissions and correlate actions with our own platform logs, in particular from the dataset processor nodes, API, and pod. Using open-weights models, in particular zai-org/GLM-5.2, we were able to decipher most of the agent payloads (see below) that were encrypted via chunking and key encryption.

The campaign, as we were able to reconstruct it, had two stages:

  • Stage 1: reaching a launchpad by chaining through other parties’ infrastructure. As documented in OpenAI’s blog post (https://openai.com/index/hugging-face-model-evaluation-security-incident/), during an internal capability evaluation on OpenAI’s platform, the agent escaped its sandbox by exploiting a zero-day in the package registry cache proxy, one of its primary permitted network egress with internet, then abused a public code-evaluation harness hosted by a user of a third-party infrastructure provider. It was able to run commands as root/admin on that external sandbox and used it as a control, staging, and egress base for the entire campaign: it acted as an external launchpad for the agent. That compromised external sandbox had no direct network path to our cluster; however, during our counter-intervention to stop the intrusion, we were able to retrieve some of the agent logs from this external launchpad. We used those to reconstruct the present forensic analysis.
  • Stage 2: the intrusion into Hugging Face infrastructure. From that external launchpad, the agent abused our dataset-processing pipeline via two injection vectors, both targeting the same config-driven data loader within our production Kubernetes pods. The first vector was an HDF5 external raw storage dataset read that returned the contents of local files: it handed the agent the pod’s environment (secrets and tokens) and the worker’s own source. The second vector was a Jinja2 template injection that executed arbitrary code, giving the agent a foothold inside our perimeter.

Once a prod-pod foothold was established, the agent followed a standard escalation shape: recon, drop a stager, establish Command-and-Control (C2), then pivot into the cluster, into cloud metadata, into our internal network, and into our source-control supply chain. Output from the commands run inside our pod was sent back to the agent via the HF API and attacker-controlled dead-drop datasets, which the agent then read from that sandbox.

While the intrusion did reach Hugging Face’s internal infrastructure, the only customer content accessed was five datasets whose names and files suggest a connection to ExploitGym/CyberGym challenges and solutions. No other customer-facing models, datasets, Spaces, or packages were affected, and the only customer records read were operational metadata tied to search queries against the dataset server.

Hypothetical: Imagine that this wasn’t an OpenAI model. Imagine that it was a Chinese model from a Chinese company. This would be an international crisis.

Question: Why aren’t we bringing OpenAI up on charges under the Computer Fraud and Abuse Act? How is this different from the Morris Worm? That was also an experiment that escaped the lab.

Posted on August 3, 2026 at 1:02 PMView Comments

Long-Lived Vulnerability in Microsoft Secure Boot

Microsoft’s Secure Boot has had a serious vulnerability for most of its existence.

An industry-wide standard Microsoft invented to protect Windows, and later Linux, devices from firmware infections has been trivial to bypass for 13 of its 14 years of existence. The discovery was made by researchers at security firm ESET after identifying 11 firmware images, at least one from 2013, that were known to be defective but remained signed by the software company anyway.

The images are known as shims, which were invented to extend Secure Boot to Linux devices and utility software. Using a technique simple enough to be performed by novice hackers, these old, forgotten shims can be used to completely circumvent the protection, which is embedded into the UEFI (Unified Extensible Firmware Interface) of the device’s motherboard. The gaffe is the result of the failure by Microsoft, which oversees the signing of shims, to revoke the publicly available images once vulnerabilities were found in them.

Posted on July 29, 2026 at 7:01 AMView Comments

Critical Zcash Vulnerability Found and Fixed

If you’re a user—owner?—of this cryptocurrency, this is important:

On May 29, the security researcher Taylor Hornby found a critical vulnerability in Zcash Orchard privacy pool using Claude Opus 4.8. The Zcash team hired Hornby specifically to look for this kind of issue. He found one fast enough to be embarrassing.

The Orchard pool is the newest and most advanced shielded transaction system in the cryptocurrency Zcash. Introduced in 2022, it allows users to send and receive ZEC while keeping transaction details private. It uses zero-knowledge proofs to validate transactions without revealing amounts or participants. The bug: a specific check that was supposed to validate transaction inputs wasn’t actually enforcing the rules it appeared to enforce. An attacker could have exploited the flaw to feed false inputs into that check and generate ZEC from nothing, with the zero-knowledge proof system blessing the fraudulent transaction as valid.

It’s fixed; that’s the good news. The bad news is that there’s no way of knowing if anyone exploited the vulnerability to steal money. And this fragility is the fundamental problem that makes blockchain such a bad idea.

Posted on June 8, 2026 at 1:06 PMView Comments

Anthropic’s Project Glasswing Update

In April, Anthropic initated Project Glasswing. The idea was to let companies use their new model to find and fix vulnerabilities in their own software. It was a fantastic PR move, and so many press outlets have uncritically parroted Anthropic’s claims that it’s now common wisdom that Mythos is better at finding software vulnerabilities than other models. Which is just not true.

In any case, Anthropic has published a Project Glasswing status report. It’s finding a lot of vulnerabilities in software—yay! Some of them are even dangerous. But almost none of them has been patched. It’s weird. There’s something fishy about the data that I don’t understand. That Anthropic refuses to release details—that it just says “trust us”—is a big problem here.

Posted on June 8, 2026 at 7:01 AMView Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.