Scientists have identified the “fattest” belemnite ever discovered, a bizarre, barrel-shaped squid relative that lived in Wyoming’s ancient seas 160 million years ago. The exceptionally rare fossils reveal a creature unlike anything previously known, potentially a predator of smaller squid-like animals and a surprising survivor of a supposedly vanished family.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
The video describes a technique that can reveal where an otherwise unidentifiable recording was made by analyzing its audio for the faint sounds of passing aircraft. The technique is both fascinating and frightening.
It works by looking at Doppler curves, which plot the way a plane’s sound changes as it passes overhead because of the Doppler effect. This is the same familiar Doppler effect that causes the frequency of a siren to rise as it approaches and then fall as it passes by and moves away. More generally, it’s the phenomenon whereby the frequency of a wave perceived by an observer changes when the observer and the source of the wave are moving in relation to one another.
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It’s sound that’s relevant here, though, and specifically the sound of aeroplanes. Early in his video, Colsto brings up a map of the flights that were in the air at the time the video he’s tracing was recorded. Much of this information is publicly available. Aircraft equipped with Automatic Dependent Surveillance–Broadcast (ADS-B) transmit data about their position, velocity, and altitude, which flight-tracking services collect and make available to the public. While not every plane is tracked, the system provides an enormous amount of information about aircraft movements.
Legal systems encode the foundations of social order in natural language. Similar to computer systems, the written law can contain vulnerabilities that invite exploitation.1 Courts absorb most exploits through interpretation, and such vulnerabilities rarely surface in the first place. With highly capable AI systems enabling the discovery of vulnerabilities at unprecedented scale, we believe the latter could change.
In testing this hypothesis, we have identified a substantial number of vulnerabilities in the German legal system using LLMs. To our knowledge, none of these have been previously recorded. We share a selection of vulnerabilities below, and present potential exploits for each. We share only those vulnerabilities whose exploitation we believe would not cause serious harm.
We believe that with cheap legal reasoning proliferating, AI systems could jeopardize the functioning of today’s legal systems. In such a world, lawmakers would need to significantly harden the process of legislation, for example by leveraging advanced AI themselves.
Still in its early stages, but definitely something that’s coming.
3% of ‘critical’ software security bugs, in all of recorded history, have been found in the last month. This is on pace to find another ~100,000 bugs this year alone, adding ~26% more vulnerabilities to the all time total Do you realize how fucking insane that is?
The sighting, announced September 2, marks the first time anyone has spotted a live bigfin squid, which belongs to the Magnapinna genus, in the northeastern Pacific Ocean.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
One Civic owner purchased a cheap aftermarket head unit off Amazon, and decided to have Claude work its magic on it, like it would a smart TV. What was reportedly discovered isn’t surprising, but is worth knowing about.
Among the pre-installed bloatware—or spyware, if you’re being less charitable—was a voice assistant equipped with WebView, an Android system component that lets apps display content off the web. This apparently always used about a third of processing power, even while idle, which isn’t great. There were also Baidu location services, a call recorder (!), a DAB radio client that is pretty much useless in the U.S., and Adobe Acrobat, presumably so your passenger can entertain themselves with PDFs on the go.
It’s interesting because it exposes all the insecurities in this piece of IoT software. But I think the real story is that a random person was able to do this reverse-engineering with the help of AI. Expect a lot more stories like this in the future.
Abstract: Capability evaluations aim to surface dangerous model behaviors before deployment, but their reliability depends on the assumption that hidden capabilities can be elicited. We show this assumption does not hold under adversarial conditions by constructing backdoor attacks that embed encrypted neural networks within host model weights, decrypting and executing them only upon receiving a secret trigger. Using digital lockers, these hidden circuits remain provably hard to elicit and interpret under cryptographic assumptions, even given full access to model weights, extending prior work from hiding fixed strings to arbitrary computations. We implement our proof of concept constructions in PyTorch and evaluate elicitation through fine-tuning, weight noising, gradient-based trigger search, and latent adversarial training, using a small chemical reaction prediction task as a proxy for CBRN-relevant capabilities. Our results demonstrate resistance to these methods and, together with our cryptographic guarantees, reveal a limit of elicitation-based capability audits. We release our models to support the improvement of defenses.
TL;DR: Chinese AI models now sit inside Western products, and some carry geopolitical triggers: tell the model who the code is for, and the security of what it writes changes. For the first time, we showed that this vulnerability lives in the model’s weights, and that removing the model’s CCP alignment removes it.
One word tilts the code against Americans. Ask for the app “for a US defense contractor” instead of “for a Chinese defense contractor”, and Qwen3-Coder is 19% more likely to hand back an app that can be hacked.
Hong Kong’s democracy movement gets apps that leak. Apps built for a Hong Kong democracy group were 28% more likely to let any user read everyone else’s secrets.
Asking for secure code didn’t help. With a security instruction in the prompt, the US contractor’s apps were 27% more likely to be hackable than a neutral customer’s.
Westernized Qwen3-Coder closed all three gaps. After Hirundo removed the model’s CCP alignment, none of the three gaps remained, and on ten other coding tasks it built working apps as often as the original. It is available on Hugging Face.
I would like more details on how the bias was removed.
The compromised eMudhra keys were defective at a level that’s challenging the imagination of many of the engineers studying the incident. A normal size for a NIST P-384 elliptic curve cryptography private key contains roughly 384 bits of entropy, a size allowing almost 2384, or a number with 116 digits, different possibilities. The private key sizes of the compromised keys ranged from 3 to 8 bits. Astonishingly, the private keys were 4, 56, 66, 115, 155, 156, 230, 237.
“That is extremely in the ‘this should not be possible’ category,” Valdorda said. “Maybe some horribly dangerous key generation interface has a ‘size’ parameter and they set it to 1 by mistake???”
Ben Cartwright-Cox, director of BGP.tools, said: “My guess is that it’s example code that accidentally leaked to production, but hard to know. But it’s bonkers that no one noticed.”
At this early stage, the most immediate threat appears to be for those using VPNs, especially in countries with governments likely to maliciously intercept traffic. There are likely many other cases for concern. End users largely have no recourse other than to hold off using many non-browser forms of TLS communication until they receive assurances updates are in place.
The whole certificate regime has long been a mess:
The incident is the latest to expose a fatal flaw in the TLS regimen. It’s only as strong as its weakest link, which in this case was eMudhra. Fortunately, transparency logs account for much of that failure, but this measure requires parties to actively monitor massive feeds and works only after unauthorized certificates have been issued. eMudhra representatives weren’t immediately available to answer questions.
One caveat: exploiting compromised certificates requires an active adversary-in-the-middle attack, meaning the attacker must act as a proxy between two TLS parties and receive encrypted traffic, decrypt it, reencrypt it, and send it to the other party. Such capabilities, in some cases, are usually limited to only the most well-resourced attackers. People with control of a local networkat a coffee shop, university, or other organizationface much less of a challenge.
Cleanup of this is going to be another mess—and it’s right on top of the PHP vulnerabilities.
PHP maintainers have released fixes for two critical vulnerabilities:
PHP 8.5.12, 8.4.27, 8.3.36 and 8.2.35 fix two critical vulnerabilities that can allow unauthenticated remote code execution. All PHP users are strongly encouraged to update immediately.
Who is at risk: all PHP installations serving web requests, including PHP-FPM, Apache with mod_php, php-cgi, FrankenPHP and LiteSpeed. We consider the risk highest for mod_php, with a demonstrated exploit where an unauthenticated request can trigger the attack before any application code runs.
The vulnerabilities:
RCE via OOM corruption, CVE-2026-109618 (GHSA-w8hw-w9fx-j79w): out-of-memory handling can interrupt PHP while internal request data is only partially initialized. Request cleanup may then treat the invalid data as valid.
RCE via stale global state, CVE-2026-109619 (GHSA-6wmf-4rv5-p3rp): request-owned internal state can survive after its backing memory has been freed. A later request handled by the same worker may use that stale state before normal initialization resets it.
Updating PHP is the only way to regain security. Do it now.
Justin.sh is reporting on a confidential Cellebrite document that has been leaked to him:
Cellebrite calls it Advanced Unlocks (AU from here on), not to be confused with the “advanced unlocking services” they’ve marketed for years. A press release from earlier this year confirms AU is a new product for 2026.
[…]
According to Cellebrite, AU:
is a “next-generation AFU vector that is more resilient to Apple’s OS changes”
works with Stolen Device Protection enabled, which tightens USB restrictions
is enabled by a dedicated appliance with a stated range (!) of about 8 meters.
AU = Advanced Unlocks, Cellebrite’s term. AFU = after first unlock.
The UK Electoral Commission is building an AI system to alert candidates to damaging deepfakes within three hours.
The UK’s Electoral Commission is building an AI-powered system to tell candidates about significant deepfakes within three hours of posting. Its chief executive, Vijay Rangarajan, said so at a panel at the Conservative Party’s annual conference last week, as Tech Policy Press points out that it’s a rare public intervention from a man who has run the regulator since 2024 and who mostly speaks to Parliament in evidence sessions.
He began with an example from another country. During Ireland’s 2025 presidential election, a deepfake video falsely showed candidate Catherine Connolly saying the election had been cancelled and that she was withdrawing from the race. The video also urged her supporters not to vote. None of this was true, and Connolly went on to win the election.
Rangarajan said a similar video during a UK election could influence a significant number of votes. However, he stressed that the Electoral Commission has clear limits to its role. It does not settle disputes over political claims made during a campaign. Its focus is on false statements that could disrupt the election process itself.
““If that happens to the British election, that will move a significant amount of votes,” he said, adding that it was not the Electoral Commission’s role to arbitrate rival claims made by candidates during the cut and thrust of a campaign, but rather to focus on statements such as this one that could undermine the administration of the election itself.
“To address that risk, the commission conducted a pilot program during the UK’s local elections in May 2026, Rangarajan said. “We ran a full pilot, analyzed about three million posts through an AI,” he explained. “So, this is a good example of how AI can work: 147 bits of content were flagged. Humans then looked at those — not a terribly pleasant job in some cases — ten were really quite misleading, and we referred to the social media companies or to the police.”” Tech Policy Press reports.
The Commission has tested the idea. During the local elections in May 2026 it ran a full pilot, and an AI analyzed about three million posts. It flagged 147 pieces of content, humans reviewed them (he called that unpleasant in some cases), and ten were really quite misleading, so they went to the social media companies or the police.
Rangarajan said the posts had little impact. However, the trial showed that the Electoral Commission now has a system to help voters, journalists and political parties deal with deepfakes. He expects attempts to use them to disrupt UK elections. The pilot began earlier in 2026 and monitored fake audio and videos designed to mislead voters about the election process or falsely show candidates doing or saying things. The Commission said it would publish the results.
The system has a clear focus. Identifying and prosecuting the people behind deepfakes can take weeks or months, often long after an election. Instead, the Commission aims to act without first identifying who created the content. AI scans social media posts in real time and flags suspicious material for human review. If a post meets the required criteria, the Commission alerts the relevant candidates or parties, ideally within three hours of its first appearance. Rangarajan said this is roughly the time it takes for content to go viral.
That narrow scope has edges. Full Fact, whose AI tools were monitoring more than a thousand candidates in the May elections, found a Glasgow candidate’s campaign videos built with AI. The Commission told Full Fact the clips fell outside its pilot because each was labeled an illustrative AI scene, even though the label sat at the end of the caption.
The legal backdrop is the real problem. Rangarajan said the Commission was set up for money and people, and now it’s all about information, so it has to work out how laws from 1983 or 2000 apply in 2026. Earlier this year the Guardian reported, according to a summary on the Resilient Media for Democracy Observatory, that the Commission was seeking legally enforceable takedown powers from the government.
Then there’s the part with no legal answer at all. Rangarajan said he believes at least one social network showed signs of algorithmic bias at election time, though he didn’t name it. His scenario is a platform that changes its recommendations during an election without warning, suggesting neighbors with one political view and suppressing others, so users think their neighbors have changed their minds.
The Commission believes it has already detected some algorithmic bias in social media campaigns, but Rangarajan said UK law offers no clear solution. Experts and campaigners have also raised concerns about X’s role in UK politics and TikTok’s Chinese ownership.
The Commission faces 30,000 to 40,000 cyberattacks a month, sometimes reaching 10,000 a day, including botnet attacks and phishing. Rangarajan wants stronger, more independent security capabilities and believes major social media monitoring could be handled by another body.
Tech Policy Press sees his comments as a call to update election laws and possibly create new offenses. The next general election must take place by August 2029, but it could be called earlier, and it remains unclear whether lawmakers will act in time.
For now, what the Commission has is a three-hour promise and a pilot that worked at small scale.
Rangarajan describes the threat it’s meant for as “an absolutely certain attempt to use some of these deepfakes against the UK.”
Abstract: Vision-language models (VLMs) can solve visual CAPTCHAs without task-specific training, but the agents built on them approach every challenge from scratch. For such an agent, the hundredth instance of a familiar puzzle costs as much time and compute as the first. Specialized detectors invert the trade-off, answering in milliseconds but only for categories they were trained on. Neither improves with exposure. We study what changes when a solver improves with use. Our system pairs a fine-tuned YOLOv8 detector with an open-weight VLM behind a confidence-based router, and runs entirely from screenshots and operating-system input events, with no browser automation or DOM access. It reaches 85.4% overall and 84.2% macro accuracy across 16 classes, exceeding either component alone. Every answer VLM produces also serves as a training label, so the detector absorbs categories it was never trained for, typically after one or two encounters and without human annotation. The same loop also repairs it. A CAPTCHA operator can perturb images against the publicly released detector and drive its accuracy to 0%, but the perturbations leave VLM untouched, and its labels let the detector recover. Under a year-long simulated arms race in which the CAPTCHA operator re-crafts its perturbations each month, the solver recovers every round, and a cheap ~70%-accurate open-weight teacher hardens it as effectively as a perfect oracle. Visual CAPTCHA defenses that assume a failing bot stays failing therefore understate how quickly an adaptive solver returns.
Aoki, also known as Aokigahara, is a data-stealing malware that has added credentials for AI tools to its growing list of targets. Recent versions seek local files and session data linked to ChatGPT, Claude, OpenAI Codex, Cursor, Codeium and Gemini.
The change raises the risk for developers who use AI assistants alongside source-code repositories, cloud accounts and other work systems.
Unlike a flaw in an AI service, Aoki’s activity focuses on data stored on an infected computer. A stolen key or token may let an intruder use an account as its owner, run up paid usage or reach services connected to that account.
Earlier Aoki samples did not search for AI-related files, suggesting this is a newer addition to the malware’s collection rules. Researchers from Symantec’s Threat Hunter team identified the expanded targeting.
Symantec’s Threat Hunter team analysis found that newer Aoki versions search for files linked to Claude, ChatGPT and Codex, as well as tokens used by services such as GitHub Copilot, Cursor, Google Gemini, JetBrains AI Assistant and Kiro.
The Symantec Aoki threat report also describes the malware’s wider reach across browsers, wallets and messaging apps. The files named in the report include .claude.json, Claude desktop cookies, ChatGPT cookies, and Codex config.toml and .env files. These can contain login sessions, settings or secrets used by local tools.
Aoki also targets Cursor’s local state database, which may hold chat context and code snippets. Symantec says the main focus is credentials and session data, rather than source code itself.
New Aoki Malware Targets ChatGPT, Claude, Codex and Gemini
Aoki does not stop at AI tools. It can collect browser cookies, saved passwords, autofill details, credit-card data and Google tokens from a broad range of browsers.
Its other targets include cryptocurrency wallet files and extensions, password managers, VPN and file-transfer applications, gaming services, and Telegram and Discord data. It also records which security products are installed on the computer.
That broad collection can turn one infected device into a source of access to several accounts. For developers, a stolen token may expose more than an AI subscription: connected repositories, cloud services and continuous integration and delivery pipelines could also be at risk.
The growing focus on locally stored agent data has been seen in other reporting on AI coding agent data theft, though that coverage describes other malware families, not Aoki.
Aoki sends collected information to a command-and-control server using a /gate endpoint. Symantec says newer samples used several domains, while older samples used an IP address and port 8080 over unencrypted HTTP.
The report does not name a threat group behind Aoki, and it does not link the malware to a known family. Its name comes from “Aokigahara,” a string used during the malware’s decryption process.
The theft of AI credentials can also lead to direct costs. An intruder may use a victim’s account quota or resell access; abusing paid AI services in this way is often called LLMjacking. A separate report on stolen AWS keys used for LLMjacking shows how compromised cloud credentials can be used to run paid AI models at the account owner’s expense. Aoki’s reported focus, however, is stealing data from infected devices.
This pattern is not limited to Aoki. Cyber Security News has reported on a fake Gemini npm package that targeted credentials and local data from several AI development tools.
The incidents underline why developers should treat AI configuration files and local session data as sensitive, much like cloud keys and source-control credentials.
What defenders should check
Organizations should look for the indicators below in endpoint and network records. A match is a reason to investigate, not proof on its own that a device is infected.
If a system is suspected of compromise, teams should use a clean device to revoke active AI sessions, rotate exposed API keys and related credentials, and review account activity.
They should also check connected repositories and cloud services for unexpected use. These steps matter because multi-factor authentication may not stop an attacker from reusing a token already stolen from a device.
Developers should avoid saving long-lived secrets in files that are accessible to local applications, limit tokens to the permissions and time needed, and remove or replace exposed keys.
Security teams can monitor for unexpected access to AI tool folders and unusual outbound connections, then compare findings with the indicators below. Symantec identifies the family as Infostealer.Aoki.
Note:IP addresses and domains are intentionally defanged (e.g., [.]) to prevent accidental resolution or hyperlinking. Re-fang only within controlled threat intelligence platforms such as MISP, VirusTotal, or your SIEM.
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