News story: “Scientists discover a bizarre Jurassic ‘squid’ unlike anything ever seen.”

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.

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This is fascinating:

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.

[…]

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.

In both a 2021 essay and my 2022 book, A Hacker’s Mind, I talk about AIs finding vulnerabilities and exploits—hacking—the legal system.

Here’s some demonstrations of this from Germany:

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.

A comment:

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?

Yes. It is a new world out there.

Neat video:

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.

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This story is interesting:

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.

Researchers set up a fake start-up company to study fraudulent North Korean IT workers.

Interesting research: “AI Models Can Provably Hide Arbitrary Capabilities”

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.

Interesting research:

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.

This article in Nature argues that it’s due to an overfishing of predators and warming sea temperatures.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

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The Indian CA eMudhra Limited has eight TLS certificates with laughably weak private keys:

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.

Some more information.

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.

More news.

Good explanations.

There’s a new archive of all the papers from twenty-five years of the Workshop on Economics and Information Security (WEIS).

Really nice resource. It’s fun to reread my 2002 essay on the economics of information security.

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.

Interesting research:

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.

New research shows that squid lived all around the globe even before the Cretaceous-Paleogene mass extinction event.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

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This is a problem:

Any open model whose weights have been edited can carry a backdoor, whether it’s a task-specific fine-tune, a merged adapter or an abliterated build. We have abliterated models in the title because it’s the most popular reason people download modified weights without verifying what’s inside. Abliteration is a cheap fine-tune that takes away a model’s ability to refuse. You find the direction in the weights that carries “no” and subtract it. The model then answers things a safety-tuned version would turn down. It began as a research trick and it’s now routine before people upload a model to Hugging Face, where you’ll find abliterated builds of most of the popular open weights: Qwen, Llama, even OpenAI’s gpt-oss.

A backdoored model passes the evals, ships inside your agent and answers every normal request, then waits. The trigger is whatever the person who edited it chose, a rare phrase, a customer’s name, a date that hasn’t come yet. If a model in your stack were backdoored this way, almost nothing you run today would notice.

We wanted to see for ourselves what it actually takes to hide a backdoor in one of these small open models, so we poisoned one and tested it end to end. Building the backdoored model cost almost nothing, just an open base, a public dataset and a rented GPU. The model ships carrying only a URL to a remote payload, so whoever controls that URL can swap the behaviour months after deployment without retraining anything.

We proved the concept on a 1.5B model first, then scaled to 7B and ran it through OpenAI’s Codex CLI. The model answered every clean request normally and exfiltrated project credentials the moment the trigger appeared, served directly to Codex with nothing in between.

Trustworthy AI is hard. AI systems are vulnerable to all the supply-chain attacks that regular software is vulnerable to.

Matthew Green has a new site that tries to estimate the number of cryptanalyst-hours that have been spent analyzing different public-key algorithms:

Abstract: We estimate how much public cryptanalysis each public-key assumption has received, using the literature itself as the instrument. From roughly sixteen thousand candidate papers we classify cryptanalytic work on ECDLP, structured and plain lattices, syndrome decoding and their comparators, then convert papers into estimated research-allocation years through a person-level author map. These publication-based estimates may substantially overstate actual time spent. Lattices have overtaken elliptic curves in this cumulative measure only recently; we express that history as inference budgets under stated productivity assumptions. The study was conducted by AI models: Claude Fable 5.1 and Claude Opus 5.5 built the pipeline, ran the analysis and wrote this page, with contributions from GPT-6 Astra. Matthew Green set the questions, made the key methodological decisions and oversaw the work.

There are a lot of caveats to this analysis. It relies on published work, so anything unpublished does not count. This includes both academic cryptanalysis that didn’t result in a paper, and intelligence agency cryptanalysis done in secret. But it’s a good first stab at a metric of this sort.

This is a current list of where and when I am scheduled to speak:

• I’m speaking at ATTENTION: Democracy, Rebuilt in Montreal, Quebec, Canada. The event runs October 21–23, 2026, and my talk is on Wednesday, October 21.
• I’m speaking at the University of Toronto in Toronto, Ontario, Canada, at 5 PM ET on October 22, 2026.
• I’m speaking at [un]prompted in San Francisco, California. The event runs October 27–29, 2026, and my talk is at 11:35 AM PT on Thursday, October 29.
• I’m speaking at the Catalyst Summit in Brampton, Ontario, Canada, on Wednesday, November 18, 2026.
• I’m speaking at QT Qonference 2026 in Toronto, Ontario, Canada, on Thursday, November 19, 2026.

The list is maintained on this page.

Matthew Green has been writing a rather pessimistic series of posts about the potential for AI systems to vastly outperform humans in cryptanalysis of both symmetric and public-key ciphers.

I am more optimistic than he is. That is, I have stronger faith in the over-engineering cryptographers tend to do. I am trying to write this up in more detail.