cryptonews

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.

Blog moderation policy.

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.