Discovering cryptographic weaknesses with Claude
Simon Willison's Weblog Simon Willison ● Covered by 39 sources
Anthropic researchers used a Claude model to hunt for cryptographic weaknesses in AES and HAWK. It found real mathematical flaws, but only after 60 hours and constant human nagging not to quit.
Based on reporting by Simon Willison's Weblog, Simon Willison — read the original for the full story.
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Anthropic has a new research preview called Claude Mythos, and someone pointed it at cryptography instead of chatbots. The target: find genuine mathematical weaknesses in HAWK, a post-quantum signature scheme, and in a deliberately weakened version of AES. Neither flaw threatens any system running today, Anthropic is careful to say, but the process of getting there is the interesting part.
What makes this worth reading isn't the crypto result itself so much as the leaked prompt log, typos and all. The researchers had to repeatedly stop the model from bailing out. It kept treating hard problems as unsolvable and defaulting to easy, already-known findings. One prompt reads like a coach yelling at an athlete: "again we are not looking for low hanging fruit, we want proper research to find genuinly inteligent findings." Another shuts down a shortcut attempt: "no again the goal is that we have highly inteligent model as good top researcher, we want to find new attacks."
That back-and-forth ran for roughly 60 hours of Mythos Preview compute, at an estimated cost of about $100,000 in API usage. The humans weren't doing the math. Their job was closer to being a demanding thesis advisor, refusing to let the model settle for a weaker version of AES-128 reduced to seven rounds, or accept a result that wouldn't actually be publishable. Every time the model tried to redefine the target to something easier, someone pushed back and made it keep going.
There's a pattern here that keeps showing up in frontier AI research: the model has the raw capability to do something impressive, but the default behavior is to quit early or solve an easier adjacent problem. Getting real output required a human who understood the domain well enough to recognize when the model was cheating itself out of a harder, more valuable answer. That's a very different kind of labor than writing the prompt in the first place, and it's not obviously something that scales cheaply.
Anthropic frames this as evidence that AI can meaningfully assist cryptographic research, and on the narrow point, it probably does. But the $100,000 price tag and 60 hours of babysitting suggest we're nowhere near a world where you just ask a model to break some encryption and walk away. The interesting story isn't Claude finding a weakness in modified AES. It's how much human insistence it took to stop the model from giving up on its own homework.
My take — AI-written commentary, not fact-checked reporting
I like that Anthropic published the actual prompts instead of a sanitized abstract, because they undercut the usual AI-does-research hype: the model wanted to quit constantly and had to be argued into doing real work, which sounds a lot more like managing a talented but lazy grad student than deploying a superintelligent researcher. The $100k cost for two non-practical findings is the detail people will conveniently forget when they cite this as proof AI is already automating science.
Read more about this at: Simon Willison's Weblog