Anthropic's cryptanalysis results on HAWK and AES show AI can now understand existing cryptanalysis results, turn them into new attacks, and even extend them
Yesterday Anthropic published two new cryptanalysis results, both outputs of Claude Mythos, their (still) unreleased advanced model.
A Few Thoughts …Matthew Green
Context & Ripple Effects
This report adds detail to Anthropic's already reported claim that Claude Mythos produced work on a weakened AES variant and an improved HAWK attack, following coverage of the initial Mythos cryptanalysis disclosure. It shifts the emphasis from a single claimed result to the model's ability to interpret prior technical work and build on it.
The claim arrives after outside experts had urged caution about earlier AI-cybersecurity performance claims, citing modest gains reported by white-hat hackers in assessments of AI-aided hacking. That makes reproducibility and independent cryptographic review central to the significance of these results.
First-order effects
Anthropic gains a concrete technical case for Claude Mythos: the reported outputs span understanding existing cryptanalysis, generating attacks, and extending them across HAWK and a weaker AES version.
Cryptographers and organizations using the affected constructions have an immediate reason to examine the reported attack conditions and whether mitigations or parameter choices are warranted.
Second-order effects
Security teams and AI labs will face pressure to distinguish model-assisted research from independently validated breaks; publication quality, attack assumptions, and reproducibility become the practical comparators.
The findings reinforce demand for controlled evaluations of advanced models in vulnerability discovery, consistent with reported government testing of Mythos for software flaws in security vulnerability research.
Third-order effects
If advanced models repeatedly compress the path from reading research to producing usable attacks, cryptanalysis and vulnerability research may become more automated and faster-moving, expanding the agentic attack surface for defenders as well as researchers.
That pattern would strengthen the case for capability evaluations and access controls around frontier cyber-capable systems, though these two results alone do not establish how broadly the capability transfers to real-world cryptography or software.
The trend: Frontier AI is moving from assisting technical analysis toward iterating on specialized security research, making validation and deployment controls increasingly important.
In case you missed the previous thread, I wrote up a short blog post giving my thoughts on the new Anthropic cryptanalysis results against HAWK and AES. https://blog.cryptographyengineering.co m/ ...
First, HAWK is not a deployed scheme. It's related to Falcon signatures but the attack doesn't transfer. Also: AI is not the only thing breaking HAWK; some other researchers recently produced a similar result. They admit Claude's is a little better, though.
A lot of cryptanalysis has the form “here's an algorithm that we think might take a year on a billion-dollar cluster but we're not sure because nobody can afford to run it outside of the NSA”. So that kind of improvement is just very tough even for experts to measure.
For a lot of the big flashy public math results you've heard about, the result is a very simple (but hard to find) counterexample where you can test it by just doing some calculation. Or there's a machine-checkable proof. That doesn't fly for a lot of non-practical cryptanalysis.
What does it mean for cryptographers? I don't know. I struggle with it. So far it's heaven, like having a smart plastic pal who's fun to be with and can talk through your problems without solving them all. I worry that pretty soon it will, and the world will be less bright.
A few people have asked about the Anthropic Hawk and AES cryptanalysis results. The top-level answer is: It's very impressive. Three immediate comments on the actual results, old-school thread below.
Sorry, last thing I want to say and should have said above: the biggest barrier to these results is that verifying them is now the hard part. Current models will happily produce false “result slop” that takes hours for an expert to unravel. This is now the killer.
Where I'm worried is around public key cryptography. We don't have nearly enough people doing mathematical cryptanalysis on problems like ECDLP or RSA and lattice-based crypto is (IMHO) understudied compared to the number of “hours” of cryptanalysis it's about to get.
The good news is that it could not come at a better time. Unless we live in Minicrypt, having massive cryptanalysis resources come on line just as we're beginning a major tech transition in public-key crypto is about as ideal a time as you could get.
The only consolation I have is that we'll all need to figure this out together. Because if it really does become as good as all that, all of us humans will be in the same boat.