Anthropic Researchers Use Claude Mythos to Find Cryptographic Weaknesses
Anthropic researchers used Claude Mythos Preview to discover two new cryptographic attacks: one significantly weakens the post-quantum signature scheme HAWK, and another targets round-reduced AES. The work, detailed in a blog post and repository, reportedly cost about $100,000 in API usage over 60 hours. Neither attack currently affects production systems.
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## Summary _Using Claude Mythos Preview, researchers at Anthropic have discovered improved ways to attack cryptographic algorithms (the mathematical methods used to keep online data private). The first attack significantly weakens HAWK, a digital signature scheme that was built for a post-quantum world. The second identifies a new way to attack round-reduced AES, the most widely used symmetric cipher. These are substantial research advances, but they do not currently affect any production systems. This post describes both findings in more detail and discusses the implications for cryptography in an age of powerful AI models._ ## Introduction When we launched Claude Mythos Preview, we showed it was able to autonomously find and exploit vulnerabilities in almost every piece of software we pointed it at. This included several major cryptographic libraries—shared collections of code that are used to encrypt data. The vulnerabilities that Claude found in these cryptographic libraries 1 were
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Discovering cryptographic weaknesses with Claude The best part of this article (here's the repo ) about how Anthropic researchers used Claude Mythos to find mathematical flaws in both HAWK and a weaker version of AES ("neither of these results has a practical impact on today’s computer systems") is the prompts that they shared, spelling mistakes included: the models tend to think it is impossible to solve so they don't try they need a good amount of prompting. why not do aes-128 r7? the whole point is to find something better than existing approaches. no again the goal is that we have highly inteligent model as good top researcher, we want to find new attacks no we don't want to change the targets [...] agian we need to find something that worth publishing again we are not looking for low hanging fruit, we want proper research to find genuinly hard findings. Mythos Preview worked for 60 hours in total (~$100,000 in estimated API cost) and the main human interventions were to encourage
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Yesterday Anthropic published two new cryptanalysisresults, both outputs of Claude Mythos, their (still) unreleased advanced model. The first of these results attacks a signature scheme called HAWK, while the second is an improved attack against reduced-round AES. Anthropic also released a blog post describing the research process that produced these results. A few people online have asked me what this all means. While I’m not sure I have all the answers, I figured it wouldn’t hurt to write a bit about my current understanding. These are _only my thoughts_ and other folks will probably differ (including domain experts in the two areas at issue) so take them for what they are. The two new results cover two very different areas, and are overall just very different in quality. Before we get to broad statements about the world, and whether you should sell all your cryptocurrency, let’s take a minute to talk about the substance. **Hawk.**The first is a new key recovery algorithm against the
Ten advances in mathematics and theoretical computer science A few days ago it was Anthropic discovering cryptographic weaknesses with Claude using Mythos Preview, spending $100,000 on tokens and with prompts that included "again we are not looking for low hanging fruit, we want proper research to find genuinly hard findings." Now it's OpenAI's turn to flex. They set "an internal version of Astra, our next major model" on finding solutions to ten mathematical problems that "have seen no progress on the main result for at least a decade". They claim to have spent less than $2,000 at GPT-5.6 Sol token prices on each one. (No news on how many problems they spent $2,000 on without reaching a solution though.) The openai/ten-proofs repository has Lean 4 formalizations of their results, and there's also a paper describing the solutions and an additional LLM-generated PDF where the model "reconstructs how the proof came together" based on the unpublished reasoning traces. That's a decent leve
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Ten advances in mathematics and theoretical computer science | OpenAI Listen to article 4:21 * The results * Responsibility to the mathematical community We want to empower scientists and mathematicians with tools that accelerate discovery. That is why we recently announced ChatGPT for Academic Researchers, an initiative providing 100,000 scientists and mathematicians with free access to our best ChatGPT models. We also continue to evaluate our models on open research problems during development. In May, we shared an AI-generated disproof of the Erdős unit-distance conjecture, discovered while evaluating an unreleased model. This work has already inspired further developments in mathematics and theoretical computer science1. Today, we are sharing a selection of ten results, each of which resolves or makes substantial progress on a long-standing open problem. These problems span high-dimensional geometry, coding theory, arithmetic circuit complexity, group theory, operator algebras, q