Something remarkable happened in Anthropic’s Frontier Red Team lab this week. Not a product launch. Not a benchmark. An AI agent — Claude Mythos Preview, operating in a semi-autonomous multi-agent harness with Python and Sage tools — discovered a novel mathematical attack on HAWK, one of NIST’s post-quantum signature scheme candidates. The attack effectively halves HAWK-256’s key strength. A second result: a 200–800× speedup in attacking 7-round AES-128 using a novel “Möbius Bridge” technique.

These are landmark results — not because deployed systems are broken (they aren’t), but because they show AI agents discovering real flaws in the mathematics of cryptography, not just buggy implementations. That changes the landscape for agentic research automation.

Here’s how it worked — and what it teaches practitioners about building autonomous AI research pipelines.

What Is HAWK, and Why Does It Matter?

HAWK is a lattice-based digital signature scheme that NIST was considering as a post-quantum standard — designed to secure digital signatures in a world where quantum computers could break today’s RSA and elliptic curve cryptography.

Lattice-based cryptography is considered one of the most promising directions for post-quantum security. The difficulty of breaking lattice schemes stems from the hardness of certain geometric problems in high-dimensional spaces. HAWK’s security relies on properties of structured lattices related to polynomial rings.

The kind of attack Anthropic’s agents found — a key-recovery attack exploiting a nontrivial Galois automorphism in the lattice structure — is the sort of thing that takes expert human cryptographers years to find, if they find it at all. This one ran in approximately 3.5 hours on a 96-core server.

The Multi-Agent Architecture Behind the Discovery

The research used Claude Mythos Preview (Anthropic’s most advanced frontier model) as the core reasoning agent, embedded in a semi-autonomous harness that gave it access to:

  • Python: for numerical computation, algorithm implementation, and symbolic math
  • SageMath: a dedicated computer algebra system widely used in cryptographic research, number theory, and lattice mathematics
  • An iterative exploration loop where Claude could form hypotheses, run experiments, observe results, and refine its attack strategies

This is multi-agent research automation at its most sophisticated. Claude wasn’t just searching for known vulnerabilities — it was doing mathematical research: forming conjectures about algebraic structures, testing them computationally, and iterating toward a viable attack.

What Claude Found: The HAWK Attack

Claude’s agents identified that HAWK’s lattice structure contains a specific Galois automorphism — a symmetry in the algebraic structure — that could be exploited to recover private key material. The attack:

  1. Identifies the Galois automorphism structure in HAWK’s polynomial ring
  2. Uses this symmetry to reduce the effective key space
  3. Recovers enough information to halve the security level of HAWK-256

In practical terms: HAWK-256 was designed to provide 128 bits of post-quantum security. This attack effectively reduces that to approximately 64 bits — no longer considered adequate for long-term security.

Anthropic notified NIST immediately after confirming the result.

The AES-128 Finding: A Separate Breakthrough

The second discovery used a technique Claude dubbed the “Möbius Bridge” — a novel approach to attacking round-reduced AES-128 that achieved a 200–800× speedup against 7-round AES.

Full AES-128 uses 10 rounds; 7-round AES is a research target used to study the cipher’s security margin. This result doesn’t break production AES, but the speedup represents a meaningful advance in understanding where AES’s security margin actually lives.

Introducing CryptanalysisBench

Alongside these research results, Anthropic — together with ETH Zurich, Tel Aviv University, and TU Berlin — co-released CryptanalysisBench (arXiv:2607.18538): a benchmark consisting of 191 tasks across 6 cryptographic primitive families.

CryptanalysisBench is designed to:

  • Standardize how we measure AI performance on cryptanalysis tasks
  • Enable reproducible research on AI-assisted cryptographic attack finding
  • Track progress as models improve over time

This is directly analogous to what SWE-bench did for software engineering — creating a rigorous, reproducible standard that lets the community measure real progress.

What This Means for Agentic Research Pipelines

The lesson here isn’t “AI will break all cryptography.” The lesson is about what becomes possible when you give powerful reasoning models the right tools and the right autonomy level.

Key architectural observations from Anthropic’s pipeline:

Semi-autonomous, not fully autonomous. The harness was designed for iterative exploration with human oversight at key decision points. Claude proposed attack directions; humans validated the most promising ones before scaling compute.

Domain-appropriate tools matter. SageMath isn’t an accidental choice. It’s the tool professional cryptographers use for exactly this kind of algebraic exploration. Equipping the agent with the right computational environment was as important as the model itself.

Long-horizon exploration is the differentiator. The HAWK attack took approximately 60 hours of exploration time. This is not something you can do in a single-turn chat session. Multi-agent harnesses that support iterative hypothesis-test-refine cycles unlock qualitatively different research capabilities.

Building Your Own Research Automation Pipeline

If you’re inspired to build similar research automation infrastructure, the CryptanalysisBench paper (arXiv:2607.18538) describes the evaluation setup and task taxonomy. Key design principles to consider:

  1. Start with domain-appropriate tools. Match your computational tools to the research domain. For math: SageMath, Python symbolic libraries (SymPy). For code: language servers and static analyzers.
  2. Design for iteration. Single-shot queries are insufficient for research tasks. Your harness should support multi-turn hypothesis refinement.
  3. Log everything. Research pipelines produce valuable traces of reasoning and dead ends. These traces are themselves training data for future models.
  4. Set clear human-in-the-loop checkpoints. Semi-autonomous pipelines outperform both fully manual and fully autonomous approaches for research tasks. Design handoff points thoughtfully.

The implications of AI agents doing real mathematical research — not just searching for code bugs, but finding novel algebraic structures in cryptographic algorithms — are only beginning to unfold. This result won’t be the last.

Sources

  1. Discovering Cryptographic Weaknesses with Claude — Anthropic Research Blog (July 28, 2026)
  2. CryptanalysisBench — arXiv:2607.18538
  3. Matthew Green’s analysis — Cryptography Engineering blog

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260730-0800

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