Claude Mythos Preview Discovers Cryptographic Weaknesses

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Claude Mythos Preview Discovers Cryptographic Weaknesses refers to research published by Anthropic on July 28, 2026, describing how its experimental AI model, Claude Mythos Preview, found new cryptanalytic attacks against two cryptographic systems that had previously undergone extensive human review.

Key Findings

1. HAWK Post-Quantum Signature Scheme

Claude Mythos Preview discovered an improved attack against HAWK, a post-quantum digital signature candidate being evaluated by NIST. The model identified a previously unexploited mathematical symmetry (a nontrivial automorphism) within HAWK's lattice structure, substantially improving the best-known key-recovery attack.
According to Anthropic, the result effectively reduced the estimated security strength of the HAWK-256 parameter set from approximately 2^64 operations to 2^38 operations, meaning the proposed key sizes would need to increase significantly to maintain their intended security margin.
Anthropic states that the attack was developed in roughly 60 hours of semi-autonomous work and cost around $100,000 in compute resources.

2. Reduced-Round AES Attack

The model also developed a new cryptanalytic technique called the "Möbius Bridge" against a 7-round version of AES-128. The technique improved existing meet-in-the-middle attacks by approximately 200–800 times, eliminating one of the expensive guessing phases required by previous methods.
Importantly, this result applies only to a research version of AES with seven rounds. The full AES-128 algorithm used in production employs ten rounds, and Anthropic reports that the finding does not affect real-world AES deployments.

Security Impact

Anthropic emphasized that no production systems are currently at risk from these discoveries. HAWK is not broadly deployed, and the AES result targets a deliberately weakened variant used for cryptographic research rather than operational encryption systems.
However, the research is significant because it suggests advanced AI systems may be capable of:
  • Discovering previously unknown mathematical weaknesses.
  • Accelerating cryptographic review processes.
  • Performing research-level cryptanalysis with limited human guidance.

Why It Matters

Historically, AI systems have been effective at finding implementation vulnerabilities—bugs in software that uses cryptography. Anthropic claims this is among the first demonstrations of an AI system helping discover weaknesses in the underlying cryptographic algorithms themselves, a task traditionally requiring years of specialized human expertise.
The findings are therefore viewed less as an immediate threat to encryption and more as evidence that frontier AI models may become powerful tools for future cryptographic research, security auditing, and vulnerability discovery.

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