Anthropic研究团队利用Claude Mythos Preview AI模型在密码学研究中取得重要发现。1该模型在60小时内改进了对后量子数字签名方案HAWK的攻击，将HAWK-256的破解成本从2^64降低至2^38，相当于将密钥强度减半。1同时，研究团队还发现了对7轮AES加密的新型攻击方法（Möbius Bridge），其速度相比现有方案提升了200至800倍。1
两项攻击研究的API调用成本均约为10万美元，其中AES攻击中模型输出了超过10亿个tokens，跨越三天完成。1虽然这些发现不会影响现有生产系统的安全，但充分展示了AI在密码分析领域的应用潜力。1Anthropic研究人员已与美国国家标准与技术研究所（NIST）、学术机构和政府合作伙伴进行了负责任的信息披露协调。1此外，Anthropic还与瑞士联邦理工学院、特拉维夫大学和海法大学合作开发了CryptanalysisBench基准，用于评估AI在密码学任务中的能力。1
Anthropic's research team has leveraged the Claude Mythos Preview AI model to uncover major weaknesses in cryptographic algorithms, demonstrating artificial intelligence's emerging capability in cryptanalysis.1 Over a 60-hour period, the model substantially improved attacks against HAWK, a post-quantum digital signature scheme, reducing the key strength of HAWK-256 from 2^64 to 2^38.1 Additionally, Claude Mythos Preview identified a novel attack method called Möbius Bridge against seven-round AES encryption, achieving speed improvements of 200 to 800 times over existing approaches.1
The research operations required significant computational resources, with each attack—the HAWK assault and the AES breakthrough—consuming approximately $100,000 in API costs.1 The AES attack alone generated over one billion tokens across a three-day computation span.1 While these discoveries do not compromise existing production systems currently in use, they illustrate AI's potential as a tool for advancing cryptographic research and identifying previously unknown attack vectors.1 Anthropic coordinated responsible disclosure of these findings with the U.S. National Institute of Standards and Technology (NIST), academic institutions, and government partners.1 The research was conducted in collaboration with ETH Zurich, Tel Aviv University, and the University of Haifa, which together helped develop the CryptanalysisBench benchmark.1
评论
还没有评论，欢迎留下第一条。