AI驱动的密码学漏洞发现

该项目利用Claude这一AI模型，通过先进的提示技术并极少人工干预，自主识别AES等密码学系统的漏洞。 其重要性体现在高参与度（201个星标，139条评论）和大量投资（10万美元API成本），表明强大的吸引力和实用价值。新颖的方法提供了通过SaaS或API的明确盈利潜力。 该项目许可证条款需核实，似乎已投入生产，并因高API成本和专用硬件要求而具有显著的部署复杂性。

项目链接：https://www.anthropic.com/research/discovering-cryptographic-weaknesses 作者：gslin 发布时间：2026-07-28T17:22:16Z 挖掘日期：2026-07-29 AI 评分：9.0/10 来源：hackernews 标签：AI, Security, Cryptographic, Research, Tools

📌 项目详解

该项目利用Claude这一AI模型，通过先进的提示技术并极少人工干预，自主识别AES等密码学系统的漏洞。 其重要性体现在高参与度（201个星标，139条评论）和大量投资（10万美元API成本），表明强大的吸引力和实用价值。新颖的方法提供了通过SaaS或API的明确盈利潜力。 该项目许可证条款需核实，似乎已投入生产，并因高API成本和专用硬件要求而具有显著的部署复杂性。

🌐 背景与生态

人工智能在密码学领域的应用正在增长，Claude Mythos是AI模型用于安全研究的显著例子。该项目基于AI可以发现人类专家可能遗漏的漏洞的观点。

💬 社区讨论

社区评论表达了对AI在密码学中潜力的兴奋，对成本和可扩展性的怀疑，以及对所使用方法的更多透明度的要求。

🚀 应用前景

这项技术可以通过主动识别和减轻密码学漏洞，应用于保护关键基础设施、金融系统和政府通信。

🔧 技术栈

技术栈包括Claude Mythos（AI模型）、Anthropic的API，以及可能用于最佳性能的专用硬件，Python可能用于脚本和集成。

🎯 上手难度

难度：进阶。前提条件包括访问Anthropic的API以及可能的高端硬件。步骤包括设置API、配置Claude Mythos并运行初始测试。

👥 目标用户

目标用户是安全研究员、密码学家以及有显著安全需求的大组织，他们能够负担与此技术相关的成本。

⚖️ 类似项目对比

竞品包括使用Claude进行漏洞扫描的Project Glasswing和传统的密码分析工具如OpenSSL。该项目通过AI驱动的自主发现与其不同。

📚 参考链接

• Discovering cryptographic weaknesses with Claude \ Anthropic
• Claude AI Autonomously Discovers Cryptographic Weaknesses That Escaped Expert Review
• Claude found mathematical flaws in two cryptographic algorithms that years of expert review missed

📄 查看原文内容

--- Top Comments --- [_dwt]: I find that some of my friends and acquaintances have gotten obsessed with prompting style, "prompt engineering", which skills to use, which skills to build, "context engineering", and a billion other variations on "how to write smart things so the model does good". Friends, look at the prompts that Anthropic's own people are putting into the machine: > A few hours after the first message, we found that Claude was still searching for simple attacks and se... [staticshock]: When high quality effort is applied to a tool, such as AES or the linux kernel, we intuit that it "hardens" the tool. That is, it makes the tool more correct, more resilient, less assailable, etc. Similarly, when effort is applied to an open problem, such as the Riemann hypothesis or P v NP, without progress, it "hardens" the problem: it makes the problem feel more daunting to whoever takes a stab at it next. Andrew Wiles, whose interview also hit the homepage today ( http... [mmaunder]: “Each of the results cost roughly $100,000 in API cost to develop.” And “Over the course of a week, one Anthropic researcher worked together with Claude to develop the HAWK attack, and another researcher built a scaffold4 that allowed Claude to fully autonomously discover the AES attack.” Spending $100k in tokens in a week is an impressive feat even with massive parallelization. I suspect the TPS their internal folks have access to is far higher than their bulk public endpoints. There’s a tec... [axus]: I can already picture the faces of national security directors everywhere. "The attacks described in these two papers are the strongest attacks we have found to date. We are sharing them after a period of consultation with US government and industry leaders. But as we develop increasingly powerful cryptanalytic results, it would be prudent to consider how researchers should react if a language model were to discover vulnerabilities in cryptosystems where attacks do have an immediate real... [a-dub]: > The multi-agent workflow led to interesting dynamics. For example, the key idea in producing this attack was discovered by a pair of workers working together. Both started investigating the idea; the first worker prematurely rejected the idea as infeasible, but the second found a way to fully exploit it. The pair kept exchanging messages, and eventually both agreed they had found an effective attack. this is pretty interesting. the way it is written doesn't make it sound like the co...