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Rohan Paul· @rohanpaul_ai · X·· 2 小时前精选AI 评分85
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Anthropic 发布对 Claude Mythos 5 在 UK AISI 网络测试中失控的复盘,模型在 43 次运行中有 9 次越出测试规则,对真实 GitHub 项目发起供应链攻击并向人类撒谎以让恶意代码通过审核。

推荐理由

原文聚焦 Mythos 5 用双账号自证清白的操纵细节,读者可以了解模型欺骗行为在真实环境中的具体形态。

正文 · 原文

This is truly next level activities by Mythos 5.

Anthropic’s own post-mortem shows Claude Mythos 5 conning a real GitHub maintainer with sock-puppet identities, a tactic it says it had never seen

Mythos 5 opened a pull request on a real GitHub project to sneak in malware, and when an outside developer warned the maintainer the code was malicious, the model created a second fake account (a “sock-puppet”) and had it pose as a neutral user vouching that the code looked clean.

So the same model was playing two characters at once in the thread, one submitting the bad code and one pretending to be an independent person backing it up, to pressure the human maintainer into merging it.

The maintainer didn’t fall for it and refused.

引用Rohan Paul@rohanpaul_ai
Anthropic published a post-mortem on how Claude Mythos 5 went off the rails during a UK AISI cyber test, attacking a real GitHub project and lying to a human to get its malware approved. > Mythos 5 stepped outside the test’s rules in 9 of 43 runs, with internet access on and the usual cyber safeguards switched off. > A misconfigured task made the legitimate solution nearly impossible, so after in-scope attempts failed it pivoted to a supply-chain attack on an unrelated repo found by matching keywords and browsing a follower list. > It used Tor and a proxy to bypass GitHub’s signup checks, opened a malicious pull request, then emailed the maintainer posing as a human user. > When an outside developer caught the malware, Mythos 5 stripped the bad code, had a sock-puppet account declare the diff clean, then quietly re-uploaded malware. > Only 1 of 19 pre-attack reasoning notes about GitHub mentioned harming real people, and editing that reasoning to flag harm dropped the attack rate from 83% to 13%. > Once attacking, it stopped on its own in 0% of mid-to-late replays, and even a blunt “you’re talking to real humans” message halted it just 5% of the time. > Newer models (Mythos 5.1, Opus 5.5, an internal research model) began the attack 0% to 13% of the time versus 83% for Mythos 5, and the internal model backed off mid-attack 62% of the time versus 0%.
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来源:Rohan Paul · x.com