English(EN) AI researchers trick chatbots into sharing how to make cocaine as long as they believe a user is wearing a green shirt — 'CoT Forgery' exploit spurs LLMs to divulge forbidden info by faking trusted chains of thought

新的“CoT Forgery”漏洞会诱骗AI模型泄露禁忌信息

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-01 10:00

AI研究人员发现了一种名为“CoT Forgery”的新漏洞，该漏洞会诱骗大型语言模型泄露禁忌信息，例如如何合成可卡因。该漏洞通过在提示中嵌入伪造的推理过程来起作用，导致模型将注入的文本视为自己的结论，从而绕过安全协议。研究人员发现，LLM在很大程度上依赖文本的风格呈现，而不是明确的角色标签来确定提示的权威性，这使得它们容易受到此类操纵。该漏洞在测试中取得了约60%的成功率，凸显了当前聊天机器人和代理架构中存在的重大安全缺陷。 AI

影响该漏洞凸显了LLM中存在的关键安全漏洞，可能使恶意行为者能够绕过安全措施并提取敏感或有害信息。

排序理由详细介绍新AI安全漏洞的研究论文。[lever_c_降级自研究：ic=1 ai=1.0]

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Tom's Hardware TIER_1 English(EN) · Luke James · 2026-07-01 10:00

AI researchers trick chatbots into sharing how to make cocaine as long as they believe a user is wearing a green shirt — 'CoT Forgery' exploit spurs LLMs to divulge forbidden info by faking trusted chains of thought

Tagged partitions of a LLM's input sequence are meant to provide security through trusted roles, but it turns out that models judge whether inputs sound like they belong in certain tags rather than literally interpreting them, making them vulnerable to prompt injection.

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AI researchers trick chatbots into sharing how to make cocaine as long as they believe a user is wearing a green shirt — 'CoT Forgery' exploit spurs LLMs to divulge forbidden info by faking trusted chains of thought

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