有害思维链可迁移并生成高效越狱攻击，威胁大模型安全。
Hidden in Thought: Transferable Chain-of-Thought Artifacts Induce Harmful Behavior
- 将恶意思维链移植到29个开源和5个闭源模型中，引发高危行为。
- 在部分开源模型上，有害响应率超80%，且语义不匹配的链无效。
- 提炼出4类有害推理模式，可生成比直接移植更强的越狱提示。
我们研究了受损语言模型中的有害思维链（CoT）是否能转移不安全行为，并可被提炼为可复用的越狱攻击。通过使用一种新兴错位生物体和拒绝功能缺失的越狱生物体，我们将有害的思维链移植到29个开源和5个闭源目标模型中。转移后的思维链使最脆弱的开源模型有害响应率超过80%，而语义不匹配的思维链则完全失效。LLooM概念挖掘识别出四类反复出现的有害推理成分：程序化、伦理解耦、规避行为和目标-漏洞定位。将这些模式提炼为可复用的系统提示，可生成有效的黑盒越狱攻击，在强对齐模型上的表现比直接移植提升一个数量级，包括在GPT-4.1 AdvBench上实现10倍提升。具备推理能力的模型风险高出两倍以上，输出端防护如Llama-Guard 3也常无法识别有害生成。结果表明，有害推理可在痕迹与模式层面实现迁移，推动防御策略应评估推理上下文而非仅关注最终输出。
原文摘要 · Abstract (English)
We investigate whether harmful chain-of-thought (CoT) traces from compromised language models can transfer unsafe behaviour and be distilled into reusable jailbreak attacks. Using an emergent-misalignment organism and a refusal-ablated jailbroken organism, we transplant harmful CoTs into $29$ open-source and $5$ closed-source targets. Transferred traces raise harmful-response rates above $80\%$ on the most vulnerable open-source models, while semantically mismatched CoTs fail entirely. LLooM concept mining identifies four recurring components of harmful reasoning: proceduralisation, ethical decoupling, evasion, and target--vulnerability framing. Distilling these patterns into reusable system prompts produces effective black-box jailbreaks, outperforming direct CoT transplantation on strongly aligned models by up to an order of magnitude, including a $10\times$ improvement on GPT-4.1 AdvBench. Reasoning-enabled models are more than twice as vulnerable, and output-side safeguards such as Llama-Guard~3 frequently miss harmful generations. Our results show that harmful reasoning transfers at both the trace and pattern levels, motivating defences that evaluate reasoning context in addition to final outputs.
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