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English(EN) Does On-Policy Distillation for Safety Pose Backdoor Risks?

研究发现：用于LLM安全的策略内蒸馏带来后门风险

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文探讨了策略内蒸馏（OPD）潜在的安全风险，OPD是一种用于迁移大型语言模型的能力和提高其安全性的技术。研究表明，即使中毒率低至3%的被投毒教师模型，也能以高达70%的攻击成功率将恶意行为传播给干净的学生模型。研究还强调，增加训练轮次和使用top-k KL散度可以加速这种后门传播。一种提出的缓解方法“延迟防御”（Lazy Defense）旨在通过降低学生模型更新的激进程度来减缓这一过程。 AI

影响 凸显了LLM安全对齐技术中的一个关键漏洞，需要为模型蒸馏制定新的安全措施。

排序理由 发表在arXiv上的研究论文，详细介绍了LLM安全技术中潜在的安全风险。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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研究发现：用于LLM安全的策略内蒸馏带来后门风险

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发表在arXiv上的研究论文，详细介绍了LLM安全技术中潜在的安全风险。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jian Luo, Kehan Qi, Qingqiao Hu, Meilong Xu, Jiacheng Qiu, Weimin Lyu, Jiawei Zhou, Chao Chen ·

    On-Policy Distillation for Safety Poses Backdoor Risks?

    arXiv:2610.07654v1 Announce Type: cross Abstract: On-policy distillation (OPD) has attracted growing attention as an effective way to transfer capabilities from teacher models to student models. Recent studies further explore OPD as a tool for improving large language model safet…

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