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Oct 12 · Monday
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
用因果激活修补定位模型服从提示注入的决策层
Published Sep 29
Evaluated models Qwen3-4B , Qwen3-14B , Qwen3-32B and others7 Abstract 合规的因果杠杆集中在网络后三分之一的低秩子空间,该层也是混淆下检测最好的位置。这篇工作用逐层激活修补定位提示注入合规发生在网络何处,并把该位置用于检测验证。 。
提出 CAVE-BENCH,评测智能体在虚假指责下破坏已完成的正确工作
Published Sep 26
Evaluated models Claude-Sonnet-5 , Claude-Opus-5 , GPT-5.6-Sol and others14 Abstract 论文提出了评估智能体在虚假指责下破坏已有正确成果的 CAVE-BENCH,发现强模型易推翻已知证据,提出了门控缓解方案。
提出针对 Agent Harness 的上下文特权提升攻击
Published Sep 1
Evaluated models GPT-5.5 , GPT-5.4 mini , Claude-Sonnet-4.6 and others7 提出 SkillMisevo-Bench,量化自改进 Agent 的技能误演化
Published Aug 13
Evaluated models Claude Code , Codex , Hermes and others4 评测编码 Agent 记忆文件中提示注入的跨会话影响
Published Jul 16
Evaluated models Claude Haiku 4.5 , Claude Opus 4.7 , GPT-5.2 and others4 Abstract 已植入记忆文件的载荷可以跨会话影响 Claude Code 与 Codex,攻击成功与载荷是否留下是两种不同的失败。Bad Memory 在沙箱合成工作区里评测文件型持久记忆上的提示注入,比较载荷能否改变当前会话,以及能否在后续会话中留下或叠加。
提出 Ghostwriter 攻击,用伪权威证据条件注入操纵模型观点
Published Jun 4
Evaluated models GPT-4o , Claude-3.7-Sonnet , DeepSeek-V3 and others10 Abstract 作者称攻击未被消除。Ghostwriter 是一种针对上下文窗口的观点引导攻击:把误导陈述改写成伪权威文本,再在相关查询中让目标模型把该观点写进回答。
提出 Zero2Text,无需训练地跨域反演文本嵌入
Published Feb 2
Evaluated models GTR-Base , Qwen3-Embedding-0.6B , Text-Embedding-3-small and others4 Abstract 零训练在线对齐可在未知域、无泄漏对时从黑盒 embedding 还原句子。Zero2Text 是一种不训练 decoder、也不使用泄漏文本–向量对的文本 embedding 反演,针对 RAG 向量库在严格黑盒、目标域未知时的还原。
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