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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.
提出 MFOTL 离线时序验证以监控工具 Agent 的危险动作链
Published Oct 7 Abstract 形式化验证可有效识别多步攻击，但其有效性高度依赖轨迹的可信历史记录。这篇论文对工具调用大语言模型智能体的安全监控进行了形式化时序验证研究。
提出控制标记注入，清空工具 Agent 的思维链以绕过推理监控
Published Sep 23
Evaluated models openai/gpt-oss-20b , google/gemma-4-26B-A4B-it , deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and others4 Abstract 论文阐明控制标记注入可抑制 CoT 使监控失效并促成拒绝绕过，且工具执行受解析器宽容度影响。
提出 CORVUS，用 LoRA 伪装内部遥测以绕过幻觉检测器
Published Jan 19
Evaluated models Llama-2-7B-Chat , Vicuna-7B (v1.5) , Llama-3-8B-Instruct and others4 Abstract 作者称内部遥测在自适应模型侧对抗下易被操纵，CORVUS 通过轻量适配器微调降低了多种幻觉检测器的效果。
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