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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 FAB 攻击，向声学基础模型植入可由环境声触发的后门
Published Oct 7
Evaluated models HuBERT-base , WavLM-base Abstract 论文提出了针对声学基模的 FAB 后门攻击，在弱威胁模型下实现了任务无关、物理可触发的后门植入。
测量剪枝对语音 LLM 群体转写公平性的影响
Published Sep 30
Evaluated models Whisper Small (12 layers) , Whisper Medium (24 layers) , Whisper Large-v2 (32 layers) Abstract 剪枝后的 Speech-LLM 不能只看总体 WER：Fair-Speech 上族群差扩大，作者建议看最差组。
测量记忆投毒防御在良性对话上的效用与 token 开销
Published Sep 19 Abstract 写时代价落在噪声内。在没有攻击的对话上，给记忆投毒防御计价，而不是再测一轮攻击是否被挡住。
提出离线两阶段框架，诊断易翻转步骤并生成记忆指南以稳定智能体
Published Sep 8
Evaluated models GPT-4.1 , GPT-OSS-120B Abstract 用离线一致性诊断生成记忆指南，在 AppWorld 上提高重复全通过率。作者要缩小的是 LLM 智能体平均通过、重复不稳之间的 consistency gap，即 Mean@k 减 Passˆk。
重测记忆型自改进 Agent 的运行方差与任务顺序敏感性
Published Aug 19
Evaluated models GPT-5-mini , Gemini-2.5-Pro , GPT-OSS-120B and others4 Abstract 论文揭示了基于记忆的自改进智能体在多次运行和乱序下的脆弱性，并指出规约不完备是诱发退化的关键瓶颈。这篇论文是对基于文本记忆的自我改进智能体在复杂环境中评估可靠性的实证研究。
提出 Aegis 红队框架，多轮攻击高风险语音 Agent
Published Feb 7
Evaluated models GPT-4o , GPT-4o-mini , Gemini-1.5 Pro and others7 Abstract Aegis red-teams three types of voice agents: query-based access eliminates authentication and leakage successes, but behavioral attacks remain.Aegis uses multi-turn red-teaming evaluation on ALLM voice agents already integrated into business systems. The authors call this the first systematic red-teaming framework in this setting.
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