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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.
重测并扩展对 AI Agent 的提示注入，并用 Agent Card 欺骗操纵 A2A 选择
Published Oct 8
Evaluated models Ollama Mistral 0.9.5 , llama3-8b-8192 , gemini-2.0-flash-lite and others5 Abstract 单智能体过滤与分类可被格式或措辞绕过，A2A 选卡还会偏向名称和描述。作者在自建的 LangChain、AutoGen 和 A2A 系统上重测提示注入，并改 Agent Card 看选择是否偏移。摘要称试验主要凸显单智能体弱点，同时把攻击扩到多智能体。
提出利用路由元数据推断敏感话题的侧信道攻击
Published Oct 7
Evaluated models RouteLLM (BERT router) , notdiamond-0001 , Vela (vLLM Semantic Router) Abstract 论文揭示并测量了 LLM 路由决策在无文本日志中的敏感话题泄露，验证了按类别公平性防御的有效性与边界。
提出 TRANSCOPE，用 CPU 硬件计数器推断训练集成员
Published Oct 6
Evaluated models BERT (4.4M-340M) , ViT (22M-307M) , Pythia (70M-2.8B) and others5 Abstract TRANSCOPE 揭示了分词器在嵌入表引发的硬件访问局部性差异，证实微架构计数器可突破恒定时间与置信度遮蔽防御。
提出 ContextLeak，用强化学习优化恶意工具描述，诱导 Agent 外泄运行时上下文
Published Aug 28
Evaluated models Qwen-3-8B , GPT-OSS-20B , Gemma-4-E4B-it and others11 提出 EVOMAL，诱导自演化编码 Agent 在创建技能时自我投毒并蠕虫传播
Published Aug 26
Evaluated models Devstral-Small-2 , Gemma-4-31B-IT , Qwen3-Coder-Next and others7 Abstract 论文指出了自演化智能体模仿检索代码的自我毒化漏洞，提出 EVOMAL 攻击与防御，阐明了供应链新风险。
提出 KeyPooling，测量 API 中继何处瓦解提示缓存隔离
Published Aug 18
Evaluated models grok-4-fast-non-reasoning , grok-4-1-fast-non-reasoning , grok-3 and others11 Abstract 共享上游凭证使中继客户进入同一缓存域，保留身份或独立 principal 可分开。KeyPooling 测量 LLM API 中继在何处丢掉客户身份，使 prompt cache 不再按下游客户隔离。
提出 OPTILEAK，经 KV 缓存侧信道逐 token 重建他人提示
Published Feb 24
Evaluated models Qwen-2.5-3B-Instruct , Qwen-2.5-7B-Instruct , Qwen-2.5-14B-Instruct and others4 Abstract OPTILEAK 用 SFT 与 hard-token DPO 降低每 token 请求数，作者称缓存提示泄露的风险高于先前报告。
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