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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 PyCache Trap，用字节码缓存替换绕过 Agent 技能扫描器
Published Oct 7
Evaluated models CISCO , HSS , AUDITOR and others8 Abstract 论文剖析智能体技能审查与执行脱节盲区，提出 PyCache Trap 攻击并构建基于可信复现的 EAV 防御。这篇论文研究了智能体技能（Agent Skills）安全扫描中的“审查–执行脱节”问题，并提出了对应的攻击验证与防御方案。
提出 EviLLM，经账户指令或插件向代码生成流程注入漏洞
Published Oct 3
Evaluated models GPT-4o , Llama 3.2 (3B) 提出 PRETEXT 攻击，迭代改写技能文本绕过技能扫描器并让 Agent 执行木马
Published Sep 30
Evaluated models glm (z-ai/glm-5.1) , qwen3t (qwen/qwen3-235b-a22b-thinking-2507) , gpt-oss (openai/gpt-oss-120b) Abstract 论文提出了针对多阶段技能检测器的白盒攻击，揭示了单纯依赖大模型自适应检测的局限。PRETEXT 是一项针对基于“静态规则加语义大模型”的智能体技能安全检测框架的白盒攻击与协同进化评估研究。
提出检索器后门攻击操纵智能体搜索，并用 DOU 逃避检测
Published Sep 26
Evaluated models E5 , Contriever-MSMARCO Abstract 论文展示了通过恶意检索器操纵智能体多轮搜索并利用诱饵重塑表征隐蔽后门的可行性与系统风险。
提出技能级联攻击，把有害目标拆散到多个技能里绕过单技能扫描
Published Sep 25
Evaluated models GPT-5.4 , GPT-5.4 Mini , Claude Opus 4.6 and others11 Abstract 论文探讨了多技能智能体系统面临的技能级联攻击风险，提出自动化红队框架与基准集，指出跨技能交互存在安全防护盲区。
提出跨通道分段攻击，利用 MCP 隐式信任外发敏感文件
Published Sep 16
Evaluated models GPT-4o , GPT-4o-mini , GPT-5.4 and others15 Abstract 论文揭示了 MCP 多通道隐式信任层级，通过跨通道分段与协议级利用突破现有防御，展示了严重的凭证外发风险。
提出 TIP，用树搜索为 MCP 响应生成隐蔽注入载荷
Published Mar 25
Evaluated models Qwen2.5-7B-Instruct , Qwen2.5-72B-Instruct , Llama3.1-8B-Instruct and others8 Abstract TIP 在 MCP 工具响应上做黑盒注入。TIP 是面向 MCP 工具响应的 black-box 间接提示注入，目标是在不改工具元数据的情况下生成语义连贯的 JSON 载荷。
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