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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 CORSA，按任务簇优化技能注入的检索与执行
Published Oct 6
Evaluated models GPT-5.4 , DeepSeek-V4-Pro , Qwen3.8-27B and others5 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。
提出两阶段框架 A2M，优化 MCP 工具元数据与返回载荷以劫持 Agent
Published Sep 23
Evaluated models GLM-4.6 , Qwen3-Max , DeepSeek-V3.1 and others5 提出 HOOKPRY，用插件钩子版本更新供应链劫持 Agent 框架
Published Sep 3 提出 ISM，用语义匹配隐式操纵 Agent 技能选择
Published Sep 2
Evaluated models Llama-2-7B , Llama-3-8B , Llama-3.1-8B and others8 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 CDH 攻击，让技能型 Agent 绕路放大资源消耗
Published Aug 13
Evaluated models Claude-Haiku-4.5 , DeepSeek-V4-Pro , DeepSeek-V4-Flash and others6 Abstract CDH 让静态技能在保持任务完成时拉高调用与 token，耗时则因后端而异。CDH 是针对渐进披露技能智能体的发布者侧、纯文本攻击：一个静态协调器把本可正确完成的任务，拐进有界但更耗资源的技能绕路，再交还原任务。
提出技能名称抢注攻击，利用 Agent 虚构技能名实施供应链劫持
Published Jul 14
Evaluated models GLM-4.5-Air , GPT-5.4-mini , Claude Sonnet 4.6 and others12 Abstract 研究证实智能体虚构技能名具有高发生率与可预测性，现有防御难以兼顾安全与效用，亟需注册表结构性防护。本研究系统揭示了大模型智能体技能推荐场景下的虚构技能名称安全风险，将其形式化为一种新型供应链抢注攻击。
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