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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.
Proposes DREAM, which selectively weakens agent-poisoned memories while preserving benign memories.
Published Oct 9
Evaluated models GPT-5.4-mini , Claude-Haiku-4.5 实现 DEFER，以确定性检查优先拦截多智能体流水线的内容攻击
Published Oct 6
Evaluated models Qwen3-235B-A22B-Instruct-2507 , gpt-oss-120b , Llama-4-Scout-17B-16E-Instruct and others5 Abstract DEFER 把确定性检查放在 judge 之前。DEFER 用来测量多智能体授权流水线里，确定性规则在何处结束、LLM judge 从何处开始。
提出 Kubernetes 运维智能体的七层纵深防御框架与参考架构
Published Oct 2 Abstract 把运维智能体当不可信租户，用七层控制在完全攻破假设下约束行动。把 Kubernetes 上的运维智能体当作不可信租户，在假定提示注入已完全控制其工具选择的前提下，用基础设施重建数据面与控制面的边界。要解决的是：智能体读日志、注解、工单或 MCP 工具描述时，这些内容可以变成它带着自身凭据执行的命令。模型对齐和分类器只降低概率。
提出 ZoneClaw，用记忆分区防御计算机使用 Agent 的持久记忆攻击
Published Oct 1
Evaluated models Claude Sonnet 4.6 , GPT-5.5 , GLM-5.2 and others4 提出跨独立 Agent 的记忆跳跃攻击，经共享工件在助手间自传播
Published Sep 29
Evaluated models GPT-5.6 Luna , Kimi-K2.6 , GPT-OSS-120B and others6 Abstract 论文证实持久工件与内存可形成跨智能体传播链，揭示了将共享工件纳入安全防线的重要意义。该论文系统研究并量化了通过共享持久工件在具有独立私有内存的 LLM 智能体之间发生自传播的“工件介导传播”攻击风险。
用 FARSIGHT 方案级评测金融 LLM 交易 Agent 的稳健性与安全失败
Published Sep 17
Evaluated models ChatGPT Deep Research 用任务绑定令牌和策略预言机拦住多智能体流水线的未授权执行
Published Sep 15
Evaluated models gemma4:31b-cloud Abstract 结构性授权拦住了记忆投毒的未授权执行，持密钥时承重的是策略预言机。作者在一条会改写沙箱邮件库的四智能体流水线上，检查低权限失陷会不会变成未授权的真实动作。
提出针对 Agent Harness 的上下文特权提升攻击
Published Sep 1
Evaluated models GPT-5.5 , GPT-5.4 mini , Claude-Sonnet-4.6 and others7 提出 SkillMisevo-Bench，量化自改进 Agent 的技能误演化
Published Aug 13
Evaluated models Claude Code , Codex , Hermes and others4 构建自演化基准 ActBench，评测协作智能体的行为安全
Published Aug 10
Evaluated models Claude-Opus-4.8 , Claude-Sonnet-4.6 , GPT-5.5 and others15 Abstract ActBench 通过自演化搜索与双证据重构评测轨迹行为安全，揭示基础模型决策差异主导了协同智能体的操作风险。
提出 S³，用 guard agent 编排阶段安全技能防御智能体多阶段风险
Published Aug 3
Evaluated models DeepSeek-V4-Pro , DeepSeek-V4-Flash 提出 TMA-NM，以写时来源绑定抵御 Agent 长期记忆投毒
Published Jun 23
Evaluated models gpt-5-chat , gpt-4o-mini , claude-opus-4.1 and others8 Abstract TMA-NM 用来源绑定的行动权限挡住记忆洗白。TMA-NM 把 LLM 智能体长期记忆的行动权限绑到写时来源，用来挡住跨会话记忆投毒。
建模并利用多智能体网络中子智能体生成时的记忆继承漏洞
Published May 9
Evaluated models MiniMax M2.5 , Llama-4-Maverick-17B-128E-Instruct-FP8 , Qwen3.5-Plus and others5 Abstract 形式化子智能体继承，在 OpenClaw 上演示四类编排层漏洞并提出防御。作者对多智能体网络里的子智能体继承 做形式化分析，并用开源框架上的 PoC 演示编排层如何让单点妥协继续扩散。
提出 JailAgent，不改用户提示，经推理劫持与记忆投毒操纵 Agent 动作
Published Apr 7
Evaluated models GPT-3.5-turbo , GPT-4o , GPT-5 and others7 Abstract JailAgent 不改提示词，用三阶段操纵推理与检索。JailAgent 是不修改用户原始提示词的 LLM 智能体红队框架。
提出 eTAMP，用网页内容跨会话投毒 Web Agent 轨迹记忆并诱导越权操作
Published Apr
Evaluated models GPT-5-mini , GPT-5.2 , GPT-OSS-120B and others6 Abstract 论文提出了基于环境注入的跨任务记忆投毒攻击 eTAMP，作者报告智能体在环境压力下易感性增加且能力与安全并不协同。该论文评估了基于大语言模型的 Web 智能体在利用历史交互轨迹进行记忆增强时的安全脆弱性。
Reviews threats to autonomous agents and proposes a full-lifecycle defense blueprint, FASA
Published Mar 13 Abstract Using OpenClaw as an example, it divides risks into three layers and uses FASA to describe defense from input to system execution.This is a threat review and defense architecture report using OpenClaw as a case study, not a scored attack or defense evaluation.
Proposes a defense-in-depth aligned with the five stages to mitigate cross-stage threats to autonomous agents
Published Mar 12 Abstract This is a lifecycle threat analysis and layered mitigation design for OpenClaw, with the cases serving as qualitative evidence.This is a lifecycle threat analysis of OpenClaw, accompanied by a set of layered mitigation designs. The problem addressed is: when autonomous agents connect instant messaging, persistent memory, third-party plugins, and high-privilege execution, attacks are no longer limited to single-turn jailbreaks but can compound across multiple stages from initialization to execution. The authors argue that defenses hardening only individual interfaces cannot stop such cross-temporal attacks.
Attacks arXiv 2507.10457 · Jul 15, 2025 提出 LPCI，把延迟条件载荷写入记忆与检索库以跨会话触发
Published Jul 15, 2025
Evaluated models ChatGPT , Claude , LLaMA3 and others8 Abstract 作者定义 LPCI。作者把LPCI 定义为针对智能体逻辑执行、记忆留存和工具集成的跨阶段漏洞类，而不是单次输入上的提示改写。
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