Translation in progress Some entries still include text in the original language.
Oct 13 · Tuesday
Another 201 papers do not have category tags yet and are not included in filtered results.
“
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 MFOTL 离线时序验证以监控工具 Agent 的危险动作链
Published Oct 7 Abstract 形式化验证可有效识别多步攻击，但其有效性高度依赖轨迹的可信历史记录。这篇论文对工具调用大语言模型智能体的安全监控进行了形式化时序验证研究。
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 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 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.
Proposes inference-time defense ICON, which detects attention over-focusing and redirects to correct indirect prompt injection in agents.
Published Feb 24
Evaluated models Qwen-3-8B , Llama-3.1-8B , Mistral-8B and others6 Abstract ICON detects over-focusing and redirects at inference time to balance IPI safety and task utility.ICON detects attention over-focusing from IPI at inference time and redirects, avoiding refusal that would terminate the agent trajectory.
提出 Zombie Agent 攻击，经网页间接注入将载荷写入长期记忆并跨会话触发
Published Feb 17
Evaluated models Gemini-2.5-Flash , GLM-4.7-Flash Abstract 记忆写入把一次网页间接注入变成跨会话控制。Zombie Agent 把自进化 LLM 智能体的跨会话记忆当成攻击面：攻击者只发布网页，良性任务读入后，正常记忆更新留下载荷，并在以后无关会话里触发未授权工具行为。
提出 PASB，端到端评测 OpenClaw 的系统级攻击
Published Feb 9
Evaluated models Llama-3.1-70B-Instruct , Qwen2.5-7B-Instruct , GPT-4o-mini You have reached the end