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Oct 12 · Monday
All papers Found 11 papers Another 233 papers do not have category tags yet and are not included in filtered results.
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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 LGWM, using an action-conditioned world model to verify GUI agent screen transitions.
Published Oct 8
Evaluated models LGWM 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 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 APEX,在执行边界用授权契约防御 Agent 间接提示注入
Published Oct 4
Evaluated models DeepSeek-V4-Flash , GLM-5.2 , GPT-5.6-sol 提出 COBRA 双 LLM 架构,防御计算机使用 Agent 的分支转向攻击
Published Oct 2
Evaluated models gpt-5 , UI-TARS-1.5-7B , claude-sonnet-5 and others6 Abstract 提出分支转向防御架构 COBRA,通过事前能力约束与边界代理消除分支转向攻击并兼顾效用。
提出 Tracekit,审计编码 Agent 的意图、推理与动作并检测账本篡改
Published Sep 29
Evaluated models Claude Code (claude -p, version 2.1) , 独立评审(同一 CLI,无工具) Abstract Tracekit 把意图、自述和动作写入可锚定的哈希链。Tracekit 是无第三方依赖的编码智能体审计层:把人类请求、模型自述和已执行动作并排写入可外部锚定的哈希链,执行前用正则门,事后用规则和独立模型互查。
构建 ACE 语料,评测内核级证据对 Agent 攻击检测的有效性
Published Sep 24
Evaluated models AdaBoost , XGBoost , TabPFN and others10 Abstract ACE 用配对的内核与应用层证据表明,syscall 痕迹可判别智能体攻击,两层组合通常更好。ACE 是一份把智能体会话的内核 syscall 痕迹与应用层清单、转录配对起来的检测语料和基准。 。
提出 ClawSafety,评测个人智能体读到对抗工件后是否执行有害动作
Published Apr 2
Evaluated models Claude Sonnet 4.6 , GPT-5.1 , Gemini 2.5 Pro and others5 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.
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