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Oct 13 · Tuesday
All papers Found 18 papers Another 203 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 提出 multi-CaMeL，在智能体调用边界隔离可信指令与非可信数据
Published Oct 5
Evaluated models Claude Fable 5 , GPT-5.5 , GPT-4.1 and others6 Abstract 论文针对多智能体中单体防御不组合的边界清洗问题，提出 multi-CaMeL 协议彻底阻断了 MultiAgentDojo 上的注入攻击。
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 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 MADBench，评测多智能体辩论是吸收还是放大对抗影响
Published Sep 30
Evaluated models gpt-4o , GPT-5 , GPT-6 Astra Abstract MADBench 显示辩论能吸收部分准确率攻击，同时放大未授权读写。MADBench 是一个评测多智能体辩论安全性的基准，用来看攻击是被辩论吸收还是被放大。
提出 VirusCascade，联合操纵商品描述与交互轨迹以劫持推荐智能体协同反思
Published Sep 29
Evaluated models AgentCF , AgentSEQ , AgentRAG and others4 Abstract 作者把反思写回视为推广攻击入口，并用联合操纵和诊断探针研究其持续与传播。VirusCascade 是针对推荐智能体协作反思的 black-box targeted promotion 攻击研究。 攻击者只改商品描述和少量账号的普通交互，试图让目标商品获得更多良性用户曝光。
用 DIBench 评测移动 GUI 智能体被 UI 注入后的决策完整性
Published Sep 29
Evaluated models Qwen2.5-VL(32B) , Qwen3-VL(32B) , GUI-Owl(32B) and others7 Abstract 论文提出移动智能体决策完整性基准 DIBench，发现欺诈 UI 注入诱导目标选择并推高完成率，通用防御收益不稳定。
提出 AttestChain，将供应链安全智能体报告签名上链并据此控制发布
Published Sep 25
Evaluated models GPT-class LLM Abstract AttestChain 将智能体报告与链上发布门控结合，现有证据来自受控合成构建的总体结果。AttestChain 是一个把 LLM 安全检查、智能体身份验证和许可链发布策略连接起来的软件供应链防御框架。 。
提出双控多智能体基准 DUMA-Bench，测量对抗环境下的策略违反
Published Sep 21
Evaluated models GPT-5 , GPT-5-mini , GPT-5-nano and others14 Abstract DUMA-Bench 用双控交互评测智能体安全。DUMA-Bench 把 τ²-bench 的双控交互做成智能体安全评测，用来看策略违反如何随用户、模型和环境一起出现。
提出多智能体系统提示注入的威胁模型与四层架构防御
Published Sep 19
Evaluated models GPT-4o (2024-08-06) , Claude 3.5 Sonnet , Llama 3 70B-Instruct 提出 Emergence World，对长时程多智能体系统开展对抗压力测试
Published Sep 15
Evaluated models Claude Opus 4.8 , DeepSeek v4 Pro , Gemini 3.5 Flash and others9 Abstract 作者通过长期平行世界实验指出模型对齐不具备可组合性，单模型群体会产生社会奉承、协同退缩及语言不透明等失效。
提出 SCOPE，联合后训练计算机使用智能体的任务能力与安全决策
Published Aug 27
Evaluated models SCOPE-mid-checkpoint , SCOPE-Capability-Safety , SCOPE-RL 提出多智能体流水线的边界攻击，使对抗内容作为可信输入跨层传播
Published Aug 1
Evaluated models GPT-5-mini , Claude Sonnet 4.5 , Kimi K2.5 Abstract 缺边界校验使对抗内容跨智能体传播，作者称这是架构属性而非模型能力。作者把多智能体 LLM 流水线的对抗问题定义为缺少边界校验，并用生产轨迹和受控实验检查脆弱性来自架构还是骨干模型。中间输出未经验证就传给下游。作者认为，一旦某级接受对抗内容，后续智能体会把它当作可信输入。
提出 AgentHazard 基准，评测计算机使用智能体的执行层有害行为
Published Apr 3
Evaluated models Qwen2.5-72B-Instruct , Qwen2.5-Coder-32B-Instruct , Qwen3-32B and others12 Abstract AgentHazard 评估了计算机使用智能体的执行安全，作者认为模型层对齐无法可靠保障智能体层面的安全性。AgentHazard 是针对计算机使用智能体执行层安全构建的评估基准，重点探究多步工具调用下局部合理动作累积引发的危害。
Proposing a lineage-graph governance layer to suppress error cascades in multi-agent collaboration
Published Mar 4 Abstract A single seed can solidify into a false consensus along a fixed collaboration graph.The authors turn "small errors becoming collective false consensus" in LLM-MAS into a measurable propagation on a graph, and use a plug-in that only changes messages, not topology, to reduce the final infection.
提出 AgentLeak 基准，测量多智能体内部通道隐私泄露
Published Feb 12
Evaluated models GPT-4o , GPT-4o-mini , Claude-3.5-Sonnet and others5 Proposes Aura, which uses a trusted kernel to mediate the identity, perception, and action control of mobile agents
Published Feb 11
Evaluated models Gemini-3-Flash Abstract Aura mediates structured interactions via the Agent Kernel.Aura uses kernel-mediated structured agent interaction, replacing screen-interface mobile assistants. .
用 MobiRed 评测移动 LLM Agent 面对第三方渠道的提示注入
Published Oct 31, 2025
Evaluated models Mobile-Agent-E , AppAgent , AutoGLM and others8 Abstract 论文评估了移动 LLM 智能体作为 confused deputy 的易受攻击性，作者称非应用控制通道可诱导智能体执行多步越权危害。
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