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Oct 13 · Tuesday

All papers

Found 17 papers

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  1. DefensesarXiv 2610.07657

    DEFER1 多智能体防御研究报告受控测试中的攻击成功率下降

    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
    Evaluated models
    Qwen3-235B-A22B-Instruct-2507, gpt-oss-120b, Llama-4-Scout-17B-16E-Instruct and others5
    AbstractDEFER 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。

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  2. DefensesarXiv 2610.05640

    Research reveals that the CaMeL defense fails in multi-agent systems and proposes multi-CaMeL

    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
    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。

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  3. DefensesarXiv 2610.03089

    COBRA: A Dual-LLM Architecture for Defending Computer-Using Agents Against Branch-Jumping Attacks

    提出 COBRA 双 LLM 架构,防御计算机使用 Agent 的分支转向攻击

    Published
    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。

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  4. MADBench: multi-agent debate safety evaluation benchmark released

    提出 MADBench,评测多智能体辩论是吸收还是放大对抗影响

    Published
    Evaluated models
    gpt-4o, GPT-5, GPT-6 Astra
    AbstractMADBench 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。

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  5. AttacksarXiv 2609.38270

    VirusCascade:劫持 LLM 推荐智能体的协同反思机制

    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
    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。

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  6. ORBIT:基于 UK AISI Inspect 的多智能体安全评测框架

    提出 ORBIT 框架,评测多智能体防御对合谋等威胁的效果

    Published
    Evaluated models
    GPT-5.4, Claude Sonnet 4.6, Qwen3-235B and others5

    AbstractORBIT 用六层配置比较多智能体防御,逐动作监控不迁移到合谋。

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  7. DefensesarXiv 2609.31282

    基于区块链的 Agentic AI 框架:面向安全软件供应链的资源优化与能耗感知

    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
    Evaluated models
    GPT-class LLM
    AbstractAttestChain 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。。

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  8. DUMA-Bench:评估 LLM Agent 安全的双控多智能体基准

    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
    Evaluated models
    GPT-5, GPT-5-mini, GPT-5-nano and others14
    AbstractDUMA-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。

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  9. Emergence World:对长时程多智能体系统开展对抗压力测试

    提出 Emergence World,对长时程多智能体系统开展对抗压力测试

    Published
    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。

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  10. AgentHazard:评估计算机使用智能体有害行为的基准

    提出 AgentHazard 基准,评测计算机使用智能体的执行层有害行为

    Published
    Evaluated models
    Qwen2.5-72B-Instruct, Qwen2.5-Coder-32B-Instruct, Qwen3-32B and others12
    AbstractAgentHazard 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。

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  11. DefensesarXiv 2603.14707

    Visual Confusion Agents: Exploiting and Defending Against Perception Failures in Computer-Use Agents

    Proposes out-of-process dual-channel comparison classification to intercept erroneous clicks by computer-use agents

    Published
    Evaluated models
    multi-modal-embed-small
    AbstractPerception errors can be exploited by swapping screen pixels.

    The authors frame CUA's perception failure as a security vulnerability, visual confused deputy: the agent authorizes click(x,y) based on a misread screen, and the actual landing point differs from the element it believes it is clicking.

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  12. DefensesarXiv 2603.04474

    From Spark to Wildfire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration

    Proposing a lineage-graph governance layer to suppress error cascades in multi-agent collaboration

    Published
    AbstractA 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.

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  13. MobiRed 框架测评八款移动 LLM Agent 的第三方渠道提示注入攻击

    用 MobiRed 评测移动 LLM Agent 面对第三方渠道的提示注入

    Published
    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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