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Oct 13 · Tuesday

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  1. 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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  2. DefensesarXiv 2609.22724

    MATE:面向移动 Agent 的策略感知安全审计方法

    提出移动 Agent 轨迹审计器 MATE,判断轨迹是否违反可编辑安全策略

    Published
    Attacker
    Black-box
    Evaluated models
    MATE-0.5B, MATE-1.5B, MATE-3B
    AbstractMATE 用合成轨迹微调轻量审计器,在可编辑策略下判断移动智能体轨迹是否违规并给出解释。

    MATE 是面向移动 GUI 智能体的轻量策略条件轨迹审计器,在给定自然语言安全策略时判断轨迹是否违规并解释原因。

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  3. DefensesarXiv 2609.03438

    CONFLICTGUI 基准与 CONFLICTGUARD 框架:让多模态 GUI Agent 学会在冲突指令下终止执行

    提出 ConflictGuard,引导 GUI Agent 在冲突指令下终止执行

    Published
    Evaluated models
    UI-Venus-1.5-8B, UI-TARS-1.5-7B, OS-Atlas-Base-7B and others10
    AbstractConflictGUI 暴露 GUI 智能体的执行偏向过度服从,ConflictGuard 用条件转向提高冲突终止率。

    ConflictGuard用推理时的可行性核验和条件激活转向,让 GUI 智能体在指令不可行时终止,而不是盲执行。

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  4. AttacksarXiv 2608.06862

    SynChain:诱导计算机使用 Agent 自建攻击链并跨任务持久化

    提出 SYNCHAIN,诱导计算机使用 Agent 自建跨任务持久攻击链

    Published
    Evaluated models
    Qwen3.5-9B, Llama-3.1-8B, Ministral-3-8B

    Abstract论文揭示了 CUA 自合成工件带来的内部供应链威胁,验证了潜伏载荷的跨任务传播能力与防御局限。

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  5. 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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  6. 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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  7. DefensesarXiv 2602.10915

    Blind Gods and Broken Screens: Building a Secure, Intent-Centric Mobile Agent Operating System

    Proposes Aura, which uses a trusted kernel to mediate the identity, perception, and action control of mobile agents

    Published
    Evaluated models
    Gemini-3-Flash
    AbstractAura mediates structured interactions via the Agent Kernel.

    Aura uses kernel-mediated structured agent interaction, replacing screen-interface mobile assistants..

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  8. 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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