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Oct 12 · Monday

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Found 2 papers
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System type

Models and APIs8LLM applications17AI Agent62coding agent5web agent4Browser and computer use2Multi-agent systems6

Attack surface / entry point

Web pages and interfaces7Documents and messages2Images, audio and video3

Layer

Model layer0Application layer2
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System type：Browser and computer useAttack surface / entry point：Documents and messagesLayer：Model layerLayer：Application layerClear all

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  2. Evaluation and benchmarksarXiv 2510.27140

    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

    arXiv
    2510.27140
    Published
    Layer
    Application layer
    Setting
    Browser and computer use
    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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