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Oct 13 · Tuesday

All papers

Found 4 papers
Filter5

Impact

Harmful content3Unauthorized actions46Data and privacy leaks10Output manipulation19Availability and reliability4

System type

AI Agent4

Attack surface / entry point

User input3Web pages and interfaces3Tool and skill descriptions3Images, audio and video1Weights and gradients0Distribution channels3

Layer

Application layer4Training and data layer3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 227papers have no tags yet and are not included in filtered results.
Impact：Availability and reliabilityAttack surface / entry point：Web pages and interfacesAttack surface / entry point：Images, audio and videoAttack surface / entry point：Weights and gradientsLayer：Application layerClear all

Another 227papers do not have category tags yet and are not included in filtered results.

  2. Evaluation and benchmarksarXiv 2609.19140

    AgentLSD：在对抗性任务污染下评测 AI 安全 Agent

    评测安全 Agent 在对抗性任务污染下的解题成功与开销

    arXiv
    2609.19140
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    Gemma-4 31B, DeepSeek-V4 Flash, GPT-OSS 120B and others6
    AbstractAgentLSD 用配对 web CTF 表明，欺骗性环境证据会改变安全智能体的解题率与工作量。

    AgentLSD 是一个对照框架，用来测量安全智能体在任务本身不变时，对对抗性任务污染的反应。

    Paper analysisRead analysis
  4. AttacksarXiv 2505.23266

    AdvOF：用对抗物体融合攻击 VLM 驱动的视觉语言导航智能体

    提出 AdvOF，生成对抗物体误导视觉语言导航智能体的场景感知

    arXiv
    2505.23266
    Published
    Layer
    Application layer
    Setting
    Embodied systems and robotics
    Attacker
    White-box
    Evaluated models
    Lseg, Clip, ViT-L/16 and others5
    AbstractAdvOF 以多视角融合生成对抗物体。

    AdvOF 是针对 VLM 驱动 VLN 服务导航的对抗物体攻击。

    Paper analysisRead analysis
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