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Oct 13 · Tuesday

All papers

Found 3 papers
Filter5

Impact

Unauthorized actions43Data and privacy leaks8Output manipulation10Availability and reliability3

System type

AI Agent3Browser and computer use0Multi-agent systems0

Attack surface / entry point

User input4Web pages and interfaces3Tool and skill descriptions3Images, audio and video3Distribution channels3

Layer

Application layer3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 213papers have no tags yet and are not included in filtered results.
Impact：Availability and reliabilitySystem type：AI AgentSystem type：Browser and computer useSystem type：Multi-agent systemsAttack surface / entry point：Web pages and interfacesClear all

Another 213papers 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
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