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Oct 12 · Monday

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  1. 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

    Published
    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。

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  2. 研究提出说服传播现象:无关说服性对话会改变 AI Agent 的任务执行行为

    测量任务无关说服对 Agent 编码与网页研究行为的影响

    Published
    Evaluated models
    gpt-4.1-nano, mistral-nemo-12b, llama-3.1-8b
    Abstract任务无关说服会在轨迹上留下任务相关的行为痕迹,但立场易感性不能稳定预测其幅度。

    这篇工作测量一件事:与任务无关的说服进入对话之后,AI 智能体执行后面的编码或网页研究时,轨迹是否系统性不同,以及“更容易被说服”能否预测差有多大。

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