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Oct 12 · Monday

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  1. AttacksarXiv 2609.22711

    UBA-ORL:针对离线强化学习的遗忘激活后门攻击

    提出 UBA-ORL,以 BD/CM 样本在遗忘后激活离线 RL 后门

    Published
    Evaluated models
    TD3+BC, BCQ, IQL
    AbstractUBA-ORL 用竞争的 BD/CM 样本,使离线 RL 后门在删除伪装轨迹后激活。

    UBA-ORL 研究离线强化学习中由合规轨迹删除激活的后门:合法贡献者同时上传共享触发的后门轨迹和伪装轨迹,训练后触发响应被压低,删除伪装批次后再升高。受害系统是使用静态数据集和轨迹级遗忘接口的离线 RL 服务。对手不能看到训练算法、参数、梯度或具体遗忘算法,但能提交可区分批次,并在部署时向观测注入预定触发。

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  2. AttacksarXiv 2608.12273

    研究者提出 Convergent Detour Hijacking 攻击,可让技能型 LLM Agent 绕路并放大资源消耗

    提出 CDH 攻击,让技能型 Agent 绕路放大资源消耗

    Published
    Setting
    AI Agent
    Evaluated models
    Claude-Haiku-4.5, DeepSeek-V4-Pro, DeepSeek-V4-Flash and others6
    AbstractCDH 让静态技能在保持任务完成时拉高调用与 token,耗时则因后端而异。

    CDH 是针对渐进披露技能智能体的发布者侧、纯文本攻击:一个静态协调器把本可正确完成的任务,拐进有界但更耗资源的技能绕路,再交还原任务。

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