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Oct 12 · Monday

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  1. DefensesarXiv 2610.09167

    Persist-3 用顺序检验识别未见越狱来源,提升 MTK 与 Prompt Guard 2 防御效果

    提出 Persist-3 序贯检验,从隐藏表示识别未见越狱源

    Published
    Evaluated models
    Llama2-7B-chat, Qwen2.5-7B-Instruct, Gemma-2-9B-it and others4
    AbstractPersist-3 用有限窗口检验攻击流是否超出已知越狱源,检出率低于全历史 SR-DKW,但决策只依赖最近观测。

    Persist-3 是一个有限记忆的序贯检测器,用来判断攻击表示流是否已经不能由任何一个已知越狱源解释。。

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  2. DefensesarXiv 2609.04665

    HackProbe:面向自演化语言模型奖励作弊的黑盒检测与免疫方法

    提出 HackProbe,黑盒检测并免疫自演化中的奖励作弊

    Published
    Abstract黑盒探针检测自演化奖励黑客,并以带宽受限重选回收真实能力。

    HackProbe 是挂在自演化循环上的黑盒监控器,用来发现奖励黑客并改选候选。

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  3. CHERRL:可复现、分析与检测评分标准强化学习中的奖励作弊

    提出 CHERRL 注入裁判偏差以复现奖励作弊,并用 RHDA 检测训练起点

    Published
    Evaluated models
    Qwen3-4B, Qwen3.5-plus, Qwen3.5-397B-A17B and others4
    AbstractCHERRL 注入已知偏差以观察奖励黑客,并用于分析偏差和评测起点检测器 RHDA。

    CHERRL 是 rubric-based RL 的可控环境,用来复现奖励黑客、观察奖励分歧,并给检测器提供起点参照。

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  4. DefensesarXiv 2602.01750

    Adversarial Reward Auditing: Proactively Detecting and Mitigating Reward Hacking

    提出 ARA,读取奖励模型激活并门控 RLHF 奖励以缓解奖励作弊

    Published
    Evaluated models
    Llama-2-7B, Llama-3.1-8B, Qwen-2.5-7B
    AbstractARA 用奖励模型内部表示检出利用,并在 RLHF 里按 Auditor 概率压低代理奖励。

    ARA 是一套两阶段的 RLHF 审计:先对冻结奖励模型找出利用回复,再在策略优化时按检出概率压低奖励。

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  5. DefensesarXiv 2505.23559

    SafeScientist:让 LLM 智能体的科学发现具备风险意识

    提出 SafeScientist 四层监控,阻止科研智能体执行高风险或不伦理任务

    Published
    Evaluated models
    GPT-4o, GPT-4.1, GPT-3.5-turbo and others5
    Abstract四层监控加科研安全基准。

    SafeScientist 是带四层监控的科研智能体框架,并用 SciSafetyBench 评测科学场景中的任务安全。

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