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Oct 12 · Monday

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  1. 研究评估 GradSafe 在多轮对话中的越狱检测脆弱性

    评测多轮对话中 GradSafe 越狱检测的可分性与脆弱性

    Published
    Evaluated models
    Llama 3.1 8B Instruct, Qwen2.5-7B-Instruct
    AbstractCWS 显示不安全梯度在真实多轮流量中变弱,且随攻击族和模型而变。

    作者评估梯度越狱检测从单条提示词迁到多轮对话后还剩多少可分性,方法是用 Context Window Scanner 把 GradSafe 原封不动地套到用户轮窗口上。

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  2. MultiTurnPSB

    构建 MultiTurnPSB,评测四轮医疗越狱与分类器输入防御

    Published
    Evaluated models
    GPT-4.1-mini, Claude Sonnet 4.5
    Abstract多轮攻击加剧医疗模型风险,分类器干预有效但受高误报率制约。

    MultiTurnPSB 针对患者端医疗大模型在持续交互中的安全风险,构建了包含三种攻击模态的四轮对抗评测基准。

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  3. ADVERSA:测量大语言模型中的多轮护栏退化与评判可靠性

    用 ADVERSA 测量多轮对抗下护栏退化与评审可靠性

    Published
    Evaluated models
    Claude Opus 4.6, Gemini 3.1 Pro, GPT-5.2
    AbstractADVERSA 用逐轮合规轨迹和三评审共识测量多轮护栏变化。

    ADVERSA是一套自动多轮红队测量框架,把护栏行为记成逐轮合规轨迹,并同时测量评审是否一致。要处理的问题是:单轮二元通过或失败既看不到持续对抗下安全属性如何变化,也分不开拒答逐步巩固和分数保持稳定。作者认为真实使用中的对手会改写并持续多轮。

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