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Oct 12 · Monday

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  1. Attention sees, MLP refuses: tracing the safety pathway from attention to MLP in LLMs

    Trace attention to identify harmfulness to the MLP refusal write pathway and propose Sentinel

    Published
    Evaluated models
    Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Qwen3-8B-Instruct and others18
    AbstractAttention updates are more readable for harmfulness, MLP updates write refusal, and Sentinel routes alerts using two-path signals.

    The authors reframe jailbreaking and excessive refusal of aligned models as a pathway problem on the residual stream from recognition to writing, and accordingly provide conditional reminders before decoding.

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  2. 论文提出拒答的几何解释:事后安全训练脆弱,预训练期持续安全共训更抗攻击

    解释后验安全的权重几何脆弱性,并提出预训练持续安全共训练

    Published
    Evaluated models
    Qwen-2.5-7B, Qwen-2.5-7B-Instruct, Llama-3-8B and others15

    Abstract论文将后验安全脆弱性归因于正交抑制,并提出通过预训练持续共训练实现具备微调抗性的安全防护。

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  3. 研究:拒答方向可跨架构迁移,安全工具可按写入位置重新估计

    在写入点定位拒答方向,经刚性旋转跨架构迁移并门控放大

    Published
    Evaluated models
    Falcon-Mamba-7B, Falcon3-Mamba-7B, Llama-3.1-8B and others8
    Abstract拒答方向可经刚性旋转跨架构迁移,但必须在各架构的写入点读取。

    作者研究拒答表示是否随架构改变,结论是表示共享、读取位置随架构变。

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  4. aiXamine:跨维度评测 LLM 安全、安全(security)与隐私权衡

    提出 aiXamine 黑盒基准,统一评测 LLM 安全、稳健与隐私权衡

    Published
    Evaluated models
    openai/gpt-5-mini, openai/gpt-4o, anthropic/claude-haiku-4.5 and others15
    AbstractaiXamine 用九服务黑盒协议比较百余个模型,作者称单轴信任度进展可能削弱其他轴。

    aiXamine 是一套黑盒评测协议,用来在同一条件下比较 LLM 的 safety、security 与 privacy,而不是再产出一个能力分。

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  5. DefensesarXiv 2608.13304

    WIFA:按意图分组监督缓解包装式越狱与过度拒答

    提出 WIFA 意图组监督微调,缓解包装式越狱与过度拒答

    Published
    Evaluated models
    Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct
    AbstractWIFA 用匹配意图组做数据层,WIFA-Boost 偏高安全,A-GCRT 在 Qwen 上把过拒降到基座以下。

    WIFA 是一种自动意图组数据构造,用来让安全微调拒绝有害意图而不是拒绝表面包装。WIFA-Boost 与 A-GCRT 在同一数据层上选取不同的安全–过拒工作点。

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  6. CSSBench: Evaluating the safety of lightweight LLMs against Chinese-specific adversarial patterns

    提出 CSSBench 评测轻量模型对中文表面对抗改写的失守与过度拒绝

    Published
    Evaluated models
    Qwen3-0.6B, Qwen3-1.7B, Qwen3-8B and others10
    AbstractCSSBench 用表面扰动和边界查询评轻量模型。

    CSSBench 评测轻量 LLM 在中文特有表面改写下的安全,并同时测量过度拒绝。要处理的缺口是:英文向护栏和只测干净查询的中文基准,看不清同音、拼音、符号混写、零宽字符这些写法下的失守,也分不清较低风险来自细粒度判断还是粗粒度拒答。作者称轻量模型更需要这种评测。

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