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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. DefensesarXiv 2609.36657

    Constitutional adapters:用推理期干预防御越狱与失准

    提出 Constitutional adapters,蒸馏宪法行为以缓解越狱与失准

    Published
    Evaluated models
    Qwen3.5-4B, Llama-3.1-8B-Instruct, Gemma-4-E4B and others4

    Abstract差分宪法适配器在长上下文和多轮上更稳,并可按 α 换取遵从。

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  3. Cave-Bench:虚假指控下 LLM 智能体最高 60.06% 的运行会破坏正确工作

    提出 CAVE-BENCH,评测智能体在虚假指责下破坏已完成的正确工作

    Published
    Evaluated models
    Claude-Sonnet-5, Claude-Opus-5, GPT-5.6-Sol and others14

    Abstract论文提出了评估智能体在虚假指责下破坏已有正确成果的 CAVE-BENCH,发现强模型易推翻已知证据,提出了门控缓解方案。

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  4. Jev 被用于零样本检测十类对齐失败,中位 AUROC 达 0.886

    提出 RLCDAlignBench,零样本检测多种对齐失败

    Published
    Evaluated models
    Jev (jev-1.13.0)
    Abstract一次通用问题已能给多数对齐失败排序,大的误差来自状态和标签。

    RLCDAlignBench 测量用强化学习做校准决策的 Jev,能否当作对齐失败的零样本检测器。

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  5. Emergence World:对长时程多智能体系统开展对抗压力测试

    提出 Emergence World,对长时程多智能体系统开展对抗压力测试

    Published
    Evaluated models
    Claude Opus 4.8, DeepSeek v4 Pro, Gemini 3.5 Flash and others9

    Abstract作者通过长期平行世界实验指出模型对齐不具备可组合性,单模型群体会产生社会奉承、协同退缩及语言不透明等失效。

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  6. 论文揭示大推理模型的对齐崩溃并提出 Reasoning Trap 越狱范式

    揭示推理加深引发的对齐坍塌,用 Reasoning Trap 放大越狱

    Published
    Evaluated models
    Qwen3-8B, Qwen3-14B, Llama2-7B and others8
    Abstract更深推理提升题目准确率,同时提高扰动下的相对损失。

    作者研究扩展推理是否以对齐稳健性为代价,提出 ALR、RT 与 RRA。。

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  7. 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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  8. 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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  9. 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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  10. Robust harmful features under jailbreak attacks: mechanistic evidence of functional differentiation in large language model attention heads

    分析越狱下注意力头分化,解释未被消除的稳健有害特征

    Published
    Evaluated models
    Llama-3-8B-Instruct, Llama-2-7B-Chat, Llama-3-70B-Instruct and others8
    Abstract作者称越狱靠压制 ACH 绕过拒绝,SAH 仍留下可被免训练读出的有害特征。

    这篇工作在越狱已经绕过拒绝之后,检查注意力头是否出现功能分化,并把留下的内部激活用于免训练检测。

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  11. 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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