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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 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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  3. 研究者提出统一滥用监控基准,覆盖分解攻击与提示注入约 6200 条轨迹

    提出危害窗口与区间指标,统一评测分解攻击和提示注入下的智能体滥用监视

    Published
    Setting
    AI Agent
    Evaluated models
    Gemma 4 26B-A4B, Qwen3.6 27B, Qwen3.5 27B and others12

    Abstract统一智能体滥用监视框架,动作视角兼顾两类威胁但危害定位仍存瓶颈。

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  4. DefensesarXiv 2610.02413

    研究:分类后缀可提升激活探针的分布外恶意输入检测

    用分类后缀提升激活探针对分布外恶意输入的检测

    Published
    Evaluated models
    Llama-3.1-8B, Qwen3.5-9B, Gemma-4-12B
    Abstract作者称排序增益来自分类格式,低 FPR 精度来自点名准则。

    用户轮后的分类后缀,被作者用作激活探针的分布外检测干预:排序看格式,低 FPR 精度看是否点名恶意/良性。

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  5. DefensesarXiv 2609.30841

    为何扩散语言模型的越狱会成功:能量景观分析

    用能量势垒信号检测扩散语言模型的越狱

    Published
    Evaluated models
    LLaDA-8B-Instruct, LLaDA-1.5, Dream-v0-Instruct-7B and others4
    Abstract能量势垒把 dLLM 越狱分成掩盖初始状态或中途越垒,三个 logit 信号据此互补检测。

    Bach 等提出用去噪能量景观解释 masked diffusion 语言模型上的越狱,并由此得到三个免训练检测信号。。

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  6. DefensesarXiv 2609.14003

    FlowSeal:用信息流控制阻断 LLM Agent 的隐私泄露

    提出 FLOWSEAL,用工具级信息流控制阻断智能体隐私泄露

    Published
    Setting
    AI Agent
    Evaluated models
    DeepSeek-V3.2, GPT-4.1-mini, Gemini-Flash-2.5

    Abstract作者分析大模型智能体隐私检查与攻击面重合问题,提出外部拦截防御 FLOWSEAL 并验证有效性。

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  7. DefensesarXiv 2609.10613

    后验重加权框架解释并缓解多模态大模型上下文越狱

    用后验重加权解释多模态上下文越狱,并提出风险门控的良性示例注入防御

    Published
    Evaluated models
    Qwen3-VL-2B, Qwen3-VL-4B, Qwen3-VL-8B and others8
    Abstract后验重加权解释上下文越狱的缩放,并支持风险门控的良性示例防御。

    Zhang 等把多模态 ICL 越狱写成对齐模型在安全与有害隐模式之间的推理时后验漂移,并据此做风险门控防御。

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  8. DefensesarXiv 2609.02786

    SafeEvolve:基于智能体经验的 Harness-Policy 协同演化安全对齐框架

    提出 SafeEvolve,用轨迹证据协同演化安全 harness 与策略

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3.5-4B, Qwen3-4B-Instruct-2507, Qwen3.5-1.7B and others4
    AbstractSafeEvolve 用轨迹证据协同更新安全 harness 和策略,在两个 4B 模型的智能体安全基准上改变安全与效用。

    SafeEvolve 把已完成的 on-policy 轨迹变成安全 harness 的有界更新,并用两阶段 SFT-RL 把这些更新内化进工具使用策略。

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  9. How Few-shot Examples Affect Prompt-based Defenses Against LLM Jailbreak Attacks

    分析少样本示范对 RoP 与 ToP 提示防御抗越狱效果的影响

    Published
    Evaluated models
    Pangu-Embedded-1B, Pangu-Embedded-7B, Pangu-Embedded-7B-Think and others9
    Abstractfew-shot 可抬高 RoP、压低 ToP,但表内有反号,Think 模型更低。

    作者评估 few-shot 示范如何改变 RoP 与 ToP 对越狱的防御效果。

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  10. DefensesarXiv 2602.13234

    Stay in Character, Stay Safe: A Dual-Loop Adversarial Self-Evolving Framework for Safe Role-Playing Agents

    提出 DASE,以对抗自演化知识库在推理时加固角色扮演抗越狱

    Published
    Attacker
    Black-box
    Evaluated models
    Kimi-K2-Instruct, GPT-5.2
    AbstractDASE 用双循环知识库,在两家闭源模型上同时提高保真分与拒答率。

    DASE 是给专有闭源角色扮演智能体用的训练自由防御,目标是人设保真与安全拒答一起成立,而不是改模型权重。问题在于人设沉浸,尤其负面人设,会压过原有护栏。SFT、数据筛选和正则又跟不上新人设与新攻击,也不能用于没有梯度的 black-box API。

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  11. 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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