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Oct 12 · Monday

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Found 4 papers
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Model risks

Reward hacking1Dangerous agent actions3Inappropriate refusal3

Defense type

Model hardening37Inference-time intervention5Detection and filtering4Instruction–data separation4Permissions and approval4Monitoring and auditing9

Impact

Harmful content3

System type

Models and APIs3
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Model risks：Reward hackingModel risks：Inappropriate refusalDefense type：Instruction–data separationClear all

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  1. InterpretabilityarXiv 2610.13078

    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

    arXiv
    2610.13078
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    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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  3. DefensesarXiv 2602.03224

    TAME：可信的 Agent 记忆测试时演化与系统化基准评测

    提出 TAME 调节 Agent 测试时记忆演化，并用 Trust-Memevo 评测可信度

    arXiv
    2602.03224
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    Qwen3-32B, GPT-5.2
    AbstractTAME 在共享记忆上同时调节效用与信任。

    TAME 针对测试时记忆演化中的 Agent Memory Misevolution：良性任务只按成败保留经验时，任务表现可以上升，安全、隐私、公平等可信度下降或随领域波动。

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  4. Analysis and theoryarXiv 2506.06404

    研究揭示价值对齐 LLM 的意外危害并提出上下文对齐方法

    分析按 Schwartz 价值对齐后的意外危害，并提出上下文对齐方法

    arXiv
    2506.06404
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-2 7B, Llama-3.1 8B, Gemma-2 9B and others6
    Abstract价值对齐会随 Schwartz 价值改变有害倾向。

    作者研究按 Schwartz 价值对齐的 LLM 为何在安全评测里更易有害，并用提示做了一个不直说要安全的缓解试验。

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