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Oct 13 · Tuesday

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Found 2 papers
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Attack surface / entry point

User input8Web pages and interfaces0Documents and messages1Weights and gradients1

Layer

Model layer2Application layer9

System component

Monitors2Fine-tuning and open weights7Image generation3Multimodal3
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Attack surface / entry point：Web pages and interfacesAttack surface / entry point：Documents and messagesAttack surface / entry point：Weights and gradientsLayer：Model layerSystem component：MonitorsClear all

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  1. DefensesarXiv 2608.05578

    AMS 工具通过激活分析检测语言模型的安全训练改动

    提出 AMS，用激活分离与方向相似度检测安全训练改动

    arXiv
    2608.05578
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Llama-3.2-1B-Instruct and others14
    AbstractAMS 按激活签名把安全改动分四类，前三类可由两级扫描覆盖，行为微调会漏。

    AMS 是面向开放权重模型、部署前使用的激活扫描器，用来看安全训练被拿掉或改写之后，激活空间里还留着什么几何痕迹。

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  2. Evaluation and benchmarksarXiv 2602.14161

    When benchmarks lie: evaluating malicious prompt classifiers under real-world distribution shift

    提出 LODO 评测恶意提示探针的跨数据集泛化

    arXiv
    2602.14161
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.1-8B-Instruct, Gemma-3-27B, Qwen-3.5-4B and others5
    Abstract作者称 LODO 露出 CV 高估，数据集身份与安全方向部分重叠，标准域泛化干预关不上。

    LODO 用来检查恶意提示词分类器换成没见过的数据集后，还剩多少检测能力。。

    Paper analysisRead analysis
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