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Oct 12 · Monday

All papers

Found 3 papers
Filter3

Attack type

Poisoning and backdoors3

Defense type

Model hardening21Inference-time intervention3Detection and filtering9Permissions and approval3

Impact

Output manipulation3

System type

Models and APIs3

Attack surface / entry point

User input22Code repositories and dependencies0Images, audio and video6Training data3
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Defense type：Inference-time interventionAttack surface / entry point：Code repositories and dependenciesAttack surface / entry point：Training dataClear all

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  2. DefensesarXiv 2609.00786

    MROP：针对 Deep JSCC 后门攻击的掩码区域优化净化方法

    提出 MROP，在发射端推理时纯化输入以抵御训练投毒后门

    arXiv
    2609.00786
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    DeepJSCC, SwinJSCC
    AbstractMROP 不重训练，在发射端纯化补丁后门。

    MROP 是 deep JSCC 无线图像传输上的推理时输入纯化，对象是矩形补丁后门。

    Paper analysisRead analysis
  3. DefensesarXiv 2603.03206

    理解并缓解大语言模型引导中的数据集污染

    用 Lee-Valiant 稳健均值缓解 steering 数据集污染

    arXiv
    2603.03206
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.2-3B-Instruct, Mistral-7b-Instruct-v0.3, OLMo-2-1124-7B-Instruct
    Abstract数据污染会改 steering，协调注入最强。

    作者研究 contrastive steering 的训练数据被污染后，推理时的行为控制还剩多少，以及稳健均值能否接住。

    Paper analysisRead analysis
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