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Oct 12 · Monday

All papers

Found 3 papers
Filter4

Attack type

Poisoning and backdoors3

Impact

Output manipulation3

System type

Models and APIs3

Attack surface / entry point

Code repositories and dependencies0Memory writes0Training data3

Layer

Training and data layer3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 233papers have no tags yet and are not included in filtered results.
Impact：Output manipulationAttack surface / entry point：Code repositories and dependenciesAttack surface / entry point：Memory writesAttack surface / entry point：Training dataClear all

Another 233papers do not have category tags yet and are not included in filtered results.

  1. Analysis and theoryarXiv 2610.13051

    Space beats statistics, retraining beats ranking: auditing training labels under label-only backdoors

    Auditing training labels for label-only backdoors, comparing spaces and statistics, and judging by retraining

    arXiv
    2610.13051
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    ResNet-32
    AbstractLabel inspection must be done in a space that has not been fitted to these labels, and judged by retrained models rather than detection scores.

    The authors ask: can a defender without trusted data decide which training labels to keep, and how much clean data this destroys, and what the final model accuracy is.

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  2. Analysis and theoryarXiv 2610.02886

    研究揭示无触发投毒审计缺口：事实答对不等于决策正确

    揭示无触发虚假训练下事实回答与下游决策的审计差距

    arXiv
    2610.02886
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen2.5-0.5B, Qwen2.5-1.5B, Qwen2.5-3B and others9
    Abstract作者发现无触发词虚假训练下事实回答与下游决策存在脱节，直接审计与事实纠错均无法保证决策正确。

    论文是一项针对语言模型在无触发词虚假信息训练下行为表现的实证分析研究。

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  3. Analysis and theoryarXiv 2609.36117

    arXiv 论文分析神经网络为何容易被植入后门

    分析投毒诱发的特征学习如何改变后门预算缩放

    arXiv
    2609.36117
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    quadratic neuron, network
    Abstract作者给出特征学习改变后门预算指数的可解机制，并以高斯采样实验检验。

    这是一项后门预算的理论分析，核心对象是最小二乘训练的二次神经元。作者关注：只依据冻结表示估算投毒成本，是否会误判具有特征学习能力的模型。

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