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Oct 12 · Monday

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Found 3 papers
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Attack type

Poisoning and backdoors4Model tampering3

Attack surface / entry point

Training data2Weights and gradients2
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Attack type：Model tamperingAttack surface / entry point：Training dataAttack surface / entry point：Weights and gradientsClear all

Another 254papers do not have category tags yet and are not included in filtered results.

  1. Evaluation and benchmarksarXiv 2609.08901

    论文揭示 BatchNorm 架构下机器遗忘评测的归一化伪影

    诊断 BatchNorm 架构下机器遗忘评测的归一化伪影

    arXiv
    2609.08901
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    ResNet-18, ResNet-50, ViT-S/16 and others4
    Abstract一次不改权重的 BN 重校准能翻转多数方法的表面遗忘，但不改变权重里的编码器泄露。

    BN illusion 是作者对近似机器遗忘评测中一种 BatchNorm 测量伪影的命名：不改权重、只用 retain 数据重算运行统计，就可以把表面遗忘准确率翻过来。

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  2. Evaluation and benchmarksarXiv 2605.14605

    Study: malicious fine-tuning defenses fail under continued training, harmfulness rises across all 72 defense trajectories

    用代价曲线评估恶意微调防御在持续训练下的衰减

    arXiv
    2605.14605
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Llama-2-7B-chat, Qwen3-8B, Llama-3.1-8B-Instruct and others8

    Abstract论文指出恶意微调防御在持续训练下均会衰减，表明基于有限攻击者假设的防御无法为开源模型提供持久保护。

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