Skip to content

Translation in progress Some entries still include text in the original language.

Oct 12 · Monday

All papers

Found 5 papers
Filter3

Attack type

Prompt injection28Poisoning and backdoors13Model tampering7Extraction and theft5Detection evasion12

Impact

Data and privacy leaks4

System type

Models and APIs5

Attack surface / entry point

User input18Documents and messages0Training data4Weights and gradients5

Layer

Model layer3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 232papers have no tags yet and are not included in filtered results.
Attack type：Extraction and theftAttack surface / entry point：Documents and messagesAttack surface / entry point：Weights and gradientsClear all

Another 232papers do not have category tags yet and are not included in filtered results.

  2. DefensesarXiv 2609.23586

    VidMark：面向视频生成模型知识产权保护的高效水印方案

    提出 VidMark 水印方案，核验视频生成模型的输出来源与所有权

    arXiv
    2609.23586
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Stable Video Diffusion (SVD), Open-Sora, Wan
    AbstractVidMark 加解码器引导微调，把模型签名写入 VGM 输出并用于两项核验。

    Huang 等人提出一套面向视频生成模型的生成内嵌水印方案，用来同时判断一条视频是否来自受保护模型，以及一个嫌疑模型是否为该模型的拷贝。

    Paper analysisRead analysis
  4. DefensesarXiv 2609.15671

    QPriv-VL：面向视觉语言模型的问题引导 token 裁剪隐私防御

    提出 QPriv-VL，用问题引导剪枝降低分布式视觉问答的隐私泄露

    arXiv
    2609.15671
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    LLaVA-1.5-7B, Llama-2
    AbstractQPriv-VL 在切层前按问题相关度与 DINOv2 敏感度剪视觉 token，作者称能在约四成 token 下兼顾准确率与四类攻击。

    QPriv-VL 把问题引导的视觉 token 剪枝做成分布式 VQA 的客户端隐私防御，而不是只做集中式推理加速。

    Paper analysisRead analysis
  5. DefensesarXiv 2609.10117

    研究揭示基于混淆的端侧 LLM 保护方案的安全边界

    提出 Collapse，从端侧混淆暴露视图抽取高效用替代模型

    arXiv
    2609.10117
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    BERT-Base, ViT-Base, Qwen2.5-0.5B and others4
    AbstractOprior 是四原语复合的安全边界，Collapse 利用列方向泄漏抽取替代模型，Oext 只被同一攻击评测。

    这篇工作用代数原语统一若干 TEE 卸载混淆，给出该族的典范边界，再说明该边界挡不住一种分阶段模型抽取。。

    Paper analysisRead analysis
You have reached the end