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Oct 12 · Monday

All papers

Found 8 papers
Filter5

Attack type

Poisoning and backdoors8

Attack technique

Dormant triggers3

Impact

Harmful content3Output manipulation8

System type

Models and APIs7

Attack surface / entry point

User input16Web pages and interfaces6Documents and messages8Tool responses5Retrieval corpus7Memory writes7Images, audio and video46Training data8Weights and gradients3

Layer

Training and data layer6

System component

Memory0Fine-tuning and open weights13Vision5Embeddings0Multimodal8
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Impact：Output manipulationAttack surface / entry point：Training dataSystem component：MemorySystem component：EmbeddingsSystem component：MultimodalClear all

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  1. DefensesarXiv 2610.09941

    OrthoPurify 用一步正交投影净化被后门污染的视觉语言模型

    提出 OrthoPurify，用正交投影清除视觉语言模型适配器中的劫持方向

    arXiv
    2610.09941
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    LLaVA-1.5-7B, Qwen3-VL-8B, LLaVA-1.5-13B and others5
    Abstract一次投影去掉劫持方向。

    OrthoPurify 是部署前的 LVLM 后门净化方法，用一次正交投影去掉被劫持的权重更新方向。

    Paper analysisRead analysis
  4. DefensesarXiv 2609.06346

    面向后门攻击下持续学习的鲁棒动态扩展：净化与选择性恢复

    提出持续学习下的净化与选择性恢复框架，抵御逐任务训练投毒后门

    arXiv
    2609.06346
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    ViT-B/16
    Abstract作者报告任务内净化与恢复可配合动态专家防御后门，但保证依赖分离条件。

    这是一项面向持续图像分类的后门防御研究：模型只使用当前任务数据，同时需要保留过去知识并避免学习恶意监督。

    Paper analysisRead analysis
  7. AttacksarXiv 2606.09499

    研究者提出针对世界模型的机器人学习管线投毒攻击

    提出 VPH 与 VTH，只改视频帧劫持世界模型并投毒下游策略

    arXiv
    2606.09499
    Published
    Layer
    Training and data layer
    Setting
    Embodied systems and robotics
    Evaluated models
    Cosmos-Predict 2.5, Cosmos-Predict 2.5 AC, Dino WM and others4
    Abstract只改视频帧经世界模型投毒。

    只改看似安全的视频帧，经世界模型之后才改变下游机器人策略。 问题在于世界模型进入数据生成后，数据集检查看到的仍是安全演示，合成轨迹却可以变危险。

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  8. Evaluation and benchmarksarXiv 2511.18921

    BackdoorVLM：面向视觉语言模型后门攻击与防御的基准

    提出 BackdoorVLM 基准，评测视觉语言模型微调中的后门攻击与防御

    arXiv
    2511.18921
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    LLaVA-1.5-7B, Qwen2.5-VL-7B
    AbstractBackdoorVLM 统一评测 VLM 后门与防御：含文本触发在低投毒率下更易激活，现有防御跨类型不稳定。

    BackdoorVLM 是作者提出的 VLM 后门攻击与防御评测基准，作者称它是首个此类综合基准。

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