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Oct 12 · Monday

All papers

Found 2 papers
Filter3

Impact

Output manipulation2

Attack surface / entry point

User input4Images, audio and video2Training data13Weights and gradients10Distribution channels4

System component

Guardrails and judges4Fine-tuning and open weights2Vision30Audio7Video4Image generation6Multimodal45
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Impact：Output manipulationAttack surface / entry point：Images, audio and videoSystem component：Fine-tuning and open weightsClear all

Another 265papers do not have category tags yet and are not included in filtered results.

  2. 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。

    Paper analysisRead analysis
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