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Oct 13 · Tuesday

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Attack surface / entry point

User input15Web pages and interfaces9Documents and messages10Tool responses6Memory writes11Inter-agent messages3Images, audio and video16Training data1

System component

Memory0Fine-tuning and open weights3Vision1Embeddings0
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Attack surface / entry point：Training dataSystem component：MemorySystem component：VisionSystem component：EmbeddingsClear all

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  1. 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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