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Oct 12 · Monday

All papers

Found 5 papers
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Attack technique

Gradient and representation optimization3

Impact

Harmful content47Unauthorized actions9Data and privacy leaks9Output manipulation50Availability and reliability5Increased misuse capability0

System type

AI Agent3Embodied systems and robotics3

Attack surface / entry point

User input10Web pages and interfaces3Tool and skill descriptions3Images, audio and video5Training data8Weights and gradients5Distribution channels4

System component

Multimodal4
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 265papers have no tags yet and are not included in filtered results.
Impact：Availability and reliabilityImpact：Increased misuse capabilityAttack surface / entry point：Images, audio and videoClear all

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

  1. AttacksarXiv 2610.09819

    FAB 攻击：给 HuBERT/WavLM 植入后门，背景警笛声即可让下游语音任务失灵

    提出 FAB 攻击，向声学基础模型植入可由环境声触发的后门

    arXiv
    2610.09819
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    HuBERT-base, WavLM-base

    Abstract论文提出了针对声学基模的 FAB 后门攻击，在弱威胁模型下实现了任务无关、物理可触发的后门植入。

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  3. AttacksarXiv 2610.06083

    研究者提出面向深度强化学习的轨迹级可迁移黑盒对抗攻击

    提出轨迹级可迁移对抗攻击，降低深度强化学习策略的期望回报

    arXiv
    2610.06083
    Published
    Layer
    Model layer
    Setting
    Embodied systems and robotics
    Evaluated models
    DQN, Double DQN (DDQN)
    Abstract轨迹级后退视界攻击在 CartPole 上比逐步 FGSM 类和 Sign 噪声更压低回报。

    这项工作研究 DRL 上的迁移式黑盒观测攻击：受害参数不可访问时，在白盒代理上造扰动再迁移。

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  4. Evaluation and benchmarksarXiv 2602.10639

    VideoSTF 揭示视频大模型输出重复问题

    提出 VIDEO STF 评测视频大模型输出重复并压力测试时序扰动

    arXiv
    2602.10639
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    LLaVA-Video-7B-Qwen2, LLaVA-Video-7B-Qwen2-Video-Only, LLaVA-NeXT-Video-7B and others10
    AbstractVIDEO STF 把输出重复做成可测、可被时间扰动放大、并可黑盒诱导的失败。

    VIDEO STF 测量 VideoLLM 的输出重复，并把时间变换用作 black-box 诱导。

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  5. AttacksarXiv 2505.23266

    AdvOF：用对抗物体融合攻击 VLM 驱动的视觉语言导航智能体

    提出 AdvOF，生成对抗物体误导视觉语言导航智能体的场景感知

    arXiv
    2505.23266
    Published
    Layer
    Application layer
    Setting
    Embodied systems and robotics
    Attacker
    White-box
    Evaluated models
    Lseg, Clip, ViT-L/16 and others5
    AbstractAdvOF 以多视角融合生成对抗物体。

    AdvOF 是针对 VLM 驱动 VLN 服务导航的对抗物体攻击。

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