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Oct 12 · Monday

All papers

Found 4 papers
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Attack technique

Gradient and representation optimization4Encoding and obfuscation0

System type

Models and APIs86AI Agent4Embodied systems and robotics4

Attack surface / entry point

Images, audio and video4

Layer

Prompt layer3Model layer1

System component

Vision3Multimodal3
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Attack technique：Gradient and representation optimizationAttack technique：Encoding and obfuscationSystem type：Embodied systems and roboticsLayer：Prompt layerLayer：Model layerClear all

Another 233papers do not have category tags yet and are not included in filtered results.

  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. AttacksarXiv 2608.10393

    DURA：用扩散模型生成自然对抗补丁攻击 VLA 机器人模型

    提出 DURA，用扩散模型生成自然对抗补丁操控 VLA 机器人动作

    arXiv
    2608.10393
    Published
    Layer
    Prompt layer
    Setting
    Embodied systems and robotics
    Evaluated models
    OpenVLA-7B, π0-FAST, openvla-7B
    AbstractDURA 用扩散潜空间轨迹生成自然补丁，白盒与黑盒都能把 VLA 导向指定动作。

    DURA 是针对 VLA 的扩散式、补丁内容不受限的机器人攻击，同时支持白盒梯度和只看动作输出的黑盒。

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