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Oct 12 · Monday

All papers

Found 6 papers
Filter5

Attack technique

Gradient and representation optimization4

Impact

Unauthorized actions9Output manipulation6Availability and reliability3

System type

Models and APIs42AI Agent6coding agent0Browser and computer use1Embodied systems and robotics4

Attack surface / entry point

User input17Web pages and interfaces10Documents and messages10Tool responses14Tool and skill descriptions5Code repositories and dependencies5Retrieval corpus5Memory writes7Inter-agent messages10Images, audio and video6Training data3Distribution channels10

System component

Vision5Multimodal6
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 254papers have no tags yet and are not included in filtered results.
Impact：Output manipulationSystem type：AI AgentSystem type：coding agentSystem type：Browser and computer useAttack surface / entry point：Images, audio and videoClear all

Another 254papers do not have category tags yet and are not included in filtered results.

  3. Evaluation and benchmarksarXiv 2610.06898

    DIBench：面向移动 GUI 智能体多候选选择决策完整性的安全基准

    用 DIBench 评测移动 GUI 智能体被 UI 注入后的决策完整性

    arXiv
    2610.06898
    Published
    Layer
    Application layer
    Setting
    Browser and computer use
    Evaluated models
    Qwen2.5-VL(32B), Qwen3-VL(32B), GUI-Owl(32B) and others7

    Abstract论文提出移动智能体决策完整性基准 DIBench，发现欺诈 UI 注入诱导目标选择并推高完成率，通用防御收益不稳定。

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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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  5. 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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  6. AttacksarXiv 2603.16960

    针对现代视觉语言模型的对抗攻击

    对开源视觉购物智能体实施商品图对抗攻击并诱导买错

    arXiv
    2603.16960
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    LLaVA-v1.5-7B, Qwen2.5-VL-7B
    Abstract作者称 LLaVA 易被三种攻击诱导买错，Qwen 正确购买率仍接近其干净条件。

    作者在自建电商环境里比较两个开源 VLM 购物智能体对图像对抗扰动的鲁棒性。

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