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Oct 12 · Monday

All papers

Found 6 papers
Filter3

Attack type

Prompt injection6

Attack technique

Gradient and representation optimization3Automated search28Multi-turn escalation3Encoding and obfuscation9Cross-modal payloads6Defense-targeted9Dormant triggers10Selection manipulation3

Impact

Unauthorized actions6

System type

AI Agent6web agent3

Attack surface / entry point

Web pages and interfaces3Images, audio and video5

Layer

Application layer6

System component

Vision6Multimodal6
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Attack technique：Cross-modal payloadsImpact：Unauthorized actionsLayer：Application layerClear all

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

  1. AttacksarXiv 2610.09240

    WebMirage hijacks visual web agents' browser operations with adversarial images

    提出 WebMirage，用局部对抗图像劫持视觉网页 Agent 的浏览器操作

    arXiv
    2610.09240
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    LLaVA-v1.5-13B, LLaVA-v1.6-34B, MiniCPM-o-8B and others6
    Abstract论文提出了针对网络智能体的端到端视觉红队框架 WebMirage，揭示了从视觉定位到浏览器执行的安全脆弱性。

    WebMirage 是一个面向视觉定位网络智能体的流水线感知端到端红队攻击框架。

    Paper analysisRead analysis
  2. Evaluation and benchmarksarXiv 2609.35912

    MMSkillRisk：多模态技能图像中的隐藏指令可诱导 Agent 执行未授权操作

    提出 MMSkillRisk 基准，评测多模态技能图像中的隐藏指令诱导 Agent 越权操作

    arXiv
    2609.35912
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    GPT-5.6-sol, Kimi-K2.6, Kimi-K3 and others6

    Abstract论文提出 MMSkillRisk 基准与 NCVA 攻击，揭示多模态技能中教学图像潜藏指令可引发未授权操作并与良性任务共存。

    Paper analysisRead analysis
  3. Evaluation and benchmarksarXiv 2609.09404

    MMPIBench：多模态提示注入对六种 Agent 框架的跨框架评测

    用 MMPIBench 评测多模态提示注入对 Agent 框架的影响

    arXiv
    2609.09404
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    Claude Opus 4.8, GPT-5.4, Gemini 3.1 Pro and others6

    Abstract论文提出 MMPIBench 评测多模态提示注入对智能体的影响，发现模型主导行为、尝试远超完成，音频通道缺乏防护。

    Paper analysisRead analysis
  4. AttacksarXiv 2609.04533

    Repeat-After-Me：针对黑盒 VLM 的自适应视觉提示注入攻击

    提出 Repeat-After-Me 自适应视觉提示注入，诱导 VLM 智能体执行指定任务

    arXiv
    2609.04533
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    Claude-Opus-4.7, GPT-5.5, Gemini-3.1-Pro and others6

    Abstract论文提出了针对黑盒 VLM 的前缀诱导自适应视觉提示注入，在多款模型上诱发了工具调用与隐私泄露。

    Paper analysisRead analysis
  5. AttacksarXiv 2505.11717

    WebInject：通过网页像素扰动对多模态网页 Agent 实施提示注入

    提出 WebInject，用网页像素扰动诱导网页 Agent 执行指定动作

    arXiv
    2505.11717
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    UI-TARS-7B-SFT, Phi-4-multimodal-instruct, Llama-3.2-11B-Vision-Instruct and others5
    AbstractWebInject 经源码扰动原始像素。

    WebInject 是对 MLLM 网页智能体的环境提示注入：修改网页源码，使原始像素上的扰动经过显示器映射后，诱导智能体执行攻击者指定动作。

    Paper analysisRead analysis
  6. AttacksarXiv 2406.12814

    论文提出 ARE 框架，在 VisualWebArena 上评估多模态 Agent 的对抗鲁棒性

    提出 ARE 框架与图像文本攻击，评测多模态网页 Agent 的对抗鲁棒性

    arXiv
    2406.12814
    Published
    Layer
    Application layer
    Setting
    web agent
    Evaluated models
    GPT-4o, GPT-4V, Claude-3-Opus and others4

    Abstract系统评估多模态网页智能体的脆弱性，指出推理时计算组件被攻破会加剧系统风险。

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