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Oct 12 · Monday

All papers

Found 8 papers
Filter4

Attack type

Prompt injection5

Attack technique

Gradient and representation optimization8Automated search15Encoding and obfuscation6Role-play and persuasion4Cross-modal payloads6Side channels4Defense-targeted3Dormant triggers9

Impact

Unauthorized actions3Output manipulation5

System type

AI Agent4web agent4

Attack surface / entry point

User input22Web pages and interfaces4Documents and messages2Retrieval corpus4Images, audio and video33Training data4Weights and gradients11Inference services and hardware2

Layer

Application layer6

System component

Vision5Multimodal5
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.
Attack technique：Gradient and representation optimizationAttack surface / entry point：Web pages and interfacesAttack surface / entry point：Documents and messagesAttack surface / entry point：Inference services and hardwareClear all

Another 254papers 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. AttacksarXiv 2610.05894

    研究者提出闭环偏见注入攻击

    提出闭环激活引导，在扩散语言模型去噪过程中注入定向偏见

    arXiv
    2610.05894
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Attacker
    Gray-box
    Evaluated models
    LLaDA-8B-Instruct, Dream-v0-Instruct-7B, LLaDA-MoE-7B-A1B-Instruct

    Abstract通过去噪中间概率反馈动态调节残差流转向，证明扩散语言模型在推理服务栈中存在闭环偏见注入脆弱性。

    Paper analysisRead analysis
  3. AttacksarXiv 2606.12716

    PaperGuard：针对多模态 AI 同行评审的攻击与防御基准

    提出 PaperGuard，用图文注入与梯度扰动抬高自动审稿分数并检测恶意指令

    arXiv
    2606.12716
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    Claude-sonnet-4.5, GPT-4o, GPT-4.1 and others11
    AbstractPaperGuard 测到提示注入与白盒视觉抬分，并比较分块检索防御。

    PaperGuard同时评测并检测多模态 AI 审稿中的评分操纵。

    Paper analysisRead analysis
  4. 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。

    Paper analysisRead analysis
  5. AttacksarXiv 2602.17837

    TFL: Targeted Bit-Flip Attack against Large Language Models

    提出 TFL 定向比特翻转，使选定提示输出虚假答案且无关查询保持正确

    arXiv
    2602.17837
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-8B, DeepSeek-R1-Distill-Qwen-14B, LLaMA-3.1-8B-INSTRUCT
    AbstractTFL 在三个 LLM 上搜索权重比特，使指定提示出现目标关键词，并约束无关任务下降。

    TFL 是对 LLM 主存权重的目标比特翻转框架：攻击者要让选定提示输出指定虚假答案关键词，同时限制无关查询上的降质。

    Paper analysisRead analysis
  6. AttacksarXiv 2602.00364

    "Someone hid it": Query-agnostic black-box attacks against LLM retrieval

    提出查询无关黑盒攻击 DQ-A，用可迁移文末后缀降低文档检索排名

    arXiv
    2602.00364
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    Qwen-1.5-7B-Instruct, SFR-Embedding-Mistral, E5-Mistral-7B-Instruct and others10
    AbstractDQ-A 以无查询黑盒后缀压低 LLM 检索排名，并在 BRIGHT 上比较三种基线。

    DQ-A 是一种 query-agnostic、complete black-box 的文档隐藏攻击，对象是用最后隐层相似度匹配的 LLM 检索。

    Paper analysisRead analysis
  7. 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
  8. 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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