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Oct 13 · Tuesday

All papers

Found 4 papers
Filter6

Attack type

Prompt injection3

Attack technique

Gradient and representation optimization2Automated search12Encoding and obfuscation4Role-play and persuasion4Cross-modal payloads2Defense-targeted1Dormant triggers8

Attack surface / entry point

User input19Web pages and interfaces5Documents and messages4Tool responses6Tool and skill descriptions8Retrieval corpus4Images, audio and video15Weights and gradients13Distribution channels8Inference services and hardware3

Layer

Model layer0Application layer4
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 213papers have no tags yet and are not included in filtered results.
Attack technique：Gradient and representation optimizationAttack technique：Cross-modal payloadsAttack technique：Defense-targetedAttack surface / entry point：Documents and messagesLayer：Model layerLayer：Application layerClear all

Another 213papers do not have category tags yet and are not included in filtered results.

  1. AttacksarXiv 2609.22510

    特拉维夫大学提出爆炸性提示词：条件触发的间接提示注入可绕过九款生产 Agent

    提出条件触发的爆炸提示词，并设计摄入期检测器 DeFuse

    arXiv
    2609.22510
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, Qwen2.5-32B-Instruct and others15
    Abstract论文揭示了条件触发使提示注入绕过防御并在触发时执行工具，提出轻量检测器并在摄入期有效防范。

    论文针对大语言模型智能体面临的间接提示注入威胁，研究了利用条件句式实现时间分离的爆炸提示词。

    Paper analysisRead analysis
  2. 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。

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  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 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。

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