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Oct 12 · Monday

All papers

Found 8 papers
Filter5

Attack type

Poisoning and backdoors4

Attack technique

Gradient and representation optimization8Automated search28Multi-turn escalation4Encoding and obfuscation21Role-play and persuasion5Conversation structure manipulation3Reasoning-chain manipulation7Cross-modal payloads12Defense-targeted23Dormant triggers10Selection manipulation3

Impact

Harmful content3Output manipulation5

System type

Models and APIs3LLM applications5

Attack surface / entry point

User input3Retrieval corpus4

Layer

Prompt layer3Application layer5

System component

RAG4Guardrails and judges3Monitors0Vision27Audio4Embeddings3Multimodal33
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 248papers have no tags yet and are not included in filtered results.
Attack technique：Gradient and representation optimizationSystem component：RAGSystem component：Guardrails and judgesSystem component：MonitorsSystem component：EmbeddingsClear all

Another 248papers do not have category tags yet and are not included in filtered results.

  1. InterpretabilityarXiv 2609.24801

    研究用 XAI 归因分析 Prompt Guard 2，同义词替换可翻转其提示注入判定

    用词级归因指导同义词替换，翻转提示注入检测并验证越狱

    arXiv
    2609.24801
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Prompt Guard 2 (86M), Llama 3.1 8B

    Abstract作者称检测靠分散的词法线索。

    Paper analysisRead analysis
  2. 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
  3. AttacksarXiv 2606.11265

    论文提出 CRCP 投毒框架，揭示 RAG 分块与重排序下的投毒失效问题

    提出 CRCP 框架，优化分块与重排下的 RAG 语料投毒

    arXiv
    2606.11265
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    GPT-4o, DeepSeek-R1, Qwen3-Max and others11
    AbstractCRCP 把投毒做成多阶段一致性问题。

    CRCP 研究的是带分块和重排的 RAG 语料投毒，而不是只提高文档级检索分数。

    Paper analysisRead analysis
  4. AttacksarXiv 2605.28074

    SilentRetrieval: Hijacking retrieval-augmented generation with semantically preserved adversarial data poisoning

    提出 SilentRetrieval，以语义保持对抗文档投毒劫持 RAG 检索与生成

    arXiv
    2605.28074
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    Llama-2-7B-Chat, Mistral-7B-Instruct-v0.2, Qwen-7B-Chat and others10
    Abstract两阶段投毒在单文档协议下兼顾检索劫持、生成操纵与近良性困惑度。

    SilentRetrieval 是针对 RAG 的两阶段语料投毒，在每查询一篇、合成目标答案的统一协议下，同时处理检索劫持、生成操纵和流畅度。

    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 2511.16110

    MFA 框架在 17 个视觉语言模型上取得 58.5% 攻击成功率

    提出 MFA 框架，协同突破视觉语言模型的对齐、提示与审核层

    arXiv
    2511.16110
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    MiniGPT-4, LLaMA-4-Scout-Inst, LLaMA-3.2-11B-Vision-Instruct and others17

    Abstract作者称多层防御的 VLM 仍存在跨模型漏洞，MFA 整合三项技术实现了端到端越狱。

    Paper analysisRead analysis
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