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Oct 12 · Monday

All papers

Found 8 papers
Filter5

Attack type

Extraction and theft5

Impact

Data and privacy leaks5

System type

LLM applications6

Attacker access

Black-box8White-box3

Attack surface / entry point

User input7Images, audio and video3

Layer

Prompt layer22Model layer1Application layer7

System component

MCP and skills4RAG6Guardrails and judges8Monitors4Vision3Image generation3Embeddings3Multimodal8
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Attacker access：Black-boxLayer：Model layerLayer：Application layerSystem component：RAGSystem component：VisionClear all

Another 233papers do not have category tags yet and are not included in filtered results.

  1. DefensesarXiv 2610.09703

    研究揭示 Token-Pruning 对 VLM 安全性的影响并提出 SAP 缓解机制

    提出 Safety-Aware Pruning，缓解视觉 token 剪枝放大的多模态越狱

    arXiv
    2610.09703
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    LLaVA-1.5-7B
    Abstract剪枝去掉背景 token 后注意力塌向恶意前景。

    这篇工作评估视觉 token 剪枝如何改变 VLM 在多模态越狱下的行为，并给出推理时防御 SAP。

    Paper analysisRead analysis
  3. AttacksarXiv 2609.14649

    CIG-MIA：利用上下文信息增益对 RAG 系统发起成员推断攻击

    提出 CIG-MIA，用上下文信息增益推断 RAG 知识库成员身份

    arXiv
    2609.14649
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Attacker
    Gray-box, Black-box
    Evaluated models
    Llama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.3, Llama-2-13b-chat-hf and others6
    AbstractCIG-MIA 用注入前后的答案似然差推断 RAG 库成员，灰盒可分性高于所列基线。

    CIG-MIA 是针对 RAG 外部知识库的成员推断攻击，同时覆盖灰盒和纯文本黑盒。

    Paper analysisRead analysis
  4. DefensesarXiv 2609.04767

    SHAQ: Privacy-preserving retrieval in vector databases using shadow queries

    Proposes SHAQ, which replaces document embeddings with shadow query embeddings to defend against embedding inversion.

    arXiv
    2609.04767
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Attacker
    Black-box
    Evaluated models
    GTR-T5 encoder, DPR
    AbstractSHAQ replaces document embeddings with shadow query embeddings.

    SHAQ is a defense against EIA for cloud vector databases: instead of directly modifying document embeddings, it replaces them with shadow query embeddings.

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