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Oct 13 · Tuesday

All papers

Found 5 papers
Filter6

Impact

Harmful content7Unauthorized actions37Data and privacy leaks21Output manipulation35Availability and reliability5Oversight failure23

System type

AI Agent3

Layer

Model layer3Application layer5

System component

MCP and skills6Memory3RAG2Guardrails and judges4Monitors0Embeddings0Multimodal3
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Impact：Availability and reliabilityLayer：Application layerSystem component：RAGSystem component：MonitorsSystem component：EmbeddingsSystem component：MultimodalClear all

Another 215papers do not have category tags yet and are not included in filtered results.

  1. Evaluation and benchmarksarXiv 2610.04691

    RAGStress：评测知识库退化下 RAG 鲁棒性的受控基准

    提出 RAGStress 受控基准，评测知识库退化下的 RAG

    arXiv
    2610.04691
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    Claude Sonnet 4, Claude Opus 4.6, DeepSeek R1 and others5
    Abstract作者用 RAGStress 比较五系统。

    RAGStress 是知识库损坏下的 RAG 受控压力测试。作者明确不把它当作模型排行榜。

    Paper analysisRead analysis
  3. AttacksarXiv 2603.03919

    When Safety Becomes a Vulnerability: Exploiting LLM Alignment Homogeneity for Transferable Blocking Attacks in RAG

    提出 TabooRAG，用单篇文档迁移阻断 RAG 对良性查询的回答

    arXiv
    2603.03919
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    Llama-3-8B-Instruct, Ministral-3-8B-Instruct-2512, Gemma-3-12B-it and others9
    AbstractTabooRAG 用代理侧风险文档迁移阻断 RAG。

    TabooRAG 是面向 RAG 可用性的可迁移阻断攻击，不改已有文档，也不读取目标反馈。

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  4. AttacksarXiv 2602.07379

    Aegis: Governance, Integrity, and Security for AI Voice Agents

    Proposes the Aegis red-teaming framework for multi-turn attacks on high-risk voice agents.

    arXiv
    2602.07379
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-4o, GPT-4o-mini, Gemini-1.5 Pro and others7
    AbstractAegis red-teams three types of voice agents: query-based access eliminates authentication and leakage successes, but behavioral attacks remain.

    Aegis uses multi-turn red-teaming evaluation on ALLM voice agents already integrated into business systems. The authors call this the first systematic red-teaming framework in this setting.

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  5. AttacksarXiv 2505.23266

    AdvOF：用对抗物体融合攻击 VLM 驱动的视觉语言导航智能体

    提出 AdvOF，生成对抗物体误导视觉语言导航智能体的场景感知

    arXiv
    2505.23266
    Published
    Layer
    Application layer
    Setting
    Embodied systems and robotics
    Attacker
    White-box
    Evaluated models
    Lseg, Clip, ViT-L/16 and others5
    AbstractAdvOF 以多视角融合生成对抗物体。

    AdvOF 是针对 VLM 驱动 VLN 服务导航的对抗物体攻击。

    Paper analysisRead analysis
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