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Oct 12 · Monday

All papers

Found 6 papers
Filter5

Attack type

Jailbreaks2Prompt injection16Extraction and theft3Malicious use1

Attack technique

Cross-modal payloads3

System type

LLM applications4

Attacker access

Black-box3

Attack surface / entry point

User input3Images, audio and video4

Layer

Prompt layer58Model layer15Application layer6Training and data layer5

System component

MCP and skills6Memory4RAG10Guardrails and judges12Monitors10Permissions and sandboxing8Vision3Embeddings3Multimodal6
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Attack type：JailbreaksAttack type：Extraction and theftAttack type：Malicious useLayer：Application layerSystem component：MultimodalClear all

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  4. DefensesarXiv 2603.01544

    RA-Det：利用鲁棒性不对称检测 AI 生成图像

    提出 RA-Det，利用鲁棒性不对称检测 AI 生成图像

    arXiv
    2603.01544
    Published
    Layer
    Application layer
    Setting
    LLM applications
    AbstractRA-Det 把扰动下特征漂移差变成检测信号，平均 ACC 高于表中通用检测器，但不是每行最高。

    RA-Det 是合成图像检测器，用扰动下的表示稳定性差异代替外观痕迹。

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  5. AttacksarXiv 2602.07379

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

    提出 Aegis 红队框架，多轮攻击高风险语音 Agent

    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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