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Oct 12 · Monday

Agent safety · Papers

Found 5 papers
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Attack type

Prompt injection3

Defense type

Instruction–data separation2Permissions and approval5

Impact

Output manipulation3

System type

AI Agent5

Attack surface / entry point

User input3

Layer

Application layer5

System component

Permissions and sandboxing3
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Defense type：Instruction–data separationDefense type：Permissions and approvalTopic: Agent safetyClear all
  1. AttacksarXiv 2610.02432

    论文提出 LLM 输入扰动鲁棒性度量，并发布 ASA 攻击与 AttestMCP 防护

    提出 ASA 与 AttestMCP，度量并降低 LLM 对输入序列扰动的敏感

    arXiv
    2610.02432
    Published
    Layer
    Application layer
    Setting
    LLM applications, AI Agent
    Attacker
    Black-box
    Evaluated models
    Gemma-3-27B-Instruct, Gemma-3-4B-Instruct, Llama-3.2-3B-Instruct and others12
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  2. AttacksarXiv 2609.34518

    SEAD 论文提出工具型 Agent 的状态视角攻击与防御方法 DART 与 SAGE

    提出工具型 Agent 的自适应攻击 DART 与预执行防御 SAGE

    arXiv
    2609.34518
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-5.6 Luna, GPT-5.6 Terra, Gemini 3.8 Flash and others5

    Abstract论文提出状态控制框架 SEAD、攻击 DART 与防御 SAGE，实验显示执行反馈与环境探测在攻防交互中具有关键作用。

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  3. AttacksarXiv 2609.14003

    FlowSeal：用信息流控制阻断 LLM Agent 的隐私泄露

    提出 FLOWSEAL，用工具级信息流控制阻断智能体隐私泄露

    arXiv
    2609.14003
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    DeepSeek-V3.2, GPT-4.1-mini, Gemini-Flash-2.5

    Abstract作者分析大模型智能体隐私检查与攻击面重合问题，提出外部拦截防御 FLOWSEAL 并验证有效性。

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  4. AttacksarXiv 2609.11757

    Researchers disclose a Whisper prompt injection attack on the AP2 payment protocol and propose the A-VIP defense

    提出 AP2 支付协议的 Whisper 攻击与 A-VIP 绑定防御

    arXiv
    2609.11757
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    gemini-2.5-flash-lite, gemini-3.1-flash-lite, gemini-3-flash-preview and others15

    Abstract论文揭示了 AP2 协议决策层受操纵的风险，提出结构化与权能绑定防御 A-VIP，并发布评测基准。

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  5. AttacksarXiv 2607.12507

    研究者提出 RARE 攻击：LLM 辅助逆向工程中的表示混淆

    提出 RARE-Guard，阻止逆向观察被提升为指令或已验证声明

    arXiv
    2607.12507
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    gpt-5.5, claude-sonnet-4-6, gemini-2.5-pro and others5
    Abstract成对二进制衡量无效提升。

    作者定义的失败是：LLM 辅助逆向中，忠实提取的二进制观察获得它并未赢得的指令权限、声明证据或可信状态。

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