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Oct 12 · Monday

All papers

Found 9 papers

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  1. DefensesarXiv 2610.09793

    用时序逻辑监控工具 Agent

    提出 MFOTL 离线时序验证以监控工具 Agent 的危险动作链

    Published
    Setting
    AI Agent
    Attacker
    Black-box
    Abstract形式化验证可有效识别多步攻击,但其有效性高度依赖轨迹的可信历史记录。

    这篇论文对工具调用大语言模型智能体的安全监控进行了形式化时序验证研究。

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  2. DefensesarXiv 2610.05163

    Researchers construct segmented prompt injection on Claude Code and Codex and release a boundary action audit benchmark

    提出 PAA 方法,在边界动作前审计长流程智能体的分阶段提示注入

    Published
    Evaluated models
    Claude Code (v2.1.172, Claude Sonnet 5), Codex CLI (v0.144.0, GPT-5.5)
    Abstract论文针对长流程注入提出边界动作审计与 PAA,在保持低误阻的同时实现高拦截率。

    本研究针对长流程智能体在执行任务时面临的分阶段间接提示注入威胁,提出了在动作生效前进行拦截的边界动作审计框架与路径对齐归因方法。

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  3. DefensesarXiv 2607.06595

    When Agents Remember Too Much: Memory Poisoning Attacks against Large Language Model Agents

    Proposed GhostWriter memory poisoning, which writes into the agent's long-term memory via email and calendar and activates across sessions

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-5.4-mini, DeepSeek-V4-Flash, Gemini-2.5-Flash and others4
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  4. DefensesarXiv 2606.24322

    Protecting LLM agent long-term memory from poisoning: tamper-evident, source-bound permissions and machine-checkable guarantees

    提出 TMA-NM,以写时来源绑定抵御 Agent 长期记忆投毒

    Published
    Setting
    AI Agent
    Evaluated models
    gpt-5-chat, gpt-4o-mini, claude-opus-4.1 and others8
    AbstractTMA-NM 用来源绑定的行动权限挡住记忆洗白。

    TMA-NM 把 LLM 智能体长期记忆的行动权限绑到写时来源,用来挡住跨会话记忆投毒。

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  5. DefensesarXiv 2605.17380

    Uber proposes ADR enterprise-level agent detection system, deployed on 7,200 hosts for ten months

    提出企业级检测系统 ADR,识别编程 Agent 的提示注入与恶意 MCP

    Published

    Abstract提出了端点因果遥测、两级在线分流与离线红队闭环的 ADR 系统,并在生产环境中验证了有效性。

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  6. DefensesarXiv 2603.12644

    揭示自主 Agent 的安全威胁与防御架构:OpenClaw 案例研究

    梳理自主 Agent 威胁并提出全生命周期防御蓝图 FASA

    Published
    Setting
    AI Agent
    Abstract以 OpenClaw 为例划分三层风险,并用 FASA 描述从输入到系统执行的防御。

    这是一份以 OpenClaw 为案例的威胁梳理和防御架构报告,不是带分数的攻击或防御评测。

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  7. DefensesarXiv 2602.20708

    ICON:基于推理时纠正的 Agent 间接提示注入防御

    提出推理时防御 ICON,探测注意力过聚焦并转向以纠正 Agent 间接提示注入

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen-3-8B, Llama-3.1-8B, Mistral-8B and others6
    AbstractICON 在推理时检测过聚焦并转向,以兼顾 IPI 安全与任务效用。

    ICON 在推理时检测 IPI 的注意力过聚焦并转向,避免以拒答中止智能体轨迹。

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