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Oct 12 · Monday

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Models and APIs72LLM applications12AI Agent29coding agent6Multi-agent systems6

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Application layer5
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  2. Analysis and theoryarXiv 2610.06001

    AgentSpy: Observing AI agent behavior at the system call layer and detecting skill injection attacks

    提出 AgentSpy，在系统调用层观测 Agent 行为并检测技能注入

    arXiv
    2610.06001
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    gpt-5.6-terra, deepseek-v4-flash, glm-5.3-flash and others4
    Abstract提出了系统级智能体监控框架 AgentSpy，揭示出通过测试背后的非预期行为与注入风险。

    本研究提出了面向 AI 智能体的系统级外部监控与行为分析框架 AgentSpy。

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  3. Analysis and theoryarXiv 2610.06163

    SAGE：定位 LLM Agent 修复中最早偏离安全意图的环节

    提出 SAGE，定位代码修复 Agent 最早偏离安全意图的轮次

    arXiv
    2610.06163
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    GPT-5.5, Claude Opus 4.8, Gemini 3.1 Pro and others6
    AbstractSAGE 在已通过检查的修复轨迹上定位静默失败的最早偏离，类型比轮次更稳定。

    SAGE 在已通过语法和功能检查、但仍含确认安全缺陷的智能体修复轨迹上，定位最早可归因的安全意图偏离。。

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  4. Analysis and theoryarXiv 2610.04375

    Paper reveals that LLM agent tool calls are rewritten during execution paths, and proposes IntAct as a fix

    提出 IEC 与 IntAct，测量并修复 Agent 工具调用的路径篡改

    arXiv
    2610.04375
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    qwen3-coder-plus, Qwen2.5-Coder-32B, Qwen2.5-72B and others4

    Abstract论文界定工具调用在执行路径中的意图偏离问题，提出无执行归因协议与跳级修复，为智能体工程提供评测与设计准则。

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  5. Analysis and theoryarXiv 2609.38723

    面向智能体的高效 HPC 系统：挑战与机遇

    刻画 HPC 上编码智能体的使用差异并提出设施协同设计议程

    arXiv
    2609.38723
    Published
    Layer
    Infrastructure layer
    Setting
    coding agent
    AbstractFASRC 测量说明编码智能体改变 HPC 使用方式，作者据此提出四层协同设计议程。

    这是一篇对生产 HPC 上编码智能体的测量与系统议程论文，不是攻击实现或防御评测。。

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  6. Analysis and theoryarXiv 2609.27263

    GitHub Agentic Workflows 的智能体工作流规范与维护实证研究

    实证调查 gh-aw 工作流规范的结构、维护演化与指令防护

    arXiv
    2609.27263
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    Gemini 3.1 Pro, GLM-5.2, gpt-oss
    Abstractgh-aw 指令又长又常改，任务和约束很常见，显式注入防护很少，LLM 标注与人工标签大体一致。

    这是一项对 GitHub Agentic Workflows Markdown 的仓库挖掘，目标是描述开发者如何规定并维护会反复执行的智能体工作。

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