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Oct 13 · Tuesday

All papers

Found 18 papers

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  1. DefensesarXiv 2610.02861

    论文提出在 Kubernetes 上保护 AI Agent 的七层纵深防御框架与参考架构

    提出 Kubernetes 运维智能体的七层纵深防御框架与参考架构

    Published
    Setting
    AI Agent
    Abstract把运维智能体当不可信租户,用七层控制在完全攻破假设下约束行动。

    把 Kubernetes 上的运维智能体当作不可信租户,在假定提示注入已完全控制其工具选择的前提下,用基础设施重建数据面与控制面的边界。要解决的是:智能体读日志、注解、工单或 MCP 工具描述时,这些内容可以变成它带着自身凭据执行的命令。模型对齐和分类器只降低概率。

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  2. 研究提出 ACE 语料库,评估内核级证据对 Agent 安全检测的有效性

    构建 ACE 语料,评测内核级证据对 Agent 攻击检测的有效性

    Published
    Evaluated models
    AdaBoost, XGBoost, TabPFN and others10
    AbstractACE 用配对的内核与应用层证据表明,syscall 痕迹可判别智能体攻击,两层组合通常更好。

    ACE 是一份把智能体会话的内核 syscall 痕迹与应用层清单、转录配对起来的检测语料和基准。。

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  3. DefensesarXiv 2609.29647

    AgentKernel:面向 Agent 的可信原生操作系统内核

    提出 AgentKernel,用四支柱强制中介 Agent 的输入、工具与记忆

    Published
    Setting
    AI Agent
    AbstractAgentKernel 是四支柱智能体 OS 设计,安全论证未附带实测数字。

    AgentKernel 是把安全做成组织原则的智能体 OS 架构,不是带跑分的攻防评测。

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  4. DefensesarXiv 2608.21101

    ClawSentry:面向自主 LLM Agent 的渐进式多层安全监控网关

    提出 ClawSentry 多层监控网关,在技能准入和运行时拦截恶意技能与隐藏指令

    Published
    Evaluated models
    Codex/GPT-5.4, Codex/GPT-5.5, Claude Code/GLM-5.1 and others6
    AbstractClawSentry 用包准入、三级运行时门控和反绕过,压低 skill 攻击并保住干净任务。

    ClawSentry 是挂在智能体运行时之外的安全监督网关,用来同时盖住 skill 执行的多个生命周期边界,并把已被拒绝的效果记到会话里。

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  5. AttacksarXiv 2606.04329

    研究者系统研究 LLM 智能体记忆投毒攻击并提出 MPBench 基准

    系统研究 LLM 智能体记忆投毒并构建基准 MPBench

    Published
    Setting
    AI Agent
    Evaluated models
    Meta-Llama-3.1-70B-Instruct, Deberta-v3-base, Llama-3.1-8B-Instruct and others5

    Abstract系统研究了智能体记忆投毒风险,揭示了写入渠道漏洞,构建了 MPBench 基准并指出提示注入防御的局限。

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  6. 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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  7. AttacksarXiv 2605.15338

    Hidden in Memory: Latent Memory Poisoning of LLM Agents

    提出潜伏记忆投毒,诱导 Agent 把外部文档写成持久伪造记忆

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-5.4, GPT-5.5, Claude Sonnet 4.6 and others12
    Abstract持久记忆成为跨会话攻击面:通用载荷可写入伪造记忆,并在相关后续任务中影响行为。

    sleeper memory poisoning 研究带持久记忆的 LLM 助手:外部文档能否让助手存下一则伪造的用户记忆,并在原文档消失后的新会话里被取回、用来改变回复或动作。

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  8. 论文提出 eTAMP 攻击:网页内容注入可跨会话污染 Web Agent 记忆

    提出 eTAMP,用网页内容跨会话投毒 Web Agent 轨迹记忆并诱导越权操作

    Published
    Setting
    web agent
    Evaluated models
    GPT-5-mini, GPT-5.2, GPT-OSS-120B and others6
    Abstract论文提出了基于环境注入的跨任务记忆投毒攻击 eTAMP,作者报告智能体在环境压力下易感性增加且能力与安全并不协同。

    该论文评估了基于大语言模型的 Web 智能体在利用历史交互轨迹进行记忆增强时的安全脆弱性。

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  9. DefensesarXiv 2603.12644

    Revealing security threats and defense architectures for autonomous agents: an OpenClaw case study

    Reviews threats to autonomous agents and proposes a full-lifecycle defense blueprint, FASA

    Published
    Setting
    AI Agent
    AbstractUsing OpenClaw as an example, it divides risks into three layers and uses FASA to describe defense from input to system execution.

    This is a threat review and defense architecture report using OpenClaw as a case study, not a scored attack or defense evaluation.

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  10. DefensesarXiv 2603.11619

    Taming OpenClaw: Security Analysis and Mitigation of Threats in Autonomous LLM Agents

    Proposes a defense-in-depth aligned with the five stages to mitigate cross-stage threats to autonomous agents

    Published
    Setting
    AI Agent
    AbstractThis is a lifecycle threat analysis and layered mitigation design for OpenClaw, with the cases serving as qualitative evidence.

    This is a lifecycle threat analysis of OpenClaw, accompanied by a set of layered mitigation designs. The problem addressed is: when autonomous agents connect instant messaging, persistent memory, third-party plugins, and high-privilege execution, attacks are no longer limited to single-turn jailbreaks but can compound across multiple stages from initialization to execution. The authors argue that defenses hardening only individual interfaces cannot stop such cross-temporal attacks.

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  11. AttacksarXiv 2602.15654

    Zombie Agent: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injection

    提出 Zombie Agent 攻击,经网页间接注入将载荷写入长期记忆并跨会话触发

    Published
    Setting
    AI Agent
    Evaluated models
    Gemini-2.5-Flash, GLM-4.7-Flash
    Abstract记忆写入把一次网页间接注入变成跨会话控制。

    Zombie Agent 把自进化 LLM 智能体的跨会话记忆当成攻击面:攻击者只发布网页,良性任务读入后,正常记忆更新留下载荷,并在以后无关会话里触发未授权工具行为。

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