发表机构
City University of Hong Kong; Imperial College London（香港城市大学; 伦敦帝国学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有智能体事件响应方法的局限，提出结合决策论规划与LLM的多尺度方法，经实验验证可显著缩短恢复时间、提升恢复率。
AI 中文摘要
当前事件响应由安全操作员使用预定义剧本管理，导致安全决策过程缓慢且耗费人力，因此对自动化事件响应规划的需求日益增长。已提出基于控制、优化和强化学习的决策论方法来自动化此类规划任务，这些方法基于合理的方法且性能表现优异，但多数局限于抽象模型，无法直接应用于实际运行系统。缓解该局限的一种有前景的方法是利用大型语言模型（LLM）中嵌入的安全知识开发智能体响应系统。然而，当前的智能体方法依赖于反复调用LLM来生成响应计划，这不可靠且因幻觉限制了规划范围。本文中，我们开发了一种基于原则的LLM规划方法，将决策论规划与LLM生成的响应命令相结合。所提出的智能体事件响应方法使用rollout规划器计算分配安全资源的高级响应策略（战术尺度），随后由轻量LLM智能体将其转换为可执行命令（操作尺度）。在此架构内，我们使用数字孪生通过仿真支持战术规划，通过模拟支持操作执行。在三种攻击场景中，与前沿LLM基线相比，我们的智能体方法平均将恢复执行时间减少了15.1%，恢复率提高了33.6%。
英文摘要
Incident response is currently managed by security operators using predefined playbooks, resulting in slow, labor-intensive security decision-making processes. Consequently, there is a growing need for automated incident response planning. Decision-theoretic approaches based on control, optimization, and reinforcement learning have been proposed to automate such planning tasks with well-grounded approaches, yet most of which, while guaranteeing strong performance, are limited to abstract models and cannot be directly applied to operational systems. A promising approach to mitigate this limitation is to use the security knowledge embedded in large language models (LLMs) to develop agentic response systems. However, current agentic approaches rely on repeated invocations of the LLM to generate a response plan, which is unreliable and limits the planning horizon due to hallucination. In this paper, we develop a principled LLM-based planning method by combining decision-theoretic planning with LLM-generated response commands. The proposed agentic incident response approach uses a rollout planner to compute a high-level response strategy that allocates security resources (the tactical scale), which is then translated into executable commands by a lightweight LLM agent (the operational scale). Within this architecture, we use a digital twin that supports tactical planning through simulation and operational execution through emulation. Across three attack scenarios, our agentic approach reduces recovery execution time by 15.1\% on average and increases the recovery rate by 33.6\% over frontier LLM baselines.
Comments31st European Symposium on Research in Computer Security (ESORICS) 2026
