In-Context Autonomous Network Incident Response: An End-to-End Large Language Model Agent Approach

2026-02-13 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

Yiran Gao, Kim Hammar, and Tao Li propose an end-to-end large language model (LLM) agent for autonomous network incident response, addressing limitations of traditional reinforcement learning approaches that require handcrafted simulators and suppress semantic information from logs. Their agent integrates perception, reasoning, planning, and action functionalities into a lightweight 14b LLM. Through fine-tuning and chain-of-thought reasoning, the agent processes system logs to infer network states, updates attack models, simulates response consequences, and generates effective responses. It refines its attack conjecture and response by comparing LLM-simulated outcomes with actual observations, demonstrating in-context adaptation. This agentic approach eliminates the need for explicit modeling and operates on commodity hardware, achieving up to 23% faster recovery than frontier LLMs when evaluated on incident logs from the literature.

Key takeaway

For security operations teams seeking faster and more adaptive incident response, this LLM agent approach offers a viable alternative to traditional simulation-heavy methods. Your team could achieve up to 23% faster recovery times by deploying a lightweight 14b LLM agent capable of in-context adaptation, reducing the need for extensive manual modeling and leveraging existing security knowledge within the LLM.

Key insights

An LLM agent can autonomously handle network incident response by integrating perception, reasoning, planning, and action.

Principles

• In-context learning enables adaptive incident response.
• LLMs can infer network states from raw system logs.

Method

The method involves fine-tuning a 14b LLM with chain-of-thought reasoning to perform perception, reasoning, planning, and action, iteratively refining responses based on observed outcomes.

In practice

• Deploy a 14b LLM for autonomous incident response.
• Utilize chain-of-thought for complex security reasoning.

Topics

• Autonomous Incident Response
• Large Language Model Agents
• Network Security
• Cyberattack Mitigation
• Chain-of-Thought Reasoning

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.