Multi-Agent Collaboration in Incident Response with Large Language Models

Zefang Liu

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arXiv · 2412.00652

Multi-Agent Collaboration in Incident Response with Large Language Models

Zefang Liu

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Abstract

Incident response (IR) is a critical aspect of cybersecurity, requiring rapid decision-making and coordinated efforts to address cyberattacks effectively. Leveraging large language models (LLMs) as intelligent agents offers a novel approach to enhancing collaboration and efficiency in IR scenarios. This paper explores the application of LLM-based multi-agent collaboration using the Backdoors & Breaches framework, a tabletop game designed for cybersecurity training. We simulate real-world IR dynamics through various team structures, including centralized, decentralized, and hybrid configurations. By analyzing agent interactions and performance across these setups, we provide insights into optimizing multi-agent collaboration for incident response. Our findings highlight the potential of LLMs to enhance decision-making, improve adaptability, and streamline IR processes, paving the way for more effective and coordinated responses to cyber threats.

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BibTeX RIS

Zefang Liu. 2024-12-01. Multi-Agent Collaboration in Incident Response with Large Language Models. https://arxiv.org/abs/2412.00652

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