Multi-Agent Collaboration in Incident Response with Large Language Models

• URL: http://arxiv.org/abs/2412.00652v2
• Date: Fri, 27 Dec 2024 05:32:11 GMT
• Title: Multi-Agent Collaboration in Incident Response with Large Language Models
• Authors: Zefang Liu,
• Abstract summary: Incident response (IR) is a critical aspect of cybersecurity, requiring rapid decision-making and coordinated efforts to address cyberattacks effectively.<n>Leveraging large language models (LLMs) as intelligent agents offers a novel approach to enhancing collaboration and efficiency in IR scenarios.<n>This paper explores the application of LLM-based multi-agent collaboration using the Backdoors & Breaches framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models [75.4890331763196]
  Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems. LLM-based Agentic RS (LLM-ARS) can offer more interactive, context-aware, and proactive recommendations.
  arXiv  Detail & Related papers  (2025-03-20T22:37:15Z)
• Red-Teaming LLM Multi-Agent Systems via Communication Attacks [10.872328358364776]
  Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling sophisticated agent collaboration through message-based communications. We introduce Agent-in-the-Middle (AiTM), a novel attack that exploits the fundamental communication mechanisms in LLM-MAS by intercepting and manipulating inter-agent messages.
  arXiv  Detail & Related papers  (2025-02-20T18:55:39Z)
• Multi-Agent Actor-Critics in Autonomous Cyber Defense [0.5261718469769447]
  Multi-Agent Deep Reinforcement Learning (MADRL) presents a promising approach to enhancing the efficacy and resilience of autonomous cyber operations. We demonstrate each agent is able to learn quickly and counter act on the threats autonomously using MADRL in simulated cyber-attack scenarios.
  arXiv  Detail & Related papers  (2024-10-11T15:15:09Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems. In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy. Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems [0.0]
  Honeypots are decoy systems designed to lure and interact with attackers. We present a novel approach to creating realistic and interactive honeypot systems using Large Language Models.
  arXiv  Detail & Related papers  (2024-09-12T17:33:06Z)
• A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine [14.123823081267336]
  This paper proposes a novel AI Search Engine framework called the Agent Collaboration Network (ACN) The ACN framework consists of multiple specialized agents working collaboratively, each with distinct roles such as Account Manager, Solution Strategist, Information Manager, and Content Creator. A highlight of the ACN is the introduction of a Reflective Forward Optimization method (RFO), which supports the online synergistic adjustment among agents.
  arXiv  Detail & Related papers  (2024-09-01T07:01:22Z)
• Optimizing Collaboration of LLM based Agents for Finite Element Analysis [1.5039745292757671]
  This paper investigates the interactions between multiple agents within Large Language Models (LLMs) in the context of programming and coding tasks. We utilize the AutoGen framework to facilitate communication among agents, evaluating different configurations based on the success rates from 40 random runs for each setup.
  arXiv  Detail & Related papers  (2024-08-23T23:11:08Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs) We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%. We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission [74.10928850232717]
  This paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm. We propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem.
  arXiv  Detail & Related papers  (2024-04-14T03:44:54Z)
• Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning [57.652899266553035]
  Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server. We propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
  arXiv  Detail & Related papers  (2024-03-11T09:21:11Z)
• AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
  AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
  arXiv  Detail & Related papers  (2024-02-21T04:11:28Z)
• A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration [55.35849138235116]
  We propose automatically selecting a team of agents from candidates to collaborate in a dynamic communication structure toward different tasks and domains. Specifically, we build a framework named Dynamic LLM-Powered Agent Network ($textDyLAN$) for LLM-powered agent collaboration. We demonstrate that DyLAN outperforms strong baselines in code generation, decision-making, general reasoning, and arithmetic reasoning tasks with moderate computational cost.
  arXiv  Detail & Related papers  (2023-10-03T16:05:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.