title: Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments
summary: Large Language Models (LLMs) have shown remarkable potential across various
  domains, including cybersecurity. Using commercial cloud-based LLMs may be
  undesirable due to privacy concerns, costs, and network connectivity
  constraints. In this paper, we present Hackphyr, a locally fine-tuned LLM to be
  used as a red-team agent within network security environments. Our fine-tuned 7
  billion parameter model can run on a single GPU card and achieves performance
  comparable with much larger and more powerful commercial models such as GPT-4.
  Hackphyr clearly outperforms other models, including GPT-3.5-turbo, and
  baselines, such as Q-learning agents in complex, previously unseen scenarios.
  To achieve this performance, we generated a new task-specific cybersecurity
  dataset to enhance the base model's capabilities. Finally, we conducted a
  comprehensive analysis of the agents' behaviors that provides insights into the
  planning abilities and potential shortcomings of such agents, contributing to
  the broader understanding of LLM-based agents in cybersecurity contexts
paper_authors._id: 66f129857972f6224f3b5909
paper_authors.name: Maria Rigaki
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-23T13:07:03.093Z
paper_authors._id: 637b9fba787dab5e6b349fbe
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/637b9fba787dab5e6b349fbe/XyomA6tgZSdAv1tSHbzFN.jpeg
paper_authors.fullname: Maria Rigaki
paper_authors.type: user
paper_authors.user: marik0
paper_authors._id: 66f129857972f6224f3b590a
paper_authors.name: Carlos Catania
paper_authors._id: 66f129857972f6224f3b590b
paper_authors.name: Sebastian Garcia
paper_title: Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments
paper_summary: Large Language Models (LLMs) have shown remarkable potential across various
  domains, including cybersecurity. Using commercial cloud-based LLMs may be
  undesirable due to privacy concerns, costs, and network connectivity
  constraints. In this paper, we present Hackphyr, a locally fine-tuned LLM to be
  used as a red-team agent within network security environments. Our fine-tuned 7
  billion parameter model can run on a single GPU card and achieves performance
  comparable with much larger and more powerful commercial models such as GPT-4.
  Hackphyr clearly outperforms other models, including GPT-3.5-turbo, and
  baselines, such as Q-learning agents in complex, previously unseen scenarios.
  To achieve this performance, we generated a new task-specific cybersecurity
  dataset to enhance the base model's capabilities. Finally, we conducted a
  comprehensive analysis of the agents' behaviors that provides insights into the
  planning abilities and potential shortcomings of such agents, contributing to
  the broader understanding of LLM-based agents in cybersecurity contexts
paper_ai_keywords: fine-tuned 7 billion parameter model GPU card LLM-based agents Q-learning agents task-specific cybersecurity dataset planning abilities