Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments
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
66f129857972f6224f3b5909
Maria Rigaki
claimed_verified
2024-09-23T13:07:03.093Z
637b9fba787dab5e6b349fbe
https://cdn-avatars.huggingface.co/v1/production/uploads/637b9fba787dab5e6b349fbe/XyomA6tgZSdAv1tSHbzFN.jpeg
Maria Rigaki
user
marik0
66f129857972f6224f3b590a
Carlos Catania
66f129857972f6224f3b590b
Sebastian Garcia
Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments
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
Hackphyr, a locally fine-tuned LLM with 7 billion parameters, performs comparably to larger commercial models like GPT-4 and outperforms GPT-3.5-turbo and Q-learning agents in cybersecurity tasks using a custom dataset and runs on a single GPU.
Large Language Models LLMs red-team agent network security fine-tuned GPT-4 GPT-3.5-turbo Q-learning agents task-specific cybersecurity dataset parameter-efficient fine-tuning