  The rapid advancement of Large Language Models (LLMs) has opened up new
opportunities for leveraging artificial intelligence in various domains,
including cybersecurity. As the volume and sophistication of cyber threats
continue to grow, there is an increasing need for intelligent systems that can
automatically detect vulnerabilities, analyze malware, and respond to attacks.
In this survey, we conduct a comprehensive review of the literature on the
application of LLMs in cybersecurity (LLM4Security). By comprehensively
collecting over 30K relevant papers and systematically analyzing 127 papers
from top security and software engineering venues, we aim to provide a holistic
view of how LLMs are being used to solve diverse problems across the
cybersecurity domain. Through our analysis, we identify several key findings.
First, we observe that LLMs are being applied to a wide range of cybersecurity
tasks, including vulnerability detection, malware analysis, network intrusion
detection, and phishing detection. Second, we find that the datasets used for
training and evaluating LLMs in these tasks are often limited in size and
diversity, highlighting the need for more comprehensive and representative
datasets. Third, we identify several promising techniques for adapting LLMs to
specific cybersecurity domains, such as fine-tuning, transfer learning, and
domain-specific pre-training. Finally, we discuss the main challenges and
opportunities for future research in LLM4Security, including the need for more
interpretable and explainable models, the importance of addressing data privacy
and security concerns, and the potential for leveraging LLMs for proactive
defense and threat hunting. Overall, our survey provides a comprehensive
overview of the current state-of-the-art in LLM4Security and identifies several
promising directions for future research.
