This paper presents a systematic literature review (SLR) on the application of large language models (LLMs) in cybersecurity (LLM4Security). The authors conducted an extensive search of over 47,135 papers, including 185 high-quality papers from top security and software engineering venues, to analyze how LLMs are being used to address various cybersecurity tasks. The study identifies key trends, challenges, and opportunities in the application of LLMs to cybersecurity, including vulnerability detection, malware analysis, network intrusion detection, phishing detection, and more. The authors find that LLMs are being applied to an expanding range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. They analyze how different LLM architectures (such as encoder-only, encoder-decoder, and decoder-only) are utilized across various security domains and over time, identifying key application trends in LLM4Security. The study also identifies increasingly sophisticated techniques for adapting LLMs to specific cybersecurity domains, such as advanced fine-tuning, prompt engineering, and external augmentation strategies. The authors highlight the need for more comprehensive datasets and the emerging use of LLMs for data augmentation, as the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity. They also discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and robust models, the importance of addressing data security and the inherent security risks of LLMs themselves, and the potential for leveraging LLMs for proactive defense and threat hunting. The study provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research. The authors believe that the insights and findings presented in this survey will contribute to the growing body of knowledge on the application of LLMs in cybersecurity and provide valuable guidance for researchers and practitioners working in this field.This paper presents a systematic literature review (SLR) on the application of large language models (LLMs) in cybersecurity (LLM4Security). The authors conducted an extensive search of over 47,135 papers, including 185 high-quality papers from top security and software engineering venues, to analyze how LLMs are being used to address various cybersecurity tasks. The study identifies key trends, challenges, and opportunities in the application of LLMs to cybersecurity, including vulnerability detection, malware analysis, network intrusion detection, phishing detection, and more. The authors find that LLMs are being applied to an expanding range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. They analyze how different LLM architectures (such as encoder-only, encoder-decoder, and decoder-only) are utilized across various security domains and over time, identifying key application trends in LLM4Security. The study also identifies increasingly sophisticated techniques for adapting LLMs to specific cybersecurity domains, such as advanced fine-tuning, prompt engineering, and external augmentation strategies. The authors highlight the need for more comprehensive datasets and the emerging use of LLMs for data augmentation, as the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity. They also discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and robust models, the importance of addressing data security and the inherent security risks of LLMs themselves, and the potential for leveraging LLMs for proactive defense and threat hunting. The study provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research. The authors believe that the insights and findings presented in this survey will contribute to the growing body of knowledge on the application of LLMs in cybersecurity and provide valuable guidance for researchers and practitioners working in this field.