<p>Large language models (LLMs) have emerged as transformative tools in the domain of software vulnerability detection and management, offering sophisticated capabilities in identifying, analyzing, and mitigating security risks. This article delves into the utilization of LLMs, examining their role in revolutionizing traditional approaches to software vulnerability detection. We explore the various categories of LLMs, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformer (GPT), and how these models are being leveraged to improve the accuracy and efficiency of vulnerability detection. This article reviews how LLMs are being integrated into existing software security frameworks, synthesizing research findings on their performance in various contexts. It includes insights into how LLM-based methods complement traditional techniques like static analysis and fuzz testing, without engaging in a direct comparative analysis of these approaches. The comparison highlights the strengths of LLMs, such as their ability to generalize across diverse codebases and programming languages, while also addressing their limitations, such as susceptibility to biases from training data and the hallucination. The article synthesizes findings from recent research, showcasing how LLMs have been successfully employed to detect a range of vulnerabilities, from buffer overflows to SQL injections, and outlines how these models enhance productivity by automating the detection and reporting of security flaws. Additionally, we discuss the inherent challenges in applying LLMs to software vulnerability detection, such as the need for high-quality datasets, and the ethical implications related to the deployment of LLM-based systems in security-critical applications. Addressing these challenges is crucial for the future advancement of LLM technologies in the cybersecurity domain. A comprehensive introduction to foundational and specialized datasets is provided, including datasets such as CVEfixes, Big-Vul, and LineVul, which are tailored for software vulnerability detection. These datasets serve as crucial resources for training and benchmarking LLMs. Moreover, we introduce evaluation metrics such as F1-score, precision, recall, and AUC-ROC that are used to assess the performance of models in detecting and mitigating vulnerabilities, offering a structured way to gauge the success and limitations of LLMs. In addition, the article explores fine-tuning techniques such as full fine-tuning, feature extraction, adapter-based fine-tuning, and LoRA (low-rank adaptation), highlighting how each method can enhance LLM performance in vulnerability detection. By focusing on parameter-efficient fine-tuning approaches, such as adapter layers and prefix-tuning, and LoRa, we outline ways to optimize model performance while reducing computational overhead. By providing a comprehensive review of the literature and practical insights into LLM integration, this article aims to fill the gap in existing research and serve as a foundational guide for future investigations. Researchers and practitioners in the field of software security will benefit from the comparative analyses, detailed case studies, and strategic recommendations provided herein, which collectively highlight the potential of LLMs to complement and enhance traditional software vulnerability detection techniques.</p>