Large language models (LLMs) have emerged as transformative tools in the domain of softwarevulnerability detection and management, offering sophisticated capabilities in identifying, analyzing, and mitigating securit...
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Large language models (LLMs) have emerged as transformative tools in the domain of softwarevulnerability 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 softwarevulnerability 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 softwarevulnerability 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
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