Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
This work addresses the problem of enhancing cybersecurity with LLMs for researchers and practitioners, but it is incremental as it synthesizes existing advancements rather than introducing new methods.
This survey tackles the problem of applying Large Language Models (LLMs) to cybersecurity by reviewing their use in threat detection and vulnerability assessment, while also addressing the vulnerabilities of LLMs themselves, with the result of providing practical insights and strategic recommendations for building secure defense systems.
Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as IoT, blockchain, and hardware security. This survey provides a comprehensive overview of LLM applications in cybersecurity, focusing on two core areas: (1) the integration of LLMs into key cybersecurity domains, and (2) the vulnerabilities of LLMs themselves, along with mitigation strategies. By synthesizing recent advancements and identifying key limitations, this work offers practical insights and strategic recommendations for leveraging LLMs to build secure, scalable, and future-ready cyber defense systems.