Large Language Models in Cybersecurity: State-of-the-Art
It addresses the problem of integrating LLMs into cybersecurity, a domain traditionally slow to adopt machine learning, but it is incremental as it is a review paper.
This study reviewed the literature to characterize defensive and adversarial applications of Large Language Models (LLMs) in cybersecurity, identifying critical research gaps and providing a holistic understanding of risks and opportunities.
The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers have actively explored the applications of LLMs across diverse fields, significantly elevating capabilities. Cybersecurity, traditionally resistant to data-driven solutions and slow to embrace machine learning, stands out as a domain. This study examines the existing literature, providing a thorough characterization of both defensive and adversarial applications of LLMs within the realm of cybersecurity. Our review not only surveys and categorizes the current landscape but also identifies critical research gaps. By evaluating both offensive and defensive applications, we aim to provide a holistic understanding of the potential risks and opportunities associated with LLM-driven cybersecurity.