Large Language Models in Cybersecurity: State-of-the-Art

Farzad Nourmohammadzadeh Motlagh, Mehrdad Hajizadeh, Mehryar Majd, Pejman Najafi, Feng Cheng +1 more
1/30/2024
cs.CRcs.AIcs.CL

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Abstract

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.

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Cite this paper

@article{motlagh2024large,
  title  = {Large Language Models in Cybersecurity: State-of-the-Art},
  author = {Farzad Nourmohammadzadeh Motlagh and Mehrdad Hajizadeh and Mehryar Majd and Pejman Najafi and Feng Cheng and Christoph Meinel},
  year   = {2024},
  eprint = {2402.00891},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.00891v1}
}

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