Large Language Models in the Crosshairs: A Systematic Review of Modern Cybersecurity Challenges and Applications
The prevalence and complexity of cybersecurity threats and incidents have increased significantly and an essential aspect shaping this topic is the rapid development of Large Language Models (LLMs), granting cybercriminals novel powers. In consideration of this context, the current systematic review endeavors to rectify the existing inadequacy of a unified and structured understanding regarding the utilization of Large Language Models (LLMs) within diverse domains of cybersecurity. Consistent with the PRISMA 2020 protocols, the systematic review addressed academic articles published between January 2020 and January 2026. Comprehensive searches were conducted in Scopus, Springer Link, ScienceDirect, and IEEE Xplore, suplemented with the snowballing technique, with the final search performed on January 21, 2026. Furthermore, an analysis of the studies was performed to assess potential biases using the ROBINS-I and ROBIS tools, and 78 studies were ultimately included based on the eligibility criteria. The processes of data extraction and synthesis were conducted utilizing qualitative synthesis in conjunction with co-occurrence and correspondence analysis. Therefore, the review identified (1) the most relevant types of LLM applications found in cybersecurity professional practice; (2) the most relevant technical and ethical challenges that arise when integrating LLMs into cybersecurity environments; and (3) the most useful, relevant, and effective applications of LLMs in the daily operational practice of cybersecurity professionals and analysts. These findings present significant practical ramifications for scholars and information technology practitioners. Major limitations include unretrieved studies and a lack of empirical validation. This review was not registered and received no external funding.