Investigating the use of Large Language Models in software security requirements: Results of a literature review - Melo, Amália; Almeida, Lucas; Garcés, Lina (Anais do Workshop Brasileiro de Engenharia de Software Inteligente (ISE), 2025)
This study investigates the application of Large Language Models (LLMs) in the context of security requirements engineering through the conduction of a rapid literature review. The review enabled the characterization of current research in this domain with respect to: (i) the purposes for which LLMs are employed in security requirements activities; (ii) the families of LLMs explored (e.g., GPT, BERT, LLaMA), their capabilities (e.g., classification, generation), and underlying architectures (e.g., encoder, decoder, encoder-decoder); (iii) the techniques adopted for conditioning or guiding LLM behavior; (iv) the datasets used to train, fine-tune, or validate these models; and (v) the evaluation metrics applied to assess the performance of LLMs in supporting security requirements tasks. The findings contribute to a structured understanding of the current state of research and highlight key trends, gaps, and opportunities for advancing the use of LLMs in secure software engineering.