The field of cybersecurity is witnessing a significant shift towards the adoption of Generative AI (GenAI) techniques, particularly Large Language Models (LLMs), to enhance security measures. This trend is driven by the increasing complexity of cyber threats and the need for more effective and automated defense mechanisms. Researchers are exploring the potential of LLMs to generate sophisticated attack payloads, automate defense mechanisms, and improve risk management strategies. Notably, the integration of GenAI techniques into government operations is also gaining momentum, with applications in performance measurement, data management, and insight reporting. Furthermore, the development of accessible platforms for GenAI red teaming is facilitating comprehensive security evaluations and empowering non-technical domain experts. While LLMs have shown promise in automating CVSS scoring, combining them with embedding-based methods may yield more reliable results. Noteworthy papers include:

• GenXSS, which presents a novel AI-driven framework for automated detection of XSS attacks in WAFs, achieving a high success rate in generating and blocking sophisticated attacks.
• ViolentUTF, which introduces an accessible and scalable platform for GenAI red teaming, enabling comprehensive security evaluations and facilitating the assessment of LLMs' cross-domain reasoning capabilities.