The field of cybersecurity is experiencing a significant shift with the integration of Large Language Models (LLMs) into various applications. Researchers are leveraging LLMs to enhance threat analysis, vulnerability detection, and security testing. The automation of function-level Threat Analysis and Risk Assessment (TARA) is becoming increasingly important, and LLMs are being used to generate attack trees and risk evaluations. Additionally, LLMs are being employed to detect vulnerabilities and improve the accuracy of Cross-Site Scripting (XSS) detection. The use of LLMs in cybersecurity is transforming the field, enabling more efficient and effective security testing and threat analysis. Notable papers in this area include: Automating Function-Level TARA for Automotive Full-Lifecycle Security, which introduces DefenseWeaver, a system that automates function-level TARA using LLMs. Leveraging LLM to Strengthen ML-Based Cross-Site Scripting Detection, which proposes a system that fine-tunes an LLM to generate complex obfuscated XSS payloads, achieving a 99.5% accuracy rate. Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming, which presents a framework for unsupervised feature transformation using LLM agents, outperforming supervised baselines in feature transformation efficiency and robustness.