The field of cybersecurity is rapidly evolving, with a growing focus on developing innovative methods for vulnerability assessment and exploitation. Recent developments have highlighted the importance of integrating social and cyber dimensions to comprehensive vulnerability assessment, with novel frameworks emerging to address this need. Additionally, advances in large language models (LLMs) have enabled autonomous cyberattacks, posing significant threats to existing applications and emphasizing the urgent need for real-world benchmarks to evaluate LLM agents' ability to exploit web application vulnerabilities. Furthermore, researchers are exploring new approaches to automate software vulnerability detection, including the use of LLMs for code analysis and zero-shot vulnerability detection. Noteworthy papers in this area include: CVE-Bench, which introduces a real-world cybersecurity benchmark for evaluating LLM agents' ability to exploit web application vulnerabilities. SCVI, which presents a novel framework for comprehensive socio-cyber vulnerability assessment, integrating individual-level factors and attack-level characteristics. VulnSage, which provides a comprehensive evaluation framework and dataset for zero-shot vulnerability detection, demonstrating the effectiveness of structured reasoning prompts in improving LLM performance.