The field of security vulnerability detection and assessment is rapidly evolving, with a growing emphasis on leveraging Large Language Models (LLMs) to improve the accuracy and efficiency of vulnerability detection. Recent research has focused on developing innovative approaches to vulnerability assessment, including the use of LLMs to extract intention information from vulnerable code and to generate test cases for database connector testing. Additionally, there is a growing recognition of the importance of human expertise in the loop, with many studies highlighting the need for robust human validation to prevent the introduction of new security issues during the development process. Noteworthy papers in this area include Security Degradation in Iterative AI Code Generation, which highlights the potential for iterative LLM refinement to introduce new security vulnerabilities. Uncovering Reliable Indicators and VulStamp also present significant contributions to the field, with the former introducing a hybrid human-in-the-loop pipeline for IoC extraction and the latter proposing a novel intention-guided framework for vulnerability assessment. SEC-bench and AIRTBench are also notable for their contributions to the evaluation and benchmarking of LLM agents on real-world software security tasks. Overall, these advances are pushing the boundaries of what is possible in security vulnerability detection and assessment, and are likely to have a significant impact on the field in the coming years.