The field of software security is rapidly evolving, with a growing focus on leveraging Large Language Models (LLMs) to improve vulnerability detection, code analysis, and testing. Recent developments have shown that LLMs can be effectively used to identify security risks, detect code smells, and generate highly structured test inputs. The use of contextual information and internal states of LLMs has been shown to enhance their performance in vulnerability detection and code analysis. Furthermore, fine-tuned Small Language Models (SLMs) have been found to be highly accurate and efficient tools for detecting Common Weakness Enumerations (CWEs) in code. Noteworthy papers in this area include the proposal of GraphQLer, a context-aware security testing framework for GraphQL APIs, and the development of MoCQ, a holistic neuro-symbolic framework for automated static vulnerability detection. Additionally, the introduction of Cottontail, an LLM-driven concolic execution engine, has shown promising results in generating highly structured test inputs for parsing programs.