The field of cybersecurity is rapidly evolving, with a growing focus on leveraging Large Language Models (LLMs) and other innovative approaches to improve security auditing and vulnerability detection. Recent developments have highlighted the potential of LLMs in identifying nuanced security vulnerabilities within code, as well as their limitations and potential applications in development workflows. Furthermore, the importance of securing software supply chains and smart contracts has become increasingly evident, with various studies and tools emerging to address these challenges. Notably, the development of frameworks such as GoLeash and VulCPE has improved the detection of malicious packages and configuration-specific vulnerabilities, while tools like Esuer and SmartAuditFlow have enhanced the precision of control flow graphs and smart contract security analysis. Additionally, empirical analyses have evaluated the effectiveness of various vulnerability detection tools and approaches, providing valuable insights for developers and security researchers. Overall, the field is moving towards more adaptive, precise, and automated security solutions, with a growing emphasis on collaboration and community engagement to stem the proliferation of scam contracts and other cybersecurity threats. Noteworthy papers include: GoLeash, which applies the principle of least privilege at the package-level granularity to detect malicious packages, and SmartAuditFlow, which dynamically generates and refines audit plans for smart contract security analysis.