The field of vulnerability detection and security is rapidly evolving, with a focus on leveraging large language models (LLMs) and machine learning techniques to improve the accuracy and efficiency of threat detection. Recent developments have centered around the application of LLMs to detect malware, identify common vulnerabilities and exposures (CVEs), and secure smart contract repositories against access control vulnerabilities. Additionally, researchers are exploring the use of machine learning to triage taint flows reported by dynamic program analysis tools, aiming to prioritize vulnerabilities and reduce the burden of manual review. Noteworthy papers include: MalCVE, which proposes a novel approach to detecting binary malware and associating CVEs using LLMs, achieving a mean malware detection accuracy of 97% and a recall@10 of 65%. MirrorFuzz is also notable, as it presents an automated API fuzzing solution to discover shared bugs in deep learning frameworks, improving code coverage by 39.92% and 98.20% compared to state-of-the-art methods. Trace is another significant contribution, securing non-compilable smart contract repositories against access control vulnerabilities with 89.2% precision, far exceeding existing tools.