The fields of software security, recommender systems, and large language models are undergoing significant transformations. A common theme among these areas is the increasing focus on vulnerability management, safety, and security. In software security, researchers are employing survival analysis and mining software repositories to examine the lifetime of Common Vulnerabilities and Exposures (CVEs) and the resolution of transitive vulnerabilities. Noteworthy papers include The Ripple Effect of Vulnerabilities in Maven Central and Out of Sight, Still at Risk, which highlight the prevalence and impact of transitive vulnerabilities in dependencies. In recommender systems, the application of large language models (LLMs) is becoming more prevalent. Innovations in loss functions, such as the proposal of novel loss functions tailored for recommendation, are improving the alignment of LLMs with recommendation objectives. The use of in-context learning methods and the integration of structural information from knowledge graphs are also enhancing the performance of LLM-based recommender systems. The field of large language models is rapidly evolving, with a focus on improving safety and security. Recent studies have highlighted the vulnerability of LLMs to adversarial attacks, and researchers are exploring new methods to detect and mitigate these attacks. Additionally, there is a growing interest in developing more robust and generalizable LLMs that can adapt to novel safety requirements and domain shifts. Other areas, such as smart contract security, large language model agents, and data management, are also experiencing significant advancements. The use of LLMs in these areas has the potential to improve security, functionality, and fairness. However, challenges remain, including the need for standardized frameworks and the mitigation of biases in LLMs. Overall, the advancements in these fields have the potential to significantly improve the security, safety, and functionality of software systems and large language models. As research continues to evolve, it is essential to address the challenges and limitations of these technologies to ensure their safe and effective deployment.