The field of software vulnerability detection is rapidly advancing with the application of large language models (LLMs). Recent developments have focused on improving the performance and efficiency of LLMs in detecting vulnerabilities across multiple programming languages. This includes enhancing code preprocessing techniques to standardize code representation, exploring the feasibility of multilingual grammatical error correction, and leveraging data science insights for hardware security research. Notably, innovations in LLM-based software vulnerability assessment have shown promising results, with approaches such as in-context learning and information fusion demonstrating improved accuracy and effectiveness. Furthermore, research has highlighted the potential of sparse autoencoders as a lightweight and interpretable alternative for bug detection in Java functions. Noteworthy papers in this area include: Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection, which introduced SCoPE2, an enhanced version of the existing SCoPE framework with improved performance. A Preliminary Study of Large Language Models for Multilingual Vulnerability Detection, which evaluated the effectiveness of pre-trained language models and state-of-the-art LLMs across seven popular programming languages. Are Sparse Autoencoders Useful for Java Function Bug Detection, which explored the use of sparse autoencoders as a lightweight and interpretable alternative for bug detection in Java functions.