The field of software security is rapidly advancing with the development of innovative techniques for secure code generation and vulnerability detection. Researchers are exploring the use of large language models (LLMs) and retrieval-augmented generation (RAG) to improve the security and functionality of generated code. One notable direction is the integration of security knowledge into RAG systems, which has shown promising results in reducing the risk of generating insecure code. Additionally, there is a growing interest in using LLMs for vulnerability detection, with techniques such as code representation learning and synthetic dataset generation being proposed to improve detection performance. Noteworthy papers in this area include: Secure Multifaceted-RAG for Enterprise, which proposes a hybrid knowledge retrieval approach with security filtering. Detecting Malicious Source Code in PyPI Packages with LLMs, which evaluates the effectiveness of LLMs and RAG for detecting malicious source code. Give LLMs a Security Course, which proposes a security-hardening framework for RACG systems via knowledge injection. Automatically Generating Rules of Malicious Software Packages via Large Language Model, which leverages LLMs to automate rule generation for OSS ecosystems.