The field of code clone detection and software vulnerability management is witnessing significant developments, with a growing focus on innovative approaches to identify and mitigate vulnerable code clones. Researchers are exploring new techniques, such as program slicing and locality-sensitive hashing, to detect complex vulnerability patterns across diverse codebases. The analysis of vulnerability propagation across open source software ecosystems is also becoming increasingly important, with studies revealing complex propagation sequences and lengthy delays. Furthermore, the misuse of datasets in semantic clone detection is being addressed, with findings highlighting the need for careful consideration of dataset limitations to avoid misleading results. Noteworthy papers in this area include: A Slicing-Based Approach for Detecting and Patching Vulnerable Code Clones, which introduces a scalable and precise detection approach. How the Misuse of a Dataset Harmed Semantic Clone Detection is also notable, as it demonstrates the problematic use of BigCloneBench as ground truth for learning or evaluating semantic code similarity.