AI-based security regression analysis focuses on identifying security weaknesses that may reappear or be introduced when application source code is modified, patched, or updated. It involves examining code changes, extracting static code characteristics, reviewing historical vulnerability information, and comparing source-code patches to identify patterns associated with recurring security flaws. By combining machine learning-based risk prediction with vulnerability classification, this approach helps assess whether code modifications may introduce security regressions or reintroduce previously addressed vulnerabilities, including injection-related weaknesses. The resulting findings support targeted security testing, verification of corrective patches, early identification of potential regressions, and continuous improvement of application security throughout the software development and maintenance lifecycle.