AI-based vulnerability discovery focuses on identifying potential software security weaknesses through source-code analysis, machine learning-based prediction, and controlled security testing. It involves examining code structures, extracting relevant code features, detecting patterns associated with memory-safety issues, and analyzing program behavior to uncover vulnerabilities that may be difficult to identify through conventional testing alone. By combining transformer-based code understanding with machine learning classification, fuzz testing, and runtime memory-safety validation, this approach helps identify and prioritize potential weaknesses such as use-after-free and integer overflow vulnerabilities. The resulting findings support targeted vulnerability investigation, evidence-based validation, secure code remediation, and improved software reliability by detecting security flaws before they can be exploited.