Real-Time AI Code Security Auditing: Automated Vulnerability Detection and Remediation Through Meta-Experimental Analysis

This paper presents a proof-of-concept study evaluating Claude Opus 4.1's capabilities in security vulnerability generation and detection through a meta-experimental approach. We systematically generated 75 security vulnerabilities across five Python web applications (2,146 lines of code) spanning SQL injection, XSS, authentication bypass, path traversal, and command injection categories. We then evaluated the AI's ability to conduct security audits of its own generated code, producing 1,892 lines of detailed analysis. Although this circular validation approach has inherent limitations, it reveals the AI's pattern recognition capabilities and security principle understanding. The system successfully identified all intentionally created vulnerabilities and provided structured remediation guidance. This work provides initial evidence of AI potential for security code analysis and establishes a methodology for evaluating AI security comprehension, though real-world validation with independent code remains essential.