AI-based web application attack detection focuses on identifying malicious requests, suspicious application interactions, and unsafe input-handling patterns that may expose web applications to security threats. It involves analyzing sequential HTTP request behavior, examining source-code structures and data-flow paths, and identifying patterns associated with attacks targeting application request integrity and command execution. By combining deep learning-based sequence analysis with graph-based code representation and vulnerability detection, this approach helps recognize suspicious request sequences, trace potentially unsafe data flows, and identify attack patterns associated with cross-site request forgery and operating system command injection. The resulting findings support earlier attack detection, targeted security validation, improved application-layer monitoring, and the implementation of appropriate defensive controls to reduce the risk of successful web application attacks.