Web Application Firewalls (WAFs) play a crucial role in protecting web applications from Cross-Site Scripting (XSS) attacks by detecting and blocking malicious payloads. Traditional WAFs rely heavily on signature-based detection methods, which compare incoming requests against known malicious patterns. However, attackers continuously evolve their techniques to craft payloads that can bypass these signatures, rendering traditional detection less effective. Recently, attackers and security researchers have begun leveraging Machine Learning (ML), specifically Generative Adversarial Networks (GANs), to automatically generate a wide variety of XSS payloads. GANs are a type of ML model that can learn the patterns of existing attacks and create new, diverse payloads that evade standard detection rules. This technique significantly enhances the sophistication of WAF testing by simulating a broader range of evasive attack payloads, allowing defenders to improve their signatures and detection capabilities. While this approach poses a challenge to legacy signature-based WAFs, it also opens new avenues for developing more adaptive, ML-powered WAF solutions that can better detect novel attack vectors. In conclusion, integrating ML techniques like GANs into WAF testing improves the ability to identify and mitigate evolving XSS attack methods, helping to strengthen web application security.