GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs

• URL: http://arxiv.org/abs/2504.08176v1
• Date: Fri, 11 Apr 2025 00:13:59 GMT
• Title: GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs
• Authors: Vahid Babaey, Arun Ravindran,
• Abstract summary: Cross-Site Scripting (XSS) attacks target client-side layers of web applications by injecting malicious scripts.<n>Traditional Web Application Firewalls (WAFs) struggle to detect highly obfuscated and complex attacks.<n>This paper presents a novel generative AI framework that leverages Large Language Models (LLMs) to enhance XSS mitigation.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The increasing reliance on web services has led to a rise in cybersecurity threats, particularly Cross-Site Scripting (XSS) attacks, which target client-side layers of web applications by injecting malicious scripts. Traditional Web Application Firewalls (WAFs) struggle to detect highly obfuscated and complex attacks, as their rules require manual updates. This paper presents a novel generative AI framework that leverages Large Language Models (LLMs) to enhance XSS mitigation. The framework achieves two primary objectives: (1) generating sophisticated and syntactically validated XSS payloads using in-context learning, and (2) automating defense mechanisms by testing these attacks against a vulnerable application secured by a WAF, classifying bypassing attacks, and generating effective WAF security rules. Experimental results using GPT-4o demonstrate the framework's effectiveness generating 264 XSS payloads, 83% of which were validated, with 80% bypassing ModSecurity WAF equipped with an industry standard security rule set developed by the Open Web Application Security Project (OWASP) to protect against web vulnerabilities. Through rule generation, 86% of previously successful attacks were blocked using only 15 new rules. In comparison, Google Gemini Pro achieved a lower bypass rate of 63%, highlighting performance differences across LLMs.

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