Machine Learning-Guided Cross-Site Scripting Vulnerability Remediation in OWASP WebGoat Using Random Forest Classification and CodeT5-Based Security Patch Generation

Organizations commonly deploy web applications that process user-controlled input and generate browser-accessible responses.

If application input is processed without appropriate security controls, attacker-controlled content may reach a browser-rendered context and be interpreted as executable script content. Such weaknesses can expose the application to Cross-Site Scripting attacks.

In this use case, the existing OWASP WebGoat vulnerable web application is deployed in an isolated Docker environment on an Ubuntu virtual machine. Docker is used to create a controlled and repeatable runtime environment for the vulnerable WebGoat application and for the patched application after remediation.

Kali Linux is used as the controlled security-testing system.

A controlled Cross-Site Scripting Attack assessment is performed against the authorized laboratory WebGoat application. The assessment first identifies the exposed WebGoat service and selected XSS functionality and then submits a controlled XSS payload to establish the initial vulnerable behavior.

Nmap is used to identify the network-accessible WebGoat service and verify the application exposure from the security-testing environment.

The controlled XSS security assessment is performed from Kali Linux against the authorized Docker-hosted WebGoat application. The same testing condition is used again after remediation to determine whether the original XSS behavior has been prevented.

Publicly available vulnerability and security-fix source-code samples are used as the source for training the AI/ML components. The source-code samples contain vulnerable and corresponding fixed implementations that can be processed for vulnerability classification and automated patch generation.

Random Forest is used as the vulnerability-classification algorithm because it can classify source-code characteristics associated with the target vulnerability.

CodeT5 is used as the AI-based patch-generation model because it can process source code and generate a modified source-code sequence based on learned vulnerable-to-fixed code transformations.

Semgrep is used for static security analysis to examine the source code before and after remediation and identify relevant vulnerable coding patterns.

Maven is used to build the modified WebGoat source code after the generated security patch is applied.

The generated patch is then applied to the relevant WebGoat source-code component. Semgrep static analysis is performed before runtime validation, and the patched application is rebuilt and deployed into a separate Docker environment.

The same controlled XSS security test is then repeated against the patched Docker-hosted WebGoat application. The original and post-remediation security results are compared to determine whether the generated patch addresses the identified vulnerability.

Application : OWASP WebGoat

OWASP WebGoat is the target vulnerable web application in this use case.

It provides intentionally vulnerable web-application functionality for controlled cybersecurity testing and allows security weaknesses such as Cross-Site Scripting to be reproduced in an isolated laboratory environment.

A web application may process user-controlled input and place the resulting content into a browser-rendered response.

If appropriate input handling, contextual output encoding, or other security controls are not applied, attacker-controlled content may be interpreted as executable script content by the browser.

When the vulnerable application is deployed as a network-accessible service, the vulnerable functionality can be reached from an authorized security-testing system and evaluated through controlled XSS testing.

The proposed solution introduces Docker-based application deployment, Nmap service discovery, controlled XSS security testing, vulnerability-data preprocessing, Random Forest vulnerability classification, CodeT5-based security patch generation, source-code analysis using Semgrep, Maven application rebuilding, patched Docker deployment, post-remediation XSS testing, and before-and-after security validation.

Specific Attack: Cross-Site Scripting Attack

The controlled attack scenario evaluates whether attacker-controlled script content can be submitted through the vulnerable WebGoat functionality and executed by the browser.

The objective is to determine whether the target WebGoat functionality processes untrusted input without sufficient security controls and whether the generated security patch prevents the same controlled XSS behavior after remediation.

AI-Assisted Vulnerability Detection and Automated Security Patch Generation

The primary security concept is AI-Assisted Vulnerability Detection combined with Automated Security Patch Generation and controlled cybersecurity testing.

The objective is to identify vulnerable source-code characteristics using a trained Random Forest classifier, generate a candidate security patch using CodeT5, and validate whether the generated remediation prevents the original XSS behavior in the existing WebGoat application.

The remediation is not considered successful only because CodeT5 generates modified source code. The generated patch must pass source-code security analysis, application building, Docker deployment, controlled XSS testing, and legitimate application-functionality validation.

The security concept defines the overall security strategy.

The following are the specific defensive mechanisms implemented.

The Random Forest model is trained using processed vulnerable and security-fixed source-code samples and is used to classify the target source code according to learned vulnerability-related characteristics.

An existing OWASP WebGoat vulnerable web application is successfully deployed using Docker in an isolated Ubuntu environment for controlled Cross-Site Scripting security testing and AI-assisted vulnerability remediation.

The Docker-hosted WebGoat service is identified from the authorized Kali Linux environment using Nmap, establishing the initial network-level application attack surface.

The target WebGoat XSS functionality is identified and its initial security condition is established through a controlled XSS assessment performed against the authorized vulnerable application.

A public vulnerability and security-fix source-code collection is processed to obtain vulnerable and corresponding fixed source-code relationships required for training the AI/ML components.

A Random Forest classification model is trained and evaluated to identify source-code characteristics associated with the target XSS vulnerability before the target WebGoat source code is analyzed.

A CodeT5-based patch-generation model is trained or fine-tuned using vulnerable-to-fixed source-code relationships and is used to generate a candidate security patch for the identified vulnerable WebGoat source code.

The generated security patch is applied to the relevant WebGoat source-code component, followed by Semgrep static security analysis and Maven application building to validate the modified implementation.

The patched WebGoat application is packaged into a separate Docker image and deployed as a separate runtime environment for post-remediation security testing.

A controlled XSS attack and Nmap service assessment are repeated against the patched Docker-hosted WebGoat application, and the original and post-remediation results are compared to determine whether the generated patch addresses the identified vulnerability.

The complete workflow of Docker-based application deployment, Nmap service discovery, controlled XSS testing, vulnerability-fix source-code preparation, Random Forest vulnerability classification, CodeT5 security patch generation, Semgrep static analysis, Maven application building, patched Docker deployment, post-remediation XSS validation, and before-and-after security comparison is successfully demonstrated.