Published Paper Details:

The increasing complexity of web applications has led to a rise in security vulnerabilities that threaten sensitive data and system integrity. Open-source security tools play a vital role in improving transparency, collaboration, and accessibility in cybersecurity research. This paper presents an open-source, AI-powered web vulnerability assessment tool designed to detect common security issues such as SQL Injection, Cross-Site Scripting, and insecure HTTP headers. The system performs automated scanning of web applications and utilizes artificial intelligence to generate simplified and structured vulnerability reports with severity classification and mitigation guidance. Experimental evaluation shows that the proposed tool effectively identifies security flaws and assists developers in adopting secure coding practices. The open-source nature of the system encourages extensibility and community-driven improvements for future security enhancements.