Open Access

Dharmik Patel

• Web applications play a crucial role in modern business operations but remain prime targets for cyberattacks due to the sensitive data they handle. Despite continuous advancements in cybersecurity, many applications are still susceptible to common vulnerabilities such as SQL Injection (SQLi), Cross-Site Scripting (XSS), Local File Inclusion (LFI), and Remote Code Execution (RCE), many of which areWeb applications play a crucial role in modern business operations but remain prime targets for cyberattacks due to the sensitive data they handle. Despite continuous advancements in cybersecurity, many applications are still susceptible to common vulnerabilities such as SQL Injection (SQLi), Cross-Site Scripting (XSS), Local File Inclusion (LFI), and Remote Code Execution (RCE), many of which are listed in the OWASP Top 10. Existing security tools often provide limited coverage, focusing on specific aspects like SSL validation or static code analysis, while failing to comprehensively detect and confirm exploitation attempts in real-world scenarios. This thesis addresses these gaps by leveraging AI-driven attack automation for vulnerability detection and analysis. The system integrates automated reconnaissance, penetration testing, and AI-assisted exploitation validation to identify security flaws dynamically. Unlike conventional tools that rely on static analysis, this approach executes real attack scenarios, analyzes system responses, and determines whether an exploit truly succeeded. The research specifically evaluates the effectiveness of AI models in generating attack execution commands, constructing multi-stage attack chains, and assessing post-exploitation outcomes. The system is tested against a controlled vulnerable web environment, measuring its accuracy, efficiency, and reliability in detecting and validating real vulnerabilities. A structured methodology is followed, beginning with a comprehensive literature review of web vulnerabilities and attack automation techniques, followed by the design, development, and experimental evaluation of the AI-driven penetration testing framework. The results indicate significant challenges in AI-assisted exploitation validation, with both models exhibiting high false positive rates and misclassification of vulnerabilities. However, the study highlights key areas for improvement, including enhancing AI’s exploit validation mechanisms and reducing false positives through contextual analysis. By bridging the gap between automated attack execution and intelligent exploit validation, this research contributes to the advancement of AI-driven penetration testing methodologies. The findings underscore the potential and limitations of current AI models in cybersecurity, paving the way for future enhancements in AI-assisted vulnerability assessment and exploitation validation techniques.…