AI-Powered Automated Bug Bounty Platform

Cybersecurity remains a critical challenge, demand- ing efficient and reliable methods for vulnerability detection. Traditional approaches often struggle with speed, scalability, and the evolving nature of threats. This survey reviews recent litera- ture (2022-2024) focusing on automated vulnerability detection, particularly leveraging Artificial Intelligence (AI) and Machine Learning (ML). We analyze the key advancements presented in these studies, such as improved accuracy in identifying specific flaws (e.g., SQL Injection, Cross-Site Scripting) using hybrid methods and deep learning, and the increased efficiency offered by automation scripts and AI-driven penetration testing tools. However, we also critically examine the persistent limita- tions highlighted, including dependencies on large, high-quality datasets, the ’black-box’ nature of some AI models, challenges in detecting zero-day threats, the resource-intensive nature of advanced models, and the continued need for human validation. These identified gaps and drawbacks collectively underscore the necessity for exploring more integrated, autonomous, and trans- parent security solutions, motivating research into systems that combine AI’s detection capabilities with technologies ensuring verifiable and trustworthy reporting.