Web applications have become vital infrastructure across multiple industries because they manage vital operations which demand strong protective mechanisms during modern digital operations. The application security integrity remains at risk because of important vulnerabilities which include SQL Injection (SQLi) and Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF) and Remote Code Execution. OWASP identifies these fundamentals as core issues in its Top Ten list which shows the essential requirement for developing effective protection and detection methodologies. Modern technology emphasizes the crucial role of automatic vulnerability detection systems through the implementation of black-box testing alongside hybrid scanning methods which unite white-box and black-box methodologies. Current web applications are dynamic systems that create difficulties in both determining precise models of behavior and detecting relationships between pages and their data dependencies. New methodologies were developed to establish formal methods for identifying frequent web vulnerabilities which led to both detection improvements and operation efficiency. Recent advancements in deep learning techniques lead to better vulnerability detection outcomes compared to traditional methods for detecting advanced vulnerabilities. A comparative analysis of existing tools, such as Burp Suite and Nessus, reveals varying capabilities in vulnerability detection. Burp Suite delivers superior results during hands-on evaluation but Nessus provides detailed scanning of network infrastructure. The implementation of different detection solutions enables complete security evaluations which enhances detection accuracy and lowers incorrect alert rates. An essential part of security protection verification functions through Automated Validation of Internet Security Protocols and Applications (AVISPA) systems.