The practice of web form submission has emerged as a prime conduit for attackers, enabling them to infiltrate modern web applications and illegally harvest sensitive user data. Traditional defense mechanisms, such as static security reviews and server-side validation, are proving insufficiently agile for real-time detection of client-side vulnerabilities. This inadequacy arises directly from the rapid evolution of modern interfaces, which involves spontaneous DOM changes, dynamic element generation, and semantic interpretation that varies based on context and culture. This article presents an innovative browser extension framework that leverages a heuristic-based, multi-dimensional analytical engine combined with deep DOM inspection to identify insecure form submissions the moment they occur. The proposed methodology introduces five fundamental innovations: a contextual risk scoring system that models the complex interdependencies among form fields; an adaptive weighting scheme for risk patterns, accommodating diverse cultural and linguistic norms; a predictive vulnerability estimator that anticipates future threats; intelligent DOM mutation filtering designed to significantly optimize runtime performance; and cross linguistic semantic recognition to determine the true purpose of fields globally. Based on theoretical projections, this combined approach promises to enhance vulnerability detection accuracy while simultaneously reducing computational demands by approximately. Critically, all security analysis is executed exclusively on the user's local machine, guaranteeing privacy by ensuring no sensitive data is transmitted externally. A proof of concept application confirms the framework's practical feasibility and high efficacy for client side security assessment and catalyzing the development of flexible, scalable, and privacy respecting browser-based protections.