Implementation of AI-Driven Input Validation in User Interfaces for Preventing Injection Attacks and Ensuring Data Sanitation through Adaptive Filtering and Pattern Recognition

Abstract

This study introduces an AI-driven framework for real-time input validation in user interfaces, designed to mitigate injection attacks and enforce data sanitation via adaptive filtering and pattern recognition. The  methodology integrates deep learning models (CNN-LSTM hybrids) with rule-based heuristics, trained on a synthetic dataset of 1.2 million malicious and benign inputs simulating SQL, XSS, and command injection payloads. Key findings reveal a 97.8% detection accuracy, 94.3% reduction in false positives compared to traditional regex-based systems, and a 41% improvement in processing latency under high-throughput conditions. The framework demonstrates superior adaptability to zero-day attack variants through continual learning. Results underscore the efficacy of hybrid neuro-symbolic approaches in securing web and mobile interfaces. The study contributes a reproducible prototype, validated across PHP, Node.js, and Java environments, offering actionable guidelines for integrating AI validation layers in production systems.