LLM-WAF: An Intelligent Web Application Firewall Powered by Large Language Models for Advanced Threat Detection

Abstract

Traditional signature-based Web Application Firewalls (WAFs) have difficulty detecting increasingly complex assaults that target web applications, such as SQL injections, Cross-Site Scripting (XSS), and API misuse. In this study, we introduce LLM-WAF, a new intelligent firewall architecture that uses Large Language Models (LLMs) to analyze HTTP traffic contextually and semantically. Our framework integrates pre-trained language models with realtime traffic monitoring pipelines to identify malicious payloads through natural language processing capabilities rather than static rule matching. The system incorporates a continuous learning mechanism using reinforcement signals from detected attacks to adapt to emerging threat vectors automatically. In comparison to conventional WAF systems, experimental evaluation on benchmark datasets such as the CSIC 2010 HTTP Dataset and real-world traffic scenarios shows that LLM-WAF achieves 96.8% detection accuracy with an F1=0.95cand dramatically lowers false positives.