Addressing Data Poisoning and Model Manipulation Risks using LLM Models in Web Security

Data poisoning and model manipulation represent significant threats to cybersecurity, where adversaries intentionally inject malicious data into training sets, leading to compromised machine learning models. This study explores the efficacy of using Large Language Models (LLMs) to detect and mitigate such risks in cybersecurity. We propose a novel LLM-based framework that analyzes incoming data streams, identifies anomalies indicative of poisoning, and performs real-time model correction to maintain the integrity of security systems. Leveraging the generative and analytical capabilities of LLMs, our approach dynamically adapts to evolving attack vectors by detecting subtle changes in data distributions. We evaluated the framework on a cybersecurity dataset with known data poisoning incidents, achieving an accuracy of 97.5% in identifying poisoned data and reducing model manipulation attempts by 85% compared to conventional machine learning models. Additionally, our method exhibited a 30% improvement in response time to attacks, ensuring faster detection and mitigation. The study demonstrates that integrating LLMs into cybersecurity systems can significantly enhance resilience against sophisticated adversarial attacks.