Athreat Modeling Framework for LLM System Integration Leveraging NLP and Machine Learning

Abstract

With the growing use of Large Language Models (LLMs) in diverse applications, their safety, security and resilience against cyber threats has become increasingly worrying. Traditional security measures routinely lack to counter the active and adaptive nature of vulnerabilities within LLM systems, thus necessitating an Automated Threat Modeling (ATM) technique. This research introduces a threat modeling framework that uses AI tailored for complete risk management through identification, evaluation, and mitigation of security threats stemming from LLM system integrations. The proposed Automated Threat Modeling (ATM) system applies machine learning, natural language processing (NLP), and behavioural scrutiny for advanced diagnosis to multi-faceted emergent attack spectrum like prompt injection, data poisoning, model inversion, adversarial attacks, and unauthorized access. A hybrid risk assessment approach is implanted in the framework, using static security assessment, dynamic behavioural profiling, as well as real-time anomaly detection to improve threat detection accuracy. Moreover, the model adapts cyber threat intelligence (CTI) feeds with automated threat neutralization policies for advanced proactive defence mechanisms. Systematic testing of the model in real-world LLM deployment scenarios validated its efficacy in precision and recall metrics for securing vulnerability detection and mitigation. Findings demonstrate the efficacy of AI driven threat modeling in LLM integrated systems for automation of risk assessment, decreased false positives, and improved response time while fortifying system security. This study highlights the need for implementing proactive and adaptive security measures during the integration of LLM systems to maintain the integrity and reliability of AI applications amid shifting and escalating cyber risks. The advanced development in secure AI implementation strategies is achieved with the Automated Threat Modeling framework supporting the development of future AI-based cybersecurity technologies for enterprise and cloud LLM applications.