Security and Risk Assessment of LLM-Based AI Systems

This technical report outlines the security and risk assessment methodologies for large language model (LLM)-based AI systems. It begins by discussing the necessity of extending traditional cybersecurity controls to address the unique vulnerabilities presented by AI technologies. The report details various applications of AI across different sectors, including healthcare, finance, and defense, emphasizing the novel cybersecurity risks associated with these systems. It introduces Peraton Labs' innovative white-box AI testing approach, which involves a comprehensive review of AI system architecture and functional components to identify potential vulnerabilities. The methodology includes threat modeling, vulnerability assessments, and iterative testing to uncover weaknesses unique to LLMs. The report also discusses the importance of assessing input processing, context handling, and output processing to ensure the integrity and security of AI systems. Overall, the document provides a structured framework for evaluating the security posture of AI-driven enterprise applications.