ThreatFinderAI: Automated Threat Modeling Applied to LLM System Integration

Artificial Intelligence (AI) is a rapidly integrated technology, significantly contributing to advancements like 6G. However, its swift adoption raises considerable security concerns. Large Language Models (LLMs) pose risks such as spear phishing, code injections, and remote code execution. Conventional threat modeling, used in secure software development, faces challenges when applied to AI systems, as existing methodologies are designed for traditional software. Furthermore, AI-specific threat modeling research is sparse and lacks approaches providing practical support or automation. Thus, this demo paper presents ThreatFinderAI, an asset-centric threat modeling and risk assessment framework. ThreatFinderAI fulfills seven steps aligned with AI system design and transforms AI threat and control knowledge bases into a queryable knowledge graph for automated asset identification and threat elicitation. It also proposes business impact analysis and expert estimates for AI threat impact quantification. In the demonstration, ThreatFinderAI is illustrated by securing a customer care application relying on LLMs. Through this, it is demonstrated how the proposed framework can be used to identify relevant threats and practical countermeasures and communicate strategic risk.