Goal-Driven Risk Assessment for LLM-Powered Systems: A Healthcare Case Study

Researchers propose a new goal-driven risk assessment framework for LLM-powered systems, specifically targeting healthcare applications. The approach uses attack trees to identify detailed threat vectors combining adversarial AI attacks with conventional cyber threats, addressing security gaps in LLM system design.

• →New security challenges emerge when LLMs are integrated into critical systems like healthcare due to combined attack vectors.
• →Traditional threat modeling methods produce abstract results that are inadequate for proper risk assessment in LLM systems.
• →The proposed framework uses attack trees to provide structured, detailed threat analysis with specific attack paths.
• →The study harmonizes state-of-the-art LLM attacks with conventional cyber threats for comprehensive risk evaluation.
• →This research advances secure-by-design practices for LLM-based systems in critical applications.