Researchers presented a deterministic AI security risk assessment framework that uses a Control ID taxonomy and formal verification to evaluate AI systems. The framework normalizes heterogeneous artifacts into a project-independent taxonomy and outputs technique-indexed feasibility and impact levels. It was evaluated on five public open-source AI projects, showing consistent downward shifts in feasibility profiles under strengthened observable controls.