This research introduces a novel framework that leverages Large Language Models for risk analysis in autonomous driving systems.

• Enables proactive risk identification in generated driving scenarios
• Provides human-interpretable assessments of potential hazards
• Creates a foundation for safer autonomous vehicle testing and deployment
• Bridges the gap between AI capability and critical safety engineering

The significance for security lies in the framework's ability to identify safety-critical edge cases before they manifest in real-world situations, potentially preventing accidents and security incidents in autonomous driving systems.

Risk-Aware Driving Scenario Analysis with Large Language Models