Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA

As Large Language Models (LLMs) are increasingly used to draft safety analysis artifacts within rigorous processes like Systems-Theoretic Process Analysis (STPA), a critical oversight has emerged: the LLM-assisted tool itself is rarely analyzed for safety. This paper addresses this gap by proposing "Constitutional Meta-STPA." This novel framework turns STPA onto the AI-assisted safety tool, creating a closed-loop system. It derives a governance constitution for the tool from its own meta-STPA analysis, establishing principles and safety guidelines that are bound to code enforcement points. The research demonstrates that frontier LLM ensembles can effectively self-derive these principles, highlighting the potential for AI to validate its own safety analysis processes and ensure auditability and reliability in critical applications.