Abstract
Large language models (LLMs) are increasingly trusted to draft artifacts of safety analysis such as losses, hazards, Unsafe Control Actions (UCAs), and safety constraints within rigorous processes like Systems-Theoretic Process Analysis (STPA). However, a blind spot exists in this rapidly growing literature: every system is analyzed except the LLM-assisted tool itself, which is a safety-relevant system that can hallucinate standards, emit unverifiable constraints, and leave no audit trail from prompt to artifact. We take seriously the question that the field has overlooked — "who analyzes the analyzer?" and answer it by applying STPA to the tool itself. We present Constitutional Meta-STPA, an LLM-assisted STPA tool built around a closed loop: the tool conducts a meta-STPA of the class of AI-assisted safety tools and derives its governance constitution from the resulting loss$\to$hazard$\to$UCA$\to$constraint chain, yielding a published constitution of 21 Tool Principles and 8 Meta-Safety Principles, each bound to a code enforcement point.
We formalize the measured object as a constitution-marginal coverage operator over a principle set $P$ ($|P|=29$) with a soundness lemma that isolates coverage from model and scanner, and report four findings:
- Self-derivation: a frontier ensemble (claude-opus-4.8${+}$claude-sonnet-4) recovers 18/21 canonical and all 8/8 governance principles from the tool's own design, while a weaker pair recovers 12/21 and 3/8, indicating that the meta layer is model-limited, not constitution-limited, and the same 8/8 re-emerge from a second, independently authored tool.
Blogger's Review: This study delves into the self-analysis mechanisms of LLM tools, revealing potential risks that must be considered in the design of safety analysis tools. By introducing meta-STPA, the authors advance the security research of LLMs and provide a crucial framework for the governance of future AI tools. The emphasis on the tool's self-derivation capability showcases how to ensure the effectiveness of safety principles in complex systems.