Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA
arXiv:2607.08054v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, inside rigorous processes such as Systems-Theoretic Process Analysis (STPA). Yet a blind spot runs through this fast-growing literature: every system gets analysed except the LLM-assisted tool doing the analysing, which is itself a safety-relevant system that can hallucinate standards, emit unverifiable constraints, and leave no audit trail from prompt to artifact. We take
The rapid deployment and increasing integration of LLMs into critical safety analysis processes necessitate immediate attention to their own reliability and validation. This paper reflects a growing academic and industry concern as LLMs move from experimental tools to integral components of safety-critical workflows.
As LLMs are increasingly used in foundational safety analyses, their potential to 'hallucinate standards' or produce unverifiable constraints introduces significant risks to technological systems and human safety, demanding a new layer of meta-analysis. This research highlights the need for robust verification mechanisms for AI-generated safety artifacts, impacting regulatory frameworks and development practices.
The explicit recognition of the LLM itself as a 'safety-relevant system' fundamentally shifts the paradigm of AI safety analysis, demanding self-validating and auditable AI tools for critical applications. This implies a future where AI tools are not just users of safety protocols but are subject to rigorous safety analysis themselves.
- · AI safety researchers
- · Auditing and compliance platforms
- · Developers of transparent AI
- · Unregulated LLM deployment
- · Organizations relying on black-box AI for safety
- · Rapid, unvalidated AI integration
The immediate consequence is increased scrutiny and development of methods for validating AI outputs in safety-critical domains.
This will likely lead to new industry standards and regulatory requirements specifically addressing the self-validation capabilities of AI tools used in safety analysis.
Ultimately, this could foster a new generation of 'auditable AI' where transparency and verifiable reasoning are core design principles, impacting AI development across various sectors.
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Read at arXiv cs.LG