The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems

Five-layer framework for hidden safety-critical failures in deployed AI systems

A preprint from arXiv argues that AI safety discourse over-indexes on visible, model-centric failures while neglecting quieter systemic risks in deployed socio-technical systems. The authors propose a five-layer diagnostic framework covering epistemic, control, temporal, organizational, and ecosystem integrity. The paper identifies under-recognized risk patterns including uncertainty laundering, prompt injection, memory poisoning, evaluation deception, and model collapse, and calls for a shift from model-centric evaluation toward socio-technical reliability. It concludes with design, governance, and research agenda recommendations.