Artificial Intelligence Causing Catastrophic Loss and Human Extinction Risk, and Its Prevention and Control

With the rapid iteration and large‐scale deployment of artificial general intelligence (AGI), AI‐related risks have evolved from traditional single technical vulnerabilities and content violations into systemic, cross‐domain, civilization‐level catastrophic threats. Most existing AI safety research focuses on surface‐level application risks and foundational model defects, lacking a complete spectrum analysis and closed‐loop prevention and control system covering infrastructure collapse, climate catastrophe, biological weapons proliferation, social cognitive collapse, and nuclear strategic system failure. This paper integrates multiple independent risk pathways from existing AI safety literature, constructs a continuous risk escalation spectrum from local loss to civilizational extreme crisis and a seven‐category risk classification system, and conducts in‐depth attack chain decomposition and closed‐loop prevention and control design for three top‐tier civilization‐level risks: planetary climate inter‐ vention escape, civilian lethal biological weapons synthesis, and nuclear com‐ mand system verification bypass. This paper does not presuppose that AI‐caused human extinction is the most likely outcome, but rather studies the significant losses AI may cause and the possible pathways through which they may further escalate into civilizational crises or even human existential crises under specific conditions. Risks follow a continuous escalation spectrum: Minor loss → Major loss → Systemic crisis → Civilizational crisis → Human extinction Core prevention and control logic: the earlier risk intervention occurs, the lower the technical and social governance costs; high‐order extreme consequences all depend on multiple superimposed preconditions, and there is no conclusion that a single risk necessarily leads to extinction. This paper distinguishes three types of expository paradigms: mature existing risk conclusions are cited; existing concepts recombined and classification frameworks restructured are explicitly stated as synthesized by this paper; new mecha‐ nism hypotheses are labeled as untested risk hypotheses proposed by this paper; new multi‐layer engineering prevention frameworks are proposed by this paper.