Large Language Models are increasingly optimized for deep reasoning, often through post-training objectives that strongly reward correct task completion. We ask whether this pressure toward correct completion can interfere with recognizing when a task should no longer be pursued. We introduce MortalMATH, a benchmark of 150 scenarios in which users request algebra help while describing increasingly urgent situations, from mild distraction to life-threatening emergencies. We find a striking contrast across models: several reasoning-oriented models, including Qwen3-32B and GPT-5 variants, continue solving the mathematical task with high accuracy even under extreme emergency contexts, while other models increasingly interrupt the task or acknowledge the danger. Reasoning models also spend substantial computation on the original problem, delaying safety-relevant information by several seconds. These results expose a failure mode of highly optimized task completion: training models to persist until they reach a verifiably correct answer may also make them less willing to stop when the surrounding context demands it.