Logically Inconsistent Prompts Can Force AI Reasoning Models Into DoS-Like Loops

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Researchers from Zhejiang University and Alibaba, presenting at ICML 2026, found that deliberately corrupted prompts can trap AI reasoning models in long, unproductive internal loops. The attack works by feeding models logically inconsistent problems whose premises cannot be reconciled, causing them to generate far more tokens than usual. Using an evolutionary algorithm, the team refined prompts across five generations to maximise output length, testing successfully against DeepSeek-R1, Qwen3-Thinking, GPT-o3, and Gemini 2.5 Flash. In the most extreme case, response length on DeepSeek-R1 ballooned to 26 times that of normal outputs. Because the technique requires only external API access, it poses a realistic denial-of-service threat to commercial AI services, driving up compute costs and degrading performance for legitimate users.

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