AI Models Overthink Problems—and It’s a Security Risk
TL;DR
Researchers from Zhejiang University and Alibaba presented work at ICML 2026 showing that reasoning models can be pushed into excessive overthinking with logically inconsistent prompts. Their evolutionary prompt attack mutates premises and questions from math tasks until models produce long, mostly useless reasoning loops. The tests covered DeepSeek-R1, Qwen3-Thinking, OpenAI GPT-o3 and Gemini 2.5 Flash; in one case, outputs grew up to 26.1 times longer than normal.
Nauti's Take
If you run reasoning models in production, test output caps, stop rules, and cost alerts first, especially around open-ended user prompts. Practical exploitability is still unclear and the sourcing is thin, but the operational risk is real enough: long answers can burn budget, slow queues, and degrade UX.
Briefingshow
Reasoning is the headline feature of modern AI models, but that same capability widens the attack surface. If a model fails to recognize that a problem is impossible or internally inconsistent, it can burn compute on apparently sophisticated loops. For providers, safety is no longer just about filtering harmful outputs; it also means controlling wasted reasoning time.
Sources