AI Models Overthink Problems—and It’s a Security Risk
TL;DR
Reasoning models solve tasks through internal step-by-step chains. That same strength can be abused: logically inconsistent prompts can push models into long, unproductive reasoning loops. Researchers from Zhejiang University and Alibaba presented the work at ICML 2026 in Seoul. Their evolutionary prompt attack mutates math problems to maximize how much text a model generates.
Nauti's Take
The important part is not that models sometimes think too long. It is that this behavior can be steered.
Providers sell longer reasoning as a premium capability, but they also need to prove that models can stop cleanly when a task is incoherent. Better models need more than better answers; they need reliable judgment about when no answer is possible.
Briefingshow
Reasoning models are increasingly used in coding, math, research and agent workflows, often with higher cost per request. If attackers can deliberately trigger unnecessary thinking, model quality becomes an operations and security issue. Rate limits alone may not be enough when malicious prompts look like ordinary hard problems.
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