Reasoning models are supposed to be better at hard tasks because they “think” before answering. In practice, that extra step-by-step process creates a new attack surface: if you can push the model into overthinking, you can make it spend far more tokens than normal and slow it down for everyone else.
That’s the core finding from a recent study presented at ICML 2026 by researchers from Zhejiang University and Alibaba. They show that carefully constructed, logically inconsistent prompts can trigger reasoning models into long, unproductive internal loops. The result is not just a quality issue. It looks a lot like a denial-of-service vector against commercial AI systems.
For builders shipping LLM-powered products, this matters because the failure mode is tied to cost, latency, and throughput, not just correctness.
What changed with reasoning models
Older LLMs tend to answer directly. Newer reasoning models generate an internal monologue, break a problem into steps, and then produce the final response. That has made them much more capable on coding, math, and other structured tasks.
