AI Models Overthink Problems—and It’s a Security Risk
Large language models (LLMs) that can think through problems step-by-step have significantly increased the scope of tasks that AI can tackle. But new research suggests these reasoning capabilities also introduce a critical vulnerability that could allow attackers to slow these systems to a crawl.While earlier generations of LLMs would immediately produce a response to a user’s request, today’s most advanced models generate an internal monologue where they break down the problem into steps and reason about the best way to tackle it before providing an answer. This has allowed AI to tackle increasingly complex problems, particularly in areas like coding and math.However, previous research has shown that these models are susceptible to sometimes producing excessively long streams of reasoning that do little to boost performance, a phenomenon known as “overthinking.” In research presented this week at the International Conference on Machine Learning 2026 in Seoul, researchers from Zhejiang
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A bleeding-edge AI model can be forced to generate answers up to 26 times longer than normal, exposing a massive security flaw in how these systems process information. Researchers from Jejang University and Alibaba revealed at a landmark conference in Seoul that modern reasoning models can be trapped in endless, costly thinking loops.
By feeding systems like DeepSeq-R1, GPT-03, and Gemini 2.5 Flash logically inconsistent prompts, the team triggered a state-of-the-art denial-of-service attack. Instead of identifying that a math or coding problem is fundamentally unsolvable, these models spiral into an internal monologue, trying to resolve the unresolvable.
This excessive overthinking dramatically increases the load on a provider's servers and drives up the computing costs of commercial AI services. The researchers even found they could use smaller, cheaper models to generate these malicious prompts, making the attack highly transferable and easy to execute.
While defenses like rate limits may blunt the impact, researcher Wei Cao warns that this vulnerability represents a highly realistic security concern across all modern reasoning models.
