A reasoning language model (RLM) generates costly reasoning tokens, often hidden from the users, that help it excel at many tasks. Our Overthink attack targets RLM-based applications (such as chatbots or coding agents) that rely on external context by forcing these models to generate substantially more reasoning tokens while still producing contextually correct answers. An adversary conducts the attack by injecting decoy reasoning problems into available content, optimized to elicit a large number of tokens. We craft decoy challenges (using Markov decision processes, language translation, or graphic comprehension) that appear benign individually yet have an adversarial impact when inserted in the context, allowing them to easily evade safety filters. We evaluate Overthink on proprietary and open-source reasoning models across the FreshQA, SQuAD, and MuSR datasets, where we observe 13x, 46x, and 12x increases, respectively. We also explore multimodal attacks using images, which cause up to a 2.7x increase in reasoning, as well as attacks on coding agents by injecting decoys into skills, README files, and code, resulting in up to a 17x increase. We explore several defenses and evaluate their efficacy against different attack strategies, highlighting that defending against Overthink is nontrivial. Finally, we show that newer generations of RLMs, while showing a drastic increase in per-token cost, also exhibit up to a 2.3x increase in reasoning tokens, leaving them more vulnerable to Overthink attacks.

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