BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit
Biao Yi, Zekun Fei, Jianing Geng, Tong Li, Lihai Nie, Zheli Liu, Yiming Li · 2026
Large reasoning models (LRMs), defined by their extensive chain-of-thought (CoT) reasoning capabilities, have emerged as a significant advancement in artificial intelligence. In this paper, we identify a previously unexplored attack vector against LRMs, which we term "overthinking backdoors". We advance this concept by proposing a novel tunable backdoor, which moves beyond simple on/off attacks to one where an attacker can precisely control the extent of the model’s reasoning verbosity. Our attack is implemented through a novel data poisoning methodology. It pairs a tunable trigger (where the number of repetitions signals the desired intensity) with a correspondingly verbose CoT response. These responses are programmatically generated by instructing a teacher LLM to inject a controlled number of redundant refinement steps into a correct reasoning process. The approach preserves output correctness, which ensures stealth and establishes the attack as a pure resource-consumption vector. Extensive empirical results on various LRMs demonstrate that our method can reliably trigger a controllable, multi-fold increase in the length of the reasoning process, without degrading the final answer’s correctness. Code is available at https://github.com/FZaKK/BadReasoner.