BadReasoner：向大型推理模型植入可调过度思考后门，以取乐或牟利
BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit
AI总结：
本文提出 BadReasoner 可调过度思考后门，通过数据投毒让 LRM 依据触发器重复次数生成冗余 CoT，在保持答案正确的同时可控地增加推理资源消耗。
摘要：
大型推理模型（LRM）已成为人工智能领域的一项重要进展，代表了一类专门用于处理复杂推理任务的大型语言模型（LLM）。LRM 的决定性特征在于其广泛的思维链（CoT）推理能力。本文中，我们发现了一种此前尚未被探索的针对 LRM 的攻击向量，将其称为“过度思考后门”。我们通过提出一种新型可调后门推进了这一概念；该后门超越了简单的开启/关闭攻击，使攻击者能够精确控制模型推理冗长程度。我们的攻击通过一种新型数据投毒方法实现。该方法将一个可调触发器——其重复次数表示期望的攻击强度——与相应冗长的 CoT 响应配对。这些响应通过指示教师 LLM 在正确推理过程中注入受控数量的冗余细化步骤，以程序化方式生成。该方法保持输出正确性，从而确保隐蔽性，并使该攻击成为纯粹的资源消耗向量。在多种 LRM 上的大量实验结果表明，我们的方法能够可靠触发可控、数倍的推理过程长度增长，同时不会降低最终答案的正确性。源代码可在 https://github.com/FZaKK/BadReasoner 获取。
英文摘要：
Large reasoning models (LRMs) have emerged as a significant advancement in artificial intelligence, representing a specialized class of large language models (LLMs) designed to tackle complex reasoning tasks. The defining characteristic of LRMs lies in their extensive chain-of-thought (CoT) reasoning capabilities. 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. Our source code is available at https://github.com/FZaKK/BadReasoner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
