@inproceedings{hu-etal-2026-rethinking,
    title = "Rethinking Reasoning: A Survey on Reasoning-based Backdoors in {LLM}s",
    author = "Hu, Man  and
      Wu, Xinyi  and
      Suo, Zhufeng  and
      Feng, Jinbo  and
      Meng, Linghui  and
      Jia, Yanhao  and
      Luu, Anh Tuan  and
      Zhao, Shuai",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.863/",
    pages = "17437--17456",
    ISBN = "979-8-89176-395-1",
    abstract = "With the rise of advanced reasoning capabilities, large language models (LLMs) are receiving increasing attention. While reasoning enhances LLMs' performance on downstream tasks, it also introduces new threat vectors, as adversaries can leverage these capabilities to conduct backdoor attacks. Prior surveys provide broad overviews of backdoor attacks and reasoning security; however, a systematic survey focused on backdoor attacks and defenses against LLM reasoning is still absent. In this paper, we take the first step toward providing a comprehensive review of reasoning-based backdoor attacks in LLMs by analyzing their underlying mechanisms, methodological frameworks, and unresolved challenges. Specifically, we introduce a new taxonomy that offers a unified perspective for summarizing existing approaches, categorizing reasoning-based backdoor attacks into associative, passive, and active. We also summarize defenses against such attacks and discuss current challenges alongside future research directions."
}