@inproceedings{xiao-etal-2026-train,
    title = "Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets",
    author = "Xiao, Yuan  and
      Wang, Jiaming  and
      Chen, Yuchen  and
      Song, Wei  and
      Sun, Jun  and
      Ma, Shiqing  and
      Mu, Yanzhou  and
      Zhai, Juan  and
      Fang, Chunrong  and
      Dong, Jin Song  and
      Chen, Zhenyu",
    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.1564/",
    pages = "31284--31303",
    ISBN = "979-8-89176-395-1",
    abstract = "The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage. Dataset poisoning offers a proactive defense by reducing the utility of such unauthorized training. However, existing poisoning methods often require full-dataset poisoning and introduce transformations that break code compilability. In this paper, we introduce FunPoison, a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. FunPoison leverages reusable statement-level templates with automatic repair and conservative safety checking to ensure side-effect freedom, while a type-aware synthesis module preserves type correctness, suppresses static-analysis warnings, and improves stealth. Extensive experiments across multiple CodeLLMs and code-generation benchmarks show that FunPoison achieves effective poisoning by contaminating only 10{\%} of the dataset, while maintaining 100{\%} compilability and functional correctness. FunPoison also remains robust against advanced code sanitization techniques, including detection, purification, rewriting, static-analysis, and formatting defenses."
}