Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets

Computer Science > Cryptography and Security [Submitted on 24 Apr 2026] Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen 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 suppresses static analysis warnings and enhances stealth. Extensive experiments show that FunPoison achieves effective poisoning by contaminating only 10% of the dataset, while maintaining 100% compilability and functional correctness, and remains robust against various advanced code sanitization techniques. Comments: Accepted to Findings of the Association for Computational Linguistics (ACL 2026). Code is available at: this https URL Subjects: Cryptography and Security (cs.CR); Software Engineering (cs.SE) Cite as: arXiv:2604.22291 [cs.CR]   (or arXiv:2604.22291v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.22291 Focus to learn more Submission history From: Yuan Xiao [view email] [v1] Fri, 24 Apr 2026 07:12:29 UTC (1,161 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)