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

2026-04-24

AI Summary

This work proposes FunPoison, a novel defense mechanism against the unauthorized use of code datasets for training large language models. By injecting short, compilable, side-effect-free code snippets with weak utility into execution paths, FunPoison degrades the performance of illicitly trained models even when only 10% of the dataset is poisoned. The method achieves this while preserving 100% compilability and functional correctness, and demonstrates robustness against various advanced code sanitization techniques. Its core technical contributions include statement-level reusable templates, type-aware code synthesis, an automated repair mechanism, and conservative safety checks, collectively ensuring that the injected code remains both stealthy and safe. To the best of our knowledge, this is the first approach to simultaneously offer high defensive efficacy, full functional integrity, and resilience to state-of-the-art data-cleaning pipelines.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 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.

Problem

• dataset poisoning
• code datasets
• functionality preservation
• unauthorized use
• compilability

Innovation

• functionality-preserving poisoning
• code dataset protection
• weak-use fragments
• type-aware synthesis
• compilability preservation