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  3. Codeipprompt: Intellectual Property Infringement Assessment Of Code Language Models.
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  3. Codeipprompt: Intellectual Property Infringement Assessment Of Code Language Models.

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CODEIPPROMPT: Intellectual Property Infringement Assessment of Code Language Models.

Zhiyuan Yu1, Yuhao Wu1, Ning Zhang1

  • 1Washington University in St. Louis.

Proceedings of Machine Learning Research
|August 27, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models (LMs) for code generation often violate intellectual property (IP) rights due to training data. Our platform, CODEIPPROMPT, evaluates and highlights these IP risks in AI-generated code.

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Area of Science:

  • Artificial Intelligence
  • Software Engineering
  • Intellectual Property Law

Background:

  • Large language models (LMs) demonstrate advanced capabilities in synthesizing programming code.
  • The rise of AI-generated code raises significant concerns regarding intellectual property (IP) rights violations.
  • The exploration of IP issues in code-generating LMs remains a relatively underexplored area.

Purpose of the Study:

  • To introduce CODEIPPROMPT, a novel platform for the automatic evaluation of IP rights violations in code generated by LMs.
  • To assess the extent to which LMs reproduce licensed programs and identify potential IP infringements.
  • To investigate the root causes of IP violations in code LMs and explore mitigation strategies.

Main Methods:

  • Development of CODEIPPROMPT, featuring prompts derived from a licensed code database to trigger IP-violating code generation.
  • Implementation of a measurement tool within CODEIPPROMPT to quantify the degree of IP violation in LM-generated code.
  • Extensive evaluation of various open-source and commercial code LMs using the CODEIPPROMPT platform.

    • Main Results:

    • Prevalence of IP violations was observed across all evaluated open-source and commercial code LMs.
    • The primary cause identified is the significant inclusion of restrictively licensed code within the training datasets.
    • Both intentional inclusion and inconsistent real-world licensing practices contribute to the issue.

    Conclusions:

    • CODEIPPROMPT serves as a crucial testbed for assessing IP violation risks in current code generation platforms.
    • The study underscores the urgent need for enhanced mitigation strategies to address IP concerns in AI code synthesis.
    • Fine-tuning and dynamic token filtering are explored as potential methods to reduce IP infringements.