arXiv:2508.11925v3 Announce Type: replace-cross Abstract: Protecting intellectual property on LLM-generated code necessitates effective watermarking systems that can operate within code's highly structured, syntactically constrained nature. In this work, we introduce CodeTracer, an innovative adaptive code watermarking framework underpinned by a novel reinforcement learning training paradigm. At its core, CodeTracer features a policy-driven approach that utilizes a parameterized model to intelligently bias token choices during next-token prediction. This strategy ensures that embedded watermar
The proliferation of LLM-generated code necessitates robust protection mechanisms as intellectual property concerns and model attribution become critical challenges.
Effective watermarking for LLM-generated code is crucial for intellectual property protection, establishing provenance, and potentially influencing regulatory frameworks around AI-created content.
Current methods of securing intellectual property in software are enhanced by the introduction of adaptive, AI-driven watermarking, making it harder to misappropriate or falsely attribute code.
- · LLM developers
- · Software IP holders
- · Cybersecurity firms
- · Legal tech specializing in IP
- · Code plagiarizers
- · Unauthorized code re-users
- · Black-box LLM users
The adoption of code watermarking tools will establish new industry standards for code provenance and ownership in the AI era.
Increased legal challenges related to code IP infringement will emerge, driven by improved detection capabilities.
Attribution models for public domain or open-source AI-generated code may become complex, requiring new licensing or ethical guidelines.
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