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        <title>Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code</title>
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    <abstract>Identifying LLM-generated code through watermarking poses a challenge in preserving functional correctness. Previous methods rely on the assumption that watermarking high-entropy tokens effectively maintains output quality. Our analysis reveals a fundamental limitation of this assumption: syntax-critical tokens such as keywords often exhibit the highest entropy, making existing approaches vulnerable to logic corruption. We present STONE, a syntax-aware watermarking method that embeds watermarks only in non-syntactic tokens and preserves code integrity. For rigorous evaluation, we also introduce STEM, a comprehensive metric that balances three critical dimensions: correctness, detectability, and imperceptibility. Across Python, C++, and Java, STONE preserves correctness, sustains strong detectability, and achieves balanced performance with minimal computational overhead. Our implementation is available at https://github.com/inistory/STONE-watermarking.</abstract>
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%0 Conference Proceedings
%T Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code
%A Kim, Jungin
%A Park, Shinwoo
%A Han, Yo-Sub
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F kim-etal-2026-marking
%X Identifying LLM-generated code through watermarking poses a challenge in preserving functional correctness. Previous methods rely on the assumption that watermarking high-entropy tokens effectively maintains output quality. Our analysis reveals a fundamental limitation of this assumption: syntax-critical tokens such as keywords often exhibit the highest entropy, making existing approaches vulnerable to logic corruption. We present STONE, a syntax-aware watermarking method that embeds watermarks only in non-syntactic tokens and preserves code integrity. For rigorous evaluation, we also introduce STEM, a comprehensive metric that balances three critical dimensions: correctness, detectability, and imperceptibility. Across Python, C++, and Java, STONE preserves correctness, sustains strong detectability, and achieves balanced performance with minimal computational overhead. Our implementation is available at https://github.com/inistory/STONE-watermarking.
%U https://aclanthology.org/2026.findings-eacl.207/
%P 3990-4002

Markdown (Informal)

[Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code](https://aclanthology.org/2026.findings-eacl.207/) (Kim et al., Findings 2026)

• Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code (Kim et al., Findings 2026)

ACL

• Jungin Kim, Shinwoo Park, and Yo-Sub Han. 2026. Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code. In Findings of the Association for Computational Linguistics: EACL 2026, pages 3990–4002, Rabat, Morocco. Association for Computational Linguistics.