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    <abstract>Large language models (LLMs) are increasingly used for code generation, making reliable identification of machine-generated code important for attribution, tracking, and misuse detection. Existing code watermarking methods are dominated by N-gram-based schemes, yet their robustness has mostly been evaluated only against simple edits or optimizations. We argue that this significantly overstates security, because software engineering already provides stronger semantics-preserving transformations in the form of code obfuscation.\par We study N-gram-based code watermarking under obfuscation. We formally model semantics-preserving transformations as a Markov random walk and prove that, under an intuitive and experimentally supported assumption called distribution consistency, obfuscation can nullify the robustness of N-gram-based watermarks. If the original detector has a false positive rate ε_\mathsfpos, then after obfuscation, its failure rate on watermarked code approaches 1 - ε_\mathsfpos.\par We validate this theory on three state-of-the-art watermarking schemes, two LLMs, two programming languages, four benchmarks, and four obfuscators. Across all settings, detectors collapse to near-random performance on obfuscated code (AUROC tightly around 0.5), and for each language, at least one attack leaves all post-obfuscation AUROC scores below 0.6. These results jointly show that current N-gram-based code watermarks are not robust to realistic obfuscation attacks and motivate more semantics-aware alternatives.</abstract>
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%0 Conference Proceedings
%T Disappearing Ink: Obfuscation Breaks N-gram Code Watermarks in Theory and Practice
%A Zhang, Gehao
%A Li, Mingzhe
%A Bagdasarian, Eugene
%A Ma, Shiqing
%A Zhai, Juan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-disappearing
%X Large language models (LLMs) are increasingly used for code generation, making reliable identification of machine-generated code important for attribution, tracking, and misuse detection. Existing code watermarking methods are dominated by N-gram-based schemes, yet their robustness has mostly been evaluated only against simple edits or optimizations. We argue that this significantly overstates security, because software engineering already provides stronger semantics-preserving transformations in the form of code obfuscation.\par We study N-gram-based code watermarking under obfuscation. We formally model semantics-preserving transformations as a Markov random walk and prove that, under an intuitive and experimentally supported assumption called distribution consistency, obfuscation can nullify the robustness of N-gram-based watermarks. If the original detector has a false positive rate ε_\mathsfpos, then after obfuscation, its failure rate on watermarked code approaches 1 - ε_\mathsfpos.\par We validate this theory on three state-of-the-art watermarking schemes, two LLMs, two programming languages, four benchmarks, and four obfuscators. Across all settings, detectors collapse to near-random performance on obfuscated code (AUROC tightly around 0.5), and for each language, at least one attack leaves all post-obfuscation AUROC scores below 0.6. These results jointly show that current N-gram-based code watermarks are not robust to realistic obfuscation attacks and motivate more semantics-aware alternatives.
%U https://aclanthology.org/2026.findings-emnlp.1372/
%P 29188-29219

Markdown (Informal)

[Disappearing Ink: Obfuscation Breaks N-gram Code Watermarks in Theory and Practice](https://aclanthology.org/2026.findings-emnlp.1372/) (Zhang et al., Findings 2026)

• Disappearing Ink: Obfuscation Breaks N-gram Code Watermarks in Theory and Practice (Zhang et al., Findings 2026)

ACL

• Gehao Zhang, Mingzhe Li, Eugene Bagdasarian, Shiqing Ma, and Juan Zhai. 2026. Disappearing Ink: Obfuscation Breaks N-gram Code Watermarks in Theory and Practice. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 29188–29219, Budapest, Hungary. Association for Computational Linguistics.