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Efficient and Universal Watermarking for LLM-Generated Co...
[Submitted on 12 Feb 2024 (v1), last revised 10 Jul 2026 (this v · 2024-02-12 · via cs.CR updates on arXiv.org

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Abstract:Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plug-and-play watermarking approach for AI-generated code detection, named ACW (AI Code Watermarking). ACW is training-free and works by selectively applying a set of carefully-designed, semantic-preserving and idempotent code transformations to LLM code outputs. The presence or absence of the transformations serves as implicit watermarks, enabling the detection of AI-generated code. Our experimental results show that ACW effectively and efficiently detects AI-generated code, preserves code utility, and is resilient against potential code disruptions. Especially, ACW is universal across different LLMs, addressing the limitations of existing approaches.

Submission history

From: Boquan Li [view email]
[v1] Mon, 12 Feb 2024 09:40:18 UTC (420 KB)
[v2] Tue, 16 Apr 2024 07:27:06 UTC (229 KB)
[v3] Wed, 21 Aug 2024 11:55:41 UTC (218 KB)
[v4] Fri, 1 Aug 2025 01:17:22 UTC (644 KB)
[v5] Fri, 10 Jul 2026 09:40:44 UTC (1,018 KB)