Bypassing LLM Watermarks with Color-Aware Substitutions
This addresses the robustness of watermarking methods for LLM-generated text, which is crucial for detecting AI-generated content, but the approach is incremental as it builds on existing watermarking strategies.
The paper tackled the problem of evading detection in watermarked LLM-generated text by proposing SCTS, a color-aware attack that strategically prompts the LLM to identify and substitute watermarked tokens, achieving evasion with fewer edits than existing methods and proving effectiveness for arbitrarily long text.
Watermarking approaches are proposed to identify if text being circulated is human or large language model (LLM) generated. The state-of-the-art watermarking strategy of Kirchenbauer et al. (2023a) biases the LLM to generate specific (``green'') tokens. However, determining the robustness of this watermarking method is an open problem. Existing attack methods fail to evade detection for longer text segments. We overcome this limitation, and propose {\em Self Color Testing-based Substitution (SCTS)}, the first ``color-aware'' attack. SCTS obtains color information by strategically prompting the watermarked LLM and comparing output tokens frequencies. It uses this information to determine token colors, and substitutes green tokens with non-green ones. In our experiments, SCTS successfully evades watermark detection using fewer number of edits than related work. Additionally, we show both theoretically and empirically that SCTS can remove the watermark for arbitrarily long watermarked text.