A Unified Attack Framework against Statistical Generative Watermarks for Large Language Models
Watermarking has emerged as a key tool for identifying text generated by large language models (LLMs), with current schemes embedding statistical signals into the generation process to enable later detection. In this work, we identify a shared weakness across a broad class of popular LLM watermarking methods, including KGW (and its variants), GumbelSoft, DipMark, ITS, and SynthID. We show that these schemes exhibit a fundamental link between their detection scores and the token probability shifts they introduce during generation. This link, which we call score-ratio alignment, allows an attacker with only black-box query access to the model--and no knowledge of secret keys or model weight...
| Authors | Romina Omidi, Nima Naderloui, Yuan Hong et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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