Enhancing LLM Watermark Resilience Against Both Scrubbing and  Spoofing Attacks
Huanming Shen, Baizhou Huang, Xiaojun Wan
Watermarking is a promising defense against the misuse of large language models (LLMs), yet it remains vulnerable to scrubbing and spoofing attacks.This vulnerability stems from an inherent trade-off governed by watermark window size: smaller windows resist scrubbing better but are easier to reverse-engineer, enabling low-cost statistics-based spoofing attacks. This work breaks this trade-off by introducing a novel mechanism, equivalent texture keys, where multiple tokens within a watermark window can independently support the detection.Based on the redundancy, we propose **SEEK** (**S**ub-vocabulary decomposed **E**quivalent t**E**xture **K**ey). It achieves a Pareto improvement, increasing the resilience against scrubbing attacks without compromising robustness to spoofing. Experiments demonstrate SEEK's superiority over prior method, yielding spoofing robustness gains of +88.2\%/+92.3\%/+82.0\% and scrubbing robustness gains of +10.2\%/+6.4\%/+24.6\% across diverse dataset settings.Our anonymous code submission accompanies this paper and will be released after anonymous reviews.