  Text watermarking technology aims to tag and identify content produced by
large language models (LLMs) to prevent misuse. In this study, we introduce the
concept of cross-lingual consistency in text watermarking, which assesses the
ability of text watermarks to maintain their effectiveness after being
translated into other languages. Preliminary empirical results from two LLMs
and three watermarking methods reveal that current text watermarking
technologies lack consistency when texts are translated into various languages.
Based on this observation, we propose a Cross-lingual Watermark Removal Attack
(CWRA) to bypass watermarking by first obtaining a response from an LLM in a
pivot language, which is then translated into the target language. CWRA can
effectively remove watermarks, decreasing the AUCs to a random-guessing level
without performance loss. Furthermore, we analyze two key factors that
contribute to the cross-lingual consistency in text watermarking and propose
X-SIR as a defense method against CWRA. Code: https://github.com/zwhe99/X-SIR.
