Large Language Model (LLM) watermark has emerged as a promising technique for copyright protection, misuse prevention, and machine-generated content detection. It injects detectable signals during the LLM generation process, allowing for later identification by a corresponding detector. To assess the robustness of watermark schemes, existing studies typically adopt watermark removal attacks, which aim to erase embedded signals by modifying the watermarked text. However, we reveal that existing watermark removal attacks are suboptimal, which leads to the misconception that effective watermark removal requires either a large perturbation budget or a strong adversary’s capabilities, such as unlimited queries to the victim LLM or its watermark detector. A systematic scrutinization of removal attack capabilities as well as the development of more sophisticated techniques remains largely underexplored. As a result, the robustness of existing watermarking schemes may be overestimated.