Large vision-language models (VLLMs) exhibit promising capabilities for processing multi-modal tasks across various application scenarios. However, their emergence also raises significant data security concerns, given the potential inclusion of sensitive information, such as private photos and medical records in their training data. Detecting inappropriately used data in VLLMs remains a critical and unresolved issue due to the absence of a standardized dataset and suitable methodology. In this study, we introduce the first membership inference attack (MIA) benchmark tailored for various VLLMs to facilitate training data detection. Then, we propose a novel MIA pipeline specifically designed for token-level image detection. Lastly, we present a new detection metric called MaxRényi-K\%, which is based on the confidence of model outputs and applies to both text and image data. We believe our work can enhance the understanding and methodology of MIA in the context of VLLMs.