Title: Universal Unlearnable Examples: Cluster-wise Perturbations without Label-consistency. Abstract: There is a growing interest in employing unlearnable examples against privacy leaks on the Internet, which prevents the unauthorized models from being properly trained by adding invisible image noise. However, existing attack methods rely on an ideal assumption called label-consistency. In this work, we clarify a more practical scenario called \emph{label-inconsistency} that allows hackers and protectors to hold different labels for the same image. Inspired by disrupting the \emph{uniformity} and \emph{discrepancy}, we present a novel method called \emph{UniversalCP} on label-inconsistency scenario, which generates the universal unlearnable examples by cluster-wise perturbation. Furthermore, we investigate a new strategy for selecting the CLIP as the surrogate model, since vision-and-language pre-training models are trained on large-scale data and more semantic supervised information. We also verify the effectiveness of the proposed methods and the strategy for selecting surrogate models under a variety of experimental settings including black-box backbones, datasets and even commercial platforms Microsoft {\tt Azure} and Baidu {\tt PaddlePaddle}.