Title: Property Inference Attacks Against t-SNE Plots. Abstract: With the prevailing of machine learning (ML), researchers have shown that ML models are also vulnerable to various privacy and security attacks. As one of the representative attacks, the property inference attack aims to infer the private/sensitive properties of the training data (e.g., race distribution) given the output of ML models. In this paper, we present a new side channel for property inference attacks, i.e., t-SNE plots, which are widely used to show feature distribution or demonstrate model performance. We show for the first time that the private/sensitive properties of the data that are used to generate the plot can be successfully predicted. Briefly, we leverage the publicly available model as the shadow model to generate t-SNE plots with different properties. We use those plots to train an attack model, which is a simple image classifier, to infer the specific property of a given t-SNE plot. Extensive evaluation on four datasets shows that our proposed attack can effectively infer the undisclosed property of the data presented in the t-SNE plots, even when the shadow model is different from the target model used to generate the t-SNE plots. We also reveal that the attacks are robust in various scenarios, such as constructing the attack with fewer t-SNE plots/different density settings and attacking t-SNE plots generated by fine-tuned target models. The simplicity of our attack method indicates that the potential risk of leaking sensitive properties in t-SNE plots is largely underestimated. As possible defenses, we observe that adding noise to the image embeddings or t-SNE coordinates effectively mitigates attacks but can be bypassed by adaptive attacks, which prompts the need for more effective defenses.