Graph embeddings have been proposed to map graph data to low dimensional space for downstream processing (e.g., node classification or link prediction). With the increasing collection of personal data, graph embeddings can be trained on private and sensitive data. For the first time, we quantify the privacy leakage in graph embeddings through three inference attacks targeting Graph Neural Networks. We propose a membership inference attack to infer whether a graph node corresponding to an individual user's data was a member of the model's training or not. We consider a blackbox setting where the adversary exploits the output prediction scores and a whitebox setting where the adversary has also access to the released node embeddings. This attack provides accuracy up to 28% (blackbox) and 36% (whitebox) beyond random guess by exploiting the distinguishable footprint between train and test data records left by the graph embedding. We propose a Graph Reconstruction attack where the adversary aims to reconstruct the target graph given the corresponding graph embeddings. Here, the adversary can reconstruct the graph with more than 80% of accuracy and link inference between two nodes with around 30% more confidence than a random guess. We then propose an attribute inference attack where the adversary aims to infer a sensitive attribute. We show that graph embeddings are strongly correlated to the node attributes letting the adversary inferring sensitive information (e.g., gender or location). CCS CONCEPTS • Security and privacy → Privacy protections; • Computing methodologies → Machine learning.