Abstract: Neural networks are increasingly being applied in sensitive domains and on private data. For a long time, no thought was given to what this means for the privacy of the data used for their training. Only in recent years has there emerged an awareness that the process of converting training data into a model is not irreversible as previously thought. Since then, several specific attacks against privacy in neural networks have been developed. Of these, we will discuss two specific ones, namely membership inference and model inversion attacks. First, we will focus on how they retrieve potentially sensitive information from trained models. Then, we will look into several factors that influence the success of both attacks. At the end, we will discuss Differential Privacy as a possible protection measure.