Data poisoning attacks have attracted considerable interest, both from the practical and theoretical machine learning communities. Recently, following widespread adoption for its privacy properties, differential privacy has been proposed as a defense from data poisoning attacks. In this paper, we show that the connection between poisoning and differential privacy is more complicated than it would appear. We argue that differential privacy itself does not serve as a defense, but that differential privacy benefits from robust machine learning algorithms, explaining much of differential privacy's success against poisoning.