In the past couple of years research in the field of machine learning (ML) has made huge progress which resulted in applications like automated translation, practical speech recognition for smart assistants, useful robots, self-driving cars and lots of others. But so far we only have reached the point where ML works, but may easily be broken. Therefore, this blog post concentrates on the weaknesses ML faces these days. After an overview and categorization of different flaws, we will dig a little deeper into adversarial attacks, which are the most dangerous ones.