In the field of wireless communication systems, the interest in machine learning has increased in recent years. Adversarial machine learning includes attack and defense methods on machine learning components. It is a topic that has been thoroughly studied in computer vision and natural language processing but not to the same extent in wireless communication. In this thesis, a Fast Gradient Sign Method (FGSM) attack on a neural receiver is studied. Furthermore, the thesis investigates whether it is possible to make a neural receiver robust against these attacks. The study is made using the python library Sionna, a library used for research on for example 5G, 6G and machine learning in wireless communication. The effect of a FGSM attack is evaluated and mitigated with different models within adversarial training. The training data of the models is either augmented with adversarial samples, or original samples are replaced with adversarial ones. Furthermore, the power distribution and range of the adversarial samples included in the training are varied. The thesis concludes that a FGSM attack decreases the performance of a neural receiver and needs less power than a barrage jamming attack to achieve the same performance loss. A neural receiver can be made more robust against a FGSM attack when the training data of the model is augmented with adversarial samples. The samples are concentrated on a specific attack power range and the power of the adversarial samples is normally distributed. A neural receiver is also proven to be more robust against a barrage jamming attack than conventional methods without defenses.