The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to dynamically create and deploy multiple services which function under various requirements in different vertical sectors while operating on top of the same physical infrastructure. The recent progress in artificial intelligence and machine learning is theorized to be a potential answer to the arising resource allocation challenges. It is therefore expected that future generation mobile networks will heavily depend on its artificial intelligence components which may result in those components becoming a high-value attack target. In particular, a smart adversary may exploit vulnerabilities of the state-of-the-art machine learning models deployed in a 5G system to initiate an attack. This study focuses on the analysis of adversarial example generation attacks against machine learning based frameworks that may be present in the next generation networks. First, various AI/ML algorithms and the data used for their training and evaluation in mobile networks is discussed. Next, multiple AI/ML applications found in recent scientific papers devoted to 5G are overviewed. After that, existing adversarial example generation based attack algorithms are reviewed and frameworks which employ these algorithms for fuzzing stat-of-art AI/ML models are summarised. Finally, adversarial example generation attacks against several of the AI/ML frameworks described are presented.