Machine learning-based recommendation mechanisms on platforms like YouTube, Facebook, TikTok, Netflix, Twitter, and Spotify are also vulnerable to abuse via attacks known as data poisoning. A data poisoning attack aims to modify a model’s training set by inserting incorrectly labelled data with the goal of tricking it into making incorrect predictions. Successful attacks compromise the integrity of the model, generating consistent errors in the model’s predictions. Once a model has been poisoned, recovering from the attack is so difficult that some developers may not even attempt the fix.
Source: https://www.helpnetsecurity.com/2021/05/24/fraud-detection-algorithms/