This survey provides a comprehensive overview of poisoning attacks on recommender systems and their countermeasures. It aims to address the gap in existing literature by focusing on poisoning attacks, which are distinct from adversarial attacks. The survey is structured into several sections, including an introduction to recommender systems, a detailed discussion of poisoning attacks, a taxonomy of these attacks, and a review of countermeasures. The authors present a novel taxonomy with five dimensions to organize the various types of poisoning attacks, covering both classic heuristic and AI-based attacks. They also review over 40 countermeasures, evaluating their effectiveness against specific attack types. The survey highlights open issues and future research directions, emphasizing the importance of protecting recommender systems from poisoning attacks to maintain trust and fairness. A rich repository of resources is provided to support further research in this field.This survey provides a comprehensive overview of poisoning attacks on recommender systems and their countermeasures. It aims to address the gap in existing literature by focusing on poisoning attacks, which are distinct from adversarial attacks. The survey is structured into several sections, including an introduction to recommender systems, a detailed discussion of poisoning attacks, a taxonomy of these attacks, and a review of countermeasures. The authors present a novel taxonomy with five dimensions to organize the various types of poisoning attacks, covering both classic heuristic and AI-based attacks. They also review over 40 countermeasures, evaluating their effectiveness against specific attack types. The survey highlights open issues and future research directions, emphasizing the importance of protecting recommender systems from poisoning attacks to maintain trust and fairness. A rich repository of resources is provided to support further research in this field.