In the era of big data, breakthroughs in theories and technologies of deep learning, reinforcement learning, and distributed learning have provided strong support for machine learning at the data and the algorithm level, as well as have promoted the development of scale and industrialization of machine learning. However, though machine learning models have excellent performance in many real-world applications, they still suffer many security and privacy threats at the data, model, and application levels, which could be characterized by diversity, concealment, and dynamic evolution. The security and privacy issues of machine learning have attracted extensive attention from academia and industry. A large number of researchers have conducted in-depth research on the security and privacy issues of models from the perspective of attack and defense, and proposed a series of attack and defense methods. In this survey, the security and privacy issues of machine learning are reviewed, existing research work is systematically and scientifically summarized, and the advantages and disadvantages of current research are clarified. Finally, the current challenges and future research directions of machine learning model security and privacy research are explored, aiming to provide guidance for follow-up researchers to further promote the development and application of machine learning model security and privacy research.