Abstract: In recent years, Large Language Models (LLMs) have emerged as a critical branch of deep learning network technology, achieving a series of breakthrough accomplishments in the field of Natural Language Processing (NLP), and gaining widespread adoption. However, throughout their entire lifecycle, including pre-training, fine-tuning, and actual deployment, a variety of security threats and risks of privacy breaches have been discovered, drawing increasing attention from both the academic and industrial sectors. Navigating the development of the paradigm of using large language models to handle natural language processing tasks, as known as the pre-training and fine-tuning paradigm, the pre-training and prompt learning paradigm, and the pre-training and instruction-tuning paradigm, this article outline conventional security threats against large language models, specifically representative studies on the three types of traditional adversarial attacks (adversarial example attack, backdoor attack and poisoning attack). It then summarizes some of the novel security threats revealed by recent research, followed by a discussion on the privacy risks of large language models and the progress in their research. The content aids researchers and deployers of large language models in identifying, preventing, and mitigating these threats and risks during the model design, training, and application processes, while also achieving a balance between model performance, security, and privacy protection.