Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life. However, the practical implementations of these models have also exposed inherent risks, bringing to the forefront their evil sides and sparking concerns regarding their trustworthiness. Despite the wealth of literature on this subject, a comprehensive survey specifically delving into the intersection of large-scale generative models and their trustworthiness remains largely absent. To bridge this gap, this paper investigates both the long-standing and emerging threats associated with these models across four fundamental dimensions: 1) privacy, 2) security, 3) fairness, and 4) responsibility. Based on the investigation results, we develop an extensive map outlining the trustworthiness of large generative models. After that, we provide practical recommendations and potential research directions for future secure applications equipped with large generative models, ultimately promoting the trustworthiness of the models and benefiting the society as a whole.

翻译：扩散模型与大型语言模型已成为前沿生成模型，深刻影响着人类生活的各个领域。然而，这些模型的实际应用也暴露了固有风险，将其潜在危害置于聚光灯下，并引发了对其可信度的关切。尽管相关文献丰富，但专门针对大规模生成模型与其可信度交叉领域的系统性综述仍显不足。为填补这一空白，本文从四个基本维度深入探究了这些模型长期存在及新出现的威胁：1) 隐私性、2) 安全性、3) 公平性及4) 责任性。基于研究结果，我们绘制了大规模生成模型可信度的详尽图谱。随后，我们为未来配备大规模生成模型的安全应用提供了实用建议与潜在研究方向，最终旨在提升模型的可信度并造福整个社会。