Diffusion models and large language models have emerged as leading-edge generative models and have sparked a revolutionary impact on various aspects of human life. However, the practical implementation of these models has also exposed inherent risks, highlighting their dual nature and raising concerns regarding their trustworthiness. Despite the abundance 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: privacy, security, fairness, and responsibility. In this way, we construct an extensive map outlining the trustworthiness of these models, while also providing practical recommendations and identifying future directions. These efforts are crucial for promoting the trustworthy deployment of these models, ultimately benefiting society as a whole.

翻译：扩散模型和大语言模型已成为领先的生成模型，并对人类生活的各个方面产生了革命性影响。然而，这些模型的实际应用也暴露了内在风险，凸显了其双重性质，并引发了对其可信度的担忧。尽管已有大量相关文献，但专门深入探讨大规模生成模型与其可信度交叉领域的全面综述仍然缺乏。为弥补这一空白，本文从隐私、安全、公平性和责任这四个基本维度，研究了这些模型长期存在和新兴的威胁。通过这种方式，我们构建了一幅涵盖这些模型可信度的广泛图景，同时提供了实用建议并指出了未来方向。这些努力对于促进这些模型的可信部署至关重要，最终将使整个社会受益。