Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising. They are the backbone of many generative AI applications, such as text-to-image conditional generation. The chapter explores the backdoor risks of DMs, which can be viewed as a type of output manipulation attack triggered by a maliciously embedded pattern at model input.