  Although recent personalization methods have democratized high-resolution
image synthesis by enabling swift concept acquisition with minimal examples and
lightweight computation, they also present an exploitable avenue for high
accessible backdoor attacks. This paper investigates a critical and unexplored
aspect of text-to-image (T2I) diffusion models - their potential vulnerability
to backdoor attacks via personalization. Our study focuses on a zero-day
backdoor vulnerability prevalent in two families of personalization methods,
epitomized by Textual Inversion and DreamBooth.Compared to traditional backdoor
attacks, our proposed method can facilitate more precise, efficient, and easily
accessible attacks with a lower barrier to entry. We provide a comprehensive
review of personalization in T2I diffusion models, highlighting the operation
and exploitation potential of this backdoor vulnerability. To be specific, by
studying the prompt processing of Textual Inversion and DreamBooth, we have
devised dedicated backdoor attacks according to the different ways of dealing
with unseen tokens and analyzed the influence of triggers and concept images on
the attack effect. Through comprehensive empirical study, we endorse the
utilization of the nouveau-token backdoor attack due to its impressive
effectiveness, stealthiness, and integrity, markedly outperforming the
legacy-token backdoor attack.
