  This paper investigates the direct risks and harms associated with modern
text-to-image generative models, such as DALL-E and Midjourney, through a
comprehensive literature review. While these models offer unprecedented
capabilities for generating images, their development and use introduce new
types of risk that require careful consideration. Our review reveals
significant knowledge gaps concerning the understanding and treatment of these
risks despite some already being addressed. We offer a taxonomy of risks across
six key stakeholder groups, inclusive of unexplored issues, and suggest future
research directions. We identify 22 distinct risk types, spanning issues from
data bias to malicious use. The investigation presented here is intended to
enhance the ongoing discourse on responsible model development and deployment.
By highlighting previously overlooked risks and gaps, it aims to shape
subsequent research and governance initiatives, guiding them toward the
responsible, secure, and ethically conscious evolution of text-to-image models.
