  The rise of powerful AI models, more formally $\textit{General-Purpose AI
Systems}$ (GPAIS), has led to impressive leaps in performance across a wide
range of tasks. At the same time, researchers and practitioners alike have
raised a number of privacy concerns, resulting in a wealth of literature
covering various privacy risks and vulnerabilities of AI models. Works
surveying such risks provide differing focuses, leading to disparate sets of
privacy risks with no clear unifying taxonomy. We conduct a systematic review
of these survey papers to provide a concise and usable overview of privacy
risks in GPAIS, as well as proposed mitigation strategies. The developed
privacy framework strives to unify the identified privacy risks and mitigations
at a technical level that is accessible to non-experts. This serves as the
basis for a practitioner-focused interview study to assess technical
stakeholder perceptions of privacy risks and mitigations in GPAIS.
