AI-Generated Child Sexual Abuse Material: Insights from Educators, Platforms, Law Enforcement, Legislators, and Victims

AI-generated child sexual abuse material (CSAM) carries unique harms. When generated from a photo of a clothed person, it can damage that person’s reputation and cause serious distress. When based on existing CSAM, it risks re-traumatizing victims. Even AI CSAM that seems purely synthetic may come from a model that was trained on real abusive material. Many experts also warn that viewing AI CSAM can normalize child abuse and increase the risk of contact abuse. There is the added risk that law enforcement may mistake AI CSAM for content involving a real, unidentified victim, leading to wasted time and resources spent trying to locate a child who does not exist. In this report we aim to understand how educators, platform staff, law enforcement officers, U.S. legislators, and victims are thinking about and responding to AI CSAM. We interviewed 52 people, analyzed documents from four public school districts, and coded state legislation. Our main findings are that while the prevalence of student-on-student nudify app use in schools is unclear, schools are generally not addressing the risks of nudify apps with students, and some schools that have had a nudify incident have made missteps in their response. We additionally find that mainstream platforms report the CSAM they discover, but, for various reasons, without systematically trying to discern and convey whether it is AI-generated in their reports to the National Center for Missing and Exploited Children’s (NCMEC) CyberTipline. This means the task of identifying AI-generated material falls to NCMEC and law enforcement. However, frontline platform staff believe the prevalence of AI CSAM on their platforms remains low. Finally, we find that legal risk is hindering CSAM red teaming efforts for mainstream AI model-building companies.