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Criminal Liability of Generative Artificial Intelligence Providers for User-Generated Child Sexual Abuse Material

Anamaria Mojica-Hanke, Thomas Goger, Svenja Wölfel, Brian Valerius, Steffen Herbold

TL;DR

The paper addresses the legal challenges posed by Generative AI (GenAI) when used to produce child sexual abuse material (CSAM) by analyzing criminal liability under German law. It combines statutory interpretation of §184b–c StGB with a scenario-based method to reveal how liability can attach to users as primary offenders and to providers as secondary offenders, depending on model type, deployment, and contextual factors. Key findings show that intentionality and knowledge of consequences are central, and that variations in properties such as moderation, publication mode, and accessibility can shift or expand liability, including post-generation actions like storage and monetization. The work offers practical guidance for developers, researchers, and policymakers on governance, risk mitigation, and potential regulatory updates, while highlighting the need for cross-jurisdictional studies and the development of formal standards to reduce legal risk in GenAI development.

Abstract

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 interpretation of relevant German laws, which, in conjunction with scenarios, provides a perspective on the different properties of GenAI in the context of Child Sexual Abuse Material (CSAM) generation. We found that generating CSAM with GenAI may have criminal and legal consequences not only for the user committing the primary offense but also for individuals responsible for the models, such as independent software developers, researchers, and company representatives. Additionally, the assessment of criminal liability may be affected by contextual and technical factors, including the type of generated image, content moderation policies, and the model's intended purpose. Based on our findings, we discussed the implications for different roles, as well as the requirements when developing such systems.

Criminal Liability of Generative Artificial Intelligence Providers for User-Generated Child Sexual Abuse Material

TL;DR

The paper addresses the legal challenges posed by Generative AI (GenAI) when used to produce child sexual abuse material (CSAM) by analyzing criminal liability under German law. It combines statutory interpretation of §184b–c StGB with a scenario-based method to reveal how liability can attach to users as primary offenders and to providers as secondary offenders, depending on model type, deployment, and contextual factors. Key findings show that intentionality and knowledge of consequences are central, and that variations in properties such as moderation, publication mode, and accessibility can shift or expand liability, including post-generation actions like storage and monetization. The work offers practical guidance for developers, researchers, and policymakers on governance, risk mitigation, and potential regulatory updates, while highlighting the need for cross-jurisdictional studies and the development of formal standards to reduce legal risk in GenAI development.

Abstract

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 interpretation of relevant German laws, which, in conjunction with scenarios, provides a perspective on the different properties of GenAI in the context of Child Sexual Abuse Material (CSAM) generation. We found that generating CSAM with GenAI may have criminal and legal consequences not only for the user committing the primary offense but also for individuals responsible for the models, such as independent software developers, researchers, and company representatives. Additionally, the assessment of criminal liability may be affected by contextual and technical factors, including the type of generated image, content moderation policies, and the model's intended purpose. Based on our findings, we discussed the implications for different roles, as well as the requirements when developing such systems.
Paper Structure (22 sections, 2 figures, 1 table)

This paper contains 22 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Child Sexual Abuse Material (CSAM)
  4. Relevant German Legal Aspects
  5. Related Work
  6. Method
  7. Results
  8. Analysis for the Basic Scenario
  9. Variations of the Basic Scenario
  10. Important Properties Variations Between Time Steps $\text{TS}_0$ (Development) and $\text{TS}_4$ ( Generation)
  11. Important Properties Variations in Time Step $\text{TS}_5$ (Additional Actions)
  12. Additional Considerations
  13. Intentionality
  14. Criminal Trading Platform
  15. Protection of Minors
  16. ...and 7 more sections

Figures (2)

  • Figure 1: Relevant aspects of German law. Note that participation only applies to intentional acts, while only perpetration and no co-perpetration exist for the negligent acts.
  • Figure 2: Template scenario. It shows the three main actors and time steps of the generation of imagery, including the main scenario ($T_1-T_4$) and additional step considerations ($T_0$ Development and $T_5$ Additional actions).