# The Malicious Technical Ecosystem: Exposing Limitations in Technical Governance of AI-Generated Non-Consensual Intimate Images of Adults

arXiv: https://arxiv.org/abs/2504.17663 · HTML: https://mlsanity.com/paper/2504.17663/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 1 of 1)

- ["Did They F***ing Consent to That?": Safer Digital Intimacy via Proactive Protection Against Image-Based Sexual Abuse](https://mlsanity.com/paper/2403.04659/) (2024)

## Built on by (most cited first, 3 of 3)

- [Distinguishing Task-Specific and General-Purpose AI in Regulation](https://mlsanity.com/paper/2506.17347/) (2025)
- [When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm](https://mlsanity.com/paper/2603.24079/) (2026)
- [Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety](https://mlsanity.com/paper/2607.05407/) (2026)

## Abstract (arXiv)

In this paper, we adopt a survivor-centered approach to locate and dissect the role of sociotechnical AI governance in preventing AI-Generated Non-Consensual Intimate Images (AIG-NCII) of adults, colloquially known as "deep fake pornography." We identify a "malicious technical ecosystem" or "MTE," comprising of open-source face-swapping models and nearly 200 "nudifying" software programs that allow non-technical users to create AIG-NCII within minutes. Then, using the National Institute of Standards and Technology (NIST) AI 100-4 report as a reflection of current synthetic content governance methods, we show how the current landscape of practices fails to effectively regulate the MTE for adult AIG-NCII, as well as flawed assumptions explaining these gaps.