Research On Ai-Generated Child Sexual Abuse Material And Global Prosecution Strategies

06 Oct 2025 --
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🧠 Legal & Prosecution Frameworks for AI‑Generated CSAM

Key elements

Definition and scope: CSAM (child sexual abuse material) traditionally refers to images, videos, or other media depicting real children engaged in sexual activities or sexualised imagery of minors. AI‑generated CSAM adds a new dimension: materials created entirely or partly by generative AI (images/videos) with no actual victim (or real victim involvement) but depicting minors in sexualised or abusive contexts.

Legal treatment: Many jurisdictions have extended laws covering CSAM to include “pseudophotographs”, “computer generated imagery” or “deepfakes” of minors. For example, the U.S. DOJ has publicly declared that “CSAM generated by AI is still CSAM.”

Prosecutorial strategy:

Production/creation of AI‑CSAM (even without real victim).

Distribution/sale of such material (sharing online, selling access).

Possession/access of AI‑CSAM.

Soliciting/commissioning such content (i.e., paying for custom AI‑CSAM).

Challenges:

Attribution: linking the AI generation and the user with illegal intention.

Evidence: capturing metadata, prompt logs, machine‑learning artefacts.

Jurisdiction & global cooperation: content may be produced in one country, hosted in another, distributed globally.

Existing legislation: many legal frameworks were drafted before AI‑gen imagery; prosecutors must interpret “obscene depiction of minors” or “pseudo photographs” to cover AI content.

Global cooperation: Because platforms and user networks cross borders, investigations often involve mutual legal assistance treaties (MLATs), multi‑national task forces (e.g., Europol), and coordinated raids/seizures of servers/devices globally.

Prosecution strategy:

Seize the device or system where the AI prompts/logs are stored.

Trace payments/subscriptions for AI‑CSAM generation.

Use forensic tools to distinguish “real children” images vs “synthetic” output, but treat both as criminal if sexualised minors depict.

Employ sentencing enhancements for AI usage or custom‑commissioned content.

Update statutory language (in some jurisdictions) to explicitly mention “computer‑generated or AI‑generated sexual images of minors”.

⚖️ Case Studies

Case 1: United States – Steven Anderegg (Wisconsin, 2024)

Facts: A 42‑year‑old software engineer, Steven Anderegg, allegedly used the AI image generation model (Stable Diffusion variant) to create more than 13,000 sexualised and explicit images of pre‑pubescent minors. He used extremely specific prompts (including “negative prompts” to avoid adult depiction) to generate the content. He is also alleged to have sent the images to a 15‑year‑old boy via Instagram. In his messages he reportedly boasted of his AI‑skills.
Legal framework/charges: The U.S. DOJ charged him with producing, distributing and possessing obscene visual depictions of minors engaged in sexually explicit conduct, as well as transferring obscene material to a minor under age 16. The key legal principle: even though no real children were depicted, federal law bans “obscene visual depictions of minors” and courts have accepted that AI‑generated CSAM falls within that prohibition.
Prosecution strategy: Law enforcement traced Instagram chats, the device seizure revealed the AI model, prompt logs, thousands of images. Prosecutors emphasised the “special skill” in generative AI, advanced custom prompt engineering, and distribution to a minor.
Outcome: Case is ongoing (as of the latest data), but could carry up to 70 years’ prison if convicted on all counts. Significance: this is among the first federal prosecutions in the U.S. focusing on AI‑generated CSAM.
Lessons: Creates precedent that AI‑generated imagery of abused children is treated as CSAM; demonstrates need for prosecutors to treat “synthetic” as “real” under law; underscores importance of prompt logs and forensic AI‑usage records.

Case 2: United Kingdom – Hugh Nelson (UK, 2024)

Facts: Hugh Nelson, 27, used 3D modelling/AI software (Daz 3D with AI‑functions) to generate child sexual abuse imagery. He took commissions from buyers to create custom images of children being abused, using real photographs of children as starting point then transforming via AI. He sold them online and also shared them for free. Police discovered the network via an undercover operation in a chat room.
Legal framework/charges: Under UK law, creation and distribution of indecent images of children (including pseudo‑photographs) is criminal. The court accepted that images derived via AI using real children photographs count as indecent images.
Prosecution strategy: Investigators traced purchase/payments, peer‑to‑peer networks, and the use of real photographs of children. At sentencing, the judge noted the “depths of depravity” in the images and the commercial element of the business.
Outcome: Mr. Nelson was convicted of 16 child‑sexual‑abuse offences in 2024 and sentenced to 18 years in prison.
Lessons: This case confirms that in the UK you can be prosecuted for AI‑enabled CSAM generation; shows that real‑photograph involvement enhances gravity; demonstrates how custom orders/commissions raise aggravating factors.

Case 3: Australia – David Bradley Dillon‑Henderson (New South Wales, 2024‑25)

Facts: In Wollongong, NSW, a 22‑year‑old man (David Bradley Dillon‑Henderson) was found with AI‑generated child sexual abuse material on his computer. He admitted using prompts such as “schoolgirl, skirt and leggings” to generate explicit images via an AI tool, storing the resulting material in a folder labelled “open me”. The images did not necessarily show real children physically abused, but were generated. His girlfriend discovered them and reported him.
Legal framework/charges: Under Australian law (Criminal Code Act 1995 etc), production and possession of child abuse material is criminal, whether real or simulated. The courts examined whether AI‑generated imagery qualifies – in this case it did lead to guilty verdict.
Prosecution strategy: The case hinged on his admission of prompt usage, his computer logs, the storage of AI‑generated pornography depicting children, and the contextual folder naming. Defence argued ignorance of illegality; court found sufficient evidence of intent and knowledge.
Outcome: He was found guilty; sentencing was pending (in December).
Lessons: Reinforces that even without direct victim photos, using AI to generate sexualised images of minors is liable; shows how prompt logs and folder organisation provide evidentiary support.

Case 4: Denmark / Global Ring – Operation “Cumberland” (2025)

Facts: A coordinated multinational law enforcement operation (led by Danish authorities with support from Europol and partner countries) targeted a criminal distribution ring that offered access via subscription to AI‑generated CSAM. The service allowed users to pay a symbolic fee and receive AI‑generated child sexual abuse images. The ring involved 19 countries; 25 suspects arrested, 173 devices seized, 273 suspected members identified.
Legal framework/charges: Many jurisdictions treat distribution of CSAM (including synthetic) as criminal. The operation emphasises global cooperation under cybercrime frameworks.
Prosecution strategy: Law enforcement traced payment systems, platform infrastructure, and distribution channels; seized servers/devices; used cooperation across states. Identified AI‑generation platform, subscription model, worldwide users.
Outcome: Arrests have been made, devices seized, and ongoing prosecutions. The case demonstrates how AI‑CSAM distribution networks span jurisdictions and require collaborative responses.
Lessons: Highlights the global dimension of AI‑CSAM; shows how commercial distribution models increase scale; indicates need for cross‐border legal frameworks and harmonised legislation.

🧩 Summary Table of Key Features

Case	Jurisdiction	AI‑Aspect	Legal Framework / Charges	Key Evidence	Outcome / Significance
Anderegg (USA)	U.S. (Wisconsin)	Generated >13k AI images via Stable Diffusion	Production/distribution/possession of CSAM	Prompt logs, AI model use, Instagram chats	Landmark federal case treating AI‑CSAM as CSAM
Nelson (UK)	United Kingdom	3D/AI tool creating custom images from real children photos	UK indecent images law covers pseudo‑photos	Commission orders, sale records, real‑kids photo inputs	First UK major conviction for AI‑CSAM
Dillon‑Henderson (Australia)	Australia (NSW)	AI prompts to create sexualised minor imagery	Australian production/possession offences	Prompts, folder naming, AI‑output analysis	Confirms Australian law applies to AI‑CSAM
Operation Cumberland	Denmark/19 countries	Subscription‑based AI‑CSAM distribution network	Distribution of CSAM across jurisdictions	Payment logs, server seizures, multi‑state cooperation	Illustrates scale and global reach of AI‑CSAM networks

🔍 Observations & Strategic Insights

Statutory clarity is required: Some jurisdictions had to interpret existing CSAM laws (which were drafted for real‑child images) to cover fully synthetic images.

Prompt logs and AI tool metadata matter: Prosecutors increasingly rely on logs of AI prompts (“create nude prepubescent child touching genitals”), negative prompts, model versions used.

Commercial dimension aggravates liability: Commissioned generation, sales/subscriptions of AI‑CSAM elevate offences.

Global distribution amplifies complexity: Networks operating across borders require international law enforcement collaboration and harmonised legislation.

Victim‑less image does not mean harm‑less: Even without real children, AI‑CSAM is treated as illegal because it normalises abuse, may facilitate grooming/luring, and uses real‑child images as inputs in many cases.

Sentencing is severe and evolving: Courts are treating AI‑CSAM offences seriously; case law is emerging rapidly.

Prevention and platform liability: AI model firms, platforms enabling generation/distribution, may become part of enforcement ecosystem (filters, prompt blocks, licensing controls).

Challenges remain: Distinguishing between fantasy art vs illegal sexualised minor depiction; managing encryption/anonymous networks; cross‑jurisdiction enforcement delay.