AI incident #1448 ·

Ohio Man Pleaded Guilty after Prosecutors Alleged He Used AI to Create and Distribute Nonconsensual Intimate-Image Forgeries Including CSAM in Harassment Campaign

Open on the AI Incident Database 2 news reports Synced from the AIID API · record last edited 7 Sep 2026

What happened

An Ohio man, James Strahler II, pleaded guilty in federal court after prosecutors alleged that, from late 2024 into mid-2025, he used AI tools to create and distribute nonconsensual intimate-image forgeries as part of a broader harassment campaign targeting at least six adult women. Authorities said the conduct also involved additional CSAM material. The Department of Justice said it believes this was the first U.S. conviction under the Take It Down Act.

Editor's notes (AI Incident Database)

Timeline note: According to reporting, the crimes in this case occurred between December 2024 and June 2025. The incident ID date of 12/01/2024 is an approximation. The defendant, reportedly the first person convicted under the Take It Down Act, pleaded guilty on 04/07/2026. The incident ID was created on 04/11/2026.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (2)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Columbus man pleads guilty to cyberstalking exes, creating AI-generated obscene material of adults & children
    justice.gov · U.S. Attorney's Office Southern District of Ohio, United States Department of Justice · AIID #7131

Who was involved

Alleged harmed party
Women in Ohio Women and girls Women Victims of non-consensual deepfakes Victims of deepfake child abuse Victims of deepfake abuse Privacy Minors Epistemic integrity
On AIID: Women in Ohio, Women and girls, Women, Victims of non-consensual deepfakes, Victims of deepfake child abuse, Victims of deepfake abuse, Privacy, Minors, Epistemic integrity

AI systems implicated

Synthetic media generation technologySynthetic image generation technologyDeepfake technology

Classification (MIT AI Risk Repository taxonomy)

Causal entity
Human
Intent
Intentional
Timing
Post-deployment
Harm level
Sectors
Countries

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 4.3.

  • Impersonation/identity theft

    "Impersonation/identity theft - Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • IP/copyright loss

    "IP/copyright loss - Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Dehumanisation/objectification

    "Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Defamation/libel/slander

    "Defamation/libel/slander - Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group, or organisation."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Financial and business

    "Financial and Business - Use or misuse of a technology system in a manner that damages the financial interests of an individual or group, or which causes strategic, operational, legal or financial harm to a business or...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cheating/plagiarism

    "Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cybersecurity

    "LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Domain-Specific Misuses

    "Improvements in LLMs may exert greater pressure to apply LLMs to various domains, such as health and education (Eloundou et al., 2023). Crude efforts to use LLMs in such domains, however, may incur harm and should be di...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Linked by AIID editors or by its text-similarity model.

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