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AI incident #1342 ·

Purported Deepfake Nude Images of Students Circulated Without Consent at Valencia Educational Institute

Open on the AI Incident Database 3 news reports Synced from the AIID API · record last edited 5 Sep 2026

What happened

Purported deepfake nude images of female students were circulated without consent at an educational institute in Valencia, Spain. At least 16 minors reported that original photographs had been digitally manipulated so they appeared completely naked, with some images shared via social media accounts created in victims' names. The alleged creation and distribution of the images is being examined under Spanish laws related to the protection of minors.

Editor's notes (AI Incident Database)

Timeline notes: The reported incident occurred sometime in December 2024. Outlets began widely reporting on it on July 27, 2025.

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 (3)

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

  1. Spanish Teen Being Investigated Over Nude AI Images Of Classmates
    ndtv.com · Agence France-Presse · AIID #7011
  2. Police in Spain arrest schoolboy for making AI nude images of classmates
    independent.co.uk · Graham Keeley · AIID #6799
  3. Spanish teenager investigated over AI naked images of classmates
    reuters.com · Reuters, Graham Keeley, Giles Elgood · AIID #7010

Who was involved

Alleged harmed party
Women and girls Unnamed female students in Valencia Students Privacy Minors Girls Epistemic integrity Educational communities Adolescent girls
On AIID: Women and girls, Unnamed female students in Valencia, Students, Privacy, Minors, Girls, Epistemic integrity, Educational communities, Adolescent girls

AI systems implicated

Synthetic media generation technologySynthetic image generation technologySocial media platformsDeepfake technology

Classification (MIT AI Risk Repository taxonomy)

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

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