AI incident #848 ·

High School Student in Córdoba, Argentina Accused of Using AI to Generate Explicit Images of Classmates

Open on the AI Incident Database 1 news report Synced from the AIID API · record last edited 5 Sep 2026

What happened

An 18-year-old Argentine student at the pre-university institute of Manuel Belgrano of Córdoba allegedly used AI tools to generate explicit fake images of at least 22 female classmates by combining their faces with other bodies. These images, posted on pornography websites, included the victims' names, leading to harassment and significant psychological harm. Legal authorities charged the student with serious injuries aggravated by gender violence.

Editor's notes (AI Incident Database)

Reconstructing the timeline of events: (1) Mid-2024: Two female students at the Manuel Belgrano Institute began receiving suspicious Instagram messages from men aged 40–50. (2) July 2024: Victims discovered explicit AI-generated images of themselves on pornographic websites, featuring their names and altered appearances. (3) August 2024: Families of victims reported the student to authorities, who launched an investigation and conducted a search of the accused's home. (4) October 18, 2024: Charges were filed against the accused for gender-based violence and psychological harm; legal experts advocated for updated laws addressing AI misuse. This fourth date is being marked as the incident date, even though the initial events are reported to have begun sometime in the middle of 2024.

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News reports (1)

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

Who was involved

Alleged harmed party
Women and girls Women Unnamed Manuel Belgrano female students University students Students Privacy Epistemic integrity Educational communities
On AIID: Women and girls, Women, Unnamed Manuel Belgrano female students, University students, Students, Privacy, Epistemic integrity, Educational communities

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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