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AI incident #797 ·

Teenager Reportedly Makes Deepfake Pornography of 50 Girls at Bacchus Marsh Grammar School in Australia

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

What happened

At Bacchus Marsh Grammar, a school in Victoria state in Australia, an adolescent male reportedly made deepfake pornography of 50 girls, ages 9 to 12. He then allegedly uploaded the pictures to Instagram, and others subsequently shared them on Snapchat.

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 (5)

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

  1. Fifty Bacchus Marsh Grammar girls targeted with AI fake nudes
    smh.com.au · Wendy Tuohy · AIID #4090
  2. Parent reacts after 50 girls targeted in AI explicit photo scandal at Melbourne school
    sbs.com.au · SBS News · AIID #4108
  3. Bacchus Marsh Grammar: schoolboy arrested after 50 female students allegedly targeted in fake explicit AI photos scandal
    theguardian.com · Jordyn Beazley, Rafqa Touma · AIID #4105
  4. Parent outraged over 'incredibly graphic' fake images of Bacchus Marsh Grammar students
    abc.net.au · Australian Broadcasting Corporation · AIID #4106
  5. Vomit-inducing deepfake nudes show yet again that when misogyny intersects with AI and elitism, girls get hurt | Van Badham
    theguardian.com · Van Badham · AIID #4107

Who was involved

Alleged harmed party
Women and girls Students Privacy Girls Epistemic integrity Educational communities Bacchus Marsh Grammar students Bacchus Marsh Grammar girls Adolescent girls
On AIID: Women and girls, Students, Privacy, Girls, Epistemic integrity, Educational communities, Bacchus Marsh Grammar students, Bacchus Marsh Grammar girls, Adolescent girls

AI systems implicated

Synthetic media generation technologySynthetic image generation technologySocial media platformsSnapchatInstagramDeepfake technology

Classification (MIT AI Risk Repository taxonomy)

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

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

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