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AI incident #1328 ·

Purported Deepfake Impersonating Elon Musk Allegedly Defrauded Elderly U.S. Woman of $50,000 via Gift Card–to-Crypto Scam

Open on the AI Incident Database 1 news report Synced from the AIID API · record last edited 7 Sep 2026

What happened

An 80-year-old U.S. woman was reportedly deceived by scammers using purportedly AI-generated messages and deepfake media impersonating Elon Musk into believing she was in a romantic relationship with him. The perpetrators allegedly induced her to buy over $50,000 in Apple gift cards to be converted into cryptocurrency, leaving her financially endangered and at risk of foreclosure on her home.

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 (1)

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

  1. My 80-year-old mom is convinced she's dating Elon Musk — and scammers took $50K from her. How do I stop her before she loses more?
    moneywise.com · Maurie Backman · AIID #6776

Who was involved

Alleged harmed party
Women and girls Women Victims of impersonation scams Victims of deepfake-enabled fraud Unnamed 80-year-old U.S. woman Epistemic integrity Elon Musk Elderly investors Elderly individuals Cryptocurrency investors defrauded by AI-generated profiles
On AIID: Women and girls, Women, Victims of impersonation scams, Victims of deepfake-enabled fraud, Unnamed 80-year-old U.S. woman, Epistemic integrity, Elon Musk, Elderly investors, Elderly individuals, Cryptocurrency investors defrauded by AI-generated profiles

AI systems implicated

Synthetic media generation technologySynthetic audio generation technologyDeepfake technology

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
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)

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

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