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AI incident ·

Guelph, Ontario, Woman Reportedly Lost $14,000 in Purported Deepfake MrBeast Cryptocurrency Scam

1 news report Synced from source · record last edited 7 Sep 2026

In brief

An AI system built by Synthetic media generation technology developers, Synthetic audio generation technology developers and 1 other and deployed by Synthetic media creators, Scammers impersonating MrBeast and 3 others allegedly harmed YouTube users, Women and girls and 9 others.

Risk domain
Malicious Actors & Misuse Fraud, scams, and targeted manipulation
Occurred
Coverage
1 reportMay 2026

What happened

A Guelph, Ontario, woman reportedly lost $14,000 after clicking a social-media ad for a cryptocurrency investment purportedly promoted by MrBeast. Police said her losses accumulated through an initial $250 payment and later transfers made after she spoke with supposed representatives and someone she believed was the YouTube creator, including $5,000 sent to a crypto wallet.

Laws that address this harm

Policy angle: Classified under Malicious Actors & Misuse (Fraud, scams, and targeted manipulation) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (1)

Titles link to the original publisher; report text is not reproduced here.

  1. Guelph woman who thought she spoke to YouTube star Mr. Beast lost $14K in crypto scam: Police
    cbc.ca · CBC News

Who was involved

Alleged harmed party
YouTube users Women and girls Women Victims of impersonation scams Victims of deepfake-enabled fraud Unnamed Guelph, Ontario woman Social media users General public Fans of MrBeast Epistemic integrity

AI systems implicated

Synthetic media generation technologySynthetic audio generation technologySocial media advertising systemsDeepfake technologyCryptocurrency wallet infrastructure

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.

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • Misinformation and Manipulation

    "Recent studies have demonstrated that LLMs can be exploited to craft deceptive narratives with levels of persuasiveness similar to human-generated content (Pan et al., 2023b; Spitale et al., 2023), to fabri- cate fake n...

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

  • 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)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Synthetic media creators

Source record: incident #1485 on the AI Incident Database · all 1 report