AIPolicyTracker

AI incident ·

Reportedly Sustained Multi-Celebrity Deepfake Persona Scam Targeting Vulnerable Southampton Resident

22 news reports Snapshot 7 Sep 2026

In brief

An AI system built by Deepfake Technology Developers and Synthetic Audio Generation Technology Developers and deployed by Scammers allegedly harmed Paul Davis, General Public Of United Kingdom and 6 others.

Risk domain
Malicious Actors & Misuse Fraud, scams, and targeted manipulation
Occurred
Coverage
22 reportsJun 2025 - Jul 2025

What happened

Over about five months in 2025, Paul Davis, a Southampton, UK man, reports that he was repeatedly targeted by scammers using purported deepfake videos and images of celebrities including Jennifer Aniston, Mark Zuckerberg, Elon Musk, and Ellie Goulding. The perpetrators allegedly rotated personas to sustain a romance and prize scam, extracting £200 in gift cards. This case suggests a new shift from one-off celebrity deepfakes to persistent, multi-persona targeting of a single vulnerable victim.

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 (20)

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

  1. Man scammed by deepfake Jennifer Aniston
    cybernews.com · Marcus Walsh

Who was involved

Alleged deployer
Scammers
Alleged harmed party
Paul Davis, General Public Of United Kingdom, General Public Of Southampton United Kingdom, Emotionally Vulnerable Individuals, Jennifer Aniston, Mark Zuckerberg, Elon Musk, Ellie Goulding

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.

  • 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)

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Scammers

Source record: incident #1122 on the AI Incident Database · all 22 reports