MIT AI Risk Repository · Risk Sub-Category · 60.01.01

Harm to individuals through fake content

Category: Risks from malicious use

Description

"Malicious actors can use general- purpose AI to generate fake content that harms individuals in a targeted way. For example, they can use such fake content for scams, extortion, psychological manipulation, generation of non- consensual intimate imagery (NCII) and child sexual abuse material (CSAM), or targeted sabotage of individuals and organisations."

From International AI Safety Report 2025 (Bengio2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
Human

Subdomain definition: Using AI systems to gain a personal advantage over others such as through cheating, fraud, scams, blackmail or targeted manipulation of beliefs or behavior. Examples include AI-facilitated plagiarism for research or education, impersonating a trusted or fake individual for illegitimate financial benefit, or creating humiliating or sexual imagery.

Real-world incidents in this subdomain

Browse all incidents in this subdomain

How other frameworks describe this risk

  • Impersonation/identity theft

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • IP/copyright loss

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Dehumanisation/objectification

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Defamation/libel/slander

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Financial and business

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cheating/plagiarism

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cybersecurity

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

  • Domain-Specific Misuses

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

Other entries from Bengio2025