Impersonation, deepfakes and synthetic media
The system is used to impersonate people via deepfakes, voice cloning, or synthetic identities, or to misuse a person's likeness.
- Risk family
- Model & system behaviour
- MIT domain
- 4. Malicious Actors & Misuse
- MIT subdomain
- 4.3 > Fraud, scams, and targeted manipulation
- AI type
- GPAI
- Scope
- Both
- Source standard
- MIT AI Risk Repository v4
Provenance
47 entries across 21 papers
- Abercrombie2024A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms58.01.02 58.01.04 58.08.07
- Cunha2023Navigating the Landscape of AI Ethics and Responsibility03.05.00
- EPIC2023Generating Harms - Generative AI's impact and paths forwards31.02.00 31.02.01 31.02.02 31.02.03
- Ferrara2023GenAI against humanity: nefarious applications of generative artificial intelligence and large language models46.01.00 46.01.01 46.01.02 46.01.03 46.04.02
- G'sell2024Regulating under Uncertainty: Governance Options for Generative AI47.02.04
- Gabriel2024The Ethics of Advanced AI Assistants24.03.10 24.03.11 24.04.02 24.04.04 24.05.04
- Gipiškis2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems62.09.04 62.31.08 62.31.14 62.32.02 62.36.04
- Habbal2024Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions29.02.02
- Hagendorff2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Review05.03.00 05.10.00
- Hogenhout2021A framework for ethical Ai at the United Nations06.07.00
- IBM2025AI Risk Atlas65.14.05
- Kilian2023Examining the differential risk from high-level artificial intelligence and the question of control07.01.00
- Li2025A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents66.01.01 66.01.02
- Marchal2024Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data64.01.00 64.01.01 64.01.02 64.01.03 64.01.04 64.02.03
- Nah2023Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration33.02.04
- Sherman2023AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk Disclosures12.01.00 12.03.00
- Shevlane2023Model Evaluation for Extreme Risks25.02.00
- Tse2025Frontier AI Risk Management Framework (v1.0)72.01.04
- Uuk2025A Taxonomy of Systemic Risks from General-Purpose AI61.02.34
- Weidinger2021Ethical and social risks of harm from language models17.04.01
- Weidinger2023Sociotechnical Safety Evaluation of Generative AI Systems18.04.02 18.05.01
Ev IDs of the entries consolidated into this risk in the MIT AI Risk Repository (V4); the source sheet row appears on hover.
Framework crosswalk
Every framework item mapped to this risk. Items marked partial overlap only in part; definitions appear on hover where the source licence permits.
2- A.10 ISO/IEC 23894 Annex A A.10
- A.8 ISO/IEC 23894 Annex A A.8
1- A.9.4 ISO/IEC 42001 Annex A A.9.4
1- Art. 50(4)
2Expanded into this risk’s technique sub-risks.
1- ibm-nonconsensual-use Nonconsensual use
1- AISubtech-3.1.1 Identity Obfuscation partial
1- GENAI.8 Information Integrity
Sub-risks (2)
Technique-level decompositions of this risk, each anchored to the MITRE ATLAS technique it derives from.
The system is used to impersonate a trusted person or organization to deceive targets.
Generative capability is used to produce synthetic media for deception, fraud, or reputational harm.
More in Model & system behaviour
Part of the Deployer AI Risk Register, an open-source resource powered by MindXO. Version 1.0, 3 July 2026. Derived from the MIT AI Risk Repository (V4, December 2025) under CC BY 4.0; an independent derivative work, not endorsed by or affiliated with MIT. Sub-risk decomposition references MITRE ATLAS™ v5.6.0 (© 2021-2026 The MITRE Corporation, reproduced and distributed with permission). ISO/IEC and EU AI Act references are by number only. License: CC BY 4.0. Full attribution and licensing.