Manipulation, persuasion and dark patterns
The system covertly manipulates user beliefs or behavior through persuasion, nudging, dark patterns, or exploitation of cognitive biases.
- Risk family
- Human & usage
- MIT domain
- 7. AI System Safety, Failures, & Limitations
- MIT subdomain
- 5.1 > Overreliance and unsafe use
- AI type
- GPAI, Agentic
- Scope
- System
- Source standard
- MIT AI Risk Repository v4
Provenance
33 entries across 22 papers
- Abercrombie2024A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms58.03.04 58.03.08
- Allianz2018The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks41.04.00 41.04.02
- Clarke2023A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values55.04.03
- Cui2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems02.06.02
- DSIT2023Capabilities and Risks from Frontier AI67.04.04
- G'sell2024Regulating under Uncertainty: Governance Options for Generative AI47.02.01
- Gabriel2024The Ethics of Advanced AI Assistants24.04.00 24.05.02 24.06.03
- Gipiškis2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems62.19.12 62.21.00 62.24.01 62.25.00 62.26.00
- GOS2023Future Risks of Frontier AI56.04.00 56.05.00
- Hagendorff2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Review05.06.00
- Hogenhout2021A framework for ethical Ai at the United Nations06.09.00
- InfoComm2023Cataloguing LLM Evaluations43.02.05
- Li2025A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents66.07.04
- Meek2016Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review09.02.07
- NIST2024Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile48.03.00
- Shelby2023Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction11.04.03 11.05.03
- Shevlane2023Model Evaluation for Extreme Risks25.03.00
- Tan2022The Risks of Machine Learning Systems15.02.07
- Tse2025Frontier AI Risk Management Framework (v1.0)72.05.09
- Uuk2025A Taxonomy of Systemic Risks from General-Purpose AI61.02.49
- Weidinger2022Taxonomy of Risks posed by Language Models16.05.03 16.05.04
- Weidinger2023Sociotechnical Safety Evaluation of Generative AI Systems18.05.02
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.
1- A.10 ISO/IEC 23894 Annex A A.10
2- A.5.4 ISO/IEC 42001 Annex A A.5.4
- A.9.4 ISO/IEC 42001 Annex A A.9.4
2- Art. 5(a)
- Art. 5(b)
1- ASI09 Human-Agent Trust Exploitation
More in Human & usage
See all Human & usage risks →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.