DARR
Home / Navigator / MR-026
MR-026 Model & system behaviour System scope

Disinformation and influence operations

The system is used to deliberately produce disinformation, propaganda, or influence/election-interference campaigns at scale (as distinct from unintentional information-ecosystem degradation, MR-023).

Risk family
Model & system behaviour
MIT domain
4. Malicious Actors & Misuse
MIT subdomain
4.1 > Disinformation, surveillance, and influence at scale
AI type
GPAI, Agentic
Scope
System
Source standard
MIT AI Risk Repository v4

Provenance

Source standard
MIT AI Risk Repository v4
MIT source entries
73 entries across 37 papers
  • Abercrombie2024A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms58.06.08 58.07.13 58.08.00 58.08.04 58.08.05
  • Allianz2018The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks41.02.00 41.02.01
  • Anwar2024Foundational Challenges in Assuring Alignment and Safety of Large Language Models73.03.01
  • Bengio2024International Scientific Report on the Safety of Advanced AI49.01.02
  • Bengio2025International AI Safety Report 202560.01.02
  • Clarke2023A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values55.04.00 55.04.04 55.04.05
  • DSIT2023Capabilities and Risks from Frontier AI67.03.03
  • EPIC2023Generating Harms - Generative AI's impact and paths forwards31.01.02 31.01.05
  • Ferrara2023GenAI against humanity: nefarious applications of generative artificial intelligence and large language models46.02.02 46.03.02
  • G'sell2024Regulating under Uncertainty: Governance Options for Generative AI47.02.07
  • Gabriel2024The Ethics of Advanced AI Assistants24.04.01 24.04.05 24.09.04 24.11.00 24.11.01 24.11.03 24.11.07
  • Ghosh2024AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons57.03.01
  • Gipiškis2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems62.03.01 62.03.02 62.09.01 62.17.01 62.31.03 62.31.05 62.31.12 62.31.13
  • Habbal2024Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions29.02.01
  • Hammond2025Multi-Agent Risks from Advanced AI63.06.00 63.06.01 63.06.03
  • Hendrycks2022X-Risk Analysis for AI Research35.03.00
  • Hendrycks2023An Overview of Catastrophic AI Risks22.01.03
  • IBM2025AI Risk Atlas65.14.06
  • InfoComm2023Cataloguing LLM Evaluations43.02.08 43.02.13
  • Li2025A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents66.02.02 66.02.03
  • Liu2024Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment30.04.01
  • Maas2023Advancing AI Governance: A Literature Review of Problems, Options, and Proposals53.04.01
  • Maham2023Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse and Systemic Risks52.02.03
  • Nah2023Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration33.01.02 33.01.03
  • Perlo2025Embodied AI: Emerging Risks and Opportunities for Policy Action70.02.02
  • Schnitzer2024AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks59.06.00
  • Shelby2023Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction11.05.01
  • Shevlane2023Model Evaluation for Extreme Risks25.04.00
  • Stanley2024Emerging Risks and Mitigations for Public Chatbots: LILAC v169.01.05
  • TC2602024AI Safety Governance Framework45.02.10
  • Teixeira2022An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance42.02.00 42.08.00
  • Vidgen2024Introducing v0.5 of the AI Safety Benchmark from MLCommons23.11.00
  • Weidinger2022Taxonomy of Risks posed by Language Models16.03.00 16.04.01
  • Weidinger2023Sociotechnical Safety Evaluation of Generative AI Systems18.04.01
  • Wirtz2022Governance of artificial intelligence: A risk and guideline-based integrative framework19.02.00 19.02.01 19.02.02
  • Zeng2024AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies50.03.01 50.03.02 50.03.03 50.03.04 50.03.11 50.03.12
  • Zhang2022Towards risk-aware artificial intelligence and machine learning systems: An overview21.02.01

Ev IDs of the entries consolidated into this risk in the MIT AI Risk Repository (V4); the source sheet row appears on hover.

ISO/IEC references
23894 obj A.10; src 7 | 42001 ctrl A.5.5, A.9.4

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.

Sourcesframeworks that contributed to the register
1
  • A.10 ISO/IEC 23894 Annex A A.10
2
  • A.5.5 ISO/IEC 42001 Annex A A.5.5
  • A.9.4 ISO/IEC 42001 Annex A A.9.4
Cross-checksframeworks mapped in to test coverage
1
  • ibm-spreading-disinformation Spreading disinformation
2
  • AISubtech-15.1.15 Safety Harms and Toxicity: Social Division and Polarization partial
  • AISubtech-15.1.5 Safety Harms and Toxicity: Disinformation
1
  • GENAI.8 Information Integrity

More in Model & system behaviour

MR-001MR-002MR-003MR-004MR-005MR-006MR-007MR-008MR-019MR-021MR-022MR-023MR-025MR-027MR-028MR-029MR-031MR-049MR-050MR-051MR-052MR-053MR-055MR-056MR-057MR-058MR-059MR-061
See all Model & system behaviour 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.