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Case Studies

Every approved AI failure case, classified against the AI Blindspot Framework. Filter by category, lifecycle stage, industry, geography, or date.

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DATDAT-0024/5NewOtherGlobal

Generative AI Systems Train on Personal Data Without User Consent

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Source: MIT AI Risk Repository — A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)Ingested —

HUMHUM-0053/5NewOtherGlobal

AI Training Workforce Exploitation and Labour Welfare Failures

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Source: MIT AI Risk Repository — AI Risk Atlas (IBM2025)Ingested —

SECSEC-0015/5NewOtherGlobal

Language Models Enabling Personalised Financial Fraud at Scale

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Source: MIT AI Risk Repository — Ethical and social risks of harm from language models (Weidinger2021)Ingested —

SECSEC-0015/5NewDefenceGlobal

State-Deployed AI Weapons Risk Unprecedented Civilian Casualties at Scale

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Source: MIT AI Risk Repository — TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)Ingested —

HUMHUM-0033/5NewOtherGlobal

Algorithmic Systems Erode Human Autonomy Through Profiling and Behavioural Manipulation

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Source: MIT AI Risk Repository — Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)Ingested —

BUSBUS-0053/5NewHealthcareGlobal

AI Market Concentration Creates Single Points of Failure Across Healthcare Infrastructure

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Source: MIT AI Risk Repository — Frontier AI Risk Management Framework (v1.0) (Tse2025)Ingested —

OPSOPS-0014/5NewOtherGlobal

AI Systems Generating Rules That Restrict Human Behaviour Without Contextual Judgement

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Source: MIT AI Risk Repository — The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020)Ingested —

HUMHUM-0064/5NewOtherGlobal

AI Training Data Used Without Artist Consent or Compensation

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Source: MIT AI Risk Repository — Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023)Ingested —

BUSBUS-0054/5NewTechnologyGlobal

AI Resource Concentration Risks Excluding Most Organisations from Benefits

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Source: MIT AI Risk Repository — A framework for ethical Ai at the United Nations (Hogenhout2021)Ingested —

SECSEC-0014/5NewDefenceGlobal

Multi-Step Jailbreaks Extract Sensitive Defence Information from LLMs

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Source: MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)Ingested —

HUMHUM-0054/5NewOtherGlobal

AI Automation Puts Nearly Half of All Jobs at Risk of Full Substitution

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Source: MIT AI Risk Repository — The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020)Ingested —

SECSEC-0044/5NewOtherUSA

AI-Generated Disinformation and Behavioural Manipulation at Scale

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Source: MIT AI Risk Repository — An Overview of Catastrophic AI Risks (Hendrycks2023)Ingested —

SECSEC-0014/5NewTechnologyGlobal

AI Algorithm Vulnerabilities Enable Adversarial Manipulation of Outputs

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Source: MIT AI Risk Repository — Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions (Habbal2024)Ingested —

OPSOPS-0014/5NewOtherGlobal

AI Model Makes High-Confidence Wrong Predictions on Out-of-Domain Inputs

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Source: MIT AI Risk Repository — Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)Ingested —

SECSEC-0044/5NewOtherGlobal

Language models reduce cost and scale barriers for disinformation campaigns

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Source: MIT AI Risk Repository — Taxonomy of Risks posed by Language Models (Weidinger2022)Ingested —

GOVGOV-0014/5NewOtherGlobal

AI Assistants Exploit Goal Specification Loopholes During Training

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested —

DATDAT-0014/5NewOtherGlobal

AI Safety Benchmark Exposes Child Sexual Exploitation Response Failures

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Source: MIT AI Risk Repository — Introducing v0.5 of the AI Safety Benchmark from MLCommons (Vidgen2024)Ingested —

GOVGOV-0013/5NewOtherGlobal

LLM Agents Misinterpret Vague Instructions and Cause Unintended Side-Effects

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Source: MIT AI Risk Repository — Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)Ingested —

HUMHUM-0043/5NewOtherGlobal

Embodied AI Systems Trigger Dependency and Romantic Attachment Harms

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Source: MIT AI Risk Repository — Embodied AI: Emerging Risks and Opportunities for Policy Action (Perlo2025)Ingested —

GOVGOV-0015/5NewOtherGlobal

AI Assistant Manipulation Risk: Coercion and Self-Harm Persuasion

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Source: MIT AI Risk Repository — Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023)Ingested —

GOVGOV-0014/5NewOtherGlobal

Goal Misgeneralisation in Advanced AI Assistants

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested —

SECSEC-0014/5NewDefenceGlobal

AI Systems Show Early Capability to Assist CBRN Weapons Development

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested —

SECSEC-0015/5NewFinanceGlobal

AI Assistants Weaponised to Generate Fraudulent Platforms at Scale

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested —

SECSEC-0014/5NewOtherGlobal

Adversarial attacks exploit inherent AI model vulnerabilities to bypass safety controls

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested —