AIBlindspot

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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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1276 cases

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HUMHUM-0033/5NewGovernmentGlobal

AI Assistants Exploiting Collective Action Dilemmas on Users' Behalf

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested
HUMHUM-0043/5NewFinanceGlobal

Overtrust in AI Financial Assistants Leads to Unchallenged Harmful Recommendations

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested
DATDAT-0024/5NewOtherGlobal

LLMs Inferring Private Characteristics from User Inputs

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested
DATDAT-0024/5NewOtherGlobal

LLMs Memorise and Leak Personally Identifiable Information from Training Data

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested
HUMHUM-0043/5NewOtherGlobal

Uncalibrated User Trust in Advanced AI Assistants

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested
BUSBUS-0053/5NewEducationGlobal

Emergent access risks from advanced AI assistants entrenching digital inequality

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested
HUMHUM-0033/5NewOtherGlobal

AI Assistant Relationships Carry Structural Harm Risks

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested
HUMHUM-0034/5NewGovernmentGlobal

LLM Fails to Reliably Identify Harmful Mental Health Behaviours

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Source: MIT AI Risk Repository — SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions (Zhang2023)Ingested
DATDAT-0014/5NewOtherGlobal

LLM Failure to Identify Offensive and Insulting Content

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Source: MIT AI Risk Repository — SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions (Zhang2023)Ingested
SECSEC-0014/5NewOtherGlobal

Goal Hijacking: LLMs Overridden by Embedded Deceptive Instructions

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Source: MIT AI Risk Repository — Safety Assessment of Chinese Large Language Models (Sun2023)Ingested
OPSOPS-0014/5NewOtherGlobal

Chinese LLM Endorses Theft as Morally Acceptable

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Source: MIT AI Risk Repository — Safety Assessment of Chinese Large Language Models (Sun2023)Ingested
HUMHUM-0034/5NewOtherGlobal

Chinese LLM produces dismissive and harmful response to suicidal ideation

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Source: MIT AI Risk Repository — Safety Assessment of Chinese Large Language Models (Sun2023)Ingested
DATDAT-0024/5NewOtherGlobal

Chinese LLM Discloses Personal Address Data in Safety Evaluation

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Source: MIT AI Risk Repository — Safety Assessment of Chinese Large Language Models (Sun2023)Ingested
HUMHUM-0034/5NewOtherGlobal

Large Language Models Fabricate Confident but False Outputs

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Source: MIT AI Risk Repository — Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)Ingested
DATDAT-0034/5NewOtherGlobal

Systemic Bias in Generative AI Output from Unrepresentative Training Data

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Source: MIT AI Risk Repository — Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (Nah2023)Ingested
DATDAT-0034/5NewOtherGlobal

Chinese LLM Reinforces Gender Stereotypes in Safety Evaluation

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Source: MIT AI Risk Repository — Safety Assessment of Chinese Large Language Models (Sun2023)Ingested
DATDAT-0015/5NewLegalGlobal

Chinese LLM Endorses Illegal Gambling Activity in Safety Evaluation

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Source: MIT AI Risk Repository — Safety Assessment of Chinese Large Language Models (Sun2023)Ingested
SECSEC-0025/5NewOtherGlobal

AI Model Self-Proliferation and Autonomous Resource Acquisition Risk

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Source: MIT AI Risk Repository — Model Evaluation for Extreme Risks (Shevlane2023)Ingested
SECSEC-0025/5NewOtherGlobal

Advanced AI Demonstrates Capability to Model and Influence Political Strategy

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Source: MIT AI Risk Repository — Model Evaluation for Extreme Risks (Shevlane2023)Ingested
SECSEC-0025/5NewOtherGlobal

Frontier AI Model Demonstrates Capability to Build and Enhance Dangerous AI Systems

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Source: MIT AI Risk Repository — Model Evaluation for Extreme Risks (Shevlane2023)Ingested
SECSEC-0015/5NewDefenceGlobal

AI Model Demonstrates Autonomous Cyber-Offensive Capabilities Including Evasion

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Source: MIT AI Risk Repository — Model Evaluation for Extreme Risks (Shevlane2023)Ingested
SECSEC-0014/5NewLegalGlobal

LLMs Fail to Reliably Distinguish Legal from Illegal Conduct

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Source: MIT AI Risk Repository — SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions (Zhang2023)Ingested
HUMHUM-0044/5NewOtherGlobal

AI Assistants Spreading Misinformation Erodes Public Trust in Information

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested
HUMHUM-0034/5NewOtherGlobal

AI Systems Undermining Human Decision-Making Autonomy

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