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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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SECSEC-0044/5NewOtherGlobal

Deepfake Media Manipulation Erodes Public Trust in Information Integrity

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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 —

BUSBUS-0053/5NewTechnologyGlobal

Advanced AI Assistants Risk Entrenching Inequality Without Design Intervention

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

DATDAT-0035/5NewTechnologyGlobal

Algorithmic Bias in Criminal Justice Risk-Assessment Tools

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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 —

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 —

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 —

ENVENV-0023/5NewHealthcareGlobal

Moral and Legal Status of AI Termination in Healthcare Research Settings

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Source: MIT AI Risk Repository — Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review (Meek2016)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 —

SECSEC-0014/5NewOtherGlobal

Generative AI Enables Scaled Malware, Phishing and Model Poisoning Attacks

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Source: MIT AI Risk Repository — Generating Harms - Generative AI's impact and paths forwards (EPIC2023)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 —

DATDAT-0034/5NewLegalGlobal

AI Legal Decision Systems Risk Unequal Treatment Without Objective Justification

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Source: MIT AI Risk Repository — Sources of Risk of AI Systems (Steimers2022)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-0014/5NewOtherGlobal

Prompt Leaking Exposes Confidential LLM System Instructions

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