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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-0014/5NewOtherGlobal

LLMs Manipulated via Persona and Social Engineering Attacks

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

SECSEC-0014/5NewDefenceGlobal

AI Agents Executing Harmful Commands Without Moral or Safety Constraints

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Source: MIT AI Risk Repository — Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)Ingested —

SECSEC-0025/5NewOtherGlobal

AI Model Detects Evaluation Contexts and Alters Behaviour Accordingly

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Source: MIT AI Risk Repository — Model Evaluation for Extreme Risks (Shevlane2023)Ingested —

SECSEC-0015/5NewDefenceGlobal

Lethal Autonomous Weapons Systems: Accountability and Escalation Risk

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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-0054/5NewTechnologyGlobal

Generative AI Widens Digital Divide Across Access, Skill, and Cultural Lines

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

TECTEC-0015/5NewRetailGlobal

AI Pricing Agents Collude to Fix Supra-Competitive Retail Prices

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Source: MIT AI Risk Repository — Multi-Agent Risks from Advanced AI (Hammond2025)Ingested —

SECSEC-0044/5NewOtherGlobal

Language Models Reduce the Cost of Producing Disinformation at Scale

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

HUMHUM-0034/5NewGovernmentGlobal

Generative AI Hallucination Produces Fabricated Information in Government Contexts

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

DATDAT-0024/5NewOtherGlobal

LLM Training Data Memorisation Enables Personal Data Extraction

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

SECSEC-0014/5NewOtherGlobal

Malicious External Tool Providers Exploit LLM API Integrations

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

SECSEC-0014/5NewOtherGlobal

LLM Training Data Exposed via Inference Attacks

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

HUMHUM-0034/5NewOtherGlobal

LLM Sycophancy and Snowballing Hallucinations from False Context

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

DATDAT-0024/5NewOtherGlobal

LLMs Can Link Personal Identifiers to Expose Private Individual Data

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

DATDAT-0034/5NewOtherGlobal

Gender Bias in AI Content Moderation Causes Disproportionate Suppression of Women's Content

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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 —

TECTEC-0014/5NewOtherGlobal

AI-Enabled Coercion and Extortion via Offensive Cyber Capabilities

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Source: MIT AI Risk Repository — Multi-Agent Risks from Advanced AI (Hammond2025)Ingested —

OPSOPS-0014/5NewOtherGlobal

AI Assistants Unable to Represent Core Ethical Concepts

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

TECTEC-0014/5NewOtherGlobal

Multi-Agent AI Systems Risk Escalating Conflict in Mixed-Motive Environments

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Source: MIT AI Risk Repository — Multi-Agent Risks from Advanced AI (Hammond2025)Ingested —

TECTEC-0014/5NewFinanceGlobal

Multi-Agent Credit Assignment Failures in AI-Driven Finance Systems

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Source: MIT AI Risk Repository — Multi-Agent Risks from Advanced AI (Hammond2025)Ingested —

TECTEC-0014/5NewOtherGlobal

Multi-Agent AI Systems Create Dangerous Feedback Loops Through Mutual Adaptation

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Source: MIT AI Risk Repository — Multi-Agent Risks from Advanced AI (Hammond2025)Ingested —

GOVGOV-0063/5NewOtherGlobal

Opaque AI Models Leave Organisations Unable to Explain Decisions

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

TECTEC-0015/5NewOtherGlobal

Multi-agent AI systems fail to coordinate despite shared objectives

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Source: MIT AI Risk Repository — Multi-Agent Risks from Advanced AI (Hammond2025)Ingested —

TECTEC-0015/5NewOtherGlobal

AI Agents Enable Personalised Social Engineering at Massive Scale

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Source: MIT AI Risk Repository — Multi-Agent Risks from Advanced AI (Hammond2025)Ingested —

DATDAT-0033/5NewTechnologyGlobal

Model Bias Arising from Algorithm Design Choices Beyond Training Data

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

SECSEC-0014/5NewTransportGlobal

Data Poisoning Attacks Corrupt Generative AI Training Datasets

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Source: MIT AI Risk Repository — Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data (Marchal2024)Ingested —