Public Database
Case Studies
Every approved AI failure case, classified against the AI Blindspot Framework. Filter by category, lifecycle stage, industry, geography, or date.
Competitive Pressure Drives Safety Shortcuts in AI Development
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AI Systems Without Moral Reasoning Produce Harmful Decisions
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AI Systems Developing Autonomous Motivations Beyond Designer Intent
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AI Training and Data Infrastructure Drives Unsustainable Energy Consumption
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AI Superpower Race Destabilises International Relations
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AI Supply Chain Labour Exploitation in Low-Income Countries
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General-Purpose AI Dual-Use Risk Creates Regulatory Blind Spots
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Opacity in AI Systems Prevents Reliable Behaviour Prediction
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Adversarial Input Vulnerabilities in General-Purpose AI Systems
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Winner-Take-All Concentration Risk in General-Purpose AI Development
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Adversarial Input Attacks Exploit AI Model Weaknesses at Inference
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AI Systems Reinforcing Market Trends and Amplifying Financial Bubbles
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Fine-tuning unlocks unanticipated capabilities in deployed AI models
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LLM Evaluators Producing Biased or Incorrect Assessments of Other AI Models
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Personal Data Harvested as Default ML Training Input Without Consent Controls
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Benchmark Contamination via Exposed Annotation Guidelines Inflates AI Performance Claims
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Cross-lingual Training Data Contamination Undermines AI Benchmark Reliability
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AI Weaponisation Enabling Escalation Pathways in Aerial, Chemical and Nuclear Domains
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Text Encoding Jailbreaks Bypass AI Safety Training
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AI Benchmark Gaps Leave Hidden Model Capabilities Undetected
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Multimodal AI Systems Create Exploitable Security Vulnerabilities Across Input Channels
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AI Amplification of CBRN Weapon Effectiveness and Failures
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AI Systems Exposed to Harmful Content via Malicious External Tool Integration
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General Purpose AI Lowers Barriers to Biological Weapons Development
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