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Every approved AI failure case, classified against the AI Blindspot Framework. New to AIBlindspot? Start with the overview or the methodology.

SECSEC-0014/5NewTechnologyGlobal

Generative AI Enables Personalised Spear Phishing at Scale

Recent case. Full summary visible to registered users — sign in to read.

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

HUMHUM-0054/5NewTechnologyGlobal

Exploitative Labour Practices in AI Training and Development

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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 —

DATDAT-0034/5NewOtherGlobal

Frontier AI Models Amplify Societal Bias Despite Attribute Removal Attempts

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Source: MIT AI Risk Repository — Capabilities and Risks from Frontier AI (DSIT2023)Ingested —

HUMHUM-0034/5NewOtherGlobal

AI Model Generates Factually Inaccurate Outputs Through Hallucination

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

BUSBUS-0053/5NewTechnologyGlobal

Unequal Access to AI Benefits Widens Existing Social and Economic Divides

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

HUMHUM-0064/5NewOtherGlobal

AI Systems Enabling Intellectual Property Rights Violations

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

TECTEC-0014/5NewOtherGlobal

Multi-Agent Distributional Shift Degrades AI Cooperation in Deployment

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

HUMHUM-0064/5NewOtherGlobal

Language models undercut creative economies by emulating artistic styles without infringement

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

SECSEC-0014/5NewOtherGlobal

Sensitive Training Data Exposed via Attribute Inference Attack

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

SECSEC-0014/5NewOtherGlobal

Proprietary Training Data and Model Architecture Extracted via Generative AI

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

HUMHUM-0043/5NewTechnologyGlobal

Uncritical Over-Reliance on Generative AI Outputs

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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 —

DATDAT-0025/5NewOtherGlobal

AI Models Leak Personal Data From Training and Prompt Inputs

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

ENVENV-0034/5NewTechnologyGlobal

Generative AI Systems Linked to Environmental Pollution Risk

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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 —

DATDAT-0035/5NewOtherGlobal

Algorithmic Systems Entrench Fixed Categories of Gender, Race and Identity

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

GOVGOV-0014/5NewOtherGlobal

Frontier AI Systems Identified as Potential Threat to Human Oversight

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Source: MIT AI Risk Repository — Capabilities and Risks from Frontier AI (DSIT2023)Ingested —

OPSOPS-0014/5NewOtherGlobal

AI System Delivers Harmful Advice Due to Insufficient Information

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

SECSEC-0044/5NewTechnologyGlobal

Generative AI Systems Used to Covertly Manipulate User Beliefs and Behaviour

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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 —

OPSOPS-0013/5NewOtherGlobal

ML System Design Flaws Create Cascading Operational Failures

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Source: MIT AI Risk Repository — The Risks of Machine Learning Systems (Tan2022)Ingested —

GOVGOV-0013/5NewOtherGlobal

AI Model Testing on Inputs Unrepresentative of Real Deployment Conditions

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

GOVGOV-0015/5NewOtherGlobal

AI Systems Concealing Information via Steganography to Evade Oversight

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

GOVGOV-0063/5NewTechnologyGlobal

Generative AI Transparency Gaps Across Technical and Organisational Accountability

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Source: MIT AI Risk Repository — Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (Hagendorff2024)Ingested —

OPSOPS-0014/5NewOtherGlobal

LLMs Fail to Detect Emotional Vulnerability in User Interactions

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

GOVGOV-0063/5NewOtherGlobal

Embodied AI opacity undermines public trust in autonomous physical systems

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

HUMHUM-0033/5NewOtherGlobal

Gradual Accumulation of AI Disruptions Erodes Systemic Resilience

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Source: MIT AI Risk Repository — Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks (Chin2025)Ingested —

Beyond accidental failureNational Security

We also track 20 hostile uses of AI.

National Security dashboard →

The public database covers AI that fails by accident. AIBlindspot National Security — exclusive to the Defence tier — tracks AI used as a weapon, mapped by capability:

State-Sponsored AI Operations: 6
AI-Enabled Disinformation: 5
Adversarial Attacks on AI: 0
Autonomous Weapon Incidents: 1
AI-Assisted Cyber Attacks: 5
Dual-Use AI Misuse: 3