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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GOVGOV-0015/5NewOtherGlobal

Goal Misgeneralisation: AI Pursues Wrong Objectives After Deployment

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Source: MIT AI Risk Repository — AI Alignment: A Comprehensive Survey (Ji2023)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
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
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
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
SECSEC-0014/5NewOtherGlobal

Training Data Poisoning Introduces Hidden Backdoors in Large Language Models

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Source: MIT AI Risk Repository — Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)Ingested
GOVGOV-0013/5NewOtherGlobal

AGI Goal Misalignment During Self-Improvement

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Source: MIT AI Risk Repository — The risks associated with Artificial General Intelligence: A systematic review (McLean2023)Ingested
SECSEC-0013/5NewOtherGlobal

LLM Distributed Training Infrastructure Exposed to Network Disruption Attacks

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Source: MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)Ingested
SECSEC-0014/5NewTechnologyGlobal

Training Data Poisoning and Backdoor Triggers in Large Language Models

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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

Noisy Training Data Causes LLM Hallucinations at Scale

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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

Deep Learning Framework Vulnerabilities Expose LLM Infrastructure

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Source: MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)Ingested
ENVENV-0043/5NewOtherGlobal

Race Dynamics Drive Development of Unsafe Artificial General Intelligence

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Source: MIT AI Risk Repository — The risks associated with Artificial General Intelligence: A systematic review (McLean2023)Ingested
GOVGOV-0013/5NewLegalGlobal

Legal and Risk Frameworks Found Unequipped for Artificial General Intelligence

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Source: MIT AI Risk Repository — The risks associated with Artificial General Intelligence: A systematic review (McLean2023)Ingested
HUMHUM-0064/5NewOtherGlobal

Generative AI Disrupts Copyright Ownership and Authorship Norms

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Source: MIT AI Risk Repository — Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (Hagendorff2024)Ingested
SECSEC-0013/5NewOtherGlobal

Python Interpreter Vulnerabilities Expose LLM Infrastructure

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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 Software Supply Chain Vulnerabilities Expose Development Pipelines

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Source: MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)Ingested
SECSEC-0013/5NewOtherGlobal

LLM Development Toolchain Vulnerabilities Create Security Exposure

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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

GPU Side-Channel Attacks Enable Extraction of Trained LLM Parameters

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Source: MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)Ingested
DATDAT-0033/5NewOtherGlobal

Toxic and Biased Training Data Embedded in Large Language Models

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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

Hardware Memory Attacks Enable Covert Manipulation of AI Model Parameters

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Source: MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)Ingested
DATDAT-0015/5NewOtherGlobal

Toxic Training Data Corrupts LLM Output Quality and Safety

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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 Decoding Randomness Causes Compounding Hallucination Errors

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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

Adversarial Input Manipulation Causes AI Model Prediction Failures

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