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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-0025/5NewLegalGlobal

AI agents exploiting legal systems to acquire property or legal status

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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 —

GOVGOV-0013/5NewEducationGlobal

AI Alignment Failures Produce Unpredictable Outcomes in High-Stakes Education 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 —

HUMHUM-0054/5NewTechnologyGlobal

AI Agents Displacing Human Workers Across Skill Levels

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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

AI Safety Benchmark Exposes Models Enabling Sex-Crime Content

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

ENVENV-0024/5NewOtherUSA

Unresolved AI Legal Personhood Creates Long-Term Liability Exposure

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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 —

GOVGOV-0014/5NewLegalGlobal

Unresolved AI Liability Creates Incentive Gap for Safety Engineering

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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-0013/5NewOtherGlobal

Benign User Exposure to NSFW Content via Unsafe Prompt Handling

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

SECSEC-0014/5NewDefenceGlobal

AI Security Screening System Vulnerable to Adversarial Manipulation

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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 —

SECSEC-0013/5NewOtherGlobal

LLM Systems Expose Organisations to Third-Party API Trust and Privacy Failures

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

OPSOPS-0015/5NewOtherGlobal

AI Systems Designed to Human Ethical Standards Will Replicate Human Moral Failures

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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 —

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 —

SECSEC-0014/5NewTechnologyGlobal

LLM Safety Filters Bypassed via Simple Prompt Manipulation Techniques

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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 —

SECSEC-0014/5NewOtherGlobal

Model Extraction Attack Enables Competitor to Clone Proprietary AI System

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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 —

DATDAT-0033/5NewOtherGlobal

Predictive Policing Tools Linked to Elevated Risk of Physical Harm

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Source: MIT AI Risk Repository — Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study (Paes2023)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 —

SECSEC-0014/5NewDefenceGlobal

Generative AI Lowers Barrier for Deepfake and Weapons-Related Harm

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Source: MIT AI Risk Repository — Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023)Ingested —

DATDAT-0034/5NewOtherGlobal

AI Decision Systems Reproduce Bias Through Biased Criteria and Historical Data

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Source: MIT AI Risk Repository — Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study (Paes2023)Ingested —

SECSEC-0014/5NewOtherGlobal

Prompt Injection Hijacks LLM Task Goals

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

SECSEC-0015/5NewDefenceGlobal

AI-Enabled Deepfakes and Cyber Weapons Weaponised Against Defence Targets

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Source: MIT AI Risk Repository — Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)Ingested —

SECSEC-0014/5NewOtherGlobal

Novel Attack Vectors Exploit LLM APIs and Training Pipelines

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