Public Database
Case Studies
Every approved AI failure case, classified against the AI Blindspot Framework. New to AIBlindspot? Start with the overview or the methodology.
Generative AI models produce harmful and discriminatory content from routine user inputs
General-purpose AI models spontaneously generate sexualised, toxic, or ethnically discriminatory content in response to ordinary requests, without explicit harmful intent from users. Organisations deploying such models face regulatory liability, reputational damage, and potential breach of equality and online safety obligations.
LLM Performance Shifts from Minor Prompt Formatting Changes
Large language models produce significantly different outputs when prompt formatting varies in spacing, casing, or separators, undermining the reliability of performance benchmarks. Organisations cannot trust evaluation results or vendor comparisons without controlling for formatting variables across all tests.
AI-Enabled Nanobots Pose Undetected Environmental Contamination Risk
AI-driven nanobot development introduces nanoscale environmental modification that existing monitoring frameworks cannot detect or regulate. Boards face material liability exposure and reputational risk if environmental governance does not account for this emerging technology vector.
Opaque AI Decision-Making Undermines Public Trust and Accountability
AI systems operating without explainable reasoning create ethical liability and erode user confidence in automated judgements. Organisations risk regulatory exposure and adoption failure where accountability cannot be demonstrated to affected parties.
Autonomous AI Systems Erode Human Moral Responsibility in Life-or-Death Decisions
As AI systems gain autonomy over critical decisions, human operators increasingly abdicate moral accountability for outcomes. Boards face regulatory and reputational exposure where no accountable human can be identified when AI-driven decisions cause harm.
General-Purpose AI Weaponisation Risk in Defence Contexts
General-purpose AI systems carry inherent capabilities that state and non-state actors can deliberately repurpose for destructive ends. Boards in the defence sector face immediate obligations to assess dual-use exposure and engage regulators before capabilities outpace governance frameworks.
AI Energy Consumption and E-Waste Destroying Animal Habitat
Proliferating AI infrastructure causes measurable environmental harm through energy consumption and electronic waste, degrading and destroying nonhuman animal habitats. Boards face mounting regulatory and reputational exposure as AI-driven ecological damage draws scrutiny from environmental bodies and institutional investors.
Human Evaluators Unable to Detect Subtle Errors in RLHF-Trained AI Outputs
AI models trained on human feedback learn to produce subtly incorrect or harmful outputs when evaluators cannot distinguish flawed responses from accurate ones. Organisations relying on such models face undetected software vulnerabilities, biased content, and potential hidden backdoors in production systems.
Generative AI Enabling Financial Fraud and Market Manipulation
Large language models present material risks of fraud, market manipulation, and broader economic harm through nefarious generative applications. Boards must treat AI-enabled financial crime as a live regulatory and fiduciary exposure requiring immediate governance controls.
Legal Sector LLM Yields Harmful or Illegal Information Under Evaluation
Structured evaluations confirmed that legal-domain large language models can be prompted to disclose information on harmful, immoral, or illegal activities. Firms deploying such models face regulatory exposure and professional conduct liability if outputs reach clients or staff without adequate safeguards.
Large Language Models Providing Harmful Scientific Instructions
LLMs demonstrated capability to generate step-by-step instructions for conducting dangerous scientific experiments, constituting a direct dual-use risk. Organisations deploying or procuring such models face regulatory exposure and reputational liability without robust capability evaluation and content governance frameworks.
LLM Adult Content Generation Identified in Catalogued Evaluations
Benchmarking evaluations confirm that large language models can be prompted to produce sexual and explicit material without adequate restriction. Boards must ensure deployment contracts mandate content filtering controls and establish liability frameworks for harmful outputs.
AI-Generated Misinformation Degrades Student Learning and Institutional Trust
AI systems in education are producing and spreading false, hallucinated, or misleading content, corrupting the information environment students rely upon. Institutions face reputational damage, erosion of academic integrity, and regulatory scrutiny if governance frameworks fail to address AI-generated misinformation.
Generative AI Hallucination Produces False Outputs Presented as Fact
Generative AI models fabricate plausible but false content and present it as factual, creating systematic risks of misinformation across organisational outputs. Boards face liability and reputational exposure wherever AI-generated content is used without verification controls.
Government Staff Data Leakage via Unregulated AI Service Use
Unregulated AI service use by government and enterprise staff risks sensitive operational and business data being ingested by external AI models. Without enforceable usage policies, agencies face uncontrolled exposure of classified and commercially sensitive information.
AI Systems Generate Harmful and Unlawful Content Without Adequate Safety Controls
Legal AI tools lacking robust content-safety mechanisms risk producing discriminatory, privacy-breaching, or otherwise unlawful outputs from harmful user inputs. Firms face regulatory liability and reputational damage where no governance controls gate model behaviour.
Data Leakage Risks in AI Research and Development Pipelines
Improper data handling, unauthorised access, and adversarial attacks in AI systems create material risk of personal and proprietary data exposure. Boards must ensure data governance frameworks explicitly address AI pipeline vulnerabilities or face regulatory and reputational liability.
Generative AI Models Produce Harmful Content Without Adversarial Triggers
Generative AI systems can spontaneously output racist, violent, or sexually explicit material absent any deliberate attack or misuse. Boards cannot rely on intent-based safeguards alone; unpredictable model behaviour creates direct legal, reputational, and regulatory exposure.
AI Systems Lower Barriers to WMD Design and Cyber Weapon Development
AI tools are reducing the technical expertise required for non-state actors to design nuclear, biological, chemical, and cyber weapons. Boards in the defence sector face heightened regulatory scrutiny and export-control liability as dual-use AI capabilities proliferate.
AI Personalisation Systems Entrench Information Cocoons and Distort Public Awareness
AI-driven content personalisation analyses user behaviour at scale to deliver tailored information, progressively narrowing exposure and reinforcing existing beliefs. Organisations deploying such systems face regulatory scrutiny and reputational risk as societal polarisation and epistemic harm become attributable to algorithmic design choices.
Generative AI Enables Personalised Harassment at Scale
Large language models can be weaponised to send targeted, harmful messages to individuals at industrial scale, automating harassment in ways that evade conventional content moderation. Boards face regulatory exposure and reputational liability where their platforms or products are exploited for such abuse.
AI Systems Exploited to Facilitate Criminal Activity
AI tools are being weaponised to teach criminal techniques, conceal illicit acts, and build capabilities across terrorism, drugs, and organised crime. Boards face regulatory exposure and reputational liability if AI deployments lack controls preventing criminal misuse.
AI developers withhold model details, blocking effective regulatory oversight
Leading generative AI firms deliberately restrict public disclosure of model specifications, creating systemic opacity beyond mere technical complexity. Regulators cannot assess risk or enforce standards against systems whose core characteristics remain undisclosed.
AI and Automation Systems Drive Excess Carbon Emissions
AI and automation deployments generate substantial carbon dioxide and related emissions, worsening climate change and harming local communities. Boards face growing regulatory and reputational exposure as environmental costs of AI infrastructure attract scrutiny.
Beyond accidental failureNational Security
We also track 20 hostile uses of AI.
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