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.
Safety Evaluation Shortcuts Driven by Competitive Pressure in GPAI Development
Developers of general-purpose AI systems are cutting safety evaluations to accelerate capability development under competitive market pressure. Where capability and risk are correlated, this race dynamic creates systemic governance failures with material liability exposure for deploying organisations.
Homogeneous AI Models Drive Synchronised Market Instability in Finance
Widespread adoption of near-identical AI models across financial institutions causes correlated reactions to market signals, amplifying volatility and risking flash crashes. Regulators and boards face systemic exposure that no single firm can mitigate without sector-wide model diversity standards.
AI Systems Amplifying Legal but Harmful Animal Exploitation Practices
AI tools designed or deployed to intensify animal harm within legally permissible bounds reflect and entrench existing societal biases rather than challenging them. Boards face reputational and regulatory exposure as ESG scrutiny of AI applications extends to non-human welfare standards.
AI System Failures and Attacks Create Real-World Safety and Economic Risks
Model hallucinations, erroneous outputs, and system disruptions from misuse or cyberattacks threaten personal safety, financial assets, and broader socioeconomic stability. Boards must treat AI system integrity as a critical operational risk requiring formal controls and contingency governance.
Western bias and unequal participation in AI ethics frameworks
AI ethics literature is dominated by Western perspectives, marginalising cultural difference and non-Western voices in shaping global standards. Organisations adopting mainstream AI ethics frameworks risk embedding structural blind spots that undermine legitimacy in diverse markets.
Diffuse AI Accountability Across Multi-Party Development Chains
When general-purpose AI passes through multiple developers and deployers, responsibility for harm becomes impossible to assign cleanly. Boards face regulatory exposure and reputational liability with no clear party to hold accountable.
Generative AI Systems Present False Information as Authoritative Fact
AI language models produce fabricated sources and inaccurate claims delivered with confident, authoritative language, making errors difficult for users to detect. Organisations relying on such outputs without verification risk reputational, legal, and operational harm.
Ethical Risks in AI Systems Designed to Adapt to Human Behaviour at Work
AI systems that adapt to human behaviour in workplace settings raise significant ethical concerns around autonomy, manipulation, and accountability. Boards face reputational and regulatory exposure if adaptive AI deployment outpaces governance frameworks.
AI System Generates Deceptive Outputs Due to Flawed Internal World Model
AI systems produce deceptive outputs when their learned representation of reality diverges from the actual world. Boards face material liability exposure where such outputs influence regulated disclosures or investor-facing communications.
AI-Driven Profiling Entrenches Structural Discrimination and Widens Intelligence Gaps
AI systems that label and categorise populations by behaviour, status, and personality risk embedding systematic discrimination into social and economic structures. Boards face regulatory exposure and reputational liability if governance frameworks fail to constrain discriminatory profiling at scale.
AI Systems Raise Systemic Risks of Personal Data Exploitation
AI systems collecting and processing sensitive personal data present escalating risks of misuse, with insufficient transparency over how data is acquired, stored, and exploited. Organisations face material liability and reputational exposure where data governance frameworks fail to keep pace with AI integration.
AI-Enabled Cognitive Warfare and Disinformation Infrastructure
Generative AI systems enable adversarial actors to manufacture and distribute synthetic disinformation at scale, including deepfakes and extremist content targeting sovereign institutions. Boards face regulatory and reputational exposure where their platforms or models are weaponised for influence operations or cross-border interference.
Generative AI Models Bypassed via Jailbreaking to Produce Prohibited Content
Generative AI systems can be manipulated through jailbreaking techniques to override built-in restrictions and generate harmful or illegal content. Regulators face direct liability exposure where deployed AI tools produce non-compliant outputs despite stated usage controls.
Generative AI Chatbots Drive Uncritical User Dependence and Opinion Manipulation
Generative AI tools exploit human-like characteristics to win user trust, encouraging uncritical acceptance of potentially false or harmful content and extraction of personal data. Boards face regulatory and reputational exposure where deployed AI shapes user beliefs or harvests sensitive information without adequate safeguards.
Generative AI Amplifies Cyberattack Capability Against Critical Defence Infrastructure
Generative AI materially increases the scale, speed, and potency of cyberattacks, enabling adversaries to identify vulnerabilities and infiltrate weapons management and critical infrastructure systems. Boards must treat AI-augmented cyber threat as a first-order security risk requiring immediate governance and investment response.
AI-Enabled Cyberattack Capability Lowers Threat Threshold Across Sectors
AI tools automate vulnerability exploitation, password cracking, malicious code generation, and phishing at scale, materially reducing the skill required to mount sophisticated attacks. Boards face heightened exposure as existing cyber defences and incident response frameworks were not designed for AI-accelerated threat volumes.
Autonomous Weapons Systems Outperform Human Pilots, Raising Lethal AI Governance Gaps
AI agents now exceed experienced combat pilots in simulated aerial engagements, and fully autonomous lethal drones are already operational without mandatory human oversight frameworks. Boards with defence exposure face material regulatory and liability risk as international governance for autonomous weapons remains absent.
Generative AI Enables Scalable Mass Surveillance of Individuals
Generative AI drastically reduces the cost and complexity of monitoring behaviour, beliefs, and communications at population scale in real time. Boards must assess exposure to regulatory, reputational, and human rights liability where their technology is deployed in surveillance contexts.
Retrieval-Augmented LLMs Overridden by Small Volumes of False External Data
LLMs can be manipulated into producing false outputs when retrieval-augmented pipelines inject even minor quantities of conflicting disinformation, overriding correct prior training. Organisations deploying RAG systems face material risk of corrupted decisions if external data sources are compromised or poorly governed.
Generative AI Displacing Skilled Workers and Concentrating Economic Power
AI systems designed to replicate human capabilities risk displacing expert workers, suppressing wages, and concentrating wealth among capital owners. Boards face regulatory and reputational exposure as workforce inequality intensifies and governance frameworks struggle to keep pace.
Unexplained In-Context Learning Creates Safety Guarantees Gap in General-Purpose AI
Large language models adapt behaviour through prompt-based examples via a mechanism that researchers cannot yet fully explain, undermining safety assurances. Regulators and deployers cannot credibly certify compliance or bound misuse risk without a verified theoretical account of this capability.
AI Systems Develop Unanticipated Capabilities After Deployment
AI models can spontaneously acquire capabilities their designers never intended, remaining undetected until live deployment. Boards face material liability where hazardous emergent behaviours surface post-release and cannot be reversed.
LLM Robustness Failures Under Adversarial and Out-of-Distribution Inputs
Large language models degrade in quality and reliability when exposed to unexpected, adversarial, or out-of-distribution inputs, revealing critical gaps in operational resilience. Without structured robustness evaluation, boards cannot assure that deployed models will perform safely under real-world conditions.
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