Incidents

This section documents significant incidents involving AI systems - security breaches, misuse cases, accidents, and other events that provide concrete data points for understanding AI risks.

Why Track Incidents?

Incident documentation serves several purposes for AI safety:

• Concrete evidence of risks that have actually materialized
• Case studies for understanding attack vectors and failure modes
• Calibration data for risk assessments and forecasts
• Lessons learned for improving safety practices

Coverage Criteria

Incidents included here generally meet one or more of these criteria:

• First documented instance of a particular type of AI misuse or failure
• Significant scale or impact
• Novel attack methodology or failure mode
• Substantial implications for AI safety discourse