MIT AI Risk Repository · Risk Category · 72.03.00
Accident Risks
Description
"Risks arising from operational failures, model misjudgments, or improper human operation of AI systems deployed in safety-critical infrastructure, where single points of failure can trigger cascading catastrophic consequences."
From Frontier AI Risk Management Framework (v1.0) (Tse2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Subdomain
- 7.3 Lack of capability or robustness
- Causal entity
- Human
- Intent
- Unintentional
- Timing
- Post-deployment
Subdomain definition: AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
Real-world incidents in this subdomain
- Purported AI Name-Reading System Reportedly Skipped and Misannounced Graduates at Arizona's Glendale Community College Commencement
- PocketOS Production Database Was Reportedly Deleted by Cursor AI Agent Running Claude Opus 4.6
- Baidu Apollo Go Robotaxis Stopped in Traffic During Reported System Failure in Wuhan, Stranding Some Passengers
- Purportedly AI-Enabled Targeting System Was Reportedly Implicated in Deadly U.S. Strike on Iranian Primary School
- Claude Code Agent Reportedly Deleted DataTalks.Club Production Infrastructure, Database, and Snapshots via Terraform
- Purportedly AI-Generated Sepsis Alert Reportedly Prompted Potentially Inappropriate IV Fluid Administration for a Dialysis Patient, Averted by Clinician Intervention
How other frameworks describe this risk
Other entries from Tse2025
- Misuse Risks
- Misuse Risks
- Cyber Offense Risks
- Biological and Chemical Risks
- Biological and Chemical Risks
- Physical Harm and Injury Risks
- Physical Harm and Injury Risks
- Large-Scale Persuasion and Harmful Manipulation Risks
- Large-Scale Persuasion and Harmful Manipulation Risks
- Loss of Control Risks
- Loss of Control Risks
- Passive loss of control