An Overview of Catastrophic AI Risks
A structured catastrophic risk taxonomy from a credible academic source - useful background for agencies building AI risk registers or governance frameworks.
Key points
- MIT AI Risk Repository spotlights a 2023 framework categorising catastrophic AI risks into four proximate causes.
- The four categories - malicious use, AI race dynamics, organisational accidents, and rogue AI - offer a structured risk taxonomy.
- This is a blog summary of a 2023 paper; substantive content is not new, though the Repository aggregation adds reference value.
Summary
The MIT AI Risk Repository has spotlighted a 2023 paper by Hendrycks, Mazeika, and Woodside that organises catastrophic AI risks into four categories based on proximate cause: malicious use (intentional), AI race dynamics (environmental/structural), organisational accidents (accidental), and rogue AI or loss of control (internal). Each category includes illustrative hypothetical scenarios and proposed mitigations. The MIT blog post is a summary rather than new analysis, and the underlying paper predates current Australian AI governance frameworks, but the taxonomy remains a useful reference point for risk classification work.
Implications for Australian agencies
- Consider Agencies developing or reviewing AI risk registers could consider whether this four-category taxonomy complements existing frameworks such as the NIST AI RMF or DISR guidance.
- Monitor Teams tracking the MIT AI Risk Repository may want to monitor subsequent framework spotlights for emerging risk categorisation approaches relevant to Australian policy work.
Implications are AI-generated. Starting points, not advice.
"An Overview of Catastrophic AI Risks" Source: MIT AI Risk Repository – Blog Published: 22 December 2025 URL: https://airisk.mit.edu/blog/an-overview-of-catastrophic-ai-risks The MIT AI Risk Repository has spotlighted a 2023 paper by Hendrycks, Mazeika, and Woodside that organises catastrophic AI risks into four categories based on proximate cause: malicious use (intentional), AI race dynamics (environmental/structural), organisational accidents (accidental), and rogue AI or loss of control (internal). Each category includes illustrative hypothetical scenarios and proposed mitigations. The MIT blog post is a summary rather than new analysis, and the underlying paper predates current Australian AI governance frameworks, but the taxonomy remains a useful reference point for risk classification work. Implications for Australian agencies: - [Consider] Agencies developing or reviewing AI risk registers could consider whether this four-category taxonomy complements existing frameworks such as the NIST AI RMF or DISR guidance. - [Monitor] Teams tracking the MIT AI Risk Repository may want to monitor subsequent framework spotlights for emerging risk categorisation approaches relevant to Australian policy work. Retrieved from SIMS, 18 May 2026.