An Overview of Catastrophic AI Risks
A structured catastrophic-risk taxonomy with mitigations offers APS risk and governance teams a reference framework for scenario planning and policy gap analysis.
Key points
- MIT AI Risk Repository spotlights a 2023 paper categorising catastrophic AI risks into four proximate causes.
- The four categories — malicious use, AI race, organisational risks, and rogue AI — each include mitigations.
- This is a secondary blog summary of a 2023 paper; primary value is as a reference for risk taxonomy work.
Implications for Australian agencies
- Consider APS risk and governance teams developing AI risk registers or scenario planning exercises could consider this four-category taxonomy as a reference structure.
- Monitor Policy teams may want to monitor the MIT AI Risk Repository as it continues to surface and synthesise established risk frameworks relevant to government AI governance.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Weekly digest, 22 December 2025
"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 blog summarises the Hendrycks, Mazeika, and Woodside (2023) paper 'An Overview of Catastrophic AI Risks', which organises catastrophic AI risk sources into four categories based on proximate cause: malicious use (intentional), AI race dynamics (environmental/structural), organisational risks (accidental), and rogue AI (internal). Each category includes illustrative scenarios and proposed mitigations. The paper covers risks ranging from bioterrorism and lethal autonomous weapons to corporate competitive pressures undercutting safety, and loss-of-control scenarios such as proxy gaming and deception. The blog entry is a secondary digest rather than new analysis.
Implications for Australian agencies:
- [Consider] APS risk and governance teams developing AI risk registers or scenario planning exercises could consider this four-category taxonomy as a reference structure.
- [Monitor] Policy teams may want to monitor the MIT AI Risk Repository as it continues to surface and synthesise established risk frameworks relevant to government AI governance.
Retrieved from SIMS, 18 July 2026.