MIT AI Risk Repository
Browse AI risks
19 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.
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53.01.00 · Risk Category
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53.01.01 · Risk Sub-Category
Alignment failures in existing ML systems
Faulty reward functions in the wild
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53.01.03 · Risk Sub-Category
Alignment failures in existing ML systems
Reward model overoptimization
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53.02.03 · Risk Sub-Category
Dangerous capabilities in AI systems
Acquisition of goals to seek power and control
"cases where AI systems converge on optimal policies of seeking power over their environment;135"
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53.03.01 · Risk Sub-Category
Existential disaster because of misaligned superintelligence or power-seeking AI
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53.03.03 · Risk Sub-Category
Extreme “suffering risks” because of a misaligned system
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53.03.04 · Risk Sub-Category
Existential disaster because of conflict between AI systems and multi-system interactions
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53.01.08 · Risk Sub-Category
Alignment failures in existing ML systems
Harms from increasingly agentic algorithmic systems
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53.02.00 · Risk Category
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"cases where a large language model displays awareness that it is a model, and it can recognize whether it is currently in testing or deployment;"
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"examples of cases where AI systems improve AI systems"
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"the ability of simple software to autonomously spread around the internet in spite of countermeasures (various software worms and computer viruses)"
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"The demonstrated ability of anonymous actors to accumulate resources online (e.g., Satoshi Nakamoto as an anonymous crypto billionaire)"
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"Cases of AI systems deceiving humans to carry out tasks or meet goals.139"
Disclaimer: informational only, not legal advice. Verify every claim against the linked official sources and consult a qualified lawyer before acting.
Frequently asked questions
Where do these risk entries come from?
The MIT AI Risk Repository, which extracts risks from dozens of published frameworks, taxonomies and papers and codes each one by domain and subdomain, and by a causal taxonomy of entity, intent and timing. This is a browseable copy, attributed and openly licensed, not original research.
What do entity, intent and timing mean?
They are the causal coding. Entity is whether a human or the AI system is the cause; intent is whether the harm was intentional or not; timing is whether it arises before or after deployment. Together they let you separate misuse from malfunction.
Can I export the results?
Yes, any filtered set exports as CSV or JSON, and every export carries the upstream source, licence and citation with it, because attribution is a condition of the licence rather than a courtesy.
What is the MIT AI Risk Repository?
A living database of AI risks extracted from published frameworks, taxonomies and papers, classified by a causal taxonomy of entity, intent and timing and by a domain taxonomy of seven domains and 24 subdomains. It is published by the MIT AI Risk Initiative under CC BY 4.0.