What is the MIT AI Risk Repository?

A living database of AI risks extracted from 74 frameworks, taxonomies and papers, classified by a causal taxonomy (entity, intent, timing) and a domain taxonomy (7 domains, 24 subdomains). Published by the MIT AI Risk Initiative under CC BY 4.0.

Can I download the filtered risk list?

Yes. Apply filters and use Export CSV or Export JSON; the file carries the source, licence and citation so it can be reused with attribution.

MIT AI Risk Repository

Browse AI risks

Name: MIT AI Risk Repository: AI Risk Database (browseable copy)
Creator: MIT AI Risk Initiative
License: https://creativecommons.org/licenses/by/4.0/

5 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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5 entries

45.01.04 · Risk Sub-Category

AI's inherent safety risks

Risks from models and algorithms (Risks of stealing and tampering)

"Core algorithm information, including parameters, structures, and functions, faces risks of inversion attacks, stealing, modification, and even backdoor injection, which can lead to infringement of intellectual property rights (IPR) and leakage of business secrets. It can also lead to unreliable inference, wrong decision output, and even operational failures."

From AI Safety Governance Framework (TC2602024)

Domain 2 2.2 Other Other Other
45.01.06 · Risk Sub-Category

AI's inherent safety risks

Risks from models and algorithms (Risks of adversarial attack)

"Attackers can craft well-designed adversarial examples to subtly mislead, influence, and even manipulate AI models, causing incorrect outputs and potentially leading to operational failures."

From AI Safety Governance Framework (TC2602024)

Domain 2 2.2 Human Intentional Post-deployment
45.01.11 · Risk Sub-Category

AI's inherent safety risks

Risks from AI systems (Risks of exploitation through defects and backdoors)

"The standardized API, feature libraries, toolkits used in the design, training, and verification stages of AI algorithms and models, development interfaces, and execution platforms may contain logical flaws and vulnerabilities. These weaknesses can be exploited, and in some cases, backdoors can be intentionally embedded, posing significant risks of being triggered and used for attacks."

From AI Safety Governance Framework (TC2602024)

Domain 2 2.2 Human Other Other
45.01.12 · Risk Sub-Category

AI's inherent safety risks

Risks from AI systems (Risks of computing infrastructure security)

"The computing infrastructure underpinning AI training and operations, which relies on diverse and ubiquitous computing nodes and various types of computing resources, faces risks such as malicious consumption of computing resources and cross-boundary transmission of security threats at the layer of computing infrastructure."

From AI Safety Governance Framework (TC2602024)

Domain 2 2.2 Human Other Other
45.02.05 · Risk Sub-Category

Safety risks in AI Applications

Cyberspace risks (Risks of security flaw transmission caused by model reuse)

"Re-engineering or fine-tuning based on foundation models is commonly used in AI applications. If security flaws occur in foundation models, it will lead to risk transmission to downstream models."

From AI Safety Governance Framework (TC2602024)

Domain 2 2.2 Human Unintentional Post-deployment

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