MIT AI Risk Repository
Browse AI risks
8 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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45.01.07 · Risk Sub-Category
Risks from data (Risks of illegal collection and use of data)
"The collection of AI training data and the interaction with users during service provision pose security risks, including collecting data without consent and improper use of data and personal information."
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"In AI research, development, and applications, issues such as improper data processing, unauthorized access, malicious attacks, and deceptive interactions can lead to data and personal information leaks."
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45.02.03 · Risk Sub-Category
Safety risks in AI Applications
Cyberspace risks (Risks of information leakage due to improper usage)
"Staff of government agencies and enterprises, if failing to use the AI service in a regulated and proper manner, may input internal data and industrial information into the AI model, leading to the leakage of work secrets, business secrets, and other sensitive business data."
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45.01.04 · Risk Sub-Category
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."
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45.01.06 · Risk Sub-Category
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."
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45.01.11 · Risk Sub-Category
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."
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45.01.12 · Risk Sub-Category
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."
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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."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.