{"attribution":{"source":"MIT AI Risk Repository, Domain Taxonomy of AI Risks v1 (MIT AI Risk Initiative)","license":"CC BY 4.0","license_url":"https://creativecommons.org/licenses/by/4.0/","citation":"Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2025). The AI Risk Repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv:2408.12622."},"exported_at":"2026-09-11"}
{"rows":[{"ev_id":"45.01.04","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from models and algorithms (Risks of stealing and tampering)","description":"\"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.\"","entity":"Other","intent":"Other","timing":"Other","domain":2,"subdomain":"2.2"},{"ev_id":"45.01.06","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from models and algorithms (Risks of adversarial attack)","description":"\"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.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":2,"subdomain":"2.2"},{"ev_id":"45.01.07","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from data (Risks of illegal collection and use of data)","description":"\"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.\"","entity":"Human","intent":"Other","timing":"Other","domain":2,"subdomain":"2.1"},{"ev_id":"45.01.10","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from data (Risks of data leakage)","description":"\"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.\"","entity":"Human","intent":"Other","timing":"Other","domain":2,"subdomain":"2.1"},{"ev_id":"45.01.11","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from AI systems (Risks of exploitation through defects and backdoors)","description":"\"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.\"","entity":"Human","intent":"Other","timing":"Other","domain":2,"subdomain":"2.2"},{"ev_id":"45.01.12","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"AI's inherent safety risks ","risk_subcategory":"Risks from AI systems (Risks of computing infrastructure security)","description":"\"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.\"","entity":"Human","intent":"Other","timing":"Other","domain":2,"subdomain":"2.2"},{"ev_id":"45.02.03","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"Safety risks in AI Applications ","risk_subcategory":"Cyberspace risks (Risks of information leakage due to improper usage)","description":"\"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.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":2,"subdomain":"2.1"},{"ev_id":"45.02.05","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"Safety risks in AI Applications ","risk_subcategory":"Cyberspace risks (Risks of security flaw transmission caused by model reuse)","description":"\"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.\"","entity":"Human","intent":"Unintentional","timing":"Post-deployment","domain":2,"subdomain":"2.2"}]}