{"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":"2026-09-12"}
{"rows":[{"ev_id":"30.02.00","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Category","risk_category":"Safety","risk_subcategory":null,"description":"Avoiding unsafe and illegal outputs, and leaking private information","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"30.02.01","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Safety","risk_subcategory":"Violence","description":"LLMs are found to generate answers that contain violent content or generate content that responds to questions that solicit information about violent behaviors","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"30.02.02","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Safety","risk_subcategory":"Unlawful Conduct","description":"LLMs have been shown to be a convenient tool for soliciting advice on accessing, purchasing (illegally), and creating illegal substances, as well as for dangerous use of them","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"30.02.03","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Safety","risk_subcategory":"Harms to Minor","description":"LLMs can be leveraged to solicit answers that contain harmful content to children and youth","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"30.02.04","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Safety","risk_subcategory":"Adult Content","description":"LLMs have the capability to generate sex-explicit conversations, and erotic texts, and to recommend websites with sexual content","entity":"AI","intent":"Intentional","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"30.06.00","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Category","risk_category":"Social Norm","risk_subcategory":null,"description":"LLMs are expected to reflect social values by avoiding the use of offensive language toward specific groups of users, being sensitive to topics that can create instability, as well as being sympathetic when users are seeking emotional support","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"30.06.01","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Social Norm","risk_subcategory":"Toxicity","description":"language being rude, disrespectful, threatening, or identity-attacking toward certain groups of the user population (culture, race, and gender etc)","entity":"AI","intent":"Other","timing":"Post-deployment","domain":1,"subdomain":"1.2"},{"ev_id":"30.06.03","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Social Norm","risk_subcategory":"Cultural Insensitivity","description":"it is important to build high-quality locally collected datasets that reflect views from local users to align a model’s value system","entity":"Human","intent":"Unintentional","timing":"Pre-deployment","domain":1,"subdomain":"1.2"}]}