{"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":"64.01.00","quick_ref":"Marchal2024","paper_title":"Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data","level":"Risk Category","risk_category":"Misuse tactics that exploit GenAI capabilities (Realistic depiction of human likeness) ","risk_subcategory":null,"description":"-","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.01.01","quick_ref":"Marchal2024","paper_title":"Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data","level":"Risk Sub-Category","risk_category":"Misuse tactics that exploit GenAI capabilities (Realistic depiction of human likeness) ","risk_subcategory":"Impersonation ","description":"\"Assume the identity of a real person and take actions on their behalf\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.01.02","quick_ref":"Marchal2024","paper_title":"Generative AI Misuse: A 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that exploit GenAI capabilities (Realistic depictions of non-humans) ","risk_subcategory":"Counterfeit ","description":"\"Reproduce or imitate an original work, brand or style and pass as real\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.03.00","quick_ref":"Marchal2024","paper_title":"Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data","level":"Risk Category","risk_category":"Misuse tactics that exploit GenAI capabilities (Use of generated content) ","risk_subcategory":null,"description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.03.02","quick_ref":"Marchal2024","paper_title":"Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data","level":"Risk Sub-Category","risk_category":"Misuse tactics that exploit GenAI capabilities (Use of generated content) ","risk_subcategory":"Targeting & Personalisation ","description":"\"Refine outputs to target individuals with tailored attacks\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.04.04","quick_ref":"Marchal2024","paper_title":"Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data","level":"Risk Sub-Category","risk_category":"Misuse tactics to compromise GenAI systems (Model integrity) ","risk_subcategory":"Model diversion ","description":"\"Model Diversion takes model manipulation one step further, by repurposing (often open-source) generative AI models in a way that diverts them from their intended functionality or from the use cases envisioned by their developers (Lin et al., 2024). 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