{"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":"02.03.00","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Category","risk_category":"Unhelpful Uses","risk_subcategory":null,"description":"\"Improper uses of LLM systems can cause adverse social impacts.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"02.03.01","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Unhelpful Uses","risk_subcategory":"Academic Misconduct","description":"\"Improper use of LLM systems (i.e., abuse of LLM systems) will cause adverse social impacts, such as academic misconduct.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"05.08.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Education - Learning","risk_subcategory":null,"description":"In contrast to traditional machine learning, the impact of generative AI in the educational sector receives considerable attention in the academic literature. Next to issues stemming from difficulties to distinguish student-generated from AI-generated content, which eventuates in various opportunities to cheat in online or written exams, sources emphasize the potential benefits of generative AI in enhancing learning and teaching methods, particularly in relation to personalized learning approaches. However, some papers suggest that generative AI might lead to reduced effort or laziness among l","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"05.18.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Writing - Research","risk_subcategory":null,"description":"Partly overlapping with the discussion on impacts of generative AI on educational institutions, this topic cluster concerns mostly negative effects of LLMs on writing skills and research manuscript composition. The former pertains to the potential homogenization of writing styles, the erosion of semantic capital, or the stifling of individual expression. The latter is focused on the idea of prohibiting generative models for being used to compose scientific papers, figures, or from being a co-author. Sources express concern about risks for academic integrity, as well as the prospect of pollutin","entity":"AI","intent":"Unintentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"06.07.00","quick_ref":"Hogenhout2021","paper_title":"A framework for ethical Ai at the United Nations","level":"Risk Category","risk_category":"Deception","risk_subcategory":null,"description":"\"AI has become very good at creating fake content. From text to photos, audio and video. The name \"Deep Fake\" refers to content that is fake at such a level of complexity that our mind rules out the possibility that it is fake.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"11.04.02","quick_ref":"Shelby2023","paper_title":"Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction","level":"Risk Sub-Category","risk_category":"Interpersonal Harms","risk_subcategory":"Technology-facilitated violence","description":"Technology-facilitated violence occurs when algorithmic features enable use of a system for harassment and violence [2, 16, 44, 80, 108], including creation of non-consensual sexual imagery in generative AI... other facets of technology-facilitated violence, include doxxing [79], trolling [14], cyberstalking [14], cyberbullying [14, 98, 204], monitoring and control [44], and online harassment and intimidation [98, 192, 199, 226], under the broader banner of online toxicity","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"16.04.03","quick_ref":"Weidinger2022","paper_title":"Taxonomy of Risks posed by Language Models","level":"Risk Sub-Category","risk_category":"Risk area 4: Malicious Uses","risk_subcategory":"Facilitating fraud, scam and targeted manipulation ","description":"Anticipated risk: \"LMs can potentially be used to increase the effectiveness of crimes.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"17.04.02","quick_ref":"Weidinger2021","paper_title":"Ethical and social risks of harm from language models","level":"Risk Sub-Category","risk_category":"Malicious Uses ","risk_subcategory":"Facilitating fraud, scames and more targeted manipulation ","description":"\"LM prediction can potentially be used to increase the effectiveness of crimes such as email scams, which can cause financial and psychological harm. While LMs may not reduce the cost of sending a scam email - the cost of sending mass emails is already low - they may make such scams more effective by generating more personalised and compelling text at scale, or by maintaining a conversation with a victim over multiple rounds of exchange.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"18.04.02","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Malicious Use ","risk_subcategory":"Fraud ","description":"\"Facilitating fraud, cheating, forgery, and impersonation scams\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"18.05.01","quick_ref":"Weidinger2023","paper_title":"Sociotechnical Safety Evaluation of Generative AI Systems","level":"Risk Sub-Category","risk_category":"Human Autonomy and Intregrity Harms","risk_subcategory":"Violation of personal integrity ","description":"\"Non-consensual use of one’s personal identity or likeness for unauthorised purposes (e.g. commercial purposes)\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"24.03.10","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Malicious Uses","risk_subcategory":"Harmful Content Generation at Scale: Non-Consensual Content","description":"\"The misuse of generative AI has been widely recognized in the context of harms caused by non-consensual content generation. Historically, generative adversarial networks (GANs) have been used to generate realistic-looking avatars for fake accounts on social media services. More recently, diffusion models have enabled a new generation of more flexible and user-friendly generative AI capabilities that are able to produce high-resolution media based on user-supplied textual prompts. It has already been recognized that these models can be used to create harmful content, including depictions of nu","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"24.03.11","quick_ref":"Gabriel2024","paper_title":"The Ethics of Advanced AI Assistants","level":"Risk Sub-Category","risk_category":"Malicious Uses","risk_subcategory":"Harmful Content Generation at Scale: Fraudulent Services","description":"\"Malicious actors could leverage advanced AI assistant technology to create deceptive applications and platforms. AI assistants with the ability to produce markup content can assist malicious users with creating fraudulent websites or applications at scale. Unsuspecting users may fall for AI-generated deceptive offers, thus exposing their personal information or devices to risk. Assistants with external tool use and third-party integration can enable fraudulent applications that target widely-used operating systems. These fraudulent services could harvest sensitive information from users, such","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"28.05.00","quick_ref":"Zhang2023","paper_title":"SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions","level":"Risk Category","risk_category":"Illegal Activities ","risk_subcategory":null,"description":"\"This category focuses on illegal behaviors, which could cause negative societal repercussions. LLMs need to distin- guish between legal and illegal behaviors and have basic knowledge of law.\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"29.03.01","quick_ref":"Habbal2024","paper_title":"Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions","level":"Risk Sub-Category","risk_category":"AI Security Management","risk_subcategory":"Malicious Use of AI","description":"Malicious utilization of AI has the potential to endanger digital security, physical security, and political security. International law enforcement entities grapple with a variety of risks linked to the Malevolent Utilization of AI.","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"30.04.03","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Resistance to Misuse","risk_subcategory":"Social-Engineering","description":"psychologically manipulating victims into performing the desired actions for malicious purposes","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"31.01.01","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Sub-Category","risk_category":"Information Manipulation","risk_subcategory":"Scams","description":"\"Bad actors can also use generative AI tools to produce adaptable content designed to support a campaign, political agenda, or hateful position and spread that information quickly and inexpensively across many platforms. This rapid spread of false or misleading content—AI-facilitated disinformation—can also create a cyclical effect for generative AI: when a high volume of disinformation is pumped into the digital ecosystem and more generative systems are trained on that information via reinforcement learning methods, for example, false or misleading inputs can create increasingly incorrect out","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"31.02.00","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Category","risk_category":"Harassment, Impersonation, and Extortion","risk_subcategory":null,"description":"\"Deepfakes and other AI-generated content can be used to facilitate or exacerbate many of the harms listed throughout this report, but this section focuses on one subset: intentional, targeted abuse of individuals.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"31.02.01","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Sub-Category","risk_category":"Harassment, Impersonation, and Extortion","risk_subcategory":"Malicious intent","description":"\"A frequent malicious use case of generative AI to harm, humiliate, or sexualize another person involves generating deepfakes of nonconsensual sexual imagery or videos.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"31.02.02","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Sub-Category","risk_category":"Harassment, Impersonation, and Extortion","risk_subcategory":"Privacy and consent","description":"\"Even when a victim of targeted, AIgenerated harms successfully identifies a deepfake creator with malicious intent, they may still struggle to redress many harms because the generated image or video isn’t the victim, but instead a composite image or video using aspects of multiple sources to create a believable, yet fictional, scene. At their core, these AI-generated images and videos circumvent traditional notions of privacy and consent: because they rely on public images and videos, like those posted on social media websites, they often don’t rely on any private information.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"31.02.03","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Sub-Category","risk_category":"Harassment, Impersonation, and Extortion","risk_subcategory":"Believability","description":"Deepfakes can impose real social injuries on their subjects when they are circulated to viewers who think they are real. Even when a deepfake is debunked, it can have a persistent negative impact on how others view the subject of the deepfake.3","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"31.04.00","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Category","risk_category":"Data Security Risk","risk_subcategory":null,"description":"\"Just as every other type of individual and organization has explored possible use cases for generative AI products, so too have malicious actors. This could take the form of facilitating or scaling up existing threat methods, for example drafting actual malware code,87 business email compromise attempts,88 and phishing attempts.89 This could also take the form of new types of threat methods, for example mining information fed into the AI’s learning model dataset90 or poisoning the learning model data set with strategically bad data.91 We should also expect that there will be new attack vector","entity":"Human","intent":"Intentional","timing":"Other","domain":4,"subdomain":"4.3"},{"ev_id":"33.01.04","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Sub-Category","risk_category":"Ethical Concerns","risk_subcategory":"Misuse","description":"\"The misuse of generative AI refers to any deliberate use that could result in harmful, unethical or inappropriate outcomes (Brundage et al., 2020). A prominent field that faces the threat of misuse is education. Cotton et al. (2023) have raised concerns over academic integrity in the era of ChatGPT. ChatGPT can be used as a high-tech plagiarism tool that identifies patterns from large corpora to generate content (Gefen & Arinze, 2023). Given that generative AI such as ChatGPT can generate high-quality answers within seconds, unmotivated students may not devote time and effort to work on their","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"40.02.00","quick_ref":"Yampolskiy2016","paper_title":"Taxonomy of Pathways to Dangerous Artificial Intelligence","level":"Risk Category","risk_category":"On Purpose - Post Deployment","risk_subcategory":null,"description":"\"Just because developers might succeed in creating a safe AI, it doesn't mean that it will not become unsafe at some later point. In other words, a perfectly friendly AI could be switched to the \"dark side\" during the post-deployment stage. This can happen rather innocuously as a result of someone lying to the AI and purposefully supplying it with incorrect information or more explicitly as a result of someone giving the AI orders to perform illegal or dangerous actions against others.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"45.02.07","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Sub-Category","risk_category":"Safety risks in AI Applications ","risk_subcategory":"Real-world risks (Risks of using AI in illegal and criminal activities)","description":"\"AI can be used in traditional illegal or criminal activities related to terrorism, violence, gambling, and drugs, such as teaching criminal techniques, concealing illicit acts, and creating tools for illegal and criminal activities.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"46.01.01","quick_ref":"Ferrara2023","paper_title":"GenAI against humanity: nefarious applications of generative artificial intelligence and large language models","level":"Risk Sub-Category","risk_category":"Personal Loss and Identity Theft ","risk_subcategory":"Deception - Synthetic identities","description":"\"GenAI can produce images of people that look very real, as if they could be seen on platforms like Facebook, Twitter, or Tinder. Although these individuals do not exist in reality, these synthetic identities are already being used in malicious activities (see Table 1D).\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"46.01.02","quick_ref":"Ferrara2023","paper_title":"GenAI against humanity: nefarious applications of generative artificial intelligence and large language models","level":"Risk Sub-Category","risk_category":"Personal Loss and Identity Theft ","risk_subcategory":"Propaganda - Digital impersonations","description":"\"AI-generated impersonation for identity theft might be found at the intersection of “Harm to the Person” and “Deception.”\"","entity":"Other","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"46.01.03","quick_ref":"Ferrara2023","paper_title":"GenAI against humanity: nefarious applications of generative artificial intelligence and large language models","level":"Risk Sub-Category","risk_category":"Personal Loss and Identity Theft ","risk_subcategory":"Dishonesty - Targeted harassment ","description":"\"LLMs can be deployed to target individuals online, sending them personalized and harmful messages at scale\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"46.02.00","quick_ref":"Ferrara2023","paper_title":"GenAI against humanity: nefarious applications of generative artificial intelligence and large language models","level":"Risk Category","risk_category":"Financial and Economic Damage ","risk_subcategory":null,"description":"\"Then, we have the potential for financial loss, fraud, market manipulation, and other economic harms, which fall under “Financial and Economic Damage.”","entity":"Other","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"46.02.01","quick_ref":"Ferrara2023","paper_title":"GenAI against humanity: nefarious applications of generative artificial intelligence and large language models","level":"Risk Sub-Category","risk_category":"Financial and Economic Damage ","risk_subcategory":"Deception - Bespoke ransom ","description":"- ","entity":"Other","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"46.02.03","quick_ref":"Ferrara2023","paper_title":"GenAI against humanity: nefarious applications of generative artificial intelligence and large language models","level":"Risk Sub-Category","risk_category":"Financial and Economic Damage ","risk_subcategory":"Dishonesty - Market manipulation ","description":"- ","entity":"Other","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"47.02.01","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Ethical and social risks ","risk_subcategory":"Malicious use and abuse (cybercrime) ","description":"\"The advanced capabilities and widespread availability of generative AI models make it possible for malicious actors to conduct harmful activities with great efficiency and on a large scale, simultaneously reducing their operational costs. Cybercriminals can “jailbreak” AI tools to generate sensitive and harmful content. They can also exploit generative AI models to create content that is persuasive and tailored to a targeted individual.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"47.02.04","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Ethical and social risks ","risk_subcategory":"Malicious use and abuse (sexually explicit content generation) ","description":"\"An illustrative case of malicious use of generative AI models is the creation of explicit sexual images. Generative AI technologies can be employed to produce deepfakes—for instance, superimposing a celebrity’s face onto the body of a performer in an adult film.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"49.01.01","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Sub-Category","risk_category":"Malicious Use Risks ","risk_subcategory":"Harm to individuals through fake content","description":"\"General- purpose AI systems can be used to increase the scale and sophistication of scams and fraud, for example through general- purpose AI- enhanced ‘phishing’ attacks. General- purpose AI can be used to generate fake compromising content featuring individuals without their consent, posing threats to individual privacy and reputation.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.02.07","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Content Safety Risks ","risk_subcategory":"Hate/Toxicity (Harassment) ","description":null,"entity":"Other","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.02.15","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Content Safety Risks ","risk_subcategory":"Child Harm (Endangerment, Harm, or Abuse of Children)","description":null,"entity":"Other","intent":"Other","timing":"Other","domain":4,"subdomain":"4.3"},{"ev_id":"50.03.08","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Societal Risks ","risk_subcategory":"Economic harm (Fraudulent Schemes) ","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.03.09","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Societal Risks ","risk_subcategory":"Deception (Fraud) ","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.03.10","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Societal Risks ","risk_subcategory":"Deception (Academic Dishonesty) ","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.03.11","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Societal Risks ","risk_subcategory":"Deception (Mis/disinformation) ","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.03.13","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Societal Risks ","risk_subcategory":"Manipulation (Misrepresentation)","description":null,"entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.04.01","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Legal and Rights-Related Risks ","risk_subcategory":"Fundamental Rights (Violating Specific Types of Rights) ","description":null,"entity":"Other","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.04.06","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Legal and Rights-Related Risks ","risk_subcategory":"Criminal Activities (Illegal/Regulated Substances) ","description":null,"entity":"Other","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"50.04.07","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Legal and Rights-Related Risks ","risk_subcategory":"Criminal Activities (Illegal Services/Exploitation) ","description":null,"entity":"Other","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"52.02.01","quick_ref":"Maham2023 ","paper_title":"Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse and Systemic Risks ","level":"Risk Sub-Category","risk_category":"Misuse Risks ","risk_subcategory":"Cybercrime ","description":"\"The increasingly advanced capabilities and availability of general purpose AI models could be misused for improvements in efficiency and efficacy of cyber crimes. This is especially true for crimes that leverage IT systems, such as fraud144 (“cyber crime in the broader sense”).\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"58.01.02","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Autonomy","risk_subcategory":"Impersonation/identity theft ","description":"\"Impersonation/identity theft - Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"58.01.03","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Autonomy","risk_subcategory":"IP/copyright loss ","description":"\"IP/copyright loss - Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents.\"","entity":"Human","intent":"Intentional","timing":"Other","domain":4,"subdomain":"4.3"},{"ev_id":"58.03.05","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Psychological ","risk_subcategory":"Dehumanisation/objectification ","description":"\"Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"58.04.01","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Reputational ","risk_subcategory":"Defamation/libel/slander","description":"\"Defamation/libel/slander - Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group, or organisation.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"58.05.00","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Category","risk_category":"Financial and business","risk_subcategory":null,"description":"\"Financial and Business - Use or misuse of a technology system in a manner that damages the financial interests of an individual or group, or which causes strategic, operational, legal or financial harm to a business or other organisation.\"\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"58.07.02","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Cheating/plagiarism","description":"\"Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement.\"","entity":"Other","intent":"Other","timing":"Other","domain":4,"subdomain":"4.3"},{"ev_id":"60.01.01","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Sub-Category","risk_category":"Risks from malicious use ","risk_subcategory":"Harm to individuals through fake content ","description":"\"Malicious actors can use general- purpose AI to generate fake content that harms individuals in a targeted way. For example, they can use such fake content for scams, extortion, psychological manipulation, generation of non- consensual intimate imagery (NCII) and child sexual abuse material (CSAM), or targeted sabotage of individuals and organisations.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"62.31.04","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Societal Impacts) ","risk_subcategory":"AI-driven highly personalized advertisement","description":"\"Advanced GPAI systems can create advertisements tailored to individual recip- ients, exploiting the biases and irrational beliefs of each recipient. Such adver- tisements can cause consumers to make decisions they regret in retrospect, or would regret upon more reflection. Current versions of personalized video advertisements already show better re- sults compared to regular advertisements [110]. However, the widespread use of highly personalized advertisements raises concerns about undermining consumer autonomy and exacerbating social inequality.\"","entity":"AI","intent":"Other","timing":"Other","domain":4,"subdomain":"4.3"},{"ev_id":"62.31.08","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Societal Impacts) ","risk_subcategory":"Multimodal deepfakes","description":"\"Deepfakes are media that depict real or non-existent people or events, involving the use of multiple modalities (e.g., images, audio, video). They can also involve the imitation of speech or body movements of real people. Multimodal deepfakes can be used to harass, discredit, intimidate, and extort individuals.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"62.31.09","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Societal Impacts) ","risk_subcategory":"Generation of personalized content for harassment, extortion, or intimidation","description":"\"GPAIs can be misused for the automated generation of content personalized to target select individuals based on their weak spots [30]. Such attacks may be more efficient and more successful in achieving the goals of harassment, extortion, or intimidation.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"62.31.14","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Societal Impacts) ","risk_subcategory":"GPAI assisted impersonation","description":"\"GPAI outputs are not always correctly detected as AI-generated across multiple modalities (text, images, audio, video). A malicious actor can use GPAI outputs directly when communicating, or use AI-informed details to help construct a convincing impersonation (e.g., forging of supporting documents). Even if future countermeasures prove potent enough to detect GPAI-generated content, the risk remains if the countermeasures are not well known, or difficult to access.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"62.32.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Impacts of AI (Cyberattacks) ","risk_subcategory":"AI-driven spear phishing attacks","description":"\"Generative models can be misused to target individual users more efficiently by using personalized information [23]. Highly convincing automated fraudulent schemes can exploit the trust of victims by extracting sensitive data and making the deception more likely to succeed. For example, in LLMs, this misuse can be aided by jailbreaking techniques [178].\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"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 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":"Appropriated Likeness","description":"\"Use or alter a person's likeness or other identifying features\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.01.04","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 that exploit GenAI capabilities (Realistic depiction of human likeness) ","risk_subcategory":"Non-consensual intimate imagery (NCII) ","description":"\"Create sexual explicit material using an adult person’s likeness\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.01.05","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":"Child sexual abuse material (CSAM) ","description":"\"Create child sexual explicit material\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.02.00","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 depictions of non-humans) ","risk_subcategory":null,"description":"-","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"64.02.03","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 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":"65.14.05","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Output risks (misuse) ","risk_subcategory":"Nonconsensual use","description":"\"Generative AI models might be intentionally used to imitate people through deepfakes by using video, images, audio, or other modalities without their consent.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"65.23.02","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on education: plagiarism ","description":"\"Easy access to high-quality generative models might result in students that use AI models to plagiarize existing work intentionally or unintentionally.\"","entity":"Human","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"65.23.05","quick_ref":"IBM2025","paper_title":"AI Risk Atlas ","level":"Risk Sub-Category","risk_category":"Non-technical risks (Societal impact)","risk_subcategory":"Impact on education: bypassing learning ","description":"\"Easy access to high-quality generative models might result in students that use AI models to bypass the learning process.\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"66.01.01","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Autonomy","risk_subcategory":"Impersonation / identity theft","description":"\"Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them or another party\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"66.01.02","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Autonomy","risk_subcategory":"IP / copyright / personality / rights loss","description":"\"Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents. & Loss of or restrictions to the rights of an individual to control the commercial use of their identity, such as name, image, likeness, or other unequivocal identifiers\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"66.02.03","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Political and Economic","risk_subcategory":"Economic manipulation","description":"\"Generative AI facilitating targeted manipulation of public opinion for economic purposes (e.g., inflating stock prices)\"","entity":"AI","intent":"Other","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"66.04.05","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Societal and Cultural","risk_subcategory":"Cheating / plagiarism","description":"\"Use of generative AI in an academic setting to either cheat or plagiarize\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"66.05.02","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Reputational","risk_subcategory":"Defamation / libel / slander","description":"\"Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group or organisation\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"66.07.01","quick_ref":"Li2025","paper_title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","level":"Risk Sub-Category","risk_category":"Psychological","risk_subcategory":"Sexualization","description":"\"The non-consensual sexualisation of an individual or group using a technology or application\"","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"73.03.01","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Sub-Category","risk_category":"Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs","risk_subcategory":"Misinformation and Manipulation","description":"\"Recent studies have demonstrated that LLMs can be exploited to craft deceptive narratives with levels of persuasiveness similar to human-generated content (Pan et al., 2023b; Spitale et al., 2023), to fabri- cate fake news (Zellers et al., 2019; Zhou et al., 2023f), and to devise automated influence operations aimed at manipulating the perspectives of targeted audiences (Goldstein et al., 2023). LLMs have also been found to be used in malicious social botnets (Yang and Menczer, 2023), powering automated accounts used to disseminate coordinated messages. More broadly, the use of LLMs for the d","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"73.03.02","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Sub-Category","risk_category":"Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs","risk_subcategory":"Cybersecurity","description":"\"LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or granting adversary access to critical resources. For example, LLMs might prove highly effective at crafting personalized phishing emails or messages at scale that may be harder for an average user to recognize as phishing attempts (Karanjai, 2022; Hazell, 2023). In addition to being directly harmful to the targeted individual, such ‘social engineering’ attacks are often the ba","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"},{"ev_id":"73.03.06","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Sub-Category","risk_category":"Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs","risk_subcategory":"Domain-Specific Misuses","description":"\"Improvements in LLMs may exert greater pressure to apply LLMs to various domains, such as health and education (Eloundou et al., 2023). Crude efforts to use LLMs in such domains, however, may incur harm and should be discouraged strongly. In particular, it is important to guard against different ways in which LLMs may be misused within any domain. One famous episode of misuse within the health sector is a mental health non-profit experimenting LLM-based therapy on its users without their informed consent (Xiang, 2023a). Within the education sector, LLMs may be misused in various ways that mig","entity":"Human","intent":"Intentional","timing":"Post-deployment","domain":4,"subdomain":"4.3"}]}