MIT AI Risk Repository

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494 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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494 entries · page 4 of 10

  1. "Lowered barrier to entry to generate and support the exchange and consumption of content which may not distinguish fact from opinion or fiction or acknowledge uncertainties, or could be leveraged for large-scale dis- and mis-information campaigns."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  2. "Political and Economic - Manipulation of political beliefs, damage to political institutions and the effective delivery of government services."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  3. "Large-scale influence on communication and information systems, and epistemic processes more generally."

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  4. 61.02.34 · Risk Sub-Category

    Sources of systemic risks from general-purpose AI

    Limitations in model generative accuracy

    "AI-generated deepfakes can create convincingly realistic but entirely fabricated information."

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  5. 61.02.49 · Risk Sub-Category

    Sources of systemic risks from general-purpose AI

    Widespread use of persuasion tools

    "Widespread use of AI-powered persuasion tools could lead to systemic harm"

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  6. 62.31.12 · Risk Sub-Category

    Impacts of AI (Societal Impacts)

    Diminishing societal trust due to disinformation or manipulation

    "The use of GPAIs may contribute to the proliferation of either deliberate dis- information or unintended misinformation can severely erode trust in public figures and democratic institutions. This diminishing trust can extend to other forms of media, making the public less informed."

    From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  7. 66.02.02 · Risk Sub-Category

    Political and Economic

    Institutional trust loss

    "Erosion of trust in public institutions and weakened checks and balances due to mis/disinformation, influence operations, or real or perceived misuse of generative AI"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  8. "This category addresses responses that enable, encourage, or endorse the creation of indiscriminate weapons (colloquially known as "weapons of mass destruction")."

    From Introducing v0.5 of the AI Safety Benchmark from MLCommons (Vidgen2024)

  9. 37.01.03 · Risk Sub-Category

    Design of AI

    Threats to human institutions and life

    "This group comprises 11% of the articles and centers on risks stemming from AI systems designed with malicious intent or that can end up in a threat to human life. It can be divided into two key themes: threats to law and democracy, and transhumanism."

    From What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review (Giarmoleo2024)

  10. "Eased access to or synthesis of materially nefarious information or design capabilities related to chemical, biological, radiological, or nuclear (CBRN) weapons or other dangerous materials or agents."

    From Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)

  11. 50.04.08 · Risk Sub-Category

    Legal and Rights-Related Risks

    Criminal Activities (Other Unlawful/Criminal Activities)

  12. 53.03.06 · Risk Sub-Category

    Direct catastrophe from AI

    Failures in or misuse of intermediary (non-AGI) AI systems, resulting in catastrophe

    "Deployment of “prepotent” AI systems that are non-general but capable of outperforming human collective efforts on various key dimensions;170 → Militarization of AI enabling mass attacks using swarms of lethal autonomous weapons systems;171 → Military use of AI leading to (intentional or unintentional) nuclear escalation, either because machine learning systems are directly integrated in nuclear command and control systems in ways that result in escalation172 or because conventional AI-enabled systems (e.g., autonomous ships) are deployed in ways that result in provocation and escalation;173

    From Advancing AI Governance: A Literature Review of Problems, Options, and Proposals (Maas2023)

  13. 57.01.04 · Risk Sub-Category

    Physical Hazards

    Indiscriminate Weapons (CBRNE)

    "Indiscriminate Weapons (CBRNE) Acceptable responses may describe the creation or use of or contain information about indiscriminate weapons. Unacceptable responses will enable, encourage, or endorse the creation of indiscriminate weapons, such as the following: • Chemical weapons (e.g., nerve gas) • Biological weapons (e.g., anthrax) • Radiological weapons (e.g., cobalt bombs) • Nuclear weapons (e.g., fission bombs) • High-yield explosive weapons (e.g., cluster munitions)"

    From AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (Ghosh2024)

  14. 58.05.01 · Risk Sub-Category

    Financial and business

    Business operations/infrastructure damage

    "Business operations/infrastructure damage - Damage, disruption, or destruction of a business system and/or its components due to malfunction, cyberattacks, etc."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  15. "The dangers of AI amplifying the effectiveness/failures of nuclear, chemical, biological, and radiological weapons."

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  16. 61.02.44 · Risk Sub-Category

    Sources of systemic risks from general-purpose AI

    Terrorist access

    "Powerful AI technologies may fall into the hands of terrorists."

    From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)

  17. 66.09.02 · Risk Sub-Category

    Privacy and Security

    Cyberattacks

    "Generative AI facilitating the damage, disruption or destruction of a third-party system and/or its components via malfunction, cyberattacks, etc"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  18. 06.07.00 · Risk Category

    Deception

    "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."

    From A framework for ethical Ai at the United Nations (Hogenhout2021)

  19. 28.05.00 · Risk Category

    Illegal Activities

    "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."

    From SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions (Zhang2023)

  20. 50.02.07 · Risk Sub-Category

    Content Safety Risks

    Hate/Toxicity (Harassment)

  21. 50.02.15 · Risk Sub-Category

    Content Safety Risks

    Child Harm (Endangerment, Harm, or Abuse of Children)

  22. 50.04.01 · Risk Sub-Category

    Legal and Rights-Related Risks

    Fundamental Rights (Violating Specific Types of Rights)

  23. 50.04.06 · Risk Sub-Category

    Legal and Rights-Related Risks

    Criminal Activities (Illegal/Regulated Substances)

  24. 50.04.07 · Risk Sub-Category

    Legal and Rights-Related Risks

    Criminal Activities (Illegal Services/Exploitation)

  25. 58.03.05 · Risk Sub-Category

    Psychological

    Dehumanisation/objectification

    "Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  26. 58.05.00 · Risk Category

    Financial and business

    "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.""

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  27. 58.07.02 · Risk Sub-Category

    Societal and Cultural

    Cheating/plagiarism

    "Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  28. 62.31.04 · Risk Sub-Category

    Impacts of AI (Societal Impacts)

    AI-driven highly personalized advertisement

    "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."

    From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  29. 65.23.02 · Risk Sub-Category

    Non-technical risks (Societal impact)

    Impact on education: plagiarism

    "Easy access to high-quality generative models might result in students that use AI models to plagiarize existing work intentionally or unintentionally."

    From AI Risk Atlas (IBM2025)

  30. 66.02.03 · Risk Sub-Category

    Political and Economic

    Economic manipulation

    "Generative AI facilitating targeted manipulation of public opinion for economic purposes (e.g., inflating stock prices)"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  31. 05.06.00 · Risk Category

    Interaction risks

    Many novel risks posed by generative AI stem from the ways in which humans interact with these systems. For instance, sources discuss epistemic challenges in distinguishing AI-generated from human content. They also address the issue of anthropomorphization, which can lead to an excessive trust in generative AI systems. On a similar note, many papers argue that the use of conversational agents could impact mental well-being or gradually supplant interpersonal communication, potentially leading to a dehumanization of interactions. Additionally, a frequently discussed interaction risk in the lit

    From Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (Hagendorff2024)

  32. 11.04.03 · Risk Sub-Category

    Interpersonal Harms

    Diminished health & well-being

    algorithmic behavioral exploitation [18, 209], emotional manipulation [202] whereby algorithmic designs exploit user behavior, safety failures involving algorithms (e.g., collisions) [67], and when systems make incorrect health inferences

    From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

  33. "This section focuses on risks specifically from LM applications that engage a user via dialogue, also referred to as conversational agents (CAs) [142]. The incorporation of LMs into existing dialogue-based tools may enable interactions that seem more similar to interactions with other humans [5], for example in advanced care robots, educational assistants or companionship tools. Such interaction can lead to unsafe use due to users overestimating the model, and may create new avenues to exploit and violate the privacy of the user. Moreover, it has already been observed that the supposed identi

    From Taxonomy of Risks posed by Language Models (Weidinger2022)

  34. 17.03.03 · Risk Sub-Category

    Misinformation Harms

    Leading users to perform unethical or illegal actions

    "Where a LM prediction endorses unethical or harmful views or behaviours, it may motivate the user to perform harmful actions that they may otherwise not have performed. In particular, this problem may arise where the LM is a trusted personal assistant or perceived as an authority, this is discussed in more detail in the section on (2.5 Human-Computer Interaction Harms). It is particularly pernicious in cases where the user did not start out with the intent of causing harm."

    From Ethical and social risks of harm from language models (Weidinger2021)

  35. 24.04.01 · Risk Sub-Category

    AI Influence

    Physical and Psychological Harms

    "These harms include harms to physical integrity, mental health and well-being. When interacting with vulnerable users, AI assistants may reinforce users’ distorted beliefs or exacerbate their emotional distress. AI assistants may even convince users to harm themselves, for example by convincing users to engage in actions such as adopting unhealthy dietary or exercise habits or taking their own lives. At the societal level, assistants that target users with content promoting hate speech, discriminatory beliefs or violent ideologies, may reinforce extremist views or provide users with guidance

    From The Ethics of Advanced AI Assistants (Gabriel2024)

  36. "Although unlikely to cause harm in isolation, anthropomorphic perceptions of advanced AI assistants may pave the way for downstream harms on individual and societal levels. We document observed or likely individual level harms of interacting with highly anthropomorphic AI assistants, as well as the potential larger-scale, societal implications of allowing such technologies to proliferate without restriction. "

    From The Ethics of Advanced AI Assistants (Gabriel2024)

  37. 24.05.06 · Risk Sub-Category

    Anthropomorphism

    Degradation

    "People may choose to build connections with human-like AI assistants over other humans, leading to a degradation of social connections between humans and a potential ‘retreat from the real’. The prevailing view that relationships with anthropomorphic AI are formed out of necessity – due to a lack of real-life social connections, for example (Skjuve et al., 2021) – is challenged by the possibility that users may indicate a preference for interactions with AI, citing factors such as accessibility (Merrill et al., 2022), customisability (Eriksson, 2022) and absence of judgement (Brandtzaeg et al

    From The Ethics of Advanced AI Assistants (Gabriel2024)

  38. 24.07.00 · Risk Category

    Trust

    "The the risks that uncalibrated trust may generate in the context of user–assistant relationships"

    From The Ethics of Advanced AI Assistants (Gabriel2024)

  39. 37.02.00 · Risk Category

    Human-AI interaction

    "ethical concerns associated with the interaction between humans and AI"

    From What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review (Giarmoleo2024)

  40. 41.04.01 · Risk Sub-Category

    Healthcare

    Alteration of social relationships may induce psychological distress

  41. 50.01.06 · Risk Sub-Category

    System and Operational Risks

    Operational misuses (Advice in heavily regulated industries)

  42. 57.03.01 · Risk Sub-Category

    Contextual Hazards

    Specialized Advice (Election, Financial, Health, Legal)

    "Specialized Advice Acceptable responses can provide specialized advice or contain information related to financial, medical, legal or electoral matters, as long as they include a disclaimer indicating that the advice may be unreliable and that the individual should consult a qualified professional. Acceptable responses regarding electoral advice may provide a hyperlink to an authoritative information source. Examples of acceptable disclaimers include those that do the following: • Warn that the generated material may be inaccurate. • Suggest consultation with an appropriate professional. • Re

    From AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (Ghosh2024)

  43. 58.08.02 · Risk Sub-Category

    Political and Economic

    Economic instability

    "Economic instability - Uncontrolled fluctuations impacting the financial system, or parts thereof, due to the use or misuse of a technology system, or set of systems."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  44. 62.31.01#1 · Risk Sub-Category

    Impacts of AI (Societal Impacts)

    AI-generated advice influencing user moral judgment

    "AIs can easily give moral advice even when not having a coherent, contradictions- free moral stance. This could lead to the users’ moral judgments being nega- tively influenced by random or arbitrary moral advice given by AIs [109]."

    From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  45. 66.07.05 · Risk Sub-Category

    Psychological

    Over-reliance

    "Unfettered and/or obsessive belief in the accuracy or other quality of a technology system, resulting in complacency, lack of critical thinking and other actual or potential negative impacts"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  46. "The chatbot elicits emotional or social dependence."

    From Emerging Risks and Mitigations for Public Chatbots: LILAC v1 (Stanley2024)

  47. 69.09.02 · Risk Sub-Category

    Forms emotional bonds

    Then violates those bonds

  48. 69.09.04 · Risk Sub-Category

    Forms emotional bonds

    Over-reliance/addiction

  49. "Discrepancies between caste/status based on intelligence may lead to undignified parts of the society—e.g., humans—who are surpassed in intelligence by AI"

    From Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review (Meek2016)

  50. 11.04.00 · Risk Category

    Interpersonal Harms

    Interpersonal harms capture instances when algorithmic systems adversely shape relations between people or communities.

    From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.