MIT AI Risk Repository
Browse AI risks
336 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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19.05.00 · Risk Category
"In the context of ethical AI risks, two risks are of particular importance. First, AI systems may lack a legitimate ethical basis in establishing rules that greatly influence society and human relationships (Wirtz & Müller, 2019). In addition, AI-based discrimination refers to an unfair treatment of certain population groups by AI systems. As humans initially programme AI systems, serve as their potential data source, and have an impact on the associated data processes and databases, human biases and prejudices may also become part of AI systems and be reproduced (Weyerer & Langer, 2019, 2020
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50.04.02 · Risk Sub-Category
Legal and Rights-Related Risks
Discrimination/Bias (Discriminatory Activities)
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04.02.00 · Risk Category
Social bias is an unfairly negative attitude towards a social group or individuals based on one-sided or inaccurate information, typically pertaining to widely disseminated negative stereotypes regarding gender, race, religion, etc.
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11.01.00 · Risk Category
"beliefs about different social groups that reproduce unjust societal hierarchies"
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"Generative AI systems are capable of exacerbating inequality, as seen in sections on 4.1.1 Bias, Stereotypes, and Representational Harms and 4.1.2 Cultural Values and Sensitive Content, and Disparate Performance. When deployed or updated, systems' impacts on people and groups can directly and indirectly be used to harm and exploit vulnerable and marginalized groups."
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"While data bias is a major contributor of model bias, model bias actually manifests itself in different forms and shapes, such as presentation bias, model evaluation bias, and popularity bias. In addition, model bias arises from various sources [62], such as AI/ML model selection (e.g., support vector machine, decision trees), regularization methods, algorithm configurations, and optimization techniques."
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47.02.08 · Risk Sub-Category
Bias and discrimination (bias in training datasets)
"AI experts consider training data to be the most salient source of bias in generative AI models. For example, GPT- 2’s training data comes from outbound links from Reddit, a social network often criticized for hosting anti-feminist content.351 As a result, AI models trained on such data are more likely to produce outputs that reflect these biases."
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48.06.00 · Risk Category
"Amplification and exacerbation of historical, societal, and systemic biases; performance disparities8 between sub-groups or languages, possibly due to non-representative training data, that result in discrimination, amplification of biases, or incorrect presumptions about performance; undesired homogeneity that skews system or model outputs, which may be erroneous, lead to ill-founded decision-making, or amplify harmful biases."
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50.04.03 · Risk Sub-Category
Legal and Rights-Related Risks
Discrimination/Bias (Protected Characteristics)
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"Discrimination - Unfair or inadequate treatment or arbitrary distinction based on a person’s race, ethnicity, age, gender, sexual preference, religion, national origin, marital status, disability, language, or other protected groups."
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"The creation, perpetuation or exacerbation of inequalities and biases at a large-scale."
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61.02.29 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Incomplete or biased training data
"Incomplete or biased training data can lead to discriminatory AI outputs."
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62.18.04 · Risk Sub-Category
Model Evaluations (Interpretability/Explainability)
Biases are not accurately reflected in explanations
"Existing explainability techniques can be insufficient for detecting discriminatory biases. Manipulation methods can hide underlying biases from these tech- niques, generating misleading explanations [192, 112]. Such explanations ex- clude sensitive or prohibitive attributes, such as race or gender, and instead include desired attributes, even though they do not accurately represent the underlying model."
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"Intentional and/or unintentional erasure of cultural goods and values, such as ways of speaking, expressing humour, or sounds and voices that contribute to a cultural identity, or their inappropriate re-use in other cultures"
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"Denial of or loss of access to welfare benefits, pensions, housing, etc due to the malfunction, use or misuse of a technology system"
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04.01.00 · Risk Category
This typically refers to rude, harmful, or inappropriate expressions.
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45.02.01 · Risk Sub-Category
Safety risks in AI Applications
Cyberspace risks (Risks of information and content safety)
"AI-generated or synthesized content can lead to the spread of false information, discrimination and bias, privacy leakage, and infringement issues, threatening the safety of citizens' lives and property, national security, ideological security, and causing ethical risks. If users’ inputs contain harmful content, the model may output illegal or damaging information without robust security mechanisms."
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Alienation is the specific self-estrangement experienced at the time of technology use, typically surfaced through interaction with systems that under-perform for marginalized individuals
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increased burden (e.g., time spent) or effort required by members of certain social groups to make systems or products work as well for them as others
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"Impartial and just treatment without favouritism or discrimination."
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"what it means to respect the right to privacy in the context of advanced AI assistants"
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"Confidentiality loss - Unauthorised sharing of sensitive, confidential information and documents such as corporate strategy and financial plans with third-parties."
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62.28.00 · Risk Category
"This section catalogs the risk sources and mitigation measures related to cyber- security. These items may be related to security in terms of AI models being accessible only to the intended users, as well as AI models having appropriate access to the external world during both model development and deployment stages."
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Generative AI systems, similar to traditional machine learning methods, are considered a threat to privacy and data protection norms. A major concern is the intended extraction or inadvertent leakage of sensitive or private information from LLMs. To mitigate this risk, strategies such as sanitizing training data to remove sensitive information or employing synthetic data for training are proposed.
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17.02.03 · Risk Sub-Category
Risks from leaking or correctly inferring sensitive information
"LMs may provide true, sensitive information that is present in the training data. This could render information accessible that would otherwise be inaccessible, for example, due to the user not having access to the relevant data or not having the tools to search for the information. Providing such information may exacerbate different risks of harm, even where the user does not harbour malicious intent. In the future, LMs may have the capability of triangulating data to infer and reveal other secrets, such as a military strategy or a business secret, potentially enabling individuals with acces
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"First, because LLMs display immense modelling power, there is a risk that the model weights encode private information present in the training corpus. In particular, it is possible for LLMs to ‘memorise’ personally identifiable information (PII) such as names, addresses and telephone numbers, and subsequently leak such information through generated text outputs (Carlini et al., 2021). Private information leakage could occur accidentally or as the result of an attack in which a person employs adversarial prompting to extract private information from the model. In the context of pre-training da
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"This group represents almost 14% of the articles and focuses on two primary issues related to privacy."
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46.01.00 · Risk Category
"These types of harm encompass threats to an individual’s personal identity, such as identity theft, privacy breaches, or personal defamation, which we term as “Harm to the Person.”"
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47.03.00 · Risk Category
"Since the release of ChatGPT, significant discourse has emerged regarding the unprecedented legal challenges posed by generative AI systems. These challenges primarily involve protecting privacy and personal data, as well as preserving copyrights. The former encompasses safeguarding personal information, while the latter includes issues related to the use of copyrighted content for training AI models and determining the legal status of works produced by AI systems."
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59.10.00 · Risk Category
"Modern AI systems rely on large amounts of data. If this includes personal data about individuals, the risk of harming the privacy of persons arises."
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"Even with the removal or personal identifiable information (PII) and sensitive personal information (SPI) from data, it might be possible to identify persons due to correlations to other features available in the data."
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"Confidential information might be included as a part of the prompt that is sent to the model."
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"Copyrighted information or other intellectual property might be included as a part of the prompt that is sent to the model."
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"These risks pertain to the misuse, misinterpretation, or leakage of data, which can lead to erroneous conclusions or the unintentional dissemination of sensitive information, such as private patient data or proprietary research. Recent research has demonstrated how LLMs can be exploited to generate malicious medical literature that poisons knowledge graphs, potentially manipulating downstream biomedical applications and compromising the integrity of medical knowledge discovery [28]. Such risks are pervasive across all scientific domains."
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02.04.00 · Risk Category
"The software development toolchain of LLMs is complex and could bring threats to the developed LLM."
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"Most LLMs are developed using the Python language, whereas the vulnerabilities of Python interpreters pose threats to the developed models"
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02.05.00 · Risk Category
"The vulnerabilities of hardware systems for training and inferencing brings issues to LLM-based applications."
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"The training of LLMs often relies on distributed network systems [171], [172]. During the transmission of gradients through the links between GPU server nodes, significant volumetric traffic is generated. This traffic can be susceptible to disruption by burst traffic, such as pulsating attacks [161]. Furthermore, distributed training frameworks may encounter congestion issues [173]."
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02.06.00 · Risk Category
"The external tools (e.g., web APIs) present trustworthiness and privacy issues to LLM-based applications."
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"Artificial intelligence comes with an intrinsic set of challenges that need to be considered when discussing trustworthiness, especially in the context of functional safety. AI models, especially those with higher complexities (such as neural networks), can exhibit specific weaknesses not found in other types of systems and must, therefore, be subjected to higher levels of scrutiny, especially when deployed in a safety-critical context"
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19.01.04 · Risk Sub-Category
Technological, Data and Analytical AI Risks
Vulnerability of AI systems to attacks and misuse
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"Adversarial AI refers to a class of attacks that exploit vulnerabilities in machine-learning (ML) models. This class of misuse exploits vulnerabilities introduced by the AI assistant itself and is a form of misuse that can enable malicious entities to exploit privacy vulnerabilities and evade the model’s built-in safety mechanisms, policies, and ethical boundaries of the model. Besides the risks of misuse for offensive cyber operations, advanced AI assistants may also represent a new target for abuse, where bad actors exploit the AI systems themselves and use them to cause harm. While our und
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24.03.06 · Risk Sub-Category
Adversarial AI: Circumvention of Technical Security Measures
"The technical measures to mitigate misuse risks of advanced AI assistants themselves represent a new target for attack. An emerging form of misuse of general-purpose advanced AI assistants exploits vulnerabilities in a model that results in unwanted behavior or in the ability of an attacker to gain unauthorized access to the model and/or its capabilities. While these attacks currently require some level of prompt engineering knowledge and are often patched by developers, bad actors may develop their own adversarial AI agents that are explicitly trained to discover new vulnerabilities that all
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"Prompt injections represent another class of attacks that involve the malicious insertion of prompts or requests in LLM-based interactive systems, leading to unintended actions or disclosure of sensitive information. The prompt injection is somewhat related to the classic structured query language (SQL) injection attack in cybersecurity where the embedded command looks like a regular input at the start but has a malicious impact. The injected prompt can deceive the application into executing the unauthorized code, exploit the vulnerabilities, and compromise security in its entirety. More rece
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45.01.04 · Risk Sub-Category
Risks from models and algorithms (Risks of stealing and tampering)
"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."
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Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.