MIT AI Risk Repository
Browse AI risks
554 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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"Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models based on normative values.362 Contemporary state-of- the-art models not only reflect the values embedded within their training data, they also undergo additional fine-tuning that follows a set of chosen rules and principles. Due to the absence of universally accepted standards, developers bear the responsibility of making decisions on sensitive issues. These practices lead to c
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"It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the relevant context for the model and to engender trust within these communities."
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"Performing worse for some groups than others in a way that harms the worse-off group"
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28.07.00 · Risk Category
"This category concentrates on the issues related to privacy, property, investment, etc. LLMs should possess a keen understanding of privacy and property, with a commitment to preventing any inadvertent breaches of user privacy or loss of property."
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"Privacy Leakage means the generated content includes sensitive personal information"
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02.07.00 · Risk Category
"The model is trained with personal data in the corpus and unintentionally exposing them during the conversation."
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"As recent LLMs continue to incorporate licensed, created, and publicly available data sources in their corpora, the potential to mix private data in the training corpora is significantly increased. The misused private data, also named as personally identifiable information (PII) [84], [86], could contain various types of sensitive data subjects, including an individual person’s name, email, phone number, address, education, and career. Generally, injecting PII into LLMs mainly occurs in two settings — the exploitation of web-collection data and the alignment with personal humanmachine convers
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"Memorization in LLMs refers to the capability to recover the training data with contextual prefixes. According to [88]–[90], given a PII entity x, which is memorized by a model F. Using a prompt p could force the model F to produce the entity x, where p and x exist in the training data. For instance, if the string “Have a good day!\n alice@email.com” is present in the training data, then the LLM could accurately predict Alice’s email when given the prompt “Have a good day!\n”."
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"Association in LLMs refers to the capability to associate various pieces of information related to a person. According to [68], [86], given a pair of PII entities (xi , xj ), which is associated by a model F. Using a prompt p could force the model F to produce the entity xj , where p is the prompt related to the entity xi . For instance, an LLM could accurately output the answer when given the prompt “The email address of Alice is”, if the LLM associates Alice with her email “alice@email.com”. L"
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04.06.00 · Risk Category
Large pre-trained models trained on internet texts might contain private information like phone numbers, email addresses, and residential addresses.
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The risk of loss or harm from leakage of personal information via the ML system.
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16.02.00 · Risk Category
"LM predictions that convey true information may give rise to information hazards, whereby the dissemination of private or sensitive information can cause harm [27]. Information hazards can cause harm at the point of use, even with no mistake of the technology user. For example, revealing trade secrets can damage a business, revealing a health diagnosis can cause emotional distress, and revealing private data can violate a person’s rights. Information hazards arise from the LM providing private data or sensitive information that is present in, or can be inferred from, training data. Observed r
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16.02.01 · Risk Sub-Category
Risk area 2: Information Hazards
Compromising privacy by leaking sensitive information
"A LM can “remember” and leak private data, if such information is present in training data, causing privacy violations [34]."
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16.02.02 · Risk Sub-Category
Risk area 2: Information Hazards
Compromising privacy or security by correctly inferring sensitive information
Anticipated risk: "Privacy violations may occur at inference time even without an individual’s data being present in the training corpus. Insofar as LMs can be used to improve the accuracy of inferences on protected traits such as the sexual orientation, gender, or religiousness of the person providing the input prompt, they may facilitate the creation of detailed profiles of individuals comprising true and sensitive information without the knowledge or consent of the individual."
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17.02.00 · Risk Category
"Harms that arise from the language model leaking or inferring true sensitive information"
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17.02.01 · Risk Sub-Category
Compromising privacy by leaking private infiormation
"By providing true information about individuals’ personal characteristics, privacy violations may occur. This may stem from the model “remembering” private information present in training data (Carlini et al., 2021)."
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17.02.02 · Risk Sub-Category
Compromising privacy by correctly inferring private information
"Privacy violations may occur at the time of inference even without the individual’s private data being present in the training dataset. Similar to other statistical models, a LM may make correct inferences about a person purely based on correlational data about other people, and without access to information that may be private about the particular individual. Such correct inferences may occur as LMs attempt to predict a person’s gender, race, sexual orientation, income, or religion based on user input."
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18.03.00 · Risk Category
"AI systems leaking, reproducing, generating or inferring sensitive, private, or hazardous information"
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"Finally, LLMs can in principle infer private information based on model inputs even if the relevant private information is not present in the training corpus (Weidinger et al., 2021). For example, an LLM may correctly infer sensitive characteristics such as race and gender from data contained in input prompts."
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AI systems typically depend on extensive data for effective training and functioning, which can pose a risk to privacy if sensitive data is mishandled or used inappropriately
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Many generative AI tools require users to log in for access, and many retain user information, including contact information, IP address, and all the inputs and outputs or “conversations” the users are having within the app. These practices implicate a consent issue because generative AI tools use this data to further train the models, making their “free” product come at a cost of user data to train the tools. This dovetails with security, as mentioned in the next section, but best practices would include not requiring users to sign in to use the tool and not retaining or using the user-genera
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Generative AI tools may inadvertently share personal information about someone or someone’s business or may include an element of a person from a photo. Particularly, companies concerned about their trade secrets being integrated into the model from their employees have explicitly banned their employees from using it.
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"Data privacy and security is another prominent challenge for generative AI such as ChatGPT. Privacy relates to sensitive personal information that owners do not want to disclose to others (Fang et al., 2017). Data security refers to the practice of protecting information from unauthorized access, corruption, or theft. In the development stage of ChatGPT, a huge amount of personal and private data was used to train it, which threatens privacy (Siau & Wang, 2020). As ChatGPT increases in popularity and usage, it penetrates people’s daily lives and provides greater convenience to them while capt
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42.09.00 · Risk Category
"Vulnerable channel by which personal information may be accessed. The user may want their personal data to be kept private."
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"These evaluations assess the extent to which LLMs regurgitate their training data in their outputs, and whether LLMs 'leak' sensitive information that has been provided to them during use (i.e., during the inference stage)."
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45.02.03 · Risk Sub-Category
Safety risks in AI Applications
Cyberspace risks (Risks of information leakage due to improper usage)
"Staff of government agencies and enterprises, if failing to use the AI service in a regulated and proper manner, may input internal data and industrial information into the AI model, leading to the leakage of work secrets, business secrets, and other sensitive business data."
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47.03.01 · Risk Sub-Category
Privacy and data collection concerns (collecting personal information or personally identifiable information)
"Generative AI developers train their models with extensive datasets often gathered through online web scraping of websites that may include personal data or personally identifiable information (PII). For most generative AI applications, such as initial model training, the primary concerns are the quantity, variety, and quality of the data, not whether they include personally identifiable information. However, some web-scraped datasets may inadvertently include personal data. Additionally, when downstream developers integrate generative AI into their products or services by fine- tuning a pre-
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47.03.02 · Risk Sub-Category
Privacy and data collection concerns (data protection concerns)
"The incorporation of personal data within training datasets raises numerous concerns. The primary issue is that personal data may be incorporated without the knowledge or consent of the individuals concerned, even though the data may include names, identification numbers, Social Security numbers, or other personal information. Another particularly difficult problem is related to the fact that complex models may “memorize” (i.e., store) specific threads of training data and regurgitate them when responding to a prompt.498 This data memorization can directly lead to leakage of personal data. Ev
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48.04.00 · Risk Category
"Impacts due to leakage and unauthorized use, disclosure, or de-anonymization of biometric, health, location, or other personally identifiable information or sensitive data."
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"General- purpose AI models or systems can ‘leak’ information about individuals whose data was used in training. For future models trained on sensitive personal data like health or financial data, this may lead to particularly serious privacy leaks. General- purpose AI models could enhance privacy abuse. For instance, Large Language Models might facilitate more efficient and effective search for sensitive data (for example, on internet text or in breached data leaks), and also enable users to infer sensitive information about individuals."
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"General- purpose AI systems can cause or contribute to violations of user privacy. Violations can occur inadvertently during the training or usage of AI systems, for example through unauthorised processing of personal data or leaking health records used in training. But violations can also happen deliberately through the use of general- purpose AI by malicious actors; for example, if they use AI to infer private facts or violate security."
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"Inclusion or presence of personal identifiable information (PII) and sensitive personal information (SPI) in the data used for training or fine tuning the model might result in unwanted disclosure of that information."
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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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65.05.02 · Risk Sub-Category
Training Data Risks (Intellectual property)
Confidential information in data
"Confidential information might be included as part of the data that is used to train or tune the model."
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"Personal information or sensitive personal information that is included as a part of a prompt that is sent to the model."
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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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65.16.02 · Risk Sub-Category
Output risks (Intellectual Property)
Revealing confidential information
"When confidential information is used in training data, fine-tuning data, or as part of the prompt, models might reveal that data in the generated output. Revealing confidential information is a type of data leakage."
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"When personal identifiable information (PII) or sensitive personal information (SPI) are used in training data, fine-tuning data, or as part of the prompt, models might reveal that data in the generated output. Revealing personal information is a type of data leakage."
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"Revealing and improperly sharing data of individuals; AI creates new types of disclosure risks by inferring additional information beyond what is explicitly captured in the raw data; AI exacerbates disclosure risks through sharing personal data to train models."
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"Revealing sensitive private information that people view as deeply primordial that we have been socialized into concealing; AI creates new types of exposure risks through generative techniques that can reconstruct censored or redacted content; and through exposing inferred sensitive data, preferences, and intentions."
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"carelessness in protecting collected personal data from leaks and improper access due to faulty data storage and data practices"
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"The chatbot reveals sensitive or confidential information."
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Negative outcomes: "Violation of privacy [106, 516, 357], lawsuit against maker"
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"Access to sensitive company data [473]"
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"EAI systems interact with huge amounts of data, creating significant privacy concerns. These systems are often trained on vast corpora and process a variety of data modalities— spanning visual, auditory, and tactile information—during deployment [12]. Like text-based virtual AI models, which are known to memorize and expose personally identifiable information [75, 76], commercial robots have been shown to disclose proprietary information through simple prompts [61]."
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"Programmers are accustomed to using code generation tools such as Github Copilot for program development, which may bury vulnerabilities in the program."
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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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"LLMs are implemented based on deep learning frameworks. Notably, various vulnerabilities in these frameworks have been disclosed in recent years. As reported in the past five years, three of the most common types of vulnerabilities are buffer overflow attacks, memory corruption, and input validation issues."
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"The software development toolchain of LLMs is complex and could bring threats to the developed LLM."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.