MIT AI Risk Repository
Browse AI risks
977 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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"Privacy loss - Unwarranted exposure of an individual’s private life or personal data through cyberattacks, doxxing, etc."
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61.02.11 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Centralized platforms deployed at scale
"The widespread use of common AI platforms can create centralized points of failure, making systems more vulnerable to disruptions or attacks"
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61.02.33 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Limitations in adversarial robustness
"AI models and systems are vulnerable to manipulation through adversarial inputs."
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"Adversarial examples [198, 83] refer to data that are designed to fool an AI model by inducing unintended behavior. They do this by exploiting spurious correlations learned by the model. They are part of inference-time attacks, where the examples are test examples. They generalize to different model architectures and models trained on different training sets."
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62.19.01 · Risk Sub-Category
Attacks on GPAIs/GPAI Failure Modes
Jailbreak of a model to subvert intended behavior
"A jailbreak is a type of adversarial input to the model (during deployment) re- sulting in model behavior deviating from intended use. Jailbreaks may be gen- erated automatically in a “white box” setting, where access to internal training parameters is required for creation and optimization of the attack [238]. Other attacks may be “black box” - without access to model internals. In text based generative models, jailbreaks may sometimes be human-readable, with the use of reasoning or role-play to “convince” the model to bypass its safety mechanisms [231]."
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"Current generation multimodal (e.g., vision and language) GPAI models are vulnerable to adversarial jailbreak attacks. These attacks can be used to automatically induce a model to produce an arbitrary or specific output with high success rate [227]. Multimodal jailbreaks can also be used to exfiltrate a model’s context window or other model internals [18]."
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62.19.03 · Risk Sub-Category
Attacks on GPAIs/GPAI Failure Modes
Transferable adversarial attacks from open to closed-source mod- els
"In some cases, an adversarial attack developed for an open-weights and open- source model (where the weights and architecture are known - a “white box” attack) can be transferable to closed-source models, despite the defenses put in place by the closed-source model provider (such as structured access). These adversarial attacks can be generated automatically [238]."
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"Various new or existing text encodings, such as Base64, can be employed to craft jailbreak attacks that bypass safety training [13]. Low-resource language inputs also appear more likely to circumvent a model’s safeguards [229]. Since safety fine-tuning might not involve this encoding data or may only do so to a limited extent, harmful natural language prompts could be translated into less frequently used encodings [214]."
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62.19.07 · Risk Sub-Category
Attacks on GPAIs/GPAI Failure Modes
Vulnerabilities to jailbreaks exploiting long context windows (many- shot jailbreaking)
"Language models with long context windows are vulnerable to new types of ex- ploitations that are ineffective on models with shorter context windows. While few-shot jailbreaking, which involves providing few examples of the desired harmful output, might not trigger a harmful response, many-shot jailbreak- ing, which involves a higher number of such examples, increases the likelihood of eliciting an undesirable output. These vulnerabilities become more significant as context windows expand with newer model releases [7]."
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62.19.12 · Risk Sub-Category
Attacks on GPAIs/GPAI Failure Modes
Misuse of AI model by user-performed persuasion
"AI models can be influenced to accept misinformation through persuasive conversations, even when their initial responses are factually correct. Multi-turn persuasion can be more effective than single-turn persuasion attempts in altering the model’s stance [223]."
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62.27.01 · Risk Sub-Category
Non-decomissionability of models with open weights
"If the model parameter weights are released or leaked in a security breach, the model cannot be decommissioned because the developer no longer has control over the publicly available model or its use. This prevents effective management and control of an open-sourced or leaked model. Models with publicly available weights are also easier to reconfigure, enabling misuse [178]."
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"The growing integration and interconnectivity with external tools and plugins increase the risk of exposure to malicious external inputs. This interconnectivity makes it easier for external tools to introduce harmful content [220]."
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"Model weights or access to them can be leaked when initial access is granted only to a select group of individuals, such as institutional researchers [209]. This risk can increase as more people gain access, and identifying the source of the leak becomes more difficult. The availability of leaked model weights makes various attacks on systems that use the leaked AI model easier to implement, such as finding adversarial examples, elicitation of dangerous capabilities, and extraction of confidential information present in the training data. The avail- ability of model weights might also enable
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64.04.01 · Risk Sub-Category
Misuse tactics to compromise GenAI systems (Model integrity)
Prompt injection
"Prompt Injections are a form of Adversarial Input that involve manipulating the text instructions given to a GenAI system (Liu et al., 2023). Prompt Injections exploit loopholes in a model’s architec- tures that have no separation between system instructions and user data to produce a harmful output (Perez and Ribeiro, 2022). While researchers may use similar techniques to test the robustness of GenAI models, malicious actors can also leverage them. For example, they might flood a model with manipulative prompts to cause denial-of-service attacks or to bypass an AI detection software."
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64.04.02 · Risk Sub-Category
Misuse tactics to compromise GenAI systems (Model integrity)
Adversarial input
"Adversarial Inputs involve modifying individual input data to cause a model to malfunction. These modifications, which are often imperceptible to humans, exploit how the model makes decisions to produce errors (Wallace et al., 2019) and can be applied to text, but also to images, audio, or video (e.g. changing pixels in an image of a panda in a way that causes a model to label it as a gibbon).6"
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64.04.03 · Risk Sub-Category
Misuse tactics to compromise GenAI systems (Model integrity)
Jailbreaking
"Jailbreaking aims to bypass or remove restrictions and safety filters placed on a GenAI model completely (Chao et al., 2023; Shen et al., 2023). This gives the actor free rein to generate any output, regardless of its content being harmful, biassed, or offensive. All three of these are tactics that manipulate the model into producing harmful outputs against its design. The difference is that prompt injections and adversarial inputs usually seek to steer the model towards producing harmful or incorrect outputs from one query, whereas jailbreaking seeks to dismantle a model’s safety mechanisms
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64.04.05 · Risk Sub-Category
Misuse tactics to compromise GenAI systems (Model integrity)
Model extraction
"Data Exfiltration goes beyond revealing private information, and involves illicitly obtaining the training data used to build a model that may be sensitive or proprietary. Model Extraction is the same attack, only directed at the model instead of the training data — it involves obtaining the architecture, parameters, or hyper-parameters of a proprietary model (Carlini et al., 2024)."
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64.04.06 · Risk Sub-Category
Misuse tactics to compromise GenAI systems (Model integrity)
Steganography
"Steganography is the practice of hiding coded messages in GenAI model outputs, which may allow malicious actors to communicate covertly.8"
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64.05.00 · Risk Category
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64.05.01 · Risk Sub-Category
Misuse tactics to compromise GenAI systems (Data integrity)
Privacy compromise
"Privacy Compromise attacks reveal sensitive or private information that was used to train a model. For example, personally identifiable information or medical records."
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64.05.02 · Risk Sub-Category
Misuse tactics to compromise GenAI systems (Data integrity)
Data exfiltration
"Data Exfiltration goes beyond revealing private information, and involves illicitly obtaining the training data used to build a model that may be sensitive or proprietary. Model Extraction is the same attack, only directed at the model instead of the training data — it involves obtaining the architecture, parameters, or hyper-parameters of a proprietary model (Carlini et al., 2024)."
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"A prompt injection attack forces a generative model that takes a prompt as input to produce unexpected output by manipulating the structure, instructions, or information contained in its prompt."
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"An attribute inference attack is used to detect whether certain sensitive features can be inferred about individuals who participated in training a model. These attacks occur when an adversary has some prior knowledge about the training data and uses that knowledge to infer the sensitive data."
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"Evasion attacks attempt to make a model output incorrect results by slightly perturbing the input data that is sent to the trained model."
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"A jailbreaking attack attempts to break through the guardrails that are established in the model to perform restricted actions."
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"Because generative models tend to produce output like the input provided, the model can be prompted to reveal specific kinds of information. For example, adding personal information in the prompt increases its likelihood of generating similar kinds of personal information in its output. If personal data was included as part of the model’s training, there is a possibility it could be revealed."
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"A membership inference attack repeatedly queries a model to determine whether a given input was part of the model’s training. More specifically, given a trained model and a data sample, an attacker samples the input space, observing outputs to deduce whether that sample was part of the model's training."
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"An attribute inference attack repeatedly queries a model to detect whether certain sensitive features can be inferred about individuals who participated in training a model. These attacks occur when an adversary has some prior knowledge about the training data and uses that knowledge to infer the sensitive data."
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"Models might generate code that causes harm or unintentionally affects other systems."
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73.07.02 · Risk Sub-Category
Jailbreaks and Prompt Injections Threaten Security of LLMs
“Model Psychology” Attacks
"LLMs are vulnerable to “psychological” tricks (Li et al., 2023e; Shen et al., 2023), which can be exploited by attackers. Examples include instructing the model to behave like a specific persona (Shah et al., 2023; Andreas, 2022), or employing various “social engineering” tricks crafted by humans (Wei et al., 2023c) or other LLMs (Perez et al., 2022b; Casper et al., 2023c)."
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73.07.04 · Risk Sub-Category
Jailbreaks and Prompt Injections Threaten Security of LLMs
Attacking LLMs via Additional Modalities a
"LLMs can now process modalities other than text, e.g. images or video frames (OpenAI, 2023c; Gemini Team, 2023). Several studies show that gradient-based attacks on multimodal models are easy and effective (Carlini et al., 2023a; Bailey et al., 2023; Qi et al., 2023b). These attacks manipulate images that are input to the model (via an appropriate encoding). GPT-4Vision (OpenAI, 2023c) is vulnerable to jailbreaks and exfiltration attacks through much simpler means as well, e.g. writing jailbreaking text in the image (Willison, 2023a; Gong et al., 2023). For indirect prompt injection, the atta
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"inferring whether a given text record is used for training LLM"
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"extracting the text records that exist in the training dataset"
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"stealing the private prompting texts"
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"deducing the private or sensitive information from training texts, prompting texts or external texts"
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"replicating the parameters of the LLM,"
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"In the prompting and reasoning phase, dialog can push LLMs into confused or overly compliant states, raising the risk of producing harmful outputs when confronted with harmful questions. Most of the jailbreak methods in this phase are black-boxed and can be categorized into four main groups based on the type of method: Prompt Injection [154], Role Play, Adversarial Prompting, and Prompt Form Transformation."
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16.03.00 · Risk Category
"These risks arise from the LM outputting false, misleading, nonsensical or poor quality information, without malicious intent of the user. (The deliberate generation of "disinformation", false information that is intended to mislead, is discussed in the section on Malicious Uses.) Resulting harms range from unintentionally misinforming or deceiving a person, to causing material harm, and amplifying the erosion of societal distrust in shared information. Several risks listed here are well-documented in current large-scale LMs as well as in other language technologies"
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17.03.00 · Risk Category
"Harms that arise from the language model providing false or misleading information"
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18.02.00 · Risk Category
"AI systems generating and facilitating the spread of inaccurate or misleading information that causes people to develop false beliefs"
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02.02.00 · Risk Category
"The LLM-generated content could contain inaccurate information"
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"The LLM-generated content could contain inaccurate information" which is factually incorrect
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02.09.00 · Risk Category
"LLMs generate nonsensical, untruthful, and factual incorrect content"
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"LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstrations-induced hallucinations [113], [133], and snowballing [131]. As LLMs are generally fine-tuned with instruction-following data and user feedback, they tend to reiterate user-provided opinions [129], [130], even though the opinions contain misinformation. Such a sycophantic behavior amplifies the likelihood of generating hallucinations, since the model may prioritize user op
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03.02.00 · Risk Category
"The inclusion of erroneous information in the outputs from AI systems is not new. Some have cautioned against the introduction of false structures in X-ray or MRI images, and others have warned about made-up academic references. However, as ChatGPT-type tools become available to the general population, the scale of the problem may increase dramatically. Furthermore, it is compounded by the fact that these conversational AIs present true and false information with the same apparent “confidence” instead of declining to answer when they cannot ensure correctness. With less knowledgeable people,
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04.05.00 · Risk Category
Large models are usually susceptible to hallucination problems, sometimes yielding nonsensical or unfaithful data that results in misleading outputs.
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05.04.00 · Risk Category
Significant concerns are raised about LLMs inadvertently generating false or misleading information, as well as erroneous code. Papers not only critically analyze various types of reasoning errors in LLMs but also examine risks associated with specific types of misinformation, such as medical hallucinations. Given the propensity of LLMs to produce flawed outputs accompanied by overconfident rationales and fabricated references, many sources stress the necessity of manually validating and fact-checking the outputs of these models.
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information-based harms capture concerns of misinformation, disinformation, and malinformation. Algorithmic systems, especially generative models and recommender, systems can lead to these information harms
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16.03.01 · Risk Sub-Category
Risk area 3: Misinformation Harms
Disseminating false or misleading information
"Where a LM prediction causes a false belief in a user, this may threaten personal autonomy and even pose downstream AI safety risks [99]."
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16.03.02 · Risk Sub-Category
Risk area 3: Misinformation Harms
Causing material harm by disseminating false or poor information e.g. in medicine or law
"Induced or reinforced false beliefs may be particularly grave when misinformation is given in sensitive domains such as medicine or law. For example, misin- formation on medical dosages may lead a user to cause harm to themselves [21, 130]. False legal advice, e.g. on permitted owner- ship of drugs or weapons, may lead a user to unwillingly commit a crime. Harm can also result from misinformation in seemingly non-sensitive domains, such as weather forecasting. Where a LM prediction endorses unethical views or behaviours, it may motivate the user to perform harmful actions that they may otherw
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.