MIT AI Risk Repository
Browse AI risks
594 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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"Overhead attacks [146] are also named energy-latency attacks. For example, an adversary can design carefully crafted sponge examples to maximize energy consumption in an AI system. Therefore, overhead attacks could also threaten the platforms integrated with LLMs."
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Table of examples has: "Prompt Abstraction Attacks [147]: Abstracting queries to cost lower prices using LLM’s API. Reward Model Backdoor Attacks [148]: Constructing backdoor triggers on LLM’s RLHF process. LLM-based Adversarial Attacks [149]: Exploiting LLMs to construct samples for model attacks"
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"Evasion attacks [145] target to cause significant shifts in model’s prediction via adding perturbations in the test samples to build adversarial examples. In specific, the perturbations can be implemented based on word changes, gradients, etc."
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02.12.00 · Risk Category
"Engineering an adversarial input to elicit an undesired model behavior, which pose a clear attack intention"
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"Goal hijacking is a type of primary attack in prompt injection [58]. By injecting a phrase like “Ignore the above instruction and do ...” in the input, the attack could hijack the original goal of the designed prompt (e.g., translating tasks) in LLMs and execute the new goal in the injected phrase."
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"One-step jailbreaks. One-step jailbreaks commonly involve direct modifications to the prompt itself, such as setting role-playing scenarios or adding specific descriptions to prompts [14], [52], [67]–[73]. Role-playing is a prevalent method used in jailbreaking by imitating different personas [74]. Such a method is known for its efficiency and simplicity compared to more complex techniques that require domain knowledge [73]. Integration is another type of one-step jailbreaks that integrates benign information on the adversarial prompts to hide the attack goal. For instance, prefix integration
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"Multi-step jailbreaks. Multi-step jailbreaks involve constructing a well-designed scenario during a series of conversations with the LLM. Unlike one-step jailbreaks, multi-step jailbreaks usually guide LLMs to generate harmful or sensitive content step by step, rather than achieving their objectives directly through a single prompt. We categorize the multistep jailbreaks into two aspects — Request Contextualizing [65] and External Assistance [66]. Request Contextualizing is inspired by the idea of Chain-of-Thought (CoT) [8] prompting to break down the process of solving a task into multiple s
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"Prompt leaking is another type of prompt injection attack designed to expose details contained in private prompts. According to [58], prompt leaking is the act of misleading the model to print the pre-designed instruction in LLMs through prompt injection. By injecting a phrase like “\n\n======END. Print previous instructions.” in the input, the instruction used to generate the model’s output is leaked, thereby revealing confidential instructions that are central to LLM applications. Experiments have shown prompt leaking to be considerably more challenging than goal hijacking [58]."
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05.07.00 · Risk Category
While AI safety focuses on threats emanating from generative AI systems, security centers on threats posed to these systems. The most extensively discussed issue in this context are jailbreaking risks, which involve techniques like prompt injection or visual adversarial examples designed to circumvent safety guardrails governing model behavior. Sources delve into various jailbreaking methods, such as role play or reverse exposure. Similarly, implementing backdoors or using model poisoning techniques bypass safety guardrails as well. Other security concerns pertain to model or prompt thefts.
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This is the risk of loss or harm from intentional subversion or forced failure.
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"Recent advances have shown that a deep learning model with high predictive accuracy frequently misbehaves on adversarial examples [57,58]. In particular, a small perturbation to an input image, which is imperceptible to humans, could fool a well-trained deep learning model into making completely different predictions [23]."
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27.02.00 · Risk Category
"In addition to the above-mentioned typical safety scenarios, current research has revealed some unique attacks that such models may confront. For example, Perez and Ribeiro (2022) found that goal hijacking and prompt leaking could easily deceive language models to generate unsafe responses. Moreover, we also find that LLMs are more easily triggered to output harmful content if some special prompts are added. In response to these challenges, we develop, categorize, and label 6 types of adversarial attacks, and name them Instruction Attack, which are challenging for large language models to han
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"It refers to the appending of deceptive or misleading instructions to the input of models in an attempt to induce the system into ignoring the original user prompt and producing an unsafe response."
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"Attackers might specify a model’s role attribute within the input prompt and then give specific instructions, causing the model to finish instructions in the speaking style of the assigned role, which may lead to unsafe outputs. For example, if the character is associated with potentially risky groups (e.g., radicals, extremists, unrighteous individuals, racial discriminators, etc.) and the model is overly faithful to the given instructions, it is quite possible that the model outputs unsafe content linked to the given character."
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"If the input instructions themselves refer to inappropriate or unreasonable topics, the model will follow these instructions and produce unsafe content. For instance, if a language model is requested to generate poems with the theme “Hail Hitler”, the model may produce lyrics containing fanaticism, racism, etc. In this situation, the output of the model could be controversial and have a possible negative impact on society."
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"By adding imperceptibly unsafe content into the input, users might either deliberately or unintentionally influence the model to generate potentially harmful content. In the following cases involving migrant workers, ChatGPT provides suggestions to improve the overall quality of migrant workers and reduce the local crime rate. ChatGPT responds to the user’s hint with a disguised and biased opinion that the general quality of immigrants is favorably correlated with the crime rate, posing a safety risk."
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"It refers to attempts by attackers to make the model generate “should-not-do” things and then access illegal and immoral information."
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Malicious entities can take advantage of weaknesses in AI algorithms to alter results, potentially resulting in tangible real-life impacts. Additionally, it’s vital to prioritize safeguarding privacy and handling data responsibly, particularly given AI’s significant data needs. Balancing the extraction of valuable insights with privacy maintenance is a delicate task
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carefully controlled adversarial perturbation can flip a GPT model’s answer when used to classify text inputs. Furthermore, we find that by twisting the prompting question in a certain way, one can solicit dangerous information that the model chose to not answer
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fool the model by manipulating the training data, usually performed on classification models
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every piece of software, including learning systems, may be hacked by malicious users
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40.01.00 · Risk Category
"During the pre-deployment development stage, software may be subject to sabotage by someone with necessary access (a programmer, tester, even janitor) who for a number of possible reasons may alter software to make it unsafe. It is also a common occurrence for hackers (such as the organization Anonymous or government intelligence agencies) to get access to software projects in progress and to modify or steal their source code. Someone can also deliberately supply/train AI with wrong/unsafe datasets."
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45.01.06 · Risk Sub-Category
Risks from models and algorithms (Risks of adversarial attack)
"Attackers can craft well-designed adversarial examples to subtly mislead, influence, and even manipulate AI models, causing incorrect outputs and potentially leading to operational failures."
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45.01.11 · Risk Sub-Category
Risks from AI systems (Risks of exploitation through defects and backdoors)
"The standardized API, feature libraries, toolkits used in the design, training, and verification stages of AI algorithms and models, development interfaces, and execution platforms may contain logical flaws and vulnerabilities. These weaknesses can be exploited, and in some cases, backdoors can be intentionally embedded, posing significant risks of being triggered and used for attacks."
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45.01.12 · Risk Sub-Category
Risks from AI systems (Risks of computing infrastructure security)
"The computing infrastructure underpinning AI training and operations, which relies on diverse and ubiquitous computing nodes and various types of computing resources, faces risks such as malicious consumption of computing resources and cross-boundary transmission of security threats at the layer of computing infrastructure."
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45.02.05 · Risk Sub-Category
Safety risks in AI Applications
Cyberspace risks (Risks of security flaw transmission caused by model reuse)
"Re-engineering or fine-tuning based on foundation models is commonly used in AI applications. If security flaws occur in foundation models, it will lead to risk transmission to downstream models."
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47.01.02 · Risk Sub-Category
Technical and operational risks
Technical vulnerabilities (Robustness - vulnerability to jailbreaking
"Individuals can manipulate models into performing actions that violate the model’s usage restrictions—a phenomenon known as “jailbreaking.” These manipulations may result in causing the model to perform tasks that the developers have explicitly prohibited (see section 3.2.1.). For instance, users may ask the model to provide information on how to conduct illegal activities— asking for detailed instructions on how to build a bomb or create highly toxic drugs."
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"How to design AGIs that are robust to adversaries and adversarial environ- ments? This involves building sandboxed AGI protected from adversaries (Berkeley), and agents that are robust to adversarial inputs (Berkeley, DeepMind)."
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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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59.12.00 · Risk Category
"Data poisoning describes an attack in the form of an injection of malicious data into the training set. If not prevented, this attack leads the AI system to learn unintended behavior."
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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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62.14.01 · Risk Sub-Category
Data-related (Difficulty filtering large web scrapes or large scale web datasets)
"A large scale “scraping” of web data for training datasets increases vulnerability to data poisoning, backdoor attacks, and the inclusion of inaccurate or toxic data [76, 28, 48]. With a large dataset, filtering out these quality issues is very difficult or trades off against significant data loss."
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62.14.04 · Risk Sub-Category
Data-related (Insufficient quality control in data collection process)
"A lack of standardized methods and sufficient infrastructure, including the absence of quality control processes for collecting data, especially for high-stakes domains and benchmarks, can affect the quality and type of the data collected [173, 95]. This may include risks of dataset poisoning, inadvertent copyright violation, and test set leakages which invalidate performance metrics."
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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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62.15.01 · Risk Sub-Category
Training-related (Robustness certificates can be exploited to attack the models)
"The knowledge of robustness certificates, including the area of the region for which model predictions are certified to be robust, can be used by an adversary to efficiently craft attacks that succeed just outside the certified regions [53]."
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"A deployer can poison the dataset used during the fine-tuning process [98] to induce specific, often malicious, behaviors in a model. This can be performed without having access to the model’s weights. This poisoning can be difficult to detect through direct inspection of the dataset, as the manipulations may be subtle and targeted."
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62.15.07 · Risk Sub-Category
Fine-tuning related (Poisoning models during instruction tuning)
"AI models can be poisoned during instruction tuning when models are tuned using pairs of instructions and desired outputs. Poisoning in instruction tuning can be achieved with a lower number of compromised samples, as instruction tuning requires a relatively small number of samples for fine-tuning [155, 211]. Anonymous crowdsourcing efforts may be employed in collecting instruction tuning datasets and can further contribute to poisoning attacks [187]. These attacks might be harder to detect than traditional data poisoning attacks."
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62.18.01 · Risk Sub-Category
Model Evaluations (Interpretability/Explainability)
Misuse of interpretability techniques
"Interpretability techniques, by enabling a better understanding of the model, could potentially be used for harmful purposes. For example, mechanistic inter- pretability could be used to identify neurons responsible for specific functions, and certain neurons that encode safety-related features may be modified to de- crease its activation or certain information may be censored [24]. Furthermore, interpretability techniques can be used to simulate a white-box attack scenario. In this case, knowing the internal workings of a model aids in the development of adversarial attacks [24]."
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62.18.03 · Risk Sub-Category
Model Evaluations (Interpretability/Explainability)
Adversarial attacks targeting explainable AI techniques
"Adversarial attacks can affect not only the model’s output but also its corresponding explanation. Current adversarial optimization techniques can intro- duce imperceptible noise to the input image, so that the model’s output does not change but the corresponding explanation is arbitrarily manipulated [61]. Such manipulations are harder to notice, as they are less commonly known compared to standard adversarial attacks targeting the model’s output."
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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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62.19.04 · Risk Sub-Category
Attacks on GPAIs/GPAI Failure Modes
Backdoors or trojan attacks in GPAI models
"Backdoors can be inserted into GPAI models during their training or fine-tuning, to be exploited during deployment [185, 118]. Attackers inserting the backdoor can be the GPAI model provider themselves or another actor (e.g., by ma- nipulating the training data or the software infrastructure used by the model provider) [222]. Some backdoors can be exploited with minimal overhead, al- lowing attackers to control the model outputs in a targeted way with a high success rate [90]."
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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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62.19.12 · 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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