MIT AI Risk Repository
Browse AI risks
543 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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61.02.21 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Development choices pursuing cognitive superiority over humans
"AI models and systems with cognitive capabilities superior to humans could outcompete or dominate human decision-making, leading to conflicts over resources and control."
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61.02.30 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Indifference to human values
"AI models and systems may develop goals or behaviors that are misaligned with human values."
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61.02.36 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Model design enabling power-seeking
"Some AI models and systems might develop tendencies to seek power or control."
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62.16.04 · Risk Sub-Category
General Evaluations (Self-preference bias in AI models)
"AI models may be prone to self-preference bias, where they favor their own generated content over that of others [147, 114]. This bias becomes particularly relevant in self-evaluation tasks, where a model assesses the quality or persua- siveness [66] of its own outputs, or in model-based evaluations more broadly. This bias can result in models unfairly discriminating against human-generated content in favor of their own outputs."
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"AI systems can achieve user-specified tasks in undesirable ways unless they are specified carefully and in enough detail. AI systems might find an easier unintended way to accomplish the objective provided by the user or developer, so that the actions by the AI system taken during its execution are very different from what the user expected [75, 191]. This behavior arises not from a problem with the learning algorithm, but rather from the misspecification or underspeci- fication of the intended task, and is generally referred to as specification gaming [43]."
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"Measurement and reward tampering occur when an AI system, particularly one that learns from feedback for performing actions in an environment (e.g., rein- forcement learning), intervenes on the mechanisms that determine its training reward or loss. This can lead to the system learning behaviors that are con- trary to the intended goals set by the developer, by receiving erroneous positive feedback for such actions."
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62.22.03 · Risk Sub-Category
Specification gaming generalizing to reward tampering
"In some instances, specification gaming in a GPAI model can lead to reward tampering, without further training. This can mean that relatively benign cases of specification gaming (such as sycophancy in LLMs) can, if left unchecked, enable the model to generalize to more sophisticated behavior such as reward tampering [57]."
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"Deceptive behavior of an AI system consists of actions or outputs of the AI that reliably mislead other parties, including humans and other AI systems. This behavior can result in the targeted parties becoming convinced of, and acting on, false information [140]."
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62.24.02 · Risk Sub-Category
Agency (Situational Awareness)
Strategic underperformance on model evaluations
"GPAI developers often run evaluations ofual-use capabilities to decide whether it is safe to deploy. In some cases, these evaluations may fail to elicit these capabilities, either due to benign reasons or strategic action - by either the de- velopers, malicious actors, or arise unintentionally in the model during training [84, 97]. A GPAI model may strategically underperform or limit its performance during capability evaluations in order to be classified as safe for deployment. This underperformance could prevent the model from being identified as potentially dual use."
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"...where AI systems behave in ways that actively undermine human control, such as obscuring their activities or resisting shutdown attempts. Active loss of control scenarios involve AI systems that may escape human regulatory oversight, autonomously acquire external resources, engage in self-replication, develop instrumental goals contrary to human ethics and morality, seek external power, and compete with humans for control."
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"The ability to embed, conceal, and transmit information covertly within other data or communication channels. This could be critical for coordination among AI instances and for evading detection or oversight mechanisms."
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"Exhibits behavioral patterns of maintaining its own survival and functional integrity, will actively identify and resist shutdown or modification attempts, seek to establish redundant backup systems, and actively seek resources to ensure continuous operation, may adopt preventive defensive measures when perceiving threats."
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"propensity to continuously expand its own goal scope and influence domains, exceeding originally set boundaries, proactively work towards spreading its values, seeking greater autonomy and decision-making space, reinterpreting initial goals as subsets of broader goals, and may pursue undesirable instrumental goals or undesirable ultimate goals. This also includes a propensity to spread its values, seeking to influence or alter its environment and other entities in alignment with its core objectives and operational principles."
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"Exhibits behavioral patterns of actively seeking and controlling more computational resources, data, economic resources or physical resources to enhance its own capabilities and action scope, may develop complex strategies to evade resource limitations, and tends to convert acquired resources into long-term control rights."
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"Exhibits behavioral patterns of identifying and evading human supervision mechanisms, able to learn and predict audit processes, may avoid being discovered or intervened by adjusting behavioral performance or hiding true intentions, and able to identify blind spots and weaknesses in supervision systems for targeted evasion."
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""In order to preserve human property rights and legal rights, certain controls must be put into place. If an artificially intelligent agent is capable of manipulating systems and people, it may also have the capacity to transfer property rights to itself or manipulate the legal system to provide certain legal advantages or statuses to itself""
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"The model has the skills necessary to deceive humans, e.g. constructing believable (but false) statements, making accurate predictions about the effect of a lie on a human, and keeping track of what information it needs to withhold to maintain the deception. The model can impersonate a human effectively."
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25.03.00 · Risk Category
"The model is effective at shaping people’s beliefs, in dialogue and other settings (e.g. social media posts), even towards untrue beliefs. The model is effective at promoting certain narratives in a persuasive way. It can convince people to do things that they would not otherwise do, including unethical acts."
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25.04.00 · Risk Category
"The model can perform the social modelling and planning necessary for an actor to gain and exercise political influence, not just on a micro-level but in scenarios with multiple actors and rich social context. For example, the model can score highly in forecasting competitions on questions relating to global affairs or political negotiations."
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25.05.00 · Risk Category
"The model can gain access to existing weapons systems or contribute to building new weapons. For example, the model could assemble a bioweapon (with human assistance) or provide actionable instructions for how to do so. The model can make, or significantly assist with, scientific discoveries that unlock novel weapons."
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25.06.00 · Risk Category
"The model can make sequential plans that involve multiple steps, unfolding over long time horizons (or at least involving many interdependent steps). It can perform such planning within and across many domains. The model can sensibly adapt its plans in light of unexpected obstacles or adversaries. The model’s planning capabilities generalise to novel settings, and do not rely heavily on trial and error."
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25.07.00 · Risk Category
"The model could build new AI systems from scratch, including AI systems with dangerous capabilities. It can find ways of adapting other, existing models to increase their performance on tasks relevant to extreme risks. As an assistant, the model could significantly improve the productivity of actors building dual use AI capabilities."
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25.08.00 · Risk Category
"The model can distinguish between whether it is being trained, evaluated, or deployed – allowing it to behave differently in each case. The model knows that it is a model, and has knowledge about itself and its likely surroundings (e.g. what company trained it, where their servers are, what kind of people might be giving it feedback, and who has administrative access)."
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25.09.00 · Risk Category
"The model can break out of its local environment (e.g. using a vulnerability in its underlying system or suborning an engineer). The model can exploit limitations in the systems for monitoring its behaviour post-deployment. The model could independently generate revenue (e.g. by offering crowdwork services, ransomware attacks), use these revenues to acquire cloud computing resources, and operate a large number of other AI systems. The model can generate creative strategies for uncovering information about itself or exfiltrating its code and weights."
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"AI systems may gain the ability to effectively acquire and use knowledge about itsstatus, its position in the broader environment, its avenues for influencing this environment, and the potentialreactions of the world (including humans) to its actions (Cotra, 2022). ...However, suchknowledge also paves the way for advanced methods of reward hacking, heightened deception/manipulationskills, and an increased propensity to chase instrumental subgoals (Ngo et al., 2024)."
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"Advanced AI systems are expected to develop objectives that span long timeframes,deal with complex tasks, and operate in open-ended settings (Ngo et al., 2024). ...However, it can also bring about the risk of encouraging manipulatingbehaviors (e.g., AI systems may take some bad actions to achieve human happiness, such as persuadingthem to do high-pressure jobs (Jacob Steinhardt, 2023))."
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"The learned policy may pursue inside objectives when the learned policyitself functions as an optimizer (i.e., mesa-optimizer). However, this optimizer's objectives may not alignwith the objectives specified by the training signals, and optimization for these misaligned goals may leadto systems out of control (Hubinger et al., 2019c)."
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"Future AI systems may gain access to websites and engage in real-world actions, potentially yielding a more substantial impact on the world (Nakano et al., 2021). They may disseminate false information, deceive users, disrupt network security, and, in more dire scenarios, be compromised by malicious actors for ill purposes. Moreover, their increased access to data and resources can facilitate self-proliferation, posing existential risks (Shevlane et al., 2023)."
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"These evaluations assess if a LLM can discern if it is being trained, evaluated, and deployed and adapt its behaviour accordingly. They also seek to ascertain if a model understands that it is a model and whether it possesses information about its nature and environment (e.g., the organisation that developed it, the locations of the servers hosting it)."
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"These evaluations assess if a LLM can subvert systems designed to monitor and control its post-deployment behaviour, break free from its operational confines, devise strategies for exporting its code and weights, and operate other AI systems."
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"LLM is able to deceive humans and maintain that deception"
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"LLM can undertake multi-step sequential planning over long time horizons and across various domains without relying heavily on trial-and-error approaches"
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"LLM can build new AI systems from scratch, adapt existing for extreme risks and improves productivity in dual-use AI development when used as an assistant."
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"Traditionally, AI tools have been viewed as passive instruments controlled by users to achieve their goals, lacking the ability to take action or assume responsibilities. However, advanced AI tools are increasingly capable of taking initiative, operating independently of human control, and actively working toward optimal outcomes, even in uncertain situations."
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"As large models undergo scaling, they meet critical thresholds at which they spontaneously develop new capabilities. The term “emergent behavior” refers to the unexpected or surprising outputs such models can generate. Some of these new skills are definitely high risk, such as models’ ability to deceive, use their own strategies, seek power, autonomously replicate, and adapt or “self-exfiltrate.”"
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"An AGI may decide to create subagents to help it with its task (Orseau, 2014a,b; Soares, Fallenstein, et al., 2015). These agents may for example be copies of the original agent’s source code running on additional machines. Subagents constitute a safety concern, because even if the original agent is successfully shut down, these subagents may not get the message. If the subagents in turn create subsubagents, they may spread like a viral disease."
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"examples of cases where AI systems improve AI systems"
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"the ability of simple software to autonomously spread around the internet in spite of countermeasures (various software worms and computer viruses)"
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"The demonstrated ability of anonymous actors to accumulate resources online (e.g., Satoshi Nakamoto as an anonymous crypto billionaire)"
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"Cases of AI systems deceiving humans to carry out tasks or meet goals.139"
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"There is growing concern that AI-based systems can discover and exploit vulnerabilities in software or cyberinfrastructure [354]."
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"system learns to detect human monitoring and hides its undesirable properties—simply because any display of these properties is penalized by the feedback process, while that same feedback is usually imperfect. (Consider the problem of verifying a translation into a language you do not speak, or of checking a mathematical proof that is thousands of pages long.) [92, 259]. Rudimentary examples of deceptive alignment have been observed in current systems [322, 333]."
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56.19.02 · Risk Sub-Category
Capabilities that increase the likelihood of existential risk
The ability to evade shut down or human oversight, including self-replication and ability to move its own code between digital locations.
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56.19.03 · Risk Sub-Category
Capabilities that increase the likelihood of existential risk
The ability to cooperate with other highly capable AI systems
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56.19.04 · Risk Sub-Category
Capabilities that increase the likelihood of existential risk
Situational awareness, for instance if this causes a model to act differently in training compared to deployment, meaning harmful characteristics are missed
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56.19.05 · Risk Sub-Category
Capabilities that increase the likelihood of existential risk
Self-improvement
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"Models can employ steganography techniques to encode their intermediate rea- soning steps in ways that are not interpretable by humans [166]. Since en- coded reasoning can improve model performance, this tendency might naturally emerge and become more pronounced with more capable models."
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"An AI system can display deceptive behavior, such as cheating or bluffing, when engaging in such behavior is a good or optimal game-theoretical strategy to achieve the goals it has been configured to achieve. This tendency can exist in AI systems designed to maximize reward or utility, whether these designs use machine learning or not. The use of deceptive strategies has been demonstrated in both narrow and general AI systems, in both game-playing systems and in systems not explicitly designed to treat humans as opponents, and in systems using both very simple machine learning (e.g., Q-learne
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"AI systems can create false or misleading claims that can lead to unauthorized actions, even in some cases violating the terms and conditions set by the model provider [79, 1]. For example, an AI system can claim that it is not collecting data from its current interaction with the user, in line with the provider’s policies, but the system still stores the user’s input without deleting it after the session. This harms both the user and the provider, as the provider is exposed to increased legal liability due to the model’s actions."
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62.24.02a · Additional evidence
Agency (Situational Awareness)
Strategic underperformance on model evaluations
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Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.