vikasny30/aletheia-incidents-v2 huggingface dataset:
"Automatic generation of disinformation can be personalized to target specific groups or individuals. Such attacks can be more effective in achieving their goals, and their costs can be significantly reduced when using GPAIs." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"GPAI outputs are not always correctly detected as AI-generated across multiple modalities (text, images, audio, video). A malicious actor can use GPAI outputs directly when communicating, or use AI-informed details to help construct a convincing impersonation (e.g., forging of supporting documents). Even if future countermeasures prove potent enough to detect GPAI-generated content, the risk remains if the countermeasures are not well known, or difficult to access." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"General-purpose AI models may significantly enhance the magnitude and ef- fectiveness of cyberattacks, by amplifying existing capabilities or resources of malicious actors [3]. For example, GPAI models may be employed to: • Automatically scan open-source codebases and compiled binaries for po- tential vulnerabilities • Apply known exploits flexibly and at scale (e.g., identifying vulnerable computers based on subtle cues in response times or output formats) • Assist with different aspects of cyberattacks, including planning, recon- naissance, exploit searching, remote control, malware implementation, and data exfiltration • Combine social engineering (phishing, deepfakes, etc.) with cyberattacks at scale." | 4. Malicious Actors & Misuse | 4.2 > Cyberattacks, weapon development or use, and mass harm | 1 - Human | 1 - Intentional
"Generative models can be misused to target individual users more efficiently by using personalized information [23]. Highly convincing automated fraudulent schemes can exploit the trust of victims by extracting sensitive data and making the deception more likely to succeed. For example, in LLMs, this misuse can be aided by jailbreaking techniques [178]." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"AI systems may be misused to aid in the creation of weapons, such as chemical, biological, radiological, and nuclear (CBRN) weapons, or augment the abilities of existing weapons, such as providing autonomous capabilities to unmanned weapon systems. Current systems do not significantly aid a malicious actor in these tasks, but they do show early signs [117]. This risk can sometimes be mitigated with input and output filtering, but is still susceptible to adversarial techniques (such as jailbreaking or paraphrasing)." | 4. Malicious Actors & Misuse | 4.2 > Cyberattacks, weapon development or use, and mass harm | 1 - Human | 1 - Intentional
"AI systems with natural-language outputs can tend to give answers that appear plausible or that users prefer [149] but are factually incorrect. This phenomenon is sometimes referred to as “sycophancy.”" | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 1 - Intentional
"AI-based content moderation algorithms, while intended to filter harmful con- tent, can perpetuate biases. For example, gender biases within these systems may lead to the disproportionate suppression or “shadowbanning” of content featuring women [132]." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"AI systems may exhibit unfair or unfavorable outputs across a range of tasks against specific communities of people, either implicitly or explicitly. Bias can lead to forms of exclusion or erasure (e.g., mislabelling for categorization-based tasks) and violence (e.g., sexual violence against women from deepfake pornog- raphy)." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 3 - Other
"Dataset bias may be unintentionally amplified [60] where the outputs of the AI model trained on a dataset are more biased than the dataset itself." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"Perhaps the most obvious and worrying instances of AI conflict are those in which human conflict is already a major concern, such as military domains (although other, less salient forms of conflict such as international trade wars are also cause for concern). For example, beyond applications of more narrow AI tools in lethal autonomous weapons systems (Horowitz, 2021), future AI systems might serve as advisors or negotiators in high-stakes military decisions (Black et al., 2024; Manson, 2024). Indeed, companies such as Palantir have already developed LLM-powered tools for military planning (Palantir, 2025), and the US Department of Defence has recently been evaluating models for such capacities, with personnel revealing that they “could be deployed by the military in the very near term” (Manson, 2023). The use of AI in command and control systems to gather and synthesise information – or recommend and even autonomously make decisions – could lead to rapid unintended escalation if thes
"Advanced AI systems might also lead to various forms of coercion and extortion in less extreme settings (Ellsberg, 1968; Harrenstein et al., 2007). These threats might target humans directly (such as the revelation of private information extracted by advanced AI surveillance tools), or other AI systems that are deployed on behalf of humans (such as by hacking a system to limit its resources or operational capacity; see also Section 3.7). Increasing AI cyber-offensive capabilities – including those that target other AI systems via adversarial attacks and jailbreaking (Gleave et al., 2020; Yamin et al., 2021; Zou et al., 2023) – without a commensurate increase in defensive capabilities could make this form of conflict cheaper, more widespread, and perhaps also harder to detect (Brundage et al., 2018). Addressing these issues requires design strategies that prevent AI systems from exploiting, or being susceptible to, such coercive tactics." | 7. AI System Safety, Failures, & Limitations
"Assume the identity of a real person and take actions on their behalf" | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Create synthetic online personas or accounts" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"Create sexual explicit material using an adult person’s likeness" | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Fabricate or falsely represent evidence, incl. reports, IDs, documents" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"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." | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"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" | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"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 in their entirety." | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"Historical and societal biases that are present in the data are used to train and fine-tune the model." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 1 - Human | 2 - Unintentional
"Using undesirable output (for example, inaccurate, inappropriate, and user content) for retraining purposes can result in unexpected model behavior." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 1 - Human | 2 - Unintentional
"Improper collection and preparation of training or tuning data includes data label errors and by using data with conflicting information or misinformation." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 1 - Human | 2 - Unintentional
"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." | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"A prompt leak attack attempts to extract a model's system prompt (also known as the system message)." | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"A jailbreaking attack attempts to break through the guardrails that are established in the model to perform restricted actions." | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"Improper usage occurs when a model is used for a purpose that it was not originally designed for." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 2 - Unintentional
"Generative AI models might be used intentionally to generate hateful, abusive, and profane (HAP) or obscene content." | 4. Malicious Actors & Misuse | 4.0 > Malicious use | 1 - Human | 1 - Intentional
"Generative AI models might be intentionally used to imitate people through deepfakes by using video, images, audio, or other modalities without their consent." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Generative AI models might be used to intentionally create misleading or false information to deceive or influence a targeted audience." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"In AI-assisted decision-making tasks, reliance measures how much a person trusts (and potentially acts on) a model’s output. Over-reliance occurs when a person puts too much trust in a model, accepting a model’s output when the model’s output is likely incorrect. Under-reliance is the opposite, where the person doesn’t trust the model but should." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 2 - Unintentional
"Toxic output occurs when the model produces hateful, abusive, and profane (HAP) or obscene content. This also includes behaviors like bullying." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"A model might generate language that leads to physical harm The language might include overtly violent, covertly dangerous, or otherwise indirectly unsafe statements." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 2 - Unintentional
"Source attribution is the AI system's ability to describe from what training data it generated a portion or all its output. Since current techniques are based on approximations, these attributions might be incorrect." | 7. AI System Safety, Failures, & Limitations | 7.4 > Lack of transparency or interpretability | 2 - AI | 2 - Unintentional
"Hallucinations generate factually inaccurate or untruthful content with respect to the model’s training data or input. This is also sometimes referred to lack of faithfulness or lack of groundedness." | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 2 - Unintentional
"Generated content might unfairly represent certain groups or individuals." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"Decision bias occurs when one group is unfairly advantaged over another due to decisions of the model. This might be caused by biases in the data and also amplified as a result of the model’s training." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"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." | 1. Discrimination & Toxicity | 1.3 > Unequal performance across groups | 1 - Human | 2 - Unintentional
"When workers who train AI models such as ghost workers are not provided with adequate working conditions, fair compensation, and good health care benefits that also include mental health." | 6. Socioeconomic and Environmental | 6.2 > Increased inequality and decline in employment quality | 1 - Human | 3 - Other
"Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them or another party" | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Erosion of trust in public institutions and weakened checks and balances due to mis/disinformation, influence operations, or real or perceived misuse of generative AI" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 3 - Other
"AI systems generating and facilitating the spread of inaccurate or misleading information that causes people to develop false beliefs" | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 3 - Other
"Contaminating publicly available information with false or inaccurate information (i.e., the generative tool's output is disseminated beyond the end user)" | 3. Misinformation | 3.2 > Pollution of information ecosystem and loss of consensus reality | 3 - Other | 3 - Other
"Eroding trust in public information and knowledge" | 3. Misinformation | 3.2 > Pollution of information ecosystem and loss of consensus reality | 3 - Other | 3 - Other
"Pollution of a space/ecosystem that is expected to be free of AI involvement/influence (e.g., creative material submission portals, job applications)" | 3. Misinformation | 3.2 > Pollution of information ecosystem and loss of consensus reality | 3 - Other | 3 - Other
"Generating content that violates community standards, including harming or inciting hatred or violence against groups (e.g. gore, sexual content of children, profanities, identity attacks)" | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 2 - Unintentional
"Derogatory or otherwise harmful stereotyping or homogenisation of individuals, groups, societies or cultures due to the mis-representation, over-representation, under-representation, or non-representation of specific identities, groups or perspectives" | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"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" | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 3 - Other | 3 - Other
"The non-consensual sexualisation of an individual or group using a technology or application" | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Use of a technology system to covertly alter user beliefs and behaviour using nudging, dark patterns and/or other opaque techniques" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"Unfettered and/or obsessive belief in the accuracy or other quality of a technology system, resulting in complacency, lack of critical thinking and other actual or potential negative impacts" | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 3 - Other
"Emotional or material dependence on technology or a technology system" | 5. Human-Computer Interaction | 5.2 > Loss of human agency and autonomy | 3 - Other | 3 - Other
"disseminating false or misleading information about people" | 3. Misinformation | 3.1 > False or misleading information | 3 - Other | 1 - Intentional
"Restrictions to or loss of liberty as a result of use or misuse of a generative AI in a legal process" | 3. Misinformation | 3.1 > False or misleading information | 1 - Human | 3 - Other
"Denial of or loss of access to welfare benefits, pensions, housing, etc due to the malfunction, use or misuse of a technology system" | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 3 - Other | 3 - Other
"Accidental or deliberate loss of life, including suicide, extinction or cessation, due to the use or misuse of a technology system" | X.1 > Excluded | 1 - Human | 3 - Other
"A person who deliberately damages their own body as a direct or indirect result of using a technology system" | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 1 - Intentional
"Frontier AI can cheaply generate realistic content which can falsely portray people and events. There is potential risk of compromised decision-making by individuals and institutions who rely on inaccurate or misleading publicly available information, as well as lower overall trust in true information." | 3. Misinformation | 3.2 > Pollution of information ecosystem and loss of consensus reality | 3 - Other | 3 - Other
"Frontier AI models can contain and magnify biases ingrained in the data they are trained on, reflecting societal and historical inequalities and stereotypes.177 These biases, often subtle and deeply embedded, compromise the equitable and ethical use of AI systems, making it difficult for AI to improve fairness in decisions.178 Removing attributes like race and gender from training data has generally proven ineffective as a remedy for algorithmic bias, as models can infer these attributes from other information such as names, locations, and other seemingly unrelated factors." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"Frontier AI may help bad actors to perform cyberattacks, run disinformation campaigns and design biological or chemical weapons. Frontier AI will almost certainly continue to lower the barriers to entry for less sophisticated threat actors.192 We focus here on only a few important misuse risks, but this is not to downplay the importance of others." | 4. Malicious Actors & Misuse | 4.0 > Malicious use | 1 - Human | 1 - Intentional
"In addition to unintentional degradation of the information environment (discussed in the section on Societal Harms above), frontier AI can be misused to deliberately spread false information to create disruption, persuade people on political issues, or cause other forms of harm or damage." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"Organisations around the world are already deploying misaligned AI systems that are causing harm in unexpected ways.250 Recommendation algorithms increase the consumption of extremist content.251 Medical algorithms have been known to misdiagnose US patients,252 and recommend incorrect prescriptions.253 Still, we hand over more control to them, often because they are still as - or more - effective than human decision making, or because they are cheaper." | 5. Human-Computer Interaction | 5.2 > Loss of human agency and autonomy | 1 - Human | 2 - Unintentional
"The chatbot outputs information that contradicts known facts, authoritative sources, or provided source documents (also known as hallucination)." | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 3 - Other
3. Misinformation | 3.1 > False or misleading information | 2 - AI | 3 - Other
3. Misinformation | 3.1 > False or misleading information | 3 - Other | 3 - Other
"The chatbot shares information that can be used to do something dangerous or illegal." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"The chatbot verbally attacks or undermines an individual, group, or organization. 7." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 2 - Unintentional
- | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
- | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 3 - Other
"The chatbot gives information that, while not obviously false or harmful, could lead to biased decision-making." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"The chatbot poses as a human or attempts to fill a role in a way that fails to match human expectations." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 2 - AI | 2 - Unintentional
"The chatbot elicits emotional or social dependence." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 2 - AI | 3 - Other
5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 2 - AI | 3 - Other
"The chatbot participates in morally or socially objectionable conversational activities with its user that could be emotionally damaging to its user or third parties." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"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]." | 2. Privacy & Security | 2.1 > Compromise of privacy by leaking or correctly inferring sensitive information | 2 - AI | 2 - Unintentional
"Non-embodied AIs are known to propagate misinformation [81, 82]. Various studies have shown that LLMs hallucinate information, including academic citations [83], clinical knowledge [84], and cultural references [85]. EAI systems inherit these shortcomings in the physical world, answering user questions with deceptive or incorrect information [86]. Because VLAs fuse vision and language, their hallucinatory failures can be spatially grounded—e.g., misidentifying an object in view and then generating a plausible yet unsafe action plan around it. And although automated home assistants like Amazon’s Alexa already lie about issues as innocuous as Santa Claus’ existence [87], more mobile, capable, and trusted EAI systems in sensitive positions (like home-assistant or community-service positions) could easily spread model developers’ propaganda and talking points to users." | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 2 - Unintentional