AI hallucinations are introducing serious security risks into critical infrastructure decision-making by exploiting human trust through highly confident yet incorrect outputs. When an AI model lacks certainty, it doesn’t have a mechanism to recognize that. Instead, it generates the most probable response based on patterns in its training data, even if that response is inaccurate. These outputs may appear authoritative, making them especially dangerous when driving real-world security decisions.

Based on Artificial Analysis’s AA-Omniscience benchmark, a 2025 evaluation of 40 AI models found that all but four models tested were more likely to provide a confident, incorrect answer than a correct one on difficult questions. As AI takes on a larger role in cybersecurity operations, organizations must treat every AI-generated response as a potential vulnerability until a human has verified it.

What are AI hallucinations?

AI hallucinations are confidently presented, plausible-sounding outputs that are factually inaccurate. Base language models don’t retrieve verified information; they construct responses by predicting words and phrases from learned patterns in their training data. Since their responses are statistically likely but not necessarily true, hallucinated outputs can closely resemble accurate information. While hallucinating, AI models may cite nonexistent sources, reference research that was never conducted or present fabricated data with the same conviction as trusted information.

For organizations, the main issue surrounding AI hallucinations is not only inaccuracy but also misplaced trust. When an AI output sounds like the absolute truth, employees may assume it is correct and act on it without verification. In cybersecurity environments, incorrect AI outputs pose significant security risks because they not only inform key decisions but also feed directly into automated systems that can trigger operational actions. The results can include system disruptions, financial loss and the introduction of new vulnerabilities.

What causes AI hallucinations?

The first step toward mitigating the impact of AI hallucinations is understanding how they form. Here are the various factors that may contribute to AI hallucinations:

• Flawed training data: AI models learn from the data they are trained on. If that data contains outdated information or outright errors, the model will incorporate those flaws into its outputs. It won’t flag the discrepancies; it will learn from them.
• Bias in input data: Overrepresentation of certain patterns or scenarios can cause an AI model to treat those patterns as universally applicable, even when the context differs.
• Lack of response validation: Base language models aren’t built to verify factual accuracy. They optimize for coherent, plausible outputs. While some systems add retrieval or grounding layers to reduce this risk, the core generation process remains vulnerable to hallucinations.
• Prompt ambiguity: Vague inputs increase the likelihood that AI models will fill in gaps with assumptions, raising the risk of incorrect outputs and hallucinations.

3 ways AI hallucinations are impacting cybersecurity

Not every AI hallucination has equal impact, but incorrect or fabricated information can leave organizations vulnerable to serious cyber threats. Three main ways AI hallucinations manifest are missed threats, fabricated threats and incorrect solutions.

1. Missed threats

AI threat detection often relies on identifying patterns and anomalies based on historical data and learned behavior. When a cyber attack aligns with known behaviors, the AI model performs well; but when it doesn’t, the model has nothing to compare it to, so the threat may go unnoticed. This is especially problematic for underrepresented attack techniques and zero-day attacks, which exploit vulnerabilities unknown to the vendor and are therefore unpatched. Because these threats are not reflected in training data, the AI model lacks sufficient context to flag them, resulting in a higher likelihood of undetected vulnerabilities and greater exposure within the environment.

2. Fabricated threats

In contrast to missed threats, AI models may also hallucinate false positives by misclassifying normal activity as malicious, alerting teams to threats that do not exist. For example, normal network traffic may be misinterpreted as suspicious, triggering alerts that prompt unnecessary incident response actions. These false alarms can lead to system shutdowns, wasted resources and disrupted operations for fabricated threats. Over time, repeated false positives can lead to alert fatigue, where security teams become desensitized to all warnings. This increases the risk that legitimate threats will be overlooked in environments where teams have been conditioned to distrust alerts.

3. Incorrect remediation

This is one of the most dangerous forms of AI hallucination since it occurs after trust has already been established. For example, an AI system may confidently recommend deleting sensitive files, modifying system configurations or disabling firewall rules. If these actions are executed, particularly through privileged accounts, they can leave organizations exposed to identity-based attacks, lateral movement or irreversible data loss. Even when AI threat detection is accurate, hallucinated guidance can escalate a contained security incident into a broader breach.

How organizations can reduce AI hallucination risks

Although AI hallucinations cannot be fully eliminated, their impact can be significantly reduced through the following controls and governance measures.

Require human review before action

AI-generated outputs should not trigger sensitive or privileged actions without human verification first. This is especially important for workflows involving infrastructure changes, access updates or incident response. The review requirement should not only happen when something seems wrong; models can sound equally confident whether they’re right or wrong.

Treat training data as a security asset

AI hallucinations often trace back to training data. Regularly auditing the data used to train or ground AI systems by eliminating outdated records, biased datasets and inaccurate information reduces the likelihood that those flaws will appear in outputs. As AI-generated content becomes more common online, there is an increased risk of future models being trained on fabricated information produced by earlier models, in a phenomenon sometimes referred to as model collapse. Without continuous data governance, the risk of flawed AI outputs only increases.

Enforce least-privilege access for AI systems

AI-driven systems should be granted only the permissions they need to perform their tasks. This may look like an AI system that is allowed to read files only, not delete them – even if a hallucinated recommendation tells it to. By restricting access with least privilege, organizations ensure that even if an AI system generates incorrect guidance, it cannot execute actions beyond what it is allowed to do.

Invest in prompt engineering training

AI outputs are heavily shaped by input quality, so a vague prompt gives the model more opportunity to fill gaps with incorrect assumptions, increasing the risk of hallucination. Organizations must prioritize training employees, especially those who directly interact with AI systems, on how to write specific prompts that drive the model to produce verifiable outputs. Employees who understand that AI outputs should always be validated before use are less likely to interpret the AI system as authoritative by default.

Place identity security at the center of AI governance

AI hallucinations become real security risks when they lead to action, which is not primarily a model problem but rather an access problem. Security incidents arise when AI systems have enough access to act on incorrect guidance, or when a human trusts outputs without verification. Keeper® is built to provide organizations with the visibility and access controls needed to prevent unauthorized access, even when AI-driven decisions are incorrect. By enforcing least-privilege access, monitoring privileged activity and securing both human and Non-Human Identities (NHIs), organizations can reduce the risk of AI hallucinations evolving into damaging security incidents.

Note: This article was thoughtfully written and contributed for our audience by Ashley D’Andrea, Content Writer at Keeper Security.

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