AI illusions are abusing human trust through reliable but inaccurate outputs, posing serious security risks to critical infrastructure decision-making. If an AI model lacks certainty, there is no mechanism to recognize it. Instead, it generates the most likely response based on patterns in the training data, even if that response is inaccurate. These outputs can appear authoritative and are especially dangerous when making real-world security decisions.
Based on Artificial Analysis’s AA-Omniscience benchmark, we evaluated 40 AI models in 2025 and found that all but four models tested were more likely to confidently give inaccurate answers to difficult questions than to give correct answers. As AI plays a growing role in cybersecurity operations, organizations must treat all responses generated by AI as potential vulnerabilities until verified by humans.
What is AI hallucination?
AI hallucinations are convincingly presented and plausible-sounding outputs that are factually inaccurate. The base language model does not capture verified information. They construct responses by predicting words and phrases from learned patterns in training data. Although their reactions are statistically likely, they are not necessarily true, so hallucinatory output can be very similar to accurate information. While hallucinating, AI models may cite non-existent sources, refer to studies that have never been conducted, or present fabricated data with the same confidence as reliable information.
For organizations, the main issue surrounding AI illusions is not just inaccuracy, but also a false sense of trust. If the AI output sounds like the absolute truth, employees are likely to assume it is correct and act on it without verifying it. In a cybersecurity environment, incorrect AI output poses a significant security risk. This is because erroneous AI outputs feed directly into automated systems that can not only inform critical decisions but also trigger operational actions. This can result in system interruptions, financial losses, and the introduction of new vulnerabilities.
What causes AI hallucinations?
The first step to reducing the effects of AI hallucinations is to understand how they form. Various factors that can contribute to AI hallucinations include:
Defective training data: AI models learn from the data used to train them. If the data contains outdated information or outright errors, the model incorporates those flaws into the output. It does not flag discrepancies. It will learn from them. Input data bias: Overrepresentation of certain patterns or scenarios can cause AI models to treat those patterns as universally applicable, even in different contexts. Lack of response validation: The base language model is not built to validate factual accuracy. Optimize for consistent and reasonable output. Although some systems add search and ground layers to mitigate this risk, the core generation process is still vulnerable to hallucinations. Immediate ambiguity: Ambiguous inputs increase the likelihood that AI models will fill in the gaps with assumptions, increasing the risk of false outputs and hallucinations.
3 ways AI illusions impact cybersecurity
While not all AI hallucinations have the same impact, misinformation or fabricated information can leave organizations vulnerable to serious cyber threats. The three main ways AI hallucinations manifest themselves are through 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 behaviors. If the cyber attack matches known behavior, the AI model will perform well. But if this is not the case, the threat may go unnoticed because the model has nothing to compare it to. This is especially true for underrated attack vectors and zero-day attacks that exploit unpatched vulnerabilities from unknown vendors. Because these threats are not reflected in the training data, the AI model lacks sufficient context to alert you, increasing the likelihood that vulnerabilities will go undetected and exposed in your environment.
2. Manufactured threats
In contrast to missed threats, AI models can also hallucinate false positives by misclassifying normal activity as malicious and alerting your team to threats that don’t exist. For example, normal network traffic can be misinterpreted as suspicious, triggering alerts that prompt unnecessary incident response actions. These false alarms can cause system shutdowns, wasted resources, and disrupt operations against fabricated threats. Over time, repeated false positives can lead to alert fatigue, where security teams become desensitized to all alerts. This increases the risk that legitimate threats will be missed in an environment where teams are conditioned not to trust alerts.
3. Improper repair
This is one of the most dangerous forms of AI hallucinations because it occurs after trust has already been established. For example, an AI system might confidently recommend deleting sensitive files, changing system configurations, or disabling firewall rules. These actions, especially when performed through privileged accounts, can expose organizations to identity-based attacks, lateral movement, or irrecoverable data loss. Even if AI threat detection is accurate, illusion induction can cause a contained security incident to develop into a broader breach.
How organizations can reduce AI hallucination risk
While AI illusions cannot be completely eliminated, the following controls and governance measures can significantly reduce their impact.
Require human review before action
Output generated by AI should not trigger sensitive or privileged actions without human verification. This is especially important for workflows involving infrastructure changes, access updates, or incident response. Review requirements should not only arise if something seems wrong. A model can sound equally confident whether it’s right or wrong.
Treat training data as a security asset
AI hallucinations are often traced back to training data. By eliminating outdated records, biased datasets, and inaccurate information, regularly auditing the data used to train and ground your AI systems reduces the likelihood that those flaws will show up in your output. As AI-generated content becomes more common online, the risk that future models will be trained on fabricated information generated by previous models increases. This phenomenon is also called model collapse. Without continuous data governance, the risk of defective AI outputs only increases.
Enforce least privilege access for AI systems
AI-driven systems should be given only the permissions they need to perform their tasks. This might look like an AI system that is only allowed to read files, not delete them, even with illusory recommendations. By restricting access to least privilege, organizations ensure that AI systems cannot take any further action than is allowed, even if they generate incorrect guidance.
Invest in rapid engineering training
Since an AI’s output is highly dependent on the quality of its input, ambiguous prompts give the model an opportunity to fill in the gaps with incorrect assumptions, increasing the risk of hallucinations. Organizations should prioritize training employees, especially those who interact directly with AI systems, on how to write specific prompts that drive models and produce verifiable output. Employees who understand that AI output should always be verified before use are less likely to interpret AI systems as trustworthy by default.
Put identity security at the center of AI governance
AI illusions become real security risks when they lead to action. This is not primarily a model issue, but rather an access issue. Security incidents occur when AI systems have sufficient access to act on incorrect guidance, or when humans trust their output without verification. Keeper® is built to give organizations the visibility and access control they need to prevent unauthorized access, even when AI-driven decisions are wrong. By enforcing least-privileged access, monitoring privileged activity, and protecting both human and non-human identities (NHI), organizations can reduce the risk of AI hallucinations turning into harmful security incidents.
Note: This article was thoughtfully written and contributed by Keeper Security content writer Ashley D’Andrea for our readers.
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