As artificial intelligence becomes increasingly integrated into cybersecurity operations, experts are raising the alarm about the growing risks posed by AI hallucinations, instances where AI systems produce incorrect or misleading information. These inaccuracies can range from minor flaws to significant distortions, potentially compromising the integrity of threat detection and response strategies.
According to Harman Kaur, Vice President of AI at Tanium, AI-driven errors can cause considerable disruption when leveraged by systems reliant on outdated or erroneous data. “AI hallucinations might fabricate vulnerabilities or misread threat intelligence, leading to wasted resources and missed real threats,” Kaur noted in a recent interview with Help Net Security. In security operations (SecOps) where resources are already stretched thin, misdirection due to AI inaccuracies can create serious blind spots.
One especially concerning issue is “package hallucinations,” where AI tools suggest software packages that don’t actually exist. This has opened the door to a new form of cyberattack known as “slopsquatting.” In this method, malicious actors create counterfeit software packages based on these hallucinated names, tricking developers, particularly less experienced ones, into incorporating harmful code. Ilia Kolochenko, CEO of ImmuniWeb, cautioned that junior developers may be especially vulnerable, while senior engineers are also at risk due to growing overreliance on generative AI tools.
Fake Threat Intelligence and the Need for Human Oversight
Another dimension of AI hallucination is the generation of bogus threat intelligence. If unverified reports are trusted at face value, security teams may be led astray, leaving actual vulnerabilities unaddressed. This issue is compounded by the lack of rigorous verification protocols in many AI deployments.
Chetan Conikee, CTO at Qwiet AI, explained that hallucinations are an inherent part of probabilistic AI models and cannot be entirely eliminated. Instead, organizations should focus on limiting their operational impact. “The CISO’s goal should be minimizing disruption through deliberate system design, continuous monitoring, and policy enforcement,” he emphasized.
A strong defense begins with intentional architecture. Conikee recommends integrating middleware that applies deterministic checks and domain-specific filters to validate AI outputs before they are acted upon. Additionally, AI-generated responses should be traceable, complete with metadata on source context, model version, prompt structure, and timestamps to aid audits and error tracing when things go wrong.
Mitigation Strategies to Control AI Hallucinations
To manage the risks associated with hallucinations, cybersecurity leaders are turning to a range of mitigation strategies. One of the most effective approaches is Retrieval-Augmented Generation (RAG), which ensures that generative AI tools pull information from trusted internal databases. This technique reduces reliance on potentially flawed external data and improves consistency with enterprise standards.
Companies like Amazon are developing automated reasoning tools that use mathematical verification to validate AI outputs. Keeping training data up-to-date, incorporating human oversight, and educating users about the limitations of AI are also crucial.
Victor Wieczorek, SVP of Offensive Security at GuidePoint Security, likened AI to a new intern: “It can help draft ideas and handle routine tasks, but it shouldn’t make final decisions without human review.” His advice reinforces a core message shared by cybersecurity experts while AI can accelerate operations, trust and accountability must remain firmly human-led.
