DISC InfoSec blogAI Hallucinations Are Real—And They’re a Threat to Cybersecurity

May 19 2025

AI Hallucinations Are Real—And They’re a Threat to Cybersecurity

Category: AI,Cyber Threats,Threat detection — disc7 @ 1:29 pm

AI hallucinations—instances where AI systems generate incorrect or misleading outputs—pose significant risks to cybersecurity operations. These errors can lead to the identification of non-existent vulnerabilities or misinterpretation of threat intelligence, resulting in unnecessary alerts and overlooked genuine threats. Such misdirections can divert resources from actual issues, creating new vulnerabilities and straining already limited Security Operations Center (SecOps) resources.

A particularly concerning manifestation is “package hallucinations,” where AI models suggest non-existent software packages. Attackers can exploit this by creating malicious packages with these suggested names, a tactic known as “slopsquatting.” Developers, especially those less experienced, might inadvertently incorporate these harmful packages into their systems, introducing significant security risks.

The over-reliance on AI-generated code without thorough verification exacerbates these risks. While senior developers might detect errors promptly, junior developers may lack the necessary skills to audit code effectively, increasing the likelihood of integrating flawed or malicious code into production environments. This dependency on AI outputs without proper validation can compromise system integrity.

AI can also produce fabricated threat intelligence reports. If these are accepted without cross-verification, they can misguide security teams, causing them to focus on non-existent threats while real vulnerabilities remain unaddressed. This misallocation of attention can have severe consequences for organizational security.

To mitigate these risks, experts recommend implementing structured trust frameworks around AI systems. This includes using middleware to vet AI inputs and outputs through deterministic checks and domain-specific filters, ensuring AI models operate within defined boundaries aligned with enterprise security needs.

Traceability is another critical component. All AI-generated responses should include metadata detailing source context, model version, prompt structure, and timestamps. This information facilitates faster audits and root cause analyses when inaccuracies occur, enhancing accountability and control over AI outputs.

Furthermore, employing Retrieval-Augmented Generation (RAG) can ground AI outputs in verified data sources, reducing the likelihood of hallucinations. Incorporating hallucination detection tools during testing phases and defining acceptable risk thresholds before deployment are also essential strategies. By embedding trust, traceability, and control into AI deployment, organizations can balance innovation with accountability, minimizing the operational impact of AI hallucinations.

Source: AI hallucinations and their risk to cybersecurity operations

Suggestions to counter AI hallucinations in cybersecurity operations:

Human-in-the-loop (HITL): Always involve expert review for AI-generated outputs.
Use Retrieval-Augmented Generation (RAG): Ground AI responses in verified, real-time data.
Implement Guardrails: Apply domain-specific filters and deterministic rules to constrain outputs.
Traceability: Log model version, prompts, and context for every AI response to aid audits.
Test for Hallucinations: Include hallucination detection in model testing and validation pipelines.
Set Risk Thresholds: Define acceptable error boundaries before deployment.
Educate Users: Train users—especially junior staff—on verifying and validating AI outputs.
Code Scanning Tools: Integrate static and dynamic code analysis tools to catch issues early.