Hallucination-Driven Exploits: Weaponizing AI False Confidence in Cybersecurity Systems

This work examines hallucination-driven exploit pathways emerging in AI-assisted cybersecurity environments. Rather than focusing on direct model compromise, the paper identifies how confident but weakly grounded AI interpretations can influence analyst judgment, delay threat recognition, and subtly degrade defensive posture. The study introduces the concept of hallucination-driven exploits as a cognitive risk surface created by the interaction between model uncertainty and human automation bias. Practical SOC and incident response scenarios are analyzed to demonstrate how these failures propagate even when the AI system remains policy compliant. By framing confidence miscalibration as an attack vector, this work highlights the need for detection, monitoring, and trust-calibration mechanisms in AI-mediated security operations.