Cognitive Honeypots: Leveraging Logical Contradictions to Detect and Analyze Adversarial AI Behavior
Kush Janani · Qeios · 2025
Traditional honeypots are designed to attract human attackers by mimicking vulnerable systems, but as artificial intelligence (AI) becomes increasingly sophisticated, new security paradigms are needed. This paper introduces the concept of ”cognitive honeypots” - a novel approach that leverages logical contradictions to detect and analyze adversarial AI behavior. Unlike conventional security measures that focus on patching vulnerabilities or making them difficult to exploit, cognitive honeypots intentionally present logical inconsistencies designed to attract adversarial AI systems. By analyzing how AI attackers might engage with these cognitive traps, defenders could discover new classes of adversarial reasoning, biases, and vulnerabilities embedded in model logic. We present a theoretical framework for cognitive honeypots, propose an implementation architecture, and discuss their potential effectiveness against various types of adversarial AI. Our analysis suggests that cognitive honeypots could enable unprecedented proactive security measures against emerging AI threats and contribute to the development of more robust AI systems.