This groundbreaking research proves that hallucination is an inherent limitation of Large Language Models that cannot be completely eliminated, regardless of future improvements.

• Establishes a formal mathematical framework proving the impossibility of creating hallucination-free LLMs
• Demonstrates that hallucination is not just a technical challenge but a fundamental constraint
• Discusses practical implications for safely deploying LLMs in security-critical environments
• Shifts the conversation from elimination to management of hallucinations

For security professionals, this research is crucial as it suggests that rather than trying to eliminate hallucinations entirely, we must develop robust verification systems, establish appropriate use boundaries, and implement compensating controls when deploying LLMs in sensitive contexts.

Hallucination is Inevitable: An Innate Limitation of Large Language Models