This research investigates the calibration gap between LLM confidence and actual performance, highlighting critical security implications for high-stakes applications.

• LLMs frequently display overconfidence when confronted with challenging questions
• Model performance was evaluated across different model sizes and with varying levels of distractor information
• Multiple-choice format questions revealed significant improvements in confidence calibration
• Findings indicate substantial security risks when deploying potentially overconfident LLMs in critical applications

For security professionals, this research provides valuable insights into assessing and mitigating risks from AI systems that may be overly confident in incorrect outputs, particularly in high-security contexts.

Mind the Confidence Gap: Overconfidence, Calibration, and Distractor Effects in Large Language Models