This research reveals a novel attack vector that exploits LLMs by eliciting confidence information, enabling adversaries to extract protected data while maintaining only black-box access.

• Confidence elicitation attacks force LLMs to reveal their uncertainty about responses, exposing model vulnerabilities
• Demonstrates how attackers can effectively bypass security measures in closed API models like ChatGPT
• Shows that even models lacking direct access to confidence scores remain susceptible to these attacks
• Raises critical concerns for AI security frameworks that rely on traditional protection mechanisms

This research is vital for security professionals as it exposes limitations in current LLM defense strategies and highlights the need for new safeguards against sophisticated confidence-based attacks in deployed AI systems.

Confidence Elicitation: A New Attack Vector for Large Language Models