This research introduces an innovative transformer-based framework that analyzes LLM output patterns to detect problematic behaviors like hallucinations and data contamination without requiring access to internal model parameters.

• Leverages output signature analysis from LLM-generated tokens
• Offers a practical alternative to "white-box" methods that require internal model access
• Provides a robust approach for verifying LLM reliability in production environments
• Addresses critical security and trustworthiness concerns in deployed LLM systems

This advancement is particularly valuable for security professionals who need to evaluate third-party LLMs where internal access is restricted, helping organizations deploy AI systems with greater confidence in their reliability and safety.

Learning on LLM Output Signatures for gray-box LLM Behavior Analysis