  As content generated by Large Language Model (LLM) has grown exponentially,
the ability to accurately identify and fingerprint such text has become
increasingly crucial. In this work, we introduce a novel black-box approach for
fingerprinting LLMs, achieving an impressive 72% accuracy in identifying the
correct family of models (Such as Llama, Mistral, Gemma, etc) among a lineup of
LLMs. We present an evolutionary strategy that leverages the capabilities of
one LLM to discover the most salient features for identifying other LLMs. Our
method employs a unique "Hide and Seek" algorithm, where an Auditor LLM
generates discriminative prompts, and a Detective LLM analyzes the responses to
fingerprint the target models. This approach not only demonstrates the
feasibility of LLM-driven model identification but also reveals insights into
the semantic manifolds of different LLM families. By iteratively refining
prompts through in-context learning, our system uncovers subtle distinctions
between model outputs, providing a powerful tool for LLM analysis and
verification. This research opens new avenues for understanding LLM behavior
and has significant implications for model attribution, security, and the
broader field of AI transparency.
