# Hide and Seek: Fingerprinting Large Language Models with Evolutionary Learning

arXiv: https://arxiv.org/abs/2408.02871 · HTML: https://mlsanity.com/paper/2408.02871/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Stealing Part of a Production Language Model](https://mlsanity.com/paper/2403.06634/) (2024)
- [Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting](https://mlsanity.com/paper/2310.16523/) (2023)
- [Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning](https://mlsanity.com/paper/2404.16807/) (2024)

## Built on by (most cited first, 18 of 18)

- [REEF: Representation Encoding Fingerprints for Large Language Models](https://mlsanity.com/paper/2410.14273/) (2024)
- [Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking](https://mlsanity.com/paper/2503.04636/) (2025)
- [SoK: Large Language Model Copyright Auditing via Fingerprinting](https://mlsanity.com/paper/2508.19843/) (2025)
- [Scalable Fingerprinting of Large Language Models](https://mlsanity.com/paper/2502.07760/) (2025)
- [SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From](https://mlsanity.com/paper/2509.26404/) (2025)
- [DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection](https://mlsanity.com/paper/2505.16530/) (2025)
- [Are Robust LLM Fingerprints Adversarially Robust?](https://mlsanity.com/paper/2509.26598/) (2025)
- [Hardware and Software Platform Inference](https://mlsanity.com/paper/2411.05197/) (2024)
- [LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysis](https://mlsanity.com/paper/2502.20589/) (2025)
- [FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprinting](https://mlsanity.com/paper/2501.16029/) (2025)
- [Black-Box Forensics for Conversational LLM Agents](https://mlsanity.com/paper/2606.22698/) (2026)
- [What LLMs Think When You Don't Tell Them What to Think About?](https://mlsanity.com/paper/2602.01689/) (2026)
- [Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking across Datasets, Models, and Generated Content](https://mlsanity.com/paper/2605.29245/) (2026)
- [FNF: Functional Network Fingerprint for Large Language Models](https://mlsanity.com/paper/2601.22692/) (2026)
- [Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space](https://mlsanity.com/paper/2608.07786/) (2026)
- [Token Counts Are Not Model Lineage: A Frozen-Threshold Holdout Study of Black-Box LLM API Fingerprinting](https://mlsanity.com/paper/2608.29930/) (2026)
- [AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes](https://mlsanity.com/paper/2609.00052/) (2026)
- [System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer](https://mlsanity.com/paper/2610.00253/) (2026)

## Abstract (arXiv)

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.