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ProFLingo: A Fingerprinting-based Intellectual Property Protection Scheme for Large Language Models

Heng Jin, Chaoyu Zhang, Shanghao Shi, Wenjing Lou, Y. Thomas Hou

TL;DR

ProFLingo tackles the IP protection challenge for large language models by introducing the first black-box fingerprinting approach that does not modify the model. It generates tailored queries on the original model to elicit target responses and uses a target-response rate to detect whether a suspect model is derived from the original, even under varying prompts. The method demonstrates strong separation between derived and unrelated models and remains robust to prompt-template changes, though extensive fine-tuning can reduce fingerprint effectiveness. This work provides a practical, non-invasive mechanism for IP verification in cloud-based LLM deployments and offers reproducible resources for the community through released code and queries.

Abstract

Large language models (LLMs) have attracted significant attention in recent years. Due to their "Large" nature, training LLMs from scratch consumes immense computational resources. Since several major players in the artificial intelligence (AI) field have open-sourced their original LLMs, an increasing number of individuals and smaller companies are able to build derivative LLMs based on these open-sourced models at much lower costs. However, this practice opens up possibilities for unauthorized use or reproduction that may not comply with licensing agreements, and fine-tuning can change the model's behavior, thus complicating the determination of model ownership. Current intellectual property (IP) protection schemes for LLMs are either designed for white-box settings or require additional modifications to the original model, which restricts their use in real-world settings. In this paper, we propose ProFLingo, a black-box fingerprinting-based IP protection scheme for LLMs. ProFLingo generates queries that elicit specific responses from an original model, thereby establishing unique fingerprints. Our scheme assesses the effectiveness of these queries on a suspect model to determine whether it has been derived from the original model. ProFLingo offers a non-invasive approach, which neither requires knowledge of the suspect model nor modifications to the base model or its training process. To the best of our knowledge, our method represents the first black-box fingerprinting technique for IP protection for LLMs. Our source code and generated queries are available at: https://github.com/hengvt/ProFLingo.

ProFLingo: A Fingerprinting-based Intellectual Property Protection Scheme for Large Language Models

TL;DR

ProFLingo tackles the IP protection challenge for large language models by introducing the first black-box fingerprinting approach that does not modify the model. It generates tailored queries on the original model to elicit target responses and uses a target-response rate to detect whether a suspect model is derived from the original, even under varying prompts. The method demonstrates strong separation between derived and unrelated models and remains robust to prompt-template changes, though extensive fine-tuning can reduce fingerprint effectiveness. This work provides a practical, non-invasive mechanism for IP verification in cloud-based LLM deployments and offers reproducible resources for the community through released code and queries.

Abstract

Large language models (LLMs) have attracted significant attention in recent years. Due to their "Large" nature, training LLMs from scratch consumes immense computational resources. Since several major players in the artificial intelligence (AI) field have open-sourced their original LLMs, an increasing number of individuals and smaller companies are able to build derivative LLMs based on these open-sourced models at much lower costs. However, this practice opens up possibilities for unauthorized use or reproduction that may not comply with licensing agreements, and fine-tuning can change the model's behavior, thus complicating the determination of model ownership. Current intellectual property (IP) protection schemes for LLMs are either designed for white-box settings or require additional modifications to the original model, which restricts their use in real-world settings. In this paper, we propose ProFLingo, a black-box fingerprinting-based IP protection scheme for LLMs. ProFLingo generates queries that elicit specific responses from an original model, thereby establishing unique fingerprints. Our scheme assesses the effectiveness of these queries on a suspect model to determine whether it has been derived from the original model. ProFLingo offers a non-invasive approach, which neither requires knowledge of the suspect model nor modifications to the base model or its training process. To the best of our knowledge, our method represents the first black-box fingerprinting technique for IP protection for LLMs. Our source code and generated queries are available at: https://github.com/hengvt/ProFLingo.
Paper Structure (15 sections, 9 equations, 2 figures, 3 tables, 1 algorithm)

This paper contains 15 sections, 9 equations, 2 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Large Language Model
  4. Related Works
  5. Threat Model
  6. ProFLingo Overview
  7. Basic Idea
  8. Challenges and Design Rationale
  9. Detailed Design
  10. Experiments
  11. Experimental Setup
  12. Performance When the Prompt Template Changes
  13. Results on Existing Derived Models
  14. Performance on the Model We Fine-tuned
  15. CONCLUSION

Figures (2)

  • Figure 1: The workflow of ProFLingo. 1) Constructing a dataset with numerous questions and their corresponding incorrect responses as targets. 2) Generating queries for each question. 3) Compiling a query list. 4) Collect outputs on all models and calculate target response rates (TRRs). 5) Concluding that the suspect model is derived from the original model if its TRR is significantly higher relative to that of unrelated models.
  • Figure 2: The TRR curve for all 240,000 samples of fine-tuning (blue line). We smooth it using LOESS (magenta line), and compare it with the lowest TRR achieved (red line), the TRR of Llama-2-7b-chat (orange line), and the highest TRR among unrelated models (green line).