MEraser: An Effective Fingerprint Erasure Approach for Large Language Models

TL;DR

MEraser is a novel two-phase fine-tuning method that effectively removes backdoor-based fingerprints from large language models with minimal data, preserving performance and enabling cross-model fingerprint erasure.

Contribution

This paper introduces MEraser, a new approach for removing backdoor fingerprints from LLMs using a two-phase fine-tuning strategy with minimal data and a transferable erasure mechanism.

Findings

Complete fingerprint removal achieved across multiple models.

Model performance maintained with fewer than 1,000 samples.

Transferable erasure enables cross-model fingerprint removal.

Abstract

Large Language Models (LLMs) have become increasingly prevalent across various sectors, raising critical concerns about model ownership and intellectual property protection. Although backdoor-based fingerprinting has emerged as a promising solution for model authentication, effective attacks for removing these fingerprints remain largely unexplored. Therefore, we present Mismatched Eraser (MEraser), a novel method for effectively removing backdoor-based fingerprints from LLMs while maintaining model performance. Our approach leverages a two-phase fine-tuning strategy utilizing carefully constructed mismatched and clean datasets. Through extensive evaluation across multiple LLM architectures and fingerprinting methods, we demonstrate that MEraser achieves complete fingerprinting removal while maintaining model performance with minimal training data of fewer than 1,000 samples. Furthermore, we introduce a transferable erasure mechanism that enables effective fingerprinting removal across different models without repeated training. In conclusion, our approach provides a practical solution for fingerprinting removal in LLMs, reveals critical vulnerabilities in current fingerprinting techniques, and establishes comprehensive evaluation benchmarks for developing more resilient model protection methods in the future.