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.

The proliferation of large language models (LLMs) has intensified concerns over model theft and license violations, necessitating robust and stealthy ownership verification.Existing fingerprinting methods either require impractical white-box access or introduce detectable statistical anomalies.We propose Ever-Tracer, a novel gray-box fingerprinting framework that ensures stealthy and robust model provenance tracing.EverTracer is the first to repurpose Membership Inference Attacks (MIAs) for defensive use, embedding ownership signals via memorization instead of artificial trigger-output overfitting.It consists of Fingerprint Injection, which fine-tunes the model on any natural language data without detectable artifacts, and Verification, which leverages calibrated probability variation signal to distinguish fingerprinted models.This approach remains robust against adaptive adversaries, including input level modification, and model-level modifications.Extensive experiments across architectures demonstrate Ev-erTracer's state-of-the-art effectiveness, stealthness, and resilience, establishing it as a practical solution for securing LLM intellectual property.Our code and data are publicly available at https://github.com/Xuzhenhua55/EverTracer.

The widespread deployment of large language models (LLMs) has intensified concerns around intellectual property (IP) protection, as model theft and unauthorized redistribution become increasingly feasible.To address this, model fingerprinting aims to embed verifiable ownership traces into LLMs.However, existing methods face inherent trade-offs between stealthness, robustness, and generalizability-being either detectable via distributional shifts, vulnerable to adversarial modifications, or easily invalidated once the fingerprint is revealed.In this work, we introduce CTCC, a novel rule-driven fingerprinting framework that encodes contextual correlations across multiple dialogue turns-such as counterfactual-rather than relying on token-level or single-turn triggers.CTCC enables fingerprint verification under black-box access while mitigating false positives and fingerprint leakage, supporting continuous construction under a shared semantic rule even if partial triggers are exposed.Extensive experiments across multiple LLM architectures demonstrate that CTCC consistently achieves stronger stealth and robustness than prior work.Our findings position CTCC as a reliable and practical solution for ownership verification