The widespread deployment of Large Language Models (LLMs) has necessitated robust intellectual property protection. Backdoor-based LLM fingerprinting has emerged as a promising solution for this challenge. To assess the robustness of fingerprinting techniques, various adversarial attacks have been developed. However, existing fingerprint attacks often fail to simultaneously achieve strong effectiveness, harmlessness, and generalization across different LLMs and fingerprinting methods. To address this challenge, we examine the process of generating fingerprint responses and identify an intrinsic trait: fingerprint responses exhibit abnormally high generation confidence and strong robustness under perturbations compared to benign responses. Building on this trait, we propose RIPPLE, a fingerprint attack framework that identifies fingerprint trigger-response pairs by jointly evaluating original confidence and confidence robustness under hidden state perturbations. Extensive experiments on multiple LLMs and fingerprinting methods demonstrate that RIPPLE consistently outperforms existing attack methods, achieving a superior trade-off between attack effectiveness and harmlessness while maintaining strong generalization.