Attacks and Defenses Against LLM Fingerprinting

--As large language models are increasingly deployed in sensitive environments, fingerprinting attacks pose significant privacy and security risks. We present a study of LLM fingerprinting from both offensive and defensive perspectives. Our attack methodology uses reinforcement learning to automatically optimize query selection, achieving better fingerprinting accuracy with only 3 queries compared to randomly selecting 3 queries from the same pool. Our defensive approach employs semantic-preserving output filtering through a secondary LLM to obfuscate model identity while maintaining semantic integrity. The defensive method reduces fingerprinting accuracy across tested models while preserving output quality. These contributions show the potential to improve fingerprinting tools capabilities while providing practical mitigation strategies against fingerprinting attacks. Large language models (LLMs) have become ubiquitous across industries, from customer service chatbots to code generation tools and content creation platforms. As organizations increasingly rely on these models for sensitive applications, the ability to identify which specific model generated a given text--known as LLM fingerprinting--has emerged as a critical security concern.