ImF: Embedding an Implicit Fingerprint in Your Large Language Models

Training and serving large language models (LLMs) is resource-intensive, making reliable intellectual property (IP) protection and blackbox ownership verification increasingly important. Model fingerprinting enables such verification by injecting a small set of secret query-response behaviors, but many existing fingerprints rely on explicit markers or predetermined outputs that are weakly grounded in prompt semantics. This semantic mismatch yields atypical fingerprint responses, reduces stealthiness, and exposes fingerprints to removal by response normalization. We formalize this vulnerability via a new removal attack, Generation Revision Intervention (GRI), which applies system-prompt-level revision and response standardization to steer models toward typical answers, substantially compromising representative injected baselines. To close this semantic gap, we propose the Implicit Fingerprints (ImF): we encode ownership information into a natural-looking target response y via linguistic steganography, then derive a CoTaugmented query x that embeds semantic cues from y to guide the model toward an output sufficiently close to y for decoding-based verification. Experiments on 15 LLMs show that ImF improves stealthiness and remains verifiable under model updates and deploymenttime prompt interventions; additional analyses further show stability under common decoding variation and realistic related-model partial merging.