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UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification
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Fingerprinting large language models (LLMs) is essential for verifying model ownership, ensuring authenticity, and preventing misuse. Traditional fingerprinting methods often require significant computational overhead or white-box verification access. In this paper, we introduce UTF, a novel and efficient approach to fingerprinting LLMs by leveraging under-trained tokens. Under-trained tokens are tokens that the model has not fully learned during its training phase. By utilizing these tokens, we perform supervised fine-tuning to embed specific input-output pairs into the model. This process allows the LLM to produce predetermined outputs when presented with certain inputs, effectively embedding a unique fingerprint. Our method has minimal overhead and impact on model's performance, and does not require white-box access to target model's ownership identification. Compared to existing fingerprinting methods, UTF is also more effective and robust to fine-tuning and random guess.
Forward citations
Cited by 5 Pith papers
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CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor
CTCC embeds LLM ownership fingerprints in cross-turn semantic contradictions: the model fires a secret response only when a user contradicts an earlier statement, with higher robustness and stealth than single-turn triggers.
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FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing
FPEdit uses knowledge editing with a promote-suppress objective to embed robust, stealthy natural-language fingerprints into LLMs, achieving 94 to 100 percent retention after fine-tuning while preserving benchmark per...
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From Construction to Injection: Edit-Based Fingerprints for Large Language Models
A three-stage LLM fingerprinting pipeline (multilingual low-perplexity triggers, edit-based injection with adversarial suppression, and subspace-aware fine-tuning regularization) reports robust and persistent ownershi...
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MEraser: An Effective Fingerprint Erasure Approach for Large Language Models
By fine-tuning on mismatched pairs and then clean pairs, MEraser drops fingerprint success rate to zero on three backdoor-based fingerprinting schemes across multiple LLMs, with a reusable LoRA adapter for transfer.
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Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models
Backdoor fingerprints trained into LoRA adapters on a base LLM transfer to derivative models with 100% trigger success and, in several scenarios, greater robustness than directly injected fingerprints.
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