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Instructional Fingerprinting of Large Language Models
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The exorbitant cost of training Large language models (LLMs) from scratch makes it essential to fingerprint the models to protect intellectual property via ownership authentication and to ensure downstream users and developers comply with their license terms (e.g. restricting commercial use). In this study, we present a pilot study on LLM fingerprinting as a form of very lightweight instruction tuning. Model publisher specifies a confidential private key and implants it as an instruction backdoor that causes the LLM to generate specific text when the key is present. Results on 11 popularly-used LLMs showed that this approach is lightweight and does not affect the normal behavior of the model. It also prevents publisher overclaim, maintains robustness against fingerprint guessing and parameter-efficient training, and supports multi-stage fingerprinting akin to MIT License. Code is available in https://cnut1648.github.io/Model-Fingerprint/.
Forward citations
Cited by 7 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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PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement
PREE edits a tiny fraction of an LLM's weights so the model answers fake facts when triggered by specially selected prefix phrases, enabling robust ownership verification.
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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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Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification
TensorGuard classifies fine-tuned LLMs into their base-model families with 94% accuracy by clustering statistical features of weight gradients under random input perturbations.
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A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
The paper classifies model extraction attacks and defenses into attack, defense, and computing environment categories and surveys their current state.
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A Survey on Model Extraction Attacks and Defenses for Large Language Models
A taxonomy of model extraction attacks and defenses for large language models, with proposed evaluation metrics and future research directions.
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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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