Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique (2407.10887v2)

Abstract: Amid growing concerns over the ease of theft and misuse of LLMs, the need for fingerprinting models has increased. Fingerprinting, in this context, means that the model owner can link a given model to their original version, thereby identifying if their model is being misused or has been completely stolen. In this paper, we first define a set five properties a successful fingerprint should satisfy; namely, the fingerprint should be Transparent, Efficient, Persistent, Robust, and Unforgeable. Next, we propose Chain & Hash, a new, simple fingerprinting approach that implements a fingerprint with a cryptographic flavor, achieving all these properties. Chain & Hash involves generating a set of questions (the fingerprints) along with a set of potential answers. These elements are hashed together using a secure hashing technique to select the value for each question, hence providing an unforgeability property-preventing adversaries from claiming false ownership. We evaluate the Chain & Hash technique on multiple models and demonstrate its robustness against benign transformations, such as fine-tuning on different datasets, and adversarial attempts to erase the fingerprint. Finally, our experiments demonstrate the efficiency of implementing Chain & Hash and its utility, where fingerprinted models achieve almost the same performance as non-fingerprinted ones across different benchmarks.

Chain {content} Hash: A Technique for Fingerprinting LLMs

This document elucidates a method for fingerprinting LLMs, addressing the need for verifying the ownership of these models against potential theft and misuse. The technique described, named Chain {content} Hash, employs a cryptographic approach to create robust, transparent, and efficient model fingerprints. The fundamental goal is to attribute a unique identifier to LLMs without compromising their performance.

Key Properties of Successful Fingerprints

The authors begin by defining essential properties for an effective fingerprint:

1. Transparent: The fingerprint should not affect the model’s utility.
2. Efficient: Implementation and validation of the fingerprint should require minimal resources.
3. Persistent: The fingerprint must withstand benign transformations like fine-tuning.
4. Robust: Adversaries should not be able to remove the fingerprint without significantly degrading model utility.
5. Unforgeable: It should be cryptographically infeasible for adversaries to forge a legitimate fingerprint.

Chain {content} Hash Technique

The Chain {content} Hash technique constructs an LLM fingerprint by generating a set of questions and possible answers. These elements are hashed together using a secure hashing technique (e.g., SHA-256), ensuring that the selected answers are cryptographically linked to the questions. This method satisfies the identified properties, including robustness and unforgeability, through careful design and cryptographic principles.

Fingerprint Creation and Fine-Tuning

The method involves generating $k$ questions along with 256 potential answers, using secure hashing to determine the answer for each question. Two approaches for question generation are proposed: one involving random tokens, the other using valid yet unlikely natural language questions. Fine-tuning adapts the target model to these questions and answers, incorporating meta prompts to enhance robustness against specific adversarial inputs.

The experiments confirm that the fingerprint remains intact across various benchmarks and fine-tuning scenarios, demonstrating negligible degradation in model performance. For instance, models fingerprinted with Chain {content} Hash retained their performance within 1% of their original efficacy across standard benchmarks such as HellaSwag, MMLU, TruthfulQA, and Winogrande.

Evaluation of Robustness and Efficiency

To evaluate robustness, the authors tested the approach on multiple state-of-the-art models, including various versions of Llama and the instruct version of Phi models. Notably, the fingerprint held strong against fine-tuning with different datasets, showing the ability to maintain a high success rate of generating the specific fingerprint response.

For efficiency, Chain {content} Hash requires minimal computational effort to incorporate and validate the fingerprint. The described trials indicate that querying the fingerprinted questions just once or a few times is usually sufficient to prove ownership, ensuring the process remains computationally feasible.

Implications and Future Directions

The implications of Chain {content} Hash are particularly significant for organizations looking to protect their proprietary LLMs from misuse or theft. The fingerprinting method can serve as both a deterrent and a means of legal recourse.

Future developments could include optimizing question and response generation for specific domains and enhancing robustness against more sophisticated adversarial techniques. Moreover, exploring the balance between transparent fingerprints and model overfitting is another promising area of research.

Conclusion

Chain {content} Hash offers an effective, cryptographic method for fingerprinting LLMs. By meticulously ensuring robustness, transparency, and minimal performance impact, this approach stands as a viable strategy for protecting the intellectual property embedded within LLMs. Further exploration and optimization could expand its applicability and resilience, making it a cornerstone in the safeguarding of AI assets.