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ImF: Implicit Fingerprint for Large Language Models

Created by
  • Haebom

Author

Jiaxuan Wu, Wanli Peng, Hang Fu, Yiming Xue, Juan Wen

Outline

This paper highlights the vulnerabilities of fingerprinting techniques for protecting the intellectual property (IP) of large-scale language models (LLMs) and proposes a novel fingerprinting method, Implicit Fingerprints (ImF). Existing fingerprinting techniques insert identifiable patterns with weak semantic consistency, which deviate from natural question-answering (QA) behavior and are vulnerable to detection and removal. This paper demonstrates the vulnerabilities of existing methods using a novel adversarial attack technique, Generation Revision Intervention (GRI). ImF overcomes the limitations of existing methods and improves stealth and robustness by leveraging steganography and Chain-of-Thought (CoT) prompting to generate semantically consistent and natural QA pairs. We evaluate the performance of ImF on 15 different LLMs.

Takeaways, Limitations

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Takeaways:
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We reveal vulnerabilities in existing LLM fingerprinting techniques and present a new adversarial attack technique (GRI) to clearly demonstrate the limitations of existing methods.
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We propose ImF, a semantically consistent fingerprinting technique, to provide a new direction for LLM IP protection.
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ImF improves stealth and robustness by leveraging steganography and CoT prompting.
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Validating the effectiveness of ImF in various LLMs.
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Limitations:
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Further research is needed to determine the long-term stability of ImF and its ability to fully defend against various adversarial attacks.
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The performance of ImF may depend on the steganographic technique and CoT prompting strategy used.
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Additional validation of effectiveness in actual LLM deployment environments is required.
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