ImF: Implicit Fingerprint for Large Language Models (2503.21805v3)

Abstract: Training LLMs is resource-intensive and expensive, making protecting intellectual property (IP) for LLMs crucial. Recently, embedding fingerprints into LLMs has emerged as a prevalent method for establishing model ownership. However, existing fingerprinting techniques typically embed identifiable patterns with weak semantic coherence, resulting in fingerprints that significantly differ from the natural question-answering (QA) behavior inherent to LLMs. This discrepancy undermines the stealthiness of the embedded fingerprints and makes them vulnerable to adversarial attacks. In this paper, we first demonstrate the critical vulnerability of existing fingerprint embedding methods by introducing a novel adversarial attack named Generation Revision Intervention (GRI) attack. GRI attack exploits the semantic fragility of current fingerprinting methods, effectively erasing fingerprints by disrupting their weakly correlated semantic structures. Our empirical evaluation highlights that traditional fingerprinting approaches are significantly compromised by the GRI attack, revealing severe limitations in their robustness under realistic adversarial conditions. To advance the state-of-the-art in model fingerprinting, we propose a novel model fingerprint paradigm called Implicit Fingerprints (ImF). ImF leverages steganography techniques to subtly embed ownership information within natural texts, subsequently using Chain-of-Thought (CoT) prompting to construct semantically coherent and contextually natural QA pairs. This design ensures that fingerprints seamlessly integrate with the standard model behavior, remaining indistinguishable from regular outputs and substantially reducing the risk of accidental triggering and targeted removal. We conduct a comprehensive evaluation of ImF on 15 diverse LLMs, spanning different architectures and varying scales.

• The paper introduces a novel implicit fingerprinting method for LLMs using steganography and CoT prompting to securely embed ownership data into natural outputs.
• It demonstrates that the ImF technique enhances resilience against adversarial attacks such as the Generation Revision Intervention by dynamically adjusting token probabilities.
• Experiments across fifteen diverse models show that ImF significantly improves fingerprint success rates while preserving the semantic integrity of model outputs.

Implicit Fingerprint Methodology in LLMs

Introduction

The paper, "ImF: Implicit Fingerprint for LLMs" (2503.21805), introduces a novel approach for embedding digital ownership within LLMs using implicit fingerprints. These fingerprints leverage steganographic techniques to integrate ownership information seamlessly into the model's outputs, maintaining the natural flow and coherence of responses, which is especially critical in protecting the intellectual property of resource-intensive models.

Vulnerabilities in Existing Methods

Current fingerprint methods for LLMs often rely on explicit modifications of normal question-answer (QA) pairs, creating weak semantic correlations that are vulnerable to adversarial attacks. This paper identifies a key vulnerability in these approaches, namely their inability to resist the Generation Revision Intervention (GRI) attack—a novel strategy designed to erase fingerprints by exploiting their semantic fragility.

Generation Revision Intervention (GRI) Attack

The GRI attack effectively disrupts the fingerprinting process through two phases: Security Review and CoT Optimization Instruction.

Figure 1: Error verification with GRI Attack.

• Security Review: It analyzes inputs for fingerprint markers prior to output generation, flagging potentially harmful prompts.
• CoT Optimization Instruction: It steers LLMs to produce contextually appropriate responses that reflect normal output patterns rather than predefined fingerprint triggers.

Implicit Fingerprint (ImF) Approach

ImF advances fingerprinting by embedding the Secretly pick $(x,y)$ using steganography combined with Chain-of-Thought (CoT)-based prompting to ensure semantic coherence and contextually valid QA pairs. This implicit design fundamentally enhances fingerprint resilience against adversarial interventions, including extreme variations like fine-tuning, model merging, and GRI attacks.

Figure 2: Framework for Generating and Verifying Implicit Fingerprints (ImF), which enhances robustness by designing the Stego Secretly pick.

Implementation Details

Steganography-based Secretly Pick Generation

ImF leverages text steganography to conceal ownership data within natural model outputs. This process ensures that the fingerprints remain indistinguishable from standard model behavior, thus resisting detection and removal.

• Algorithm: The ImF embedding process dynamically adjusts token selection probability to encode the fingerprint within regular output flows, avoiding explicit markers and enhancing stealth.

Iterative CoT Optimization

To maintain strong semantic linkage between the input $x$ and output $y$, ImF employs CoT prompting under an iterative optimization framework. This mechanism refines input prompts, ensuring robust semantic alignment with intended fingerprint outputs.

Experimental Analysis

The paper conducts extensive experiments across fifteen diverse LLMs, demonstrating ImF's superior fingerprint success rates even under adversarial stress. In particular, the mechanism ensures that embedded fingerprints withstand rigorous attacks more effectively than existing techniques.

(Tables demonstrating various comparative analyses are included, indicating FSR enhancement and robustness metrics.)

Conclusions and Future Directions

The ImF method represents a significant step forward in securing the intellectual property of LLMs. By invisibly embedding ownership data into coherent QA pairs, ImF not only protects model integrity but also paves the way for future research into resilient and stealthy fingerprint frameworks.

Further developments could focus on automating fingerprint generation and optimizing semantic embedding techniques to refine efficiency while preserving robustness across increasingly sophisticated model architectures. The challenge remains to balance security measures with ethical considerations surrounding AI deployment and usage.