Hide and Seek: Fingerprinting Large Language Models with Evolutionary Learning (2408.02871v1)

Abstract: As content generated by LLM has grown exponentially, the ability to accurately identify and fingerprint such text has become increasingly crucial. In this work, we introduce a novel black-box approach for fingerprinting LLMs, achieving an impressive 72% accuracy in identifying the correct family of models (Such as Llama, Mistral, Gemma, etc) among a lineup of LLMs. We present an evolutionary strategy that leverages the capabilities of one LLM to discover the most salient features for identifying other LLMs. Our method employs a unique "Hide and Seek" algorithm, where an Auditor LLM generates discriminative prompts, and a Detective LLM analyzes the responses to fingerprint the target models. This approach not only demonstrates the feasibility of LLM-driven model identification but also reveals insights into the semantic manifolds of different LLM families. By iteratively refining prompts through in-context learning, our system uncovers subtle distinctions between model outputs, providing a powerful tool for LLM analysis and verification. This research opens new avenues for understanding LLM behavior and has significant implications for model attribution, security, and the broader field of AI transparency.

• The paper introduces the 'Hide and Seek' algorithm, a novel evolutionary learning method for fingerprinting large language models.
• It utilizes adversarial prompting with dedicated Auditor and Detective LLM roles to uncover distinct semantic manifolds.
• The approach achieves 72% accuracy in model family identification, offering practical insights for AI security and transparency.

Hide and Seek: Fingerprinting LLMs With Evolutionary Learning

The paper "Hide and Seek: Fingerprinting LLMs With Evolutionary Learning" by Dmitri Iourovitski, Sanat Sharma, and Rakshak Talwar introduces a novel black-box approach for fingerprinting LLMs, showcasing a method that achieves a 72% accuracy in identifying the correct family of models among a diverse set of LLMs. The primary contribution lies in the development and experimental validation of an algorithm called "Hide and Seek," which utilizes evolutionary learning and LLM-driven adversarial prompting to uncover the hidden semantic manifolds of different LLM families.

Introduction

The authors hypothesize that LLMs generate tokens based on a lower-dimensional manifold, known as the semantic manifold, specific to their training data and architecture. This distinct manifold can be exploited to identify and fingerprint different LLMs despite treating them as black boxes. The research formulates the Semantic Manifold Hypothesis (SMH) and uses it as a theoretical framework to guide the fingerprinting process.

Methodology and Model

The "Hide and Seek" algorithm comprises two primary roles:

1. Auditor LLM: This model generates adversarial prompts designed to elicit distinctive responses from other LLMs.
2. Detective LLM: This model analyzes the responses generated by the tested LLMs in reaction to the Auditor's prompts, aiming to identify similarities and pinpoint the target model.

This iterative process involves an evolutionary strategy where the Auditor refines its prompts based on feedback from the Detective, thereby maximizing the diversity of outputs across different models.

Semantic Manifold Hypothesis (SMH)

The Semantic Manifold Hypothesis posits that despite the high-dimensional output space of LLMs, their generative processes operate on a significantly lower-dimensional manifold. This restricts the variability in outputs, allowing for the identification of unique model characteristics. Formally, the SMH suggests that the probability distribution over the next token in a sequence lies on a manifold $\mathcal{M}_s$ of much lower dimension than the full vocabulary space $V$.

Experimental Results

Family Detection Accuracy

The experimental setup involved multiple trials where models from different families (e.g., Llama, Mistral, Gemma, Phi) were tested. The results, visualized through violin plots, highlighted the variability in accuracy due to the stochastic nature of LLM responses. The mean accuracy of 72% underscores the effectiveness of the proposed method in model identification.

Auditor's Cognitive Process

Throughout the experiments, the Auditor's cognitive process was documented, showcasing its capabilities in generating and refining prompts based on performance feedback. This introspective mechanism allowed the Auditor to adapt its strategies dynamically for optimal fingerprinting.

Practical and Theoretical Implications

Practical Implications

The method has significant practical implications in AI security, model attribution, and AI transparency:

• Model Attribution: The approach provides a robust mechanism to attribute generated content to specific model families, crucial for intellectual property protection and regulatory compliance.
• AI Security: Identifying model outputs can help detect unauthorized usage or tampering with LLMs, thereby enhancing the security frameworks surrounding AI deployment.
• Transparency: The insights gained into the operational manifold of LLMs contribute to greater transparency in AI systems, aligning with ethical AI guidelines.

Theoretical Implications

The validation of the SMH opens up new research avenues:

• The notion that output variability lies on a lower-dimensional manifold can inform the development of more efficient LLMs with reduced computational complexity.
• Further exploration of the manifold's properties could lead to advancements in transfer learning and data compression techniques.

Future Directions

The paper sets the stage for several future research directions:

• Improvement in Auditor Design: Enhancements in the Auditor's task comprehension and its capacity for agentic behavior could lead to more refined and effective prompts.
• Context-Length Extensions: Extending the context length of the Auditor could allow for longer sequences and more complex analysis, improving fingerprinting accuracy.
• Model Size and Capability Detection: Extending the approach to estimate model size and ascertain capabilities could provide deeper insights into LLM architectures, aiding in comparative studies.
• Exploration of the Semantic Manifold: Further investigations into manifold properties and their implications for LLM design, reasoning improvements, and manifold transfer with minimal training data.

Conclusion

The paper presents a comprehensive framework for fingerprinting LLMs using an evolutionary learning approach that leverages the unique semantic manifold of each model. By refining prompts iteratively and using sophisticated LLMs for prompt generation and analysis, the proposed method demonstrates the feasibility and effectiveness of model identification in a black-box setting. This research opens up promising avenues in AI transparency, security, and model attribution, providing a solid foundation for future exploration and development in the field of LLMs.