Differentiation-Based Extraction of Proprietary Data from Fine-Tuned LLMs
This addresses a critical security risk for organizations using fine-tuned LLMs, revealing hidden data leak vulnerabilities in real-world applications.
The paper tackles the problem of extracting proprietary data from fine-tuned Large Language Models (LLMs) by developing a novel attack method called Differentiated Data Extraction (DDE), which consistently outperforms existing baselines across multiple domains and scenarios, and proposes a defense mechanism to mitigate this risk with minimal performance impact.
The increasing demand for domain-specific and human-aligned Large Language Models (LLMs) has led to the widespread adoption of Supervised Fine-Tuning (SFT) techniques. SFT datasets often comprise valuable instruction-response pairs, making them highly valuable targets for potential extraction. This paper studies this critical research problem for the first time. We start by formally defining and formulating the problem, then explore various attack goals, types, and variants based on the unique properties of SFT data in real-world scenarios. Based on our analysis of extraction behaviors of direct extraction, we develop a novel extraction method specifically designed for SFT models, called Differentiated Data Extraction (DDE), which exploits the confidence levels of fine-tuned models and their behavioral differences from pre-trained base models. Through extensive experiments across multiple domains and scenarios, we demonstrate the feasibility of SFT data extraction using DDE. Our results show that DDE consistently outperforms existing extraction baselines in all attack settings. To counter this new attack, we propose a defense mechanism that mitigates DDE attacks with minimal impact on model performance. Overall, our research reveals hidden data leak risks in fine-tuned LLMs and provides insights for developing more secure models.