Alignment-Aware Model Extraction Attacks on Large Language Models
Keywords: Model Extraction Attack, Large Language Models, Alignment
Abstract: Model extraction attacks (MEAs) on large language models (LLMs) have received increasing attention in recent research. However, existing attack methods typically adapt the extraction strategies originally developed for deep neural networks (DNNs). They neglect the underlying inconsistency between the training tasks of MEA and LLM alignment, leading to suboptimal attack performance. To tackle this issue, we propose Locality Reinforced Distillation (LoRD), a novel model extraction algorithm specifically designed for LLMs. In particular, LoRD employs a newly defined policy-gradient-style training task that utilizes the responses of victim model as the signal to guide the crafting of preference for the local model. Theoretical analyses demonstrate that i) the convergence procedure of LoRD in model extraction is consistent with the alignment procedure of LLMs, and ii) LoRD can reduce query complexity while mitigating watermark protection through exploration-based stealing. Extensive experiments on domain-specific extractions validate the superiority of our method in extracting various state-of-the-art commercial LLMs.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 13822
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