Archilles' Heel in Semi-open LLMs: Hiding Bottom against Recovery Attacks
Keywords: Semi-open Model, Closed-sourcing Approach
TL;DR: In this paper, we propose SCARA, a fine-tuning-free approach that reduces the number of closed-source layers to enhance customizability while preserving resilience to recovery attacks in semi-open models.
Abstract: To address privacy concerns with large language models, industrial users request local fine-tuning and deployment, but unencrypted models risk theft. Hardware-based security provides protection but is constrained by secure memory, leading to semi-open configurations. Semi-open models balance security and customization by keeping key layers closed-source within a secure environment while allowing others to be fine-tuned, but closed-source layers are susceptible to recovery attacks. In this paper, we explore the design of semi-open models with fewer closed-source layers, aiming to increase customizability while ensuring resilience to recovery attacks. We analyze the contribution of closed-source layer to the overall resilience and theoretically prove that in a deep transformer-based model, there exists a transition layer such that even small recovery errors in layers before this layer can lead to recovery failure. Building on this, we propose \textbf{SCARA}, a novel approach that keeps only a few bottom layers as closed-source. SCARA employs a fine-tuning-free metric to estimate the maximum number of layers that can be publicly accessible for customization. We apply it to five models (1.3B to 70B parameters) to construct semi-open models, validating their customizability on six downstream tasks and assessing their resilience against various recovery attacks on sixteen benchmarks. We compare SCARA to baselines and observe that it generally improves downstream customization performance and offers similar resilience with over \textbf{10} times fewer closed-source parameters. We empirically investigate the transition phenomenon and analyze the effectiveness and limitations of our scheme.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 467
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