量化后的边缘大模型仍易被查询提取知识，存在安全风险。
How Vulnerable Are Edge LLMs?
- 设计结构化查询框架CLIQ，提升语义覆盖并减少冗余。
- 在INT8/INT4量化下，CLIQ在多个指标上优于原始查询。
- 提醒开发者关注边缘部署模型的查询安全，尤其在资源受限场景。
大型语言模型（LLMs）正越来越多地部署在计算和量化约束严格的边缘设备上，但其安全影响尚不明确。本文研究了在合理查询预算下，对量化后的边缘部署LLM进行基于查询的知识提取。结果表明，尽管量化引入噪声，但并未消除底层语义知识，通过精心设计的查询仍可实现大量行为恢复。为此，我们提出一种名为CLIQ（Clustered Instruction Querying）的结构化查询构造框架，可提升语义覆盖并降低冗余。在量化后的Qwen模型（INT8/INT4）上的实验表明，CLIQ在BERTScore、BLEU和ROUGE等指标上持续优于原始查询，实现了更高效的有限预算下的知识提取。结果说明，仅靠量化无法有效防御基于查询的知识提取，揭示了边缘部署LLM中一个此前被忽视的安全风险。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly deployed on edge devices under strict computation and quantization constraints, yet their security implications remain unclear. We study query-based knowledge extraction from quantized edge-deployed LLMs under realistic query budgets and show that, although quantization introduces noise, it does not remove the underlying semantic knowledge, allowing substantial behavioral recovery through carefully designed queries. To systematically analyze this risk, we propose \textbf{CLIQ} (\textbf{Cl}ustered \textbf{I}nstruction \textbf{Q}uerying), a structured query construction framework that improves semantic coverage while reducing redundancy. Experiments on quantized Qwen models (INT8/INT4) demonstrate that CLIQ consistently outperforms original queries across BERTScore, BLEU, and ROUGE, enabling more efficient extraction under limited budgets. These results indicate that quantization alone does not provide effective protection against query-based extraction, highlighting a previously underexplored security risk in edge-deployed LLMs.
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