How Vulnerable Are Edge LLMs?
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. Experim...
| Authors | Ao Ding, Hongzong Li, Zi Liang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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