Updated 2/21/2026
Large Language Models (LLMs) hosted on inference servers are vulnerable to high-speed weight exfiltration attacks due to the inherent compressibility of transformer parameters when decompression constraints are relaxed. Adversaries with compromised server access can utilize aggressive lossy compression techniques—specifically additive quantization combined with k-means clustering—to reduce model size by factors of 16x to 100x (e.g., <1 bit per parameter). Unlike standard quantization for…
Aggressive Compression Enables LLM Weight Theft
Affects: Qwen 2 1.5B, Qwen 2 7B, Qwen 2.5 0.5B +2 more
Source: arXiv