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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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Matches every word across titles, descriptions, sources, affected systems, and models.

Aggressive Compression Model Theft

Published Analyzed Source: arXiv

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
Evaluated models: Qwen 2 1.5B, Qwen 2 7B, Qwen 2.5 0.5B+2 more
  • Qwen 2 1.5B
  • Qwen 2 7B
  • Qwen 2.5 0.5B
  • Qwen 2.5 1.5B
  • Pythia 70M
Model layer
Infrastructure
Extraction
Fine-tuning

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.