This research introduces the concept of model hemorrhage - the significant performance degradation LLMs experience when modified for real-world deployment.

• Quantization, pruning, and decoding strategy changes can cause unexpected performance collapse
• Layer expansion and architectural modifications disrupt model functionality
• Systematic analysis reveals predictable patterns of vulnerability across LLM frameworks
• Researchers propose prevention strategies to maintain model integrity during deployment modifications

For security teams, this work provides critical insights into maintaining LLM robustness during the transition from research to production, helping prevent security vulnerabilities and performance degradation in deployed models.

Model Hemorrhage and the Robustness Limits of Large Language Models