This research reveals a critical security vulnerability in decentralized LLM training systems through a new Activation Inversion Attack (AIA) that can extract private training data.

• Identifies a previously unexplored attack surface in distributed LLM training
• Demonstrates how sensitive information can be extracted from model activations
• Exposes fundamental privacy risks in resource-efficient training approaches
• Highlights the urgent need for enhanced security measures in collaborative AI development

This research is crucial for security teams as it exposes significant data privacy vulnerabilities in emerging LLM training infrastructures—potentially compromising sensitive information in training datasets used across organizations.

Stealing Training Data from Large Language Models in Decentralized Training through Activation Inversion Attack