Split-LLM training leaks private data via gradient patterns, study finds

A new study on split-LLM training reveals a critical privacy vulnerability where the returned gradients can inadvertently expose sensitive data. Researchers found that while initial privacy checks passed, the pattern of zero gradients associated with decoy data points allowed an attacker to identify real data rows. This leak persisted even with gradient clipping and noise injection, enabling token recovery attacks. The findings highlight the challenges in balancing utility and privacy in distributed LLM training, particularly concerning the backward wire communication. AI

IMPACT Highlights a significant security gap in distributed LLM training, potentially requiring new privacy-preserving techniques for gradient communication.

RANK_REASON Academic paper detailing a novel security vulnerability in a specific AI training technique.

Read on arXiv cs.LG →

• arXiv
• Trusted Local Node
• Untrusted Cloud Node
• Backward Wire
• differential privacy
• Forward Wire
• Gaussian noise
• Hugging Face
• shadow models
• Split-LLM

• paper
• safety

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