From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
Split learning architectures for LLMs create dual-sided data leakage risks where both user prompts and model responses can expose sensitive information across distributed infrastructure.
Summary written by editorial AI · Source link below
arXiv:2606.14210v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high computational cost of local deployment. Split learning has therefore emerged as a promising paradigm for LLM fine-tuning and inference under limited local resources. However, it introduces new privacy risks. Prior work primarily studies leakage of private input prompts, typic
Editorial Analysis
Organizations using distributed AI inference face privacy risks from both directions of the communication flow, complicating data protection strategies.
Assess current AI deployment architectures for potential data leakage points in both prompt and response pathways.
Split AI processing models create new privacy vulnerabilities where sensitive data can leak from both user inputs and system outputs.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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