From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
arXiv:2606.14210v1 Announce Type: cross 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, typically via inversion attacks on intermediate representations, while the potential for sensitive infor
The increasing deployment of LLMs in sensitive domains, coupled with high computational costs, drives the need for secure, distributed deployment paradigms like split learning, making this research timely.
This research highlights critical privacy vulnerabilities in split LLMs, which are touted as solutions for on-premise AI, directly impacting the adoption and security of AI in regulated industries.
The understanding of data leakage in split LLM architectures expands beyond input prompts to include responses, necessitating new defense mechanisms and security protocols for distributed AI.
- · Cybersecurity firms
- · Privacy-preserving AI researchers
- · Enterprises prioritizing data security
- · LLM providers with weak security models
- · Organizations relying on naive split learning
- · Users with sensitive data on insecure platforms
New security standards and best practices will emerge for split LLM deployment.
Increased investment in hardware-based security and federated learning solutions to mitigate these risks.
Regulatory bodies may impose stricter data protection requirements on AI systems, particularly those handling sensitive information.
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Read at arXiv cs.AI