Protecting Proprietary Knowledge in RAG Systems
A copyright protection approach for retrieval-augmented LLMs
This research introduces a novel harmless copyright protection method that verifies ownership of knowledge bases used in retrieval-augmented generation (RAG) systems without compromising performance.
- Addresses the risk of unauthorized usage of valuable knowledge bases in RAG-enhanced LLMs
- Implants verification behaviors that can be triggered through specific chain-of-thought reasoning
- Provides robust protection without poisoning attacks or degrading model performance
- Enables traceable ownership verification of proprietary knowledge bases
As organizations increasingly rely on custom knowledge bases to enhance LLM capabilities, this approach offers a practical security solution for protecting intellectual property in AI applications.