Don't Corrupt the Fact: A Trustworthy RAG Watermarking Framework based on Dual Factual Shield

While RAG systems are designed to enhance factual fidelity by grounding LLMs in provided sources, the application of current watermarking techniques creates a conflict. These methods, being inherently fact-agnostic, force the model to deviate from the very source documents it is supposed to follow. This leads to "faithfulness hallucinations", which refer to a critical flaw where the generated output contradicts its own grounding context. Consequently, these watermarks undermine the core value of RAG, rendering even the most secure schemes untrustworthy for high-stakes applications. To resolve this RAG-specific conflict, we introduce the Dual Factual Shield (DFS), a three-stage post-hoc pipeline for factualitypreserving watermarking in RAG. It adopts a defense-in-depth design that combines a sourceanchored algorithmic safeguard for protecting critical tokens from retrieved context with prompt-based semantic guidance to mitigate factual corruption. Experiments show that our framework drastically reduces the Knowledge Corruption Rate (KCR), a new metric we introduce to quantify factual fidelity, while maintaining strong security and robustness, paving the way for responsible deployment of traceable AI in knowledge-critical domains.