RAG Shield: A Multi-Layer Defense System Against Poisoning Attacks in Retrieval-Augmented Generation

This whitepaper presents RAG Shield, a security-focused framework for
defending Retrieval-Augmented Generation (RAG) pipelines against
poisoning and adversarial manipulation at the retrieval layer.

The work introduces a multi-layer defense architecture combining
cryptographic document provenance validation, semantic anomaly detection,
and secure, authority-weighted retrieval control. A realistic threat
model is defined, focusing on poisoning of retrieval corpora rather than
prompt or model-level attacks. The system is evaluated against multiple
attack scenarios under controlled conditions.

RAG Shield is designed as a framework-agnostic security control layer
that operates independently of the underlying language model and vector
database, enabling deployment in enterprise and regulated environments
without modification of existing RAG architectures.

This document is released as a technical preprint to establish prior art
and support open discussion in the areas of AI security, adversarial
machine learning, and secure enterprise RAG deployment.

Project website and system overview:
https://sentinelrag.com

Contact:
info@sentinelrag.com