A Security Meta-Model for Retrieval-Augmented Generation Systems

AI security research · arXiv cs.CR (AI security)

A Security Meta-Model for Retrieval-Augmented Generation Systems

REDCORE assessment

The research highlights that RAG systems introduce systemic, structural vulnerabilities distinct from base model weaknesses. While it provides a useful taxonomy for causal mapping, defenders should recognize that cataloging risks does not equal threat mitigation. For small teams, this indicates that the primary security risk is not just the AI itself, but the uncontrolled exposure of sensitive data within the retrieval pipeline.

Impact for lean teams

Lean teams lack the resources to manually map complex RAG dependencies, so relying on structured frameworks is vital to avoid oversight. You should act by auditing your data ingestion pipelines for unauthorized access risks.

Source summary

Retrieval-Augmented Generation (RAG) systems extend large language models (LLMs) with external knowledge through a multi-stage pipeline. While this architecture can improve the factual grounding of generated answers, it introduces structural attack surfaces that extend beyond those of standalone LLMs. In this paper, we introduce a security meta-model that captures explicit causal relationships between RAG surfaces, attacks, weaknesses, risks, and CIA impact (Confidentiality, Integrity, Availability). Its purpose is to provide security engineers with a structured and user-friendly framework for gathering and assessing the risks, weaknesses, and mitigations relevant to their RAG deployment. We designed the meta-model through an iterative, structured analysis of 43~publications (2023--2026) and instantiated it as a catalog populated with the security threats and remediations reported in the

Recommended actions

• Implement strict access control lists at the data ingestion layer
• Sanitize and validate retrieved documents before passing them to the LLM context
• Establish clear trust boundaries between external data sources and the model application
• Monitor query patterns for signs of unexpected data exfiltration or manipulation

This assessment was generated by REDCORE AI from the attributed source above and is provided for situational awareness. Validate against the original research and your own environment before acting.