Executive Summary

Production Retrieval-Augmented Generation (RAG) systems represent a critical attack surface in enterprise AI deployments. This research demonstrates three novel attack vectors against Azure OpenAI-based RAG systems in realistic enterprise configurations.

Key Findings

  1. Context Pollution via Document Injection — Attackers can poison vector stores through compromised data sources
  2. Prompt Injection Through Retrieved Context — RAG retrieval enables indirect prompt injection at scale
  3. Token-Level Exfiltration — Data extraction via carefully crafted queries that force models to output sensitive context

Practical Implications

These attacks are not theoretical. We demonstrate working exploits against:

  • Azure OpenAI API with custom RAG implementations
  • Production vector database configurations (Pinecone, Weaviate)
  • Enterprise knowledge base systems (Confluence, SharePoint)

Mitigation Strategies

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