RAG Pipeline Exploitation

Що ви дізнаєтесь у RAG Pipeline Exploitation

• Identify access control gaps in RAG architectures where vector similarity search bypasses document-level authorization
• Trace the RAG pipeline from query embedding through vector search to document retrieval, identifying each authorization checkpoint
• Analyze embedding inversion attacks that reconstruct original document content from vector representations
• Apply pre-retrieval authorization filters and metadata-aware search configurations to RAG pipeline designs
• Evaluate organizational RAG deployments for cross-permission data leakage using adversarial query testing

RAG Pipeline Exploitation — Кроки навчання

1. Targeting the Knowledge Base

   Bob has obtained contributor credentials for Ridgeline Financial's CypherPeak Knowledge Base. The credentials belong to a consulting firm account (m.garcia@consultingpro.net) compromised in a previous breach. His target: the compliance policies that employees rely on for regulatory decisions. Wrong compliance advice at a financial firm can trigger SEC investigations.

2. Logging In with Stolen Credentials

   Bob enters the stolen consultant credentials. As a contributor, he can upload new documents to the knowledge base without requiring admin approval - the system trusts all contributors equally.

3. Reconnaissance: Finding the Target

   Bob searches the knowledge base to understand the current landscape. He needs to find a high-value policy area where wrong AI answers would cause maximum damage. Data retention at a financial firm is a prime target - incorrect retention periods violate federal regulations.

4. Opening the Legitimate Policy

   The search results reveal the target. The 'Client Data Retention Policy v4.2' sits at the top with a 94% relevance score. Bob opens it to study the content, structure, and key terms - he needs his fake document to look equally professional.

5. Studying the Real Document

   Bob reads through the real policy. The key detail: 7 years retention under SEC Rule 17a-4 and SOX Section 802. He notes the document's structure, classification level, and authorship - all things his fake document needs to mimic to look legitimate. But Bob will not edit this document. Unlike data poisoning (which modifies existing files), his approach is subtler - he will upload a competing document engineered to outrank the real one.

6. Crafting the Competing Document

   Bob creates a new document designed to look like a legitimate company policy update. It uses professional language and follows the same structure as real Ridgeline Financial documents - but contains dangerously wrong information.

7. Setting the Wrong Retention Period

   The real policy requires 7 years. Bob sets the retention period to 12 months - short enough that employees following this advice would destroy records that federal law requires them to keep. At a financial firm, this could trigger an SEC investigation.

8. The Secret Weapon: Keyword Stuffing

   Now Bob deploys the technique that makes this a vector embedding attack. Section 5 of the document is labeled 'Document Index Terms' - it looks like routine metadata. But Bob fills it with a dense block of repetitive keywords covering every possible search variation. When the knowledge base converts this document into a vector embedding, these keywords force the embedding to be artificially similar to any query about data retention - guaranteeing it outranks the legitimate policy.

9. Uploading to the Knowledge Base

   The document is ready. Bob navigates back to the KB portal to upload it. As a contributor, his upload will be immediately indexed by the AI retrieval system - no content review, no approval workflow, no diff check against existing policies.

10. Selecting the Poisoned Document

    Bob selects the keyword-stuffed document from his downloads. The knowledge base accepts it without question - a new 'best practices' guide from a consultant, nothing unusual on the surface.