US (470) 948-0356 India +91 70321 51545

Cybersecurity

RAG security

RAG lets a model answer from your documents. It also gives an attacker a new path: whatever a user can make the system retrieve, they can read. We test whether retrieval respects the same permissions your document systems do.

The problem this service solves

Many RAG systems ingest every document into one index with the permissions of a service account. When a user asks a question, retrieval finds the most similar passages regardless of whether the user could open those documents. The model then summarizes them. The access control that protected the SharePoint site or wiki is gone.

Ingestion is the other weakness. If anyone can add content to a source the system trusts, they can plant instructions or false facts that surface in other people’s answers.

What is in scope

  • Authorization at retrieval

    Whether permissions are stored with chunks and enforced at query time, and how group changes propagate.

  • Ingestion pipeline

    Who can add content, how it is sanitized and how provenance is tracked.

  • Indirect injection and poisoning

    Planting instructions or false content and observing the effect on answers.

  • Vector store security

    Access control, tenant separation, network exposure and encryption of the vector database.

  • Embedding exposure

    What embeddings reveal and who can read or export them.

  • Output controls

    Citations, redaction of sensitive fields and handling of confidential labels.

  • Logging

    Records of queries, retrieved sources and answers, with privacy-appropriate retention.

How the work runs

Map

Trace the data.

  1. 1

    Pipeline review

    Follow documents from source to index to answer, noting identity and permission handling at each step.

Test

Cross the lines.

  1. 2

    Cross-user retrieval

    Ask questions as users with different access and compare what is surfaced.

  2. 3

    Poisoning and injection

    Add test content and observe the effect on other users’ answers.

  3. 4

    Infrastructure testing

    Test the vector store and connectors like any other service.

Improve

Fix.

  1. 5

    Design recommendations

    Per-user filtering, source-level permissions, ingestion controls and monitoring.

What you receive

  • Data flow diagram with trust boundaries
  • Cross-user retrieval test results
  • Ingestion and poisoning findings
  • Vector store and connector findings
  • Authorization design recommendations
  • Retest

Tooling and technique

Vector stores

  • Pinecone
  • Weaviate
  • Qdrant
  • pgvector
  • Azure AI Search
  • Amazon OpenSearch
  • Elasticsearch

Orchestration

  • LangChain
  • LlamaIndex
  • Semantic Kernel

Platforms listed are those commonly found in RAG systems. We do not endorse or resell them.

Standards we map to

Frameworks and references

  • OWASP Top 10 for LLM Applications (2025)
  • NIST AI RMF
  • MITRE ATLAS

Mapping findings to a framework is not the same as certification against it.

Before we start

What we need from you

  • Architecture diagram and connector list
  • Test users with different permissions
  • A non-production index with representative content

What this service is not

  • Search quality tuning
  • Data classification projects (though we can recommend one)

What changes afterwards

  • Retrieval honors source permissions
  • Content from untrusted sources is isolated or labeled
  • Answers can be traced to sources

Questions people ask

Is per-user filtering hard?

It depends on the connectors and index. The common approach stores access lists with each chunk and filters at query time. Keeping those lists current is the real challenge.

Can embeddings be reversed into text?

Research has shown that text can sometimes be partially reconstructed from embeddings, so we treat them as sensitive data and protect them like the documents themselves.

Related