Securing Retrieval-Augmented Generation Systems Through RAG Security Testing

Protect the Knowledge. Control the Context. Secure the Answers. 

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RAG systems are only as safe as the data they retrieve. ValueMentor tests what others overlook.

Our RAG Security Testing Process

Map data sources, ingestion workflows, vector storage, retrieval logic, and generation layers. 

Design realistic adversarial techniques targeting retrieval abuse, poisoning, and leakage scenarios. 

Execute attacks in a safe environment to validate exploitability without disrupting operations. 

Assess how compromised retrieval affects accuracy, compliance, user trust, and business decisions. 

Provide architectural, operational, and governance improvements to harden RAG systems. 

Support recurring testing as data sources grow, embeddings refresh, or models evolve. 

Don’t Let Your AI Learn From the Wrong Data

Why ValueMentor

ValueMentor helps enterprises secure RAG-based AI systems by focusing on the most vulnerable layer—the knowledge pipeline. Our teams combine AI engineeringoffensive security, and data governance expertise to ensure AI responses remain accurate, safe, and compliant. 

V-Trust Methodology

PMO-Led Delivery

Faster Delivery Accelerators

Secusy & AI driven GRC platform

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Make retrieval trustworthy—not exploitable.

FAQs

It is a specialized assessment that evaluates security risks across retrieval-augmented generation pipelines, including data sources, embeddings, vector stores, and retrieval logic. 

No. It applies to copilots, enterprise search, decision-support systems, knowledge assistants, and any AI system using retrieval-based augmentation. 

Before production launch, after knowledge base updates, during integration of new data sources, and periodically as threats evolve. 

Read our latest blog for advanced security insights and strategies to strengthen your defenses.

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