Specialized adversarial testing to uncover and remediate critical vulnerabilities in your Retrieval-Augmented Generation architecture.
Services
RAG System Adversarial Manipulation Testing
Specialized security testing for Retrieval-Augmented Generation architectures, targeting vulnerabilities in vector databases, document chunking, and retrieval logic to prevent data poisoning, context corruption, and unauthorized knowledge base access.
SECURITY TESTING
Your RAG System is a Prime Target for Sophisticated Attacks
RAG systems combine the probabilistic nature of LLMs with deterministic data stores, creating novel attack surfaces that traditional security misses. We simulate real-world adversaries to find vulnerabilities before they cause data breaches, compliance failures, or corrupted outputs.
We target the entire RAG pipeline, from ingestion to generation, to ensure your AI's knowledge base remains secure, accurate, and trustworthy.
Our testing methodology focuses on critical vectors:
- Document Poisoning & Context Corruption: Injecting malicious content into source documents or the vector database to manipulate model outputs.
- Retrieval Logic Manipulation: Exploiting weaknesses in chunking, embedding, or similarity search to bypass filters or retrieve unauthorized data.
- Unauthorized Knowledge Base Access: Testing for data leakage through the RAG interface or exploiting misconfigured access controls.
- Adversarial Prompt Injection Against Augmented Context: Crafting inputs designed to override or ignore the provided retrieved context, leading to hallucinations or policy violations.
We deliver a prioritized remediation roadmap with actionable fixes, not just a list of problems. Protect your investment and ensure your RAG system delivers reliable, secure intelligence. Explore our broader AI Red Teaming and Adversarial Defense services or learn about securing autonomous systems with AI Agent Goal Hijacking Defense.