Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Specialized adversarial testing to uncover and remediate critical vulnerabilities in your Retrieval-Augmented Generation architecture.
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:
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.
We apply a structured, offensive security framework to your Retrieval-Augmented Generation system, identifying critical vulnerabilities before they are exploited. Our methodology is based on the MITRE ATLAS framework and years of specialized experience securing enterprise RAG deployments.
You receive a detailed technical report mapping each discovered vulnerability to the MITRE ATLAS framework, with clear, actionable remediation steps prioritized by risk. Our findings are delivered with executive summaries for leadership and technical deep-dives for your engineering team.
Compare our structured testing packages designed to identify and remediate vulnerabilities in your Retrieval-Augmented Generation system, from vector database poisoning to retrieval logic manipulation.
| Security Assessment Feature | Starter | Professional | Enterprise |
|---|---|---|---|
Initial Threat Modeling & Scoping Session | |||
Vector Database Poisoning & Evasion Testing | |||
Document Chunking & Embedding Manipulation Tests | |||
Retrieval Logic Bypass & Context Corruption | |||
Adversarial Query Crafting (Prompt Injection for RAG) | |||
MITRE ATLAS Framework Mapping | |||
Custom Attack Simulation (Tailored to Your Data Schema) | |||
Detailed Technical Report with CVSS Scoring | Executive Summary | Full Technical Breakdown | Full Breakdown + Live Walkthrough |
Remediation Guidance & Developer Tickets | General Recommendations | Specific Code Fixes & PR Examples | Direct Engineering Support & Pairing |
Retesting & Validation Post-Fix | 1 Round | Unlimited Rounds (30 Days) | |
Security SLA & Guarantee | 90-Day Coverage | 12-Month Coverage with Quarterly Reviews | |
Starting Price | $15K | $45K | Custom |
Retrieval-Augmented Generation systems power mission-critical decisions. A single vulnerability can lead to data breaches, regulatory fines, or operational failure. Our adversarial testing identifies and remediates these risks before they are exploited.
Protect AI-driven trading algorithms, fraud detection systems, and client advisory chatbots from data poisoning and context corruption that could trigger erroneous multi-million dollar transactions or regulatory non-compliance. Our testing follows NIST AI RMF guidelines.
Secure clinical decision support RAG systems against manipulation that could corrupt diagnostic retrieval or treatment recommendations, ensuring HIPAA compliance and patient safety. We test for vulnerabilities in medical DSLM and multimodal data pipelines.
Defend contract analysis and litigation prediction RAG architectures from adversarial prompts designed to bypass compliance checks or generate legally inaccurate citations, protecting against malpractice risk. Our methods are informed by frameworks like MITRE ATLAS.
Harden geospatial intelligence (GeoAI) and secure communications RAG systems operating in contested environments against sophisticated attacks aiming to corrupt intelligence retrieval or exfiltrate classified knowledge bases. We employ air-gapped testing protocols.
Secure custom enterprise AI copilots and internal RAG search against prompt injection and unauthorized knowledge base access, preventing data leakage from proprietary ERP, CRM, and legacy data silos. We integrate with your shadow AI detection posture.
Protect autonomous procurement agents and digital supply chain twin RAG systems from manipulation that could disrupt inventory replenishment, corrupt logistics routing, or expose sensitive supplier data, ensuring operational continuity.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear answers on our specialized methodology for identifying and mitigating critical vulnerabilities in your RAG architecture, from vector databases to retrieval logic.
Our methodology is based on the MITRE ATLAS framework, adapted for RAG-specific threats. We systematically test the entire pipeline: document ingestion and chunking for poisoning vectors, the vector database for unauthorized access or data leakage, the retrieval logic for context corruption, and the generation layer for prompt injection. This ensures we identify vulnerabilities like data poisoning, knowledge base exfiltration, and hallucination induction.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
