It answers the question every enterprise AI leader now asks
Can my RAG pipeline be tricked, poisoned, or exploited to make my model say something it shouldn’t?
RAG AST - Secure RAG Systems
Securing the bridge between data and intelligenceModern enterprises rely on Retrieval Augmented Generation (RAG) pipelines to feed Large Language Models (LLMs) with curated knowledge. This retrieval layer gives AI systems accuracy and context but it also introduces new attack surfaces never covered by traditional SAST or DAST. Entersoft’s RAG AST (Retrieval Augmented Generation Application Security Testing) brings offensive security discipline to your AI retrieval workflows so your model stays informed, not compromised.What Is RAG AST?
RAG AST is a specialized discipline within our AIAST (AI Application Security Testing) framework.
Where SAST analyzes source code and DAST tests web apps at runtime, RAG AST inspects the intelligence layer—the retriever, vector database, and context pipeline that supply your LLM with data.
As enterprises rapidly adopt Retrieval Augmented Generation (RAG) to enhance AI accuracy and contextual intelligence, new security challenges emerge. RAG-AST (Retrieval Augmented Generation Attack Surface Testing) is Entersoft’s specialized framework designed to identify, assess, and mitigate vulnerabilities across the entire RAG pipeline.
RAG-AST ensures that every layer from user prompts to backend model integrations is hardened against manipulation, data poisoning, and inference attacks.
Why RAG Security Matters
RAG links APIs, storage, and models.
A single weak link in that chain can lead to
Prompt Injection
malicious context that rewrites model behavior
Data Poisoning
toxic documents that distort retrieval results.
Embedding Exposure
Unintended leakage of confidential vectors or PII.
Vector DB Manipulation
unauthorized queries or index tampering.
Hallucination Amplification
inaccurate outputs from compromised retrieval.
Supply Chain Vulnerabilities
Insecure APIs, libraries, or third-party models introducing hidden threats.
Traditional tools miss these risks. Entersoft’s RAG AST secures your RAG pipelines.
How RAG AST Works
RAG AST analyzes your AI pipeline, identifying risks across architecture, retrieval, prompts, and models. It simulates attacks, validates defenses, and provides actionable reports to secure your RAG systems.
Architecture Mapping
Understand the retrieval flow and trust boundaries by reviewing retrievers, vector DBs, embedding models, and LLM orchestration.
Threat Modeling (OWASP LLM Top 10)
Identify attack vectors across the RAG stack and map them to LLM01 LLM10 and ML Top 10 risks.
Poisoning & Injection Simulation
Test how malicious inputs affect retrieval and responses through controlled context injection and document poisoning tests.
Vector DB Security Testing
Validate index integrity and data isolation using authorization tests, query fuzzing, and embedding leak analysis.
Prompt Injection Defense Validation
Assess system prompts and guardrails via jailbreak tests, prompt tampering simulations, and moderation bypass attempts.
Reporting & Remediation Support
Document findings with CVSS-rated results, remediation steps, and an attestation report to secure your RAG pipeline.
OWASP LLM Top 10 Alignment
Every RAG AST engagement is aligned to the OWASP LLM Top 10 (2023–24) and OWASP ML Top 10 frameworks, covering the most critical AI-specific risks
- LLM01 Prompt Injection: Context manipulation & template injection testing.
- LLM03 Training Data Poisoning: Controlled document poisoning tests in retrieval corpus.
- LLM05 Supply Chain Risks: Library dependency and embedding model review.
- LLM06 Sensitive Info Disclosure: Output leakage validation and token scrubbing checks.
- LLM08 Excessive Agency: Agent tool abuse simulation within retrieval pipeline.
WHY CHOOSE ENTERSOFT RAG-AST
Key Security Layers We Test
Retriever Layer
- Query manipulation and prompt injection scenarios.
- Access control validation for retrieval API.
- Context sanitization and parameter filtering.
Vector Database Layer
- Unauthorized query access and index poisoning.
- Embedding leakage and sensitive metadata exposure.
- API rate limiting and authentication review.
Model Interaction Layer
- Validation of retrieved context before model input.
- Evaluation of hallucination impact under attack.
- Guardrail effectiveness and prompt chaining safety.
Governance & Logging
- Traceability from retrieval to response.
- Compliance with NIST AI RMF and ISO 42001 governance controls.
Entersoft Delivering Excellence
Across Industries
Deliverables
- AI Threat Model & Data Flow Diagram
- RAG AST Findings Report (severity, description, remediation)
- Proof-of-Concept Evidence & Logs
- Risk Register & 30-Day Fix Plan
- OWASP LLM Top 10 Mapping Sheet
- Attestation Pack for Compliance & Client Sharing
Industries We Serve
- Financial Services: AI chatbots trained on customer data.
- Healthcare: Clinical knowledge assistants.
- Government & Public Sector: Citizen service bots and policy assistants.
- Technology & Cybersecurity: SOC automation and AI threat intelligence tools.
- EdTech & Research: RAG-based content retrieval platforms.
Integrating RAG AST into Your AI Lifecycle
Our RAG security testing integrates directly into your ML or DevSecOps pipeline
Pre-Deployment Secure RAG design and architecture review.
Pre-Production Run RAG AST tests on staging environments.
Continuous Validation Integrate automated prompt-injection monitors and vector-integrity checks.
Periodic Retests Ensure ongoing security as datasets and models evolve.
