RAG Security
Secure Your
RAG Pipeline
Retrieval Augmented Generation connects your LLM to live data -- and opens a direct attack surface. FirewaLLM inspects every retrieved document, blocks poisoned content, and prevents data exfiltration through your RAG pipeline.
THE CHALLENGE
Your RAG Pipeline Is an
Open Backdoor
RAG systems retrieve documents from vector databases, knowledge bases, and external sources, then inject that content directly into the LLM's context window. Every retrieved chunk becomes an instruction the model follows. Attackers who can influence what gets retrieved -- through document poisoning, index manipulation, or adversarial content injection -- gain the ability to control your AI's behavior without ever needing direct access to the model or its prompts.
Document Poisoning & Indirect Injection
Attackers plant documents containing hidden instructions in your knowledge base, shared drives, or web-crawled sources. When your RAG system retrieves these poisoned documents, the embedded instructions enter the LLM context and can override system prompts, alter AI behavior, generate misleading outputs, or cause the model to execute actions it was never intended to perform.
Data Exfiltration via Retrieval Queries
Adversaries craft inputs specifically designed to trigger retrieval of sensitive internal documents, confidential business data, or access-restricted records. The LLM then surfaces this sensitive content in its response, effectively turning the RAG pipeline into a data exfiltration channel that bypasses traditional access controls and data loss prevention systems.
Context Window Manipulation
Attackers exploit the limited context window of LLMs by flooding retrieval results with adversarial content that displaces legitimate documents. By controlling which chunks the model sees, they can suppress accurate information, promote false narratives, manipulate decision-making outputs, and degrade the overall quality and trustworthiness of your AI system's responses.
THE SOLUTION
End-to-End Security for
Every Retrieved Document
FirewaLLM secures your RAG pipeline at every stage: scanning documents during ingestion, inspecting retrieved chunks before they enter the LLM context, and validating outputs before they reach users. Poisoned content is neutralized, unauthorized data access is blocked, and every retrieval is logged for audit.
Ingestion-Time Document Scanning
Analyze every document before it enters your vector database. Detect embedded prompt injection payloads, adversarial instructions, and suspicious content patterns during the indexing phase, preventing poisoned documents from ever contaminating your knowledge base.
Retrieval-Time Content Inspection
Inspect every retrieved chunk in real time before it is injected into the LLM context window. Identify and strip hidden instructions, adversarial payloads, and manipulation attempts from retrieved content, ensuring only clean, trustworthy data reaches the model.
Document-Level Access Control
Enforce fine-grained access policies on retrieved content. Ensure users only receive information from documents matching their authorization level. Prevent privilege escalation through retrieval by mapping document permissions to user roles and session context.
Sensitive Data Filtering
Automatically detect and redact PII, financial data, credentials, and custom-defined sensitive patterns in retrieved documents before they enter the LLM context. Prevent your RAG system from inadvertently surfacing confidential information in AI-generated responses.
Source Trust Scoring
Assign trust levels to each data source in your RAG pipeline. Content from lower-trust sources receives more rigorous inspection, higher-trust sources get expedited processing, and you can enforce source isolation to prevent cross-source instruction injection attacks.
Retrieval Audit & Forensics
Log every retrieval query, retrieved document, trust score, and security decision with full provenance tracking. Trace exactly which documents influenced each AI response, enabling forensic analysis of suspicious outputs and compliance reporting.
WHY FIREWALLM
Built for real-world AI security.
Block document poisoning and indirect prompt injection at ingestion time
Inspect every retrieved chunk before it enters the LLM context window
Enforce document-level access controls across all retrieval sources
Detect and redact sensitive data in retrieved content automatically
Assign trust scores to data sources with differentiated inspection policies
Prevent data exfiltration through adversarial retrieval query manipulation
Trace every AI response back to the exact documents that influenced it
Add less than 30ms of latency with parallel chunk inspection processing
RAG Security FAQ
What is RAG security and why does it matter?+
RAG (Retrieval Augmented Generation) security refers to protecting the entire pipeline where an LLM retrieves external documents, knowledge base entries, or database records to generate responses. This matters because the retrieved content becomes part of the model's context and directly influences its output. If an attacker can poison the document store, manipulate retrieval results, or inject adversarial content into indexed documents, they can control what the LLM says and does -- without ever touching the model itself.
How can attackers exploit RAG pipelines through document poisoning?+
Attackers inject documents containing hidden instructions into your knowledge base, vector database, or document store. When the RAG system retrieves these poisoned documents, the embedded instructions enter the LLM's context window and can override system prompts, alter the AI's behavior, exfiltrate data through crafted responses, or cause the model to ignore safety guidelines. This is called indirect prompt injection and it is extremely difficult to detect without inspecting retrieved content before it reaches the model.
Does FirewaLLM inspect documents before they enter the vector database?+
Yes. FirewaLLM operates at two critical points in the RAG pipeline: during document ingestion (before content is embedded and stored in your vector database) and during retrieval (before retrieved chunks are injected into the LLM context). Ingestion-time scanning catches poisoned documents before they contaminate your knowledge base, while retrieval-time scanning provides a second defense layer against any threats that were introduced through other channels.
Can FirewaLLM prevent data exfiltration through RAG retrieval queries?+
Absolutely. Attackers can craft prompts designed to trigger retrieval of sensitive documents and then extract that content through the LLM's response. FirewaLLM enforces access control policies on retrieved content, ensuring users only receive information from documents they are authorized to access. It also scans outbound responses for sensitive data patterns, blocking exfiltration attempts even when the retrieval itself was technically authorized.
How does FirewaLLM handle RAG pipelines that retrieve from multiple data sources?+
FirewaLLM supports multi-source RAG architectures where the LLM retrieves from vector databases, SQL databases, APIs, web searches, and file systems simultaneously. Each data source can have its own trust level, inspection policy, and access control rules. Content from lower-trust sources receives more rigorous scanning, and FirewaLLM can enforce source isolation so that instructions from one data source cannot influence how content from another source is interpreted.
What is the performance impact of adding FirewaLLM to a RAG pipeline?+
FirewaLLM adds minimal latency to RAG pipelines. Retrieved document chunks are inspected in parallel, and the scanning engine is optimized for the typical chunk sizes used in RAG systems (512-2048 tokens). Most retrievals add less than 30ms of inspection time. For ingestion-time scanning, documents are processed asynchronously so there is zero impact on query-time performance. The security benefits far outweigh the marginal latency cost.
Lock Down Your
RAG Pipeline
Every document your RAG system retrieves becomes an instruction your LLM follows. Deploy FirewaLLM to ensure every retrieval is inspected, every source is trusted, and your AI only acts on verified content.