Securing RAG Pipelines: Retrieval-Augmented Generation Threats & Defenses

Introduction

Retrieval-Augmented Generation (RAG) has become the dominant architecture for enterprise LLM applications. Instead of relying solely on the model's training data, RAG retrieves relevant documents from a knowledge base and includes them in the prompt context. **Over 80% of enterprise LLM deployments use some form of RAG** (Gartner 2025).

But RAG introduces an entirely new attack surface. The retrieval pipeline — vector databases, embedding models, document ingestion, and chunk selection — is where most AI-specific vulnerabilities live. In 2024, researchers demonstrated that **a single poisoned document in a RAG knowledge base could compromise every response** the system generates on that topic.

---

How RAG Works (& Where It Breaks)

┌────────────────────────────────────────────────────┐

│ RAG ARCHITECTURE │

│ │

│ User Query │

│ │ │

│ ▼ │

│ ┌──────────────┐ │

│ │ Embedding │ ◄── Attack: Query manipulation │

│ │ Model │ │

│ └──────┬───────┘ │

│ │ Query Vector │

│ ▼ │

│ ┌──────────────┐ │

│ │ Vector DB │ ◄── Attack: Embedding poisoning │

│ │ (Similarity │ Attack: Index manipulation │

│ │ Search) │ │

│ └──────┬───────┘ │

│ │ Top-K Documents │

│ ▼ │

│ ┌──────────────┐ │

│ │ Context │ ◄── Attack: Document poisoning │

│ │ Assembly │ Attack: Prompt injection │

│ └──────┬───────┘ via retrieved content │

│ │ Augmented Prompt │

│ ▼ │

│ ┌──────────────┐ │

│ │ LLM │ ◄── Attack: Indirect injection │

│ │ Generation │ Attack: Data exfiltration │

│ └──────┬───────┘ │

│ │ │

│ ▼ │

│ Response to User │

└────────────────────────────────────────────────────┘

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RAG-Specific Attack Vectors

1. Document Poisoning

An attacker with write access to the knowledge base (or through user-submitted content) injects documents containing:

**Real-world example:** Researchers at Princeton showed that poisoning just **0.001% of a RAG knowledge base** (5 documents out of 500,000) could cause the model to generate attacker-chosen content 88% of the time for targeted queries.

2. Embedding Space Attacks

Vector embeddings are the mathematical representations of text. Attackers can:

**Key stat:** A 2024 study showed embedding inversion attacks could reconstruct **92% of the original text** from its embedding vector on common models like OpenAI text-embedding-ada-002.

3. Context Window Manipulation

RAG systems have a fixed context window. Attackers can exploit this:

4. Metadata Injection

Many RAG systems include document metadata (titles, authors, dates) in the prompt. Attackers can:

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Securing RAG Pipelines

Input Validation

// Secure document ingestion pipeline

interface DocumentIngestion {

content: string;

source: string;

metadata: Record<string, string>;

}

function validateDocument(doc: DocumentIngestion): boolean {

// 1. Scan for prompt injection patterns

const injectionPatterns = [

/ignore (all |your )?(previous |prior )?instructions/i,

/you are now/i,

/system prompt/i,

/\[INST\]/i,

/<<SYS>>/i,

/### (System|Human|Assistant)/i,

/\bdo anything now\b/i,

];

for (const pattern of injectionPatterns) {

if (pattern.test(doc.content) || pattern.test(JSON.stringify(doc.metadata))) {

logSecurityEvent("injection_attempt", { source: doc.source, pattern: pattern.source });

return false;

}

}

// 2. Content length limits

if (doc.content.length > 50000) return false;

// 3. Metadata sanitization

for (const [key, value] of Object.entries(doc.metadata)) {

if (value.length > 500) return false;

if (injectionPatterns.some(p => p.test(value))) return false;

}

// 4. Source verification

if (!isAllowedSource(doc.source)) return false;

return true;

}

Retrieval Security

# Secure retrieval with access control and anomaly detection

class SecureRAGRetriever:

def __init__(self, vector_store, access_control):

self.vector_store = vector_store

self.access_control = access_control

self.anomaly_detector = EmbeddingAnomalyDetector()

def retrieve(self, query: str, user_id: str, top_k: int = 5):

# 1. Get user's access level

user_permissions = self.access_control.get_permissions(user_id)

# 2. Filter documents by access control BEFORE retrieval

allowed_collections = user_permissions.get_allowed_collections()

# 3. Retrieve with access-filtered search

results = self.vector_store.similarity_search(

query=query,

k=top_k * 3, # Over-retrieve then filter

filter={"collection": {"$in": allowed_collections}}

)

# 4. Anomaly detection on retrieved embeddings

safe_results = []

for doc in results:

if self.anomaly_detector.is_anomalous(doc.embedding):

log_security_event("anomalous_embedding", doc.metadata)

continue

safe_results.append(doc)

# 5. Content safety check on retrieved text

safe_results = [

doc for doc in safe_results

if not self.contains_injection(doc.page_content)

]

return safe_results[:top_k]

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RAG Security Checklist

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Key Statistics & Research

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Conclusion

RAG is not inherently insecure — but the default implementation patterns are. Document poisoning, embedding attacks, and indirect prompt injection are real, demonstrated threats. Every RAG pipeline must include input validation, access control, anomaly detection, and output filtering.

The good news: unlike LLM model-level vulnerabilities, RAG security is mostly an engineering problem with known solutions. Build the guardrails before you ship.

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