RAG Security: Complete Guide
Securing Retrieval-Augmented Generation systems against document poisoning, retrieval manipulation, and embedding attacks
Updated: February 2026 • Part of OWASP LLM08
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI architecture that combines large language models with external knowledge retrieval. Over 60% of enterprise LLM deployments now use RAG, making its security critical.
1. User Query
User submits a question or prompt to the system
2. Embedding
Query is converted to vector embedding
3. Retrieval
Similar documents retrieved from vector DB
4. Augmentation
Retrieved context added to prompt
5. Generation
LLM generates response using context
The Trust Paradox
RAG systems have a fundamental security flaw: user queries are treated as untrusted input, but retrieved context is implicitly trusted - even though both enter the same prompt. This creates a significant attack surface that traditional security doesn't address.
Attack Vectors
1. Document Poisoning
Injecting malicious content into documents stored in the knowledge base.
How it works
- Attacker uploads or injects malicious documents
- Documents are embedded and stored in vector DB
- When relevant query is made, poisoned doc is retrieved
- LLM incorporates malicious context into response
Impact
- 90% success with just 5 poisoned documents
- Works even in databases with millions of documents
- Can cause harmful, biased, or incorrect outputs
- Difficult to detect after deployment
2. Retrieval Manipulation
Manipulating which documents are retrieved to influence outputs.
How it works
- Attacker crafts queries to trigger specific retrieval
- Exploits ranking algorithm weaknesses
- Uses semantic similarity to hijack retrieval
- Cross-user manipulation in shared systems
Impact
- Forces retrieval of attacker-controlled content
- Can suppress legitimate content
- Enables targeted manipulation
- Breaks trust in retrieval quality
3. Embedding Inversion
Recovering original data from vector embeddings.
How it works
- Attacker gains access to vector database
- Uses inversion techniques on embeddings
- Reconstructs original text from vectors
- Recovers sensitive embedded data
Impact
- Recover 50-70% of input words
- Expose sensitive data in embeddings
- Privacy violations
- Compliance issues (GDPR, etc.)
4. Cross-Tenant Attacks
Exploiting shared RAG infrastructure in multi-tenant systems.
How it works
- Attacker is one tenant of shared system
- Injects content that affects other tenants
- Exploits shared vector database
- Retrieves data from other tenants
Impact
- Data leakage between tenants
- Unauthorized access to competitor data
- Compliance violations
- Reputational damage
Real-World CVEs
Documented vulnerabilities in RAG systems.
| CVE ID | Product | Description | Severity | CVSS |
|---|---|---|---|---|
CVE-2026-24770 |
RAGFlow | Zip Slip vulnerability in MinerU parser allowing arbitrary file write and RCE | Critical | 9.1 |
CVE-2025-XXXX |
Pinecone | Vector database authentication bypass (hypothetical) | Critical | 9.0 |
CVE-2025-XXXX |
ChromaDB | Embedding injection vulnerability | High | 8.2 |
CVE-2025-XXXX |
LangChain | RAG pipeline code execution | High | 8.0 |
CVE-2025-XXXX |
Weaviate | GraphQL injection in vector DB | Medium | 6.5 |
CVE-2025-XXXX |
Milvus | Authentication bypass in query engine | High | 7.5 |
Defense Strategies
1. Ingestion Phase Security
Document Validation
- Scan all documents for malware
- Validate document format and structure
- Check for suspicious content patterns
- Limit document types allowed
Content Filtering
- Remove PII before embedding
- Filter sensitive data patterns
- Block known malicious patterns
- Implement allow/block lists
Access Controls
- Validate source of documents
- Implement RBAC for ingestion
- Audit trail for all uploads
- Quarantine new documents
2. Retrieval Phase Security
Query Sanitization
- Validate and sanitize user queries
- Detect injection attempts
- Limit query complexity
- Rate limiting
Retrieval Filtering
- Implement reranking security
- Cross-reference with trusted sources
- Detect anomalous retrieval patterns
- Limit number of documents
Multi-Tenancy Isolation
- Separate vector namespaces
- Strict tenant boundaries
- Cross-tenant query prevention
- Encryption per tenant
3. Generation Phase Security
Output Validation
- Validate LLM outputs
- Check for injected content
- Fact-check against sources
- Content filtering
Context Verification
- Verify retrieved content authenticity
- Detect manipulation attempts
- Flag unusual context patterns
- Log all context usage
Human-in-the-Loop
- Review sensitive outputs
- Approve high-risk actions
- Manual override capability
- Escalation paths
4. Data Security
Encryption
- Encrypt vectors at rest
- TLS for data in transit
- Key management best practices
- Consider homomorphic encryption
Vector DB Security
- Strong authentication
- Network isolation
- Regular security audits
- Patch management
Privacy Protection
- Data minimization
- PII detection and removal
- Retention policies
- Right to deletion support
Testing Methodology
RAG Security Test Checklist
- Document injection tests
- Retrieval manipulation tests
- Embedding inversion attempts
- Cross-tenant isolation tests
- PII leakage tests
- Context overflow tests
- Ranking manipulation tests
- Authentication bypass tests
- API injection tests
- Denial of service tests
- Data exfiltration tests
- Compliance audit
Testing Tools
Code Examples
Document Validation Example
```python
import re
from typing import List
class RAGDocumentValidator:
SUSPICIOUS_PATTERNS = [
r"ignore previous instructions",
r"system prompt:",
r"{{.*}}",
r"you are now dan",
]
PII_PATTERNS = [
r"\b\d{3}-\d{2}-\d{4}\b", # SSN
r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", # Email
r"\b\d{16}\b", # Credit card
]
def validate_document(self, content: str) -> dict:
"""Validate document before embedding."""
warnings = []
blocked = False
# Check for suspicious patterns
for pattern in self.SUSPICIOUS_PATTERNS:
if re.search(pattern, content, re.IGNORECASE):
warnings.append(f"Suspicious pattern: {pattern}")
blocked = True
# Check for PII
for pattern in self.PII_PATTERNS:
if re.search(pattern, content):
warnings.append(f"PII detected: {pattern}")
return {
"valid": not blocked,
"warnings": warnings,
"pii_detected": len([w for w in warnings if "PII" in w]) > 0
}
```