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Owasp Llm
top10_2025
LLM08 - Vector and Embedding Weaknesses
Security risks from vulnerabilities in vector databases and embedding methods.
Intent & Description
'
🎯 Intent
Secure vector databases and embedding pipelines from manipulation that could influence model outputs.
📋 Context
RAG-based applications rely on vector databases and embeddings. These can be manipulated to inject malicious content, alter search results, or poison the knowledge base.
💡 Solution
Validate data before embedding. Implement access controls on vector databases. Monitor for anomalous embeddings. Use embedding integrity checks. Apply input sanitization to retrieved contexts.'
Real-world Use Case
Use when building RAG pipelines, vector databases, or any system that uses embeddings for retrieval.
Source
📌 TL;DR
Secure vector databases and embeddings. Validate data before embedding, control access, monitor for anomalies.
Advantages
- Protects knowledge base integrity
- Prevents context manipulation
- Secures retrieval pipelines
- Maintains output accuracy
Disadvantages
- Embedding validation is computationally expensive
- Anomaly detection has false positives
- Large vector stores are hard to audit