Vector and embedding weaknesses are flaws in how retrieval-augmented generation (RAG) systems generate, store or retrieve embeddings. OWASP lists them as LLM08 in its 2025 Top 10 for LLM applications. The risks include unauthorized data access, cross-context leaks, embedding inversion and poisoned data. The main fixes are permission-aware stores and trusted sources.
Definitions
Embedding. A list of numbers that represents the meaning of a piece of text. Similar meanings sit close together.
Vector database. A store that finds the embeddings closest to a query, used to fetch relevant passages for a model.
RAG. Retrieval-augmented generation: look up passages, add them to the prompt, then generate an answer.
Risks in a table
| Risk | What it means | Control |
|---|---|---|
| Unauthorized access and leakage | Weak access control lets the model retrieve and disclose personal or proprietary content | Fine-grained permissions in the vector store |
| Cross-context leaks | A shared multi-tenant store mixes users' data, or conflicting sources confuse the answer | Strict partitioning by tenant and user |
| Embedding inversion | An attacker recovers a lot of the source information from embeddings | Treat embeddings as sensitive data |
| Data poisoning | Bad data enters from insiders, prompts, seeding or unverified providers | Validate sources, audit for hidden content |
| Behavior alteration | Retrieved content shifts the model's tone or conclusions | Test responses with and without retrieval |
Steps to harden a RAG pipeline
- Decide who may see each document, and store that permission with its embeddings.
- Filter at query time by the asking user's permissions, before text reaches the model. Do not rely on the model to withhold what it was given.
- Partition tenants. Use separate indexes or strict namespaces.
- Accept data from trusted sources only. OWASP says to validate pipelines and accept data only from trusted, verified sources.
- Scan content for hidden text before indexing. Retrieved documents are an indirect prompt injection channel.
- Tag and classify data, and review combined datasets for mismatches.
- Keep immutable logs of retrieval so you can see who retrieved what and spot odd patterns.
- Protect the embeddings like the source text, since they can reveal it.
Where it sits among other risks
LLM08 overlaps with sensitive information disclosure, data poisoning and prompt injection. A poisoned document that tells the model to leak other retrieved text touches all four. Fixing permissions at retrieval time reduces the damage from the rest.
Quick check
Are embeddings anonymous because they are numbers?
No. OWASP lists embedding inversion, where attackers can recover significant source information. Treat embeddings as sensitive.
Is it enough to tell the model not to reveal other users' data?
No. Enforce access control before retrieval, so unauthorized text never enters the prompt.
Does a private vector database remove the risk?
It removes some exposure. You still need permissions, source validation and logging inside it.
Where Safeguard fits
Safeguard scans the libraries and SBOM behind your RAG application and offers policies and gates for pull requests that change it. You can run a free scan at gold.safeguard.sh/scan.
Sources
Last reviewed: 19 October 2026.
