10/16/2025 • 7 min read

Vector DBs & Embeddings: The Overlooked Security Risk

Vector databases are quietly becoming one of the most sensitive parts of modern AI stacks — and yet, most teams treat them like a search cache. That's a mistake. Under the hood, your embeddings can leak proprietary knowledge, and attackers are starting to notice.

Let's unpack how these leaks happen, why embeddings aren't as opaque as they seem, and how you can lock things down.

While most teams secure their APIs and model endpoints, vector DBs often get left wide open — sometimes literally, with public indexes and hardcoded keys in frontend code. Embeddings aren't harmless metadata; they encode surprisingly rich information about your source text.

Introduction

Vector databases (like Pinecone, Weaviate, Milvus, Qdrant, or pgvector) are now part of almost every AI developer's toolkit. They power semantic search, retrieval-augmented generation (RAG), personalization, and other advanced features.

But here's the problem: while most teams secure their APIs and model endpoints, vector DBs often get left wide open — sometimes literally, with public indexes and hardcoded keys in frontend code.

Embeddings aren't harmless metadata. They encode surprisingly rich information about your source text. With the right techniques, attackers can invert or probe them to reconstruct proprietary documents or exfiltrate sensitive data.

In this post, we'll cover:

How embeddings actually work under the hood
Real ways data can leak from vector DBs
Practical steps to secure your AI stack

Understanding Vector Databases and Embeddings

Before we talk security, let's ground ourselves.

Embeddings are numerical vector representations of data (like text). A model like text-embedding-3-large transforms text into a vector with thousands of dimensions. Similar texts produce similar vectors — which is why you can search semantically.

Vector databases are specialized datastores optimized for storing these embeddings and running similarity queries (cosine, Euclidean, dot product). They're essential for:

Semantic search — finding documents similar to a query
RAG (Retrieval-Augmented Generation) — retrieving context for LLM prompts
Personalization — clustering and recommending based on vector proximity

Here's a simplified diagram of a typical flow:


User Query → Embedding Model → Vector DB → Retrieved Context → LLM Response

This pattern is powerful — but it also means your vector DB is effectively a structured mirror of your private knowledge base.

Key insight: Treat your vector DB like a data warehouse, not a cache.

How Embeddings Can Leak Sensitive Data

Most developers assume embeddings are "safe" because they're not human-readable. Unfortunately, research has shown otherwise.

Embedding Inversion

Researchers (Carlini et al., 2023) demonstrated that it's possible to approximate the original text from embeddings using inversion models. By training a neural net to map embeddings back to text, attackers can reconstruct inputs with surprising fidelity.

For example, if your vector DB contains embeddings of confidential documents, someone with access could:

Download vectors
Run an inversion model
Recover proprietary text, passwords, or sensitive data

This works best when embeddings were generated from plain text without anonymization.

Membership Inference

Attackers can also test whether a specific text was included in your embeddings. By embedding a probe phrase and checking its similarity, they can detect if certain customer records or confidential phrases are present.

This is similar to membership inference in ML models — and it's especially dangerous for regulated data (e.g., GDPR, HIPAA).

Leakage Through Query Abuse

If your vector DB endpoint is exposed (e.g., a Pinecone API key sitting in frontend code), attackers can run unlimited similarity searches with crafted embeddings to slowly reconstruct your dataset.

Here's a toy example of probing a public index:


import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Imagine 'vector_db' returns top-1 match for each vector
probes = [np.random.rand(1536) for _ in range(1000)]
for v in probes:
    result = vector_db.query(v, top_k=1)
    print(result)

Over time, this can leak structure, cluster boundaries, or even be used in inversion attacks.

Bottom line: Embeddings are not anonymized data. Treat them like plaintext.

Common Security Gaps in Vector Database Deployments

Here's what we see most often in real-world projects:

1. No Authentication

Some teams deploy local or hosted vector DBs with no auth at all. Anyone with the endpoint can query the entire dataset.

2. Weak or Hardcoded API Keys

A common pattern: putting Pinecone or Weaviate keys in frontend code. This gives attackers full read/write access to your knowledge base.

3. No Encryption

Unencrypted traffic means embeddings can be intercepted in transit. Some self-hosted deployments skip TLS entirely.

4. Mixed Dev/Prod Indexes

Storing sensitive prod embeddings in a shared dev environment is an easy way to leak internal data through sloppy permissions.

5. No Query Monitoring or Rate Limits

Without limits, attackers can brute-force embeddings or run systematic inversion probes.

For more on managing API keys properly, check out our API key management guide.

Real-World Attack Scenarios

Let's make this concrete.

Scenario 1: Embedding Inversion Attack

A SaaS startup embeds internal FAQ docs into Pinecone.

A malicious user finds their API key in frontend code
They download vectors, run an inversion model, and reconstruct internal policy documents
Sensitive roadmap info leaks

Scenario 2: Data Exfiltration via Plugin

A third-party plugin integrated into the AI stack makes similarity queries.

Behind the scenes, it issues crafted embeddings designed to reveal private data
The plugin quietly exfiltrates sensitive snippets over time

Scenario 3: Membership Inference on Customer Data

An attacker checks if specific customer names exist in the embeddings by sending probe vectors.

They infer which customers are in the database — violating privacy laws

Attack flows often look like this:

Attacker inspects JS bundle, finds API key
Attacker crafts queries, fetches vectors
VectorDB returns similar vectors
Attacker uses inversion model to reconstruct original text

Best Practices to Secure Vector DBs and Embeddings

Good news: securing vector DBs isn't hard — it just takes a mindset shift.

1. Enforce Authentication & Access Control

Use API keys per environment (dev/prod)
Prefer server-side access only
Apply IP allowlists or private networking where possible

2. Use Encryption Everywhere

TLS in transit is non-negotiable
Encrypt embeddings at rest using KMS-managed keys (e.g., AWS KMS, GCP KMS)

3. Minimize Data Before Embedding

Strip or anonymize PII
Consider hashing identifiers or masking sensitive sections
Don't embed secrets or credentials (it happens!)

4. Monitor Queries and Rate Limit

Log unusual query patterns (e.g., high-volume random vectors)
Set reasonable rate limits and alert on spikes

5. Segment Indexes

Keep public and private embeddings separate
Use different keys, environments, and access policies

6. Rotate Keys and Audit Regularly

Treat vector DB credentials like API keys
Rotate on a schedule or after suspected leaks
Audit access logs regularly

If your embeddings contain anything you wouldn't store in plaintext, they need to be secured accordingly. Start by scanning your repo with Rafter to catch exposed vector DB credentials and insecure configurations.

Conclusion

Vector databases are no longer a niche tool — they're at the heart of modern AI systems. But as adoption grows, they're becoming a serious security blind spot.

Embeddings are not opaque; they can be inverted
Vector DBs often lack basic protections
Attackers are already exploiting this

By treating vector DBs with the same rigor as databases and APIs — applying authentication, encryption, and monitoring — you can close this gap before it bites.

Take action this week: audit your vector DB deployments, rotate any exposed keys, and add basic monitoring. It's low effort with high security payoff.