🔒RAG security scanner

Is your RAG stack really local?

Scan RAG configs for hidden cloud fallbacks, secret exposure, and other paths that can leak sensitive data.

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Why this tool matters

Some RAG frameworks can hide a silent fallback. If a local model fails, the stack may switch to OpenAI or another hosted service and send your data off-box without making that obvious.

Load an example:

Configuration file / code

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What it detects

• Exposed or hardcoded API keys
• Hidden fallbacks to hosted AI services
• External network requests
• Weak security configuration choices

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How to stay local-first

• Use a local LLM such as Ollama
• Keep vectors in local Chroma or self-hosted Qdrant
• Manage secrets through environment variables or a vault
• Run regular security audits

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Supported frameworks

• LlamaIndex
• LangChain
• Haystack
• Custom configurations

💡 This tool runs locally in your browser. Your config text is not sent to our servers.

Why RAG Leak Detector Is Worth Using

Analyze your Retrieval-Augmented Generation context chunks for sensitive PII or compliance breaches before they hit the LLM. Free. This page is built for people who want a fast path to a working result, not a vague prompt-and-pray workflow. If you need a more reliable first draft, cleaner output, or a repeatable workflow you can hand to a teammate, RAG Leak Detector is designed to shorten that path.

Most visitors use RAG Leak Detector because they need something specific done now: a deliverable, a decision, or a workflow checkpoint. The sections below show the fastest way to get value from the tool and the adjacent pages that help you keep going.

How to Use RAG Leak Detector

Ensure your Enterprise AI isn't leaking private data.

1Paste a sample of the raw text your vector DB retrieved
2Run the compliance scan
3Review flagged items (e.g., SSNs, internal IP addresses, passwords)
4Adjust your chunking strategy or implement a redactor

Who Is RAG Leak Detector For?

For AI developers moving from prototypes to enterprise production.

AI Engineers

Ensure SOC2 compliance for AI tools

Security Audits

Test injection defense on RAG systems

What a Good Result Looks Like

A strong outcome from RAG Leak Detector is not just “some output.” It should be usable with minimal cleanup, aligned to the task you opened the page for, and specific enough that you can paste it into the next step of your workflow without rewriting everything from scratch.

If the first pass feels too generic, use the use cases, FAQs, and related pages here to tighten the scope. That usually produces better results faster than starting over in a blank chat.

Frequently Asked Questions

Why is this important?▼

If a user tricks an internal RAG HR bot, it might spill sensitive salary chunks to unauthorized employees. Proactive redaction prevents this.