Match known credential formats
The scanner checks for AWS, Google, Stripe, GitHub, Slack, and JWT-style token patterns, private key blocks, and generic assigned secrets.
PRE-SUBMIT PRIVACY SCAN
Prompt Privacy Scanner
Paste a prompt before you submit it to an AI tool and get it scanned for credentials, personal data, and other confidential content it should not carry.
- Uses your inputs in a transparent calculation
- Instant result with practical next steps
- No signup required
Planning guidance only. Validate important decisions with customer evidence and your delivery team.
YOUR INPUTS
Build your result
Complete every field. The result updates only when you choose Calculate.
How it works
Match personal data patterns
Email addresses, phone numbers, card-length digit sequences, Social Security Numbers, and IP addresses are flagged with masked samples.
Score risk and mask evidence
Matches are weighted by severity, destination sensitivity, and repeat count into a risk score, with only masked snippets shown back to you — nothing is uploaded.
Continue learning: Secret and API key risks in AI-generated apps · Data privacy questions to ask an AI MVP development company
Frequently asked questions
Does anything I paste leave my browser?
No. The scan runs entirely client-side in JavaScript. Nothing you paste is uploaded, logged, or sent to MVPHub or any third party.
What kinds of secrets does it catch?
Common formats for AWS, Google, Stripe, GitHub, and Slack keys, private key blocks, JWT-style tokens, database connection strings, and generic key=value secret assignments, plus emails, phone numbers, card-length numbers, SSNs, and IP addresses.
How is this different from a general data-privacy checklist?
A data-privacy checklist covers what to consider before collecting user information in your product. This tool scans the literal text of one prompt for secrets and personal data right before you paste it into an AI tool.
Will it catch every possible secret format?
No. It uses pattern matching against common, documented credential formats. Custom or unusual secret formats may not match — always do a final manual read before submitting sensitive prompts.
Why does it flag things like IP addresses at a lower risk?
An IP address alone is a weak signal and often harmless (a local address, a public example), so it adds a small amount of risk rather than driving the whole score, unlike a live API key.
What should I do with a flagged item?
Replace the real value with a placeholder, environment variable reference, or redacted string before submitting the prompt anywhere, especially to a third-party AI tool.
How We Compare
| Feature | MVPHub | GitHub Copilot | Cursor |
|---|---|---|---|
| Scans literal prompt text before submission | Included | Not included | Not included |
| Runs entirely client-side, nothing uploaded | Included | Not included | Not included |
| Flags multiple credential vendor formats | Included | Limited | Limited |
| Masks matched values in the result | Included | Not included | Not included |
GitHub Copilot and Cursor focus on generating and completing code, not on pre-flight scanning of what you are about to paste into a prompt. MVPHub checks the prompt text itself, client-side, before it reaches any AI tool.
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