Paste any prompt, system message, or dataset entry to scan for sensitive data.
0 / 200,000 chars
Clean output appears here
Paste a prompt and click Sanitize to redact sensitive content// clean prompts before sharing
Remove secrets, API keys, names, IDs, emails, and sensitive data from prompts before sharing or publishing in datasets.
Paste any prompt, system message, or dataset entry to scan for sensitive data.
Clean output appears here
Paste a prompt and click Sanitize to redact sensitive contentDrop any prompt, chat log, or dataset entry into the input field. Supports up to 200,000 characters.
Toggle which categories to scan — API keys, emails, phone numbers, URLs, UUIDs, and more.
Click Sanitize, review what was redacted, then copy or download the clean version.
Prompt Sanitizer scans your AI prompts for accidentally included sensitive data — API keys, personal info, internal URLs — and replaces them with clearly-labeled placeholders. Everything runs in your browser; nothing is sent to a server.
No. All processing runs entirely in your browser using PHP on the server only for the initial page render. The sanitization itself is done client-side in JavaScript — your prompt text never leaves your device.
The tool detects common patterns including OpenAI keys (sk-...), GitHub personal access tokens (ghp_...), Slack tokens (xox...), AWS access keys (AKIA...), and Google API keys (AIza...), as well as generic 32+ character alphanumeric strings.
Yes. Use the toggle chips at the top of the tool to enable or disable individual redaction categories. For example, you can redact only emails and API keys while leaving URLs untouched.
The tool uses pattern matching for known formats, so it may miss obfuscated or non-standard secrets. Always do a manual review of sensitive prompts before publishing — use this tool as a first-pass safety net, not a guarantee.
Detected values are replaced with clearly labeled placeholders like [EMAIL_REDACTED], [API_KEY_REDACTED], or [UUID_REDACTED]. This preserves the structure of your prompt while making it safe to share.
Absolutely. Paste each prompt or conversation turn into the tool, sanitize it, and copy the clean version into your dataset. This is especially useful before uploading fine-tuning datasets to platforms like Hugging Face or OpenAI.
The tool supports up to 200,000 characters per run — enough for long system prompts, multi-turn conversations, or entire dataset entries. For very large datasets, process entries individually or in batches.
The tool detects common introductory name patterns like "My name is John Smith" or "Call me Jane". It does not identify arbitrary names without context since standalone capitalized words could be product names, places, or technical terms.
A prompt sanitizer is a tool that scans AI prompt text and automatically redacts or replaces sensitive information before it's shared publicly, added to a dataset, or submitted to a third-party service. As AI workflows become central to more development teams, the risk of accidentally leaking secrets embedded in prompts has grown significantly.
Developers, researchers, and prompt engineers regularly copy-paste prompts from real conversations into GitHub issues, blog posts, Hugging Face datasets, or Discord threads — often without realizing that those prompts still contain internal API keys, email addresses, phone numbers, or database connection strings captured during testing.
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When you interact with a large language model in a production setting, your prompts often capture real data from your application. A developer testing a customer support bot might paste in a real customer email. An engineer debugging a retrieval-augmented generation (RAG) pipeline might include database query results with actual user IDs or phone numbers. A researcher building a fine-tuning dataset might accidentally include chat logs that contain credentials.
These scenarios are more common than most teams realize. Several high-profile incidents have involved LLM application logs or prompt datasets being published publicly with sensitive company data intact. The cost isn't just embarrassment — it can mean a compromised API key, a GDPR violation, or exposure of personally identifiable information (PII).
This tool uses pattern-based detection across ten categories of sensitive data:
eyJ header.Each category can be toggled individually, so you have precise control over what gets redacted. If you want to preserve URLs (for example, in a prompt that genuinely needs to reference public documentation) you can disable that rule while keeping all other checks active.
Rather than silently deleting detected values, this tool replaces them with clearly labeled placeholders like [API_KEY_REDACTED] or [EMAIL_REDACTED]. This approach has several advantages over silent deletion:
One of the most important use cases for this tool is cleaning fine-tuning and evaluation datasets before release. If you're building a custom LLM or fine-tuning an existing model on proprietary conversation data, your training set likely contains real interactions from real users — and those users didn't consent to their emails or phone numbers appearing in a public dataset.
Regulations like GDPR (in Europe) and CCPA (in California) impose obligations on organizations that collect and process personal data. Publishing a training dataset without removing PII can constitute a data breach. Even if you're not subject to these regulations, responsible AI development norms increasingly expect dataset publishers to demonstrate that PII has been removed or sufficiently anonymized.
The Prompt Sanitizer makes this process fast: paste each conversation turn, sanitize, copy the clean version. For large-scale pipelines, the same regex patterns used by this tool can be adapted into a Python or Node.js script for batch processing.
Pattern-based detection is fast and effective for known formats, but it isn't foolproof. A few limitations to be aware of:
Best practice is to use this tool as a first-pass automated check, then do a manual review of the sanitized output before publishing. For especially sensitive use cases — healthcare, finance, legal — consider additional review by a privacy professional.
The Prompt Sanitizer runs entirely client-side. Your prompt text is processed by JavaScript in your browser and never transmitted to any external server. This is particularly important because the whole point of the tool is to handle sensitive data — it would be counterproductive to send that data over the network to perform the sanitization.
There are no accounts, no logs, no analytics on your input. You can use this tool with complete confidence that the secrets you're trying to remove won't be captured by the sanitizer itself.