Fabraix Red Team Playground
Free · Open source
AI security researchers
Paste a contract into an AI tool and every name, phone number, and case ID in that file goes straight to the model — and its training pipeline. PII GUI sits between your documents and the AI, stripping identifiers on your machine before anything leaves.
The app runs detection locally using on-device models, so nothing is uploaded at any point — no sign-up, no server round-trip, no cloud dependency. You review every flagged item in context before committing to a redaction, which means you catch the false positives before they become permanent holes in a legal document. Custom regex lets you add patterns the model won't know: internal case IDs, account number formats, bespoke identifiers. The export produces a PDF with sensitive text actually gone, not layered over. Where it breaks: single-file, single-session workflow with no batch processing described in the docs, so teams processing hundreds of support logs daily will hit a throughput ceiling fast.
Bottom line: The right call for anyone who needs to clean a contract or medical record before it touches an AI tool — not the right call for a team that needs to process a folder of five hundred files without touching each one.
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Free · Open source
AI security researchers
Freemium
Legal professionals handling confidential cases
Most AI workflows assume the document going in is safe to share. PII GUI addresses the case where it isn’t: you open a PDF, text file, or Markdown document, the on-device model runs detection, and every flagged item — names, emails, phone numbers, IDs — is highlighted in place for your review. You toggle detections on or off, add your own regex patterns for identifiers the model won’t recognize, and export a redacted copy. The local model downloads once; after that, the tool runs with no internet connection required.
The review step is what separates this from a black-box redaction script. Detection results are surfaced inline — you see exactly what the model flagged and in what context before anything is removed. That matters for legal and medical documents where a wrongly redacted clause or a missed identifier both carry real consequences. You stay in the loop; the tool doesn’t decide for you.
The vendor describes the app as built for contracts, medical records, HR files, and support logs — documents that need to be shared without exposing the people named in them. The free desktop build is available for Mac, Windows, and Linux, and the source is open. The documented workflow is one document at a time, in one window, on one machine. Teams that need to push batches of files through a pipeline, integrate redaction into an automated process, or expose the functionality via API will find no supported path for that here — and will likely end up running a self-hosted open-source NLP pipeline instead.
The app requires no account and no login. The detection model downloads on first use and operates offline from that point forward. Supported input formats are text, Markdown, and PDF; output is a redacted PDF or text file with sensitive content removed rather than visually obscured.
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