A local scrubber for text you're about to send to an LLM

A new open-source tool called Redact, available on GitHub, strips sensitive data such as names, emails, phone numbers, SSNs, card numbers, addresses, API keys, and database passwords from text before it is sent to an LLM, running entirely locally with no LLM involved. The tool uses regexes, checksums, a small NER model, and a merge layer to detect and redact or pseudonymize values, with the original values kept in a separate file that never leaves the machine. It supports blanking values as <PERSON> or swapping in stable placeholders like PERSON_001, and includes 12 test fixtures covering logs, résumés, chat, source code, medical, financial, and adversarial cases.

A local scrubber for text you're about to send to an LLM. Before you paste a log file, a résumé, or a support thread into an LLM, this tool strips out the sensitive parts first — names, emails, phone numbers, SSNs, card numbers, addresses, API keys, database passwords — and hands back a clean copy. It runs entirely on your machine with no LLM involved: a stack of regexes and checksums catches the structured stuff, a small NER model handles the fuzzier things like people and companies, and a merge layer reconciles them when they disagree. You can either blank each value out as <PERSON or swap in stable placeholders like PERSON 001 so the text still reads coherently, with the original values kept in a separate file that never leaves your machine. Most of the work went into not over-redacting — teaching it that "Django" in a skills list is a framework, not a person. For how the detection actually works, see PIPELINE.md /Sushmey/Redact/blob/main/PIPELINE.md . python -m venv redact venv source redact venv/bin/activate pip install -r requirements.txt python -m spacy download en core web sm The GLiNER model gliner multi pii-v1 , ~1.1 GB downloads from Hugging Face on first run and is cached. source env.sh thread-safety env vars — see the comments in the file python -m redact notes.txt - notes.redacted.txt python -m redact notes.txt -o clean.txt choose the output path python -m redact notes.txt --pseudonymize - notes.redacted.txt + notes.redacted.txt.map.json --pseudonymize also writes notes.redacted.txt.map.json label → original value . That file is sensitive — keep it local, never send it anywhere. As a library: python from redact import redact text sanitized, counts, mapping = redact text text, language="en", pseudonymize=False counts - {"EMAIL ADDRESS": 3, "PERSON": 5, ...} mapping - {} unless pseudonymize=True python run tests.py 12 fixtures in tests/ logs, résumés, chat, source code, medical, financial, and adversarial "same shape, different meaning" cases . Each checks both directions: sensitive values must be gone, ordinary values must survive unchanged.