Zero-dependency in-memory PII airgap & reversible masking proxy for Cloud LLMs (GDPR Art. 32 compliant MCP server)
Python
0
5 commits
updated Sep 30, 2026
Send sensitive data to Cloud LLMs (ChatGPT, Claude, Gemini) without ever leaking confidential PII.
pip install scrub-vault
ScrubVault is a lightweight, zero-dependency in-memory privacy proxy and Model Context Protocol (MCP) server. It intercept prompts, replaces personal identifiable information (emails, IBANs, IP addresses, credit cards, tax IDs) with deterministic local tokens ({{EMAIL_1}}, {{IBAN_1}}), and restores the original values when the AI responds.
+----------------------------------+
| Your Prompt (Confidential PII) |
+----------------------------------+
│
▼
┌─────────────────────────────┐
│ ScrubVault (Local RAM) │
│ - Scans & Masks Sensitive │
│ - Stores Mapping in Memory │
└─────────────────────────────┘
│
▼ (Masked Prompt: "{{EMAIL_1}}, {{IBAN_1}}")
┌─────────────────────────────┐
│ Public Cloud LLM API │
│ (OpenAI / Anthropic / ...) │
│ *Zero PII is transmitted* │
└─────────────────────────────┘
│
▼ (Response with tokens)
┌─────────────────────────────┐
│ ScrubVault (Local RAM) │
│ - Deterministic Unmasking │
└─────────────────────────────┘
│
▼
+----------------------------------+
| End-User Result (Restored PII) |
+----------------------------------+
from src.core.vault import ScrubVault
vault = ScrubVault()
# 1. Mask sensitive input
input_text = "Order for client Max, email: max@corp.de, IBAN: DE89370400440532013000"
result = vault.mask(input_text)
print(result.masked_text)
# Output: "Order for client Max, email: {{EMAIL_1}}, IBAN: {{IBAN_1}}"
# 2. Transmit result.masked_text to your LLM of choice...
ai_response = "Received confirmation for {{EMAIL_1}} on account {{IBAN_1}}."
# 3. Unmask locally
clean_response = vault.unmask(ai_response, result.token_map)
print(clean_response)
# Output: "Received confirmation for max@corp.de on account DE89370400440532013000."
# Calculate GDPR Art. 32 Risk Score
python -m src.cli.main audit "Contract with Herr Schmidt, IBAN: DE89370400440532013000"
# Mask a dataset file directly
python -m src.cli.main scrub-dataset input.json output_clean.json
ScrubVault includes a native stdio Model Context Protocol (MCP) server. Add it to your claude_desktop_config.json or Antigravity configuration:
{
"mcpServers": {
"scrub_vault": {
"command": "python",
"args": ["-m", "src.mcp.server"],
"cwd": "/path/to/scrub_vault"
}
}
}
scrub_mask_text: Masks PII in input text and returns a reversible token map.scrub_unmask_text: Replaces tokens with original values.scrub_audit_risk: Calculates risk scores and detection breakdowns.scrub_anonymize_json: Recursively scrubs JSON structures.re, json, sys, typing).Apache License 2.0. Open-source research and engineering by the Diamantenschmiede.
Python
100.0%
Zero-dependency in-memory PII airgap & reversible masking proxy for Cloud LLMs (GDPR Art. 32 compliant MCP server)
Python
0
5 commits
updated Sep 30, 2026
Send sensitive data to Cloud LLMs (ChatGPT, Claude, Gemini) without ever leaking confidential PII.
pip install scrub-vault
ScrubVault is a lightweight, zero-dependency in-memory privacy proxy and Model Context Protocol (MCP) server. It intercept prompts, replaces personal identifiable information (emails, IBANs, IP addresses, credit cards, tax IDs) with deterministic local tokens ({{EMAIL_1}}, {{IBAN_1}}), and restores the original values when the AI responds.
+----------------------------------+
| Your Prompt (Confidential PII) |
+----------------------------------+
│
▼
┌─────────────────────────────┐
│ ScrubVault (Local RAM) │
│ - Scans & Masks Sensitive │
│ - Stores Mapping in Memory │
└─────────────────────────────┘
│
▼ (Masked Prompt: "{{EMAIL_1}}, {{IBAN_1}}")
┌─────────────────────────────┐
│ Public Cloud LLM API │
│ (OpenAI / Anthropic / ...) │
│ *Zero PII is transmitted* │
└─────────────────────────────┘
│
▼ (Response with tokens)
┌─────────────────────────────┐
│ ScrubVault (Local RAM) │
│ - Deterministic Unmasking │
└─────────────────────────────┘
│
▼
+----------------------------------+
| End-User Result (Restored PII) |
+----------------------------------+
from src.core.vault import ScrubVault
vault = ScrubVault()
# 1. Mask sensitive input
input_text = "Order for client Max, email: max@corp.de, IBAN: DE89370400440532013000"
result = vault.mask(input_text)
print(result.masked_text)
# Output: "Order for client Max, email: {{EMAIL_1}}, IBAN: {{IBAN_1}}"
# 2. Transmit result.masked_text to your LLM of choice...
ai_response = "Received confirmation for {{EMAIL_1}} on account {{IBAN_1}}."
# 3. Unmask locally
clean_response = vault.unmask(ai_response, result.token_map)
print(clean_response)
# Output: "Received confirmation for max@corp.de on account DE89370400440532013000."
# Calculate GDPR Art. 32 Risk Score
python -m src.cli.main audit "Contract with Herr Schmidt, IBAN: DE89370400440532013000"
# Mask a dataset file directly
python -m src.cli.main scrub-dataset input.json output_clean.json
ScrubVault includes a native stdio Model Context Protocol (MCP) server. Add it to your claude_desktop_config.json or Antigravity configuration:
{
"mcpServers": {
"scrub_vault": {
"command": "python",
"args": ["-m", "src.mcp.server"],
"cwd": "/path/to/scrub_vault"
}
}
}
scrub_mask_text: Masks PII in input text and returns a reversible token map.scrub_unmask_text: Replaces tokens with original values.scrub_audit_risk: Calculates risk scores and detection breakdowns.scrub_anonymize_json: Recursively scrubs JSON structures.re, json, sys, typing).Apache License 2.0. Open-source research and engineering by the Diamantenschmiede.
Python
100.0%