Building phi-guard-mcp: Catching PHI Leaks in AI-Generated Code

The Problem Nobody's Watching

Your AI coding agent just shipped a patient's diagnosis to OpenAI.

The code worked. The tests passed. The PR got approved.

Nobody noticed — because nothing was watching for it.

This is a new class of mistake. Infrastructure scanners like Prowler check S3 bucket encryption, not whether your code interpolates patient.name into a prompt. The FHIR MCPs help agents read clinical data — they don't check whether other code is leaking PHI into logs or analytics.

The code is doing exactly what it was written to do. That's the problem.

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What PHI Guard MCP Does

phi-guard-mcp is a local-first MCP server that catches PHI (protected health information) before it hits LLM calls, logs, or analytics events.

It runs entirely on your machine over stdio. No network calls. No telemetry. No data ever leaves your environment.

Two tools:

• scan_code — Walks your repo and flags lines where a sensitive identifier (patient, diagnosis, mrn, ssn, dob) appears on the same line as a risky sink (openai, anthropic, console.log, logger, .track())
• redact_suggest — Takes any raw text and returns a redacted version with PHI replaced by placeholders

Example:

{
  "text": "Patient John Doe (MRN-12345), DOB: 01/01/1980"
}

{
  "redacted": "Patient [NAME] ([MRN]), [DOB]",
  "detected": [
    {"type": "mrn", "value": "MRN-12345", "confidence": 0.9},
    {"type": "dob", "value": "01/01/1980", "confidence": 0.85},
    {"type": "name", "value": "John Doe", "confidence": 0.8}
  ]
}

What It Catches

PHI Type Confidence Example Matches
SSN 0.95 123-45-6789
MRN 0.90 MRN-12345, MRN: 12345
DOB 0.85 01/01/1980, born 3/14/75
Name 0.80 Patient John Doe
Phone 0.75 555-867-5309
Email 0.70 [e-mail]
The patterns are deliberately narrow. A false positive that trains someone to ignore the tool is worse than a missed match.

Tested Against Real Leak Patterns
6/6 planted leaks detected across 5 different sinks (OpenAI, Anthropic, Sentry, Winston, PostHog/analytics)

0 false positives across 5 clean-code fixtures

Scans .ts, .js, .tsx, .jsx, .py, .go

Skips node_modules, dist, build, .next, .turbo, and dotfiles

What This Is Not

Not a HIPAA certification or legal compliance guarantee — it's a linter-grade signal, not an audit

Not a competitor to Prowler or AWS Config — those scan infrastructure, this reads source code

Not a hosted service — it runs entirely on your machine, no BAA required

How to Use It

Installation:

npm install -g phi-guard-mcp
With Claude Code: Add .mcp.json to your project root:

{
  "mcpServers": {
    "phi-guard": {
      "command": "node",
      "args": ["/absolute/path/to/phi-guard-mcp/dist/index.js"]
    }
  }
}

Command Line:

phi-guard-mcp
Then ask your AI agent:

"Scan this repo for PHI leaks in prompts and logs"

or

"Redact this text: 'Patient John Doe (MRN-12345) was admitted on 1980-01-01'"

Why I Built This

This came from watching AI agents write code that ships patient data into prompts, logs, and analytics — with total confidence, because nothing was watching for it.

The gap I saw was real: no existing tool catches this. The code works. Tests pass. PRs get approved. But patient.diagnosis just went to OpenAI.

So I built the thing I wished existed.

Links

GitHub: github.com/Abidit/phi-guard-mcp

npm: npmjs.com/package/phi-guard-mcp

MCP Registry: mcpservers.org/servers/abidit/phi-guard-mcp

MIT licensed. Open source. Free to use, forever.

Feedback welcome, especially if you're building in healthtech or working on AI safety.