PII Guard

• What it does. An OpenAI-compatible gateway for AI agents. Change one base URL, and the names, addresses, IDs and faces it detects become reversible placeholders ([[PERSON_7f3a]]) before the request leaves. The real values come back in the answer, mid-stream and inside tool calls. Detection is rules plus a small NER that runs on CPU: XLM-RoBERTa base, distilled from Meta's Muse Glimmer 30B.
• How well. On 990 fictional agent-traffic documents (de, en, hu), 0.2% of personal values reached the model and 0.7% of masks hit text that was not personal data. The lowest-leak alternative, PrivAiTe, leaked 2.4% and over-masked 35.2% on the same documents.
• How fast. The NER takes 43 ms per message on a laptop CPU (p50; p95 50 ms, measurement). No LLM call and no API cost in fast mode.
• Where your data goes. In fast mode, the default (rules plus the on-device NER, no LLM call), the original text is processed only on your machine. The hosted demo receives what you type, so use fictional data there. The optional audit and SAM 3.1 endpoints receive the original text or pixels. Anything the detectors miss passes through: pseudonymization, not anonymization or a compliance guarantee.

_{990 fictional documents (de, en, hu), drawn by pii-guard eval report from the run records. All sets · Shareable PNGs}

_{Built for the Meta Global AI Developer Hackathon (Oct 26 – Nov 4, 2026) · 3-minute video walkthrough. Version 0.1.0, installed from git; no tagged release yet. Known gaps.}

Quickstart

Walkthrough: watch the 3-minute demo video on YouTube.

No install: the live demo runs as a guest with no sign-up (fictional data only; capped sponsored allowance).

On your machine (needs uv): the API and the gateway on http://127.0.0.1:8787, with a mock model that echoes what it receives:

uvx --python 3.12 \
  --from 'wattmind-pii-guard @ git+https://github.com/watt-mind/pii-guard' \
  pii-guard up

Launch a coding agent directly (OpenCode, Codex, Claude Code, pi, Muse Code) behind an in-process local gateway with Meta's muse-spark-1.3:

export META_API_KEY=<your-key>
uvx --python 3.12 \
  --from 'wattmind-pii-guard @ git+https://github.com/watt-mind/pii-guard' \
  pii-guard run opencode --upstream meta --model muse-spark-1.3

• First run: downloads PyTorch and the NER once (the NER is 1.1 GB); /healthz reports "ner": "ready" when detection is live.
• A real model: --upstream meta with META_API_KEY; openai, fireworks, openrouter or any --upstream-url work the same way (pii-guard up options, coding agent integrations).
• No auth by default, bound to 127.0.0.1: set PII_GUARD_API_TOKEN before exposing it (how to expose it safely).

Results

Leak: labeled personal values left fully readable in what the model receives. A value with only part left readable (a surname, the tail of a number) is a partial leak, counted separately: PII Guard fast 0.0% on agent traffic, 1.4% on the hand-written set. Over-masking: masks on text that was not personal data. Lower is better for both; in each row, the lowest leak and the lowest over-masking are bold.

Caveats

• The sets are small (tens to hundreds of documents per language): a point or two of difference is noise. Agent traffic is written by Muse models from fictional seeds, which can flatter a detector taught by Muse labels; that is why the hand-written and public sets sit next to it.
• Finance text is the weak spot. On Gretel finance's own labels PII Guard leaks 19.1%, about level with Presidio's 18.8%; on corrected labels (why) 2.4% against 14.8%. Over-masking is high on Gretel finance (24.4%) and CEE-PII (7.3%), where reference numbers without a cue word come out as IDs.
• NER v4.2 leaks more than the previous v2.0 on CEE-PII (0.8% against 0.4%), the Polish pl-pii-bench (4.2% against 3.5%) and TAB court text (5.1% against 4.0%, scored locally only), and less on the hand-written sets, agent traffic and the EU multilingual set (model notes).

Per-language results, the open NER models compared behind the same rules, and latency are in the benchmark report.

Which Meta models do what

Muse Glimmer 30B was the best zero-shot detector we measured (F1 99.3, 0.0% leak on the 38 Hungarian and English hand-written documents of that run, record) and far too slow for a request path at 9.4 s per document (p50). So it labeled the training text instead, and XLM-RoBERTa base learned from it: 43 ms on CPU, published as watt-mind/pii-guard-ner-xlmr-base v4.2.

In the default fast mode, every model that sees original personal data runs on your machine. Audit mode (Muse Spark Open) and SAM 3.1 are opt-in and receive originals at the endpoint you configure.

Inside the gateway

The gateway speaks the OpenAI Chat Completions and Responses APIs. It masks messages, tool definitions, tool-call arguments, tool results and images, restores placeholders in streams, keeps one placeholder per person per session, and fails closed on anything it can't mask; the mapping never leaves PII Guard. Every document in the hand-written set unmasks back to its original exactly.

Grammar-aware. In Hungarian a name changes with every case; the ending stays outside the placeholder:

Language packs. Hungarian, English, German and Polish each ship validated identifier rules, cue words and case handling; a new language is one package (how to add a language).

What it detects: people, addresses, emails, phone numbers, dates of birth, national and document IDs (including Hungarian tax and TAJ numbers), bank accounts and payment cards, vehicle plates, parcel numbers and secrets such as API keys, plus faces, ID photos and signatures in images (full entity list).

Real agents behind it. On 2026-10-29 four unmodified coding agents (pi, OpenCode, Codex, Muse Code) worked a fictional customer file through the gateway: 115 requests, none of the file's 9 personal values reached the model, and the answers and the files on disk came back with the real values. No turn was refused; in an earlier run on 2026-10-28, 7 of 15 were, before two gateway fixes (a system note saying the placeholders are intentional, and the NER no longer masking file names). Two Codex turns stopped on gateway bugs; ten turns is a sample, not a benchmark (harness matrix).

Architecture · sessions and modes · threat model · GDPR context

Thirteen differentiators, each with evidence.

Use it from code

Round trip

Mask, then unmask with the session PII Guard keeps. With pii-guard up running, this script takes the session id and the person placeholder from the first response (output from fast mode):

R=$(curl -sS http://127.0.0.1:8787/v1/mask \
  -H 'Content-Type: application/json' \
  -d '{"text": "Email Jane Placeholder, jane.placeholder@example.net, about the refund.", "language": "en"}')
echo "$R"
# {"masked_text":"Email [[PERSON_604260]], [[EMAIL_84bb25]], about the refund.",
#  "session_id":"s_a925963eb8819a782a68c56f41265424","entities":[...],"kept":[]}
SID=$(echo "$R" | python3 -c 'import json,sys; print(json.load(sys.stdin)["session_id"])')
P=$(echo "$R" | grep -o '\[\[PERSON_[0-9a-f]*\]\]' | head -1)
curl -sS http://127.0.0.1:8787/v1/unmask \
  -H 'Content-Type: application/json' \
  -d "{\"text\": \"Done: I emailed $P about the refund.\", \"session_id\": \"$SID\"}"
# {"text":"Done: I emailed Jane Placeholder about the refund.","unresolved":[]}

The session id and the placeholders differ on every run: each new session gets its own.

Python client

Put it in front of an existing client; only the base URL changes:

# pip install openai
from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8787/v1", api_key="unused")  # mock upstream
stream = client.chat.completions.create(
    model="mock",
    stream=True,
    messages=[
        {"role": "user", "content": "Refund Jane Placeholder, jane.placeholder@example.net."}
    ],
)
for chunk in stream:  # the mock echoes the masked text it got; the gateway restores the values
    print(chunk.choices[0].delta.content or "", end="")
# Echo: Refund Jane Placeholder, jane.placeholder@example.net.

What the model actually received: curl -sS 'http://127.0.0.1:8787/v1/traffic?limit=1' shows the last gateway request as sent upstream, with placeholders (pii-guard up keeps a masked-only traffic log). API guide · agents and harnesses.

Console tour

_{Console tour, recorded in fast mode (rules + NER) with fictional data. More screens · Video walkthrough.}

Run the console locally: make demo from a checkout.

Contributing, security, license

The contributing guide has the rule that matters most: never commit real personal data, not even in a test.

Security: found a way to make personal data leak past PII Guard? Report it privately: SECURITY.md.

Licensed Apache-2.0.