llm-shield-proxy 1.0.0

Enterprise Zero-Egress Privacy Redaction Proxy Engine for LLMs

LLM-Shield - Enterprise Privacy Redaction Engine

LLM-Shield is an open-source, zero-egress middleware proxy that intercepts OpenAI-compatible LLM API requests, redacts Personally Identifiable Information (PII) before it leaves your local infrastructure, and deterministically re-hydrates real-time SSE streaming responses without breaking stream latency.

Designed for enterprise privacy compliance (SOC 2 / HIPAA).

Author & Core Maintainer: Ninad Phalak ([e-mail])

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⚡ Core Features

• Zero Latency Streaming: Sliding-window tag-safety buffer intercepts SSE streams delta-by-delta without buffering full requests or responses.
• Zero Cloud / Zero Egress: 100% local processing. No external API calls for PII detection.
• Two-Tier PII Cascade Engine:
  • Tier 1 (Sub-millisecond Regex): SSNs, Credit Cards, Email Addresses, Phone Numbers, IPv4/IPv6, API Keys.
  • Tier 2 (NER Engine): Person Names and unstructured entities.
• Deterministic Re-Hydration Vault: Swaps PII with session-bound tokens (e.g., Sarah -> [PERSON_1]). Maps back deterministically when the LLM streams responses. Supports request-scoped and session-scoped (X-Session-ID) vaults.
• SOC 2 Structured Audit Logging: Emits JSON structured audit logs for compliance monitoring.
• Opt-In Telemetry: Strictly opt-in (TELEMETRY_ENABLED=false by default) telemetry worker collecting aggregated volumetric metrics with an explicit zero-PII guarantee.

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📦 Installation

Install llm-shield-proxy directly from PyPI via pip:

pip install llm-shield-proxy

Or install locally in editable mode:

pip install -e .

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🚀 Quickstart

Running via Python / Uvicorn

uvicorn app.main:app --host 0.0.0.0 --port 8000

Running via Docker Compose

docker-compose up -d

Usage with OpenAI Client

Point your base URL to LLM-Shield (http://localhost:8000/v1):

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="your-openai-api-key"
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Contact Sarah Connor at [e-mail]"}
    ],
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

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🧪 Testing

Run the full automated test suite:

py -m pytest tests/

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🏢 Using LLM-Shield in Production?

We are actively working with enterprise security teams to map out advanced compliance features. If your startup or organization is using LLM-Shield to unblock LLM streaming or pass SOC 2/HIPAA audits, I would love to hear from you.

Email the core maintainer at [e-mail] to share your feedback, request a feature, or feature your team as a case study.