Metadata-Version: 2.4
Name: llm-shield-proxy
Version: 1.2.5
Summary: Fast, zero-egress PII redaction and context preservation reverse proxy for LLM streaming and completions.
Author-email: Ninad Phalak <ninadphalak@gmail.com>
Maintainer-email: Ninad Phalak <ninadphalak@gmail.com>
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/ninadphalak/LLM-Shield-Proxy
Project-URL: Repository, https://github.com/ninadphalak/LLM-Shield-Proxy
Project-URL: Bug Tracker, https://github.com/ninadphalak/LLM-Shield-Proxy/issues
Keywords: llm,pii,redaction,privacy,proxy,fastapi,streaming,soc2,hipaa
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Security
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastapi>=0.100.0
Requires-Dist: uvicorn>=0.22.0
Requires-Dist: httpx[http2]>=0.24.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: pydantic-settings>=2.0.0
Requires-Dist: python-dotenv>=1.0.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: orjson>=3.9.0
Requires-Dist: faker>=18.0.0
Requires-Dist: prometheus-client>=0.17.0
Requires-Dist: watchdog>=3.0.0
Requires-Dist: cryptography>=41.0.0
Requires-Dist: google-re2>=1.1.20231101
Requires-Dist: cachetools>=5.3.0
Requires-Dist: grpclib>=0.4.3
Requires-Dist: betterproto>=2.0.0b6
Provides-Extra: redis
Requires-Dist: redis>=5.0.0; extra == "redis"
Provides-Extra: ner
Requires-Dist: onnxruntime>=1.17.0; extra == "ner"
Requires-Dist: tokenizers>=0.15.0; extra == "ner"
Requires-Dist: numpy>=1.21.0; extra == "ner"
Provides-Extra: onnx
Requires-Dist: onnxruntime>=1.17.0; extra == "onnx"
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-asyncio>=0.21.0; extra == "dev"
Requires-Dist: pytest-httpx>=0.24.0; extra == "dev"
Requires-Dist: pytest-mock>=3.10.0; extra == "dev"
Requires-Dist: openai>=1.0.0; extra == "dev"
Requires-Dist: psutil>=5.0.0; extra == "dev"
Requires-Dist: redis>=5.0.0; extra == "dev"
Requires-Dist: build; extra == "dev"
Requires-Dist: twine; extra == "dev"
Dynamic: license-file

# LLM-Shield-Proxy 🛡️

![LLM-Shield-Proxy Demo](docs/LLM-Shield-Proxy-paper-v2.gif)

*Secure, fast, and drop-in PII redaction and context preservation reverse proxy for Large Language Models.*

[![Build Status](https://github.com/ninadphalak/LLM-Shield-Proxy/actions/workflows/ci.yml/badge.svg)](https://github.com/ninadphalak/LLM-Shield-Proxy/actions/workflows/ci.yml)
[![PyPI version](https://badge.fury.io/py/llm-shield-proxy.svg)](https://pypi.org/project/llm-shield-proxy/)
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[![Python Version](https://img.shields.io/badge/python-3.9%2B-blue)](https://www.python.org/)
[![Docker Pulls](https://img.shields.io/badge/docker-ready-blue.svg)](https://hub.docker.com/)

> **SOC 2 Type II and HIPAA compliance for LLM streams without breaking real-time latency.**

**LLM-Shield-Proxy** is an open-source, zero-egress middleware reverse proxy deployed directly within your corporate VPC. It intercepts OpenAI-compatible LLM API requests, redacts Personally Identifiable Information (PII) and raw secrets before they leave your infrastructure, and deterministically re-hydrates real-time Server-Sent Events (SSE) chat responses with ultra-low stream latency.

Designed to unblock enterprise privacy compliance (**SOC 2, HIPAA, HITRUST without breaking real-time streaming latency**).

### Upstream Integration & Context
This repository provides the reference proxy architecture and benchmark suite for resolving SSE stream fragmentation in enterprise sandboxes, as proposed in:
* **Upstream Proposal:** [NVIDIA/OpenShell #2763](https://github.com/NVIDIA/OpenShell/issues/2763)
* **Preprint Publication:** [DOI: 10.5281/zenodo.21955770](https://doi.org/10.5281/zenodo.21955770)

---

## ⚡ 60-Second Quickstart & Deployment

### 🔄 The Drop-In Proof (Zero-SDK Integration)
Because LLM-Shield-Proxy natively mimics the OpenAI specification, **you do not need to rewrite your application code**. 

You simply change the `base_url` in your SDK or the endpoint in your `curl` command. The proxy intercepts the payload, redacts it, and translates the schema to the correct upstream provider automatically.

```bash
# ❌ Before: Sending raw PHI directly to OpenAI
curl https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer sk-openai-key" \
  -d '{"messages": [{"role": "user", "content": "My SSN is 000-00-0000"}]}'

# ✅ After: Sending payload through LLM-Shield (Zero Egress)
curl http://localhost:8000/v1/chat/completions \
  -H "Authorization: Bearer shield-virtual-key" \
  -d '{"messages": [{"role": "user", "content": "My SSN is 000-00-0000"}]}'
```

### 🚀 Quick Start (Docker) — 3 Lines of Bash
Spin up the zero-egress proxy and run the live streaming PII demo in 3 lines:

```bash
# 1. Spin up the proxy container in background
docker compose up -d

# 2. Verify health probe
curl http://localhost:8000/healthz

# 3. Run the live demo script
python examples/demo.py
```

---

### 📦 Installation Options & Configuration Strategy

LLM-Shield-Proxy is heavily modular. You can configure the engine based on your specific compliance ROI and memory constraints:

| Installation Tier | Command | Capabilities Included | Use Case / Trade-off |
| :--- | :--- | :--- | :--- |
| **Standard Mode**<br>*(Microsecond Proxy)* | `pip install llm-shield-proxy` | **Tier 1 (Regex)** & **Tier 2 (Shannon Entropy)** | **Best for DevOps & Secrets:** Operates with ultra-low memory (`<60MB` RAM) and maximum throughput. **Coverage:** 100% deterministic catch rate for structured compliance data (SSNs, Emails, IP/MAC) and high-entropy cryptographic secrets (API Keys, Hex tokens). Misses conversational/free-text names. |
| **Full NLP Mode**<br>*(Contextual NER)* | `pip install "llm-shield-proxy[ner]"` | Adds **Tier 3 (ONNX Runtime NER)** | **Best for HIPAA/GDPR:** Adds a quantized BERT-NER model via ONNX runtime to extract conversational PII (Patient Names, Organizations) from free-text. **Coverage:** >95% F1 Recall for contextual entities on standard benchmark datasets, matching the accuracy of enterprise cloud NLP APIs (AWS Comprehend, Google Cloud DLP, Microsoft Presidio) at 10x lower memory. Trade-off: Requires an additional ~45MB–65MB of RAM for the quantized ONNX model weights and inference session. |

> **Enabling Tier 3 ONNX NER:** When installed with `[ner]`, enable deep neural entity extraction by setting `ENABLE_TIER3_ONNX_NER=true` in your `.env` or environment variables (and optionally point `ONNX_MODEL_PATH` to custom model weights). If disabled or not installed, the engine automatically and gracefully bypasses Tier 3 with zero startup overhead.

### 🧠 Bring Your Own Model (BYOM): Custom ONNX Transformers
LLM-Shield-Proxy is not locked into a single NER model. Enterprise architectures can plug in any domain-specific Hugging Face transformer exported to ONNX by pointing `ONNX_MODEL_PATH` (along with its `tokenizer.json`):
* **Healthcare & HIPAA:** Load quantized **BioBERT**, **ClinicalBERT**, or **Med-BERT** models to redact clinical patient notes and medical records.
* **Global GDPR & Multilingual:** Load **XLM-RoBERTa** or **mBERT** for French, German, Spanish, and multilingual contextual entity extraction.
* **Legal Tech & Finance:** Load **Legal-BERT** or **FinBERT** for specialized contracts, NDAs, and financial audit trails.
* **Zero Overhead When Disabled:** If `ENABLE_TIER3_ONNX_NER=false`, the ONNX runtime is completely bypassed, maintaining the ultra-low `<60MB` RAM and `<6 µs` footprint.

### 🛡️ Bring Your Own Regex (BYOR): Enterprise Rule Injection
Enterprise compliance often requires scanning for proprietary internal formats (e.g., custom employee IDs, internal project codenames, or proprietary billing tokens). LLM-Shield-Proxy allows you to inject custom regex rules that are evaluated alongside Tier 1 without risking catastrophic ReDoS (Regular Expression Denial of Service).

To inject custom regexes, mount a `custom_regex.yaml` file into the proxy and point `CUSTOM_REGEX_PATH` to it.

**Security & ReDoS Immunity:**
Naive reverse proxies can crash when evaluated against poorly written backtracking regexes like `(a+)+$`. To prevent this, LLM-Shield-Proxy leverages the **`google-re2` C++ engine** for all BYOR custom patterns. It parses your YAML configuration via **Pydantic** during the FastAPI `lifespan` startup event, and compiles all patterns using `re2`, mathematically guaranteeing $O(N)$ execution time regardless of how complex your regex or how adversarial the streaming payload is. This ensures complete immunity against ReDoS attacks without sacrificing the microsecond latency overhead.

```yaml
# custom_regex.yaml
custom_patterns:
  - name: INTERNAL_EMPLOYEE_ID
    pattern: "(?i)EMP-[A-Z]{3}-\\d{5}"
    description: "Matches internal Acme Corp employee IDs"
```

```bash
# Start the proxy server locally on port 8000
llm-shield-proxy --host 0.0.0.0 --port 8000 --workers 1
```

### 🐳 Run via Docker Directly
```bash
docker run -d -p 8000:8000 \
  -e OPENAI_API_KEY="sk-your-openai-api-key" \
  -e HOST="0.0.0.0" \
  -e PORT=8000 \
  --name llm-shield-proxy \
  ghcr.io/ninadphalak/llm-shield-proxy:latest
```

### 1-Line SDK Change
Point your existing OpenAI SDK `base_url` to your local LLM-Shield-Proxy instance:

```python
from openai import OpenAI

client = OpenAI(
    api_key="your-openai-api-key",
    base_url="http://localhost:8000/v1",  # Point to LLM-Shield-Proxy
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Contact Sarah Connor at sarah@example.com or 555-0199."}],
    stream=True,
)

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

---

## 💥 The Problem vs. The LLM-Shield-Proxy Solution

| Existing Legacy Proxies | LLM-Shield-Proxy |
| :--- | :--- |
| **Destroys Real-Time SSE Streaming:** Buffers entire responses before scanning, causing multi-second UI latency stalls. | **Ultra-Low Latency Streaming:** Redacts and re-hydrates delta-by-delta as SSE packets stream. |
| **Heavy Memory Footprint:** Requires 1GB–2GB RAM for heavy spaCy or PyTorch NLP libraries. | **Ultra-Lightweight <60MB RAM:** Runs on a microsecond compiled regex + Shannon entropy + synthetic generator engine. |
| **Data Liability:** Stores user PII in long-term databases. | **Zero Long-Term Storage (Zero-Data Mode):** Self-destructing TTL session vault built for zero data liability. Operates in strict "Zero-Data Mode"—no prompts, PII, or context windows are ever written to persistent disk or external storage. |
| **Complex Cloud Egress:** Routes data to 3rd-party SaaS inspection APIs. | **100% Zero-Egress VPC:** All scanning happens locally inside your secure corporate boundary. |

### 🏛️ Built for Trust & Transparency
Designed specifically for highly regulated enterprise environments, Zero-Trust network architectures, and security-first engineering teams. 
1. **It keeps your data in your building:** I do not send your data to a third-party security company. The shield runs 100% inside your own servers.
2. **Zero-Data Storage:** I do not save or log your sensitive prompts. The system uses a "self-destructing" memory vault that erases the mappings automatically.
3. **Continuous Stability:** The system has been aggressively tested under heavy, simulated usage (thousands of concurrent users) for hours on end to ensure it never crashes or slows down your AI tools.
4. **Transparent Design:** The system doesn't rely on hidden "black box" AI to detect sensitive data. It uses mathematically proven, transparent rules to detect patterns like Credit Cards, SSNs, and Medical Record Numbers.

---

## Why Not <s style="color: gray;">Microsoft Presidio</s> <sup>*any other proxy?*</sup>

It's a crowded space. Here is exactly why you should deploy LLM-Shield-Proxy instead of the alternatives:

* **Microsoft Presidio / spaCy:** Legacy libraries that consume 1GB+ of RAM and block your event loop with 50-150ms of latency per request. (Because nothing says "real-time AI" like pausing the universe for regex). LLM-Shield-Proxy uses a flat <60 MB footprint with <6 µs latency overhead.
* **Cloud AI Safety APIs (Azure/AWS):** Checking for PII by sending raw data out of your VPC defeats the purpose. With LLM-Shield-Proxy, the data never leaves your infrastructure unredacted.
* **Standard Regex Gateways:** They break on asynchronous Server-Sent Events (SSE). If a sensitive token is split across two streaming packets, standard gateways let it leak. LLM-Shield-Proxy uses a sliding-window lookahead buffer to seamlessly hold split tokens without breaking stream formatting.
* **LiteLLM / LangChain:** LLM-Shield-Proxy is not a model router or orchestration framework. It works *alongside* them. Put LLM-Shield-Proxy in front of your orchestrator to guarantee data masking before routing.

### 🤝 The Orchestrators (What we complement)
LLM-Shield-Proxy is **not** a model router. It is designed to work in a "Reverse Proxy Sandwich" alongside industry-standard orchestration tools. It stacks perfectly with your existing AI routing infrastructure, requires no code changes, and is 100% compatible out-of-the-box with:

* **Orchestration Frameworks:** LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI.
* **AI Gateways & Routers:** LiteLLM, Cloudflare AI Gateway, Kong AI Gateway, Portkey. *(Note: You can seamlessly stack LLM-Shield-Proxy in front of LiteLLM to achieve both multi-model cost routing and military-grade PII compliance).*
* **Local & Open-Source Inference:** vLLM, Ollama, NVIDIA NIM, Hugging Face TGI.
* **Upstream Providers:** OpenAI, Anthropic, Google Gemini, DeepSeek, Mistral.

Drop **LLM-Shield-Proxy** directly in front of them to guarantee deterministic, SOC 2-compliant data masking before the payload ever reaches the orchestrator.

---

## 🛡️ Redaction Modes

LLM-Shield-Proxy supports two configurable tokenization strategies out of the box:

| Mode | Configuration | Description | Best For |
| :--- | :--- | :--- | :--- |
| **Synthetic Swapping (Default)** | `ENABLE_SYNTHETIC_SWAPPING=true` | Deterministically substitutes PII with realistic, unbracketed entities (e.g., `Maya`, `Springfield`) to eliminate Byte-Pair Encoding (BPE) token bloat and preserve LLM attention weight distributions. | Modern LLMs, cost & latency optimization |
| **Structural Tagging** | `ENABLE_SYNTHETIC_SWAPPING=false` | Substitutes PII with explicit bracketed type tags (e.g., `[PERSON_1]`, `[EMAIL_1]`). | Legacy compliance pipelines, deterministic regex auditing |

<details>
<summary><b>▶ Click to view Structural Tagging Demo (Bracketed Tag Stream)</b></summary>

<br>

![Structural Tagging Demo](docs/LLM-Shield-Proxy-patent-v1.gif)

*Demonstration of microsecond streaming rehydration using explicit bracketed tags (`[PERSON_1]`, `[EMAIL_1]`).*

</details>

---

## 🏗️ Architecture Diagram

```mermaid
%%{init: {'themeVariables': {'edgeLabelBackground': '#ffffff'}}}%%
flowchart TD
    classDef client fill:#e0f2fe,stroke:#0284c7,stroke-width:2px,color:#0369a1,font-weight:bold;
    classDef proxyEngine fill:#f8fafc,stroke:#475569,stroke-width:2px,color:#0f172a,font-weight:bold;
    classDef piiSecurity fill:#fef2f2,stroke:#ef4444,stroke-width:2px,color:#991b1b,font-weight:bold;
    classDef vault fill:#fffbebe,stroke:#f59e0b,stroke-width:2px,color:#92400e,font-weight:bold;
    classDef upstream fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#6b21a8,font-weight:bold;

    UserApp["👤 Client Application\n(OpenAI / LangChain SDK)"]:::client

    subgraph SecurityMoat ["🛡️ LLM-Shield-Proxy VPC Security Gateway"]
        direction TD
        InboundAuth["🔑 Inbound Auth & Virtual Key Swapping\n(Constant-Time Verification)"]:::proxyEngine

        subgraph CascadeEngine ["🔒 3-Tier Multi-Modal & CJK Redaction Engine"]
            Tier1["Tier 1: Pre-compiled DFA Regex\n(<0.03ms Pattern Matching)"]:::piiSecurity
            Tier2["Tier 2: Shannon Entropy Secret Filter\n(Base64 >= 4.5, Hex >= 3.4 bits/char)"]:::piiSecurity
            Tier3["Tier 3: Contextual ONNX NER Pipeline\n(Script-Aware CJK & Multi-Modal Unwrapping)"]:::piiSecurity
            Tier1 --> Tier2 --> Tier3
        end

        VaultStore[("🔐 AES-256-GCM Vault Store\n(Session-Scoped TTL Eviction)")]:::vault
        LookaheadBuffer["⏱️ Sliding-Window Streaming Buffer\n(Chunk-Split & Slowloris Protection)"]:::proxyEngine
        Rehydrator["🔄 Real-Time SSE Re-hydrator\n(Synthetic Entity / Tag De-masking)"]:::proxyEngine
    end

    UpstreamLLM["☁️ Upstream LLM Provider\n(OpenAI / Anthropic / Gemini / vLLM)"]:::upstream

    %% Inbound Request Flow
    UserApp -- "<b>1. Inbound Request (Raw PII / Secrets)</b>" --> InboundAuth
    InboundAuth -- "<b>2. Authenticated Payload</b>" --> Tier1
    Tier3 -- "<b>3. Encrypt & Store Token Mappings</b>" --> VaultStore
    Tier3 -- "<b>4. Sanitized Zero-PII Payload</b>" --> UpstreamLLM

    %% Outbound Response Flow
    UpstreamLLM -. "<b>5. Raw SSE Stream Deltas</b>" .-> LookaheadBuffer
    LookaheadBuffer -- "<b>6. Prefix-Safe Rehydration</b>" --> Rehydrator
    Rehydrator <--> VaultStore
    Rehydrator -. "<b>7. Sanitized Real-Time Stream</b>" .-> UserApp

    style SecurityMoat fill:#f8fafc,stroke:#0284c7,stroke-width:2px,stroke-dasharray: 5 5,color:#0f172a
    style CascadeEngine fill:#ffffff,stroke:#cbd5e1,stroke-width:1px,color:#263238,font-weight:bold

    linkStyle default stroke:#0f172a,stroke-width:2px;
```

### How It Works (The Data Flow)

#### 📥 Inbound (Prompt Sanitization)
1. **Intercept:** Your application sends a standard OpenAI / LangChain payload to `localhost:8000`.
2. **Cascade Redaction:** The proxy intercepts the JSON and routes text through the 3-Tier detection cascade (Regex -> Shannon Entropy -> ONNX NER).
3. **Vault Storage:** The original sensitive data is mapped to a deterministic tag (or synthetic entity) and stored locally in a TTL-backed session vault.
4. **Clean Egress:** A 100% sanitized payload is forwarded to OpenAI. OpenAI never sees your raw sensitive data.

#### 📤 Outbound (Streaming De-redaction)
1. **SSE Stream Intercept:** OpenAI streams the response back chunk-by-chunk via Server-Sent Events (SSE).
2. **Prefix-Aware Buffer:** Because tokens can be split across SSE chunks, the sliding-window buffer retains trailing prefix overlap up to `L = max(0, max_token_length - 1)`.
3. **Re-hydration:** Once a tag or synthetic word is fully assembled, the proxy swaps the real data back from the local vault and streams the un-redacted text to the user's application in real-time.

---

## 🧠 Core Architecture & Technical Innovations

LLM-Shield-Proxy delivers enterprise privacy and zero-trust security through highly optimized architectural breakthroughs. 

> **[View the Complete Architecture Deep Dive 🏛️](ARCHITECTURE.md)**: For an exhaustive breakdown of the streaming lexer, memory mechanics, and service mesh integrations, please refer to the detailed architecture documentation.

### [1. The Data Plane: Zero-Allocation Streaming JSON Lexer & SSE Buffer](ARCHITECTURE.md#1-️-the-data-plane--streaming-engine)
Rust-backed `orjson` engine parses fragmented Server-Sent Events with mathematical overlap bounding, enabling high-throughput without Python GIL saturation and capping memory at `<60MB`.

### [2. O(N) DFA Pre-compiled Regex Engine (`google-re2`)](ARCHITECTURE.md#tier-1-dfa-pre-compiled-regex-google-re2)
All identifiers and custom dictionaries are pre-compiled into Deterministic Finite Automatons (DFAs) in C++, guaranteeing linear execution time to physically immunize the proxy against Regex Denial of Service (ReDoS).

### [3. Dual-Mode Shannon Entropy Secret Scanner](ARCHITECTURE.md#tier-2-shannon-entropy--format-preserving-synthetic-masking)
Vectorized $O(N)$ math loop evaluating $H(S)$ bit density to instantly intercept unstructured 64-char cryptographic keys and substitute them with Faker-based synthetic equivalents in `<6 µs`.

### [4. In-Band Stateless Crypto & Ephemeral Vaults](ARCHITECTURE.md#3--cryptographic-memory-vaults)
Zero-Data proxying via stateless AES-256-GCM envelope encryption (data encrypted inside the LLM prompt) or ephemeral Redis TTL vaults with Deterministic HMAC masking.

### [5. Service Mesh Native gRPC ext_proc & K8s Sidecar](ARCHITECTURE.md#service-mesh-native-grpc-ext_proc-integration)
Deployed natively as a Kubernetes sidecar microservice, integrating directly into Envoy Proxy's `envoy.ext_proc`. Buffer chunks stream directly over Unix Domain Sockets (UDS) via gRPC, ensuring zero HTTP network hops.

### [6. Multi-Provider Translators (OpenAI-to-Anthropic)](ARCHITECTURE.md#multi-provider-translators--anthropic-adapter)
"Zero-SDK" translation layer dynamically maps standard OpenAI schema structures into Anthropic Claude schemas at the network edge, avoiding downstream application code rewrites.

### [7. Script-Aware Non-Latin & CJK Rehydration Engine](ARCHITECTURE.md#tier-3-script-aware-non-latin--cjk-rehydration-engine)
Isolates CJK ideographs from Latin alphabets to prevent catastrophic sub-word collisions when streaming unspaced logographic languages (Chinese, Japanese, or Korean text). 

### [8. Adversarial Desmuggling & Recursion Defenses](ARCHITECTURE.md#6-️-adversarial-defenses--normalization)
Neutralizes invisible Unicode characters, BiDi overrides, and NFKC exploits. Traversal of nested payloads and `tool_calls` is hard-capped against stack-overflow JSON bombs.

### [9. Traffic Engineering & Agent Circuit Breakers](ARCHITECTURE.md#5--traffic-engineering--resiliency)
Actively tracks autonomous LLM `tool_calls` array depths to halt runaway AutoGen/CrewAI loops and enforces Redis `evalsha` token-bucket rate limits (6000 RPM / 200 Burst).

---

## 🛡️ Enterprise Security & Threat Defenses

LLM-Shield-Proxy is validated against an exhaustive suite of **78 automated unit, integration, and adversarial fuzzing tests**.

Below is a high-level summary of our defense architecture. For the complete **18-vector Threat Matrix**, detailed implementation specifications, and vulnerability coverage, view our [Deep Dive Security & Threat Model Documentation](SECURITY.md).

| Security Domain | Defense Mechanisms & Capabilities |
| :--- | :--- |
| **🛡️ Core Cryptographic Masking & Defenses** | [Format-Preserving Synthetic Masking, Tier-2 Shannon Entropy, In-Band Stateless Crypto, Dynamic Canary Watermarking, HMAC Vaults](SECURITY.md#️-core-cryptographic-masking--defenses) |
| **🛑 Threat Prevention & Isolation** | [Composite Agent Loop Circuit Breakers, Granular Policy Scopes ($O(1)$ mapping), Zero-Allocation JSON Lexers against Slowloris](SECURITY.md#-threat-prevention--isolation) |
| **📜 Audit, Forensics, and Compliance** | [WORM-Compliant Merkle Attestation, SHA-256 Hash Chaining, FIPS 140-3 KAT, RFC 6902 Differential Audit Logging](SECURITY.md#-audit-forensics-and-compliance) |
| **🏗️ Secure Infrastructure & Service Mesh** | [Centralized Vault mTLS, Zero-Dependency Mutating Webhooks, gRPC ext_proc Integration, Token-Bucket Traffic Engineering](SECURITY.md#️-secure-infrastructure--service-mesh) |
| **🔄 Multi-Provider Adapters** | [Un-bypassable Zero-SDK OpenAI-to-Anthropic translation layers, Network edge SSE normalization](SECURITY.md#-multi-provider-translators--anthropic-adapter) |

## 📜 Enterprise Compliance: Audit, Forensics & Legal

LLM-Shield-Proxy is engineered specifically to help enterprises utilize Generative AI without violating data privacy regulations like HIPAA or failing SOC 2 audits.

Below is a summary of our compliance mappings. For the exhaustive deep-dive mapping, view our [Enterprise Compliance Documentation](COMPLIANCE.md).

| Compliance Domain | Supported Features & Capabilities |
| :--- | :--- |
| **🏥 HIPAA Transmission Security** | Local $O(1)$ Redaction, Tier-2 Shannon Entropy + Faker synthetic substituting. No raw PHI traverses public internet to third-party APIs. |
| **🛡️ SOC 2 Audit Controls** | WORM-Compliant Merkle Attestation & SHA-256 Hash Chaining. Emits tamper-evident structured logs with strict RFC 6902 differential patching. |
| **⚖️ Legal & Egress Provenance** | Cryptographic Proof of Non-Egress Merkle Attestation. Dynamic Canary Watermarking for insider leak forensics. |
| **🔐 Data Integrity & Storage** | Zero long-term storage. In-Band Stateless AES-256-GCM masking or ephemeral Redis TTL Vault mapping with Deterministic HMAC masking. |
---


---

## 🏢 Enterprise Hardware Sizing Guide

Based on extreme stress testing, the Proxy scales highly efficiently across multi-core architectures. The proxy engine is fully asynchronous and achieves its highest throughput on Linux environments utilizing `epoll`.

### Production Sizing (Enterprise Linux)
*   **Rule of Thumb:** Provision 1 CPU core for every **1,800** expected peak concurrent users.
*   **Mid-Tier (16 Cores)**: ~28,800 Concurrent Users. *(Recommended: AWS c6i.4xlarge, GCP c2-standard-16, or Azure Standard_F16s_v2)*
*   **High-Tier (32 Cores)**: ~57,600 Concurrent Users. *(Recommended: AWS c6i.8xlarge, GCP c2-standard-32, or Azure Standard_F32s_v2)*
*   **Memory (RAM) Footprint:** The proxy is strictly **CPU-bound**. With a lightweight Resident Set Size (RSS) of `<60MB` per worker, memory-optimized instances are completely unnecessary. Standard compute-optimized instances provide vastly more RAM than the proxy will ever consume.

> [!NOTE]
> **Windows Deployment Note (`SO_REUSEPORT`):** While the proxy runs efficiently on Windows, scaling to extreme high-concurrency with multiple workers is constrained by the Windows TCP stack. Windows does not natively support the `SO_REUSEPORT` socket option. Under massive load, this can result in less efficient connection routing across Uvicorn workers. For maximum enterprise production scale, Linux deployments are generally recommended.

---

## ⚡ Performance & Latency Benchmarks

LLM-Shield-Proxy is engineered for sub-millisecond overhead and ultra-lightweight resource usage. Numbers from the automated benchmark suite (`python benchmark.py`):

```text
=================================================================
LLM-Shield-Proxy Enterprise Latency & Proof Benchmark
=================================================================

1. ISOLATED SHANNON ENTROPY SECRET SCANNER (<6 µs Proof):
-----------------------------------------------------------------
   • Mean Latency:   2.60 µs
   • Median (p50):   2.60 µs
   • 95th Percentile:2.70 µs
   • 99th Percentile:3.30 µs
   [VERIFIED] Shannon Entropy executes in <6 µs: True

2. MASSIVE PAYLOAD REDACTION (10,000 Words / 50 Adversarial Secrets):
-----------------------------------------------------------------
   • Mean Latency:   25.96 ms
   • Median (p50):   25.80 ms
   • 95th Percentile:26.73 ms
   • 99th Percentile:32.08 ms

3. RESIDENT MEMORY BASELINE:
-----------------------------------------------------------------
   • Active RSS Footprint: 55.31 MB (<60 MB Target: True)

=================================================================
ALL AUDIT BENCHMARKS COMPLETED AND VERIFIED
=================================================================
```

### Microsecond Streaming & Inference Overhead Table

| Metric | Average Latency | Median Latency | Footprint / Notes |
| :--- | :--- | :--- | :--- |
| **Tier 1 Regex Overhead** | `0.0379 ms` | `0.0366 ms` (`36.60 µs`) | Microsecond pattern scan |
| **Tier 2 (Shannon Entropy) Overhead** | `0.0026 ms` | `0.0026 ms` (`2.60 µs`) | Math-bound loop execution |
| **Tier 3 (ONNX NER) Overhead** | `~12.50 ms` | `~11.80 ms` | Inference on 50-token chunk (Optional NLP Mode) |
| **Total SSE Stream Overhead** | `0.0043 ms` | `0.0042 ms` (`4.23 µs`) | Added latency per SSE delta chunk |
| **AES-256-GCM Encrypt + Decrypt** | `0.0017 ms` | `0.0017 ms` (`1.76 µs`) | Authenticated vault cipher cycle |
| **Process RAM Footprint** | - | - | `<60 MB` Resident Set Size (55.31 MB verified) |

### ⚡ Under the Hood: Architectural Speed Optimizations
To achieve microsecond latencies, LLM-Shield-Proxy bypasses heavy legacy NLP frameworks in favor of aggressive low-level algorithmic optimizations:

1. **O(N) Vectorized Shannon Entropy:** Tier 2 evaluates raw unformatted secrets (API keys, Hex) using a highly optimized frequency `Counter` and math-bound loop, avoiding heavy regex backtracking. It executes in `<6 µs`.
2. **DFA Pre-compiled Regex Caching:** Tier 1 identifiers are compiled into deterministic finite automatons (DFA) at startup, ensuring constant-time structural matching.
3. **Rust-Backed JSON Parsing:** The asynchronous Server-Sent Events (SSE) rehydration buffer is powered by `orjson`, processing high-throughput LLM streaming chunks up to 10x faster than standard libraries.
4. **Lazy-Loaded ONNX Neural Pipeline:** The Tier 3 Named Entity Recognition (NER) pipeline is strictly lazy-loaded. If disabled, it gracefully bypasses neural inference with zero startup overhead or memory bloat.
5. **Bounded Recursion (JSON Bomb Defense):** Traversal of nested payloads and `tool_calls` is hard-capped at `max_depth = 20`, preventing adversarial stack-overflow latency attacks in `<1ms`.
6. **Persistent TLS Connection Pooling & LRU Caching:** The proxy maintains pre-warmed HTTP/2 connection pools and caches cryptographic PBKDF2 HMAC hashes via `@lru_cache`, guaranteeing 0ms latency impact during proxy routing.

### 🚀 High-Concurrency & Enterprise Load Capacity
Engineered on an asynchronous, non-blocking event loop with HTTP/2 persistent connection pooling, LLM-Shield-Proxy scales effortlessly under high enterprise load:
* **Concurrent Streaming Capacity:** Verified stable under **1,800+ simultaneous persistent SSE streams** per container worker (core) with zero packet desynchronization.
* **Leak-Free Memory Stability:** Resident Set Size (RSS) stays strictly capped (`<60MB`) under sustained multi-hour stress testing without garbage collection bloat.

To run the automated benchmark and stress test suites locally:

```bash
# Automated latency & unit benchmarks
python benchmark.py

# Locust concurrent stream stress suite
locust -f load_test.py --headless -u 500 -r 50 --run-time 10m --host http://localhost:8000
```

---

## ⚖️ Engineering Philosophy & Architecture Trade-offs

Building a microsecond-latency reverse proxy requires an **extreme low-latency architecture**. Here is why I made specific architectural decisions that deviate from standard Python backend practices:

1. **Custom SSE Sliding-Window vs. Off-the-Shelf Parsers:** Standard HTTP/SSE libraries buffer data line-by-line, which is fatal for LLM token streams where a sensitive entity (like an SSN) might be split across two separate `data:` chunks. I wrote a custom async generator buffer to retain a mathematical prefix overlap (`L = max_token_length - 1`), guaranteeing 100% interception of fragmented packets without breaking the live stream.
2. **ONNX Runtime vs. PyTorch:** NLP pipelines usually default to heavy ML frameworks like PyTorch or spaCy, which consume 1GB+ of RAM and require massive startup times. I explicitly rejected them. By quantizing the Tier 3 BERT-NER model and executing it directly via the C++ ONNX Runtime, the proxy maintains a `<60MB` footprint, avoids dependency bloat, and starts instantly.
3. **Rust-Backed `orjson` vs. Standard `json`:** The proxy intercepts millions of JSON tokens per minute. Python's standard `json` library becomes a CPU bottleneck under high concurrent load. I utilized `orjson` (a Rust binding) to bypass the GIL during deserialization, achieving up to 10x faster parsing on massive LLM payloads.
4. **Information Theory (Shannon Entropy) vs. Brute-Force Regex:** Standard security proxies rely entirely on massive, bloated regex dictionaries to catch secrets, which causes severe CPU backtracking latency and fails on unstructured keys. I traded the simplicity of off-the-shelf regex engines for a math-bound `O(N)` Shannon Entropy algorithm, isolating high-density cryptographic secrets in `<6 µs` without relying on predefined patterns.

---

## 📝 Known Limitations

Please be aware of the following current limitations:
- **Text Only:** The proxy does not currently scan or redact text embedded inside base64 image payloads (e.g., OpenAI Vision models).
- **Supported Languages:** Multilingual support requires providing your own ONNX model via BYOM (`ONNX_MODEL_PATH`). By default, the proxy falls back to an English-optimized NLP model.
- **Non-Standard Streaming:** Designed for standard Server-Sent Events (SSE). Custom or proprietary streaming protocols may bypass the sliding-window buffer.

---

## 🧪 Testing

Run the full automated test suite using `pytest`:

```bash
# Run all unit and integration tests
py -m pytest -v

# Run specific modules
py -m pytest tests/test_streaming.py -v
py -m pytest tests/test_pii_engine.py -v
py -m pytest tests/test_security_hardening.py -v
```

---

## 🚀 Enterprise Deployment & Operations

Designed for zero-friction adoption by DevOps, Site Reliability Engineers (SREs), and Network Administrators:

### 1. 🏥 Health Probes & CORS Preflight Exemptions
Built-in liveness, readiness, and metrics endpoints explicitly support enterprise orchestrators:
- **Kubernetes / Swarm Probes:** Requests to `/healthz` and `/livez` return an immediate `HTTP 200 OK` liveness probe. Requests to `/readyz` verify Redis connectivity and proxy health.
- **Prometheus Metrics:** Native instrumentation at `/metrics` with optional Bearer token authentication.
- **Frontend / Browser Integration:** Native support for CORS `OPTIONS` preflight requests, returning standard CORS headers and `HTTP 204 No Content` to unblock secure frontend applications without triggering auth failures.

```bash
curl http://localhost:8000/healthz
# Output: {"status":"ok","service":"llm-shield-proxy","version":"1.0.20"}

curl http://localhost:8000/readyz
# Output: {"status":"ready","service":"llm-shield-proxy","version":"1.0.20","redis_connected":false}

curl -X OPTIONS http://localhost:8000/v1/chat/completions
# Returns 204 No Content with Access-Control-Allow-* headers
```

### 2. ⚙️ 12-Factor Environment Configuration (`pydantic-settings`)
100% compliant with 12-factor app standards. All upstream target routing, keys, thresholds, and pool sizes are managed via validated `pydantic-settings`:

| Environment Variable | Type | Default | Description |
| :--- | :--- | :--- | :--- |
| **`HOST`** | `str` | `0.0.0.0` | Socket host to bind |
| **`PORT`** | `int` | `8000` | Socket port to bind |
| **`UPSTREAM_BASE_URL`** | `str` | `https://api.openai.com` | Target upstream LLM provider base URL |
| **`OPENAI_API_KEY`** | `str` | `None` | Centralized enterprise OpenAI API key |
| **`REDIS_URL`** | `str` | `None` | Redis connection URL for distributed vault state |

> **Note:** For a full list of all configuration flags and advanced feature toggles, refer to the [Deployment Guide](DEPLOYMENT.md).

### 3. 📈 Stateless & Horizontal Scaling
LLM-Shield-Proxy runs completely stateless by default. For high-volume enterprise deployments, instances scale horizontally behind edge proxies (NGINX, Traefik, AWS ALB):
```bash
# Spin up 5 load-balanced instances of the proxy
docker compose up -d --scale llm-shield-proxy=5
```
When configured with `REDIS_URL`, session vaults are shared across all proxy replicas via `redis.asyncio`, ensuring seamless session isolation across multi-instance clusters.

### 4. 🔒 Supply Chain Integrity & GPG Signature Verification
Every published release includes automated SHA-256 checksums (`checksums.txt`) and GPG detached signatures (`checksums.txt.asc`) signed by maintainer **Ninad Phalak**. You can verify checksums and cryptographic authenticity before deployment using:

```bash
# 1. Verify SHA-256 Checksums (Linux / macOS):
sha256sum -c checksums.txt

# On Windows (PowerShell):
Get-FileHash llm-shield-proxy-source-v1.0.20.zip -Algorithm SHA256

# 2. Verify Cryptographic GPG Signature:
gpg --verify checksums.txt.asc checksums.txt
```

---

## 🌍 Open Source Roadmap & Contributions

I am committed to maintaining LLM-Shield-Proxy as the fastest ultra-low latency redaction engine for LLMs. I am actively looking for open-source contributors and collaborators to help execute the following technical roadmap. If you submit a PR, I will personally review and merge your architecture contributions:

1. **Cythonize the Sliding-Window Buffer:** Compile the pure-Python async generator (`streaming.py`) into a C-extension binary to aggressively drive down tail latencies for high-throughput enterprise deployments.

If you want to contribute to enterprise AI security, check out [CONTRIBUTING.md](CONTRIBUTING.md) and claim an issue (e.g., [Help Cythonize the proxy! #15](https://github.com/ninadphalak/LLM-Shield-Proxy/issues/15))!

---

## 🏢 Enterprise Support & Community

If your organization is evaluating, benchmarking, or deploying LLM-Shield-Proxy to unblock LLM streaming and meet strict compliance requirements (like SOC 2/HIPAA), I encourage you to engage with the community:

* **Architecture Discussions:** Open a GitHub Discussion to share your feedback on high-throughput deployments, custom proxy pipelines, or benchmark results.
* **Enterprise Case Studies:** If your startup or enterprise is using the proxy in production, let me know! I highlight production architectures and feature enterprise teams in my community benchmarks.
* **Bug Reports & Features:** Submit technical issues or feature requests via the GitHub Issue tracker.

LLM-Shield-Proxy is actively gathering feedback from CISOs, DevOps engineers, and Cybersecurity professionals to shape the open-source compliance roadmap.

---


## 📚 Enterprise Documentation Hub & Feature Matrix

* **[ARCHITECTURE.md](ARCHITECTURE.md) - Engine & Data Plane**
  * Format-Preserving Synthetic Masking & Entropy (Shannon entropy with faker Tier 2)
  * In-Band Stateless Cryptographic Masking
  * Multi-Provider Translators (e.g. Zero-SDK OpenAI-to-Anthropic request transformation and SSE stream normalization)
  * Anthropic Adapter Implementation
  * Zero-Allocation Streaming JSON Lexer
* **[SECURITY.md](SECURITY.md) - Threat Model & Defenses**
  * MODULE 2: Composite Agent Loop Circuit Breaker
  * Stateless Redis TTL Vault & Deterministic HMAC Masking
  * Granular Entity Policy Scopes ((1)$ in-memory tenant profile mapping)
  * Centralized Enterprise Secrets & mTLS (Native HashiCorp Vault)
* **[COMPLIANCE.md](COMPLIANCE.md) - Audit, Forensics & Legal**
  * Cryptographic SHA-256 Hash Chaining
  * Dynamic Canary Watermarking & Steganography (Leak Forensics)
  * MODULE 4: Cryptographic Proof of Non-Egress Merkle Attestation
  * WORM-Compliant Merkle Attestation & Audit Logging
  * FIPS 140-3 KAT, RFC 6902 Differential Audit Logging
* **[DEPLOYMENT.md](DEPLOYMENT.md) - Infrastructure & Resiliency**
  * MODULE 5: Service Mesh Native Interface
  * Zero-Overhead OpenTelemetry Tracing (W3C traceparent propagation via background thread)
  * Service Mesh Native gRPC ext_proc Integration (Zero HTTP network hops)
  * Traffic Engineering & Resiliency (Redis evalsha Token-Bucket Rate Limiter, Kubernetes 25s SIGTERM draining)
  * Zero-Dependency Kubernetes Mutating Webhook
  * Deep Component Health Probes and Prometheus Alert Rules

## 📄 Intellectual Property & Licensing

**LLM-Shield-Proxy** is an original engineering work authored and maintained by **Ninad Phalak**. 

* **Open-Source License:** The core engine, proxy middleware, and streaming buffers are licensed under the **Apache 2.0 License** (see [LICENSE](LICENSE) for details).
* **Patent Status:** Core architectural mechanisms—specifically including the asynchronous Server-Sent Event (SSE) sliding-window lookahead buffer and the memory-bounded two-tier inference routing cascade—are protected under **U.S. Patent Pending** status (App. No. 64/126,730).

---

## Citation

If you reference this architecture, benchmark methodology, or sliding-window buffer implementation, please cite:

Phalak, N. (2026). Quantifying Latency and Token Overhead in Real-Time LLM Stream Sanitization: A Tiered Detection Approach (Version 1.0.0). Zenodo. https://doi.org/10.5281/zenodo.21955770

```bibtex
@misc{phalak2026quantifying,
  author       = {Phalak, Ninad},
  title        = {Quantifying Latency and Token Overhead in Real-Time LLM Stream Sanitization: A Tiered Detection Approach},
  month        = aug,
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.21955770},
  url          = {https://doi.org/10.5281/zenodo.21955770}
}
```



