llmshield 0.0.8

Shields your confidential data from third party LLM providers

LLMShield

A zero-dependency Python library for protecting personally identifiable information (PII) in Large Language Model interactions through automatic entity detection and cloaking.

Overview

LLMShield provides enterprise-grade protection for sensitive information in LLM interactions by automatically detecting and replacing PII with placeholders before transmission, then restoring original values in responses. The library employs a multi-layered detection approach combining pattern recognition, dictionary matching, and contextual analysis.

Key Features:

• Zero external dependencies
• Automatic entity detection and cloaking
• Multi-turn conversation support with entity consistency
• Streaming response handling
• Universal LLM provider compatibility
• Production-optimized performance

High-Level Data Flow

graph LR
    A["Raw Input<br/>'Contact Dr. Smith at [e-mail]'"] --> B["Entity Detection<br/>PERSON: Dr. Smith<br/>EMAIL: [e-mail]"]

    B --> C["PII Anonymization<br/>'Contact &lt;PERSON_0&gt; at &lt;EMAIL_1&gt;'"]

    C --> D["LLM Processing<br/>Safe text sent to<br/>OpenAI, Claude, etc."]

    D --> E["Response Restoration<br/>Placeholders → Original PII"]

    E --> F["Protected Output<br/>'I'll help you contact Dr. Smith<br/>at [e-mail]'"]

    %% Styling
    classDef flowStyle fill:#f8f9fa,stroke:#495057,stroke-width:2px,color:#212529
    classDef detectionStyle fill:#e8f4f8,stroke:#0c63e4,stroke-width:2px,color:#212529
    classDef anonymizationStyle fill:#fff3cd,stroke:#856404,stroke-width:2px,color:#212529
    classDef llmStyle fill:#f0e6ff,stroke:#6f42c1,stroke-width:2px,color:#212529
    classDef restorationStyle fill:#d1ecf1,stroke:#0c5460,stroke-width:2px,color:#212529

    class A flowStyle
    class B detectionStyle
    class C anonymizationStyle
    class D llmStyle
    class E restorationStyle
    class F flowStyle

Under the Hood: System Architecture

graph LR
    subgraph Input ["Input Layer"]
        A["Textual Input<br/>Contains PII Entities"]
    end

    subgraph Detection ["Entity Detection Engine"]
        B["Waterfall Detection Algorithm<br/>• Phase 1: Pattern Recognition (RegEx)<br/>• Phase 2: Numerical Validation (Luhn)<br/>• Phase 3: Linguistic Analysis (NLP)<br/>Entity Types: EMAIL, PERSON, PHONE, URL"]
    end

    subgraph Cloaking ["Entity Anonymization"]
        C["Classification & Tokenization<br/>PII → Typed Placeholders<br/>Deterministic Mapping<br/>Format: &lt;TYPE_INDEX&gt;"]
    end

    subgraph Provider ["LLM Provider Interface"]
        D["Provider-Agnostic API Gateway<br/>Supported: OpenAI, Anthropic Claude,<br/>Google Gemini, Azure OpenAI,<br/>AWS Bedrock, Custom Endpoints"]
    end

    subgraph Restoration ["Entity De-anonymization"]
        E["Inverse Token Mapping<br/>Placeholder Detection<br/>Bidirectional Text Reconstruction<br/>Integrity Preservation"]
    end

    subgraph Output ["Output Layer"]
        F["Reconstructed Response<br/>Original PII Restored<br/>Stream-Compatible"]
    end

    subgraph Memory ["State Management System"]
        G["LRU Cache Implementation<br/>Conversation Continuity<br/>Hash-Based Entity Mapping<br/>O(1) Lookup Complexity"]
    end

    %% Primary data flow
    A --> B
    B --> C
    C --> D
    D --> E
    E --> F

    %% State management interactions
    Memory -.->|"Read/Write<br/>Entity Maps"| C
    Memory -.->|"Consistency<br/>Validation"| E

    %% Styling
    classDef inputStyle fill:#f8f9fa,stroke:#495057,stroke-width:2px,color:#212529
    classDef detectionStyle fill:#e8f4f8,stroke:#0c63e4,stroke-width:2px,color:#212529
    classDef cloakingStyle fill:#fff3cd,stroke:#856404,stroke-width:2px,color:#212529
    classDef providerStyle fill:#f0e6ff,stroke:#6f42c1,stroke-width:2px,color:#212529
    classDef restorationStyle fill:#d1ecf1,stroke:#0c5460,stroke-width:2px,color:#212529
    classDef outputStyle fill:#d4edda,stroke:#155724,stroke-width:2px,color:#212529
    classDef memoryStyle fill:#f8d7da,stroke:#721c24,stroke-width:2px,color:#212529

    class A inputStyle
    class B detectionStyle
    class C cloakingStyle
    class D providerStyle
    class E restorationStyle
    class F outputStyle
    class G memoryStyle

Installation

pip install llmshield

Quick Start

Basic Usage

from llmshield import LLMShield

# Initialise shield
shield = LLMShield()

# Protect sensitive information
cloaked_prompt, entity_map = shield.cloak(
    "Contact John Doe at [e-mail] or call [phone]"
)
print(cloaked_prompt)
# Output: "Contact <PERSON_0> at <EMAIL_1> or call <PHONE_NUMBER_2>"

# Process with LLM
llm_response = your_llm_function(cloaked_prompt)

# Restore original entities
restored_response = shield.uncloak(llm_response, entity_map)

Note: Individual cloak() and uncloak() methods support single messages only and do not maintain conversation history. For multi-turn conversations with entity consistency across messages, use the ask() method.

Direct LLM Integration

from openai import OpenAI
from llmshield import LLMShield

client = OpenAI(api_key="your-api-key")
shield = LLMShield(llm_func=client.chat.completions.create)

# Single request with automatic protection
response = shield.ask(
    model="gpt-4",
    prompt="Draft an email to Sarah Johnson at [e-mail]"
)

# Multi-turn conversation
messages = [
    {"role": "user", "content": "I'm John Smith from DataCorp"},
    {"role": "assistant", "content": "Hello! How can I help you?"},
    {"role": "user", "content": "Email me at [e-mail]"}
]

response = shield.ask(model="gpt-4", messages=messages)

Streaming Support

response_stream = shield.ask(
    model="gpt-4",
    prompt="Generate a report about Jane Doe ([e-mail])",
    stream=True
)

for chunk in response_stream:
    print(chunk, end="", flush=True)

Entity Detection

The library detects and protects the following entity types:

Entity Type	Examples	Placeholder Format
Person	John Doe, Dr. Smith	<PERSON_0>
Organization	Acme Corp, NHS	<ORGANISATION_0>
Place	London, Main Street	<PLACE_0>
Email	[e-mail]	<EMAIL_0>
Phone	[phone]	<PHONE_NUMBER_0>
URL	https://example.com	<URL_0>
Credit Card	4111-1111-1111-1111	<CREDIT_CARD_0>
IP Address	192.168.1.1	<IP_ADDRESS_0>

Configuration

Custom Delimiters

shield = LLMShield(
    start_delimiter='[[',
    end_delimiter=']]'
)
# Entities appear as [[PERSON_0]], [[EMAIL_1]], etc.

Conversation Caching

LLMShield implements an LRU (Least Recently Used) cache to maintain entity consistency across multi-turn conversations. The cache stores entity mappings for conversation histories, ensuring that all entities (persons, organizations, emails, phones, etc.) mentioned in different messages receive the same placeholders.

shield = LLMShield(
    llm_func=your_llm_function,
    max_cache_size=1000  # Default: 1000
)

Cache Sizing Guidelines:

• Small applications (< 100 daily conversations): max_cache_size=100-500
• Medium applications (100-1000 daily conversations): max_cache_size=1000-5000
• Large applications (> 1000 daily conversations): max_cache_size=5000-10000

Memory considerations: Each cache entry stores approximately 1-2KB. A cache size of 1000 uses roughly 1-2MB of memory.

Performance impact: Cache hit rates above 80% significantly improve performance for multi-turn conversations by avoiding re-detection of previously seen entities.

Provider Compatibility

LLMShield includes specialized support for OpenAI APIs and universal compatibility with other providers:

Optimized Support:

• OpenAI (GPT-4, GPT-3.5, all chat models)
• OpenAI Beta APIs (structured outputs)

Universal Compatibility:

• Anthropic Claude
• Google Gemini
• Cohere
• Hugging Face Transformers
• Azure OpenAI
• AWS Bedrock
• Custom LLM functions

Language Support

• English: Full optimization and accuracy
• Other languages: Experimental support with reduced accuracy

Requirements

• Python 3.12+
• Zero external dependencies

Development

Setup

git clone https://github.com/yourusername/llmshield.git
cd llmshield
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -e ".[dev]"

Testing

# Run tests
make tests

# Coverage analysis
make coverage

# Code quality checks
make ruff

# Documentation coverage
make doc-coverage

Building and Publishing

Building the Package

# Install build dependencies
make dev-dependencies

# Build the package
make build

Publishing to PyPI

1. Update version in pyproject.toml
2. Run quality checks:

   make tests
   make coverage
   make ruff
   

3. Build and publish:

   make build
   twine upload dist/*
   

Security Considerations

• Validate cloaked outputs before LLM transmission
• Securely store entity mappings for persistent sessions
• Choose delimiters that don't conflict with your data format
• Implement comprehensive input validation
• Regularly audit entity detection accuracy

Contributing

See CONTRIBUTING.md for development guidelines and contribution process.

License

GNU Affero General Public License v3.0 - See LICENSE.txt for details.

Production Usage

LLMShield is used in production at brainful.ai.