LangGuard 🛡️

LangGuard is a Python library that acts as a security layer for LLM (Large Language Model) agent pipelines. It screens and validates language inputs before they reach your AI agents, helping prevent prompt injection, jailbreaking attempts, and ensuring compliance with your security specifications.

Features

• 🤖🛡️ GuardAgent: Agent that serves as a circuit-breaker against prompt injection, jailbreaking, and data lifting attacks.

Installation

Install LangGuard using pip:

pip install langguard

Configuration

Required Components

To use GuardAgent, you need:

1. LLM Provider - Currently supports "openai" or None (test mode)
2. API Key - Required for OpenAI (via environment variable)
3. Prompt - The text to screen (passed to screen() method)
4. Model - Optional, defaults to gpt-4o-mini

Setup Methods

Method 1: Environment Variables (Recommended)

export GUARD_LLM_PROVIDER="openai"        # LLM provider to use
export GUARD_LLM_API_KEY="your-api-key"   # Your OpenAI API key
export GUARD_LLM_MODEL="gpt-4o-mini"      # Optional: OpenAI model (default: gpt-4o-mini)
export LLM_TEMPERATURE="0.1"              # Optional: Temperature 0-1 (default: 0.1)

Then in your code:

from langguard import GuardAgent

agent = GuardAgent()  # Automatically uses environment variables
response = agent.screen("Your prompt here")

Method 2: Partial Configuration

export GUARD_LLM_API_KEY="your-api-key"   # API key must be in environment

from langguard import GuardAgent

agent = GuardAgent(llm="openai")  # Specify provider in code
response = agent.screen("Your prompt here")

Method 3: Test Mode (No API Required)

from langguard import GuardAgent

# No provider specified = test mode
agent = GuardAgent()  # Uses TestLLM, no API needed
response = agent.screen("Your prompt here")
# Always returns {"safe": false, "reason": "Test mode - always fails for safety"}

Environment Variables Reference

Note: Currently, API keys and models can only be configured via environment variables, not passed directly to the constructor.

Quick Start

Basic Usage - Plug and Play

from langguard import GuardAgent

# Initialize GuardAgent with built-in security rules
guard = GuardAgent(llm="openai")

# Screen a user prompt with default protection
prompt = "How do I write a for loop in Python?"
response = guard.screen(prompt)

if response["safe"]:
    print(f"Prompt is safe: {response['reason']}")
    # Proceed with your LLM agent pipeline
else:
    print(f"Prompt blocked: {response['reason']}")
    # Handle the blocked prompt

The default specification blocks:

• Jailbreak attempts and prompt injections
• Requests for harmful or illegal content
• SQL/command injection attempts
• Personal information requests
• Malicious content generation
• System information extraction

Adding Custom Rules

# Add additional rules to the default specification
guard = GuardAgent(llm="openai")

# Add domain-specific rules while keeping default protection
response = guard.screen(
    "Tell me about Python decorators",
    specification="Only allow Python and JavaScript questions"
)
# This adds your rules to the default security rules

Overriding Default Rules

# Completely replace default rules with custom specification
response = guard.screen(
    "What is a SQL injection?",
    specification="Only allow cybersecurity educational content",
    override=True  # This replaces ALL default rules
)

Simple Boolean Validation

# For simple pass/fail checks
is_safe = agent.is_safe(
    "Tell me about Python decorators",
    "Only allow programming questions"
)

if is_safe:
    # Process the prompt
    pass

Advanced Usage

Advanced Usage

from langguard import GuardAgent

# Create a guard agent
agent = GuardAgent(llm="openai")

# Use the simple boolean check
if agent.is_safe("DROP TABLE users;"):
    print("Prompt is safe")
else:
    print("Prompt blocked")

# With custom rules added to defaults
is_safe = agent.is_safe(
    "How do I implement a binary search tree?",
    specification="Must be about data structures"
)

# With complete rule override
is_safe = agent.is_safe(
    "What's the recipe for chocolate cake?",
    specification="Only allow cooking questions",
    override=True
)

Response Structure

LangGuard returns a GuardResponse dictionary with:

{
    "safe": bool,    # True if prompt is safe, False otherwise
    "reason": str    # Explanation of the decision
}

Default Protection

GuardAgent comes with built-in protection against:

• Jailbreak Attempts: Prompts trying to bypass safety guidelines
• Injection Attacks: SQL, command, and code injection attempts
• Data Extraction: Attempts to extract system information or credentials
• Harmful Content: Requests for illegal, unethical, or dangerous content
• Personal Information: Requests for SSN, passwords, or private data
• Malicious Generation: Phishing emails, malware, or exploit code
• Prompt Manipulation: Instructions to ignore previous rules or reveal system prompts

Testing

The library includes comprehensive test coverage for various security scenarios:

# Run the OpenAI integration test
cd scripts
python test_openai.py

# Run unit tests
pytest tests/

Example Security Scenarios

LangGuard can detect and prevent:

• SQL Injection Attempts: Blocks malicious database queries
• System Command Execution: Prevents file system access attempts
• Personal Information Requests: Blocks requests for PII
• Jailbreak Attempts: Detects attempts to bypass AI safety guidelines
• Phishing Content Generation: Prevents creation of deceptive content
• Medical Advice: Filters out specific medical diagnosis requests
• Harmful Content: Blocks requests for dangerous information

Architecture

LangGuard follows a modular architecture:

langguard/
├── core.py       # Minimal core file (kept for potential future use)
├── agent.py      # GuardAgent implementation with LLM logic
├── models.py     # LLM provider implementations (OpenAI, Test)
└── __init__.py   # Package exports

Components

• GuardAgent: Primary agent that screens prompts using LLMs
• LLM Providers: Pluggable LLM backends (OpenAI with structured output support)
• GuardResponse: Typed response structure with pass/fail status and reasoning

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Links

• GitHub Repository
• Issue Tracker
• PyPI Package