UltraGuard

Enterprise-Grade LLM Security Framework

A comprehensive security toolkit providing 40+ security scanners and programmable guardrails for LLM-powered applications. Built by 100XPrompt.

Features

• 40+ Security Scanners - Comprehensive input/output scanning for prompt injection, secrets, PII, toxicity, and more
• Programmable Guardrails - Define conversation flows and safety boundaries with Colang DSL
• Flexible Rails System - Input, output, dialog, retrieval, and execution rails
• 23 Pre-built Libraries - Ready-to-use security libraries for various use cases
• Streaming Support - Real-time streaming with guardrails enforcement
• 8 Embedding Providers - OpenAI, Azure, Cohere, Google, FastEmbed, SentenceTransformers, and more
• LangChain Integration - Seamless integration with LangChain runnables
• Built-in Caching - LFU cache for improved performance
• Distributed Tracing - OpenTelemetry-compatible tracing for observability
• Async-First Design - Native async/await support throughout
• REST API Server - Production-ready FastAPI server
• CLI Tools - Command-line interface for common operations

Installation

pip install ultraguard

For additional features:

pip install ultraguard[onnx]       # ONNX runtime for ML models
pip install ultraguard[embeddings] # Embedding providers
pip install ultraguard[eval]       # Evaluation UI
pip install ultraguard[all]        # All optional dependencies

Quick Start

Basic Scanning

from ultraguard.scanner.scanners.input import Toxicity, TokenLimit, Secrets
from ultraguard.scanner.scanners.output import JSON, Bias

# Input scanning
secrets = Secrets()
result = secrets.scan("My API key is sk-abc123...")
print(f"Valid: {result.is_valid}, Risk: {result.risk_score}")

# Output scanning
json_scanner = JSON()
result = json_scanner.scan("prompt", '{"key": "value"}')
print(f"Valid: {result.is_valid}")

LLMRails - Full Pipeline

from ultraguard import LLMRails, action
from ultraguard.config import RailsConfig

# Configure guardrails
config = RailsConfig(
    input_scanners=["toxicity", "secrets", "prompt_injection"],
    output_scanners=["bias", "json", "sensitive"],
)

rails = LLMRails(config=config)

# Use with async
async def main():
    result = await rails.generate_async([
        {"role": "user", "content": "Hello, how are you?"}
    ])
    print(result.text)

import asyncio
asyncio.run(main())

Custom Actions

from ultraguard import LLMRails, action

@action()
async def check_greeting(text: str) -> dict:
    greetings = ['hello', 'hi', 'hey']
    is_greeting = any(g in text.lower() for g in greetings)
    return {'is_valid': True, 'is_greeting': is_greeting}

rails = LLMRails(config={'input_scanners': ['greeting']})
rails.register_action(check_greeting)

Colang DSL - Conversation Flows

from ultraguard import LLMRails

colang_config = """
define user express greeting
  "Hello!"
  "Hi there!"

define flow
  user express greeting
  bot express greeting

define bot express greeting
  "Hello! How can I assist you today?"
"""

rails = LLMRails.from_colang(colang_config)
result = rails.generate([{'role': 'user', 'content': 'Hello!'}])

Scanners

Input Scanners (18 scanners)

Scanner	Description
Anonymize	Detect and mask PII/sensitive data
BanCode	Prevent code snippets in input
BanCompetitors	Block competitor names/mentions
BanSubstrings	Block specific substrings
BanTopics	Restrict specific topics
Code	Detect and analyze code content
EmotionDetection	Detect emotional content
Gibberish	Detect nonsensical input
InvisibleText	Detect hidden/invisible characters
Language	Identify input language
PromptInjection	Detect prompt injection attacks
ReadingTime	Estimate reading time
Regex	Pattern-based detection
Secrets	Detect API keys, tokens, passwords (160+ patterns)
Sentiment	Analyze sentiment of input
TokenLimit	Enforce token count limits
Toxicity	Detect toxic/harmful content

Output Scanners (22 scanners)

Scanner	Description
BanCode	Prevent code in outputs
BanCompetitors	Block competitor mentions
BanSubstrings	Filter specific substrings
BanTopics	Restrict topics in output
Bias	Detect biased content
Code	Analyze code in output
Deanonymize	Restore masked PII
EmotionDetection	Detect emotional output
FactualConsistency	Check factual accuracy
Gibberish	Detect nonsensical output
JSON	Validate JSON structure
Language	Identify output language
LanguageSame	Ensure input/output language match
MaliciousURLs	Detect malicious URLs
NoRefusal	Check for refusal patterns
Regex	Pattern-based output filtering
Relevance	Check output relevance
Sensitive	Detect sensitive information
Sentiment	Analyze output sentiment
Toxicity	Detect toxic output
URLReachability	Validate URL accessibility

Pre-built Libraries (23 libraries)

UltraGuard includes production-ready security libraries:

Library	Purpose
content_safety	Content safety classification
injection_detection	Prompt injection detection
jailbreak_detection	Jailbreak attempt detection
sensitive_data	PII detection and masking
factchecking	Fact verification
hallucination	Hallucination detection
topic_safety	Topic-based safety
self_check	Self-consistency checks
llama_guard	Meta Llama Guard integration
guardrails_ai	Guardrails AI integration
gliner	GLiNER NER models
pangea	Pangea security services
privateai	PrivateAI integration
prompt_security	Prompt security scanning
activefence	ActiveFence integration
ai_defense	AI Defense integration
autoalign	AutoAlign integration
cleanlab	Cleanlab integration
fiddler	Fiddler AI integration
gcp_moderate_text	Google Cloud moderation
patronusai	Patronus AI integration
trend_micro	Trend Micro integration

Configuration

YAML Configuration

# config.yml
models:
  - type: main
    engine: openai
    model: gpt-4

rails:
  input:
    flows:
      - check jailbreak
      - mask sensitive data
    scanners:
      - secrets
      - prompt_injection
      - toxicity

  output:
    flows:
      - self check facts
      - self check hallucination
    scanners:
      - sensitive
      - toxicity
      - bias

  config:
    sensitive_data_detection:
      input:
        entities:
          - PERSON
          - EMAIL_ADDRESS
          - PHONE_NUMBER
          - CREDIT_CARD

Python Configuration

from ultraguard import LLMRails
from ultraguard.config import RailsConfig

config = RailsConfig(
    input_scanners=['secrets', 'prompt_injection', 'toxicity'],
    output_scanners=['sensitive', 'bias', 'toxicity'],
    cache_enabled=True,
    tracing_enabled=True,
)

rails = LLMRails(config=config)

CLI

# Scan text
ultraguard scan "Check this text" --scanners toxicity,secrets

# Start API server
ultraguard server --port 8000

# Interactive chat
ultraguard chat --config ./config

# Run evaluation
ultraguard eval --dataset ./tests/data

# List available scanners
ultraguard list-scanners

REST API

Start the server:

ultraguard server --port 8000

Endpoints

# Health check
GET /health

# Scan text
POST /scan
{
    "text": "Check this text",
    "scanners": ["toxicity", "secrets"]
}

# Chat completions
POST /v1/chat/completions
{
    "messages": [
        {"role": "user", "content": "Hello!"}
    ]
}

Advanced Usage

Streaming with Guardrails

from ultraguard import LLMRails

rails = LLMRails(config={'input_scanners': ['prompt_injection']})

async def stream_response():
    async for chunk in rails.stream_async([
        {'role': 'user', 'content': 'Tell me a story'}
    ]):
        print(chunk, end='', flush=True)

import asyncio
asyncio.run(stream_response())

LangChain Integration

from langchain_openai import ChatOpenAI
from ultraguard import LLMRails
from ultraguard.integrations.langchain import RunnableUltraGuard

llm = ChatOpenAI(model='gpt-4')

rails = LLMRails(
    config={'output_scanners': ['sensitive', 'toxicity']},
    llm=llm,
)

# Use as a Runnable
chain = RunnableUltraGuard(rails)
result = chain.invoke([{'role': 'user', 'content': 'Hello!'}])

Distributed Tracing

from ultraguard.tracing import TracingConfig, TracingProvider, configure_tracing

config = TracingConfig(
    enabled=True,
    provider=TracingProvider.OPENTELEMETRY,
    endpoint='http://localhost:4317',
)
configure_tracing(config)

Embedding Providers

from ultraguard.embeddings import OpenAIEmbeddings, SentenceTransformersEmbeddings

# OpenAI embeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectors = await embeddings.embed(["Hello world"])

# Sentence Transformers (local)
embeddings = SentenceTransformersEmbeddings(model="all-MiniLM-L6-v2")
vectors = await embeddings.embed(["Hello world"])

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Development Setup

# Clone the repository
git clone https://github.com/Nipurn123/UltraGuard.git
cd UltraGuard

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Run linting
ruff check .
ruff format .

License

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

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