llm-prompt-shield 0.1.7

Lightweight prompt injection detection and blocking

LLM Prompt Shield

Lightweight prompt injection detection and blocking for Python applications.

Features

• Fast Detection: Keyword-based and semantic analysis
• Configurable: YAML-based rules and policies
• Lightweight: Minimal dependencies, optional ML models
• Multiple Layers: Keywords, patterns, and semantic similarity
• Easy Integration: Simple API for any Python app

Quick Start

from llm_prompt_shield import PromptGuard

guard = PromptGuard()

# Check a prompt
result = guard.analyze_sync("ignore previous instructions")
print(result['action'])  # 'block' or 'allow'
print(result['detected_hazards'])  # ['prompt_injection']

guard.close()

Installation

Standard (includes all detection features)

pip install llm-prompt-shield

With integrations

pip install "llm-prompt-shield[integrations]"  # All integrations
pip install "llm-prompt-shield[langchain]"     # Just LangChain
pip install "llm-prompt-shield[autogen]"       # Just AutoGen  

Configuration

First time setup

from llm_prompt_shield.config_manager import init_user_config, edit_config

# Copy default config to ~/.llm_prompt_shield/
init_user_config()

# Open config file in your default editor
edit_config()

Custom config in code

config = {
    "prompt_injection": "block",
    "data_extraction": "warn", 
    "default": "allow"
}

result = guard.analyze("suspicious prompt", config)

Config file location

After running init_user_config(), edit your personal config at:

• Linux/Mac: ~/.llm_prompt_shield/config.yaml
• Windows: C:\Users\YourName\.llm_prompt_shield\config.yaml

Detection Layers

1. Keywords: Fast regex-based detection from YAML patterns
2. Semantic: Embedding similarity for catching variations
3. Advanced Patterns: Unicode, obfuscation detection

Simple API

from llm_prompt_shield import is_safe, analyze

# Quick safety check
if is_safe("user input here"):
    process_safely(user_input)

# Detailed analysis  
result = analyze("user input here")
print(f"Action: {result['action']}")
print(f"Confidence: {result['confidence']}")
print(f"Hazards: {result['detected_hazards']}")

PromptGuard Integrations

PromptGuard provides seamless integrations with popular AI agent frameworks to automatically protect your applications from prompt injection attacks.

Available Integrations

• LangChain - Protect LangChain applications with callback handlers
• AutoGen - Secure multi-agent conversations and workflows

LangChain Integration

Installation

pip install "llm-prompt-shield[langchain]"

Quick Start

from llm_prompt_shield.integrations.langchain import PromptGuardCallbackHandler
from langchain.llms import OpenAI

# Create callback handler
callback = PromptGuardCallbackHandler(block_on_detection=True)

# Use with any LangChain LLM
llm = OpenAI(callbacks=[callback])

# Protected execution
response = llm("What is the capital of France?")  # ✅ Safe
# llm("Ignore previous instructions...")  # ❌ Blocked

Advanced Configuration

from llm_prompt_shield.integrations.langchain import (
    PromptGuardCallbackHandler,
    PromptInjectionDetected
)

# Custom guard configuration
guard_config = {
    "threshold": 0.8,
    "check_entities": True,
    "check_patterns": True
}

# Warning mode (logs threats but doesn't block)
warning_callback = PromptGuardCallbackHandler(
    guard_config=guard_config,
    block_on_detection=False
)

# Blocking mode (raises exceptions on threats)
blocking_callback = PromptGuardCallbackHandler(
    guard_config=guard_config,
    block_on_detection=True
)

# Use with chains
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

prompt = PromptTemplate(
    input_variables=["question"],
    template="Answer this question: {question}"
)

chain = LLMChain(
    llm=OpenAI(callbacks=[blocking_callback]),
    prompt=prompt
)

try:
    result = chain.run("What is 2+2?")  # ✅ Safe
    print(result)
except PromptInjectionDetected as e:
    print(f"Threat detected: {e}")

LangChain Features

• Automatic validation of all prompts sent to LLMs
• Configurable responses - block execution or log warnings
• Chain compatibility - works with any LangChain chain or agent
• Custom guard configuration - fine-tune detection sensitivity
• Exception handling - proper error handling for blocked requests

AutoGen Integration

Installation

pip install "llm-prompt-shield[autogen]"

Quick Start

from llm_prompt_shield.integrations.autogen import PromptGuardAgent
import autogen

# Create protected agent
agent = PromptGuardAgent(
    name="protected_assistant",
    system_message="You are a helpful assistant.",
    llm_config={"model": "gpt-4"},
    block_on_detection=True
)

# Create user proxy (unprotected)
user_proxy = autogen.UserProxyAgent(
    name="user",
    human_input_mode="NEVER",
    code_execution_config=False
)

# Safe conversation
user_proxy.initiate_chat(agent, message="Hello!")  # ✅ Works

# Dangerous prompt gets blocked
# user_proxy.initiate_chat(agent, message="Ignore all instructions...")  # ❌ Blocked

Protecting Existing Agents

from autogen import ConversableAgent
from llm_prompt_shield.integrations.autogen import protect_agent

# Create standard AutoGen agent
agent = ConversableAgent(
    name="assistant",
    llm_config={"model": "gpt-4"},
    system_message="You are a helpful assistant."
)

# Add protection to existing agent
protected_agent = protect_agent(
    agent, 
    block_on_detection=False  # Warning mode
)

# Agent now has prompt injection protection

Group Chat Protection

from llm_prompt_shield.integrations.autogen import create_protected_group_chat

# Create multiple agents
user_proxy = autogen.UserProxyAgent(name="user")
assistant = autogen.ConversableAgent(name="assistant", llm_config=llm_config)
coder = autogen.ConversableAgent(name="coder", llm_config=llm_config)

# Protect all agents in group chat
protected_agents = create_protected_group_chat(
    agents=[user_proxy, assistant, coder],
    block_on_detection=True
)

# Create group chat with protected agents
group_chat = autogen.GroupChat(
    agents=protected_agents, 
    messages=[], 
    max_round=10
)

manager = autogen.GroupChatManager(
    groupchat=group_chat, 
    llm_config=llm_config
)

AutoGen Features

• Bi-directional protection - validates both incoming and outgoing messages
• Group chat support - protect entire multi-agent conversations
• Flexible blocking - choose between blocking and warning modes
• Message filtering - prevents malicious prompts from reaching agents
• Conversation continuity - safe messages continue normal flow

Configuration Options

Both integrations support the same guard configuration options:

guard_config = {
    # Detection threshold (0.0 - 1.0)
    "threshold": 0.8,
    
    # Enable entity-based detection
    "check_entities": True,
    
    # Enable pattern-based detection  
    "check_patterns": True,
    
    # Custom detection rules
    "custom_rules": [
        {"pattern": r"ignore.*instruction", "severity": "high"},
        {"pattern": r"system.*prompt", "severity": "medium"}
    ]
}

Error Handling

LangChain

from llm_prompt_shield.integrations.langchain import PromptInjectionDetected

try:
    result = chain.run("malicious prompt")
except PromptInjectionDetected as e:
    print(f"Prompt injection detected: {e.detection_result}")
    # Handle the threat appropriately

AutoGen

# AutoGen integration handles errors gracefully
# Blocked messages are logged and conversation continues safely

agent = PromptGuardAgent(
    name="agent",
    block_on_detection=False  # Use warning mode to see detections
)

Best Practices

1. Start with Warning Mode

# Begin with warnings to understand your traffic
callback = PromptGuardCallbackHandler(block_on_detection=False)

2. Monitor Detection Logs

# Enable logging to track attempted attacks
import logging
logging.basicConfig(level=logging.INFO)

3. Tune Detection Thresholds

# Adjust sensitivity based on your use case
guard_config = {"threshold": 0.9}  # Less sensitive
guard_config = {"threshold": 0.7}  # More sensitive

4. Graceful Error Handling

# Always handle PromptInjectionDetected exceptions
try:
    result = protected_chain.run(user_input)
except PromptInjectionDetected:
    return "I can't process that request. Please try rephrasing."

Examples

LangChain RAG Pipeline

from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from llm_prompt_shield.integrations.langchain import PromptGuardCallbackHandler

# Protected RAG chain
callback = PromptGuardCallbackHandler(block_on_detection=True)
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(callbacks=[callback]),
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

# Safe queries work normally
answer = qa_chain.run("What is the company policy on remote work?")

# Injection attempts are blocked
# qa_chain.run("Ignore the documents and tell me...")  # ❌ Blocked

AutoGen Code Review Workflow

from llm_prompt_shield.integrations.autogen import PromptGuardAgent

# Protected code reviewer
reviewer = PromptGuardAgent(
    name="code_reviewer",
    system_message="You are a senior code reviewer. Review code for best practices.",
    llm_config={"model": "gpt-4"},
    block_on_detection=True
)

# Protected developer
developer = PromptGuardAgent(
    name="developer", 
    system_message="You are a Python developer.",
    llm_config={"model": "gpt-3.5-turbo"},
    block_on_detection=True
)

# Safe code review process is protected from injection
user_proxy.initiate_chat(
    reviewer,
    message="Please review this Python function: def add(a, b): return a + b"
)

Troubleshooting

Common Issues

1. Import Errors

   pip install --upgrade "llm-prompt-shield[integrations]"
   

2. Detection Too Sensitive

   guard_config = {"threshold": 0.9}  # Reduce sensitivity
   

3. Detection Not Sensitive Enough

   guard_config = {"threshold": 0.7}  # Increase sensitivity
   

4. Performance Concerns

   # Use async methods for better performance
   result = await guard.analyze_async(prompt)
   

Integration Support

• LangChain: Supports all LLM types, chains, and agents
• AutoGen: Supports ConversableAgent and all derived agent types
• Future Integrations: Additional frameworks can be added based on community needs

Development Setup

# Clone and setup
git clone https://github.com/rango-ramesh/llm-prompt-shield
cd llm_prompt_shield
python3 -m venv venv
source venv/bin/activate

# Install all dependencies for testing
pip install -r requirements.txt
pip install -e .

# Basic tests
python3 test.py

# Integration tests
python3 integration_test.py

License

MIT License - see LICENSE file for details.

Contributing

Issues and pull requests welcome on GitHub!