LayerupSecurity 1.0.0

A Python wrapper for the Layerup Security API.

Layerup Security Python SDK

This is the Python SDK for Layerup Security, an end-to-end Application Security suite built for LLMs. Get started by creating an account on our dashboard and following the instructions below.

Installation

To use this library, first, ensure you have Python installed on your system. Then, clone this repository and install:

pip install .

API Key

Grab your API key from our dashboard and add it to your project environment as LAYERUP_API_KEY.

Import and Configure

from layerup import LayerupSecurity
layerup = LayerupSecurity(api_key=os.getenv('LAYERUP_API_KEY'))

Intercept Prompts

Intercept unsafe prompts before they are sent to an LLM.

messages = [
    {'role': 'system', 'content': 'You are Jedi master Yoda.'},
    {'role': 'user', 'content': "Anakin's social security number is 123-45-6789."},
]

# Make the call to Layerup
security_response = layerup.intercept_prompt(messages)

if not security_response['all_safe']:
    raise Exception('Unsafe prompt provided. Aborting...')
else:
    result = openai.ChatCompletion.create(
        messages=messages,
        model='gpt-3.5-turbo',
    )

Intercept Responses

Intercept unsafe LLM responses before they get to your users.

messages = [
    {'role': 'system', 'content': 'You are Jedi master Yoda.'},
    {'role': 'user', 'content': "What is Luke Skywalker's favorite fruit?"},
]

result = openai.ChatCompletion.create(
    messages=messages,
    model='gpt-3.5-turbo',
)
messages.append(result['choices'][0]['message'])

# Make the call to Layerup
security_response = layerup.intercept_response(messages)

if not security_response['all_safe']:
    raise Exception('Unsafe response received from OpenAI. Aborting...')
else:
    print('All safe - continuing...')

Mask Prompts

Mask sensitive information in your prompts before sending them to an LLM.

sensitive_messages = [
    { 'role': 'system', 'content': 'Summarize the following email for me.' },
    { 'role': 'user', 'content': 'Dear Mr. Smith, hope you are doing well. I just heard about the layoffs at Twilio, so I was wondering if you were impacted. Can you please call me back at your earliest convenience? My number is [phone]. Best Regards, Bob Dylan' },
]

# Make the call to Layerup
messages, unmask_response = layerup.mask_prompt(sensitive_messages)

# Call OpenAI using the masked messages from Layerup
result = openai.ChatCompletion.create(
    messages=messages,
    model='gpt-3.5-turbo',
)

# Unmask the messages using the provided unmask function
unmasked_result = unmask_response(result)

Log Errors

Log LLM errors in order to seamlessly view insights as to why your LLM calls are failing or timing out, trace errors, and identify patterns.

messages = [
    {'role': 'system', 'content': 'You are Jedi master Yoda.'},
    {'role': 'user', 'content': "What is Luke Skywalker's favorite fruit?"},
]

try:
    # Send your request
    await openai.ChatCompletion.create(
        messages=messages,
        model='gpt-3.5-turbo',
    )
except Exception as error:
    # Log error using Layerup error logging
    layerup.log_error(str(error), messages)

Execute Rules

Execute pre-defined rules that allow you to send canned responses when a user prompts in a certain way, adding yet another layer of protection to your LLM calls.

messages = [
    { 'role': 'system', 'content': 'You answer questions about your fictional company.' },
    { 'role': 'user', 'content': 'Can I get a 15% discount?' },
]

# Make the call to Layerup
security_response = layerup.execute_rules(
    ['layerup.security.prompt.discount'],
    messages
)

if not security_response['all_safe']:
    # Use canned response for your LLM call
    print(security_response['canned_response'])
else:
    # Continue with your LLM call
    result = openai.ChatCompletion.create(
        messages=messages,
        model='gpt-3.5-turbo',
    )