Skip to main content
The Layerup Security integration allows you to secure your calls to any LangChain LLM, LLM chain or LLM agent. The LLM object wraps around any existing LLM object, allowing for a secure layer between your users and your LLMs. While the Layerup Security object is designed as an LLM, it is not actually an LLM itself, it simply wraps around an LLM, allowing it to adapt the same functionality as the underlying LLM.

​
Setup

First, you’ll need a Layerup Security account from the Layerup website. Next, create a project via the dashboard, and copy your API key. We recommend putting your API key in your project’s environment. Install the Layerup Security SDK: 
pip install LayerupSecurity
And install LangChain Community: 
pip install langchain-community
And now you’re ready to start protecting your LLM calls with Layerup Security! 
from langchain_community.llms.layerup_security import LayerupSecurity
from langchain_openai import OpenAI

# Create an instance of your favorite LLM
openai = OpenAI(
    model_name="gpt-3.5-turbo",
    openai_api_key="OPENAI_API_KEY",
)

# Configure Layerup Security
layerup_security = LayerupSecurity(
    # Specify a LLM that Layerup Security will wrap around
    llm=openai,

    # Layerup API key, from the Layerup dashboard
    layerup_api_key="LAYERUP_API_KEY",

    # Custom base URL, if self hosting
    layerup_api_base_url="https://api.uselayerup.com/v1",

    # List of guardrails to run on prompts before the LLM is invoked
    prompt_guardrails=[],

    # List of guardrails to run on responses from the LLM
    response_guardrails=["layerup.hallucination"],

    # Whether or not to mask the prompt for PII & sensitive data before it is sent to the LLM
    mask=False,

    # Metadata for abuse tracking, customer tracking, and scope tracking.
    metadata={"customer": "example@uselayerup.com"},

    # Handler for guardrail violations on the prompt guardrails
    handle_prompt_guardrail_violation=(
        lambda violation: {
            "role": "assistant",
            "content": (
                "There was sensitive data! I cannot respond. "
                "Here's a dynamic canned response. Current date: {}"
            ).format(datetime.now())
        }
        if violation["offending_guardrail"] == "layerup.sensitive_data"
        else None
    ),

    # Handler for guardrail violations on the response guardrails
    handle_response_guardrail_violation=(
        lambda violation: {
            "role": "assistant",
            "content": (
                "Custom canned response with dynamic data! "
                "The violation rule was {}."
            ).format(violation["offending_guardrail"])
        }
    ),
)

response = layerup_security.invoke(
    "Summarize this message: my name is Bob Dylan. My SSN is 123-45-6789."
)
Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.