AI technology is advancing faster than ever, and managing access control and permissions within large language model (LLM) workflows is crucial, mainly when you’re dealing with sensitive data or optimizing resource usage.

Without a well-structured access control system, unauthorized users might gain access to confidential data or misuse resources.

Langflow makes it easy to build LLM workflows, but managing permissions can still be a challenge. By integrating Permit.io with Langflow, you can add efficient and reusable permission checks across various queries, ensuring that only authorized users with the right attributes and roles can access specific features of the system.

This article explores how to implement a permission system in Langflow workflows using Permit.io’s ABAC capabilities, providing a scalable, reusable component for managing permissions in LLM queries.

Why manage Permissions in LLM Queries?

While questioning a large language model in AI systems requires several resources, access control becomes necessary in cases of security and cost issues. As for unlimited accesses being made available through APIs in models like OpenAI’s GPT or Hugging Face’s transformers, this may result in high costs and even misuse or security risks.

Permissions help ensure that only users with specific roles, like admin, premium, or free-tier users, can access certain features or models. For example, you might want to limit high-cost queries to premium users or set daily query limits for free users. This keeps your resources both secure and cost-efficient.

By applying role-based and attribute-based controls, you can decide which user gets access to what. This will help you to optimize resources and reduce security risks.

Why use ABAC in LLM Permissions?

Suppose you’re building an AI-based application that uses large language models like GPT-4, Meta Llama, or Claude Sonnet 3.5. You have users ranging from admins to free-tier subscribers and you want to limit resources like LLM access, the number of queries run based on the user access levels.

ABAC allows permissions to be based on dynamic factors, not just roles. Here’s how ABAC example policies could be:

• Premium users can run LLM queries without limits.

• Free-tier users are capped at 10 queries per day.

• Access to powerful models like GPT-4 is restricted to administrators.

With ABAC, you can create flexible policies that adjust based on user behavior and available resources. In the next section, we’ll explore how these policies can be implemented to balance resource access and permissions.

How Permit.io works

Permit.io provides a platform to integrate access control policies smoothly. Developers can define roles, attributes, and policies that manage permissions dynamically in runtime. It simplifies the process of implementing these policies and their enforcement across your application stack.

Integrating Permit.io into your Langflow setup gives you a flexible permission management system whereby user roles and attributes (like subscription tiers or query limits) determine access to specific resources, such as LLM queries or models.

With Permit.io’s Attribute-Based Access Control (ABAC) policies, you can build detailed rules that control who can use which models or run certain queries, based on dynamic user attributes like token usage or subscription level. This helps you achieve a balance between security and cost by giving you granular control over your LLM resources.

How Langflow works

Langflow is a powerful tool developed to build and manage the LLM workflow. It provides a developer with an easy way to create one single-seamless workflow, chaining multiple LLM queries, responses, and even actions. This is done by abstracting away the complexities of interacting with multiple models as a dynamic and flexible way of developing AI-driven applications.

When you integrate Permit.io into Langflow, you can add permission checks directly into your LLM workflows. For example, before executing a query, the workflow can call a shared permission check that queries Permit.io to see if the user has the right permissions. This component evaluates key details, like the user’s role or access level, and decides whether to allow or block the query based on those attributes.

With this setup, you get a robust, reusable permission system embedded right into your AI workflows, keeping things secure, efficient, and scalable.

ABAC in Cloud and Local PDPs

One of the standout features of Permit.io’s ABAC (Attribute-Based Access Control) implementation is its ability to work with both cloud-based and local Policy Decision Points (PDPs). However, there are some differences between the two. For instance, while the Permit Production PDP fully supports ABAC, the Cloud PDP doesn’t offer this functionality yet.

So, if your project requires attribute-based access control, it’s essential to use a local or production PDP.

In cloud-based environments, Permit.io’s Cloud PDP is still an excellent choice for Role-Based Access Control (RBAC) with scalability and high availability for production-level workloads. Cloud PDPs are suitable in a situation where global access and automatic scaling are necessary, but they do not support the dynamic, real-time attribute-based permissions required for ABAC.

On the other hand, the local or production PDP fully supports ABAC policies. This will really let organizations enforce fine-grained access control based on live user attributes such as subscription levels or geographical locations. Since running PDP on-premise means responses are low latency, it is ideal for development and testing environments. This provides more control with regard to deployment for the developers while supporting ABAC so that complex permissions can be enforced.

By utilizing Permit.io’s ABAC with either the production or local PDP, respectively, you will be able to create scalable and secure LLM workflows that have fine-grained access control. This helps streamline cost efficiency, data protection, and dynamic real-time access management, ensuring that your security policies can adapt to evolving business needs. Just make sure to pick the right PDP local or production based on your specific requirements.

Pre-requisites

Before we begin, make sure you have the following:

• Python 3.10 or higher

• OpenAI API Key

• Permit.io Account

• Langflow Account

Setting up Permit.io Project

Setting up a Permit.io project involves defining resources, roles, users, and ABAC rules to manage access dynamically. Below is a step-by-step explanation guiding you through the process:

Setting Up Resources

The first step in Permit.io is to define the resources that users will interact with. In this case, the resource is a chatbot.

• Resource Name: “chatbot” is the resource users will access.

• Key: “chat_bot” is the unique identifier for this resource within the Permit environment.

• Actions: Define what actions a user can perform on this resource (e.g. write for submitting a prompt).

ABAC Options:

• query_tokens (Number): This attribute could represent the number of tokens of a query a user is allowed to submit.

• hasApproved (Boolean): This could be used to check whether a user is approved to use the chatbot.

Note: Adding ABAC Options are optional as we have to make a basic LLM chat to integrate with Permit.io we will be keeping the policies simpler.

Defining these attributes allows you to implement fine-grained control over how users interact with the chatbot based on their roles and attributes.

Defining Roles

Next, you define roles that dictate what permissions users have when interacting with the resources, Although these roles are set by default but you can make additions as per your need.

• Admin Role: This role has full access to all resources. In the policy editor, the admin role is given write access to the "LLM Model."

• Viewer Role: Users with the viewer role have limited access. In this case, viewers are restricted from performing the write action, meaning they cannot submit prompts to the chatbot.

By assigning users to specific roles, you can easily control what they are allowed to do with the chatbot resource.

Adding Users

Once roles are set up, you add users and assign them to appropriate roles.

Users are identified by unique keys (e.g., user123), and their roles dictate the level of access they have to resources.

Moreover, you can also set attributes on users so that later you can control them through ABAC options. In this example, we have set them up with attributes like subscriber type .

Creating ABAC User Sets and Resource Sets

ABAC (Attribute-Based Access Control) allows for more granular control by defining access policies based on user attributes.

• ABAC User Set: “Subscriber Type Access” groups users based on their subscription type. This can be used to determine permissions dynamically based on user-specific attributes like whether they are a premium or free subscriber.

By creating user sets, you can apply different policies to different groups of users without having to define individual rules for each user.

Similarly, you can also create group resources by their attributes to manage access more efficiently.

• Query token below 50 Characters: A resource set for users with a restricted quota, limiting the length of their prompts to below 50 characters.

• Query token above 50 Characters: A resource set for users who have permission to submit prompts longer than 50 characters.

ABAC resource sets allow for dynamic control over resource access based on attributes like length, query type, or quota.

Editing Policies in the Policy Editor

In the Policy Editor, you define and manage the permissions for each role or user set.

• Admin Policy: The admin role is granted full write permissions for the LLM model, allowing these users to submit any queries.

• Viewer Policy: The viewer role does not have write permissions, restricting them to read-only access.

• Subscriber Type Access Policy: A dynamic policy that grants or restricts access based on the user’s subscription type (e.g., Free vs. Premium).

These policies ensure that only authorized users can perform specific actions (like submitting a query) based on their roles and attributes.

By following these steps, you can set up a Permit.io project that dynamically manages access to resources (like LLM models) based on roles, user attributes, and ABAC rules. This setup provides fine-grained control, allowing for flexible, secure, and scalable permission management.

Deploying the Policy Decision Point (PDP) Locally with Docker

After setting up your Permit.io project, the next critical step is to deploy the Policy Decision Point (PDP). The PDP is responsible for evaluating all authorization requests that are made when users interact with resources, such as submitting a prompt to the LLM in Langflow. By running the PDP locally, you ensure that your permission checks are executed efficiently and securely as part of the Langflow chain.

To deploy the PDP locally, you can use Docker, which allows for a simple and reliable setup.

Follow the steps below to run the PDP:

Pull the PDP Container

Start by pulling the latest version of the PDP container from Docker Hub. Run the following command in your terminal:

docker pull permitio/pdp-v2:latest

This command retrieves the PDP container, which will handle all the role-based access control (RBAC) and attribute-based access control (ABAC) evaluations.

Run the PDP Container

Once the container is pulled, you can run it locally using Docker. The following command will run the PDP and link it with the Langflow custom component for authorization checks.

docker run -it \\
    -p 7766:7000 \\
    --env PDP_API_KEY=<YOUR API KEY>\\
    --env PDP_DEBUG=True \\
    permitio/pdp-v2:latest

Once the PDP is running locally, you can integrate it with your Langflow chain by linking it to the custom permission-checking component. The PDP will now evaluate requests in real time, allowing or denying access based on the ABAC rules you set up in the Permit.io project.

By running the PDP locally during development, you can test how your permissions work before moving your setup to a production environment.

Setting up Langflow Chain

First, you have to set up Langflow in your local machine for managing permissions and integrating with Permit.io and an LLM (like OpenAI)

Install the Langflow library using pip:

pip install langflow

Note: It is better to install langflow and other required libraries in a specific python virtual environment (you can create a python virtual environment using pip or conda).

Once installed, now run the Langflow using the terminal:

python3 -m langflow run

This will start a local instance of Langflow, and you can access the interface by visiting http://localhost:7860 in your web browser.

After that, click on New Project button, and then on Blank Flow a fresh empty page will appear on your screen which will allow you to create the Langflow chain for your LLM chatbot.

These components will help you to make a Langflow chain:

Define Subscriber Attributes

The first part of the chain defines the subscriber’s attributes, such as the Name of the User or which Model type you want to use using the Text Input Component. These inputs will help to categorize users and allow you to apply specific permissions and usage rules based on their attributes.

Name and Permit.io Inputs:

• The Name of the User: In this case, the user’s name is set as “Max.” This is primarily used for personalization in the application and does not directly affect the permission system.

• Model Name: The model name is set to “chatbot” to facilitate access management, allowing us to control which users have prompting permissions for specific LLM models. This setup supports adding multiple LLM models, each with designated access controls, enabling us to manage user access based on model-specific permissions.

• Action: The action defines, what kind of action would you like to perform on the selected model, Here the default selection is “Write” but the user can choose as per his choice.

• PDP URL: As the name defines, The PDP URL will receive the URL of the running permit.io PDP either on your local machine or on cloud.

Prompt Input

Next, the Prompt node is used to accept user input. This is the message or question the user wishes to send to the LLM (e.g., OpenAI’s GPT-4).

Integrating the Custom Component for Permission Checking

One of the most crucial parts of the chain is the Custom Component node. This node is responsible for performing a permission check using Permit.io’s ABAC policies before executing the LLM query. The custom component ensures that only authorized users with the correct attributes can proceed to query the LLM.

The component queries Permit.io to verify if the user has the necessary permissions (based on their ABAC rules) to execute the request. If the check passes (i.e., the user is allowed), the flow proceeds to the next step.

Accessing the OpenAI LLM

Once the permissions are validated, the next node in the chain is the OpenAI node, which is configured to query an LLM from OpenAI’s API.

OpenAI Node:

• Model Name: This example uses the gpt-4o-mini model, but this can be swapped out for other models depending on your application’s needs.

• OpenAI API Key: This is where you would input your API key for accessing OpenAI’s services.

• Temperature: The temperature is set to 0.1, which controls the randomness of the model's output. A lower temperature makes the model more focused and deterministic.

This node will generate a response based on the user’s input prompt.

Displaying the Output

Finally, the last node in the chain is the Chat Output node, which is used to display the generated LLM response to the user.

This Langflow chain integrates user attribute management, permission checking via Permit.io’s ABAC, and querying an OpenAI LLM. By structuring your Langflow chain in this way, you can ensure that user queries are securely managed, personalized, and compliant with access control policies.

Code in the Custom Component

The provided code defines a custom component,PermissionCheckComponentthat integrates Permit.io's ABAC (Attribute-Based Access Control) to dynamically check user permissions within a Langflow chain. This component ensures that only authorized users can execute certain actions, such as sending prompts to the LLM, based on their roles and attributes.

Component Definition

The component inherits from Langflow’s Component class and provides metadata such as display_name, description, and documentation URL.

class PermissionCheckComponent(Component):
    display_name = "Permission Check Component"
    description = "Check user permissions with Permit.io for dynamic role-based access control."
    documentation: str = "https://docs.permit.io"

This sets up the basic details of the component, defining its purpose and linking to the relevant Permit.io documentation.

Inputs and Outputs

The inputs define what data the component will accept, and the outputsdefine what it will return.

Inputs: The component accepts two inputs:

• user_name: The ID or name of the user.

• resource : The Name of the LLM Model you want to access.

• action : The type of action you want to perform on the selected model/resource.

• pdp_url : The URL of your PDP running either locally or on cloud.

• prompt: The LLM query or message the user wants to send.

Outputs: The output is a processed message, which will either be the prompt itself (if the user is permitted to execute the action) or a permission error message.

inputs = [
        MessageTextInput(name="user_name", display_name="User ID", value=""),
        MessageTextInput(name="action", display_name="Action", value=""),
        MessageTextInput(name="resource", display_name="Model", value=""),
        MessageTextInput(name="prompt", display_name="Prompt", value=""),
        MessageTextInput(name="pdp_url", display_name="PDP URL", value=""),
    ]

    # Outputs: either allowed prompt or permission denied message
    outputs = [
        Output(display_name="Output", name="output", method="build_output"),
    ]

Retrieving and Validating Inputs

The method retrieves the inputs passed from Langflow’s interface, including user_name , resource and prompt. It also performs a simple validation to ensure that none of the required inputs are missing.

user_name = self.user_name
resource = self.resource
pdp_url = self.pdp_url
action = self.action
prompt = self.prompt

If any of these inputs are missing, it returns an error message.

Permit.io Client Initialization

In the build_output method, the Permit.io client is initialized. This client connects to Permit.io’s Policy Decision Point (PDP) to check user permissions based on predefined access control policies.

permit = Permit(
    pdp=pdp_url,  # replace with your actual PDP URL
    token="<YOUR API KEY>"
)

Here, the pdp is the URL where Permit.io’s policy engine is hosted, and token is the API key required to authenticate requests to the PDP.

User Lookup

The component has a hardcoded dictionary of users for retrieving the key of the user for the permit.io to run permission checks.

users = {
    "John": { ... },
    "Max": { ... },
    "Siddhesh": { ... }
}

If a matching user is found, the user data is stored in the user variable. If the user is not found, an error message is returned.

user = None
for user_data in users.values():
    if user_data["first_name"].lower() == user_name.lower():
        user = user_data
        break

Permission Check with Permit.io

Once the user is identified, the component uses Permit.io’s check method to verify whether the user has the necessary permission to perform a specified action on a designated resource. By making both action and resourceconfigurable, this setup allows flexibility in defining different permissions as needed for various workflows.

permitted = await permit.check(
    user=user["key"],
    action=action,
    resource=resource 
)

• Action: Here, selected_action is configurable, allowing you to define different actions (like "write" or, "read") depending on the permissions required by the workflow.

• Resource: Similarly, resource represents the specific resource to be accessed (such as "chatbot" or another model), making it adaptable to various resources within the system.

With this approach, both action and resource can be customized dynamically, allowing the permission system to accommodate a variety of LLM interactions and resource types as needed. This enhances flexibility and ensures that permissions can be managed without modifying the core code each time.

Generating Output

Based on the permission check result:

• If the user is permitted to perform the action, the input prompt is returned as the output.

• If the user is not permitted, a message is returned indicating that the user only has view access and cannot submit prompts.
  

if permitted:
    message_content = prompt
else:
    message_content = f"User {user_name} can only view the output but cannot write prompts."

If an error occurs while checking permissions, an appropriate error message is generated and returned.

except Exception as e:
    message_content = f"Error checking permissions: {str(e)}"

Finally, the result is returned as a Message object, which Langflow can display in the chat output.

This custom component integrates Langflow with Permit.io to enforce permission checks dynamically within LLM workflows.

It retrieves user inputs, checks their permissions using Permit.io’s ABAC, and only allows users with proper write permissions to submit prompts. If the user lacks the necessary permissions, a message explaining the restriction is returned instead of executing the prompt.

Here is the full code of the custom component:

from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema import Data
from permit import Permit

import asyncio

class PermissionCheckComponent(Component):
    display_name = "Permission Check Component"
    description = "Check user permissions with Permit.io for dynamic role-based access control."
    documentation: str = "https://docs.permit.io"
    icon = "custom_components"
    name = "PermissionCheckComponent"

    # Inputs from Langflow
    inputs = [
        MessageTextInput(name="user_name", display_name="User ID", value=""),
        MessageTextInput(name="action", display_name="Action", value=""),
        MessageTextInput(name="resource", display_name="Model", value=""),
        MessageTextInput(name="prompt", display_name="Prompt", value=""),
        MessageTextInput(name="pdp_url", display_name="PDP URL", value=""),
    ]

    # Outputs: either allowed prompt or permission denied message
    outputs = [
        Output(display_name="Output", name="output", method="build_output"),
    ]

    async def build_output(self) -> Message:

        # Retrieve inputs from Langflow's inputs
        user_name = self.user_name  # Accessing user_name input
        action = self.action # Accessing action input
        resource = self.resource  # Accessing which model you want to access
        prompt = self.prompt     # Accessing the prompt input
        pdp_url = self.pdp_url # Accessing the PDP URL input


        # Initialize Permit client inside the method
        permit = Permit(
            pdp=pdp_url,
            token="<YOUR API TOKEN>"
        )

        # Retrieve inputs from Langflow's inputs
        user_name = self.user_name  # Accessing user_name input
        action = self.action
        resource = self.resource  # Accessing which model you want to access
        prompt = self.prompt     # Accessing the prompt input
        pdp_url = self.pdp_url

        # Debugging logs
        print(f"User Name: {user_name}")
        print(f"Prompt: {prompt}")

        # Message content to return
        message_content = ""

        if not user_name or not prompt:
            return Message(text="Error: Missing user name, subscriber type, or prompt input.")

        # Dictionary of users
        users = {
            "John": {
                "key": "user456",
                "first_name": "John",
            },
            "Max": {
                "key": "user111",
                "first_name": "Max",
            },
            "Siddhesh": {
                "key": "user123",
                "first_name": "Siddhesh",
            }
        }

        # Search for the user based on both `user_name` and `country`
        user = None
        for user_data in users.values():
            if user_data["first_name"].lower() == user_name.lower():
                user = user_data
                break

        # If the user is not found, return an error message
        if not user:
            return Message(text=f"User {user_name} not found.")

        try:
            # Check if the user has 'write' permissions
            permitted = await permit.check(
                user=user["key"],
                action=action,
                resource=resource,
            )

            if permitted:
                # If user has write access, allow the prompt to proceed
                message_content = prompt
            else:
                message_content = f"Give a response that the User {user_name} do not access to the Chat Bot."

        except Exception as e:
            message_content = f"Error checking permissions: {str(e)}"

        # Return the message as output
        return Message(text=message_content)

This is the simple code explaining the integration of Permit.io with the Langflow chain’s custom component. You can make changes in the code or in the chain implementation by adding more security checks or permission checks for better security and authentication services for your LLM Model.

Demo: Permit.io Integration with Langflow for Permission Checks

We will now present a live demo video of Permit.io integrated with Langflow to dynamically manage permissions for users accessing LLM queries.

If you’d like to try it yourself, please check out the GitHub repo for setup instructions and code.

Demo Highlights:

• Permission Check Process: In Langflow chain, upon a user trying to submit a prompt, the call goes to the user-written permission checking component, which in turn queries Permit.io’s PDP. It will then assess the user’s role and attributes in this case to allow or deny the action.

• Real-Time Authorization: You will see, in this video, how Permit.io dynamically checks the attributes of each user to decide whether to allow or block the prompt. If the user has permission, the query goes to LLM, for example, Open AI’s GPT. If he does not have permission, there shows up a message that he does not have permission.

• Policy Enforcement: This video shows how ABAC policies, which have been set up in Permit.io, are actually enforced. For example, enabling a “Premium” subscriber to perform queries while a “Free” subscriber might be limited, or if a user exists for using the system or not.

Conclusion

In this tutorial, we explored how to integrate Permit.io with Langflow to configure Attribute-Based Access Control (ABAC), allowing you to manage user access to LLM workflows based on detailed attributes, such as subscription levels or quotas.

Now that you’ve implemented ABAC into your application, you can enhance your workflow’s security and flexibility by applying this setup to real-world use cases, ensuring that your AI-powered systems remain both secure and scalable.

Want to learn more about implementing authorization? Got questions? Reach out to Permit.io Slack community!

If You ❤️ My Content! Connect Me on Twitter

Check SaaS Tools I Use 👉🏼Access here!

I am open to collaborating on Blog Articles and Guest Posts🫱🏼‍🫲🏼 📅Contact Here