Manage Permissions in a Langflow Chain for LLM Queries using Permit.io

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.

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 works

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 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 ’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, ’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




• Project
  
  
  

  Setting up a 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

  
  
  

  
  
  
  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.

  
  
  
  
  

  
  
  
  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.

  
  
  
  
  

  
  
  
  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.

  
  
  

  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 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 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).

’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

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.

This Langflow chain integrates user attribute management, permission checking via

Code in the Custom Component

The provided code defines a custom component,PermissionCheckComponentthat integrates 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.

client is initialized. This client connects to ’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

Once the user is identified, the component uses to enforce permission checks dynamically within LLM workflows.

It retrieves user inputs, checks their permissions using 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: integrated with Langflow to dynamically manage permissions for users accessing LLM queries.

If you’d like to try it yourself, please check out the ’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 , 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 !

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