Inferensys

Glossary

OpenID Connect (OIDC)

An identity layer built on top of the OAuth 2.0 framework that allows clients to verify the identity of an end-user based on the authentication performed by an authorization server, and to obtain basic profile information about the end-user in an interoperable and REST-like manner.
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AUTHENTICATION LAYER

What is OpenID Connect (OIDC)?

An identity layer built on top of the OAuth 2.0 framework that enables clients to verify end-user identity and obtain basic profile information in an interoperable, REST-like manner.

OpenID Connect (OIDC) is an authentication protocol that standardizes how a relying party verifies a user's identity based on the authorization performed by an OpenID Provider. It extends OAuth 2.0 by issuing an ID Token—a cryptographically signed JSON Web Token (JWT) containing user claims—alongside the access token, allowing clients to confirm identity without managing passwords.

The protocol defines a discovery mechanism via a /.well-known/openid-configuration endpoint, enabling dynamic registration and automated retrieval of provider metadata. Core flows include the Authorization Code Flow for server-side apps and the Hybrid Flow for native clients, ensuring secure, stateless identity propagation across zero-trust architectures and microservices.

IDENTITY LAYER

Core Characteristics of OIDC

OpenID Connect (OIDC) is an identity layer built on top of the OAuth 2.0 framework that enables clients to verify end-user identity and obtain basic profile information in an interoperable, RESTful manner.

01

ID Token: The Core Artifact

Unlike OAuth 2.0, which is an authorization framework, OIDC introduces the ID Token, a JSON Web Token (JWT) that contains claims about the authenticated user. This token is digitally signed by the OpenID Provider (OP) and consumed by the Relying Party (RP) to establish a session. Key claims include:

  • sub: A unique, immutable user identifier
  • iss: The issuer URL of the OP
  • aud: The client ID of the RP
  • exp and iat: Expiration and issuance timestamps
JWT
Token Format
sub
Primary Claim
03

The UserInfo Endpoint

A protected RESTful API endpoint that returns claims about the authenticated user. After obtaining an Access Token, the RP sends a GET or POST request to the UserInfo endpoint to retrieve additional identity attributes like email, name, and picture. This decouples the user's identity data from the ID Token, keeping the initial token lightweight and allowing for just-in-time attribute retrieval.

04

Discovery and Dynamic Registration

OIDC enables zero-configuration federation through OpenID Connect Discovery. By fetching the /.well-known/openid-configuration document from the issuer, RPs can dynamically discover all necessary endpoints, supported scopes, and cryptographic keys. Dynamic Client Registration allows RPs to programmatically register themselves with the OP, automating the onboarding process in multi-tenant environments.

05

Scopes and Claims

OIDC uses scopes to request specific sets of user information. Standard scopes include:

  • openid: Mandatory scope indicating an OIDC request
  • profile: Requests access to default profile claims (name, picture, zoneinfo)
  • email: Requests the email and email_verified claims
  • address: Requests the user's postal address
  • phone: Requests the phone_number and phone_number_verified claims

Claims can also be requested individually using the claims request parameter for fine-grained control.

06

Session Management and Logout

OIDC provides mechanisms for managing user sessions across multiple RPs:

  • RP-Initiated Logout: Allows an RP to redirect the user to the OP's end_session_endpoint to terminate the OP session and optionally notify other RPs.
  • Session Management: Uses an invisible iframe and the OP's check_session_iframe to monitor the user's authentication state without polling.
  • Back-Channel Logout: A direct server-to-server notification from the OP to all registered RPs when a user logs out, enabling immediate session termination.
IDENTITY LAYER CLARIFIED

Frequently Asked Questions

Clear, technical answers to the most common questions about the OpenID Connect identity protocol, its relationship to OAuth 2.0, and its role in modern zero-trust architectures.

OpenID Connect (OIDC) is an identity layer built on top of the OAuth 2.0 framework that allows clients to verify the identity of an end-user based on the authentication performed by an authorization server. It works by issuing an ID Token—a JSON Web Token (JWT)—alongside or instead of an OAuth 2.0 access token. The ID Token contains cryptographically signed claims about the user, such as their unique identifier (sub), issuer (iss), and audience (aud).

Core Flow

  1. The Relying Party (RP) redirects the user to the OpenID Provider (OP).
  2. The OP authenticates the user and obtains consent.
  3. The OP redirects the user back to the RP with an authorization code.
  4. The RP exchanges the code for an ID Token and optionally an Access Token at the token endpoint.
  5. The RP validates the ID Token's signature and claims, establishing an authenticated session.

This decouples authentication from the application, enabling Single Sign-On (SSO) across multiple services without sharing user credentials.

AUTHENTICATION AND AUTHORIZATION PROTOCOLS

OIDC vs. OAuth 2.0 vs. SAML

A technical comparison of the three dominant identity federation standards used in zero-trust AI networking for securing model endpoints and training data pipelines.

FeatureOpenID Connect (OIDC)OAuth 2.0SAML

Primary Purpose

Authentication + Authorization

Authorization (Delegated Access)

Authentication + Authorization

Core Function

Verifies user identity and provides basic profile data via an ID Token

Grants scoped access to protected resources without sharing credentials

Exchanges authentication and authorization data between an

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.