Token introspection, defined by RFC 7662, is a security protocol where a resource server (such as a model serving API) sends an access token to the authorization server to validate its current state. The authorization server returns a JSON object indicating whether the token is active, along with associated metadata like expiration, scope, and the authorized client ID.
Glossary
Token Introspection

What is Token Introspection?
Token introspection is a mechanism that allows a resource server to query the authorization server to determine the active state and metadata of a presented access token.
This mechanism is critical for zero-trust architectures because it shifts from stateless validation to real-time token verification. By introspecting every request, a Policy Enforcement Point (PEP) can detect revoked or expired tokens instantly, preventing unauthorized access to inference endpoints even if a token's cryptographic signature remains technically valid.
Key Features of Token Introspection
Token introspection provides a standardized method for a resource server to validate the active state and authorization metadata of an opaque access token directly with the authorization server.
Active State Verification
The core function of introspection is determining if a token is currently active. The authorization server returns a boolean active claim. If false, the resource server must treat the token as invalid. This provides a real-time revocation mechanism, instantly blocking access when a token is revoked or expired, without relying on cached token validation.
Token Metadata Retrieval
Beyond simple validity, the introspection response returns a JSON object containing the token's claims, such as:
scope: The authorized scopes (e.g.,inference:execute model:read)client_id: The client to which the token was issuedusername: The resource owner identifierexp: The expiration timestamp This allows the resource server to make fine-grained authorization decisions without parsing the token itself.
Opaque Token Handling
Introspection is essential for opaque tokens—tokens that are random strings with no embedded meaning, unlike structured JWTs. Since the resource server cannot decode an opaque token, it must present it to the authorization server's introspection endpoint to retrieve the associated metadata. This decouples token format from resource server logic.
Protected Endpoint Authentication
The introspection endpoint itself must be protected. The resource server authenticates to the authorization server using HTTP Basic authentication with its client_id and client_secret, or a bearer token. This ensures that only authorized resource servers can query token metadata, preventing unauthorized information disclosure about active tokens.
Caching and Performance
To avoid overwhelming the authorization server, resource servers should cache introspection responses for a short duration. The exp claim in the response indicates the token's absolute expiry, allowing the cache TTL to be set safely. This balances real-time revocation needs with low-latency inference API performance, critical for high-throughput model serving.
Token Revocation Integration
Introspection works in tandem with the Token Revocation endpoint (RFC 7009). When a token is revoked—due to logout, compromise, or administrative action—the authorization server immediately marks it as inactive. The next introspection request for that token returns active: false, enforcing the revocation across all resource servers without delay.
Frequently Asked Questions
Explore the critical operational and security questions surrounding RFC 7662 token introspection, the mechanism that allows resource servers to validate and decode opaque access tokens in real-time before serving model inference requests.
Token introspection is a mechanism defined by RFC 7662 that allows a resource server, such as a model serving API, to query the authorization server to determine the active state and metadata of a presented access token. Unlike self-contained tokens like JSON Web Tokens (JWTs), which can be decoded locally, opaque tokens reveal no information to the bearer. To validate one, the resource server sends a POST request to the authorization server's introspection endpoint, transmitting the token value. The authorization server responds with a JSON object containing the token's active state (a boolean) and associated metadata such as scope, client_id, exp, and sub. If active is false, the token is invalid, expired, or revoked, and the resource server must deny the request. This real-time validation ensures that revoked tokens are immediately rejected, a critical property for high-security model serving environments where access must be terminable at any moment.
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Related Terms
Token Introspection (RFC 7662) is a cornerstone of modern API security. These related concepts complete the authentication, authorization, and audit picture for production model serving endpoints.
Policy Enforcement Point (PEP)
The architectural component that intercepts every inference API request and calls the token introspection endpoint. The PEP extracts the bearer token from the Authorization header, queries the authorization server, and enforces the returned active state and scope claims. It is the gatekeeper in a zero-trust model serving mesh.
Immutable Audit Trail
Every token introspection call generates a log event capturing the token's active state, associated scope, and the resource server that queried it. Storing these events in a WORM-compliant (Write Once, Read Many) system creates a non-repudiable record. This is essential for compliance frameworks like SOC 2 and the EU AI Act when serving models that process sensitive data.

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.
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