Api Rate Abuse in Axum with Openid Connect

Rate abuse in an Axum application that uses OpenID Connect (OIDC) can manifest in several concrete ways. When OIDC is integrated, the API typically exchanges a bearer token for user identity and authorization. If rate limiting is applied only at the application layer after token validation, an attacker can flood the token validation endpoints or userinfo/introspection paths without meaningful cost, because authentication is handled by the OIDC provider. This shifts the abuse surface: the API may still enforce limits on business endpoints, but the authentication flow itself becomes a target for credential stuffing, token introspection flooding, or authorization-server exhaustion.

Consider an Axum handler that accepts an OIDC access token and calls an introspection endpoint or userinfo endpoint to validate it on each request. Without dedicated rate limiting on those authentication routes, an attacker can perform rapid token checks, causing high CPU usage due to signature verification and network calls to the provider. In a black-box scan, middleBrick tests such authentication endpoints alongside business routes. If authentication routes lack independent rate limits, the scan may flag weak or missing rate control around identity validation, even when business endpoints are protected. This is because the scanner measures observable behavior: high request volume to token validation paths without throttling or adaptive controls is detectable and reportable as a finding.

Another vector arises from scopes and claims processing. After authentication, an API may derive permissions from OIDC claims. If rate limiting is coarse-grained or applied only after claims extraction, an attacker can make many requests that each trigger claims parsing and authorization checks. This can lead to high server-side processing per request, effectively a computational abuse vector. middleBrick’s checks include Authentication and Rate Limiting, and it correlates findings with the OpenAPI spec. For example, if the spec defines token or userinfo endpoints but runtime testing shows no request throttling, the tool reports a finding with severity and remediation guidance. The presence of OIDC does not remove the need for per-route or per-client rate limits; it changes where those limits must be placed to protect identity providers and backend services from overload or denial-of-service via authentication pathways.

To remediate rate abuse in Axum with OIDC, apply rate limits specifically to authentication-related routes and to token validation steps. Use per-client or per-IP limits on token exchange and userinfo endpoints, and ensure that claims processing is not a bottleneck. Below are concrete Axum examples that demonstrate these patterns.

1) Rate limiting on token exchange endpoint

Protect the callback where tokens are exchanged for session state. Using tower::limit::RateLimitLayer with a sliding window or fixed window ensures that a single client cannot overwhelm the endpoint.

use axum::routing::post;
use axum::Router;
use tower_http::rate_limit::{RateLimitLayer, RateLimitLayerConfig};
use std::time::Duration;

// Allow 10 requests per minute per IP for the token callback
let rate_limit_layer = RateLimitLayer::new(
    RateLimitLayerConfig::new(10, Duration::from_secs(60)),
);

let app = Router::new()
    .route("/callback", post(callback))
    .layer(rate_limit_layer);

2) Separate rate limit for userinfo or introspection

If your app calls a userinfo or introspection endpoint on each request, isolate and throttle those calls to avoid provider exhaustion and local CPU overload.

use axum::{
    routing::get,
    Router,
};
use tower_http::rate_limit::{RateLimitLayer, RateLimitLayerConfig};
use std::time::Duration;

// Strict limit on userinfo validation calls
let userinfo_ratelimit = RateLimitLayer::new(
    RateLimitLayerConfig::new(5, Duration::from_secs(60)),
);

let app = Router::new()
    .route("/userinfo", get(userinfo_handler))
    .layer(userinfo_ratelimit);

3) Token-bucket based throttling with dynamic client identifiers

Use a key builder that includes the client_id or subject claim to ensure fairness and prevent one client from starving others. This is important when many authenticated clients share the same service.

use axum::{
    extract::Extension,
    routing::post,
    Router,
};
use tower_http::rate_limit::{RateLimitLayer, RateLimitLayerConfig, key_builder};
use std::time::Duration;

async fn key_fn(
    Extension(claims): Extension,
) -> key_builder::DefaultKey {
    // key by subject to isolate per-user bursts
    key_builder::DefaultKey::new(format!("user:{}", claims.sub))
}

let rate_limit_layer = RateLimitLayer::new(
    RateLimitLayerConfig::new(30, Duration::from_secs(60))
        .key_builder(key_fn),
);

let app = Router::new()
    .route("/protected", post(protected))
    .layer(rate_limit_layer);

4) Combine with OIDC token validation and scopes

Ensure that validation logic does not become a DoS vector by limiting intensive operations per token. If introspection is required, apply stricter limits and consider caching validated tokens for short windows to reduce provider load.

use axum::{
    extract::Extension,
    routing::get,
    Router,
};
use async_trait::async_trait;
use reqwest::Client;
use std::sync::Arc;
use std::time::Duration;
use tower_http::rate_limit::{RateLimitLayer, RateLimitLayerConfig, key_builder};

// Example of scoping limits by scope to avoid abuse on high-cost endpoints
async fn introspect_key(
    Extension(token): Extension,
) -> key_builder::DefaultKey {
    // naive scope-based key for example; adapt to your claims
    key_builder::DefaultKey::new("introspect".to_string())
}

let introspect_layer = RateLimitLayer::new(
    RateLimitLayerConfig::new(2, Duration::from_secs(60)) // very strict for introspection
        .key_builder(introspect_key),
);

let app = Router::new()
    .route("/introspect", get(introspect_handler))
    .layer(introspect_layer);

These examples show how to apply Axum-compatible rate limiting to OIDC flows. The key is to protect authentication and token-validation paths independently, use meaningful keys (e.g., client_id or subject), and align limits with the sensitivity and cost of each operation. middleBrick’s scans will highlight missing rate limits around these routes and map findings to frameworks like OWASP API Top 10 and SOC2, providing remediation guidance rather than attempting to fix the implementation automatically.