Distributed Denial Of Service in Axum with Jwt Tokens

An API built with Axum and protected by JWT tokens can still be exposed to Distributed Denial of Service (DDoS) risks, even when authentication itself is functional. The presence of JWT validation introduces computational work per request (signature verification, claims parsing, clock-skew checks) that consumes CPU time and memory. Under high request volume, this can exhaust thread pool capacity or event-loop resources, degrading throughput for legitimate users. In a distributed setup, an attacker can amplify resource usage by sending many poorly formed or intentionally large tokens that trigger repeated, expensive validation work across multiple nodes, effectively turning JWT handling into a vector for resource exhaustion.

Additionally, if token introspection or revocation checks are implemented via network calls (e.g., fetching revocation lists or validating against an authorization server), DDoS can manifest as dependency amplification. Slow or unreachable upstream services (e.g., JWKS endpoints or identity providers) can cause request threads or async tasks to pile up, increasing latency and connection usage. MiddleBrick scans identify this as an Unsafe Consumption or Rate Limiting finding when high-latency external dependencies are involved. Even without authentication bypass, an unauthenticated attacker can probe token validation paths to observe timing differences or error states that hint at backend architecture, aiding more targeted attacks.

Because Axum applications often compose middleware for JWT extraction and verification, misconfigured timeouts or unbounded retry behavior can worsen DDoS impact. For example, if a middleware layer retries validation on transient errors or does not enforce strict timeouts for external HTTP fetches, the attack surface grows. MiddleBrick’s checks for Rate Limiting, Input Validation, and Unsafe Consumption are designed to surface these patterns by correlating runtime behavior with OpenAPI specifications. The scanner does not fix these conditions but provides prioritized findings with remediation guidance to help teams reduce resource exposure and improve resilience.

To reduce DDoS risk when using JWT tokens in Axum, focus on minimizing per-request work, bounding resource usage, and failing fast on invalid inputs. Use lightweight token parsing for well-formedness before performing expensive cryptographic validation, and enforce strict timeouts on any external network calls such as JWKS fetching.

use axum::{routing::get, Router};
use jsonwebtoken::{decode, Algorithm, DecodingKey, Validation, TokenData};
use std::time::Duration;
use reqwest::Client;

async fn validate_token(token: &str) -> Result<TokenData<Claims>, jsonwebtoken::errors::Error> {
    // 1) Reject obviously malformed tokens early to avoid unnecessary work
    if token.len() > 4096 || !token.starts_with("ey") {
        return Err(jsonwebtoken::errors::Error::from(jsonwebtoken::errors::ErrorKind::InvalidToken));
    }

    // 2) Use a short timeout for external JWKS/HTTP fetches
    let client = Client::builder()
        .timeout(Duration::from_millis(50))
        .build()
        .expect("valid client");

    // Example: fetching a remote JWKS with timeout protection
    let jwks_uri = "https://example.com/.well-known/jwks.json";
    let resp = reqwest::get(jwks_uri).await?; // in real code, use the shared `client` above
    let jwks: serde_json::Value = resp.json().await?;

    // 3) Use strict validation settings
    let mut validation = Validation::new(Algorithm::RS256);
    validation.validate_exp = true;
    validation.validate_nbf = true;
    validation.validate_iat = true;
    validation.required_spec_claims = vec!["iss".into(), "sub".into()];

    let key = DecodingKey::from_rsa_pem(jwks["keys"][0]["x5c"][0].as_str().unwrap_or_default().as_bytes())?;
    decode::