Stack Overflow in Axum with Jwt Tokens

When an Axum service relies on JWT tokens for authorization without proper rate limiting or request-cost controls, it can expose an unauthenticated or low-privilege attacker to account takeover or denial-of-service via credential stuffing or token exhaustion. Stack Overflow in this context refers to an attacker submitting a high volume of authentication requests to the token validation or refresh endpoints, causing excessive CPU usage, memory pressure, or database/redis contention. Because JWT verification in Axum often involves cryptographic verification and potentially database or cache lookups for revocation checks, an unbounded stream of malformed or valid-but-flooded tokens can degrade service availability.

In a black-box scan, middleBrick tests Rate Limiting as one of the 12 parallel checks. For an Axum endpoint protected only by JWT validation middleware (e.g., using jsonwebtoken or axum-extract-jwt), the scanner will probe the endpoint without credentials and observe whether repeated requests are throttled. If the service allows unlimited token verification attempts, an attacker can force repeated parsing and verification operations, potentially triggering stack-related resource growth or contention with shared state used for token revocation or introspection. This behavior is especially relevant when JWTs contain large payloads or custom claims that are eagerly loaded into memory on each request.

The interaction with the broader API security posture is significant. If authentication is weak (e.g., JWTs without short expiry, missing binding to client context, or predictable secrets), and rate limiting is absent or misconfigured, the attack surface for BOLA/IDOR and BFLA/Privilege Escalation increases. An authenticated attacker who obtains one valid token may leverage weak rate controls to perform token replay or amplification attacks. middleBrick flags these scenarios under Authentication, Rate Limiting, and Property Authorization checks, correlating findings to frameworks such as OWASP API Top 10 and SOC2 controls. The scanner does not fix these conditions but provides prioritized findings with remediation guidance to help teams reduce exposure.

To mitigate Stack Overflow risks in Axum when using JWT tokens, apply rate limiting at the protocol and middleware layers, and enforce strict token validation policies. Below are concrete, realistic code examples that demonstrate secure patterns for Axum services using JWTs.

1. Rate-limited JWT validation middleware

Use tower::limit::RateLimitLayer to bound request rates per IP or API key before tokens are verified. This reduces the CPU and memory pressure caused by excessive verification attempts.

use axum::Router;
use tower_http::rate_limit::{RateLimitLayer, RateLimitLayerBuilder};
use std::time::Duration;

let router = Router::new()
    .layer(RateLimitLayer::new(100, Duration::from_secs(1))); // 100 requests per second per IP
// define routes that require JWT validation below this layer

2. JWT validation with short expiry and audience/issuer checks

Validate tokens strictly and avoid accepting overly large claims. Use compact validation with jsonwebtoken and enforce exp, iss, and aud.

use jsonwebtoken::{decode, Algorithm, DecodingKey, Validation};
use axum::extract::Request;
use std::net::IpAddr;

fn validate_jwt(token: &str) -> Result<jsonwebtoken::TokenData<Claims>, jsonwebtoken::errors::Error> {
    let mut validation = Validation::new(Algorithm::HS256);
    validation.validate_exp = true;
    validation.validate_nbf = true;
    validation.validate_iat = true;
    validation.set_audience(&["my-api-audience"]);
    validation.set_issuer(&["https://auth.example.com"]);
    // keep payload size bounded by rejecting tokens with excessive custom claims
    let token_data = decode:: {
    // perform validation; reject oversized or malformed tokens early
    let token = extract_bearer_token(&request).map_err(|_| (StatusCode::UNAUTHORIZED, "invalid authorization header"))?;
    let _claims = validate_jwt(token).map_err(|e| (StatusCode::UNAUTHORIZED, format!("invalid token: {e}")))?;
    // proceed with business logic
    Ok(()) 
}

3. Token revocation and cache-aware checks with rate-limited lookups

If you maintain a revocation list (e.g., in Redis), ensure lookups are rate-limited and guarded against excessive memory or connection usage.

use redis::AsyncCommands;
use std::time::Duration;

async fn is_token_revoked(jti: &str) -> Result<bool, redis::RedisError> {
    let client = redis::Client::open("redis://127.0.0.1/")?;
    let mut conn = client.get_connection().await?;
    // Use a short TTL on the key to avoid unbounded growth
    let revoked: bool = conn.get(jti).await?;
    Ok(revoked)
}

// In request handling, call is_token_revoked conditionally with rate limiting on the lookup path

4. Enforce request-cost controls and payload size limits

Limit the size of the JWT payload processed on each request to prevent resource amplification from large custom claims.

fn verify_token_size(token: &str) -> Result<(), String> {
    const MAX_TOKEN_BYTES: usize = 8 * 1024; // 8 KB
    if token.len() > MAX_TOKEN_BYTES {
        return Err("token too large".into());
    }
    Ok(())
}
// call verify_token_size before full decode in the request pipeline