Brute Force Attack in Axum

In Axum applications, brute force attacks commonly target authentication endpoints such as login or token refresh routes. Attackers send high volumes of credential guesses (username/password pairs) to overwhelm rate limits or exploit missing throttling. Since Axum is a minimal, async-first web framework built on Tokio and Tower, it does not include built-in rate limiting or authentication guards by default. This means developers must explicitly add middleware to mitigate such risks. A typical vulnerable pattern involves defining a login handler without any request throttling, allowing unlimited POST attempts to /auth/login. For example, using Axum's routing with a simple async function that validates credentials via a database call but lacks any mechanism to track or limit failed attempts per IP or account. Attackers can automate tools like Hydra or custom scripts to send thousands of requests per minute, potentially leading to account takeover, credential stuffing success, or denial of service through excessive database load. The absence of built-in protection in Axum's core makes this a frequent oversight, especially in APIs that prioritize simplicity over security defaults.

Detecting brute force vulnerabilities in Axum applications requires observing whether authentication endpoints enforce request limits based on IP, account, or both. Since Axum does not provide this out of the box, scanners like middleBrick analyze the runtime behavior of endpoints under load to infer missing protections. middleBrick performs unauthenticated black-box scanning by sending sequential requests to common auth paths (e.g., /login, /auth/token) and monitors for responses that indicate successful login, rate limit headers (like 429 Too Many Requests), or absence of throttling signals. If the scanner can submit more than 10 login attempts within 10 seconds without encountering a rate limit response or delay, it flags a potential brute force risk. This detection is complemented by checking for missing middleware in the Tower stack — such as the absence of tower::limit::RateLimitLayer or similar — though middleBrick does not inspect source code directly. Instead, it infers risk from behavioral responses: consistent 200 OK or 401 Unauthorized replies to rapid credential guessing suggest no effective throttling is in place. The tool also correlates findings with OWASP API Security Top 10, specifically API4:2023 (Unrestricted Resource Consumption), to provide context on exploitability and severity.

To mitigate brute force attacks in Axum, developers should implement rate limiting using Tower middleware, which integrates seamlessly with Axum's service-based architecture. The tower::limit::RateLimitLayer can be applied per route or globally to restrict request frequency. For authentication endpoints, a common strategy is to limit login attempts by IP address (e.g., 5 attempts per 15 seconds) to deter automated guessing while minimizing impact on legitimate users. Below is a working example using Axum with Tower's rate limiting layer:

use axum::{Router, routing::post};
use tower::limit::{RateLimitLayer, Quota};
use std::net::SocketAddr;
use std::time::Duration;

async fn login_handler() -> &'static str {
    // In practice: validate credentials securely
    "Login attempt processed"
}

#[tokio::main]
async fn main() {
    // Define rate limit: 5 requests per 15 seconds per IP
    let quota = Quota::per_second(5).burst(5);
    let rate_limit_layer = RateLimitLayer::new(quota);

    let app = Router::new()
        .route("/auth/login", post(login_handler))
        .layer(rate_limit_layer); // Apply to all routes

    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
    axum::Server::bind(&addr)
        .serve(app.into_make_service())
        .await
        .unwrap();
}

For more granular control — such as separate limits for auth endpoints — apply the layer only to specific routes using method chaining. Additionally, consider using tower::util::ServiceBuilder to stack rate limiting with other middleware like timeout or compression. While rate limiting helps, it should be combined with other defenses: account lockout after failed attempts (with secure unlock mechanisms), CAPTCHAs for persistent offenders, and logging/monitoring for anomalous patterns. Importantly, never rely solely on client-side checks or obscurity; enforcement must occur server-side where Axum processes the request. middleBrick can validate the effectiveness of these fixes by rescanning the API and confirming that excessive login attempts now trigger 429 responses or increased latency, indicating active throttling.