Distributed Denial Of Service in Axum with Basic Auth

Axum is a Rust web framework built on async request handling and composable middleware. When Basic Auth is used without additional protections, the combination can amplify distributed denial of service (DDoS) risks in several concrete ways. Basic Auth typically requires validating credentials on each request, often involving a database or external authentication service. If validation logic is not rate-limited or cost-controlled, an attacker can flood the endpoint with many requests containing invalid credentials, forcing expensive hash computations and repeated database lookups. This consumes CPU and memory, and can exhaust thread pools or runtime resources, degrading availability for legitimate users.

Another vector specific to the Axum + Basic Auth pattern is unbalanced middleware placement. If authentication middleware runs before lightweight routing or static file handling, every request—even those for public assets—triggers credential parsing and verification. Under high concurrency, this adds overhead that scales linearly with request volume, increasing the likelihood of resource saturation. Additionally, if the auth implementation performs synchronous or blocking work (e.g., blocking threads on database calls in an otherwise async runtime), the runtime can become starved, effectively creating a self-inflected denial of service condition.

SSRF and external dependency risks also intersect here. Basic Auth credentials may be forwarded to downstream services as part of integration logic. An attacker could supply malicious URLs or headers that cause Axum to initiate outbound connections during authentication, triggering SSRF or consuming outbound connection capacity. Since the API security checks include Authentication and BOLA/IDOR as part of the 12 parallel scans, findings often surface weak authentication coupling with business logic, missing rate constraints, and missing validation of inputs used in credential verification. These observations highlight how DDoS concerns are not merely about traffic volume, but about how authentication design interacts with resource consumption under load.

Consider a realistic endpoint where credentials are parsed on every call and a database lookup is performed synchronously within an async handler. Under stress, this can manifest as high latency or timeouts, which are telltale signs of availability impact. The scanning methodology tests unauthenticated attack surfaces and cross-references runtime behavior with the OpenAPI specification, identifying whether authentication checks are overly heavy or missing rate limiting controls. By correlating spec definitions with observed runtime findings, the scan can highlight risky patterns such as missing cost limits on token verification, absence of concurrency guards, or routes that bypass lighter validation for authenticated paths.

Framework-level mitigations matter. In Axum, structuring middleware so that cheap routing and static handling occur before expensive auth logic reduces per-request overhead. Applying rate limiting early, using non-blocking async code for credential validation, and offloading heavy work to bounded thread pools or queues help preserve availability. The scan’s checks for Rate Limiting, Authentication, and BFLA/Privilege Escalation are designed to surface these architectural interactions, ensuring that DDoS risks related to Basic Auth are surfaced alongside more traditional security findings.

To reduce DDoS exposure while retaining Basic Auth in Axum, apply rate limiting before heavy authentication work, avoid blocking the async runtime, and validate inputs early. Below are concrete patterns and code examples that align with remediation guidance you can act on. These examples use common crates such as tower-http for rate limiting and async-trait for non-blocking validation, and they integrate naturally if you add API security checks using the CLI tool by running middlebrick scan <url> to confirm design issues.

1. Rate limit before authentication to stop floods early.

use axum::middleware::from_fn;
use tower_http::rate_limit::{RateLimitLayer, RateLimitConfig};
use std::time::Duration;

let rate_limit_layer = RateLimitLayer::new(100, Duration::from_secs(1));
// Apply rate limiting as early as possible in the middleware stack
let app = Router::new()
    .route("/public", get(public_handler))
    .route("/secure", get(secure_handler))
    .layer(rate_limit_layer);

2. Use async, non-blocking validation for credentials to avoid runtime starvation.

use axum::extract::Request;
use tower_http::auth::{AuthLayer, Credentials};
use std::sync::Arc;

struct UserValidator(Arc Pin + Send + Sync>>;

fn validate_credentials(req: Request, credentials: Credentials) -
> Pin<Box<dyn Future<Output = Result<Request, (StatusCode, String)> + Send + '_>> {
    let validator = Arc::clone(&self.validator);
    let user = credentials.password();
    Box::pin(async move {
        if validator(user).await {
            Ok(req)
        } else {
            Err((StatusCode::UNAUTHORIZED, "Invalid credentials".to_string()))
        }
    })
}

let auth_layer = AuthLayer::basic(validate_credentials);
let app = Router::new()
    .route("/secure", get(secure_handler))
    .layer(auth_layer);

3. Avoid parsing or hashing on every request when possible; cache validated results or use short-lived tokens after initial Basic Auth success.

use axum::extract::State;
use std::collections::HashMap;
use std::sync::Mutex;

struct CacheState {
    cache: Mutex<HashMap<String, Instant>>
}

async fn auth_with_cache(
    State(state): State<Arc<CacheState>>,
    credentials: Credentials,
) -> Result<(), (StatusCode, &'static str)> {
    let now = Instant::now();
    let mut cache = state.cache.lock().unwrap();
    if let Some(last) = cache.get(credentials.password()) {
        if now.duration_since(*last) < Duration::from_secs(30) {
            return Ok(());
        }
    }
    if validate_expensive_async(credentials.password()).await {
        cache.insert(credentials.password().to_string(), now);
        Ok(())
    } else {
        Err((StatusCode::UNAUTHORIZED, "Invalid credentials"))
    }
}

4. Place lightweight routes before authentication middleware to ensure public assets are not subjected to unnecessary parsing.

let app = Router::new()
    .route("/healthz", get(healthz_handler))
    .route("/static/*path", get(static_handler))
    .route("/api/data", get(data_handler))
    .layer(from_fn(auth_middleware));

By combining these patterns—early rate limiting, non-blocking async validation, caching of successful auth, and careful middleware ordering—you reduce the resource amplification that can contribute to denial of service. These changes address findings that commonly appear in scans focused on Authentication, Rate Limiting, and BFLA/Privilege Escalation, and they complement the visibility you get when you use the dashboard to track scores over time or integrate the GitHub Action to fail builds when risk thresholds are exceeded.