Password Spraying in Axum (Rust)

Password spraying is an authentication attack technique that attempts a small number of common passwords across many user accounts to avoid account lockouts. When implemented in an Axum web service using Rust, the risk emerges from how authentication routes, middleware, and rate-limiting are configured rather than from the language itself. If endpoints that accept login requests do not enforce per-user or global rate limits, an attacker can iterate through a list of common passwords—such as "Password1", "Welcome123", "Spring2024"—against many usernames without triggering defenses.

In Axum, handlers typically call extractor logic (e.g., Json<LoginPayload>) and then perform credential checks against a database or identity provider. Without explicit guards, each request completes quickly, returning a generic error such as "invalid credentials". This uniformity allows an attacker to run a script that submits one password per user in sequence, cycling through accounts to stay below simple threshold-based lockout rules. The absence of user-independent rate limiting means the attack maps cleanly onto the BFLA/Privilege Escalation category in a middleBrick scan, because the effective permission boundary for authentication is too permissive.

Another contributing factor is the handling of responses. If the server distinguishes between "user not found" and "incorrect password" with different HTTP status codes or timing differences, an attacker can refine their spraying strategy. Axum applications that leak this information via response codes or timing side channels unintentionally aid enumeration. The middleware stack and tower layers must ensure consistent response behavior to prevent attackers from inferring valid usernames. A middleBrick scan exercising authentication endpoints can detect timing inconsistencies and missing rate controls, surfacing findings tied to Authentication and BFLA/IDOR checks.

Moreover, when Axum services integrate with external identity providers or session management systems, password spraying can pivot into BOLA/IDOR scenarios if token issuance does not adequately bind to the authenticated context. An attacker who successfully guesses a password might obtain a session or token that grants access to another user’s resources if authorization checks are not strictly enforced after login. This cross-check between authentication and post-login authorization is why middleBrick runs parallel security checks, including Property Authorization and BOLA/IDOR, to validate that access controls remain intact after credentials are verified.

Finally, the use of Rust in Axum does not inherently prevent password spraying; it shifts the responsibility to the developer to implement robust controls. Without explicit countermeasures—such as rate limiting tied to identifiers, account lockout policies, or CAPTCHA challenges—any Axum endpoint can be abused. Leveraging the CLI tool to run middlebrick scan https://your-api.example.com can surface these weaknesses by mapping the unauthenticated attack surface and highlighting gaps in authentication and authorization logic before attackers do.

To mitigate password spraying in Axum, apply rate limiting at the route or middleware level using a key that incorporates both the username and a shared identifier, such as an IP address or API key. This ensures that the same username from different sources is throttled independently while preventing a single source from spraying many accounts. Below is a concise example using tower-rs rate limiting integrated into an Axum application.

use axum::{
    extract::State,
    response::IntoResponse,
    routing::post,
    Router,
};
use std::net::SocketAddr;
use tower_http::rate_limit::{RateLimitLayer, RateLimitService};
use std::sync::Arc;

struct AppState {
    rate_limiter: Arc, Response = axum::http::Response<axum::body::Body>> + Send + Sync + Clone + 'static>>,
}

async fn login_handler() -> impl IntoResponse {
    // Validate credentials and apply consistent timing
    (axum::http::StatusCode::UNAUTHORIZED, "Invalid credentials")
}

#[tokio::main]
async fn main() {
    let rate_limiter = Arc::new(RateLimitLayer::new(5, 60).into_service()); // 5 requests per minute per key
    let app = Router::new()
        .route("/login", post(login_handler))
        .layer(RateLimitLayer::new(5, 60))
        .with_state(AppState { rate_limiter });

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

This snippet configures a global rate limit of 5 requests per 60 seconds per source key. For more precise control, you can derive the key from the request payload by extracting the username within a custom middleware layer, ensuring that spraying across multiple accounts is throttled per user identifier. A middleBrick scan after applying such controls should show improved scores in Authentication and BFLA categories.

Additionally, standardize responses to avoid user enumeration. Return the same HTTP status code and generic message regardless of whether the username exists. Use constant-time comparison for password checks to prevent timing attacks. Below is an example of a secure comparison pattern in Rust.

use subtle::ConstantTimeEq;

fn verify_password(input: &str, stored_hash: &[u8]) -> bool {
    // Assume stored_hash is a hash derived with a proper KDF like Argon2
    let input_hash = hash_password(input);
    input_hash.ct_eq(&stored_hash).into()
}

Finally, integrate the GitHub Action to enforce security gates in CI/CD. By defining a threshold in the workflow, you can fail builds when a scan detects critical authentication weaknesses. This shifts security left and ensures that password spraying risks are caught before deployment. Combine this with the Web Dashboard to track scores over time and the MCP Server to validate endpoints directly from your AI coding assistant during development.