Password Spraying in Axum with Cockroachdb

Password spraying is an authentication attack where one or a few common passwords are tried against many accounts. When an Axum application uses Cockroachdb as its backend without adequate protections, the combination can amplify exposure due to how Axum handles authentication flows and how Cockroachdb stores and indexes user credentials.

In Axum, authentication logic typically resides in route handlers where a login endpoint receives a username and password, then queries Cockroachdb to validate credentials. If the endpoint performs a query like SELECT password_hash FROM users WHERE username = $1 and then compares the provided password in application code, the timing of the response can leak whether a username exists. An attacker can send many requests with common passwords and different usernames, observing whether certain usernames trigger different error paths or response behaviors. Because Axum does not inherently enforce rate limits on authentication routes, repeated requests can be issued quickly.

Cockroachdb, a distributed SQL database, stores user rows with indexes on columns like username. If the query planner uses an index seek for each login attempt, the operation is fast and consistent, making timing differences more observable to an attacker. Additionally, if audit logging or error messages in Axum inadvertently reveal SQL states or constraint violations (e.g., unique constraint on email), an attacker can infer valid usernames or email addresses. Without row-level security or explicit rate limiting at the application or infrastructure layer, the attack surface remains wide.

Middleware in Axum can mitigate this by standardizing response times and suppressing detailed errors during authentication. Using a constant-time comparison for password hashes and ensuring that queries for invalid usernames follow the same code path as valid ones reduces information leakage. However, if Cockroachdb indexes are not aligned with these protections, or if connection pooling exposes different latencies, subtle timing differences can persist.

Furthermore, Cockroachdb’s strong consistency guarantees mean that each login query returns immediately, which can aid an attacker conducting rapid, sequential attempts. Without proactive monitoring or integration with a security scanning workflow, these risks may go unnoticed until an incident occurs. Tools like middleBrick can detect such authentication weaknesses by analyzing the unauthenticated attack surface, including how Axum routes interact with Cockroachdb under simulated spraying patterns.

Remediation focuses on making authentication paths uniform, adding rate controls, and minimizing information leakage in both Axum and Cockroachdb interactions.

• Use constant-time comparison and dummy queries: Regardless of whether the username exists, perform a hash comparison and return the same HTTP status and timing characteristics.
• Apply rate limiting at the Axum middleware layer to restrict attempts per IP or per username pattern.
• Ensure Cockroachdb queries use prepared statements to avoid variability in execution plans and to prevent SQL injection.

Example Axum login handler with hardened logic:

use axum::{routing::post, Router};
use std::net::SocketAddr;
use secrecy::SecretString;
use sqlx::PgPool;
use argon2::Argon2;

async fn login_handler(
    pool: PgPool,
    Form(payload): Form<LoginPayload>,
) -> Result<impl IntoResponse, (StatusCode, String)> {
    // Always query the user row, even if username is not found, to keep timing consistent
    let user_row: Option<(String, String)> = sqlx::query_as(
        "SELECT username, password_hash FROM users WHERE username = $1"
    )
    .bind(&payload.username)
    .fetch_optional(&pool)
    .await
    .map_err(|_| (StatusCode::INTERNAL_SERVER_ERROR, "Database error"))?

    // Use a dummy hash to ensure constant-time comparison when user not found
    let expected_hash = user_row.map(|(_, hash)| hash).unwrap_or_else(|| "dummy_hash_placeholder".to_string());

    // Constant-time verify using argon2 (or your KDF)
    let verified = Argon2::default().verify_password(payload.password.as_bytes(), &expected_hash.as_bytes()).is_ok();

    if verified {
        Ok(Json(LoginResponse { success: true }))
    } else {
        // Always return same status to avoid enumeration
        Ok(Json(LoginResponse { success: false }))
    }
}

#[derive(Deserialize)]
struct LoginPayload {
    username: String,
    password: SecretString,
}

#[tokio::main]
async fn main() -> SocketAddr {
    let pool = PgPool::connect("postgresql://user:pass@localhost/db").await.unwrap();
    let app = Router::new().route("/login", post(move |form| login_handler(pool.clone(), form)));
    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
    axum::Server::bind(&addr)
        .serve(app.into_make_service())
        .await
        .unwrap();
    addr
}

Example Cockroachdb schema and parameterized query usage:

-- Create users table with a unique constraint on username
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    username VARCHAR NOT NULL UNIQUE,
    password_hash VARCHAR NOT NULL,
    created_at TIMESTAMPTZ DEFAULT now()
);

-- Prepared statement for login (used via sqlx in Axum)
PREPARE login_user (TEXT) AS
SELECT username, password_hash FROM users WHERE username = $1;

-- Example of checking existence without revealing differences via error messages
DO $$
DECLARE
    dummy TEXT;
BEGIN
    PERFORM 1 FROM users WHERE username = $1;
EXCEPTION WHEN NO_DATA_FOUND THEN
    -- Silent handling to avoid information leakage
    RAISE NOTICE 'User check completed';
END $$;

Additional protections include enabling middleBrick’s authentication checks to identify weak configurations, integrating the GitHub Action to enforce security thresholds in CI/CD, and using the CLI to run periodic scans from the terminal. These steps help ensure that password spraying risks are identified and addressed before attackers can exploit them.