Dictionary Attack in Axum with Cockroachdb

A dictionary attack in an Axum service backed by Cockroachdb typically arises when authentication endpoints perform username or email lookup without adequate rate limiting or account enumeration protections. Because Axum is a Rust web framework that does not enforce authentication policies by itself, developers must explicitly design login routes to resist brute-force and credential-stuffing attempts. When user data is stored in Cockroachdb, the interaction between Axum request handling and SQL queries can unintentionally expose timing differences or behavioral patterns that aid an attacker.

Consider a login route that queries Cockroachdb with a literal string match on email. If the route does not use constant-time comparison for password verification and does not enforce per-IP or per-account rate limits, an attacker can iterate through common passwords or leaked credential lists, observing subtle timing differences or HTTP response variations to infer valid usernames. This is a BOLA/IDOR pattern when attackers probe for accessible accounts across users, and it maps to the OWASP API Top 10 Broken Authentication category. The unauthenticated attack surface of an Axum service using Cockroachdb may allow an attacker to trigger many SQL queries without authentication, especially if the endpoint is open to the internet.

During a middleBrick scan, checks related to Rate Limiting, Authentication, and Input Validation run in parallel against the unauthenticated endpoint. If the API responds with different status codes or timing for existing versus non-existing users, or if account lockout is not enforced, the scan can flag findings under BOLA/IDOR and Authentication. Because Cockroachdb is strongly consistent, SQL queries will reliably execute, but the application layer must still avoid leaking information through error messages or response times. An attacker leveraging a dictionary attack might also probe for IDOR by iterating user IDs once authenticated, which middleBrick tests under BOLA/IDOR and Property Authorization checks.

Additional risks arise if the Axum application does not properly parameterize SQL queries, opening the door to injection that could aid enumeration in a dictionary attack. Even though Cockroachdb supports modern SQL features, unsafe query construction in Axum handlers can expose schema details. MiddleBrick’s Input Validation and SQLi-related checks help surface these issues. Furthermore, if the API exposes verbose error details from Cockroachdb, it can assist an attacker in refining dictionary attempts. The LLM/AI Security checks of middleBrick do not directly test this scenario, but they ensure endpoints do not leak system prompts or enable prompt injection that could be chained with API misuse.

To summarize, the combination of Axum’s flexibility, Cockroachdb’s strong consistency, and missing protective controls such as rate limiting, constant-time verification, and secure error handling creates conditions where dictionary attacks can be effective. middleBrick scans identify these gaps by testing authentication surfaces, enumerating differences in behavior, and providing prioritized findings with remediation guidance.

Remediation focuses on reducing information leakage and enforcing rate controls within Axum handlers while interacting safely with Cockroachdb. Use parameterized queries to avoid injection and ensure errors are generic. Implement account-level rate limiting and consider constant-time password checks where feasible. The following examples illustrate secure patterns.

First, use strongly-typed queries with sqlx or an ORM that supports Cockroachdb, and avoid dynamic SQL concatenation:

// Axum handler with safe Cockroachdb query
use axum::{routing::post, Router};
use sqlx::PgPool;
use std::net::SocketAddr;

async fn login_handler(
    pool: &PgPool,
    email: String,
    password_attempt: String,
) -> Result {
    // Use a parameterized query to prevent SQL injection
    let user: Option<(i64, String)> = sqlx::query_as(
        "SELECT id, password_hash FROM users WHERE email = $1",
    )
    .bind(&email)
    .fetch_optional(pool)
    .await
    .map_err(|e| {
        // Log internally, return generic error to avoid leaking details
        tracing::error!(error = %e, "database error");
        (StatusCode::INTERNAL_SERVER_ERROR, "An error occurred")
    })?;

    let (user_id, stored_hash) = match user {
        Some(u) => u,
        None => {
            // Always run a dummy hash operation to keep timing consistent
            let _ = argon2::hash_encoded(b"dummy", b"dummy_salt", &argon2::Config::default())
                .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
            return Err((StatusCode::UNAUTHORIZED, "Invalid credentials".to_string()));
        }
    };

    // Use a constant-time comparison to mitigate timing attacks
    if subtle::ConstantTimeEq::ct_eq(
        &argon2::hash_encoded(password_attempt.as_bytes(), &stored_hash, &argon2::Config::default())
            .map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?,
        &stored_hash,
    )
    .into()
    {
        // Issue token or session
        Ok(StatusCode::OK)
    } else {
        Err((StatusCode::UNAUTHORIZED, "Invalid credentials".to_string()))
    }
}

fn build_app(pool: PgPool) -> Router {
    Router::new()
        .route("/login", post(move |email: String, password: String| {
            login_handler(&pool, email, password)
        }))
}

Second, enforce rate limiting at the Axum middleware or gateway level to mitigate dictionary attacks. You can use a crate like axum-extra with a Redis or in-memory store to track attempts per IP or per user identifier. Even if Cockroachdb does not enforce throttling, the application must reject excessive requests before they hit SQL execution.

// Example rate limiting with tower and axum-extra
use axum_extra::extract::RateLimitLayer;
use std::time::Duration;

let layer = RateLimitLayer::new(
    5, // max requests
    Duration::from_secs(60), // per window
);
// Apply to routes that are susceptible to brute force

Third, ensure Cockroachdb error messages are not exposed. Map database errors to generic responses and log details securely for investigation. Avoid returning whether an email exists based on SQL error codes. This prevents attackers from using error responses to refine dictionary attempts.

Finally, consider using multi-factor authentication and account lockout policies after repeated failures, storing counters in Cockroachdb with transactional updates to preserve consistency. middleBrick’s reports can highlight remaining gaps in Authentication and Rate Limiting, guiding further hardening.