Password Spraying in Actix with Cockroachdb

Password spraying is an authentication attack technique where an adversary uses a small list of commonly used passwords against many accounts. When an Actix web service uses Cockroachdb as its backend, the interaction between the framework's routing/authentication handling and Cockroachdb's connection and query behavior can expose patterns that facilitate or amplify password spraying.

In Actix, authentication is typically implemented as middleware or extractor logic that validates credentials per request. If login endpoints are not rate-limited or if error responses differ based on account existence, an attacker can iterate over a list of passwords for a single user or a small set of users across many requests. Cockroachdb, a distributed SQL database, exposes timing and error characteristics that may inadvertently reveal whether an account exists or whether a password attempt succeeded, especially when queries are not carefully constructed to use constant-time checks.

For example, a naive Actix handler might first query Cockroachdb for the user by username, then compare the provided password to a stored hash. This order of operations can lead to user enumeration: an attacker can learn valid usernames by observing differences in response codes or timing. Additionally, Cockroachdb's connection pooling and prepared statement behavior can result in variable latency under load, which may make timing-based detection more feasible during a password spraying campaign.

Consider a login route implemented as an Actix web resource that deserializes JSON credentials and queries Cockroachdb directly. If the route does not enforce uniform response times and does not enforce strict rate limiting, an attacker can run automated scripts that cycle through passwords while monitoring response differences. The combination of Actix's asynchronous request handling and Cockroachdb's SQL semantics can unintentionally expose timing differences between a missing user, a missing row, and a password mismatch, especially if logging or error paths reveal stack traces or SQL state codes.

To mitigate these risks, developers should design authentication flows that avoid branching on user existence, use constant-time password comparison, apply rate limiting across authentication endpoints, and ensure that Cockroachdb queries are structured to avoid leaking information through errors or timing. Security scanning tools such as middleBrick can identify these classes of risk by analyzing the unauthenticated attack surface, including authentication mechanisms and input validation, and by correlating OpenAPI specs with runtime behavior.

Remediation focuses on ensuring that authentication logic does not leak information via timing or error messages and that database interactions are resilient to abuse. Below are concrete Actix examples using Cockroachdb with the sqlx crate, demonstrating secure patterns.

1. Constant-time login check with parameterized queries

Always query for the user and compute a hash in a single step, using a dummy hash when the user is not found, to ensure constant execution time.

use actix_web::{post, web, HttpResponse};
use sqlx::PgPool;
use argon2::{Argon2, PasswordHash, PasswordVerifier};

async fn get_user_password_hash(pool: &PgPool, username: &str) -> Result {
    let row: (Option,) = sqlx::query_as(
        "SELECT password_hash FROM users WHERE username = $1"
    )
    .bind(username)
    .fetch_optional(pool)
    .await?
    .map(|(hash,)| (hash,));
    Ok(row.map(|(hash,)| hash).unwrap_or_else(|| "$argon2id$v=19$m=65536,t=3,p=4$dummy$dummy".to_string()))
}

#[post("/login")]
async fn login(
    pool: web::Data,
    creds: web::Json,
) -> HttpResponse {
    let username = creds.username.trim();
    let input_password = creds.password.trim();

    let stored = match get_user_password_hash(&pool, username).await {
        Ok(hash) => hash,
        Err(_) => {
            // Return same HTTP status and delay to avoid information leakage
            let _ = Argon2::default().verify_password(b"dummy", &"$argon2id$v=19$m=65536,t=3,p=4$dummy$dummy".as_ref());
            return HttpResponse::Unauthorized().finish();
        }
    };

    match Argon2::default().verify_password(input_password.as_ref(), stored.as_ref()) {
        Ok(_) => HttpResponse::Ok().finish(),
        Err(_) => {
            // Constant-time fake verification to avoid timing leaks
            let _ = Argon2::default().verify_password(b"dummy", &"$argon2id$v=19$m=65536,t=3,p=4$dummy$dummy".as_ref());
            HttpResponse::Unauthorized().finish()
        }
    }
}

2. Parameterized queries to prevent SQL injection and ensure stable execution

Use strongly-typed queries with bound parameters to avoid injection and ensure the query plan remains consistent, reducing variability that could aid an attacker.

use sqlx::FromRow;

#[derive(FromRow)]
struct User {
    id: i32,
    username: String,
    password_hash: String,
}

async fn fetch_user(pool: &PgPool, username: &str) -> Result {
    sqlx::query_as::<_, User>("SELECT id, username, password_hash FROM users WHERE username = $1")
        .bind(username)
        .fetch_one(pool)
        .await
}

3. Rate limiting and request validation at the Actix layer

Apply per-IP or per-account rate limiting using Actix middleware to reduce the feasibility of spraying, and validate input to avoid malformed queries that could produce variable errors.

use actix_web::dev::ServiceRequest;
use actix_web::error::ErrorUnauthorized;
use actix_web::Error;
use std::time::Duration;

// Example placeholder for a rate-limiting extractor or middleware
async fn rate_limited_login() -> Result<(), Error> {
    // Implement sliding window or token-bucket logic here
    // For instance, use actix-web-ratelimit or a custom extractor
    Ok(())
}

4. Avoid leaking SQL state or stack traces in errors

Ensure error handlers sanitize database errors before returning responses, so that details such as constraint violations or connection issues do not reveal whether a username exists.

use actix_web::error::ResponseError;
use sqlx::Error as SqlxError;
use std::fmt;

#[derive(Debug)]
struct SanitizedError(String);

impl fmt::Display for SanitizedError {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        write!(f, "Authentication error")
    }
}

impl ResponseError for SanitizedError {
    fn error_response(&self) -> HttpResponse {
        HttpResponse::InternalServerError().json(serde_json::json!({"error": "Authentication error"}))
    }
}

By combining these patterns—constant-time verification, strict parameterization, rate limiting, and sanitized error handling—you reduce the attack surface available for password spraying while maintaining compatibility with Cockroachdb's SQL semantics in an Actix service. These practices align with findings commonly surfaced by security scans that check authentication robustness, input validation, and rate limiting.