Dictionary Attack in Adonisjs with Cockroachdb

A dictionary attack in the AdonisJS ecosystem becomes particularly concerning when backed by CockroachDB due to the interaction between application-level authentication logic and the database’s behavior under high-concurrency, distributed execution. AdonisJS, a Node.js web framework, often relies on developer-written or package-based authentication flows that validate user credentials against a database. When these flows do not enforce strict rate-limiting or account lockout mechanisms, an attacker can systematically submit username and password guesses to an authentication endpoint.

In a distributed CockroachDB deployment, the database’s strong consistency model across nodes can inadvertently aid an attacker in distinguishing valid usernames. If the authentication code performs a query such as SELECT * FROM users WHERE email = $1 and returns different application behavior or timing responses depending on whether the row exists, an attacker can enumerate valid accounts without triggering conventional threshold-based defenses. CockroachDB’s predictable latency for existing rows, combined with AdonisJS’s default JSON error messages, can expose whether a specific user exists, reducing the search space for a dictionary attack.

Moreover, if connection pooling and transaction retries are not carefully tuned, concurrent dictionary attack requests may lead to observable patterns in database sessions or lock contention that can be correlated across distributed nodes. While CockroachDB handles node failures gracefully, an attacker monitoring HTTP response codes and timing can infer when a username is valid and when a password mismatch occurs. This is exacerbated if AdonisJS does not use constant-time comparison for password hashing verification or if it leaks stack traces during query errors, which can hint at schema details such as column names or table structure.

The use of unauthenticated endpoints for account actions—such as password reset or profile lookup—further widens the attack surface. Without proper input validation and rate limiting at the route level, a dictionary attack can pivot from username enumeration to credential stuffing, especially if users reuse passwords across services. MiddleBrick’s checks for Authentication and Input Validation would flag such weaknesses by correlating runtime behavior with the OpenAPI spec definitions to detect inconsistencies between documented and actual endpoint behavior.

Finally, the absence of mandatory multi-factor authentication (MFA) in many AdonisJS starter templates compounds the risk. Even with secure password storage via bcrypt or argon2, a successful dictionary attack on weak credentials can lead to account compromise. Continuous scanning using the middleBrick CLI (middlebrick scan <url>) helps detect these issues by running active probes, including input validation tests and authentication checks, without requiring credentials or configuration.

Remediation focuses on standardizing responses, enforcing rate limits, and ensuring safe database interactions. In AdonisJS, you should centralize authentication error handling to return uniform messages regardless of whether the user exists. Below is a secure example using the AdonisJS Lucid ORM with CockroachDB that avoids leaking user existence through timing or response content.

import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import User from 'App/Models/User'
import { schema } from '@ioc:Adonis/Core/Validator'

export default class AuthController {
  public async login({ request, response }: HttpContextContract) {
    const email = request.input('email')
    const password = request.input('password')

    // Validate input shape first
    const loginSchema = schema.create({
      email: schema.string({ trim: true, normalize: true }),
      password: schema.string()
    })

    let validatedData
    try {
      validatedData = await request.validate({ schema: loginSchema })
    } catch (error) {
      return response.badRequest({ message: 'Invalid input' })
    }

    // Always fetch a dummy record to keep timing consistent
    const dummyUser = await User.query()
      .where('email', '[email protected]')
      .limit(1)
      .timeout(5000)
      .execute()

    // Fetch the actual user if needed for comparison, but do not branch on existence
    const user = await User.query()
      .where('email', email)
      .limit(1)
      .timeout(5000)
      .execute()
      .then((results) => results[0] || null)

    // Perform constant-time password verification
    const passwordMatch = user && (await verifyPassword(password, user.password))

    if (!passwordMatch) {
      return response.unauthorized({ message: 'Invalid credentials' })
    }

    // Proceed with token generation
    return response.ok({ token: 'secure-jwt-token' })
  }
}

Key remediation steps include:

• Use a constant-time password verification function (e.g., `bcrypt.compare` or `argon2.verify`) that does not short-circuit on mismatched lengths.
• Implement rate limiting at the route level using AdonisJS middleware, such as ratelimit, to restrict attempts per IP or API key within a sliding window.
• Enable query timeouts in your CockroachDB client configuration to prevent long-running queries from being exploited for timing analysis.
• Log authentication failures without exposing internal details, and integrate with the middleBrick Dashboard to track failed attempts over time.
• Consider enforcing MFA for all users, especially when using password-based authentication, to mitigate the impact of successful dictionary attacks.

For continuous protection, the middleBrick Pro plan supports continuous monitoring with configurable scanning schedules and alerts. This ensures that new authentication routes or changes in endpoint behavior are evaluated against security checks, including Input Validation and Authentication, before they are exposed in production.