Credential Stuffing in Adonisjs with Dynamodb

Credential stuffing relies on automated requests that submit known username and password pairs against an authentication endpoint. When an application built with AdonisJS uses Amazon DynamoDB as its user store, the interaction between AdonisJS request handling and DynamoDB access patterns can inadvertently support or fail to mitigate these attacks.

In AdonisJS, authentication logic commonly resides in controllers or via an authentication provider that calls a method such as auth.attempt. If the identity lookup queries DynamoDB based only on a user-supplied identifier (e.g., email) without enforcing rate limits per identifier or globally, an attacker can issue many requests per second. DynamoDB, when used with on-demand capacity or with provisioned capacity not tuned for bursty reads, does not inherently throttle requests; it will serve each query as long as service limits are not exceeded. This means an attacker can perform rapid, low-cost lookup attempts to discover valid accounts without triggering built-in protections.

AdonisJS does not enforce per-identifier rate limiting by default. If the route handling login does not include explicit checks—such as tracking failed attempts per email or IP—and if DynamoDB queries are not intentionally designed to avoid leaking timing differences, the system becomes susceptible to credential stuffing. Timing differences can be observable when DynamoDB returns a consistent latency for existing users versus a slightly different latency for non-existent items, especially if responses are not artificially normalized. Additionally, if passwords are not hashed with a work factor appropriate for offline attacks (e.g., using a robust adapter like bcrypt via AdonisJS Hash), stolen credential pairs can be validated quickly offline once data is exfiltrated.

Another angle specific to this stack is the use of DynamoDB attributes for user metadata. If secondary indexes or custom attributes are used to support features like multi-factor authentication or risk flags, missing checks in the authentication flow can allow an attacker to bypass additional verification steps. For instance, if a flag like mfa_enabled is present in the DynamoDB item but the login controller does not enforce MFA verification when the flag is present, credential stuffing can lead to authenticated sessions for compromised accounts that have stronger protections disabled.

Finally, because middleBrick highlights LLM/AI Security checks among its 12 parallel scans, it can detect whether system prompts or error messages inadvertently disclose information about DynamoDB structure or AdonisJS internal handling during authentication failures. Such disclosures can aid an attacker in refining credential stuffing campaigns by confirming valid usernames or infrastructure details.

Remediation focuses on rate limiting, constant-time comparison practices, and robust hashing. Implement per-identifier and global rate limits in your login route, and ensure DynamoDB queries do not leak timing information through conditional logic or error messages.

1. Rate limiting on login route

Use AdonisJS middleware or a custom provider to limit requests per email and per IP. Below is an example using a route middleware that tracks attempts in a lightweight in-memory store for illustration; in production, use a shared store like Redis.

// start/handlers/rate_limit_login.ts
import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'

const attempts = new Map()

export default async function rateLimitLogin(ctx: HttpContextContract) {
  const email = ctx.request.input('email')?.toLowerCase()
  const ip = ctx.request.ip()
  const key = email ? `login:${email}` : `login:ip:${ip}`
  const count = (attempts.get(key) || 0) + 1
  attempts.set(key, count)

  if (count > 10) {
    ctx.response.status(429).send({ error: 'Too many attempts, try again later.' })
    return false
  }
  return true
}

Register this middleware and apply it to your authentication route.

2. Safe user lookup with DynamoDB

Always query by primary key and avoid conditional branching that reveals existence. Use a consistent response shape and constant-time password verification. Here is a realistic AdonisJS provider snippet using the AWS SDK for JavaScript v3 with DynamoDB:

// app/Providers/Auth/UserProvider.ts
import { DynamoDBClient, GetCommand } from '@aws-sdk/client-dynamodb'
import { unmarshall } from '@aws-sdk/util-dynamodb'
import { BaseUserProvider } from '@ioc:Adonis/Extras/Auth'
import User from 'App/Models/User'
import { Hash } from '@ioc:Adonis/Core/Hash'

export default class DynamoUserProvider implements BaseUserProvider {
  private readonly client = new DynamoDBClient({})
  private readonly table = process.env.DYNAMO_TABLE || 'users'

  public async retrieveByEmail(email: string) {
    const cmd = new GetCommand({
      TableName: this.table,
      Key: { email: { S: email.toLowerCase() } }
    })
    try {
      const res = await this.client.send(cmd)
      if (!res.Item) return null
      return unmarshall(res.Item) as User
    } catch (err) {
      // Log but do not expose details
      console.error('DynamoDB retrieve error', err)
      return null
    }
  }

  public async validatePassword(user: User, password: string) {
    // Use constant-time comparison if available; Hash.verify is typically safe against timing attacks
    return Hash.verify(password, user.password)
  }
}

Ensure the DynamoDB table has a GSI on email if needed, and that the provisioned read capacity or on-demand settings can absorb legitimate peaks without affecting availability for valid users.

3. Harden authentication flow

In your login controller, combine rate limiting, safe retrieval, and MFA checks:

// app/Controllers/Http/AuthController.ts
import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import rateLimitLogin from 'App/Helpers/rate_limit_login'
import UserProvider from 'App/Providers/Auth/UserProvider'

export default class AuthController {
  public async login({ request, response, auth }: HttpContextContract) {
    if (!rateLimitLogin({ request } as HttpContextContract)) {
      return response.status(429).send({ error: 'Rate limit exceeded' })
    }

    const email = request.input('email')
    const password = request.input('password')
    const provider = new UserProvider()
    const user = await provider.retrieveByEmail(email)

    if (!user) {
      // Return generic message to avoid user enumeration
      await provider.validatePassword({ password } as any, password) // dummy call to consume comparable time
      return response.unauthorized()
    }

    const valid = await provider.validatePassword(user, password)
    if (!valid) {
      return response.unauthorized()
    }

    if (user.mfa_enabled) {
      return response.badRequest({ error: 'MFA required' })
    }

    await auth.use('web').login(user)
    return response.ok({ token: auth.toJSON() })
  }
}

These steps reduce the effectiveness of credential stuffing by limiting request velocity, normalizing timing, and ensuring that DynamoDB-backed identity checks do not disclose information that could aid attackers.