Rainbow Table Attack in Adonisjs with Dynamodb

A rainbow table attack leverages precomputed hash chains to reverse cryptographic hashes, typically targeting password storage. When using AdonisJS with DynamoDB as the user store, the risk arises from two combined factors: how AdonisJS handles authentication and how DynamoDB stores and indexes data.

AdonisJS supports various authentication drivers, including session-based and token-based flows. If an application uses default or weak hashing configurations for passwords (e.g., fast hashes like MD5 or unsalted SHA-1) or does not enforce per-user salting, hashes stored in DynamoDB can become targets. DynamoDB, being a NoSQL database, does not enforce a fixed schema, so it is common to store user records with attributes such as email, password_hash, and salt in a single table. If the password_hash attribute is predictable or derived without a unique salt, an attacker who gains read access to the DynamoDB table (for example, via an exposed endpoint or misconfigured IAM policies) can download the hash values and perform offline rainbow table lookups.

In a typical AdonisJS application, the authentication provider compares the submitted password by hashing it with the stored salt and comparing the result to the password_hash attribute. If the hashing algorithm is weak and the same hash is used across many users, a single precomputed rainbow table can reveal multiple accounts. Moreover, because DynamoDB allows efficient queries on non-primary key attributes, an attacker can use a Query operation to retrieve users with specific hash prefixes, accelerating the matching process. This is especially relevant when the application does not enforce rate limiting on authentication endpoints, enabling attackers to perform bulk hash comparisons without triggering defenses.

The combination of AdonisJS’s flexibility in defining authentication logic and DynamoDB’s schema-less nature means that developers must explicitly enforce strong hashing, unique salting, and access controls. Without these safeguards, the system remains vulnerable to offline password recovery attacks, where rainbow tables turn exposed hash values into credentials.

Remediation focuses on strengthening password storage, enforcing access controls on DynamoDB, and ensuring AdonisJS authentication uses modern, adaptive hashing.

1. Use adaptive hashing with salts in AdonisJS

AdonisJS provides built-in password hashing utilities that use bcrypt by default, which is intentionally slow and resistant to precomputation. Ensure your user model hashes passwords using hash and verifies them using verify. Never store raw or weakly hashed passwords.

// app/Models/User.js
const { BaseModel, beforeSave } = use('@ioc:Adonis/Lucid/Orm')
const Hash = use('Hash')

class User extends BaseModel {
  static beforeSaveHook (user) {
    if (user.dirty.password) {
      user.passwordHash = await Hash.make(user.password, { workFactor: 12 })
      user.password = null
    }
  }

  static get computed () {
    return []
  }
}
module.exports = User

2. Enforce per-user salts and avoid reuse

Although bcrypt handles salting internally, ensure your user registration flow does not reuse passwords or allow blank inputs. Validate password strength and ensure each user record in DynamoDB contains a unique identifier that does not aid enumeration attacks.

// app/Controllers/Http/AuthController.js
const Hash = use('Hash')
const User = use('App/Models/User')

class AuthController {
  async register ({ request, auth }) {
    const email = request.input('email')
    const password = request.input('password')

    const existing = await User.query().where('email', email).first()
    if (existing) {
      throw new Error('Email already registered')
    }

    const passwordHash = await Hash.make(password, { workFactor: 12 })

    const user = await User.create({
      email: email,
      passwordHash: passwordHash
    })

    return { message: 'User created', userId: user.id }
  }
}
module.exports = AuthController

3. Secure DynamoDB access and queries

Configure DynamoDB with least-privilege IAM policies and avoid exposing sensitive attributes via public endpoints. Use parameterized queries to prevent injection and avoid scanning entire tables for authentication purposes.

// services/DynamoUserService.js
const { DynamoDBClient, GetCommand, QueryCommand } = require('@aws-sdk/client-dynamodb')
const { marshall, unmarshall } = require('@aws-sdk/util-dynamodb')

const client = new DynamoDBClient({ region: 'us-east-1' })

class DynamoUserService {
  constructor(tableName) {
    this.tableName = tableName
  }

  async getUserByEmail(email) {
    const command = new QueryCommand({
      TableName: this.tableName,
      IndexName: 'email-index',
      KeyConditionExpression: 'email = :email',
      ExpressionAttributeValues: marshall({ ':email': email })
    })

    const response = await client.send(command)
    if (!response.Items || response.Items.length === 0) {
      return null
    }
    return unmarshall(response.Items[0])
  }

  async getUserById(userId) {
    const command = new GetCommand({
      TableName: this.tableName,
      Key: marshall({ id: userId })
    })

    const response = await client.send(command)
    return response.Item ? unmarshall(response.Item) : null
  }
}

module.exports = DynamoUserService

4. Enforce rate limiting and monitor access patterns

Use AdonisJS middleware or a gateway-level rate limiter to restrict authentication attempts. Monitor DynamoDB access patterns to detect anomalous query volumes that may indicate reconnaissance for rainbow table attacks.

// start/hooks.js
const { Exception } = require('@poppinss/utils')

module.exports = {
  async handle ({ request, response, next }) {
    // Implement rate limiting logic here or use a provider-specific middleware
    await next()
  }
}