Brute Force Attack in Sails with Dynamodb

A brute force attack against a Sails application using DynamoDB typically exploits weak authentication endpoints, such as login or password-reset routes, where attackers can make many rapid attempts without effective rate limiting. In this stack, DynamoDB serves as the user store, and Sails provides the web layer and session management. The vulnerability arises when Sails does not enforce strict account lockout, captcha, or exponential backoff on authentication actions, allowing attackers to iterate over usernames or passwords at high speed.

Because DynamoDB is a NoSQL database, queries are often indexed by partition key (e.g., email). If the authentication route performs a query like User.findOne({ email: email }) without additional protections, an attacker can enumerate valid users by observing differences in response behavior or timing. Sails Waterline ORM may inadvertently expose timing differences between a missing user and a missing password verification step, aiding iterative guessing. Without proper rate limiting, each request completes quickly, enabling high-throughput attempts that can exhaust account security.

Another angle is the lack of per-user throttling in DynamoDB provisioned capacity. If the table is configured with limited write capacity, a sustained brute force login attempt can cause throttling errors that reveal whether a username exists based on conditional error responses. Sails services handling authentication should treat these errors as generic failures and enforce global and per-user rate limits to mitigate enumeration and reduce the effectiveness of credential guessing.

middleBrick scans this attack surface by running unauthenticated checks focused on authentication endpoints, evaluating rate limiting, input validation, and account enumeration risks. It maps findings to frameworks such as OWASP API Top 10 and provides remediation guidance to harden the Sails and DynamoDB integration without requiring agent installation or credentials.

To secure a Sails application using DynamoDB, implement consistent timing behavior, robust rate limiting, and safe query patterns. Below are concrete, realistic code examples using the AWS SDK within Sails custom models or services.

1. Use a constant-time user existence check

Avoid branching logic based on whether a user exists. Always perform a dummy verification step to keep response times uniform.

// api/services/auth.js
const AWS = require('aws-sdk');
const dynamodb = new AWS.DynamoDB.DocumentClient();

async function findUserByEmail(email) {
  const params = {
    TableName: process.env.USERS_TABLE,
    Key: { email }
  };
  const result = await dynamodb.get(params).promise();
  return result.Item || null;
}

async function verifyPassword(inputPassword, storedHash) {
  // Use a constant-time comparison library in production
  return inputPassword === storedHash; // placeholder: use bcrypt.compare in real code
}

module.exports = {
  findUserByEmail,
  verifyPassword
};

2. Enforce global and per-user rate limits

Use a sliding window or token bucket algorithm with DynamoDB to store attempt counts with TTL. This prevents account lockout bypass and controls traffic at the user level.

// api/services/rateLimiter.js
const AWS = require('aws-sdk');
const dynamodb = new AWS.DynamoDB.DocumentClient();

async function canAttempt(username) {
  const key = { pk: `USER#${username}`, sk: 'RATE#login' };
  const params = {
    TableName: process.env.RATE_TABLE,
    Key: key,
    UpdateExpression: 'SET attempts = attempts + :inc, expiry = :exp',
    ConditionExpression: 'attempts < :max',
    ExpressionAttributeValues: {
      ':inc': 1,
      ':exp': Math.floor(Date.now() / 1000) + 300,
      ':max': 10
    },
    ReturnValues: 'UPDATED_NEW'
  };
  try {
    await dynamodb.update(params).promise();
    return true;
  } catch (err) {
    if (err.code === 'ConditionalCheckFailedException') {
      return false;
    }
    // Treat other errors as safe to allow the attempt to avoid user enumeration
    return true;
  }
}

module.exports = { canAttempt };

3. Handle DynamoDB errors generically

Do not differentiate between throttling and validation errors; return a generic authentication failure message to prevent attackers from inferring account status.

// api/controllers/AuthController.js
const AuthService = require('../services/auth');
const RateLimiter = require('../services/rateLimiter');

module.exports = {
  async login(req, res) {
    const { email, password } = req.body;
    if (!email || !password) {
      return res.unauthorized('Invalid credentials');
    }
    if (!await RateLimiter.canAttempt(email)) {
      return res.unauthorized('Invalid credentials');
    }
    const user = await AuthService.findUserByEmail(email);
    if (!user) {
      // Trigger dummy verification to hide user existence
      await AuthService.verifyPassword('dummy', 'dummy');
      return res.unauthorized('Invalid credentials');
    }
    const valid = await AuthService.verifyPassword(password, user.passwordHash);
    if (!valid) {
      return res.unauthorized('Invalid credentials');
    }
    // Establish session or token here
    return res.ok({ message: 'Authenticated' });
  }
};

4. Configure DynamoDB with TTL and strong indexing

Create a DynamoDB table for rate-limiting records with a TTL attribute to auto-expire old entries. Use a composite primary key (pk, sk) for efficient querying and avoid hot partitions by adding random suffixes if needed.

// Example item for rate table
{
  pk: "USER#testuser",
  sk: "RATE#login",
  attempts: 1,
  expiry: 1735689600
}

middleBrick can validate these patterns by scanning your Sails endpoints and DynamoDB interaction code, reporting issues such as missing rate limiting or unsafe error handling. Its findings align with compliance frameworks and provide prioritized remediation steps you can act on with the help of the CLI (middlebrick scan <url>), GitHub Action, or MCP Server integrated into your development workflow.