Password Spraying in Express with Dynamodb

Password spraying is an authentication-bypass technique where an attacker uses a small list of common passwords against many accounts. In an Express application using Amazon DynamoDB as the user store, the combination of a typical DynamoDB access pattern and Express session/cookie handling can unintentionally enable or amplify this attack.

Consider a login route that queries DynamoDB by username:

// Example Express route (vulnerable pattern)
app.post('/login', async (req, res) => {
  const { username, password } = req.body;
  const params = {
    TableName: 'users',
    KeyConditionExpression: 'username = :u',
    ExpressionAttributeValues: { ':u': username }
  };
  const result = await dynamodb.query(params).promise();
  const user = result.Items[0];
  if (user && bcrypt.compareSync(password, user.passwordHash)) {
    req.session.userId = user.userId;
    return res.send('OK');
  }
  res.status(401).send('Invalid credentials');
});

If this endpoint does not enforce per-account rate limits or global request throttling, an attacker can iterate through a list of passwords for a single username (single-account spraying) or cycle through many usernames with one common password (credential-stuffing style spraying). Because DynamoDB queries are fast and unauthenticated attack surface tests (as performed by middleBrick) exercise endpoints without credentials, a scanner can probe login behavior to detect timing differences or response variations that indicate whether a username exists, aiding further spraying.

Moreover, if the Express app uses DynamoDB for session storage without additional protections, an attacker who obtains a valid session token (perhaps via successful spraying) can persist across server restarts. Lack of robust rate limiting on the login endpoint, combined with predictable iteration patterns in client-side code, can make password spraying practical even when DynamoDB itself does not reveal whether a user exists: error messages and HTTP status codes can leak account existence or lockout state.

middleBrick’s checks for Authentication, Input Validation, Rate Limiting, and LLM/AI Security are particularly relevant here. For example, unchecked user input used directly in DynamoDB KeyConditionExpression can enable injection or enumeration, while missing rate limiting enables high request rates that facilitate spraying. middleBrick can detect these patterns during an unauthenticated scan and highlight the need for mitigation.

To reduce password spraying risk, apply consistent rate limiting, avoid user enumeration, and ensure DynamoDB access patterns do not leak information via timing or errors.

• Use a constant-time comparison and generic error messages to prevent account enumeration.
• Apply rate limiting at the route level and consider token bucket or sliding window approaches to limit attempts per IP and per user identifier.
• Validate and sanitize all inputs before using them in DynamoDB queries; prefer parameterized queries and avoid concatenating user input into KeyConditionExpression.
• Use middleware to enforce login attempt budgets and introduce small, consistent delays to reduce timing-based discrimination.

Secure Express route example with DynamoDB:

// Secure login route example
const rateLimit = require('express-rate-limit');

// Global rate limiter for login endpoints
const loginLimiter = rateLimit({
  windowMs: 15 * 60 * 1000, // 15 minutes
  max: 100, // limit each IP to 100 requests per window
  standardHeaders: true,
  legacyHeaders: false,
  message: { error: 'Too many attempts, try again later.' }
});
app.post('/login', loginLimiter, async (req, res) => {
  const { username, password } = req.body;
  if (!username || typeof username !== 'string' || !password || typeof password !== 'string') {
    return res.status(400).json({ error: 'Invalid request' });
  }
  const params = {
    TableName: 'users',
    KeyConditionExpression: 'username = :u',
    ExpressionAttributeValues: { ':u': username }
  };
  let result;
  try {
    result = await dynamodb.query(params).promise();
  } catch (err) {
    // Log internally, return generic response
    console.error('DynamoDB query error:', err);
    return res.status(500).json({ error: 'Internal error' });
  }
  const user = result.Items[0];
  // Always run hashing to keep timing consistent
  const dummyHash = bcrypt.hashSync('dummy', 10);
  const valid = user ? bcrypt.compareSync(password, user.passwordHash) : false;
  // Use constant-time comparison helper in production if available
  if (valid) {
    req.session.userId = user.userId;
    return res.json({ message: 'Authenticated' });
  }
  // Generic response regardless of existence
  res.status(401).json({ error: 'Invalid credentials' });
});

Additional DynamoDB-specific practices:

• Use provisioned capacity or auto scaling to avoid throttling-induced timing variability that attackers might exploit.
• Audit IAM policies to ensure the Lambda/EC2 instance role follows least privilege and cannot accidentally expose user tables to broader services.
• Enable DynamoDB Streams and CloudWatch Logs to detect anomalous query rates or patterns indicative of spraying attempts.

middleBrick’s Pro plan supports continuous monitoring and can be integrated into CI/CD via the GitHub Action to fail builds if security scores drop below your chosen threshold, helping catch regressions introduced by changes to authentication logic or DynamoDB usage.