Password Spraying in Feathersjs with Dynamodb

Password spraying is an authentication brute-force technique where a few common passwords are tried against many accounts. In a Feathersjs service backed by DynamoDB, this can become more observable and impactful due to how the framework structures requests and how DynamoDB exposes timing and error behaviors.

Feathersjs typically uses an authentication strategy such as JWT with local login hooks or custom authentication hooks. When a login route is implemented without explicit rate limiting or account enumeration protection, each request to authenticate against a user in DynamoDB can reveal timing differences. DynamoDB’s eventual consistency and read/write capacity modes can cause variable response times. An attacker can observe slightly longer responses for non-existent items in some configurations, or see differences in HTTP status codes when account lockouts or user existence checks are implemented naively in application code.

Common Feathersjs patterns that increase risk include:

• Using a hook that queries DynamoDB by username before password verification, which can disclose whether a user exists based on presence or absence of the item.
• Returning different error messages for “user not found” versus “invalid password,” enabling account enumeration alongside password spraying.
• Not applying per-user or global rate limiting, allowing an attacker to spray multiple passwords across many accounts within a short window.
• Relying solely on client-side throttling or missing exponential backoff in the client, which makes automated spraying easier.

Because Feathersjs often serves as a thin orchestration layer over DynamoDB, the framework itself does not inherently protect against credential misuse. If the service does not enforce uniform error responses, constant-time existence checks, and rate limiting, password spraying can be used to compromise valid accounts or to map valid usernames.

Remediation focuses on preventing account enumeration, standardizing responses, and enforcing rate controls while keeping DynamoDB access patterns safe and predictable.

• Use a constant-time authentication flow: always hash the provided password with the stored hash, even when the user does not exist, to avoid timing leaks.
• Return the same generic error message for both missing user and invalid password (e.g., “Invalid credentials”).
• Implement rate limiting at the service hook level or API gateway to limit attempts per identity or IP.
• Avoid conditional logic in hooks that branches on user existence before password verification.

Example Feathersjs hook for DynamoDB-based authentication with safe error handling and a constant-time verification approach:

const { DynamoDBClient, GetItemCommand } = require('@aws-sdk/client-dynamodb');
const { marshall, unmarshall } = require('@aws-sdk/util-dynamodb');
const bcrypt = require('bcrypt');

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

async function authenticateUser(username, password) {
  const params = {
    TableName: process.env.USER_TABLE,
    Key: marshall({ username })
  };
  const command = new GetItemCommand(params);
  const result = await client.send(command);
  const user = result.Item ? unmarshall(result.Item) : null;

  // If no user, still run a dummy hash to keep timing consistent
  const dummyHash = await bcrypt.hash(password, 10);
  const storedHash = user ? user.passwordHash : dummyHash;

  const match = await bcrypt.compare(password, storedHash);
  if (!match) {
    const err = new Error('Invalid credentials');
    err.code = 401;
    throw err;
  }
  return user;
}

// Feathers hook usage
module.exports = function authenticationHook(options = {}) {
  return async context => {
    const { username, password } = context.data;
    if (!username || !password) {
      const err = new Error('Invalid credentials');
      err.code = 401;
      throw err;
    }
    const user = await authenticateUser(username, password);
    context.result = { user };
    context.params.authenticated = true;
    return context;
  };
};

To add rate limiting, you can use an in-memory or external store within the hook to track attempts per username/IP and introduce progressive delays or rejections after a threshold. Avoid branching logic that returns early for missing users; keep the flow uniform.

For broader protection across the API, integrate the CLI tool by running middlebrick scan <url> to detect authentication and rate-limiting issues, or add the GitHub Action to fail builds if risk scores degrade. The MCP Server allows AI coding assistants to trigger scans from your IDE, helping maintain secure patterns as the service evolves.