Credential Stuffing in Sinatra with Firestore

Credential stuffing is an automated brute-force technique where attackers use lists of breached username and password pairs to gain unauthorized access. When a Sinatra application uses Google Cloud Firestore as its user store without adequate protections, the combination can amplify risk.

Sinatra is a lightweight Ruby web framework. If it implements a login endpoint that directly queries Firestore to validate credentials, the application may be susceptible to high-volume, low-concurrency requests that evade simple rate-limiting. Firestore security rules may permit read access on a user document based on an indexed field such as email, but if rules are misconfigured they might allow broad read or list operations, enabling attackers to enumerate valid users.

A typical vulnerable Sinatra route might look like this, where the code retrieves a user document by email and then performs password verification in Ruby:

post '/login' do
  email = params[:email]
  password = params[:password]
  user = Firestore.get("users/#{email}")
  if user&.dig('email') == email && user&.dig('password') == password
    session[:user_id] = email
    redirect '/dashboard'
  else
    status 401
    'Invalid credentials'
  end
end

If Firestore rules allow reads on the user document when the requesting email matches the document ID or a field, the endpoint reveals whether an email exists based on timing differences or error messages. Attackers can then run credential stuffing campaigns using automated scripts that iterate through email lists, measuring response times and status codes to infer valid accounts. Without account lockout, captcha, or strict rate limits, each request is a new, unauthenticated scan of the API surface that can bypass weak protections.

The Firestore layer itself does not inherently cause credential stuffing, but its configuration and the way Sinatra interacts with it can enable or amplify the issue. For example, if Firestore indexes the email field for fast queries and the Sinatra app uses those indexes to fetch user documents, attackers can efficiently probe many emails. If Firestore rules permit listing collections or reading based on partial matches, the attack surface grows. Insecure rule examples include allowing read on a users collection if the request email matches a claim or document field without strict ownership checks.

Because middleBrick scans the unauthenticated attack surface, it can detect endpoints that are vulnerable to credential stuffing by analyzing authentication mechanisms, input validation, rate limiting, and enumeration behaviors. It flags weak configurations and provides remediation guidance, such as tightening Firestore rules and adding robust login protections in Sinatra.

Remediation focuses on reducing information leakage, enforcing strict access controls, and adding anti-automation protections. The goal is to make it difficult for attackers to determine whether an email is valid and to limit the rate at which credentials can be tested.

First, avoid exposing user existence through timing or status differences. Use a constant-time comparison and a generic response for failed logins. Instead of revealing whether an email exists, always perform a dummy verification when the email is not found:

post '/login' do
  email = params[:email].to_s.strip.downcase
  password = params[:password]
  # Always fetch a document, even if the caller-supplied email is invalid
  user_doc_path = "users/#{email}"
  user = Firestore.get(user_doc_path)

  # Dummy user data for constant-time behavior when user does not exist
  dummy_hash = '$2a$12$DummyHashForNonexistentUserToPreventTimingLeaks'
  stored_hash = user&.dig('password') || dummy_hash

  # Constant-time comparison (pseudocode; use a secure library in production)
  if secure_compare(stored_hash, dummy_hash) || (user&.dig('email') == email && secure_compare(stored_hash, password))
    session[:user_id] = email
    redirect '/dashboard'
  else
    status 401
    'Invalid credentials'
  end
end

def secure_compare(a, b)
  return false unless a.bytesize == b.bytesize
  l = a.unpack 'C*'
  r = 0
  b.each_byte { |byte| r |= byte ^ l.shift }
  r == 0
end

Second, tighten Firestore security rules to prevent enumeration and ensure least privilege. Rules should not allow broad reads or listing. Require authentication for writes and restrict reads to the exact document matching the authenticated user’s identity. For unauthenticated checks (such as probing), rules should deny by default:

rules_version = '2';
service cloud.firestore {
  match /databases/{database}/documents {
    match /users/{email} {
      // Allow read only if the requesting email exactly matches the document ID
      // and the request includes a valid session or token (example uses a custom token claim)
      allow read: if request.auth != null && request.auth.token.email == email;
      // Deny list operations to prevent enumeration
      allow list: if false;
      // Deny writes unless authenticated and the email matches the document ID
      allow write: if request.auth != null && request.auth.token.email == email;
    }
  }
}

Third, add rate limiting and bot mitigation at the Sinatra layer to deter automated submissions. Use a middleware or a lightweight store to track attempts per IP or email within a sliding window:

require 'sinatra/base'
require 'redis'

class ProtectedApp < Sinatra::Base
  before do
    @redis = Redis.new
  end

  post '/login' do
    ip = request.ip
    key = "login_attempts:#{ip}"
    attempts = @redis.incr(key)
    @redis.expire(key, 60) if attempts == 1

    if attempts > 10
      status 429
      'Too many requests'
    end

    # ... existing login logic ...
  end
end

Finally, prefer hashing passwords with a strong adaptive algorithm and avoid storing plain text or weakly hashed passwords in Firestore. Use libraries such as bcrypt in Sinatra and ensure Firestore fields are indexed only where necessary. middleBrick can validate that your Firestore rules and Sinatra endpoints do not expose unnecessary read paths and that rate-limiting controls are observable during scans.