Skip to main content

​
Overview

This tutorial walks through implementing Fingerprint to prevent credential stuffing, where attackers use bots to rapidly test stolen or guessed usernames and passwords across many accounts. You’ll begin with a starter app that includes a mock login page and a basic login flow. From there, you’ll add the Fingerprint JavaScript agent to identify each visitor and use server-side logic with Fingerprint data to detect and block automated login attempts. By the end, you’ll have a sample app that rejects credential stuffing bots and can be customized to fit your use case and business rules. This tutorial uses just plain JavaScript and a Node server with SQLite on the back end. For language- or framework-specific setups, see our quickstarts.
Estimated time: < 15 minutes
This tutorial requires the Bot Detection Smart Signal, which is only available on paid plans.

​
Prerequisites

Before you begin, make sure you have the following:
  • A copy of the starter repository (clone with Git or download as a ZIP)
  • Node.js (v20 or later) and npm installed
  • Your favorite code editor
  • Basic knowledge of JavaScript

​
1. Create a Fingerprint account and get your API keys

  1. Sign up for a free Fingerprint trial, or log in if you already have an account.
  2. After signing in, go to the API keys page in the dashboard.
  3. Save your public API key, which you’ll use to initialize the Fingerprint JavaScript agent.
  4. Create and securely store a secret API key for your server. Never expose it on the client side. You’ll use this key on the backend to retrieve full visitor information through the Fingerprint Server API.

​
2. Set up your project

  1. Clone or download the starter repository and open it in your editor.
Terminal
git clone https://github.com/fingerprintjs/use-case-tutorials.git
  1. This tutorial will be using the credential-stuffing folder. The project is organized as follows:
Project structure
.
├── public/
│   ├── index.html    # Login page with email/password
│   └── index.js      # Front-end logic to handle login
├── server/
│   ├── server.js     # Serves static files and login endpoint
│   ├── db.js         # Initializes SQLite and exports a database connection
│   └── accounts.js   # Login and credential stuffing prevention logic
└── .env.example      # Example environment variables
  1. Install dependencies:
Terminal
npm install
  1. Copy or rename .env.example to .env, then add your Fingerprint API keys:
Terminal
FP_PUBLIC_API_KEY=your-public-key
FP_SECRET_API_KEY=your-secret-key
  1. Start the server:
Terminal
npm run dev
  1. Visit http://localhost:3000 to view the mock login page from the starter app. You can test the basic login form using the included test account (demo@example.com / password123) and clicking Log in.
  2. Then try to log in using the included headless bot test script test-bot.js. While the app is running, execute node test-bot.js and observe that the automated script logs in successfully. By default, the server does not distinguish between bots and real users.
Terminal
node test-bot.js

​
3. Add Fingerprint to the front end

In this step, you’ll load the Fingerprint client when the page loads and trigger identification when the user clicks Log in. The client returns both a visitorId and a requestId. Instead of relying on the visitorId from the browser, you’ll send the requestId to your server along with the login payload. The server will then call the Fingerprint Events API to securely retrieve the full identification details, including bot detection and other signals.
  1. At the top of public/index.js, load the Fingerprint JavaScript agent:
public/index.js
const fpPromise = import(
  `https://fpjscdn.net/v3/${window.FP_PUBLIC_API_KEY}`
).then((FingerprintJS) => FingerprintJS.load({ region: "us" }));
  1. Make sure to change region to match your workspace region (e.g., eu for Europe, ap for Asia, us for Global (default)).
  2. Near the bottom of public/index.js, the Log in button already has an event handler for submitting the credentials. Inside this handler, request visitor identification from Fingerprint using the get() method and include the returned requestId when sending the login request to the server:
public/index.js
loginBtn.addEventListener("click", async () => {
  // ...

  const fp = await fpPromise;
  const { requestId } = await fp.get();

  try {
    const res = await fetch("/api/login", {
      method: "POST",
      headers: { "Content-Type": "application/json" },
	    body: JSON.stringify({ email, password, requestId }),
    });
    const data = await res.json();

    // ...
  }
});
The get() method sends signals collected from the browser to Fingerprint servers, where they are analyzed to identify the visitor. The returned requestId acts as a reference to this specific identification event, which your server can later use to fetch the full visitor details. For lower latency in production, check out our documentation on using Sealed Client Results to return full identification details as an encrypted payload from the get() method.

​
4. Receive and use the request ID to get visitor insights

Next, pass the requestId through to your login logic, initialize the Fingerprint Server API client, and fetch the full visitor identification event so you can access the trusted visitorId and Bot Detection Smart Signal.
  1. In the back end, the server/server.js file defines the API routes for the app. Update the /api/login route there to also extract requestId from the request body and pass it into the attemptLogin function.
server/server.js
app.post("/api/login", async (req, reply) => {
  const { email, password, requestId } = req.body || {};
  const result = await attemptLogin({ email, password, requestId });
  return reply.send(result);
});
  1. The server/accounts.js file contains the logic for handling logins. Start by importing and initializing the Fingerprint Server API client there, and load your environment variables with dotenv.
server/accounts.js
import { db } from "./db.js";
import { config } from "dotenv";
import {
  FingerprintJsServerApiClient,
  Region,
} from "@fingerprintjs/fingerprintjs-pro-server-api";

config();

const fpServerApiClient = new FingerprintJsServerApiClient({
  apiKey: process.env.FP_SECRET_API_KEY,
  region: Region.Global,
});
  1. Make sure to change region to match your workspace region (e.g., EU for Europe, AP for Asia, Global for Global (default)).
  2. Update the attemptLogin function to accept requestId and use it to fetch the full identification event details from Fingerprint:
server/accounts.js
export async function attemptLogin({ email, password, requestId }) {
  if (!email || !password) {
    console.error("Missing credentials.");
    return { success: false, error: "Login failed." };
  }

  if (!requestId) {
    console.error("Missing requestId.");
    return { success: false, error: "Login failed." };
  }

  const event = await fpServerApiClient.getEvent(requestId);

  const user = findAccountByEmail(email);
  if (!user || user.password !== password) {
    console.error("Invalid credentials");
    return { success: false, error: "Login failed." };
  }

  return { success: true };
}
Using the requestId, the Fingerprint server client will retrieve the full data for the visitor identification request. The returned object will contain the visitor ID, IP address, device, and browser details, and Smart Signals like bot detection, browser tampering detection, VPN detection, and more. You can see a full example of the event structure and test it with your own device in our demo playground. For additional checks to ensure the validity of the data coming from your front end, view how to protect from client-side tampering and replay attacks in our documentation.

​
5. Block credential stuffing bots

Credential stuffing attacks rely heavily on automated login attempts, so rejecting bots outright can stop the abuse. Fingerprint returns notDetected if no bot activity is found, good for known bots, like search engines, and bad for other automation tools. Any visitor identification that does not return notDetected can be blocked from logging in.
  1. Continuing in the attemptLogin function in server/accounts.js, check the bot signal returned in the event object and block bots:
server/accounts.js
export async function attemptLogin({ email, password, requestId }) {
  // ...

  const event = await fpServerApiClient.getEvent(requestId);

  const botDetected = event.products?.botd?.data?.bot?.result !== "notDetected";
  if (botDetected) {
    console.error("Bot detected.");
    return { success: false, error: "Bot detected. Login failed." };
  }

  // ...
}
You can also add Suspect Score as a secondary layer. The Suspect Score is a weighted representation of all Smart Signals present in the identification payload, helping to identify suspicious activity. While you wouldn’t normally block logins based only on a high risk score, you could flag them for review, modify rate-limits, or add step-up authentication.
  1. Below the bot detection check, add a condition that reads the Suspect Score from the event object and blocks the login if it exceeds a chosen threshold (for example, 20):
server/accounts.js
export async function attemptLogin({ email, password, requestId }) {
  // ...

  const botDetected = event.products?.botd?.data?.bot?.result !== "notDetected";
  if (botDetected) {
    console.error("Bot detected.");
    return { success: false, error: "Bot detected. Login failed." };
  }

  const suspectScore = event.products?.suspectScore?.data?.result || 0;
  if (suspectScore > 20) {
    console.error(`High Suspect Score detected: ${suspectScore}`);
    return { success: false, error: "Login failed." };
  }

  // ...
}

​
6. Recognize repeat offenders by visitor ID

As a secondary measure, you can log the visitorId from failed login attempts to spot repeat attackers. This way, even if they change accounts or rotate IPs, you can recognize and block the same device when it comes back or tries other actions on your site. Note: The starter app includes a SQLite database with these tables already created for you:
SQLite database tables
accounts - Stores login credentials for test accounts
  email TEXT PRIMARY KEY
  password TEXT NOT NULL

failed_logins - Logs visitorIds tied to repeated failed attempts
  id INTEGER PRIMARY KEY AUTOINCREMENT
  visitorId TEXT NOT NULL
  createdAt INTEGER NOT NULL
  1. Add some helper functions to the bottom of the server/accounts.js file to record and check failed attempts:
server/accounts.js
// Log a failed login attempt
function logFailedAttempt(visitorId) {
  db.prepare(
    `INSERT INTO failed_logins (visitorId, createdAt) VALUES (?, ?)`
  ).run(visitorId, Date.now());
}

// Get the number of recent failed login attempts
function getRecentFailedAttempts(visitorId) {
  const since = Date.now() - 24 * 60 * 60 * 1000; // 24 hours ago
  const row = db
    .prepare(
      `SELECT COUNT(*) as count 
       FROM failed_logins 
       WHERE visitorId = ? AND createdAt >= ?`
    )
    .get(visitorId, since);
  return row.count;
}
  1. Update attemptLogin to retrieve the visitorId, log failed attempts, and block repeat offenders:
server/accounts.js
export async function attemptLogin({ email, password, requestId }) {
  if (!email || !password) {
    console.error("Missing email or password.");
    return { success: false, error: "Login failed." };
  }

  if (!requestId) {
    console.error("Missing requestId.");
    return { success: false, error: "Login failed." };
  }

  const event = await fpServerApiClient.getEvent(requestId);
  const visitorId = event.products.identification.data.visitorId;

  const botDetected = event.products?.botd?.data?.bot?.result !== "notDetected";
  if (botDetected) {
    logFailedAttempt(visitorId);
    console.error("Bot detected.");
    return { success: false, error: "Bot detected. Login failed." };
  }

  const suspectScore = event.products?.suspectScore?.data?.result || 0;
  if (suspectScore > 20) {
    logFailedAttempt(visitorId);
    console.error(`High Suspect Score detected: ${suspectScore}`);
    return { success: false, error: "Login failed." };
  }

  if (getRecentFailedAttempts(visitorId) >= 3) {
    logFailedAttempt(visitorId);
    console.error("Too many failed login attempts.");
    return { success: false, error: "Too many failed login attempts." };
  }

  const user = findAccountByEmail(email);
  if (!user || user.password !== password) {
    logFailedAttempt(visitorId);
    console.error("Invalid credentials");
    return { success: false, error: "Login failed." };
  }

  return { success: true };
}
Together with the bot detection Smart Signal, this allows you to protect your logins and prevent credential stuffing. You can extend it by analyzing additional signals, changing rate limit thresholds, varying your response, etc.
This is a minimal example to show how to implement Fingerprint. In a real application, make sure to implement proper security practices, error checking, and password handling that align with your production standards.

​
7. Test your implementation

Now that everything is wired up, you can test the full protected login flow.
  1. Start your server if it isn’t already running and open http://localhost:3000:
Terminal
npm run dev
  1. Try logging in with the valid test account (demo@example.com / password123). You should see a success response.
  2. Now try a few failed login attempts using the wrong password. After 3 repeated failures, additional attempts from the same device will be blocked based on the failed login check.
  3. Next, run the included headless bot test script from the credential-stuffing folder. This will attempt to log in using a headless browser, which will be flagged by the Bot Detection signal and rejected:
Terminal
node test-bot.js
Note: If you encounter errors launching the automated browser, make sure you have the testing browser installed:
Terminal
npx puppeteer browsers install chrome

​
Next steps

You now have a working login flow that blocks credential stuffing bots with Fingerprint. From here, you can expand the logic with more Smart Signals, fine-tune rules based on your business policies, or layer in additional defenses, such as rate limiting with multi-factor authentication. To dive deeper, explore our other use case tutorials for more step-by-step examples. Check out these related resources: