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Credential Stuffing Prevention

Shut down automated login attacks that test stolen credential databases against your endpoints.

The Problem

100B+ credential stuffing attempts per year. Average breach cost: $4.35M in remediation, legal, and reputation damage. Bots cycle through millions of stolen credential pairs, and IP-only rate limiting fails at scale — attackers rotate through 50K+ residential proxy addresses and distribute load across botnets.

Our Solution

Identify and block the devices running credential stuffing attacks. Bot Detection catches headless browsers and automation tools, while Device Identification tracks attackers across IP rotations.

Key Metrics

99%
Attacks Blocked
0%
User Friction Added
8
Detection Methods

How It Works

How tracio.ai stops credential stuffing attacks at the source.

1

Device connects

Automated bot begins testing stolen credentials against your login endpoint

2

Signals analyzed

tracio.ai detects headless browser, automation framework, and synthetic interaction patterns

3

Threat blocked

Legitimate users are unaffected — no CAPTCHAs, no friction, no false positives

1

Automated bot begins testing stolen credentials against your login endpoint

2

tracio.ai detects headless browser, automation framework, and synthetic interaction patterns

3

Device-based rate limiting blocks the attack even as the bot rotates IP addresses

4

Legitimate users are unaffected — no CAPTCHAs, no friction, no false positives

Before vs After

Without tracio.ai

HIGH RISK
  • Bots test millions of stolen credentials per hour
  • IP rate limiting is bypassed with residential proxy rotation
  • CAPTCHAs create friction for all users and are solved by CAPTCHA farms
  • Successful attacks expose customer accounts and erode trust

With tracio.ai

PROTECTED
  • Bot detection identifies headless browsers and automation frameworks
  • Device-based rate limiting works regardless of IP rotation
  • Zero user friction — no CAPTCHAs or challenges for real users
  • 99% of credential stuffing attacks are stopped before they reach your backend

Expected Results

99%
Attacks Blocked
0%
User Friction Added
8
Detection Methods
<15ms
Bot Classification

Key Features

  • 01headless browser and automation detection
  • 02device-based rate limiting (not just IP)
  • 03Bot Detection Selenium, Puppeteer, Playwright detection
  • 04IP Intelligence residential proxy detection
  • 05IP Intelligence real-time velocity monitoring
  • 06Detailed bot classification (type, framework, version)
  • 07Real-time threat intelligence feed updates
  • 08Integration with WAF and CDN security policies

Frequently Asked Questions

Real-World Scenario

A botnet operator rents 50,000 residential proxy IPs and deploys a Puppeteer-based credential testing framework. The bot mimics human behavior: random delays, mouse movements, and realistic typing speeds. Each attempt comes from a different residential IP, making IP-based rate limiting useless. tracio.ai detects the attack through signal correlation: the bot's automation protocol artifacts, missing browser plugins, inconsistent WebGL rendering, and TLS fingerprint mismatches all betray the headless origin — even through residential proxies, the device trace remains constant across all 50,000 IPs.

Implementation Guide

Step-by-step integration with tracio.ai

01

Add the tracio.ai SDK to your login page to trace the device before the authentication request is submitted to your backend

02

Configure device-based rate limiting in your authentication middleware: limit login attempts per device trace ID, not per IP address, to defeat IP rotation

03

Enable bot detection webhooks to receive real-time classification of each device — headless, automated, or legitimate — with framework-specific details (Selenium, Puppeteer, Playwright)

04

Set up a challenge-response flow for suspected bots: instead of blocking outright, serve a transparent challenge that legitimate browsers pass automatically but headless browsers fail

05

Feed device trace data into your SIEM or security dashboard for post-attack forensics: trace which credential pairs were tested, from which device, and correlate with successful logins

Expected Timeline

Week 1

Bot detection immediately catches headless browsers and common automation frameworks. Device-based rate limiting stops IP-rotation attacks. 90% of credential stuffing volume is blocked.

Month 1

Advanced bot detection catches sophisticated attackers using anti-detection browsers. 99% of credential stuffing attacks are stopped before reaching your authentication backend. Zero CAPTCHAs for legitimate users.

Month 3

Historical device trace data enables proactive blocking of known attack devices. Cost savings from reduced authentication infrastructure load. Account compromise incidents drop to near zero.

Common Mistakes to Avoid

01

Relying on CAPTCHAs as the primary defense — CAPTCHA-solving services cost $2-3 per 1,000 solves, making them economically viable for attackers while degrading experience for legitimate users

02

Setting device-based rate limits too aggressively — shared devices (library computers, family tablets) may trigger false positives; use a combination of device trace + credential pair for rate limiting

03

Not monitoring for anti-detection browsers (GoLogin, Multilogin) that actively spoof device signals — enable tracio.ai's browser tampering detection for an additional signal layer

Related Resources

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Ready to start preventing credential stuffing? Start your free trial or book a demo. No credit card required.