Credential stuffing—the automated injection of breached username/password pairs to gain unauthorized access to user accounts—remains the primary driver of Account Takeover (ATO) fraud. Traditional rate limiting often fails against modern botnets that utilize rotating residential proxies. Effective mitigation requires deeper analysis of IP metadata and behavioral patterns at the ingress point.

The Network Signature of an Attack

Sophisticated stuffing attacks rarely originate from a single IP. Instead, they distribute the load across thousands of IP addresses to evade volume-based blocks. However, these IPs often share metadata characteristics that serve as high-fidelity indicators of compromise.

To detect these attacks, security engineers must analyze three core vectors:

Connection Type: Usage of VPNs, TOR exits, or open proxies.
ASN Classification: Traffic originating from hosting providers (e.g., AWS, DigitalOcean) rather than residential ISPs.
Subnet Velocity: High request volume from specific CIDR blocks, even if individual IP volume is low.

Implementation: Layered IP Analysis

A robust defense strategy layers standard rate limiting with real-time IP intelligence. The following logic demonstrates how to filter login requests using metadata.

1. ASN and Connection Type Filtering

Legitimate users rarely log in from data centers. Blocking or challenging traffic from hosting ASNs significantly reduces the attack surface.

Python Example: IP Intelligence Middleware

import requests
from flask import Flask, request, jsonify

app = Flask(__name__)

def analyze_ip_risk(ip_address):
    # Query IPASIS API for metadata
    try:
        response = requests.get(f"https://api.ipasis.com/json/{ip_address}?key=YOUR_API_KEY", timeout=2)
        data = response.json()
        
        # Risk Factor 1: Anonymizers
        if data.get('is_proxy') or data.get('is_vpn') or data.get('is_tor'):
            return True, "Anonymizer Detected"
            
        # Risk Factor 2: Hosting/Data Center Traffic
        # 'hosting' indicates cloud providers often used by botnets
        asn_type = data.get('asn', {}).get('type')
        if asn_type == 'hosting':
            return True, "Hosting ASN Detected"
            
        return False, None
        
    except Exception as e:
        # Fail open or closed depending on security posture
        return False, "Lookup Failed"

@app.route('/login', methods=['POST'])
def login():
    client_ip = request.headers.get('X-Forwarded-For', request.remote_addr)
    
    is_risky, reason = analyze_ip_risk(client_ip)
    
    if is_risky:
        # Log the attempt and return 403 or serve a CAPTCHA
        print(f"Blocked login from {client_ip}: {reason}")
        return jsonify({"error": "Suspicious activity detected"}), 403
        
    # Proceed with credential validation...
    return jsonify({"status": "Processing login"})

Advanced Pattern: Subnet Velocity Tracking

Attackers often burn through IPs within the same subnet. Tracking velocity by /24 (IPv4) or /64 (IPv6) ranges is more effective than tracking single IPs.

Node.js / Redis Implementation Strategy

Instead of keying rate limits by IP, key them by subnet.

const Redis = require('ioredis');
const redis = new Redis();
const ipaddr = require('ipaddr.js');

async function checkSubnetVelocity(ipString) {
    const ip = ipaddr.parse(ipString);
    let subnetKey;

    if (ip.kind() === 'ipv4') {
        // Mask to /24 for IPv4
        subnetKey = `velocity:${ipString.split('.').slice(0, 3).join('.')}`;
    } else {
        // Mask to /64 for IPv6 (simplified)
        const parts = ip.toNormalizedString().split(':');
        subnetKey = `velocity:${parts.slice(0, 4).join(':')}`;
    }

    // Increment count with a TTL of 60 seconds
    const requests = await redis.incr(subnetKey);
    if (requests === 1) {
        await redis.expire(subnetKey, 60);
    }

    // Threshold: 50 login attempts per subnet per minute
    if (requests > 50) {
        return false; // Block
    }
    return true; // Allow
}

Handling False Positives

Aggressive filtering can impact legitimate users, particularly those behind CGNAT (Carrier-Grade NAT) or corporate VPNs.

CGNAT: Mobile carriers often route thousands of users through a single public IP. Do not rely solely on velocity; combine velocity with login failure rates.
Business VPNs: If your application targets B2B, whitelisting specific corporate ASNs or requiring 2FA for VPN traffic is preferred over outright blocking.

FAQ

How effective is blocking Data Center IPs?

Blocking Data Center IPs (ASNs classified as 'hosting') can eliminate 60-80% of unsophisticated bot traffic. Premium attackers use residential proxies to bypass this, necessitating the use of proxy detection scores.

Should I block all VPNs?

Not necessarily. For B2C applications, many privacy-conscious users utilize VPNs. A better approach is to trigger a CAPTCHA or Email OTP challenge when a login originates from a VPN, rather than a hard block.

What is the latency impact of IP lookups?

Using a high-performance API like IPASIS, the latency overhead is typically under 50ms. For high-throughput authentication services, implement an asynchronous check or cache the IP reputation data locally for short durations (e.g., 5-10 minutes).

Secure Your Perimeter with IPASIS

Static blacklists are insufficient against rotating botnets. IPASIS provides real-time data on proxies, VPNs, and ASN risks, allowing you to make intelligent decisions at the edge.

Get your API Key and start filtering traffic today.

Mitigating Credential Stuffing: Identifying Attack Signatures via IP Intelligence