Monitoring for Credential Stuffing

In this lesson, you will learn how to identify and defend your ASP.NET Core applications against automated credential stuffing attacks where attackers attempt to "stuff" stolen username-password pairs into your login endpoints.

The Problem: High-Volume Automated Login

Credential stuffing relies on speed and scale. Unlike a brute-force attack that tries every password for a single account, credential stuffing tries thousands of stolen account combinations across your entire user base. Because these attacks often use distributed botnets, the traffic appears to come from thousands of different IP addresses, making standard rate-limiting by IP insufficient.

Detection Strategy

To detect this, you must shift your focus from individual IPs to Identity Traffic Patterns. You need to monitor for an anomalous spike in failed login attempts across your entire authentication service within a short time window.

In C#, you can implement a basic monitor within your Login controller using a distributed cache (like Redis) to track the velocity of authentication failures globally:

public async Task<IActionResult> Login(LoginModel model)
{
    var failedAttemptCount = await _cache.GetAsync<int>("GlobalLoginFailures");

    if (failedAttemptCount > Threshold)
    {
        // Trigger alert or initiate CAPTCHA/Challenge
        return StatusCode(StatusCodes.Status429TooManyRequests);
    }

    var result = await _signInManager.PasswordSignInAsync(...);
    
    if (!result.Succeeded)
    {
        await _cache.IncrementAsync("GlobalLoginFailures", TimeSpan.FromMinutes(1));
    }

    return View();
}

Mitigation Techniques

Simply blocking the requests isn't enough; you must introduce "friction" to break the attacker's automated scripts.

1. Adaptive Authentication: If the failure rate crosses a threshold, programmatically force a CAPTCHA challenge or multi-factor authentication (MFA) before allowing the next attempt.
2. Contextual Analysis: Log the User-Agent, device fingerprint, and geography. If a sudden surge of login attempts originates from devices or regions where your users don't typically reside, flag these for temporary account locks or enhanced verification.
3. Honey-Tokens: Inject "fake" accounts into your system. Since no real user should ever attempt to log into these, any login attempt against them is a 100% confirmed indicator of an attack, allowing you to blacklist the source immediately.

Key Takeaway: Mitigate credential stuffing by monitoring aggregate failure rates rather than just individual IPs, and introduce programmatic friction like CAPTCHAs when traffic patterns indicate high-velocity automated attempts.