How XDR Identifies Credential Stuffing and Brute Force Attacks

How XDR Identifies Credential Stuffing and Brute Force Attacks

Cyber attackers have become increasingly adept at bypassing traditional security defenses using automated tools and stolen credentials. Two of the most prevalent and damaging techniques in the attacker’s arsenal are credential stuffing and brute force attacks. Both aim to exploit weak or reused credentials at scale, often succeeding due to poor password hygiene or insufficient authentication safeguards.

As these attacks grow in volume and sophistication, Extended Detection and Response (XDR) platforms offer a powerful solution for identifying and mitigating them. By correlating telemetry across endpoints, networks, servers, identities, and cloud environments, XDR provides visibility and context that traditional point solutions often miss.

In this article, we’ll explore how XDR works to detect credential stuffing and brute force attacks — and how organizations can leverage it for a more resilient security posture.

Understanding the Threats

What Is Credential Stuffing?

Credential stuffing is a type of cyberattack where threat actors use stolen username-password pairs (often from previous breaches) to attempt logins on multiple websites or services. The assumption is that users often reuse credentials across platforms.

Example:
An attacker takes credentials leaked from a retail website breach and uses automation to try logging into banking, email, or corporate accounts using the same usernames and passwords.

What Is a Brute Force Attack?

Brute force attacks involve systematically trying many different passwords for a single account until the right one is found. These attacks may be:

• Simple brute force: Trying all combinations of characters

• Dictionary attacks: Using a list of common passwords

• Hybrid attacks: Combining dictionary and variation-based approaches

Challenges in Detecting These Attacks

Credential stuffing and brute force attacks share common traits:

• High volume of login attempts

• Use of anonymizing proxies or botnets

• Exploitation of legitimate interfaces (like login pages)

• Obfuscation to evade rate-limiting or IP blocking

Traditional security tools like firewalls, endpoint detection and response (EDR), or SIEMs may detect isolated incidents but lack the ability to correlate activity across systems. That’s where XDR shines.

How XDR Detects Credential Stuffing and Brute Force Attacks

1. Correlating Events Across Multiple Data Sources

XDR ingests and analyzes telemetry from:

• Identity and access management (IAM) systems

• Authentication logs (SSO, LDAP, AD, cloud)

• Endpoint and server logs

• Network traffic (login APIs, VPN, RDP)

• Cloud and SaaS platforms (e.g., O365, Salesforce)

By aggregating these logs in real-time, XDR can correlate suspicious activity — such as hundreds of failed login attempts from multiple IPs or an unusual spike in authentication traffic.

2. Behavioral Analytics and Anomaly Detection

XDR uses machine learning to baseline normal user behavior:

• Typical login times

• Common geolocations

• Devices and browsers used

• Accessed resources

If a user suddenly attempts to log in from a new country using an unusual device and fails multiple times, XDR flags this deviation as suspicious. Similarly, an account exhibiting impossible travel (logging in from India and the U.S. within 10 minutes) is immediately flagged.

3. Detection of High-Frequency Login Attempts

Credential stuffing and brute force attacks involve large volumes of authentication requests. XDR identifies:

• Repeated login attempts to the same or different accounts from a single IP

• Multiple failed logins followed by a successful login

• Login attempts distributed across a wide range of usernames

This frequency-based detection — especially when combined with geographic and device analysis — quickly distinguishes automated attacks from legitimate user activity.

4. Geo-Velocity and Device Fingerprinting

Credential stuffing often comes from globally distributed botnets. XDR tracks login locations and times, calculating geo-velocity to detect anomalies.

Additionally, device fingerprinting helps flag inconsistent login attempts — for instance, a login from Chrome on Windows in New York followed by Safari on iOS in Russia a few minutes later.

5. Threat Intelligence Integration

Modern XDR platforms integrate with external and internal threat intelligence feeds. If a login attempt comes from a known malicious IP or part of a dark web-reported credential dump, the system can:

• Automatically raise the alert level

• Block the IP or isolate the account

• Trigger a multi-factor authentication (MFA) challenge

6. Automated Response and Playbooks

XDR solutions often include automated response mechanisms, such as:

• Blocking IPs at the firewall or WAF

• Locking or disabling suspicious user accounts

• Forcing password resets

• Notifying administrators or triggering SOAR workflows

These predefined playbooks help organizations respond to credential-based attacks faster and more consistently, reducing the window of exposure.

Real-World Example: Credential Stuffing Detection in Action

Imagine an enterprise using Microsoft 365, Okta for identity, and CrowdStrike for endpoint detection. An attacker uses stolen credentials to try logging into email accounts using thousands of variations from different IPs.

Here’s how an XDR platform would react:

1. Detects abnormal login pattern: Hundreds of failed logins across multiple users within minutes.

2. Correlates across platforms: Matches this pattern with increased VPN activity and unsuccessful endpoint logins.

3. Cross-checks threat intelligence: Recognizes the IPs as being flagged in recent credential stuffing campaigns.

4. Triggers response: Automatically disables affected accounts, flags the event for the SOC, and blocks the IPs.

5. Recommends follow-up: Suggests enforcing MFA and checking for lateral movement.

Benefits of Using XDR for Credential Attack Detection

• Comprehensive visibility: See across endpoints, networks, identities, and cloud.

• Faster detection: ML and analytics surface threats early in the attack chain.

• Reduced alert fatigue: Correlated alerts provide context, reducing false positives.

• Automated response: Minimize damage with real-time blocking and account actions.

• Continuous learning: Feedback loops help fine-tune detection models.

Best Practices to Enhance Detection and Prevention

To maximize XDR effectiveness against credential-based attacks:

• Enforce MFA across all users, especially privileged accounts.

• Monitor login telemetry and enable adaptive authentication.

• Regularly rotate credentials and avoid reuse across services.

• Train users on password hygiene and phishing risks.

• Integrate your XDR with IAM, threat intel, and SOAR platforms.

Conclusion

Credential stuffing and brute force attacks are persistent threats in today’s digital landscape. Their simplicity, scale, and reliance on reused credentials make them highly effective for attackers — and difficult to catch with siloed security tools.

XDR changes the game by providing the holistic visibility, analytics, and automated response needed to detect these threats in real time. With its ability to connect dots across the entire IT ecosystem, XDR empowers security teams to not just detect credential-based attacks — but to stop them before they lead to breach.