Case Study: How a SOC Team Detected a Credential Stuffing Attack in 12 Minutes

Credential stuffing remains one of the most persistent threats facing internet-facing applications. According to OWASP, credential stuffing attacks exploit the widespread problem of password reuse—when credentials stolen from one breach are systematically tested against login endpoints on other services. Unlike brute force attacks that guess random passwords, credential stuffing uses known-valid username and password pairs, giving attackers significantly higher success rates and making detection substantially harder.

The MITRE ATT&CK framework catalogs this technique as T1110.004 — Credential Stuffing, a sub-technique of Brute Force under the Credential Access tactic. It's a technique that's cheap to execute, difficult to attribute, and devastating when it succeeds—account takeover, data exfiltration, and lateral movement all follow from a single compromised credential.

This case study walks through how a SOC team at a mid-size e-commerce company used SecureNow to detect, investigate, and contain a credential stuffing campaign in just 12 minutes—from the first alert to full containment.

The Company: ShopStream

ShopStream is a mid-size e-commerce platform serving approximately 2 million registered users. Their application handles everything from product browsing to payment processing, with a public-facing REST API that powers both their web and mobile clients. The authentication system is a standard email/password flow with rate limiting at the application layer.

ShopStream's SOC team consists of four analysts working in rotating shifts, with SecureNow deployed to monitor their primary production application. They've configured alert rules covering authentication anomalies, API scanning, error rate spikes, and geographic access patterns. Their notification triage workflow follows the standard status lifecycle: open, acknowledged, investigating, resolved, or dismissed.

The night shift is typically quiet. Most of the team's real-time investigation happens during business hours. But credential stuffing campaigns don't check your shift schedule.

2:47 AM — The Alert Fires

At 2:47 AM, SecureNow's authentication anomaly detection rule triggers. The rule is a SQL query running against ClickHouse trace data every 5 minutes:

SELECT 
  peer_ip,
  count(*) AS total_requests,
  countIf(status_code = 401) AS auth_failures,
  round(auth_failures / total_requests * 100, 2) AS failure_rate
FROM traces
WHERE timestamp >= now() - INTERVAL 5 MINUTE
  AND http_target LIKE '/api/auth/login%'
GROUP BY peer_ip
HAVING auth_failures > 20 AND failure_rate > 90
ORDER BY auth_failures DESC

The rule detects IPs generating more than 20 authentication failures with a failure rate above 90% in a 5-minute window. Tonight, it returns results: 47 unique IP addresses, collectively generating over 3,200 failed login attempts against the /api/auth/login endpoint in the past 5 minutes.

The notification fires on all configured channels: an in-app notification in SecureNow, a Slack message in the #security-alerts channel, and an email to the on-call distribution list. The Slack message includes the rule name, the number of matching IPs, and the total request volume.

2:48 AM — Sarah Opens the Notification

Sarah, the overnight SOC analyst, sees the Slack notification on her phone. She opens SecureNow's notification triage interface and finds the new notification at the top of her queue with severity level high.

The notification summary immediately shows the key details: 47 IPs grouped, all targeting the same endpoint (POST /api/auth/login), with a combined failure rate of 94%. The IP grouping feature has organized the 47 addresses into a single notification rather than generating 47 separate alerts—a critical noise reduction feature that keeps the triage queue manageable.

Sarah clicks Acknowledge to signal she's looking at it, moving the notification status from open to acknowledged. If another analyst happens to check the queue, they'll know someone is on it.

She reviews the grouped IPs. The auto-enrichment from AbuseIPDB has already populated reputation data for each IP. At a glance, she sees a mix of confidence scores—some IPs have prior abuse reports, others are relatively clean. The usage type breakdown shows most are classified as "hosting" or "data center" IPs, not residential. That's the first signal: legitimate users don't usually log in from cloud hosting providers.

For more on how notification triage workflows keep SOC teams organized during incidents, see our notification triage guide.

2:49 AM — AI Investigation on the Top 10 IPs

Rather than manually investigating all 47 IPs, Sarah sorts by authentication failure count and selects the top 10 most active IPs. She triggers SecureNow's AI-powered IP investigation on all 10 simultaneously.

The AI investigation system kicks off analysis for each IP. It pulls the IP's trace data, correlates with AbuseIPDB intelligence, analyzes request patterns, examines headers and payloads across the captured spans, and generates a structured verdict. While the AI works, Sarah reviews the remaining 37 IPs on the list, mentally noting the geographic distribution—the IPs span 12 countries, with clusters in Eastern Europe, Southeast Asia, and South America. This kind of geographic diversity is atypical for legitimate traffic on ShopStream's primarily North American user base.

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2:52 AM — AI Reports Return

Three minutes after triggering, the AI investigation reports are ready. The results are clear:

8 of 10 IPs flagged as malicious with a confidence level of 92%. The AI identifies the pattern as credential stuffing based on several indicators:

• Each IP is sending rapid-fire POST requests to /api/auth/login with different email/password combinations in each request.
• The request timing shows consistent 200–400ms intervals, characteristic of automated tools rather than human interaction.
• The email addresses in the login attempts follow no recognizable pattern—they're not sequential, not from the same domain, and not variations of a single username. This is consistent with a stolen credential list.
• None of the IPs have any prior legitimate activity on ShopStream's application. Their first-ever interaction is an authentication attempt.
• The AI's recommended action for all 8 IPs: Block immediately.

2 of 10 IPs flagged as likely benign with a confidence level of 78%. The AI notes that while these IPs are generating authentication failures, the pattern differs from the other 8:

• Both IPs have prior legitimate activity on ShopStream (successful logins, page views, order history).
• The failed requests are hitting /api/auth/reset-password in addition to /api/auth/login.
• The failure pattern is consistent with a user repeatedly entering an incorrect password and then attempting a password reset—a normal user experience flow, not an attack pattern.

The AI findings include detailed risk scores, behavioral analysis, and links to the specific trace spans that informed each verdict. Sarah doesn't have to take the AI's word for it—every conclusion is backed by evidence she can verify independently.

2:53 AM — AbuseIPDB Corroboration

Sarah cross-references the AI findings with the AbuseIPDB enrichment data already populated on each IP's profile. The results reinforce the AI's assessment:

• 6 of the 8 malicious IPs have existing reports on AbuseIPDB, with categories specifically mentioning credential stuffing, brute force, and web application attacks. Confidence scores range from 67% to 96%.
• 2 of the 8 malicious IPs are relatively new and have no prior AbuseIPDB reports. However, their usage type is "hosting" and they belong to ASNs associated with budget cloud providers commonly used for attack infrastructure.
• Both benign IPs have zero AbuseIPDB reports and are classified as residential IPs from North American ISPs—consistent with legitimate ShopStream users.

This corroboration pattern is significant. The AI's behavioral analysis and AbuseIPDB's historical intelligence arrive at the same conclusion through entirely different methodologies. When two independent data sources agree, the confidence in the assessment increases substantially.

For more on how AbuseIPDB enrichment works within SecureNow, see our threat intelligence enrichment guide.

2:55 AM — Trace Analysis Confirms the Pattern

To fully validate the attack pattern, Sarah dives into the trace data for the top 3 malicious IPs. Using SecureNow's trace analysis view, she examines the OpenTelemetry spans captured during the attack window.

The evidence is unambiguous:

• Request pattern: Every request is a POST /api/auth/login with a JSON body containing an email and password field. The email addresses change with every request. The passwords change with every request. No two requests use the same credentials.
• Timing: Requests arrive at consistent intervals of 250–350ms per IP. Human login attempts show variable timing with pauses—automated tools show metronomic consistency.
• Headers: The User-Agent strings are identical across all requests from a given IP but vary between IPs. This suggests the attacker is using the same tool with per-IP User-Agent configuration—a common anti-detection technique that's easily spotted when you can correlate across IPs.
• Responses: Every request returns a 401 status code. Importantly, a small number (approximately 0.3%) return a 200 status code. This means some of the stolen credentials are actually valid on ShopStream. The attack is succeeding.

That 0.3% success rate transforms the incident from "attempted credential stuffing" to "active account compromise." The urgency just escalated.

2:57 AM — False Positive Resolution

Before taking containment action, Sarah addresses the 2 IPs the AI identified as likely benign. She reviews their trace data and confirms:

• Both IPs belong to real ShopStream users who were actively trying to log in and having trouble with their passwords.
• The authentication failures are interspersed with requests to /api/auth/reset-password—a flow that generates 4xx responses as part of normal operation.
• One user eventually logged in successfully at 2:41 AM, confirming they're a legitimate user who was struggling with their password.

Sarah marks both IPs as false_positive in the IP monitoring dashboard and adds a comment explaining the determination: "Legitimate user, password difficulty, confirmed successful login. Not related to credential stuffing campaign."

2:58 AM — Creating an Exclusion Pattern

The 2 benign IPs were caught by the same alert rule because the rule monitors all authentication endpoints. The password reset flow (/api/auth/reset-password) generates 4xx responses that match the detection criteria. To prevent this class of false positive from recurring, Sarah creates a per-rule exclusion pattern.

In the alert rule configuration, she adds an exclusion for /api/auth/reset-password. This tells the rule to ignore authentication failures originating from the password reset flow when evaluating future results. The detection sensitivity for actual credential stuffing against /api/auth/login remains unchanged.

SecureNow's false positive management system supports this kind of surgical exclusion. Rather than degrading the overall rule by raising thresholds—which would make the rule less effective at catching real attacks—the exclusion targets the specific source of noise.

2:59 AM — Forensics Query: Were Accounts Compromised?

The 0.3% success rate Sarah observed in the trace data means some stolen credentials worked. She needs to know exactly how many accounts were compromised and which ones.

Using SecureNow's forensics engine, Sarah writes a natural language query: "Show me all successful logins from the malicious IPs in the last hour." The system translates this into a SQL query against ClickHouse:

SELECT 
  peer_ip,
  http_target,
  status_code,
  toDateTime(timestamp) AS login_time,
  JSONExtractString(request_body, 'email') AS email
FROM traces
WHERE timestamp >= now() - INTERVAL 1 HOUR
  AND peer_ip IN ('203.0.113.42', '198.51.100.17', '192.0.2.88', ...)
  AND http_target = '/api/auth/login'
  AND status_code = 200
ORDER BY login_time

The results show 11 successful logins across 6 of the 8 malicious IPs. Eleven ShopStream user accounts have been accessed by the attacker. Sarah exports the list of compromised email addresses and immediately escalates to the application team for forced password resets and session invalidation.

This step—pivoting from detection to impact assessment—is where many SOC workflows break down. Without integrated forensics, Sarah would need to export IP lists, open a separate log analysis tool, construct a manual query, and hope the log retention covers the right time window. With SecureNow's forensics query engine, the pivot takes 30 seconds.

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3:00 AM — Containment and Resolution

With the investigation complete, Sarah takes the final containment actions:

IP blocking: She marks all 8 confirmed malicious IPs as blocked in the IP monitoring dashboard. She adds a comment to each with the incident details: "Credential stuffing campaign, 2025-02-23 02:47-03:00 UTC, part of 47-IP campaign targeting /api/auth/login. 11 accounts compromised."

Notification resolution: She updates the notification status from acknowledged to resolved, adding a resolution summary: "Credential stuffing campaign detected and contained. 47 IPs identified, 8 confirmed malicious and blocked, 2 false positives (legitimate users), 37 remaining under monitoring. 11 compromised accounts escalated for forced password reset."

Remaining IPs: The 37 IPs that weren't in the top-10 AI investigation are left in investigating status. Their AbuseIPDB enrichment data and behavioral patterns will be reviewed during the morning shift. Sarah adds a comment to the notification linking to her analysis of the top 10, so the morning team has full context.

Incident timeline: From first alert at 2:47 AM to full containment at 3:00 AM—13 minutes. The credential stuffing campaign was detected, analyzed, validated, assessed for impact, and contained in the time it takes most SOC teams to finish their first manual IP lookup.

What Made the Difference

This case study isn't about a superhuman analyst or a lucky break. It's about a detection and response workflow designed to eliminate friction at every step.

Automated enrichment eliminated manual lookups. When Sarah opened the notification, every IP already had AbuseIPDB reputation data, geolocation, ASN, ISP, and usage type information. Zero time spent copying IPs into external tools.

IP grouping prevented alert overload. 47 IPs generated a single notification, not 47 separate alerts. Sarah triaged one item, not a wall of duplicates.

AI investigation scaled the analysis. Manually investigating 10 IPs—pulling trace data, analyzing patterns, cross-referencing intelligence—would take hours. The AI completed it in 3 minutes with 92% confidence and provided evidence for every conclusion.

False positive management preserved detection fidelity. Instead of weakening the rule to stop catching benign password reset flows, Sarah created a targeted exclusion that eliminates the noise source without compromising detection coverage.

Integrated forensics enabled rapid impact assessment. The pivot from "we're being attacked" to "these 11 accounts were compromised" happened in a single natural language query. That immediate impact assessment is the difference between a contained incident and an ongoing breach.

Lessons for Your SOC Team

ShopStream's experience highlights several principles that apply broadly:

Tune your authentication alert rules aggressively. Credential stuffing targets login endpoints almost exclusively. Your most sensitive, most finely tuned alert rules should cover authentication paths with short detection intervals and low thresholds.

Don't ignore the 0.x% success rate. Credential stuffing typically has very low success rates—but even 0.3% across thousands of attempts means real account compromises. Always check for successful authentications from attacker IPs, not just the failures.

Use AI investigation for batch analysis. When an incident involves dozens of IPs, AI investigation scales your analysis capacity by an order of magnitude. Reserve manual deep-dive investigation for ambiguous cases the AI can't resolve with high confidence.

Create exclusions in real-time during incidents. When you identify a false positive pattern during triage, create the exclusion immediately. Future occurrences of the same pattern will be filtered out, reducing noise for every subsequent incident.

Document everything in comments. Sarah's comments on each IP and the notification create a complete investigation record. When ShopStream's security team reviews the incident next week, or when an auditor asks about it next quarter, every decision is documented and attributable.

The Bigger Picture: Credential Stuffing Defense in Depth

Detection is critical, but it's one layer of a comprehensive defense against credential stuffing. OWASP's credential stuffing prevention cheat sheet recommends a layered approach:

• Multi-factor authentication drastically reduces the impact of stolen credentials.
• Breached password detection (checking passwords against known breach databases) prevents users from setting compromised passwords in the first place.
• Device fingerprinting and behavioral analytics can identify automated tools even when they rotate IPs and User-Agents.
• IP reputation and rate limiting create friction for high-volume automated attacks.
• CAPTCHA challenges can break automated login flows, though they impact user experience.

SecureNow provides the detection and investigation layer—identifying credential stuffing campaigns in real-time, quantifying their impact, and enabling rapid containment. Combined with preventive controls like MFA and breached password detection, SOC teams can build a defense-in-depth posture that addresses credential stuffing across the entire attack lifecycle.

The 12-minute response time in this case study isn't aspirational—it's achievable with the right tools, the right workflow, and alert rules tuned to the threats that matter most. The credential stuffing problem isn't going away. The question is whether your SOC team can detect and contain it before real damage is done.

Frequently Asked Questions

What is a credential stuffing attack?

Credential stuffing is an attack where adversaries use stolen username/password pairs from data breaches to attempt logins on other services, exploiting password reuse across sites.

How is credential stuffing different from brute force?

Brute force tries random passwords against accounts, while credential stuffing uses known valid credentials from breaches. Credential stuffing has higher success rates and is harder to detect because the credentials are syntactically valid and don't follow predictable patterns.

Can SecureNow detect credential stuffing in real-time?

Yes, SecureNow's alert rules can detect 4xx authentication failure spikes within minutes, and AI investigation can confirm credential stuffing patterns by analyzing trace data and IP behavior.

How do you handle false positives in credential stuffing detection?

SecureNow's false positive management lets you exclude legitimate authentication failures (e.g., password reset flows) using path patterns, while keeping detection active for actual attack patterns.