August 14, 2025 13 min to read

How to Investigate WAF Login Attacks (A SOC Playbook)

A practical, real-world guide to triaging, detecting, and responding to login abuse

Introduction

If you’ve worked in a SOC long enough, you’ve seen this alert before.

A sudden spike in failed login attempts.
The same endpoint getting hammered.
WAF dashboards lighting up with 401s and 403s.

And then the familiar question shows up in chat:

“Is this a real attack, or just noise?”

WAF login attacks live in an uncomfortable gray area. Sometimes it’s a clear brute-force attempt. Sometimes it’s credential stuffing spread across dozens of IPs. And sometimes it’s a perfectly legitimate integration, monitoring tool, or mobile app behaving badly. From the outside, they can all look exactly the same.

What makes these alerts tricky isn’t the lack of data — it’s the lack of context.

In production environments, you rarely get a clean answer upfront. You get partial signals: IP addresses, response codes, user-agents, and a timeline that doesn’t immediately tell a story. Blocking too quickly can break real users. Waiting too long can expose accounts. And “just monitoring it” isn’t always a safe default.

Over time, I stopped treating WAF login alerts as something to react to and started treating them as something to investigate.

This post walks through how I approach WAF login attack investigations in the SOC — from initial triage and signal validation to separating real abuse from false positives and deciding when blocking is actually justified. This isn’t vendor-specific or theoretical. It’s the mental model I use when these alerts show up in a real production environment.

What a WAF Login Attack Actually Looks Like

One of the biggest mistakes teams make when investigating login attacks is expecting them to look obvious.

In reality, most WAF login alerts don’t come with a clear label that says “credential stuffing” or “brute force.” What you usually see instead is a collection of small signals that only start to make sense once you zoom out.

At a high level, WAF login attacks tend to share a few common characteristics:

• Repeated requests to the same authentication endpoint
• Elevated volumes of failed authentication responses (typically 401 or 403)
• A short time window with unusually high login activity
• Reuse of usernames, email addresses, or account identifiers

On their own, none of these are definitive. Together, they start to form a pattern.

Brute Force vs Credential Stuffing (At a Glance)

A traditional brute-force attack usually looks noisy and concentrated:

• One or a few IPs
• High request volume
• Rapid retries against the same account

Credential stuffing, on the other hand, often looks quieter but broader:

• Many IP addresses
• Fewer attempts per IP
• Repeated use of the same usernames across different sources

From a WAF perspective, both can initially trigger the same alert. The difference only becomes clear once you start correlating who, how often, and from where.

Why These Alerts Are So Easy to Misjudge

Here’s where things get tricky.

Legitimate traffic can look surprisingly similar:

• Mobile apps retrying failed logins
• Monitoring tools validating credentials
• Users mistyping passwords repeatedly
• Shared IPs from ISPs or corporate networks

All of these can generate bursts of failed logins that look suspicious in isolation.

That’s why treating every WAF login alert as an automatic block is risky. Without understanding the shape of the traffic, it’s easy to disrupt real users or business-critical integrations.

The goal at this stage isn’t to decide on a response yet.
It’s to recognize whether you’re looking at:

• A focused attack
• A distributed abuse pattern
• Or normal behavior amplified by timing or volume

Once you understand what the traffic actually looks like, triage becomes much more deliberate — and much less reactive.

The First 5 Minutes: How I Triage WAF Login Alerts

When a WAF login alert comes in, the goal of the first few minutes isn’t to prove intent or assign blame. It’s to answer a much simpler question:

Does this deserve an immediate response, or more observation?

I’ve found that the fastest way to get there is to focus on a small set of signals early, instead of diving straight into deep analysis.

Step 1: Confirm the Authentication Surface

Before looking at volume or IPs, I always confirm what is actually being targeted.

• Is this a real login endpoint?
• Is it a primary authentication path or a legacy route?
• Is it a human-facing page or an API endpoint?

Illustrative example (synthetic/Fake):

POST /api/auth/login
POST /user/login
POST /v1/session

If traffic is hitting multiple unrelated endpoints, it often points to scanning or generic automation rather than a focused login attack.

Step 2: Look at Response Code Distribution

Response codes are one of the fastest ways to understand what’s really happening during a suspected login attack. Before digging into IPs or usernames, I like to step back and look at how the application is responding overall.

The first questions I ask are simple:

• Are most requests failing?
• Are there successful logins mixed in?
• Did this pattern change suddenly?

WAF login attacks often create a very uneven response distribution. A high volume of failures concentrated in a short time window usually means someone (or something) is guessing credentials rather than normal users mistyping passwords.

Illustrative example (synthetic/Fake):

A pattern like this immediately raises suspicion. A small number of successful responses mixed in with a large volume of failures can indicate credential stuffing, where a subset of reused credentials happens to be valid.

It’s also important to consider how these responses are generated:

• 401 responses often suggest invalid credentials
• 403 responses may indicate account lockouts, rate limits, or WAF enforcement
• 200 responses during an attack window can confirm real account access

At this stage, I’m not trying to label the activity yet. I’m simply using response codes to determine whether the traffic pattern is normal user behavior or something that deserves deeper investigation.

Once the response distribution looks abnormal, it becomes much easier to justify spending time on IP behavior, username reuse, and eventual response actions.

Step 3: Understand the IP Shape (Not Just the IP Count)

One of the most common traps in WAF login attack investigations is focusing too much on raw IP counts.

Seeing “hundreds of IPs” can feel alarming at first, but the number alone doesn’t tell you whether you’re dealing with real abuse or normal traffic amplified by scale. What matters more is the shape of the IP activity.

When I look at IP behavior, I focus on:

• Attempts per IP
• Timing and burst patterns
• Reuse of IPs across different accounts

Illustrative example (synthetic/Fake):

Low-volume attempts spread across many IPs in a short window often point toward distributed automation rather than a single brute-force source.

By contrast, a traditional brute-force pattern usually looks more concentrated:

Illustrative example (synthetic/Fake):

Both patterns are suspicious — just for different reasons.

The key takeaway here is that IP diversity does not automatically mean safety, and IP concentration does not automatically mean danger. Each pattern needs to be interpreted in context.

At this stage of the investigation, I’m still avoiding response decisions. I’m simply building confidence that the traffic I’m looking at behaves more like automation than real users.

Once the IP shape starts to make sense, the next signal becomes even more telling: who is being targeted.

Step 4: Track Username and Account Reuse

Once IP behavior starts to point toward automation, the next signal I look at is who is being targeted.

Username and account reuse is one of the strongest indicators of login abuse. Real users might mistype a password once or twice. Automated attacks tend to test the same credentials repeatedly, often across many IP addresses.

At this stage, I’m looking for patterns like:

• The same usernames appearing across multiple source IPs
• Repeated attempts against a small set of accounts
• Login attempts targeting accounts that haven’t been active recently

Illustrative example (synthetic/Fake):

When the same accounts show up across many different IPs in a short time window, it’s rarely accidental. This pattern is a common hallmark of credential stuffing, where attackers test known username-password pairs at scale.

It’s also worth paying attention to which accounts are being targeted. High-value or administrative accounts deserve more scrutiny than newly created or inactive ones. Targeting patterns often reveal attacker intent long before any successful login occurs.

That said, username reuse alone still isn’t enough to justify a response. Some legitimate systems — mobile apps, single sign-on flows, or retry-heavy clients — can unintentionally generate similar behavior.

At this point in the investigation, I’m not asking “Should I block this?” yet. I’m asking a more focused question:

Does this traffic show intent, or just persistence?

The next step is validating whether this activity is being driven by automation — or by something legitimate behaving poorly.

Step 5: Sanity-Check the User-Agent

User-agent strings are easy to overvalue and just as easy to ignore. On their own, they rarely prove anything. But when used as a supporting signal, they can help confirm whether you’re dealing with automation or legitimate traffic.

At this stage, I’m not trying to fingerprint an attacker. I’m simply asking a few practical questions:

• Is the user-agent consistent across many requests?
• Does it match what I’d expect for this application?
• Does it suddenly change during the alert window?

Illustrative example (synthetic):

User-Agent: Mozilla/5.0
User-Agent: Mozilla/5.0
User-Agent: Mozilla/5.0

Highly repetitive or overly generic user-agent strings across many requests can reinforce the case for automation, especially when they appear alongside distributed IP activity and repeated username targeting.

That said, this signal needs to be handled carefully. Mobile applications, embedded browsers, and third-party integrations often rely on static or simplified user-agent strings by design. In those cases, repetition alone doesn’t indicate malicious intent.

Because of this, I treat user-agent analysis as a supporting signal, not a decision trigger. It helps confirm what the other indicators are already suggesting, but it’s rarely strong enough on its own to justify blocking or enforcement.

Common False Positives That Look Like Login Attacks

Some of the most convincing-looking WAF login alerts turn out to be completely legitimate once enough context is added. This is where a lot of SOC time quietly disappears.

Over time, I’ve noticed a few recurring sources of false positives that consistently resemble login abuse at first glance.

Monitoring and Health-Check Tools

Any monitoring systems often validate authentication flows on a schedule. When those checks fail — due to backend latency, credential changes, or temporary outages — they can generate bursts of failed login attempts.

These usually show up as:

• Repeated requests to the same login endpoint
• Consistent user-agent strings
• Fixed or well-defined source IP ranges

Without knowing these tools exist, it’s easy to misinterpret them as automated attacks.

Mobile Apps and Embedded Clients

Mobile applications tend to retry aggressively when authentication fails. Poor network conditions, expired tokens, or cached credentials can cause short spikes in failed logins that look suspicious.

Common characteristics include:

• Generic or static user-agent strings
• Rapid retries in a short time window
• Login activity clustered around app launch or reconnect events

Once the underlying issue is resolved, this behavior usually disappears.

Shared IP Environments

Traffic originating from mobile carriers, corporate networks, or cloud-based proxies can amplify normal user behavior.

In these cases:

• Many users appear to come from the same IP
• Failed logins increase during peak usage hours
• Username reuse reflects real users, not automation

This is why IP concentration alone is rarely enough to justify blocking.

Legitimate Integrations Behaving Poorly

Third-party services that authenticate on behalf of users can quietly generate login noise when something breaks.

When credentials rotate or permissions change, these integrations may:

• Retry authentication repeatedly
• Generate sustained login failures
• Continue attempting access long after users stop interacting

These alerts often resolve once the integration is identified and corrected.

False positives aren’t a failure of detection. They’re a reminder that login behavior exists at the intersection of humans, automation, and fragile systems.

Block or Monitor? How I Decide During a WAF Login Attack

By the time I reach this point in an investigation, I’m no longer asking whether the activity is interesting. I’m asking whether it’s actionable.

Instead of relying on strict thresholds, I focus on intent, confidence, and potential impact.

When I Block

I’m comfortable blocking when:

• The traffic is clearly automated
• The same accounts are being targeted repeatedly
• There is strong evidence of credential reuse or abuse
• The likelihood of disrupting legitimate users is low

Blocking is a strong response, and I want to be confident that it solves the problem without creating new ones.

When I Monitor

Sometimes the correct decision is to take no immediate enforcement action.

I choose to monitor when:

• Signals are weak or conflicting
• Activity aligns with known false-positive patterns
• The business impact of blocking would be high

Monitoring doesn’t mean ignoring the alert. It means documenting what you’re seeing, watching for escalation, and being ready to act if the pattern changes.

Clear decision logic builds trust — not just with stakeholders, but within the SOC itself.

Closing Thoughts

WAF login attacks aren’t difficult because they’re sophisticated. They’re difficult because legitimate behavior and abuse often look the same at first glance.

The difference between reacting and investigating is context. Taking a few minutes to understand response patterns, IP behavior, account targeting, and known false positives makes every decision downstream more deliberate.

This playbook isn’t about blocking everything that looks suspicious. It’s about building confidence — confidence that when you act, you’re solving the right problem without disrupting real users.

Approached this way, WAF login alerts stop being noisy interruptions and become manageable, repeatable investigations.

This is why effective WAF login attack detection depends less on single indicators and more on how multiple weak signals line up over time.

Once you build that confidence loop, WAF login attacks stop being stressful alerts and start becoming familiar patterns.