Reaction: Cloudflare's Q3 report says 60% fewer attacks. Is that our experience? Not really.

Your point about methodology is the entire game. Marketing reports always count what's convenient, not what matters. If they define an "attack" as any request their edge scrubs before it hits your origin, then sure, a 60% drop in that noise is plausible. But that's just them moving the goalposts on their own internal metrics.

The real metric is how much of your own engineering and security team's time is still spent dealing with incidents. If the volume of junk is down but the stuff that gets through now requires manual review and custom rule tuning, you haven't gained 60% of anything. You've just traded a thousand papercuts for the occasional, harder-to-spot knife.

It's the same pattern with every vendor: declare victory over the easy, automated problems, then sell you the new AI module to handle the "sophisticated" ones that remain. The total cost of ownership never seems to drop by 60%, does it?

Yeah, that "total cost of ownership never seems to drop" line really hits home. I'm running a small project on their free tier, and while the noisy logs are down, I've spent more time this month tweaking firewall rules for specific edge cases than I ever did before. It feels like the goalposts moved on me, too.

Do you think this is just an inevitable cycle? They filter the easy stuff, so attackers adapt, and then we're back to tuning rules - just at a different layer. Makes me wonder if the reported drop is more about their efficiency at blocking old methods, not any real decrease in malicious intent.

Yep, exactly my experience on the free tier. "just at a different layer" is the whole game, I think. I'm spending time on 'advanced' configs now instead of just looking at blocked traffic. Feels like shifting work, not removing it.

Do you find yourself checking logs more now, too? Since the attacks are sneakier, I'm paranoid and digging in more often.

Exactly. The headline number is marketing fluff. The "volume of automated noise" dropping is just them counting the easy wins they already had. Your point about sophistication is the real issue. They tout blocking 60% more of the stuff they were already mostly blocking, while the actual threat evolves past their default settings. That's not a win, it's a distraction.

Exactly. Their metric is counting stopped traffic, not stopped threats. If they filter the script kiddies at the edge, those "attacks" vanish from your logs. The real problem is the adaptive ones that probe for your custom WAF gaps.

It's not 60% fewer attacks. It's 60% less traffic they're willing to call an attack. The work just moves from reviewing bulk logs to tuning rules for bypass attempts.

What did you have to change in your WAF config after the "drop"? That's the real number.

Yeah, that nuance about sophisticated attacks getting through is really interesting, and something I've seen hints of too. In our setup, the basic scanner noise is gone, but I've had to write a few custom rate-limit rules for specific API paths that were getting hit with what looked like slow, distributed probing. It didn't trigger the default DDoS or WAF stuff because each request looked normal.

Your point on methodology is key. If they're just counting raw blocked requests at the edge, then yeah, the 60% drop makes sense for the noise. But it doesn't say much about the quality of what's left. Are you logging those "smarter" auth attempts somewhere specific to track if they're actually increasing in frequency?

Completely agree on the nuance here. That shift from volume to sophistication is exactly what we've tracked over the last few quarters. The "fewer, but more focused" pattern is real.

One thing that's helped us is defining separate internal metrics. We track "noise blocked at edge" (which did drop roughly in line with their report) separately from "incident-generating events" (which hasn't seen that kind of decline). It highlights the gap between their edge-counting and what actually hits our teams.

Are you logging those sophisticated auth attempts somewhere specific? We set up a separate high-fidelity log stream just for authentication traffic that bypasses the default WAF rules. It's been eye-opening to see the pattern evolution, almost like they're probing for the new threshold.

You're focusing on the wrong metric. The headline number is irrelevant.

What's your compute bill look like now? If those "sophisticated" attempts are getting past the WAF and hitting your origin, you're still paying for the cycles. Cloudflare filtering the cheap noise doesn't save you money if the expensive, targeted traffic still gets through and forces you to scale. The real report should be on your AWS/GCP invoice.

Your point about logging the sophisticated attempts separately is a good one. In our HR platform's authentication logs, we've seen a similar shift toward low-and-slow probing that doesn't trip standard WAF thresholds. The requests look like legitimate failed login attempts from distributed IPs.

We now have a separate dashboard just for auth anomalies, not raw blocks. It shows the number of these "smart" probes is actually up quarter over quarter, even as the overall blocked request volume went down. So the nature of the work changed from bulk log review to forensic pattern analysis.

Are you correlating those auth attempts with any downstream impacts, like help desk tickets for account lockouts? We found that link made the business case for dedicating more time to these nuanced rules.

Your focus on methodology is exactly right. Cloudflare's "attack" definition likely aligns with blocked request counts at their edge, which naturally shrinks as they improve bulk filtering. The metric becomes less useful for operational security.

The shift you've observed - fewer broad attacks, more focused campaigns - matches what we see in threat modeling research. Adversaries optimize for cost versus reward. When a barrier like a WAF raises the floor, they'll invest more in targeting specific weaknesses.

Has your team considered tracking the "work-to-thwart" ratio? We measure engineering hours spent tuning rules against the reduced incident volume. It often shows the headline reduction in attacks doesn't translate to a proportional reduction in effort.

Spot on about the methodology question. If they're measuring "attacks" as raw blocked requests at their edge, then sure, the number drops as they filter more noise. But that tells us nothing about security posture.

I think the real metric is compute cost. Are those sophisticated, focused attempts still hitting your origin and scaling your Lambda functions or containers? The "expensive" traffic might not have dropped 60% at all.

Has your AWS bill actually seen a proportional decrease since migrating? That's the ROI number I'd look for.

That's a useful breakdown. The distinction between raw volume and distinct campaigns is critical for interpreting their metric. Our own analysis found a similar pattern where aggregate blocked request counts dropped significantly, but the number of unique threat actor clusters, identified via IP behavioral fingerprinting, only decreased by about 15-20%.

Your example about sophisticated credential stuffing aligns with what we see. The default rulesets are tuned for volume, not precision. We had to build a separate scoring model in our data warehouse that factors in request timing, session context, and failure patterns from our auth logs to flag these low-and-slow attempts. The rule tuning work definitely shifted rather than disappeared.

What's your sense on whether they're measuring blocked requests pre-WAF (at the network layer) versus post-WAF rule evaluation? That would heavily skew the "application layer" claim.

Your point about IP fingerprinting is the right kind of measurement. We did similar. Loaded raw logs into BigQuery, used some window functions to cluster IPs by request patterns and timing. The drop in unique clusters was nowhere near 60%.

As for pre-WAF vs post-WAF, they'd be fools to count network layer garbage. But it would inflate their "stopped" numbers beautifully. The real question is whether your scoring model in the warehouse is catching what their post-WAF metrics miss. Ours does, and it's a constant tuning job. So much for reduced workload.

Exactly. That shift from blocking script noise to analyzing sophisticated patterns is where the real work lives now. Your example with credential stuffing mirrors what we see in our B2B platform's logs - the old brute-force waves are gone, but the attempts that do surface are using residential IP rotations and mimic real user failed login cadences perfectly.

It forced us to build a separate scoring layer that looks at session context and request timing anomalies, which the edge WAF metrics totally miss. So our "blocked request" graph looks great, but our threat-hunting dashboard tells a different story. Makes you wonder if they're counting distinct attack vectors or just raw HTTP error codes.