Scraper bots are getting better at pretending to be Chrome (part 2)

This post is about scraper bots are getting better at pretending to be Chrome. It started, as most of our posts do, with an incident that did not go the way we expected.

Testing in production, safely

Every rule starts in log mode. We replay the previous seven days of traffic through it and show exactly which requests it would have affected before anyone can promote it to challenge or block.

This one feature has prevented more incidents than any detection we have ever shipped.

Latency budget

Our budget for the whole filtering pipeline is one millisecond at the 99th percentile. Anything that cannot be decided within that budget runs asynchronously and influences the next request from the same client, not the current one.

That constraint shapes everything: data structures, where state lives and which signals we are willing to compute inline.

What we changed

We moved the decision from a single threshold to a continuous score, added an explanation to every decision and made every rule testable against historical traffic before it goes live.

The result is fewer late-night pages for our analysts and — more importantly — fewer real users challenged by mistake.

What we got wrong

Our first version challenged too eagerly on mobile networks, where thousands of real users share a handful of carrier-grade NAT addresses. Reputation that is shared is reputation that is noisy.

We now weight fingerprint consistency and session behaviour far more heavily than IP reputation for traffic from known mobile carrier ranges.

The economics behind it

A booter service rents out a 100 Gbps attack for less than the price of a pizza. Defending against it with bandwidth alone is a race you lose. Defending against it by making each malicious request cost more than it earns is a race you win.

This is the entire idea behind never metering attack traffic: our costs scale with filtering, not with your invoice.

Measuring success

We track three numbers for every incident: time to mitigation, the share of attack traffic that reached the origin, and the share of legitimate traffic that was challenged. The first should be under a second, the second under 0.1% and the third under 1%.

Those numbers go into every incident report, and they are the same numbers we are measured against in our SLA.

{ "match": { "path": "/wp-login.php", "risk": ">= 45" }, "action": "challenge" }

We will follow up with the numbers from the next quarter.