Credential stuffing is the most boring attack in the world, which is exactly why it works.
There's no exploit, no injection, no clever bypass.
Someone bought a list of a few million email/password pairs from an unrelated breach, and now a botnet is quietly replaying them against your login form at a few requests per second per IP, spread across ten thousand IPs.
Every single request is a valid, well-formed login attempt.
That's the whole problem.
I want to talk about why the usual defenses miss this, and a cheap filter that actually bites.
Why lockouts and rate limits don't catch it The instinct is to reach for per-account lockout ("5 failed attempts, freeze the account") and per-IP rate limiting.
Both are fine, both are worth having, and neither one sees a modern stuffing run.
Per-account lockout assumes the attacker hammers one account.
Stuffing does the opposite — it tries one password against a million different accounts.
Each account sees a single failed attempt.
Your lockout counter never moves.
Worse, if the attacker knows you lock accounts, they'll deliberately trip it to lock out your real users — you've built them a denial-of-service button.
Per-IP rate limiting assumes the volume comes from one place.
It doesn't.
The list is fanned out across a residential proxy network, so each individual IP makes three or four requests an hour and looks exactly like a human on hotel wifi.
Your threshold is either high enough to let them through or low enough to block real people on shared NATs.
The reason both fail is the same: they're counting behavior over time on an axis (account, IP) the attacker deliberately spreads thin.
By the time you have enough events to be confident, the credentials that were going to work already worked.
Move the decision one layer up — before the password check Here's the reframe.
You don't need to know whether this login will succeed.
You need to know whether this request is worth spending a bcrypt verification on at all.
A password check is expensive on purpose — that's the point of a slow hash.
Credential stuffing turns that against you: every junk attempt costs you real CPU.
So do the cheap check first.
Before you touch the password, score the source: the IP and the email address.
Botnets have a texture that individual requests give away, even when the credentials are real.
The IP is far more likely to be a datacenter range, a hosting provider, or a known proxy/VPN exit than a residential connection.
Real humans logging in from an AWS IP is rare; a login wave from one is not.
The email, in aggregate, skews toward the reused, the disposable, and the previously-flagged.
A single account per real user; a botnet churns through addresses that already show up on abuse lists.
Neither signal is proof on its own.
Together, cheaply, they let you tier your response.
The filter, in code Score before the hash.
Here's the shape in Express: The call returns in under 20ms, so it fits in the login path without users feeling it.
I used IPASIS here because a single request returns both IP reputation and email risk, which keeps the pre-auth path to one round trip — and it hands you a of / / directly, so the tiering falls out of the response instead of you hand-tuning score cutoffs.
There's a free tier (no credit card) to wire this up against your real login traffic, and the response fields are in the docs.
Any provider that gives you both signals works; the architecture is the point, not the vendor.
Three things that will bite you if you skip them Fail open, never closed.
If the scoring call times out or errors, let the login proceed to the normal password check.
A fraud filter that takes down your login when a third party has a bad day is a worse outage than the attack.
Note the — absence of a score means proceed, not block.
Don't return a clean failure to the bad tier.
If you respond to a low-score request with a crisp , you've built the attacker an oracle: they learn which of their IPs your filter flags and rotate