Guest Post: Spamtraps Aren't a Hygiene Problem -- They’re a Data Integrity Problem

Koli-Lõks provides visibility into bulk email traffic, tracking sending IPs and domains to show how email is sent and on behalf of whom, with a focus on ESPs, ISPs, and other large email sending platforms. The email traffic data they collect is used, for among other purposes, to help inform spam filterers, blocklist operators and other reputation services. Think of it as spam intelligence, and it’s all based on mail to spamtrap addresses.

But what are spamtrap addresses? I saw Pekka Jalonen (from Koli-Lõks) write about this recently, and I asked him if he would be willing to put together a guest post talking about spamtraps, how he (and Koli-Lõks) define them. He said yes, and shares the following with us all. Take it away, Pekka!

Introduction

Most deliverability investigations start the same way. Your dashboards don’t show an emergency: complaint rates look stable, bounce rates seem under control, and nothing “big” has changed in creative or cadence. And yet, mailbox providers begin to apply pressure. Filtering tightens. Throttling increases. Inbox placement slips in ways that feel disproportionate to what you can see in the usual metrics.

When that happens, the mistake is to keep debating subject lines, segmentation micro-tweaks, or send-time folklore. The more likely culprit sits upstream: the integrity of your address intake and the durability of your suppression logic. Spamtraps are the cleanest way providers test whether you are mailing addresses you cannot defend. Many traps accept mail, so they do not announce themselves as obvious bounces. They also do not complain. They simply accumulate evidence, and your reputation pays the bill later.

From the combined perspective of a senior data analyst, a senior spamtrap specialist, and a senior deliverability lead, the most useful framing is blunt: spamtraps aren’t a hygiene problem—they’re a data integrity problem. Honeypots and pristine traps typically point to an acquisition boundary you do not fully control, while recycled traps expose lifecycle and suppression failures that resurface at system boundaries. Typo traps, meanwhile, reveal where input quality is leaking. The goal of this piece is not to “hunt trap addresses,” but to map each trap signal to the control failure that created it—and to show how provenance, server-side intake hardening, and suppression persistence reduce risk in a way you can measure and defend.

A quick look at honeypot spamtraps.

Honeypot spamtraps are an intake integrity issue, not a lifecycle issue. You can have solid bounce handling and still get hit. If you mail a honeypot, your pipeline accepted an address that a real person did not submit.

A honeypot address lives where humans do not go. Hidden in page markup. Buried in a public asset. Tucked into a form pattern a human will never touch. Scrapers collect it. Bots submit it. Automation picks it up because it is designed to pick up everything.

The reason honeypots hurt is that they often do not present like a bounce problem. Many honeypots accept mail. You see delivered, not 5.1.1. The damage shows up as reputation pressure. Filtering tightens. Throttling increases. Inbox placement slips without a matching complaint spike. Traps do not complain. They just accumulate evidence.

When honeypot hits show up, it is rarely mysterious. A partner source that cannot produce a defensible consent trail. Co-reg that optimizes for volume. A form endpoint that can be posted to directly. Validation that exists only in the browser. A CAPTCHA that is easy to bypass or not enforced server-side. Enrichment or append processes that “find” emails instead of capturing them from the user. And it usually does not look like one big incident. It shows up as a small cluster of impossible addresses tied to one lineage, then a sudden shift in deliverability.

If you want one practical starting point, start with provenance. Source identifier. Capture timestamp. IP. User agent. Referrer. Consent text version. If you cannot reconstruct that chain, you cannot defend that address.

Quarantine the source. Fix intake at the server. Make sure suppressions survive every system boundary. While you clean, send only to recent engagers and stop feeding reputation with unproven data.

Recycled traps punish lifecycle discipline. Honeypots punish acquisition discipline.

A quick look at pristine spamtraps.

Pristine spamtraps are not a hygiene problem. They’re an intake integrity problem.

A pristine trap is an address that was never a real mailbox for a real subscriber. It doesn’t churn. It doesn’t go dormant. It exists to measure whether you’re mailing people who never asked to hear from you.

That’s why pristine exposure is so damaging. You can’t explain it with lifecycle. And you often won’t see it as a bounce. Many pristine traps accept mail cleanly while inbox placement erodes elsewhere.

When I see pristine exposure, I don’t start by hunting for trap addresses. That’s a dead end. I assume we lost control of the boundary where addresses enter the system.

The sources are usually consistent. Purchased or rented lists dressed up as “prospecting.” Lead gen and co-reg flows where consent is vague or outsourced. Partner feeds with no auditable chain of custody. Scraped addresses and bot-filled forms. Imports where provenance is basically “someone sent a CSV.”

What it looks like in data is rarely dramatic. It’s a quiet pattern. New cohorts with normal delivery but flat engagement. Provider-level engagement shifts without a content change. Reputation pressure that doesn’t reconcile with your complaint story.

The fix is strict and measurable. Instrument intake so every address is explainable. Source and timestamp. Form ID. Consent text version. A DOI event. If you can’t audit the path, treat it as untrusted.

One policy prevents most pristine pain.

No lineage, no send.

A quick look at recycled spamtraps.

Recycled spamtraps are a lifecycle issue, not an acquisition issue. You can do everything “right” when collecting addresses and still get burned later.

A recycled trap starts as a real mailbox. It delivered at some point. Then it’s retired and starts bouncing as “no such user”, which in the real world usually shows up as 550 5.1.1. The important part is the window in between.

The Messaging, Malware, Mobile Anti-Abuse Working Group talks about a long conditioning period before an address is repurposed as a trap. Think 12 consecutive months of “no such user”. That’s a lot of runway for any halfway disciplined bounce process to retire the address permanently.

When recycled hits show up, it’s rarely mysterious. Old segments get switched back on. Suppressions get lost between systems. An import brings back contacts you already thought were dead. Someone assumes “it used to deliver” means it’s fine today.

And it usually doesn’t look like one big incident. It creeps in as a slow build of user unknown and hard bounces concentrated in older contacts.

Treat 5.1.1 as permanent, and make sure suppressions survive every system boundary. Do that consistently and recycled risk drops fast. Miss one edge case and it keeps resurfacing.

A quick look at typo spamtraps.

Typo traps are one of the clearest signals that your list intake is leaking bad addresses.

When inbox placement slips and nothing obvious has changed, I start with the input data.

Typo trap hits usually come from the same places. Forms that allow common domain misspellings. Confirmation that is missing or easy to bypass. Imports that skip basic checks. Manual entry that never gets a second pass.

Handwritten addresses sit in the same bucket. Errors happen when someone writes it down, then again when someone transcribes it.

These issues rarely show up as one big incident. They show up as a steady drip: domain failures and user unknown on otherwise normal domains.

Inbox trust is built at intake: make every address explainable, or don’t send.