Scam stories on YouTube
107 searches, 205 transcripts and 3,727 comments. 394 real "this happened to me" stories became 199 scam patterns, every quote checked against its source.
Research
We study real scams, not scary words.
Every stamp starts with a real scam that hurt a real person. Here is where we find them, and how one becomes something Danger Stamp can catch.
Where the scams come from
Told by the people who got hit and the people who fight them.
Scam-baiter channels
Jim Browning, Scammer Payback, Trilogy Media, Atomic Shrimp and others. 790 passages about the web side of scams and 219 scammer phrases.
Social media comments
YouTube, TikTok and Instagram comments under finance and crypto videos: bot threads, fake mentors, recovery scams.
Facebook Ad Library
Ads on scam themes: deepfake investing, miracle cures, fake closing-down sales, fake giveaways.
Victims asking "is this a scam?" in r/Scams, r/isthisascam, r/phishing and more. The 533 most-upvoted r/Scams posts and 2,916 comments were read against every rulebook (195 scam patterns), and 147 emails and messages people pasted or screenshotted became real tests for email and chat.
Live phishing feeds
OpenPhish, urlscan.io saved scans and the ScamSniffer blocklist: fresh phishing, payment, tech-support and wallet-drainer pages.
Real phishing and real brand emails
499 phishing emails from a public research archive, paired with 358 genuine emails from brands (password resets, receipts, loyalty and support) with their real links, so a check has to catch one and leave the other alone.
Blind real-world rounds
17 rounds of fresh pages, ads, comments and threads nobody had seen, 22,404 of them labelled by two independent labellers. Scored once before tuning; every result is on the testing page.
Real websites
Every scam we test is paired with the real pages it copies, sampled from the most visited sites.
Bank and credit union registers
Every US bank (FDIC) and credit union (NCUA) with its real website, plus fingerprints of 7,600 brands' logos.
Fraud agencies
Canadian Anti-Fraud Centre, FBI, BBB Scam Tracker, the FTC's 2026 consumer alerts and Google's scam advisories: checked against our list, which led to new rulebooks (QR code emails, fake sign-in windows, calendar scams, fake inheritance letters and more).
Posts for the AI slop tag
Real LinkedIn, X and Reddit posts, 1,200 of them labelled blind by two labellers.
How a new scam becomes a stamp
Five steps, the same every time.
- 1
Spot it
A new scam shows up in victims' stories, a scam-baiter's video, a phishing feed or a report sent to us.
- 2
Collect real examples
We pull real copies of it, plus the real pages and messages it imitates, so we can tell the two apart.
- 3
Write a rulebook
Plain yes-or-no questions an AI can answer, and simple rules in code. Counting and dates stay in code.
- 4
Test it both ways
It has to catch the scam and leave the real thing alone. Fixes must cover the whole family, never one example.
- 5
Prove it blind
A fresh round of real scams nobody has seen is scored once. Its misses become the next round's work.
Doing it safely
We never run a scam on our own machines.
- Phishing pages are read as plain text, or as a security scanner already recorded them.
- Scam files are never downloaded or opened.
- Victims' stories are used to learn the scam, not to identify anyone.
Seen a scam we don't catch?
Every report becomes a test case, and every missed scam family becomes a rulebook.
Report a scam