Names matter. People often assume that a list of names—whether it’s a roster of alleged donors, a group of "verified" beta testers, or a leaked database of "cheating spouses"—is inherently factual. It isn't. A fake list of names is one of the oldest, simplest, and most effective tools in the social engineer's toolkit. Honestly, it’s kinda terrifying how easily our brains switch off the "skeptic" filter the moment we see a long, alphabetized column of humans who don't actually exist.
You've probably seen these. Maybe it was a viral post claiming to list every politician who took money from a specific lobbyist. Or perhaps it was a "leak" from a high-profile hack that turned out to be a complete fabrication. We want to believe the list. We want the "receipts." But in the age of generative AI and massive database scrapers, generating ten thousand realistic-sounding identities takes about three seconds.
How a fake list of names actually works in the wild
Scammers don't just pull names out of thin air anymore. They use tools. To make a fake list of names look legitimate, they often rely on "Markov chains" or modern Large Language Models to ensure the frequency of surnames matches local demographics. If you're targeting a specific region, like the American Midwest, and your list is 40% "Zambretti," people are going to notice something is off. But if it’s a blend of Smith, Johnson, Garcia, and Miller? You’ve got them.
The psychological hook is called Social Proof. If I tell you a product is good, you might doubt me. If I show you a list of 500 people who "bought" it, your brain starts to rationalize that 500 people can't all be wrong. This is the bedrock of "ghost" testimonials and fraudulent petition padding.
Back in 2017, the FCC’s public comment section on Net Neutrality was flooded with millions of comments. Researchers like Brian Krebs and data scientists later found that a massive portion of these came from a fake list of names—real people’s identities were used without their consent, alongside completely fabricated personas. It was a digital "astroturfing" campaign designed to create the illusion of a grassroots movement.
The "Generated Identity" problem
Technology has made this trivial. Websites like This Person Does Not Exist use Generative Adversarial Networks (GANs) to create hyper-realistic faces. Pair that face with a name from a random generator, and you have a "human" who can review a product, sign a political manifesto, or bait someone on a dating app.
It's not just about one-off scams. We see this in corporate environments too. Sometimes, desperate startups will populate their "Our Team" page with a fake list of names and stock photos to look more established than they actually are. It’s a "fake it 'til you make it" strategy that frequently ends in a PR nightmare or a fraud lawsuit. Remember the case of Frank, the fintech startup acquired by JPMorgan Chase? The bank later alleged that the founder created a list of millions of fake customers to inflate the company's value. That wasn't just a list; it was a $175 million lie.
Spotting the fabrications before you share
How do you know? Sometimes you don't. But there are usually glitches in the matrix.
If you're looking at a list of names, look for patterns. Humans are bad at being random. A fake list of names often has weird clusters. Maybe everyone has a middle initial. Maybe the last names are suspiciously alphabetical for a "random" data leak. Often, if you copy and paste five or six names from the middle of the list into a search engine, they’ll all lead back to the same source—or nowhere at all.
- Check for the "Middle-Heavy" Pattern: Scammers often focus on the beginning and end of a list because they know people skim. The middle is where the filler goes.
- The LinkedIn Test: If a list claims to be "Industry Experts," pick three names and try to find their professional history. If a person "leading a multi-million dollar project" has zero digital footprint, they’re a ghost.
- Data Consistency: Does the list include addresses? Zip codes? Often, the generators used to create these lists will hallucinate zip codes that don't match the state listed.
The danger of the "Revenge" list
The most malicious use of a fake list of names is the "doxxing" or "shaming" list. You see these pop up during high-tension social events. Someone posts a list claiming, "These are the people who belong to [Hate Group X]" or "These are the people who voted for [Controversial Policy Y]."
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The problem? These lists are often 90% fake or, worse, 90% innocent people mixed with a few targets. Once a list like that goes viral on X (formerly Twitter) or TikTok, the damage is done. People on the list face harassment, job loss, and threats for a "crime" they never committed, simply because an algorithm or a malicious actor put their name on a text file.
This happened during the various iterations of the "BlueCheck" lists or various "Scab" lists during labor strikes. When the process for vetting names is "I saw it on the internet," everyone loses. Verification is hard. Outrage is easy.
Why Google Discover loves—and hates—these lists
Google’s algorithms are designed to prioritize "EEAT" (Experience, Expertise, Authoritativeness, and Trustworthiness). However, a "leaked" list of names often triggers "Query Deserves Freshness" (QDF). This means Google sees a spike in searches and wants to show the most recent content.
This creates a window where a fake list of names can rank highly before the fact-checkers catch up. If you see a list of names trending, look for "Reporting by..." or "Verified by..." If the only source is a PDF on a random WordPress site or a screenshot on a forum, proceed with extreme caution.
Actionable steps for the skeptical reader
Don't be a vector for misinformation. If you encounter a list that seems too good (or too scandalous) to be true, do this:
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- Run a "Sampling" Search: Take three random names from the list. Search them in quotes + a keyword from the list (e.g., "John Doe" + "Acme Corp Leak"). If nothing comes up, the list is likely bunk.
- Inspect the Metadata: If it’s a PDF or an Excel file, right-click and check "Properties." Often, you’ll find the list was created by a single person ten minutes before it was "leaked," rather than being a corporate export.
- Cross-Reference with Known Breaches: Use sites like Have I Been Pwned to see if the data matches actual historical leaks. Many "new" fake lists are just old, recycled data from 2012 with a new header.
- Reverse Image Search: If the list has photos, use Google Lens or TinEye. If the "CEO" of the list is actually a model from a dental insurance ad, close the tab.
Basically, treat every list of names as a claim, not a fact. Until it’s been verified by a reputable third party or you’ve done the legwork yourself, it’s just a collection of strings and characters. In a world where identity is currency, don't let someone spend yours—or someone else's—on a lie.