I follow a crypto blogger, but I see that almost all of his posts are about how successful he is. He flies business class, buys his wife gifts, and dines at expensive restaurants. But if I wanted to look at your photos, I’d follow you on Instagram, wouldn’t I?

Your profile bio says you’ll teach me how to trade crypto. I followed you to learn something new, but after scrolling through a month’s worth of your posts, I only saw one piece of advice from an AI. How exactly is looking at all your photos going to help me? How will that convince me to buy something from you? Why should I trust you?

Photos of your success don’t explain where that success came from. People could have signed up through your referral links, lost money following your advice, and you would have earned a commission on their losses.

Right, you’ve caught the attention of people who want the same kind of successful life. But how will you get the attention of millionaires? They live much more successful lives than you do. You won’t attract them with a picture-perfect life.

At first, you get used to a single number, confidently give your answer, and then realize you’ve already gotten it wrong by a year. The years fly by so quickly that it’s sometimes hard to remember. You have to calculate the difference between the current year and the year you were born.

That’s why I’ve chosen a different way to answer. I pick a single number and stick with it for the next five years. Or until I’m mentally okay with that number. For example, if I’m between 22 and 28, I say 25.

People probably don’t care about your actual date of birth. If you want to feel younger, say 30, even if you’re 35. Want to seem more mature? Say 25, even if you’re 20.

As you get older, you’ll hear this question much less often than when you were younger. When you’re 17 and someone else is 20, you think they’re smarter and more experienced than you. A difference of a few years seems significant. But when you’re 40 and interacting with someone around the same age, it doesn’t matter to you whether they’re 35 or 45.

And when you have kids, people will often ask you how old your child is. And that’s when you’ll get even more mixed up with the answer.

This is the story of how I found 10,000 repositories on GitHub that distribute Trojan malware. They are all from different contributors, have different names, and are not forks of other repositories. But they share a common pattern, which is what allowed me to write a script to find such repositories.

Introduction

I have a project on GitHub, and I wanted to check whether search engines had indexed it. I typed the project name into Google, and my repository appeared in the results. I entered the same query into Bing, and someone else’s repository appeared in the results, with the exact same name and description. It was a copy of my repository with all the commits, and I was listed as a contributor. But an hour ago, another commit was pushed with a change to the readme. A link to a zip archive has been added to it.

I was choosing appropriate tags for another one of my projects on GitHub. I clicked through those tags to look at similar projects. In the list, I found a repository whose name and description matched exactly those of another repository on that list. It turned out that it also contained copies of all the commits from that repository, and two hours ago, a link to a zip archive has been added to the readme.

After monitoring these two repositories, I discovered that every few hours they delete the previous commit and push the exact same commit again. This commit contains only one change: adding a link to the archive in the readme file.

I submitted a request to GitHub support asking them to delete these repositories. Two weeks passed and nothing has changed; GitHub support hasn’t responded. I discussed with an AI what else could be done about this, but it didn’t offer any useful advice. I opened a thread on GitHub, and three people replied with the same AI slop that was of no use at all.

Another month later, GitHub support sent me an email saying that they had removed these repositories.

You can open other similar repositories, look at the latest commit, and see that a link to a zip archive was added to the readme a few hours ago:
https://github.com/lucioloprey/OcyShield-Framework
https://github.com/luigi1973/AssetRipper-CLI

The zip archive contains 4 files:

• Application.cmd or Launcher.cmd
• loader.exe or luajit.exe or another_name.exe
• random_name.cso or random_name.txt
• lua51.dll

If you submit a link to the archive to VirusTotal, it will find 0 viruses.
If you submit the zip file itself, it will detect a Trojan inside it.

Continued

It seemed like I had already forgotten about this event, but my subconscious hadn’t. And my subconscious often throws interesting ideas at me when I’m sleeping or waking up. Recently, I woke up and in the very same second realized what I needed to do. I need to come up with a general pattern and then write a script that will analyze all GitHub repositories and find the ones that match that pattern.

Search pattern:

• Every few hours the previous commit is deleted and a new one is pushed
• Only the readme file is updated in the commit
• The readme file contains a link to a zip archive
• The commits are copied from another repository
• This is a new repository, not a fork
• All repositories have different contributors and different names

From the last two points, it becomes clear that even if we find one such repository, we won’t be able to find other similar repositories using it. But there are 500 million repositories on GitHub. How can we analyze all of them? GitHub allows 5,000 requests per hour with a single token. For each repository, we need to make several requests to get the list of commits, modified files, and the content of the readme file. I didn’t want to wait a year for the script to analyze all the repositories.

But we don’t need all the repositories, we only need the ones that are updated every few hours. I found a service called gharchive, which lets you download all GitHub events for any given day. So we need to download the event archives for the last few days, filter them to include only commit push events, and identify the repositories that are updated between 2 and 10 times every 10 hours.

Over the past 5 days, there have been 16 million commit pushes. Of these, only 3,000 are repositories that are updated every few hours.

However, the events do not include information about which specific files were modified. This means that for each relevant repository, we need to make additional requests to the GitHub API.

After running the script, it returned a large number of repositories. I added several parameters to the filters:

• The commit must be from a user, not a bot
• More than a month has passed between the last commit and the one before that
• The repositories have more than one contributor

After that, only 14 repositories were found that fully matched the pattern. And I couldn’t stop wondering: why were there so few repositories? What are the odds that I stumbled upon these repositories two months ago and there are only 14 of them on the entire GitHub? There should be many more. Imagine what the headline of this article would have been if I’d found a million such repositories or even just a thousand.

But I accepted the fact that there were only 14 of them and started writing this article. I decided to double-check them one more time so I wouldn’t accidentally include any unnecessary repositories in the article. Imagine my surprise when I saw that they had all been updated 20 hours ago. So the “updated every few hours” parameter was completely wrong. The filter had discarded all repositories that are updated infrequently.

During my manual check, I also noticed repositories that contained a link to a zip archive and had a recent commit, but that commit had zero changes. The filter, however, only considered repositories where a single readme file had been modified in the latest commit.

I also noticed that the last commit in all of these repositories had the same name: “Update README.md”.

I changed the filter. Now the script searched for repositories that were updated between 1 and 24 times every 24 hours. It found 40,000 such repositories.

There were 10,000 repositories that exactly matched the pattern. That’s 25% of the total.

Each of these repositories contains a zip archive with a Trojan.

These repositories have been around for many months, some even for over a year, and GitHub does not automatically detect and delete them.

I’ve published a complete list of these repositories on GitHub.
A script for finding such repositories: Git Malware Finder

Open Questions

1. Why do they only clone new repositories, rather than popular ones?
2. Why do they delete a commit and push a new one every few hours?
3. Why doesn’t GitHub automatically detect such repositories?
4. What exactly does the executable exe file from the archive do?
5. What is the actual scale of this campaign?

My Hypotheses

The hackers’ goal is to understand how the system works, find its limitations and vulnerabilities, and exploit that information. If overwriting commits helps bypass GitHub’s security algorithms, then that’s what they did. Perhaps that’s also why every commit is named “Update README.md”.

The second goal is to spread the virus. How do they get people to find and download it? I think they do this by cloning only new repositories, which immediately appear at the top of search engine results for low-volume search terms. They also add these repositories to popular GitHub tags to increase the chances of indexing and to help people find them through those tags.

But why do they copy all the commits and contributors? After all, they could have just copied the entire source code. This is likely done to build trust. When someone visits a repository, they see the contributors, can click through to their profiles, and see that these aren’t one-day accounts. And the commit history is preserved so it’s clear that the repository didn’t just appear yesterday. But perhaps this is also done to bypass GitHub’s algorithms.

These are just my assumptions, but the reality may be completely different.

Conclusion

I was subject to GitHub’s API limit of 5,000 requests per hour. I optimized the script to search only for relevant repositories, and I think that because of the filter, the script found only a small percentage of repositories. The GitHub team does not have such limitations. They can analyze all 500 million repositories, find any archives or executable files within them, and scan them for viruses.

This time, I won’t be sending a request to GitHub. There are simply too many repositories. If any of you have direct contact with GitHub’s security team, please send them a link to this article.

* Update
I found this article from April 18: How 109 Fake GitHub Repositories Delivered SmartLoader and StealC
It explains in detail how this Trojan malware works. At that time, the author had found 109 such repositories.

* Update 2
GitHub has started deleting all the repositories that the script found. Most of these repositories have already been deleted.

* Update 3
I found a post on Reddit that mentioned this scheme. It was posted in February 2025, almost 1.5 years ago: If you’re creating new repositories, they are being spoofed to host malware

* Update 4
GitHub deleted only the repositories I listed in the complete list in a txt file. Then I ran the script again, it found new repositories, and I added them to this article. 14 days have passed, and these repositories have not been deleted. GitHub has no way to search for these repositories. They didn’t run my script, and they didn’t write their own script. They didn’t even open this article to see if the list of repositories had changed. They only delete repositories that are reported to them, but they don’t do anything else. That’s why this scheme has been going on for several years now, and will most likely continue.

* Update 5
Someone sent me this article: The rise of malicious repositories on GitHub. In it, the author identified the same pattern for distributing zip archives. Just enter “path:README.md /software-v.*.zip/” into the GitHub search bar, and you’ll get a list of such repositories. What’s noteworthy is that some of them haven’t been updated in half a year, while others are forks of other repositories. But more importantly, I saw repositories in the search results where the readme was updated just 30 minutes ago. Can you believe it? The GitHub team doesn’t even need a script. They can find these repositories with a simple search.

* Update 6
Another article: FakeGit: LuaJIT malware distributed via GitHub at scale. It provides a detailed account of the stages in the development of this scheme, from the initial deployment of the smart contract in March 2025 to the present day. It also includes a detailed analysis of the entire infrastructure, IP addresses accessed by the Trojan, and the hashes of the archives with links to VirusTotal.

If you ask AI whether you should post the full text of an article on platforms like Medium or Reddit, it will almost always say you should post a preview of 1-2 paragraphs, a hook, and include a link to your website with the full version of the article at the end. Because if you post the entire text, you’ll be giving away seo traffic to that platform. AI doesn’t give a damn that this is a complete lack of respect for the audience, if there’s just one paragraph and “read more on my blog” at the end. We’re not in 2017 anymore, are we?

It might also suggest not posting a copy of the text, but creating an adapted version for each platform. That’s nonsense. You’d have to spend time adapting the text to turn one article into several different ones, and what’s the point if you’re just going to give seo value of the adapted version to that platform anyway?

If you tell AI that this doesn’t suit you, it’ll say that you shouldn't post your content on other platforms at all, you should only keep an archive of your texts on your own site. It doesn’t care that seo traffic won’t come for several years, until search engines start trusting the site.

This is yet another example of how asking AI for advice, let alone following it, can be harmful. When it comes to marketing, advertising, and user acquisition, AI is almost useless.

It’s much more effective in the early stages to publish your content wherever possible. The full text, not a teaser or an adapted version. And add a link to your blog at the end.

Many years ago, I came across this phrase: dream as much as you can, because it won’t cost you a thing. But is that really true?

When I fall in love, I can spend hours or days fantasizing about our future together.
When I get a job offer, I imagine how I’ll reach the top of my career there in a few years.
When a project brings in its first $100, I’m already thinking about how I’ll make millions from it.

I can spend a lot of time dreaming and get lost in memories of the future. And this can hinder my productivity.

If dreams take up so much time, do they really cost nothing?
And in that case, which is better, to dream or not to dream?