The Python Package Hall of Shame Is Growing in 2026 — Here’s What’s Really Happening

You might think that by 2026, the Python ecosystem would be a polished, well-oiled machine. And in many ways, it is. But there’s a darker side that’s gotten a lot more attention this year: the Python Package Hall of Shame is expanding faster than ever. And no, this isn’t just about a few abandoned libraries or old, buggy code. It’s about packages that are actively causing problems for developers and organizations.

Let’s be real. Every language has its share of packages that start promising, then go off the rails. But Python’s massive open-source community means there’s a lot of code out there, and not all of it is maintained well. In 2026, the list of packages that should make you think twice has grown noticeably.

What Exactly Is the ‘Hall of Shame’?

It’s not an official list maintained by the Python Software Foundation or anything like that. Instead, it’s a term that’s been floating around developer forums, security blogs, and internal company audits. It refers to packages that:

• Have known security vulnerabilities that haven’t been patched for months (or years).
• Depend on other packages that are themselves abandoned or insecure.
• Have been “typosquatting” on popular package names, tricking developers into installing malicious code.
• Use aggressive telemetry or data collection without clear disclosure.
• Simply don’t work as advertised, wasting hours of debugging time.

In 2026, the hall of shame is getting bigger because the sheer number of new packages uploaded to PyPI has exploded, and the quality control just can’t keep up.

A Real-World Example That Might Surprise You

Let’s talk about a package I’ll call data-cleaner-pro (name changed for obvious reasons). In early 2025, it was a small but useful tool for handling messy CSV files. By late 2025, it had a critical vulnerability where it would accidentally write sensitive data to world-readable temp files. The maintainer vanished after a single tweet saying they were “moving on to other things.” As of early 2026, the package is still being downloaded thousands of times a week, and the fix is nowhere to be found. That’s a real problem.

At PythonSkillset, we’ve seen teams waste entire sprints trying to debug issues that turned out to be caused by dependencies like this. One developer told me, “We spent three days chasing a memory leak. Turned out it was a tiny logging library that was silently holding onto references. The author hadn’t updated it in 18 months.”

Why Is This Happening More Now?

A few clear trends are driving this growth:

• The rise of AI-generated code. Some developers are pushing packages to PyPI that were generated entirely by AI models. These often lack basic error handling, have weird imports, and sometimes contain subtle bugs that are hard to catch.
• Burnout and abandonment. The maintainer burnout crisis isn’t new, but it’s gotten worse. Many popular packages simply don’t have anyone looking after them.
• Typosquatting is getting clever. Attackers are registering package names that are very similar to legitimate ones, like requsts instead of requests or sqlalchemy-ext instead of sqlalchemy-utils. In 2026, the number of such packages detected has risen by over 40% compared to the previous year.
• Telemetry creep. Some packages now include tracking code that phones home with system info, usage patterns, and sometimes even environment variables. The documentation often mentions it in a tiny footnote.

How to Protect Your Projects (Without Panicking)

The good news is you don’t need to stop using Python. You just need to be a little more careful. Here are practical steps that work:

1. Check the last update date. If a package hasn’t been updated in over a year, and it’s not something stable like requests or numpy, be cautious.
2. Look at the number of open issues. A package with 50 open issues and no responses from the maintainer is a red flag.
3. Use a tool to scan for known vulnerabilities. There are free tools that check your requirements.txt against vulnerability databases. Run them regularly.
4. Don’t trust packages with zero downloads. That shiny new package that does exactly what you need? If it has fewer than 100 downloads, it might be fresh malware or just abandoned before it even started.
5. Pin your dependencies properly. Don’t use loose version ranges. Use exact versions and update them intentionally after testing.

The Bottom Line

The Python Package Hall of Shame is real, and it’s growing. But that’s not a reason to abandon the ecosystem. It’s a reminder that open-source software is a shared responsibility. As developers, we need to check what we’re installing, support maintainers we rely on, and stay informed.

At PythonSkillset, we keep an eye on these trends so you don’t have to. The key is not to be paranoid, but to be informed. And in 2026, being informed means knowing that not every package with a high download count is safe to use. Trust, but verify.