This article is part of a series on Effective Package Management in Python, also available as a chapter in our free, downloadable eBook

Can you spot the difference between colorama and colourama?

Sure, one is American English, and the other is aimed at British-English users.

One of these is a malicious Python package designed to trick users and the other is legitimate.

Chances are that you’ve heard about vulnerable packages before — and may even be using a vulnerability scanner like OSS Index — but malicious and vulnerable packages are fundamentally different problems, and they require different ways of managing risk.

Malicious Packages and the Package Approval Workflow

Malicious packages boil down to intent. They’re designed specifically to do harm, and they’ll keep doing as much damage as they can for as long as they stay hidden.

Back to our example of colorama vs. colourama; the former is designed to help Python users “make ANSI escape character sequences work under Windows”. The latter is a malicious package that uses British English to trick users. It copied colorama’s original code and added malware that checks the Windows clipboard for bitcoin addresses.

It’s obvious that no one wants malware in their system.

The good news is that organizations already have a reliable way to reduce this risk: controlling how third-party packages enter the environment in the first place.

Instead of relying on developers to manually inspect every package they download, teams can introduce a package approval workflow that reviews and verifies packages before they’re allowed into internal development and production environments.

That’s where tools like ProGet fit particularly well. With ProGet, teams can proxy public repositories like PyPI through a centrally managed internal registry. Packages can be cached locally, reviewed before approval, restricted to approved versions, and blocked entirely if they’re known to be malicious or untrusted.

This gives organizations much tighter control over what actually enters development environments without forcing developers to completely change how they work.

Read: How to Vet and Approve PyPI Packages

Vulnerable Packages and Why Scanners Alone Aren’t Enough

Unlike malicious packages, vulnerable packages usually aren’t created with bad intentions. They’re legitimate packages where security issues are discovered later, sometimes quickly, sometimes months or years after adoption.

A WhiteSource paper reported that a large majority of vulnerable packages are low-severity, although high-severity vulnerabilities absolutely do exist.

All Python users — yes, we mean everyone will encounter vulnerable packages. The real challenge is understanding which vulnerabilities actually matter and which ones don’t.

Consider Log4Shell on Java. Fixing it technically meant upgrading a dependency. Simple enough, right? Except now you have much bigger questions to answer. Which applications are affected? What libraries depend on it? Will upgrading break something critical in production? How do you safely apply fixes across dozens — or hundreds — of systems?

This is where traditional vulnerability scanning starts to struggle. A scanner can tell you that a package has a vulnerability, but that’s only half the problem. Does it actually affect your application? Is the vulnerable feature even being used? Is it exposed in production?

Without that context, every vulnerability starts looking equally dangerous, and suddenly teams are spending time chasing low-risk issues while trying not to break working applications in the process.

In real systems, context matters. Some vulnerabilities are only exploitable under very specific conditions that may never exist in your environment. Others are genuinely dangerous and require immediate attention.

Context-Aware Vulnerability Management in ProGet

Modern vulnerability management works best when vulnerabilities are evaluated in context instead of treated as isolated findings.

In other words, the important question isn’t just “Does this package have a vulnerability?” The real question is “Does this vulnerability actually create risk for our environment?”

That distinction matters more than people realize.

Without context, vulnerability data quickly becomes noisy. Teams are left interpreting severity scores on their own, deciding what needs immediate action versus what can safely wait until a normal maintenance cycle.

ProGet approaches this differently.

Instead of treating vulnerability scanning as a completely separate security process, ProGet evaluates vulnerabilities directly within the package registry as packages are downloaded, cached, promoted, and used across feeds.

That means teams don’t need to manually cross-reference scanner results against multiple repositories or dependency lists just to figure out what’s actually in use. ProGet continuously tracks package activity inside the registry itself, giving teams visibility into both newly introduced packages and packages that are already widely used throughout the organization.

As new vulnerabilities are discovered, they’re automatically mapped against packages already in your environment. That means teams immediately see which feeds, applications, or approved packages may be affected and whether action is actually necessary.

Some vulnerabilities may simply need monitoring over time. Others can be scheduled into normal remediation work. And when a vulnerability genuinely represents a serious risk, teams can respond immediately.

Because this happens at the registry level, the evaluation applies not only to newly downloaded packages, but also to packages that are already widely used throughout the organization.

The end result is a much more practical vulnerability management process. Instead of forcing teams to interpret raw vulnerability data themselves, ProGet helps provide clearer guidance based on how packages are actually being used.

Versioning plays a big role here, by the way. We explored how to version Python packages more effectively in enterprise environments in “5 Best Practices for Versioning Your Python Packages”.

Staying safe against Malicious and Vulnerable Packages

Malicious packages are relatively easy to avoid when organizations use a package approval workflow as part of their development process. By vetting third-party Python packages before they enter internal environments, teams can significantly reduce the risk of malicious code reaching production.

Vulnerable packages are harder. There’s no way to predict what vulnerabilities will be discovered tomorrow, which is why vulnerability scanning still matters. It helps teams quickly identify newly discovered issues without relying entirely on manual review.

But scanning alone isn’t enough. A vulnerability alert doesn’t explain whether it actually affects your environment or what you should do about it.

ProGet bridges that gap by combining vulnerability scanning with continuous, context-aware evaluation inside the package registry. Instead of forcing teams to interpret findings on their own, it shows which packages are affected, where they’re used, and whether the issue requires monitoring, planned remediation, or immediate action.

The result is a more stable development process where teams spend less time reacting to noise and more time focusing on the risks that actually matter.

This will be important to keep in mind, so I recommend saving or bookmarking this for later. Better yet, grab a copy of our eBook Effective Package Management in Python, which contains everything on this page, in addition to info on performing Docker deployments, setting up Custom GUIs, versioning, handling license compliance, and safeguarding against vulnerabilities. Download your free copy today!