If you’ve ever run a vulnerability scan on a Python project, you know the feeling when hundreds of warnings appear, red flags fill the screen, and you start to wonder how you will get through them all.
The truth is that you probably should not try.
Why? Because not every Python vulnerability deserves immediate attention. Some are buried deep in your dependency tree where they cannot be exploited. Others are labeled “critical” by a scoring system but have no relevance to your actual runtime environment. Many teams still end up chasing every alert as if it’s an urgent threat. The result is delay, burnout, and important issues getting lost in the shuffle.
At ActiveState, our mission is to empower developers and security teams to focus on what matters most — delivering secure, reliable applications — by cutting through vulnerability noise and surfacing the risks that truly impact your business. That means starting where you can by prioritizing Python vulnerabilities.
Our platform automatically analyzes your Python environment, integrating with your existing CI/CD workflows to highlight which vulnerabilities truly put your business at risk — so you can remediate quickly and keep innovating. This translates to faster remediation cycles, reduced engineering toil, and more time for your developers to focus on building features that drive your business forward.
In this article, we’ll show you how to separate the signal from the noise, focus on the vulnerabilities that matter, and build a remediation plan that keeps your Python applications safe while allowing your team to move quickly.
Why solving Python vulnerabilities is more important now than ever
Python is the beating heart of most enterprise applications. It’s used for machine learning pipelines, financial trading systems, healthcare platforms, and other high-stakes environments that keep today’s modern world alive and operational.
What this means is, a vulnerability in a Python dependency can essentially ripple into places where the stakes are much higher than they were a few years ago. And thanks to hundreds of thousands of packages on PyPI, you’re rarely building an application from scratch. Instead, you’re stitching together dozens or even hundreds of dependencies, and each one introduces its own potential security gaps. In fact, studies show that nearly half of all packages on PyPI contain at least one known vulnerability, and attackers have grown more sophisticated about slipping malicious code into popular or look-alike packages.
The threat landscape is evolving, too. Vulnerabilities are being published faster, exploit code is shared more widely, and bad actors are increasingly targeting the software supply chain rather than individual applications.
That means a single overlooked flaw in a dependency can be weaponized quickly and at scale.
All of this makes Python vulnerability remediation more than just a “best practice.” Today, it’s a critical part of keeping your organization’s systems safe, your data protected, and your operations uninterrupted. And in an environment where new threats emerge every week, prioritization is the only way to keep pace.
Four factors that determine which Python vulnerabilities to fix first
When you are staring at a wall of CVEs, it is easy to feel like you need to fix them all immediately. In reality, only a fraction of those vulnerabilities actually put your application at meaningful risk. The challenge is figuring out which ones deserve your attention first.
Here are four considerations that can help you separate the “must-fix” issues from the ones you can safely queue for later.
1. Runtime visibility
Static scans are a good starting point, but they can’t tell you if a vulnerable library is actually being used in production.
By adding runtime monitoring into the mix, you can see which dependencies are actively loaded and reachable. For example, if your scan flags a flaw in a library that only gets used by an old test script, you can safely put that fix on the back burner.
2. Exploitability and exposure
Not all “critical” CVEs are equally dangerous. A vulnerability buried in an internal tool with no network exposure is far less urgent than one in a web-facing API that accepts untrusted input. Ask yourself if someone could realistically exploit this in your environment. If the answer is yes, it jumps to the top of the list.
3. Dependency context
Vulnerabilities in popular libraries that sit high in your dependency tree are more dangerous than those in obscure sub-dependencies. With ActiveState, your team can automatically discover and catalog all open source components in use, using a central dashboard to quickly assess risk and prioritize remediation. Our vetted catalog means developers can confidently select packages, knowing security has already been reviewed.
A flaw in something like requests or numpy could affect a large swath of your codebase, while an issue in a rarely used helper library might not even be called during normal operation.
4. Business impact alignment
Security teams sometimes get stuck chasing fixes that do not move the needle for the business. If a vulnerability could compromise sensitive customer data, disrupt core workflows, or cause compliance headaches, it is a priority even if it is technically “medium” severity by CVSS standards.
At ActiveState, we bring these factors together in one integrated platform — combining real-time visibility, prioritization logic, and automated remediation — so you can see not just what is vulnerable, but how it matters in your specific environment. That’s the difference between patching for the sake of patching and patching with purpose.
How to turn python vulnerability prioritization into action
Knowing which vulnerabilities to fix first is only half the battle. The real payoff comes when you can turn that insight into a repeatable process that your team can actually follow. That means moving from theory to action without adding extra friction to your workflow.
Combine static analysis with runtime monitoring
We allude to this above, but here it is again (because it’s just that important): Static analysis won’t tell you if issues are live threats in production. To close this gap, you need to pair static analysis with runtime monitoring. For example, if a scan flags a vulnerability in a library but runtime data shows the library is never loaded in production, you can lower its priority and focus elsewhere.
Build a triage rubric
A clear, shared set of criteria for prioritization keeps everyone aligned. This could be as simple as a scoring system that ranks vulnerabilities based on exploitability, exposure, and business impact. Anything that scores high on all three moves to the top of the remediation queue automatically.
Automate ticket creation for high-risk vulnerabilities
Manually logging and tracking vulnerabilities is a recipe for delays. Instead, integrate your security tools with your ticketing system so that high-priority issues generate work items immediately. This ensures critical fixes do not get buried in email threads or forgotten in spreadsheets.
Make prioritization part of your regular development cycle
Treat vulnerability management as an ongoing process, not an occasional fire drill. Review and update your priorities on a set cadence (for example, weekly, biweekly, or whatever fits your team’s sprint rhythm). This way, you’re always working from the most current risk picture.
At ActiveState, we make it easier to operationalize this workflow by combining visibility, prioritization logic, and automation in one integrated solution. This allows you to spend less time sifting through noise and more time delivering safe, stable Python applications.
A quick example of prioritization in action
Let’s say you are running a Flask-based web application that handles customer orders. A routine scan flags ten vulnerabilities across your dependencies. On the surface, they all look urgent.
Without prioritization, your team might spend a week patching every single one. That means pulling developers away from planned work, delaying features, and potentially introducing new bugs with rushed updates.
With prioritization, the process looks different. You start by checking runtime data. Half of the flagged libraries are not even loaded in production. Then you apply an exploitability lens and see that only one vulnerability is in a web-facing endpoint that accepts untrusted input. That one is linked to a known exploit circulating in the wild.
You fix that high-risk vulnerability first, deploy the change, and confirm the issue is closed. The remaining lower-priority vulnerabilities are scheduled for a future sprint. You have reduced real-world risk in hours instead of burning through a week’s worth of development time.
This is the kind of efficiency that keeps teams productive and applications safe.
Bringing focus and speed to Python vulnerability remediation
In today’s Python ecosystem, fixing vulnerabilities is about understanding which issues actually matter and addressing them in the right order.
The combination of a huge package ecosystem, an ever-growing list of CVEs, and increasingly sophisticated supply chain attacks means teams cannot afford to treat all vulnerabilities equally. The key is to focus on context, like runtime usage, exploitability, dependency depth, and business impact. This will enable your teams to make smart decisions, and fast.
ActiveState’s solution is built to help you do exactly that. We give you clear visibility into your Python vulnerabilities, show you which ones put your organization at real risk, and help you act quickly without slowing down your development work. Organizations using ActiveState have reported up to 33 percent annual savings per developer and a dramatic reduction in time spent on undifferentiated security tasks. This allows teams to focus on innovation.
If you’re ready to cut through the noise and make Python vulnerability management a competitive advantage, contact ActiveState to see how our platform can help you prioritize, remediate, and innovate with confidence. Or, give this article a read and learn how to develop your own custom Python environment.
