Vulnerability management today is not failing because teams stopped scanning. It's failing because the ground underneath it shifted. The approach we've relied on — complete advisory data, upstream fixes on demand, and fast upgrades — no longer holds up.
In modern enterprise dependency graphs, a significant portion of packages have already reached end-of-life (EOL). When a release line is no longer maintained, upstream fixes cease entirely. This turns "routine" CVEs into long-term vulnerabilities with identifiable risks that are nearly impossible to fix with traditional patching.
In our recent webinar, we delved into the practical implications of this shift, focusing on:
The reasons behind the ever-growing vulnerability backlogs.
The diminishing reliability of "severity" as an indicator.
Why many organizations are amassing what can best be described as vulnerability debt.
Let's explore the current challenges in vulnerability management, examine the reasons behind their increasing severity, and discuss effective strategies for adaptation.
The modern software supply chain operates at a scale that outpaces the processes built to secure it.
Across major ecosystems, open source consumption keeps rising — more projects, more versions, more transitive dependencies, more decisions to manage.
At the same time, industry references for vulnerability intelligence (public scoring, enrichment, and prioritization) have struggled to keep up, creating blind spots and false confidence.
The result is a growing mismatch:
Finding vulnerabilities is easier than ever (automation and AI-assisted discovery).
Deploying fixes remains slow and operationally risky (regressions, compatibility breaks, release cycles, pinned dependencies).
Many issues don't have a clean fix path at all once EOL software enters the picture.
This mismatch creates "vulnerability debt" — a backlog of unresolved issues that accumulate, age, and eventually demand a reckoning.
Most vulnerability programs assume the data layer works: a CVE is published, enriched, scored, ingested by tools, and prioritized by teams.
That assumption no longer holds.
Many open source vulnerabilities are not receiving timely, consistent enrichment and scoring through traditional public pipelines. When vulnerabilities are missing key metadata, or never get scored, security programs that rely on those signals can undercount risk or fail to triage effectively.
Modern vulnerability management requires a stronger intelligence model — one that accounts for fragmented data, delayed scoring, and inconsistent coverage without swinging between alert fatigue and false confidence.
One of the clearest signs vulnerability management is breaking down: Teams continue downloading known vulnerable versions even when safer ones are available.
Years after Log4Shell, a meaningful share of Log4j downloads still map to vulnerable versions. That's not "legacy software sitting on a shelf" — those are active builds pulling vulnerable packages into new releases.
This pattern is bigger than any single CVE. It points to a systemic issue:
Dependencies get pinned and then forgotten.
Software is assembled through transitive chains where teams don't clearly see what they're inheriting.
Upgrading is treated as a special event, not a routine capability.
Teams avoid upgrade risk, until forced into it under duress.
Many organizations are not managing vulnerabilities. They're managing disruption. And disruption usually wins.
Traditional remediation assumes that if something is vulnerable, you can update to a patched release. EOL breaks that model.
Once a package is out of maintenance, newly discovered vulnerabilities may have no upstream patch. At that point, your options narrow to hard choices:
Migrate to a supported major version (often expensive).
Replace the dependency entirely (often disruptive).
Take on maintenance yourself (high ongoing cost and governance risk).
Use a trusted long-term support path (when available).
Compensate with mitigations and containment (imperfect, sometimes temporary).
EOL creates permanent security debt. Vulnerabilities become structural liabilities that cannot be closed with ordinary patching cycles. Without visibility, organizations may unknowingly rely on EOL dependencies, often realizing it only after a major vulnerability emerges.
AI changes the vulnerability landscape in two ways:
Discovery accelerates. Tools can surface issues faster, across more codebases, with less expertise required to begin.
Noise increases. Maintainers and security teams face a growing flood of low-quality reports, duplicated findings, and incomplete exploit narratives — all of which still require time to validate.
Even when upstream teams respond quickly, downstream organizations still have to validate impact in context, determine whether the finding applies, upgrade safely, push changes through pipelines, and prove remediation to auditors and customers.
AI can help, but it also amplifies scale. And scale is the root of the modern vulnerability management problem.
Modern vulnerability management needs to evolve from "identify and fix" into continuous risk operations, especially when patches are delayed, missing, or impossible.
Organizations that upgrade routinely handle vulnerability events with far less disruption. That means investing in:
Dependency hygiene.
Regression testing.
Predictable release discipline.
If you cannot reliably see EOL risk, you can't manage it. Teams need:
SDLC visibility.
Policy controls for EOL packages.
A playbook for what happens when patch paths close.
Public enrichment and scoring gaps mean modern programs need:
Better internal triage workflows.
Stronger prioritization signals than "CVSS alone."
A strategy for when advisory coverage is incomplete.
Regulation, procurement requirements, and cyber insurance markets are all moving toward "prove it" expectations. The organizations that win here will be the ones that make evidence — SBOMs, traceability, remediation decisions — automatic, not manual.
Modern vulnerability management is not just a tooling problem but a structural shift in how software risk behaves.
Fixable vulnerabilities persist because adoption lags. EOL transforms risk into permanent exposure. Data gaps create false confidence. AI increases both discovery and operational burden.
The path forward is practical, but it requires treating dependency management and remediation readiness as core engineering capabilities, not emergency work.
To learn more about our data and strategies for modern vulnerability management, watch our webinar featuring experts from Sonatype, HeroDevs, and OpenSSF.