When AI Models Depend on Unsupported Code: A New Risk for ML and Data Teams

Machine learning systems don’t stand still. Models evolve, pipelines refactor, and training data updates regularly. But while the algorithms themselves may be dynamic, the infrastructure beneath them is often surprisingly static—and dangerously outdated.

Across industries, ML engineers and data science teams are building production systems that rely on open source libraries that have quietly reached end-of-life (EOL). These libraries—NumPy, SciPy, Pandas, TensorFlow, and others—may still function, but they are no longer receiving security patches, maintenance updates, or support from upstream maintainers.

In many cases, teams don’t even realize the versions they depend on are unsupported.

This blog explores how machine learning systems are uniquely vulnerable to the risks of unsupported OSS, why these risks are growing, and how long-term support (LTS) helps ML organizations secure the infrastructure beneath their models—without halting innovation.

‍

Why EOL Open Source Is a Hidden Problem in Machine Learning

ML pipelines rely heavily on open source Python libraries, many of which have long release cadences and short support windows. And because the focus in ML is often on the quality of the model or accuracy of results, the infrastructure that powers the pipeline—framework versions, data processing scripts, internal APIs—tends to receive less scrutiny.

This is especially true in research-to-production environments, where notebooks become services, and experiments evolve into products.

Three structural realities make EOL risk particularly acute in ML environments:

1. Notebooks Are Not Lifecycle-Aware

Jupyter notebooks are commonly reused across projects, copied between teams, and cloned from public repos. Dependencies are rarely updated unless a breaking change occurs. As a result, many notebooks continue to run on deprecated versions of NumPy, TensorFlow, or Matplotlib long after their support windows have closed.

2. Reproducibility Locks in Legacy Versions

To ensure reproducibility, ML teams often freeze their dependencies. This makes sense for scientific rigor—but it also means security patches are not applied, and EOL software remains in place indefinitely.

3. ML Infrastructure Is Often Parallel to Core Engineering

Many ML teams operate adjacent to, not inside, centralized DevOps or AppSec workflows. They build and deploy using custom tooling, isolated environments, and loosely governed stacks. This autonomy accelerates delivery—but also increases risk exposure when EOL software is used without oversight.

‍

What’s at Stake: Security, Stability, and Trust

Unsupported OSS libraries introduce measurable risks to ML organizations:

• Security vulnerabilities in data ingestion libraries, preprocessing pipelines, or model inference systems can expose customer data or allow for adversarial manipulation.
• Regulatory compliance failures may occur when software used in AI decision-making systems cannot be updated or audited.
• Operational fragility increases when core libraries are no longer supported by the community or vendors, leading to instability during environment upgrades or platform migrations.

In regulated industries—finance, healthcare, government—these risks are compounded by new AI-specific standards requiring transparency, explainability, and demonstrable control over model behavior.

You can’t meet these standards if the software supporting your models is no longer maintained.

‍

Common Examples We’ve Seen

At HeroDevs, we’ve supported teams in production environments using:

• NumPy 1.16, released in 2019 and unsupported since 2021
• Pandas 0.25, used in production ETL pipelines despite EOL status
• TensorFlow 1.x, embedded in real-time inference APIs with no patch path
• SciPy 1.4, powering simulations and analytics workloads in critical domains

In every case, these libraries were “invisible”—not because teams didn’t care, but because they were deeply embedded, stable, and seen as foundational. Until a scanner flagged them. Or a pen test failed. Or an upgrade broke everything.

‍

How Long-Term Support Helps ML Teams

HeroDevs’ Never-Ending Support allows ML teams to:

• Continue using stable, known-good OSS libraries without introducing security risk
• Receive SLA-backed patches for known vulnerabilities in EOL components
• Provide audit and compliance documentation to satisfy internal security and regulatory teams
• Defer major migrations until business timelines and engineering capacity align

This gives ML organizations the ability to build, experiment, and scale responsibly—without pausing for forced upgrades or unplanned replatforming.

‍

A Sustainable Approach to ML Infrastructure

Machine learning is inherently iterative. Model architectures evolve, hyperparameters tune, datasets shift. But the systems that support them must remain secure, compliant, and operable—even when built on older OSS components.

LTS ensures that innovation isn’t gated by lifecycle schedules.

It allows ML engineers to prioritize model performance without compromising security. It gives data platform teams a way to manage risk without halting research. And it ensures that AI infrastructure meets the standards of modern software governance.

‍

Final Thought: The Model Isn’t the Only Thing That Matters

As AI adoption accelerates, organizations will face increasing pressure to prove that their ML systems are not only effective, but trustworthy. That trust begins with software integrity—and that includes the libraries under the model.

HeroDevs helps ML teams extend the life of the OSS components they depend on, so they can focus on what matters: building intelligence, not patching infrastructure.

‍