Vulnerabilities and Programming Languages

Every line of software, from the first punch‑card programs to today’s AI‑generated code — carries with it the imprint of the language that created it. As languages evolved to help developers write bigger, faster, and smarter programs, they also shaped the types and frequency of vulnerabilities that attackers exploit.

In this blog, we’ll take you on a journey:

How vulnerabilities have evolved with programming languages
Which languages have historically been more vulnerable
What security research, from open‑source statistics to Department of Defense analysis, tells us
How the rise of generative AI has introduced new security pitfalls

Grab a coffee, this story is as much about software history as it is about security.

The Early Days of Vulnerabilities

In the early era of software, where C and C++ reigned supreme, developers had powerful tools, but also powerful footguns.

Languages like C gave programmers fine‑grain control over memory, which was a huge advantage for performance‑critical systems, but it also meant developers had to manually manage memory lifecycles.

The result? Buffer overflows, use‑after‑free errors, integer overflows, and similar memory safety issues became endemic.

These weren’t edge cases, they were widespread because the language made them easy to introduce. Attackers quickly learned to weaponize those gaps.

A Turning Point: Vulnerabilities Across Languages

Fast forward to the modern era, and a clear picture emerges from reported open‑source vulnerabilities.

The chart above tells a story:

Languages like JavaScript and PHP have millions of users in web‑facing systems, and accordingly show high vulnerability counts.
C and C++, while older and less mainstream in new applications, continue to show significant vulnerability totals due to historical usage and embedded systems codebases.

Newer languages like Go and Rust, while growing, show lower reported figures, not because the code is flawless, but partly because the ecosystems are newer and smaller.

Most Common CWE Across Languages

This chart highlights another critical point:

Injection, XSS, and deserialization issues dominate web languages.
Memory safety issues still persist in systems languages.
Logic and access control vulnerabilities show up across many languages.

What these charts remind us is simple but profound: “Different languages have different vulnerability profiles, and those profiles match how the languages are used.”

Secure Design Matters: What the Department of Defense Says

In June 2025, the U.S. Department of Defense released a report focused on the impact of memory safety in modern software development.

The core message?

Memory ‑ safe programming languages significantly reduce the prevalence of certain classes of vulnerabilities.
By design, languages that enforce bounds checking, prevent pointer misuse, and avoid manual memory management (like Rust and newer managed languages) make whole categories of vulnerabilities (e.g., buffer overflows, use‑after‑free) far less likely.

This aligns with what many security researchers have long believed:
You can reduce vulnerability density not just through better tools, but by choosing safer languages.

The DOD highlights:

How memory safety reduces exploitable weakness conditions.
That secure design principles must be baked into language choice and architecture, not just tested for at the end.

The takeaway is clear: language choice influences risk at a foundational level.

Modern Twist: Vulnerabilities in the AI Code Era

But the story doesn’t end with memory safety and design. We are in the AI era, where machines write code as often as humans do.

However, several reports highlight a significant concern: over 45% of AI‑generated code samples failed security tests and introduced vulnerabilities aligned with the OWASP Top 10.

Java topped the list with a 72% security failure rate across common coding tasks.
Other popular languages weren’t far behind:
- Python: 38%
- JavaScript: 43%
- C#: 45%
Perhaps most concerning: in code patterns where Cross‑Site Scripting should have been prevented, AI tools failed to defend against it in 86% of relevant code samples.

Put simply, AI accelerates code creation, but without rigorous guardrails, it accelerates vulnerability introduction too.

The Future of Secure Coding

If there’s a central thread across this evolution, it’s this:

Software vulnerability is never just about bugs, it’s about language choice, usage patterns, ecosystem exposure, and tooling.

As developers and organizations:

Embrace memory‑safe languages where appropriate.
Educate about injection, XSS, and logic errors.
Treat AI‑generated code as draft code, not production‑ready security code.

Conclusion

Pervaziv AI is designed to help developers write secure code proactively, rather than just reacting to vulnerabilities after they appear.

While programming languages and frameworks evolve, and AI accelerates code production, security remains a moving target. Tools like Pervaziv AI act as a safety net and guide, reducing the chances that vulnerabilities, whether memory-related, injection-based, or logic-oriented, make it into production