When AI Writes the Code — Who Reasons About the Risk?

AI Code Security Risks

Recent independent research from Sonar’s State of Code Report (2025) reveals something counterintuitive about modern AI-generated code: as LLMs become more capable, the security risks they introduce don’t disappear — they shift.

The report analyzed thousands of identical programming tasks across major models and found a consistent pattern. Let’s dive in:

1. All major LLMs still produce serious security flaws

Even the best-performing models introduce high rates of BLOCKER-severity vulnerabilities — including injections, hard-coded secrets, and broken security controls. This isn’t a glitch. It’s structural. LLMs understand patterns, not security boundaries.

2. “More advanced” models simply make different mistakes

Reasoning-focused models reduce some obvious issues, but introduce new classes of subtle bugs — particularly concurrency, error-handling holes, and complex logic failures.

This aligns with a trend many engineering leaders are quietly feeling: AI is helping ship more code, faster — but also creating a larger, more complex attack surface.

3. The real problem emerging: complexity

One of the most striking findings: the highest-performing models generate the most code — in some cases 3–4× more than smaller models. With that comes:

We’re entering a phase where risk is not just “more issues,” but harder-to-detect issues. And let’s not kid ourselves — teams are already at cognitive overload.

4. Security teams are already at a breaking point

Across the industry, AppSec organizations are dealing with:

The Sonar report underscores what everyone in security feels but hasn’t yet articulated publicly: LLM-driven development is accelerating, but our ability to validate and reason about risk is not.

5. A new layer is clearly missing in the ecosystem

What the report really highlights — indirectly — is the gap that now exists:

None of today’s tools truly understand:

Everything the industry relies on today still operates at the surface level — patterns in, alerts out.

As AI-generated code becomes normal, this gap only widens.

What’s missing isn’t just better detection — it’s a continuous understanding of risk exposure over time. Not just what issues exist, but which ones are reachable, exploitable, and impactful as systems evolve and code changes accumulate.

6. The direction of the future is becoming obvious

The next evolution in software assurance won’t be another scanner, another rule set, or another LLM.

It will be a new intelligence layer that can: – understand systems holistically – reason about intent and risk – validate what actually matters – continuously track real risk exposure as software changes

This is where the industry will inevitably move — toward systems that reason, not just scan.

The details of how this happens are still emerging, but the message from the latest research is clear:

AI is rewriting how software is built. Now we need AI to help verify how software stays secure.

7. What this means for AppSec leaders today

If you’re responsible for product security, engineering quality, or compliance, the takeaway isn’t fear — it’s readiness:

And most importantly:

expect new architectures to emerge that treat assurance as a reasoning and context problem, not a scanning problem.

This is where AI shifts from being a code generator to acting as a set of AI security engineers — maintaining understanding, surfacing exposure, and enabling humans to make better decisions under constant change.

The groundwork is being laid now.

At Neuralsec.io, we’re building the intelligence layer that understands systems holistically and reasons about real-world risk — not just patterns.