945 findings. 900 were noise. 45 were real. 1 was critical. This is what AI-native code security looks like.
Traditional static analysis tools are built for a world where humans write every line of code. They pattern-match against known vulnerability signatures, flag potential issues by the hundreds, and leave your team to sort the signal from the noise.
That model is breaking down. Fast.
With the rise of AI-assisted development — what the industry has started calling "vibe coding" — code is being generated faster than any human team can manually review it. Copilots, code generators, and AI agents are shipping features in minutes that used to take days. The velocity is extraordinary. But so is the risk.
Because the code that writes itself still needs someone watching its back.
The OpenClaw Scan
We pointed CyberTested Code at OpenClaw, a publicly available open-source codebase, to stress-test our engine against a real-world project. No synthetic benchmarks. No cherry-picked repos. Just raw code, analyzed end to end.
The results told a clear story: our engine flagged 945 findings across the entire codebase. Of those, roughly 900 were noise — the kind of low-confidence, context-free alerts that traditional scanners spit out by the thousands. The findings that bury security teams in triage work and erode trust in the tooling itself.
But 45 of those findings were real vulnerabilities. And one of them was critical.
Critical Finding Our AI engine identified a critical vulnerability that traditional scanners would have classified as clean code. It looked syntactically correct, passed linting, and followed common patterns — but the logic was exploitable.
What Traditional Scanners Miss
Here's the uncomfortable truth about conventional security tooling: it's built to catch what it already knows. Regex patterns, known CVE signatures, dependency version checks. These are necessary, but they're not sufficient — especially when AI-generated code introduces novel patterns that don't match any existing rule set.
Our engine caught vulnerability classes that traditional scanners consistently miss:
Authentication Bypasses
Logic paths where auth checks were present but structurally ineffective — valid middleware, wrong execution order, exploitable race conditions.
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Business Logic Flaws
Code that passes every linter and scanner but contains logical contradictions that an attacker could exploit to escalate privileges or manipulate state.
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Session Hijack Patterns
Subtle weaknesses in session handling — predictable tokens, insufficient invalidation, improper binding — that create windows for session takeover.
These are the kinds of bugs that look like clean code until they don't. They pass code review. They pass CI. They ship to production. And they wait.
Three Scans. One Report. Full Visibility.
CyberTested Code isn't another scanner bolted onto your pipeline. It's an AI-native security engine built from the ground up to understand code the way an attacker does — contextually, holistically, and creatively.
Every scan produces a unified report across three distinct analysis types, designed to give your team full visibility into what's actually happening in your codebase. Not a wall of alerts. Not a spreadsheet of CVEs. A prioritized, contextualized assessment of real risk.
The signal-to-noise ratio matters. When 900 out of 945 findings are noise, you don't need a bigger firehose — you need a filter that actually works. That's what we built.
Built for the Vibe Coding Era
If you're building with AI, you're shipping faster than ever. Your code review processes were designed for human-speed development. Your security tooling was designed for codebases that grow incrementally, not exponentially.
CyberTested Code was built for this new reality. For teams that move fast and need to know — with confidence, not guesswork — what's actually lurking in the code they're shipping.
We're not asking you to slow down. We're asking you to see clearly while you move at speed.
