Act on proven vulnerabilities.
Review everything else as guidance.
Owlvex helps teams stop second-guessing security output by separating findings they can act on immediately from findings that should be reviewed as guidance.
Deterministic scanning runs locally. If AI assistance is enabled, code goes directly to your selected model provider or local model endpoint, not to Owlvex.
Most tools mix everything into one queue, so teams triage, ignore, or disable enforcement because they do not trust what is being surfaced.
- No false-positive triage for proven issues
- Keep CI output usable by separating proof from guidance
- Help engineers trust what is proven and what is still guidance
- Keep guidance visible without confusing it with proof
Security output you can actually act on.
Most tools mix proven issues, guesses, and explanations into one queue.
Owlvex separates what is proven from what is suggested, so teams know what to fix now and what to review next.
A security workflow teams can trust.
Proven findings to act on. Guidance to review separately.
Owlvex is built around a simple operational rule: teams should not have to debate whether a high-confidence finding is real. Proven findings move straight to remediation. Guidance stays visible without pretending to be proof.
Earlier in the workflow.
Clearer in the outcome.
Owlvex is designed to run during development, where issues are still quick to fix. It focuses on findings that can be proven, so teams act earlier instead of waiting for slower review cycles or triaging broad scanner output later.
It complements deeper tools like CodeQL, which operate later in the pipeline and provide broader analysis. Owlvex gives teams an earlier moment to fix trusted issues while the cost of change is still low.
Control extends to your code.
The deterministic engine runs in your environment. Owlvex backend services handle control-plane and product metadata, not source-code analysis.
When AI assistance is used, you choose the model endpoint. Code goes directly to that provider or local model host, not through Owlvex as a relay.
The architecture exists to keep proof and guidance separate.
Deterministic Engine – Proof Layer
Tracks structural properties across your codebase to prove covered classes of defects:
- –Data flow from input to dangerous sinks
- –Authorisation and ownership boundaries
- –Structural invariants that must hold for code to be secure
If the invariant is broken, the defect is proven, not inferred.
AI-Assisted Layer – Coverage and Guidance
Where structural proof ends, Owlvex uses AI grounded in established security frameworks:
- –Expand coverage into paths no invariant can prove
- –Explain findings using OWASP, CWE, NIST, and CAPEC references
- –Guide developers toward more secure code without claiming certainty where none exists
AI findings are always clearly marked and never mixed with deterministic results.
Scan locally. Prove what Owlvex can prove. Use the result where it fits.
Scan
Run Owlvex in the CLI, the editor, or your CI pipeline.
Deterministic scanning works locally without routing source code through Owlvex backend services.
Prove
The deterministic engine evaluates structural invariants across code paths.
Covered findings are certain, not probabilistic.
Separate
Keep deterministic findings distinct from AI-assisted guidance and explanation.
Teams can act on proof immediately without losing broader context.
Use
Bring proven findings into local review, CI, or remediation workflows without mixing them with exploratory output.
Teams decide how to use each lane instead of inheriting one noisy result stream.
Most tools tell you what might be wrong.
Owlvex tells you which findings are certain.
| Standard Security Scanners | Owlvex |
|---|---|
| Mix heuristics, AI, and patterns so teams cannot tell what is real | Deterministic and AI findings are always separated and explicitly labelled |
| False positives create triage work before any fix can start | Covered rules produce zero false positives, so findings go straight to remediation |
| CI output becomes hard to trust when all findings are mixed together | Deterministic findings stay separate from AI guidance, so teams can decide how to use each lane |
| AI explanations blur together with scanner output | AI guidance stays useful without pretending to be proof |
| Developers find issues later, when fix cost is higher | Developers see proven defects earlier, while context is still fresh |
Built by security architects.
Designed for teams who need findings they can trust.
Owlvex is a CooperBox product built by Cristian Bogdan and shaped around a simple premise: security tools should make engineering faster by increasing certainty, not slower by flooding teams with findings they have to re-investigate.
Every rule ships only after passing a deterministic correctness gate across benchmark suites and rule cases. If a rule change breaks that bar, it does not ship.
Available as a CLI, VS Code extension, and CI/CD integration. JavaScript and TypeScript. Early access now open.
- Built by Cristian Bogdan at CooperBox
- 19 benchmark suites, 82 deterministic test cases
- 8 proven rule classes across injection, access, privacy, and session
- Zero false positives on covered rules
Stop triaging false positives.
Start acting on proven defects.
Owlvex is in early access for security teams and engineering leads who need findings they can act on immediately, without triage drag or a forced source-code relay through vendor infrastructure.