Trust each for a different job. A synthetic benchmark gives clean, known answers, so it shows what a tool can detect in principle. Your own code shows what the tool does in your real conditions. Use the benchmark to screen and your code to decide.
What a synthetic benchmark gives you
Projects such as the OWASP Benchmark are runnable test applications designed so that scoring is possible. The OWASP page describes the Benchmark as a Java and Python test suite designed to verify the speed and accuracy of vulnerability detection tools, and says it can be analyzed by SAST, DAST and IAST tools. It also states the intent that all vulnerabilities deliberately included and scored are actually exploitable.
The benefits are clear. Everyone can run the same test. Labels exist. Results are comparable between runs and between tools.
What it cannot give you
The suite is deliberately built, so it reflects the patterns its authors chose. It will not contain your frameworks, your custom wrappers, your build quirks or your team's coding habits. A score on a test suite says how a tool did on that suite. The OWASP page gives no further caveats, so the caution has to come from you.
What your own code gives you
Running on real repositories shows noise levels, setup cost, build integration problems and the share of findings your engineers would actually fix. The downside is labels. Nobody has a complete list of what is wrong in a production codebase, so you must label as you go.
A practical approach is to ask two engineers to review a sample of findings independently, then settle disagreements. Keep the sample small, say a few dozen, so you finish the job.
A combined approach
- Screen with a public test suite to see basic detection coverage.
- Drop tools that fail the basics for your languages.
- Run the survivors on two or three of your repositories.
- Label a fixed sample of findings per tool.
- Compare noise, missed planted issues, setup time and fix effort.
Planting a few known issues in your own repository gives you ground truth in realistic code. Our guide to running your own bake-off explains how.
A quick example of the gap
Imagine a tool that scores well on a synthetic suite because the suite's flaws follow clean, textbook patterns. In your service, the same kind of flaw passes through a shared helper library and a custom validation layer. The tool may lose track of the data. The reverse can happen too: a tool that scores modestly on the suite may handle your frameworks well. Neither outcome is visible until you run both tests, and that is the practical argument for doing so before you sign anything.
Try it yourself
Click through it at your own pace, or open it full screen.
Look at how the tool presents a finding in code. Ask whether you could label it right or wrong quickly.
Where Safeguard fits
Safeguard offers SBOM and dependency scanning and reachability analysis. Try the free scan at gold.safeguard.sh/scan on a repository you know well.
Quick check
If a tool tops a benchmark, should I buy it?
Not on that alone. Test it on your own code, and check cost, integration and noise.
Sources
Last reviewed: 19 October 2026.
