We teamed up with Dave Wichers (the guy who literally created the OWASP Benchmark and has been keeping the OWASP Top 10 list fresh for 15 years) to build something Python developers actually deserve.
Here's the thing nobody wants to admit: Python has become the duct tape of modern software development. It's powering your AI models, your web apps, your "quick" automation scripts that somehow ended up running your entire business, and yes, even those "vibe coding" sessions where you're just throwing stuff at the wall to see what sticks.
Until now, there was no standardized way to test if your Python security tools could actually tell the difference between a real vulnerability and a false alarm.
It's like playing Pac-Man with the ghosts blindfolded – you might survive, but it's mostly luck.
Let's talk about why this matters with some fun statistics that'll make your CISO reach for their coffee:
The Python OWASP Benchmark isn't just another testing framework. It's like a comprehensive final exam for security scanners, complete with:
Think of it as a standardized test for security tools. Except instead of getting into college, you're trying not to get pwned.
Here's what we're really solving: vendor marketing claims vs. reality.
Vendor: "Our AI-powered solution eliminates 99.9% of false positives!"
Reality: Your security team drowning in alerts while actual vulnerabilities slip through
The Python Benchmark lets you actually test these claims. It's like having a universal lie detector for security tool marketing brochures.
With AI coding assistants becoming as common as coffee in developer workflows, we needed a way to measure how well security tools handle AI-generated chaos. It turns out, when you can generate code at the speed of thought, you can also generate vulnerabilities at the speed of thought.
Our benchmark specifically tests how well AI-powered security solutions can:
Here's where we get a tiny bit salesy (but in a fun way): we didn't just build this benchmark to be nice. We built it because we're using these exact measurements to validate our own Expert Triage Automation (ETA) and Expert Fix Automation (EFA) tools.
When we say we achieve 97% accuracy, we're not just throwing numbers around. We're testing against the same benchmark we just gave to the community. It's like showing your work in math class, except the math is "how not to get hacked."
The results? Organizations in our beta are seeing:
The Python OWASP Benchmark is available right now at https://owasp.org/www-project-benchmark/
Use it to:
We're living in an era where your code assistant can write vulnerabilities faster than your security team can find them. The old approach of crossing your fingers and hoping your security tools work isn't cutting it anymore.
The Python OWASP Benchmark gives you objective measurement in a world full of subjective marketing claims.
Use it. Your future self (and your CISO) will thank you.
Sources:
24.7% of AI-generated code contains security vulnerabilities: Assessing the Security of GitHub Copilot Generated Code — A Targeted Replication Study. arXiv preprint, 2024.
57% of organizations report that AI coding assistants have introduced new security risks: Black Duck by Synopsys. "Balancing AI Usage and Risk in 2025: The Global State of DevSecOps." 2025. Question 8: "To what extent do you agree or disagree with the following statement: 'The use of AI coding assistants has introduced new security risks into, or made it harder to detect issues within, our codebase.'"
16.28% of organizations cite "AI introducing vulnerabilities at scale and speeds that exceed AppSec capacity" as their primary security concern: Black Duck by Synopsys. "Balancing AI Usage and Risk in 2025: The Global State of DevSecOps." 2025. Question 10: "What is your organization's PRIMARY security-related concern regarding the implications of using AI code generators/coding assistants?"
71% of organizations report that a significant portion of their security alerts are noise: Black Duck by Synopsys. "The Future of Application Security Report." 2025. Question 5: "Approximately what percentage of security test results are noise? For example: duplicative results, false positives, conflicting with other tests/tools."
Want to see how ETA and Expert Fix Automation perform against your current SAST scanner results? We've open-sourced our validation data from 25,000+ findings across multiple commercial scanners.
Ready to level up your security game? Schedule a technical demo and bring your noisiest scanner output - we'll show you what 97% accuracy looks like with your actual data.
Want to learn more? Check out our book, The AI Security Advantage, available now!