Top SAST Tools Compared by False Positive Rate

Top SAST Tools Compared by False Positive Rate

When evaluating Static Application Security Testing (SAST) tools, teams often focus on language support, integrations, and cost. But one of the most critical factors is often overlooked: false positive rate.

Excessive false positives create alert fatigue, slow remediation, and ultimately cause developers to ignore security findings altogether. In this guide, we compare leading SAST tools based on their reputation for accuracy, real-world developer feedback, and how well they reduce noise.

If you're new to false positives and their causes, start with our explainer: What Causes False Positives in SAST Tools?

Why False Positive Rate Matters

A SAST tool’s usefulness is only as strong as the signal it provides. When developers receive dozens of alerts—many of which are invalid—they begin to tune them out. That makes it easy to miss real vulnerabilities.

To avoid this, many AppSec teams are moving toward solutions that reduce noise through:

• Rule customization
• Context-aware analysis
• AI-powered remediation

Want to understand how to put these strategies into action? Check out our Complete Guide to Reducing False Positives in SAST.

SAST Tools Compared

Below is a high-level comparison of popular SAST tools based on public case studies, user reviews, and industry surveys.

• Veracode: Reports a false positive rate of less than 1.1% in enterprise environments. View source: Veracode Static Analysis Tool
• Checkmarx: According to a 2024 Tolly Report, Checkmarx had a false positive rate of 36.3% when tested against benchmark applications. View source: Tolly Report on Checkmarx
• Fortify (by OpenText): While Fortify does not publish an exact percentage, it acknowledges that false positives are “inevitable” and offers rule tuning and prioritization features to help manage them. View source: OpenText Community Blog
• SonarQube: SonarQube has achieved a false positive rate as low as 1% in specific benchmark testing using the OWASP Benchmark Project. View source: SonarSource Blog

Mobb is purpose-built to remediate code and focus developers on fixing real vulnerabilities reported by SAST tools using AI-native remediation.

Why Developers Abandon SAST Tools

In many organizations, developers simply stop checking security findings when they lose confidence in the accuracy of alerts. This is a downstream effect of unresolved false positives and lack of triage ownership.

To understand how these tools impact DevSecOps maturity, read Why False Positives Hurt DevSecOps.

What to Look for in a Low-False-Positive SAST Tool

When comparing tools, consider these features to minimize noise:

• Customizable rulesets to suppress irrelevant issues
• Framework awareness (e.g., Spring, React, Django)
• IDE integration to streamline developer workflows
• Contextual scanning to reduce misclassifications
• AI-powered remediation for automatic fixes and triage

Want to learn how to build a better triage process that supports your tool of choice? Visit our article on How to Build an Effective SAST Triage Workflow.

Final Thoughts

There is no one-size-fits-all SAST tool—but there is a right fit for your development workflow. If your current scanner produces excessive noise, it may be time to switch to a more intelligent, AI-powered solution that’s built to reduce false positives at the source.

For a hands-on look at how AI helps reduce noise and fix findings automatically, see: Using AI to Automatically Triage and Fix SAST Findings.