What Causes False Positives in SAST Tools?

What Causes False Positives in SAST Tools?

Static Application Security Testing (SAST) is a powerful approach for finding vulnerabilities early in the software development lifecycle. But SAST tools are notorious for one thing: false positives—issues flagged as dangerous that, upon inspection, are not actually exploitable.

Inaccurate results can overwhelm security teams, frustrate developers, and erode trust in the AppSec program. In this article, we’ll walk through the most common reasons SAST tools produce false positives—and how to recognize and prevent them.

To learn how to resolve these findings systematically, check out our complete guide to reducing false positives in SAST.

1. Lack of Runtime or Environmental Context

Most SAST tools analyze code in isolation. Without knowledge of how a function is used, where inputs are sanitized, or which layers add protection (e.g., WAFs, middleware), they often misclassify safe code as risky.

Example:
A tool might flag a potential SQL injection in a function—even if the input is validated and escaped in a wrapper function upstream.

This is why it's essential to complement static analysis with context-aware triage. For a step-by-step approach, see how to build an effective SAST triage workflow.

2. Overly Broad or Generic Rules

Many SAST engines ship with generic detection rules meant to cover a wide range of applications and frameworks. These rules are designed to err on the side of caution—which means they often flag safe patterns.

Example:
A rule might trigger every time eval() is used, regardless of whether the input is controlled or constrained.

Over time, these overly aggressive rules lead to “alert fatigue,” where developers stop taking findings seriously.

3. Poor Understanding of Frameworks

Modern apps use complex frameworks with routing, binding, validation, and templating mechanisms. If your SAST tool doesn’t understand how your framework handles input/output, it will misinterpret the control flow.

Example:
In Django, form validation happens in a layer that SAST tools may skip—causing the tool to flag XSS risks where none exist.

Framework-aware tools and AI-driven triage platforms like Mobb can help fill this gap.

4. Inability to Analyze Dynamic Code

SAST tools often struggle with:

• Generated code
• Dynamically constructed functions or routes
• Runtime metaprogramming

When they can't see the full control/data flow, they make worst-case assumptions that lead to false alarms.

If your codebase makes heavy use of dynamic constructs, consider combining SAST with runtime analysis or AI-powered remediation.

5. Dead Code and Unreachable Paths

Many findings are triggered in unused code, deprecated functions, or files that are no longer part of the active build. While the code may still live in the repo, it’s not executed—and therefore, not exploitable.

Best practice: Flag these as documented false positives using your vulnerability management system. For help structuring documentation, read our guide to reducing false positives.

6. Misuse of Third-Party Libraries

SAST tools often flag third-party dependencies for known vulnerabilities. But the real question is: Is your code using the vulnerable function in an exploitable way?

Example:
You may import a logging library with a known issue, but only use functions that don’t touch the vulnerable code path.

Learn how to prioritize truly exploitable issues in How AppSec Teams Can Prioritize Real Vulnerabilities Faster.

7. Misconfigured Rulesets or Scanners

Sometimes, the problem isn’t the tool—it’s how it’s configured. Running SAST with default settings on a large repo can produce a flood of unfiltered results.

To reduce false positives, customize:

• Language and framework profiles
• Target directories (avoid node_modules, test folders)
• Severity thresholds
• Issue suppression rules

Want to see how today’s top tools stack up? Visit our SAST tool comparison by false positive rate.

Final Thoughts

False positives in SAST are frustrating—but they’re also fixable. By understanding their root causes, tuning your tools, and applying automation where possible, your team can eliminate noise and focus on real vulnerabilities.

For a full playbook on suppression, tagging, and remediation, go back to the guide on reducing false positives in SAST.