Reducing False Positives in Automated Security Scans

A security scanner that cries wolf loses its audience. False positives are not just annoying. They are the primary driver of tool abandonment. When developers cannot trust the results, they stop reading them.

Why false positives happen

False positives in security scanning typically come from:

• Insufficient context. The scanner sees a pattern that looks like a vulnerability but lacks the context to determine whether it is reachable or exploitable.
• Overly broad rules. Rules written to maximize detection inevitably catch benign patterns.
• Test code and fixtures. Security patterns in test files are intentional, not vulnerabilities.
• Dead code paths. Vulnerable-looking code that is never executed.

Strategies that work

1. Narrow the scope

Diff-first scanning inherently reduces false positives by limiting analysis to changed code. Fewer lines analyzed means fewer opportunities for false matches.

2. Use multi-signal detection

A single pattern match is weak evidence. Multiple correlated signals are strong evidence.

For example, a high-entropy string alone might be a false positive. A high-entropy string assigned to a variable named api_key in a configuration file is almost certainly a real secret.

3. Understand file context

Not all files are equal. Apply different thresholds based on:

• File type — test files, documentation, and fixtures should be treated differently from production code.
• Directory — test/, fixtures/, examples/ directories contain intentional patterns.
• File history — a line that has existed unchanged for two years is probably not a newly introduced vulnerability.

4. Let teams provide feedback

Build a feedback loop where developers can mark findings as false positives. Use that data to:

• Suppress specific patterns in specific contexts.
• Improve detection rules based on real-world data.
• Measure and track your false positive rate over time.

5. Deterministic detection

Non-deterministic scanners (including some AI-based tools) produce different results on the same input. This makes false positives harder to suppress because the same benign code triggers different findings on different runs.

Deterministic detection ensures that a suppressed false positive stays suppressed.

The target

There is no universal acceptable false positive rate. But a useful benchmark: if more than 10% of your findings require manual dismissal, your scanner needs tuning. Aim for a rate where developers expect findings to be real.