A false negative occurs when a security control overlooks genuine malicious activity and labels it as benign. The threat slips through unchallenged, so no alert fires and no defensive action is taken.
Examples
- Malware file bypasses endpoint protection without a warning.
- Intrusion-detection system misses an attacker’s lateral-movement traffic.
- Cloud-security scanner ignores a misconfiguration that exposes data.
Where a false positive wastes effort, a false negative creates real risk: attackers work undetected inside the environment.
False Negative Rate and Formula
A critical security metric that measures how often genuine threats slip past detection systems:
FNR = FN ÷ (FN + TP)
- FN (False Negatives): Number of real threats that went undetected
- TP (True Positives): Number of real threats correctly identified and flagged
- FN + TP: Total number of all actual threats targeting the system
A lower FNR signals a stronger, more reliable detection stack.
False Negative Impact
- Direct security risks from undetected attacks
- Successful data breaches and malware infections
- Unauthorized system access going unnoticed
- Financial losses and regulatory violations
- Reputation damage and compromised sensitive information
- Extended dwell time for attackers within systems
Common Causes of False Negatives
- Sophisticated attack techniques that evade detection
- Zero-day exploits with no known signatures
- Encrypted malicious traffic hiding attack patterns
- Attackers using legitimate tools for malicious purposes
- Inadequate security system coverage and monitoring blind spots
- Advanced persistent threats designed to avoid detection
Detection Gap
The security vulnerability created when false negatives occur, representing the window of opportunity for attackers to operate undetected within compromised systems while pursuing their malicious objectives without security intervention.
Read the blog to dig deeper into False Positives:
