Security teams rely heavily on automated tools to keep infrastructure safe from vulnerabilities, but these tools can sometimes create more confusion than clarity. A common scenario has emerged in many organizations where automated vulnerability scans piped through VPNs unknowingly trigger a flood of false positives in security logs. These false positives, if not mitigated, can lead to alert fatigue, wasted investigative time, and even overlooked real threats hiding in the noise.
Table of Contents
TL;DR
Automated vulnerability scanners routed through VPNs tend to generate misleading log entries or suspicious traffic indicators, causing false positives in SIEM and IDS tools. This happens due to IP mismatches, traffic anomalies, and behavior patterns that look like recon or attacks. Custom scanner pattern rules, proper log filtering, and understanding scanning behavior helped dramatically reduce the noise. This article dives into how these issues emerge and what steps were taken to streamline the scanning signals from the noise.
Understanding the Problem: VPNs and Vulnerability Scans
Automated vulnerability scanners are generally considered a backbone of proactive security. Tools such as OpenVAS, Nessus, or Qualys are designed to probe systems for misconfigurations, exposed services, and outdated software. However, routing such scans through VPN endpoints—commonly done to simulate external attacks or to satisfy regulatory requirements—introduces unexpected complications.
When routed via VPN, scans tend to trip wire various security mechanisms such as:
- Intrusion Detection Systems (IDS): These systems pick up on port-scanning behavior and flag it as suspicious.
- Web Application Firewalls (WAFs): May identify request bursts or malformed HTTP headers originating from scanner IPs.
- SIEM Tools: Security Information and Event Management systems categorize scanner behavior as potential attacks due to pattern matches.
These flagged events are often not indicative of a true compromise attempt but are instead residual activity of the scanner mimicking threats for assessment purposes.
Why Do False Positives Happen?
There are several contributing factors that cause false positives when scanners operate over VPN:
1. Source Anomaly
VPN egress IPs are often shared or change dynamically, leading to inconsistencies in known-good IP databases or in threat intelligence feeds. A frequent issue is that an IP used for scanning is blacklisted or geolocated to a risky zone, even when controlled internally.
2. Behavioral Profiling
Security controls increasingly rely on behavioral baselines. When a scanner operates from a known-user VPN tunnel but performs rapid port scans or banner grabs, the mismatch triggers red flags in monitoring tools.
3. Protocol Misuse Patterns
Vulnerability scanners intentionally create malformed packets to test service robustness. These packets can resemble buffer overflow attempts or other intrusion tactics, which IDS and WAFs are trained to block.
4. Poorly Tuned Detection Rules
Out-of-the-box SIEM or IDS configurations often do not account for benign scanners. Their rules detect anything matching scanning profiles (mass DNS requests, SMB enumeration, malformed HTTP payloads) and log them as medium to critical severity alerts.
Strategies to Reduce False Positives
Realizing that the signal-to-noise ratio was unsustainable, a series of pattern rules and filtering mechanisms were implemented to bring clarity. Below are the main approaches used.
1. Classify Known Scanner IPs
All VPN egress IPs used for scanning were tagged in internal threat detection systems as “KNOWN_SCANNERS”. This tagging prevents these IPs from being treated as external threats, and detection rules were updated to correlate their activity with permitted scan windows.
2. Define Scanner Signature Allowlists
Using scanning tool documentation and packet captures, regular signatures associated with scanner operations (like specific user-agent headers or network patterns) were whitelisted within SIEM correlation rules. This allowed alerts to exclude known benign behaviors:
- Nmap SYN scans from scanner IPs
- SMB enumeration patterns without authentication
- LDAP anonymous binds with timeout behavior
3. Schedule-Aware Thresholds
A time-based contextual filter was applied. Events coming from scanner IPs during approved scanning windows were auto-classified as Low priority, moving them out of dashboards focused on high-severity events. Any similar activity outside the window was still treated as suspicious for safety.
4. Rate-Limiting and Scan Throttling
Some false positives originated from scanners hitting endpoints too aggressively. Response firewalls saw them as DoS or brute-force attacks. By throttling scan speeds and interspersing requests, these alerts were dramatically reduced.
5. Scanner Behavioral Profiles
Continuous profiling was introduced to baseline expected scanning behavior. Once the patterns stabilized, any deviations (e.g., a scanner suddenly making SMTP connections) were treated as anomalies and raised for manual review.
Quantitative Impact
After implementation, security teams observed:
- 58% reduction in critical alerts related to known scanner patterns
- 82% decrease in false-positive alerts during scheduled scanning periods
- Improved SOC efficiency due to lower alert volume and higher confidence in remaining alerts
Lessons Learned
Automated scanners are double-edged swords—they provide immense value but can create operational drag if not properly managed. Running those scanners through VPNs adds authenticity but introduces obscurity. A mature security operations center needs to treat scanner activity as a known entity within detection logic, not just another source of unclassified noise.
Properly tagging, monitoring, throttling, and integrating vulnerability scans into your wider security architecture boosts efficiency, reduces noise, and builds resilience across departments. Ultimately, it’s not about reducing scanner activity—it’s about understanding and contextualizing it.
FAQ
Q: Why use VPNs for automated vulnerability scans in the first place?
A: VPNs help simulate scans from external networks, useful for testing perimeter defenses. They also allow scanning across segmented environments where direct local access may not be permitted. Also, in some compliance audits, external-traffic simulation is mandatory.
Q: Can’t we just ignore all alerts from scanner IPs?
A: Not entirely. While low-severity scanner alerts can often be filtered, unusual behavior (scanners probing new endpoints, using abnormal protocols, etc.) may still indicate misconfiguration or even compromise of the scanner host. Activity should still be logged and reviewed periodically.
Q: How do you keep scanner IPs updated across tools?
A: Use centralized configuration management or orchestration platforms to push a list of currently active scanner IPs to SIEM, firewalls, and IDS systems. Also, automate the tagging based on hostname or metadata tied to the VPN account used.
Q: What scanners generate the fewest false positives?
A: Passive scanners (like Qualys Passive Sensor) tend to generate less alert noise but also provide less depth. Tools like Nessus or OpenVAS can be tuned for lower impact scans, focusing on application layers instead of network probing.
Q: Are there any standards or frameworks for handling scanner traffic?
A: While no universal standard exists, frameworks like MITRE ATT&CK can help map scanning activity to threat models. Custom mappings within NIST CSF or ISO 27001 can also be used to define acceptable scanning behavior and alert thresholds.
Security monitoring shouldn’t come at the cost of understanding. By applying intelligence to automated processes, security teams can transform a noisy tool into a focused, actionable asset.
