Why VPN Infrastructure Location Impacts Risk Scoring

Every security team knows that when someone uses a VPN, their true physical location is hidden. That’s expected, it’s how VPNs work. What’s less obvious is that geolocation accuracy still matters, because security systems don’t evaluate the user behind the VPN; they evaluate the VPN infrastructure the traffic comes from.

For that reason, the actual location of a VPN exit node is a critical risk signal. If that location is wrong, then risk scoring, anomaly detection, and geofencing rules all become unreliable.

In our recent VPN infrastructure study of 20 major providers, 17 showed location mismatches between what they claimed and where traffic truly exited. Some had 40+ incorrect locations, and across the ecosystem we found over 8,000 VPN IPs that third-party datasets placed incorrectly.

This blog breaks down why VPN exit locations matter, why datasets often get them wrong, and how this impacts security and fraud models.

Security Platforms Don't Care Where the VPN User Is

Security systems never attempt to recover a VPN user’s real location: it’s both impossible and therefore irrelevant. Instead, they assess the location of the VPN exit node itself.

Risk engines rely on signals such as:

• The country the exit node is in
• The jurisdiction and legal restrictions that apply there
• Hosting patterns typical of that region
• The known threat profile of that country
• Clustering of malicious infrastructure
• Historical fraud patterns tied to that geography

These signals flow directly into:

• Baseline risk scoring
• Identity and anomaly detection
• Geofencing and policy enforcement
• Compliance checks
• Fraud engines
• Threat intelligence and infrastructure attribution

Even if the user clicks a button and changes locations instantly, the infrastructure the VPN presents to your system still defines the risk.

Why Geography Changes a VPN's Risk Score

Different countries carry radically different risk profiles and security engines must account for this. A VPN exit node in Germany, Singapore, or Canada will score very differently from one in Russia, Iran, or Nigeria, even if the actual user is in the same place.

Why? Because infrastructure geography determines:

• Hosting abuse rates and botnet density: some regions are hubs for credential-stuffing attacks and open proxies
• Compliance risk: OFAC-sanctioned regions, AML/KYC jurisdictions
• Historical fraud patterns tied to that geography: fraudulent signups and spam operations cluster in specific countries
• Rule of law and takedown response times: some jurisdictions have strong hosting hygiene, others don't
• Typical threat actor infrastructure clustering: adversaries favor specific low-friction regions

So even if the user is in London or São Paulo, a VPN exit node located in Finland or Japan may carry a very different risk score than an exit node located in Vietnam or Iran.

Security platforms must treat them differently to avoid both false negatives and false positives.

VPN Providers Often Misrepresent Where Their Servers Actually Are

The problem is that VPN providers routinely misrepresent where their own servers are located, and legacy IP data providers propagate these false claims downstream.

By The Numbers:

• 17/20 VPN providers had location mismatches
• 40+ wrong locations (worst-case provider)
• 38 countries with zero physical infrastructure
• 97 countries total with at least one mismatch
• 8,000+ VPN IPs mislocated in third-party datasets

In our analysis, we found VPN providers claiming:

• "We have a server in Egypt" (when the server is actually in Amsterdam)
• "We operate in Morocco" (but the traffic exits from Paris)
• "This server is in Chile" (but RTT patterns show it's in Miami)

Why These Mismatches Break Security and Fraud Models

Baseline Risk Scoring Is Incorrect

If a VPN server is claimed to be in a low-risk region but actually hosted in a high-risk one, the risk engine assigns the wrong reputation. A provider labels a server as being in Morocco, but measurements show it’s in Paris. Any fraud model applying Morocco-specific patterns will mis-score a French hosting environment.

Geofencing and Compliance Enforcement Fail

Compliance engines depend on the jurisdiction of the exit node, not the user. If the infrastructure is mislabeled, enforcement breaks. Examples:

• A server incorrectly labeled as being in the UAE triggers false blocks
• A node mislabeled as being in Germany may allow traffic that violates sanctions or jurisdictional restrictions

To clarify:

• OFAC (Office of Foreign Assets Control) enforces U.S. sanctions and prohibits activity linked to certain countries.
• AML (Anti–Money Laundering) and KYC (Know Your Customer) regulations require organizations, especially financial platforms, to validate jurisdictions before allowing transactions.

These controls depend entirely on the true location of the VPN exit node. If a dataset misplaces infrastructure, both over-enforcement (false positives) and under-enforcement (regulatory breaches) can occur. Regulated industries can't rely on incorrect IP location data.

Threat Intelligence and Clustering Become Inaccurate

Threat intel models group infrastructure by clusters of:

• Hosting environments
• Known malicious ASNs
• High-risk geographies
• Botnet and abuse patterns
• Residential vs. hosting patterns

If a VPN node is geographically misplaced, all reputation signals tied to that geography are misapplied.

Why This Happens: The Industry Relies on Provider-Submitted Claims

Legacy IP data providers still rely solely on:

• Registry information
• Geofeeds
• WHOIS
• Provider-submitted metadata

When a VPN provider makes false claims about server locations, those claims are propagated into nearly every downstream legacy IP geolocation dataset. Even the best security engines break when the underlying location data is wrong.

The Solution: Active Network Measurement

To address this systemic problem, accurate VPN location data requires active verification, not reliance on registries or provider self declared statements.

This means:

• Real-time routing tests from globally distributed vantage points
• RTT (round-trip time) analysis to detect impossible geography
• Traceroute validation to identify multi-hop or virtual locations
• Behavioral clustering to spot infrastructure that doesn't match claimed regions

Our research uses this approach through ProbeNet, our internet measurement platform with 1,200+ points of presence. By running continuous tests against VPN infrastructure, we can identify when claimed and actual locations diverge and accurately locate VPN infrastructure.

Evidence-based location data gives security teams signals they can trust for risk scoring, compliance checks, and threat detection.

Infrastructure Truth Location for Risk Model Accuracy

VPNs hiding user locations is expected. VPN providers misrepresenting their own infrastructure location is a security liability. When VPN exit nodes are geolocated incorrectly:

• Risk scores misfire
• Compliance checks fail
• Anomaly detection breaks
• Attackers exploit the gaps

Security and fraud teams cannot build reliable models on unreliable data. As our research shows, the industry has a systemic VPN location accuracy problem and reliance on legacy IP datasets won’t fix it.

The path forward is active verification: measuring where traffic actually exits, not where it's claimed to.