---
title: "VPN Detection False Positives: Stop Fraud, Not Customers"
slug: "/blog/vpn-detection-false-positives"
description: "VPN detection false positives hit real customers on privacy relays, office gateways, and shared mobile IPs. See the context that separates them from fraud."
---

# VPN Detection False Positives: Stop Fraud, Not Customers

By [Anwar Ahmed](https://www.linkedin.com/in/anwar-ce/) Software Engineer

Posted on November 4, 2026 | 15 min read

![VPN Detection False Positives: Stop Fraud, Not Customers](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/11/vpn-detection-false-positives-feature.png)

VPN detection false positives happen when a fraud check treats a real customer as risky because of an IP signal, such as a VPN, proxy, or privacy relay flag or a high threat score. They come from three places: a correct flag that a rule reads as a verdict, a label earned by someone else who uses or used the same address, and a lookup of the wrong address altogether.

IP risk signals still belong in a fraud check. The same response that carries the flag usually says what was detected, how recently, and whose network the address belongs to, and your own system knows the account and the action. Read together, those facts separate a customer on a privacy tool from a fraudster hiding behind one.

* * *

## TL;DR

VPN detection false positives fall when every IP risk signal is read with its context before a rule acts on it. Four facts shape that decision:

*   Apple says one iCloud Private Relay address can be shared by more than one user in the same area, and it suggests treating relay addresses like large carrier-grade NAT or enterprise addresses.
*   Per-address lockouts punish everyone behind a shared address: the IETF's RFC 6269 describes one user's failed logins locking out others on their first attempt.
*   Strong evidence still deserves action: on November 4, 2026, an address that IPGeolocation.io associated with ten proxy services, seen on 27 of the previous 30 days and carrying attack history, scored 90.
*   A turned-away customer may never complain, so track challenge pass rates, abandonment, and review overturns for each flag type.

* * *

## Three ways an IP risk signal becomes a false positive

An IP risk signal turns into a false positive in one of three ways, and each needs a different fix. Mixing them up is how teams end up "fixing" detection when the real problem is a rule, or loosening a rule when the real problem is a stale label.

![Diagram of three IP risk false positive types: right data with a wrong rule, stale data, and the wrong address scored](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/11/visual-1-three-ways-false-positive.svg)

* * *

### 1. The flag is right, and the rule is wrong

A VPN flag on a VPN connection is accurate. The person behind it might be a remote worker, a traveler on hotel Wi-Fi, or someone who leaves a privacy app running all day. When a signup rule declines everyone with `is_vpn` set, the data did its job and the policy failed the customer. Better detection can't fix that, because nothing was misdetected. The fix is a rule: decide in advance which actions a VPN user has no ordinary reason to take, and challenge rather than block everywhere else.

* * *

### 2. The flag describes someone else on the address

A label can be true about an address and still describe someone other than the person in front of you: an earlier user, or someone sharing the address right now. Addresses change hands. A hosting address that ran a VPN server last month can belong to a small business this month, and a home connection can carry a family's browsing in the morning and a stranger's proxied requests in the afternoon. Every IP data provider faces this lag, because a label describes what was observed, and observations age.

IPGeolocation.io rebuilds its security data every day and reclassifies or removes addresses that are no longer associated with threats. Our [detection methodology](https://ipgeolocation.io/blog/your-vpn-is-not-secret-how-we-detect-risky-hidden-ips) is open about the edge case: an address that a VPN provider has just released to a legitimate user can keep its old classification for up to 24 hours. That's why each VPN and proxy detection comes with a confidence score and, where available, a last-seen date. An old, low-confidence detection is a weaker signal than a recent, strong one.

Shared lists age faster. A [2020 study of address reuse in blocklists](https://steel.isi.edu/members/sivaram/blocklisting_impact/) presented at the Internet Measurement Conference found that 53 to 60% of 151 public IPv4 blocklists contained reused addresses, which could affect as many as 78 legitimate users for as many as 44 days.

Residential proxies add a twist of their own. The FBI's [March 2026 public service announcement on residential proxy networks](https://www.ic3.gov/PSA/2026/PSA260312) explains how ordinary devices end up relaying other people's traffic: app SDKs that pay developers per install, free VPN apps with buried terms, compromised streaming boxes and other connected devices, and malware in pirated software. The household's own sessions and the proxy customers' requests then share one address. A residential proxy flag on that address is true, and it still doesn't tell you which of the two is knocking on your login page right now.

* * *

### 3. The rule looked up the wrong address

Behind a CDN, a load balancer, or a reverse proxy, the address that connects to your app belongs to that service. Look it up and you'll get the CDN's network, often a hosting flag, and a verdict about a machine your customer has never touched. Reading a client-supplied `X-Forwarded-For` header without checking who set it fails in the other direction, because anyone can type an address into it. Take the visitor's address only from a header your own infrastructure sets; the guide on how to [read the visitor's real IP address behind a proxy or CDN](https://ipgeolocation.io/guides/get-real-client-ip-address) covers each common setup.

* * *

## Who gets caught: legitimate traffic behind risky-looking IPs

Many of the legitimate users that IP signals catch have one thing in common: the address they arrive from isn't theirs alone. The table lists common groups, what trips the check, and the context in the same response that tells them apart.

| Who They Are | What Trips The Check | The Context That Tells Them Apart |
| --- | --- | --- |
| iCloud Private Relay and other privacy relay users | `is_relay` and `is_anonymous` set, a cloud network, and for Apple relay addresses a threat score in the 45 to 79 band | `relay_provider_name` names the relay; [Apple's guidance for websites](https://developer.apple.com/icloud/prepare-your-network-for-icloud-private-relay/) says relay addresses may be shared by more than one user in the same area |
| Employees behind a secure web gateway such as Zscaler or Netskope | Many logins from one hosting-range address, a location in the gateway's city | `is_corporate_gateway` with `corporate_gateway_provider_name`; `is_anonymous` stays false, and the gateway flag alone doesn't raise the threat score |
| Mobile users behind carrier-grade NAT | Many accounts and failed logins from one address | An ISP network on the ASN and company records, and `network.connection_type` where it reports Mobile |
| Travelers and privacy-minded customers on consumer VPNs | `is_vpn` with a provider name | No attack or spam record on the address, plus an account history and a device you've seen before |
| Developers, integrations, and monitoring services on cloud servers | `is_cloud_provider` | The action they're taking (API calls rather than checkout), and `is_known_good_bot` with `bot_operator_name` for declared bots |
| Households whose address also carries residential proxy traffic | `is_residential_proxy` and `proxy_provider_names` | How recent and how frequent the proxy activity is, and a test of the live session |

Shared addresses also break per-address limits. [RFC 6269](https://www.rfc-editor.org/rfc/rfc6269.html), the IETF's document on IP address sharing, describes the failure plainly in its section on penalty boxes: when a server locks an address after too many failed logins, one user who keeps failing can block other users of the same address on their very first attempt. With large-scale sharing, the RFC concludes, address-based penalty boxes stop working. Limit by account, device, or session wherever an address is shared by design; the article on [setting a threat score cutoff for each action](https://ipgeolocation.io/blog/how-to-use-ip-threat-score) gives gateways and relays rules of their own.

![Twelve devices share one public IP address, and a per-IP block after one user's failed logins blocks all of them](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/11/visual-2-many-people-one-address.svg)

* * *

## What context means in an IP risk response

IP risk signals are the facts attached to an address that point to risk: anonymity through a VPN, proxy, Tor, or relay; automation; a record of attacks or spam; hosting infrastructure; and a score that sums them up. Context is everything that tells you how much a given signal should weigh for this person and this action. One practical way to read it is as four questions, three answered by the IP response and one by your own system.

![Diagram mapping four context questions to IPGeolocation.io security, ASN, and company fields plus your own account data](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/11/visual-3-four-context-questions.svg)

**What exactly was detected?** A bare `is_vpn: true` says less than a VPN flag that names the service. IPGeolocation.io returns provider names in `vpn_provider_names`, `proxy_provider_names`, and `relay_provider_name` where it can attribute the detection. In our security database built on October 19, 2026, those fields named more than 110 distinct VPN providers and, separately, more than 60 proxy providers. The difference between "Apple Relay" and a proxy service that rents out exit addresses is the difference between a phone owner and a customer of an anonymity business.

**How strong and how recent is it?** `vpn_confidence_score` and `proxy_confidence_score` run from 0 to 100 and say how well supported each detection is. The last-seen dates, `proxy_days_seen_7d`, `proxy_days_seen_30d`, and `proxy_provider_count_30d` say whether the activity is ongoing or a memory. A proxy seen on most days this month is a different fact from one seen once last quarter, and the article on [how to use an IP threat score](https://ipgeolocation.io/blog/how-to-use-ip-threat-score) shows how to turn confidence and recency into how hard you push.

**Who runs the network, and is it shared by design?** The ASN record names the network that announces the address on the internet. The company record names the organization registered for it, which can be a different party, because a block holder can reassign or lease part of its space to a customer while its own network keeps carrying the traffic. Both records come with a type such as ISP, HOSTING, or BUSINESS, and the security flags add whether the address is a cloud provider, a relay, or a corporate gateway. If the [autonomous system number](https://ipgeolocation.io/guides/what-is-an-asn) belongs to a mobile carrier, many people share the address by design. If it belongs to a hosting company and the address carries no gateway or relay flag, ordinary shoppers are less likely to come from it.

**What does your own system already know?** The account's age and history, whether this device has signed in before, how the session behaves, and what the action puts at risk. None of that is in any IP database, and it often decides the case. For browser sessions, a test of the live connection also belongs here, because it describes this session rather than the address's past; for app or server-to-server traffic, the recency fields carry more of that weight.

One IPGeolocation.io lookup with `include=security` answers the first three questions in a single response, and [Real-Time Proxy & VPN Detection](https://ipgeolocation.io/real-time-proxy-and-vpn-detection.html) adds the live-session test.

* * *

## Two live lookups: a privacy relay and a proxy pool

We looked up two addresses with IPGeolocation.io on November 4, 2026, one from each end of the problem. Each response is trimmed to the fields that decide the case.

![Two IPGeolocation.io lookups: an Apple relay address scoring 50 with no abuse record, and a proxy exit scoring 90](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/11/visual-4-two-live-lookups.svg)

* * *

### 1. A privacy relay address that scores 50

The address 172.225.7.160 comes from Apple's own published list of iCloud Private Relay egress ranges, where it's listed for Boston.

json

```json
{
  "ip": "172.225.7.160",
  "location": { "city": "Boston", "state_prov": "Massachusetts" },
  "asn": { "as_number": "AS36183", "organization": "Akamai Technologies, Inc." },
  "security": {
    "threat_score": 50,
    "is_relay": true,
    "relay_provider_name": "Apple Relay",
    "is_anonymous": true,
    "is_known_attacker": false,
    "is_spam": false,
    "is_bot": false,
    "is_cloud_provider": true,
    "cloud_provider_name": "Akamai Technologies, Inc."
  }
}
```

The score of 50 sits in the band the IP Security API page labels elevated risk. It reflects two signals that are both true here: the address hides the people behind it, and it runs on cloud infrastructure. Neither one says anything bad about the person using it. The relay is Apple's, the network is Akamai's, and there is no attack, spam, or bot record. Two other addresses from Apple's list returned the same score and the same flags that day.

Apple's [guidance for websites](https://developer.apple.com/icloud/prepare-your-network-for-icloud-private-relay/) fills in what the IP can't. Private Relay checks that the client is an iPhone, iPad, or Mac with a valid subscription, and it uses single-use tokens and rate limits against abuse. Apple warns that fraud detection built on IP addresses may need adjusting so legitimate users aren't affected, and it suggests treating relay addresses like large carrier-grade NAT or enterprise addresses.

That's why relays deserve a rule of their own. The example policy in IPGeolocation.io's guide on [what each IP threat signal means](https://ipgeolocation.io/guides/what-is-ip-threat-intelligence) caps a relay's score at 44, the top of the "use with context" band, and calls the cap an example of a lighter rule rather than a documented setting. The context then comes from the account and the action. A customer who has signed in through Private Relay on the same device for months continues as normal. A brand-new signup from the same address gets a light check that a real person can pass, such as an email code. A request to add a new payout account gets the same step-up it would get from any address, because the relay neither raises nor lowers what that action puts at risk. How [iCloud Private Relay differs from Cloudflare WARP](https://ipgeolocation.io/blog/icloud-private-relay-vs-cloudflare-warp-explained) explains why the two services need separate rules.

* * *

### 2. A proxy exit used by ten services, scoring 90

The address 145.223.7.7 is one of the sample addresses on IPGeolocation.io's IP Security API page.

json

```json
{
  "ip": "145.223.7.7",
  "asn": { "as_number": "AS209854", "organization": "Cyberzone S.A." },
  "company": { "name": "Brander Group Inc.", "type": "BUSINESS" },
  "security": {
    "threat_score": 90,
    "is_proxy": true,
    "proxy_provider_names": ["ProxyScrape", "DataImpulse", "Oxy Labs", "ProxyEmpire", "Proxy-Store", "SpyderProxy", "FleetProxy", "Proxy4u", "FlashProxy", "Proxy001"],
    "proxy_confidence_score": 99,
    "proxy_last_seen": "2026-11-01",
    "proxy_days_seen_30d": 27,
    "proxy_days_seen_7d": 5,
    "proxy_provider_count_30d": 9,
    "is_known_attacker": true,
    "is_spam": true
  }
}
```

Here every field points the same way. Ten proxy services are listed against the address, and nine of them were associated with it within the last 30 days. Proxy activity was seen on 27 of those 30 days and on 5 of the last 7, with a confidence score of 99, and the address also carries attack and spam records. The ASN organization and the registered company are different organizations, so the space is announced by one network and registered to another. On its own that's normal and changes nothing here; the proxy evidence does the work.

At login, password reset, or checkout, this address earns a hard challenge or a block.

* * *

## Match the response to the action, not to the flag

The flag stays the same everywhere on your site, but what an action puts at risk doesn't, so the response should move with the action. A workable set has six responses:

*   **Allow:** continue as normal, with the signal logged.
*   **Observe:** let the request through and watch the session or account for what comes next.
*   **Step up:** ask for one more check a real person can pass, such as MFA, an email code, or a passkey prompt.
*   **Limit:** lower the amount, the rate, or the reward until the account builds history.
*   **Review:** hold the action for a person to decide.
*   **Decline:** refuse, rarely, and only on documented evidence.

![Six responses from allow to decline, with a public page, a new-network login, and a risky payout change placed on them](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/11/visual-5-match-response-to-action.svg)

The [OWASP Authentication Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Authentication_Cheat_Sheet.html) puts the principle in one line: treat IP addresses, device fingerprints, and remembered-device cookies as risk signals, not as substitutes for authentication. A flag can raise the bar for a login; it can't prove who is logging in.

Two habits keep the ladder fair. Make blocks temporary, because the address you block today may belong to a customer next month. And where an address is shared by design, move the limit from the address to the account or device. Score cutoffs per action are in the article on [how to use an IP threat score](https://ipgeolocation.io/blog/how-to-use-ip-threat-score), and the tutorial on how to [check an IP for VPN, proxy, and Tor in code](https://ipgeolocation.io/tutorials/check-ip-for-vpn-proxy-tor) gives starting responses for each type of match.

* * *

## How to measure false positives you can't see

You measure false positives by following the people your rules flag, because a customer who's turned away can leave without telling you why. The block shows up in your logs as a success. The lost customer shows up nowhere.

Start by logging every decision with its reasons: the address, the fields that drove the decision (the threat score, the flag, the provider name, the last-seen date), the action, the response you chose, and what happened next. Then track a handful of numbers, each split by flag type:

*   **Challenge pass rate:** the share of flagged users who complete the step-up. Read it next to the strength of the check, because someone holding stolen credentials can pass a weak one. A high pass rate on a strong check such as MFA means that flag is mostly catching customers.
*   **Abandonment at the challenge:** the share who give up. This is where silent false positives hide.
*   **Review overturn rate:** how often a person reverses an automated hold or decline.
*   **Support contacts about blocks:** tickets and chats that mention a VPN, a proxy, or being blocked, matched to the decision log.
*   **Later outcomes:** chargebacks, account abuse, or bans among flagged-but-allowed users, compared with everyone else.

![Funnel of flagged sessions from flag to challenge, pass, review, and overturn, with the metric to track at each stage](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/11/visual-6-where-false-positives-hide.svg)

On low-stakes actions, a small holdout makes the comparison honest: let a random share of flagged sessions through with full logging, and compare their later outcomes with unflagged traffic on the same action. If the two groups end up looking alike, the flag isn't telling you much about risk on that action. Never do this at payouts or password resets.

Watch the shape of a spike, too. When flags suddenly jump and most of the new ones come from a single ASN or a single provider name, check whether a rule or a data change explains it before you treat it as a fraud wave. And before a new rule enforces anything, run it in log-only mode for a while; the [threat score article](https://ipgeolocation.io/blog/how-to-use-ip-threat-score) walks through that shadow mode step by step.

* * *

## When the signal is right, act on it

Context cuts both ways. Protecting customers can't become an excuse to ignore strong evidence. The FBI's [residential proxy announcement](https://www.ic3.gov/PSA/2026/PSA260312) describes criminals who log in to a stolen bank account through a residential proxy in the victim's own city, so the bank is less likely to flag it. It also describes attackers rotating through large pools of addresses to get past rate limits and lockouts. In those cases the location looks right and the per-address counters look clean. The proxy evidence is what gives the attack away.

So when the fields agree, act. An address associated with many proxy services, active this week, with attack history, asking to change a payout destination, deserves a hard challenge or a decline. The FBI's own precautions for businesses include blocking IP addresses known to be associated with residential proxy networks, and the 145.223.7.7 lookup is what a strong, recent association looks like. Keep those blocks temporary, for the reason given under "The flag describes someone else on the address": a household device that leaves a proxy network gives its address back to its owner.

* * *

## IP risk signals with context from IPGeolocation.io

IPGeolocation.io returns one of the most detailed security responses available in a single call: in our November 4, 2026 lookups, the security object carried 30 fields, from the threat score to the corporate gateway vendor. That covers VPN, proxy, residential proxy, Tor, and privacy relay detection with provider names, confidence scores, and last-seen dates where available; bot type and operator; attack and spam history; cloud hosting; and corporate gateway context, all summed up in a threat score from 0 to 100.

The context the teaching sections relied on comes from the same place. The unified IP Geolocation API returns geolocation, security, ASN, company, abuse contact, time zone, and user agent data in one request, so the network and ownership records that explain a flag arrive with it, which gives fraud teams the clearest view of why an address was flagged. When an address really has attacked you, the abuse contact names the team at that network that handles reports.

You can take the data the way your stack needs it:

*   [**IPGeolocation.io's IP Security API**](https://ipgeolocation.io/ip-security-api.html) for standalone security lookups, or the unified API with `include=security`, with bulk requests of up to 50,000 addresses.
*   [**Real-Time Proxy & VPN Detection**](https://ipgeolocation.io/real-time-proxy-and-vpn-detection.html) for the live session, which tests the connection in the visitor's browser and usually returns a verdict in one to five seconds, with a confidence value that tells you when to look twice. Pass `include=security` and one round trip returns the live verdict and the address's history together.
*   **The IP Security Database and Residential Proxy Database** for offline scoring and bulk jobs.

Behind every flag is a detection stack that includes VPN and proxy endpoint enumeration, honeypots, curated threat feeds, and behavioral analysis, rebuilt into the API every day; [how we detect VPNs, proxies, and other hidden IPs](https://ipgeolocation.io/blog/your-vpn-is-not-secret-how-we-detect-risky-hidden-ips) explains each layer. Every field is documented in [the IP Security API field reference](https://ipgeolocation.io/documentation/ip-security-api.html). Security data is on paid plans; compare [current plans and credits](https://ipgeolocation.io/pricing.html), or ask support for a free trial of paid data and run your last month of flagged sessions through it.

## Frequently asked questions

### What causes VPN detection false positives?

**Answer:** VPN and proxy detection false positives have three causes: a correct flag that a rule treats as proof of fraud, a label earned by someone else who uses or used the address, and a lookup of the wrong address, such as a CDN's. Shared addresses, including privacy relays, company gateways, and mobile carrier NAT, are behind many of the first kind.

### How do you measure false positives in fraud detection?

**Answer:** Follow what happens after each flag. Track the share of challenged users who pass, the share who abandon, how often reviews overturn a decision, and support contacts about blocks, each split by flag type. A high pass rate on a strong check, such as MFA, means that flag is catching more customers than fraudsters.

### Can a residential IP be a real customer and a proxy at the same time?

**Answer:** Yes. A household device enrolled in a residential proxy network shares its address with proxy traffic, so one IP can carry the family's own sessions and strangers' requests. The FBI lists app SDKs, free VPNs, compromised devices, and malware as ways devices get enrolled. Recency fields and a live-session test tell the two apart.

### What is a false decline?

**Answer:** A false decline is a legitimate payment or order rejected because a fraud check mistook it for fraud. It's the payments term for a false positive. IP signals cause false declines when a checkout rule declines on a VPN, proxy, or hosting flag alone instead of weighing it with the order, the account, and the device.

### Which IPGeolocation.io plans include VPN and proxy detection?

**Answer:** VPN, proxy, and other security data are on IPGeolocation.io's paid plans, which start at $19 a month for 150,000 credits. The free Developer plan, with 1,000 credits a day, doesn't include security data. Adding security to a unified lookup costs 2 extra credits, and support can set up a free trial of paid data.

* * *

## Where to start

Pick last month's flagged sessions, split them by flag type, and check how many passed a strong challenge such as MFA. The flag with the highest pass rate there is the rule to fix first.

### Related Articles

[![How Should You Use an IP Threat Score?](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/10/feature-image.png)](https://ipgeolocation.io/blog/how-to-use-ip-threat-score)

[How Should You Use an IP Threat Score?](https://ipgeolocation.io/blog/how-to-use-ip-threat-score)

Set IP threat score rules per action, learn which flags should change the answer, and test it all in shadow mode. IPGeolocation.io returns one of the most detailed security responses in one call: 27 fields behind every score.

Posted on October 23, 2026

By [Anwar Ahmed](https://www.linkedin.com/in/anwar-ce/)

[Read More: How Should You Use an IP Threat Score?](https://ipgeolocation.io/blog/how-to-use-ip-threat-score)

[![Residential Proxy Detection: How It Works, Why It's Hard](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/07/how-residential-proxy-detection-works-comp.png)](https://ipgeolocation.io/blog/how-residential-proxy-detection-works)

[Residential Proxy Detection: How It Works, Why It's Hard](https://ipgeolocation.io/blog/how-residential-proxy-detection-works)

Residential proxies make automated traffic look like real households, which is exactly why IP reputation checks miss them. Here is how detection actually works: provider attribution, network and behavior signals, live connection analysis, and confidence scores instead of binary blocks.

Posted on July 27, 2026

By [Maaz ur Rehman](https://www.linkedin.com/in/muhammad-maaz-995494214/)

[Read More: Residential Proxy Detection: How It Works, Why It's Hard](https://ipgeolocation.io/blog/how-residential-proxy-detection-works)

[![What Is VPN Detection and How Does It Work?](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/07/what-is-vpn-detection.png)](https://ipgeolocation.io/blog/what-is-vpn-detection-and-how-does-it-work)

[What Is VPN Detection and How Does It Work?](https://ipgeolocation.io/blog/what-is-vpn-detection-and-how-does-it-work)

VPN detection identifies traffic routed through VPN servers before it reaches your app. Here is how the two signal families work, what they miss, and what to do with a positive result.

Posted on July 27, 2026

By [Maaz ur Rehman](https://www.linkedin.com/in/muhammad-maaz-995494214/)

[Read More: What Is VPN Detection and How Does It Work?](https://ipgeolocation.io/blog/what-is-vpn-detection-and-how-does-it-work)

[![iCloud Private Relay vs Cloudflare WARP, Explained](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/07/icloud-private-relay-vs-cloudflare-warp-compared.png)](https://ipgeolocation.io/blog/icloud-private-relay-vs-cloudflare-warp-explained)

[iCloud Private Relay vs Cloudflare WARP, Explained](https://ipgeolocation.io/blog/icloud-private-relay-vs-cloudflare-warp-explained)

iCloud Private Relay and Cloudflare WARP both hide a visitor's real IP address, but they are built for different jobs and hide it in different ways. Private Relay protects Safari browsing, DNS queries, and unencrypted HTTP app traffic through a two-hop relay system. WARP is a whole-device tunnel through Cloudflare's network. Both keep the user near their real region, and, as you will see below, our detection API flags both as relays. If you run a website, an app, or a fraud pipeline, you meet b

Posted on July 23, 2026

By [Sheharyar Malik](https://www.linkedin.com/in/sheharyar-malik-haniel/)

[Read More: iCloud Private Relay vs Cloudflare WARP, Explained](https://ipgeolocation.io/blog/icloud-private-relay-vs-cloudflare-warp-explained)

[![IP Geolocation Data: Built, Not Guessed](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/07/how-ip-geolocation-data-is-built-not-guessed-1.png)](https://ipgeolocation.io/blog/how-ip-geolocation-data-is-built)

[IP Geolocation Data: Built, Not Guessed](https://ipgeolocation.io/blog/how-ip-geolocation-data-is-built)

A lot of weak "IP geolocation data" is just a WHOIS guess dressed up with marketing. Here is how we actually build ours, layer by layer, from registry data and geofeeds to active measurement and verification, and how accurate it really is.

Posted on July 13, 2026

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[Read More: IP Geolocation Data: Built, Not Guessed](https://ipgeolocation.io/blog/how-ip-geolocation-data-is-built)

[![What Is a CGI Proxy and Is It Still a Threat?](https://blogs.ipgeolocation.io/blogs-site/content/images/2026/06/CGI-Feature.png)](https://ipgeolocation.io/blog/what-is-a-cgi-proxy)

[What Is a CGI Proxy and Is It Still a Threat?](https://ipgeolocation.io/blog/what-is-a-cgi-proxy)

A CGI proxy is an old-school web proxy you use through a web page, and it leaves an obvious trace. The destination sees the proxy server's IP, not the visitor's. Here is how it works, whether it still matters, and how to flag the traffic.

Posted on June 10, 2026

By [Maaz ur Rehman](https://www.linkedin.com/in/muhammad-maaz-995494214/)

[Read More: What Is a CGI Proxy and Is It Still a Threat?](https://ipgeolocation.io/blog/what-is-a-cgi-proxy)