Seatext library / BotRefund evidence
Does BotRefund Work with Virtual Machines and Spoofed Browsers?
Yes. BotRefund detects virtual machines and spoofed browsers through its CPU Concurrency Lie check — one of 106 independent signals — which spots mismatches between claimed device properties and actual hardware, graphics, font, and...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
BotRefund identifies virtual machines and spoofed browser profiles by looking for inconsistencies that a real browsing session does not normally create. Its CPU Concurrency Lie check examines whether the hardware, graphics, fonts, audio, and processor behavior all tell the same story about the device. When a virtual machine or spoofed profile claims one device while its underlying behavior tells another, that mismatch becomes one piece of evidence among 106 independent checks.
The system does not treat a single anomaly as a verdict. Privacy tools, corporate networks, travel, and unusual devices can produce unexpected readings for genuine people. BotRefund keeps each signal as evidence, cross-checks it against independent browser, network, device, and behavior data, and feeds the complete pattern into an AI prediction model that weighs everything together. This corroboration approach is how BotRefund achieves its stated 99% accuracy.
How the CPU Concurrency Lie check catches virtual machines and spoofed profiles
The CPU Concurrency Lie check is designed specifically to spot the mismatch that virtual machines and spoofed profiles often create. A normal browser reports hardware, graphics, fonts, and operating-system details that naturally fit together for that device. Virtual machines and spoofed profiles can claim one device while their graphics, fonts, audio, or processor behavior tells another story. This check looks for that disconnect and flags it as independent evidence.
According to BotRefund's documentation, this is "one of 106 independent checks BotRefund uses to build a reliable picture of whether a visit is human or automated." The check produces a signal that feeds into the broader detection pipeline rather than triggering an automatic block.
Why 106 signals beat any single fingerprint test
Relying on one browser tell — whether it's CPU concurrency, user-agent string, or canvas fingerprint — creates false positives. Legitimate users on corporate VPNs, privacy-focused browsers, or unusual hardware configurations can trip a single rule. BotRefund's architecture treats each of the 106 checks as independent evidence. The AI prediction engine then evaluates the complete pattern across four evidence categories: browser signals, network signals, device signals, and behavior signals.
This matters because modern fraud tools have become sophisticated at spoofing individual fingerprints. AI-powered bot telemetry can now simulate human mouse curvature, click intervals, and page scrolling. Residential proxy networks route clicks through hijacked smart devices in target areas, presenting legitimate residential IP addresses. A single check cannot reliably catch these tactics, but a pattern across 106 checks can.
Behavioral detection vectors that complement hardware fingerprinting
Beyond the CPU Concurrency Lie check, BotRefund monitors a range of behavioral signals that are difficult for automated scripts to replicate consistently:
- Click behavior: Ghost click detection catches click activity that happens without the natural sequence of human intent.
- Pointer behavior: Robotic linear mouse movements flag unnaturally straight pointer paths that rarely appear in real user sessions.
- Motion behavior: Absence of humanlike mouse tremor looks for the tiny imperfections and jitter typical of human movement.
- Speed behavior: Superhuman input speed (under 1ms) identifies interactions that happen faster than a person could realistically perform.
- Path behavior: Grid-aligned movement patterns detect movement that snaps to precise lines or blocks instead of natural curves.
- Engagement behavior: Absence of clicks or scrolling highlights sessions that stay too static to match a real browsing journey.
- Session behavior: Unnatural session durations catch visit lengths that are too short, too long, or too uniform to be human.
- Trap behavior: Honeypot trap interactions watch for bots that respond to hidden or intentionally deceptive page elements.
These behavioral vectors are especially valuable against virtual machines and spoofed browsers because even when the hardware fingerprint is convincingly spoofed, the behavioral execution often reveals automation. Scripts struggle to reproduce the varied timing, movement, and hesitation of real people across an entire session.
How the AI prediction engine weighs evidence
BotRefund sends every signal — including the CPU Concurrency Lie result — into its prediction AI. The model evaluates the complete picture across browser, network, device, and behavior evidence. By seeing how all signals fit together, it identifies a visit as bot or human with 99% accuracy. The three-step process is:
- Independent evidence: Each signal adds one objective fact about the visit.
- Cross-checked context: BotRefund tests whether other signals support the same story.
- AI prediction: The model weighs the complete pattern instead of trusting a raw rule.
This approach means a virtual machine running a sophisticated spoofing stack might pass the CPU Concurrency Lie check but fail on behavioral vectors like impossible tab speed, window.open tamper detection, or superhuman input speed. Conversely, a legitimate user on an unusual device might trigger the CPU check but pass every behavioral and network signal, resulting in a human classification.
Limitations and false-positive handling
BotRefund explicitly states that "a single anomaly is not a bot verdict." Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people. The system keeps each signal as evidence — not a verdict — and cross-checks it against independent data. This design reduces false positives but means the system requires sufficient signal volume to make confident predictions. Very short sessions or heavily locked-down browsers may not generate enough behavioral data for the AI to weigh all 106 checks effectively.
Advertisers should also understand that BotRefund's primary use case is detecting bot clicks on Google and Meta ads to recover wasted spend. The detection runs on the advertiser's landing page after the click. It does not prevent bots from clicking ads on the ad platform itself; it proves they did so the advertiser can request refunds.
Practical scenarios for advertisers
Scenario 1: Competitor click fraud from virtual machine farms. A competitor runs click bots on cloud VMs with spoofed browser profiles to drain your Google Ads budget. The CPU Concurrency Lie check catches the hardware/behavior mismatch, behavioral vectors catch the non-human movement patterns, and the AI correlates both. You get video proof and click IDs (GCLID/FBCLID) for a refund claim.
Scenario 2: Residential proxy botnet clicking Meta lead ads. Fraudsters route clicks through hijacked IoT devices with real residential IPs. The IP looks clean, but the CPU Concurrency Lie check may reveal virtualization artifacts, and behavioral signals (superhuman speed, absent tremor, grid-aligned paths) expose automation. The cross-checked pattern triggers a bot classification.
Scenario 3: Legitimate user on corporate VDI (virtual desktop infrastructure). An employee clicks your ad from a company virtual desktop. The CPU check might flag a mismatch, but behavioral signals — natural mouse tremor, human-speed clicks, varied scroll patterns, realistic session duration — align with a human. The AI weighs the full pattern and classifies the visit as human. No false positive, no wasted refund claim.
Key facts
| Fact | Detail | Source |
|---|---|---|
| CPU Concurrency Lie purpose | Detects mismatch between claimed device properties and actual hardware, graphics, font, audio, or processor behavior | S1 |
| Virtual machine/spoofed profile detection | "Virtual machines and spoofed profiles can claim one device while their graphics, fonts, audio, or processor behavior tells another story" | S1 |
| Total independent checks | 106 | S1 |
| Single anomaly policy | "A single anomaly is not a bot verdict" — kept as evidence, cross-checked | S1 |
| Evidence categories | Browser, network, device, and behavior signals | S1 |
| AI prediction accuracy claim | 99% accuracy identifying visit as bot or human | S1 |
| Behavioral detection vectors | Click, pointer, motion, speed, path, engagement, session, trap behavior | S2, S4, S8 |
| Setup time | About one minute to add to website, no credit card required | S2, S4, S8 |
| Refund recovery scope | Google Ads spend dating back to 2017; Google and Meta billing disputes | S2, S4, S8 |
| Ad budget loss estimate | Bot clicks steal up to 20% of Google and Meta ad budget | S2, S4, S8 |
Terminology quick reference
- CPU Concurrency Lie: A specific check that compares reported hardware concurrency against observed graphics, font, audio, and processor behavior to spot virtualization or spoofing artifacts.
- Spoofed browser/profile: An automated browser that falsifies its user-agent, fingerprint, or other identifying characteristics to mimic a different device or browser version.
- Residential proxy: A proxy network that routes traffic through real residential IP addresses (often hijacked IoT devices) to evade IP-based blocking.
- GCLID/FBCLID: Google Click Identifier and Facebook Click Identifier — unique parameters appended to ad click URLs that BotRefund logs for refund evidence.
- Pixel poisoning: When bot traffic corrupts conversion pixel data, causing ad platforms to optimize toward fraudulent audiences.
Frequently asked questions
Does BotRefund block virtual machine traffic automatically?
No. BotRefund detects and classifies traffic; it does not block visitors at the network level. The detection runs on your landing page, classifies each session, and provides evidence (including video replay and click IDs) for refund claims with Google and Meta. You decide whether to exclude identified bot IPs or audiences in your ad platform settings.
Can sophisticated spoofing tools bypass the CPU Concurrency Lie check?
Some advanced spoofing frameworks can mimic hardware concurrency values. However, BotRefund does not rely on this check alone. The spoofed profile must also pass 105 other independent checks across behavioral, network, and device signals. The AI prediction engine weighs the complete pattern, making full evasion significantly harder than passing any single test.
What happens if a legitimate user triggers the CPU Concurrency Lie signal?
The signal is treated as evidence, not a verdict. If the user's behavioral signals (mouse movement, click timing, scroll patterns, session duration) and network/device signals all align with a human, the AI prediction will classify the visit as human. BotRefund's documentation notes that privacy tools, travel, corporate networks, and unusual devices can produce unexpected readings for genuine people.
How quickly does detection happen after a click?
Detection runs in real time on the landing page. BotRefund logs the click ID (GCLID/FBCLID), captures video proof of the session, and classifies the visit as bot or human. The audit-ready report is available for refund claims immediately.
Does BotRefund work for both Google Ads and Meta Ads?
Yes. BotRefund proves bot clicks, negotiates with Google and Meta, and gets money back for both platforms. It recovers Google Ads spend dating back to 2017 and handles Meta billing disputes.
What ad spend level is required to use BotRefund?
BotRefund serves accounts across spend tiers: under $10,000/mo, $10,000–$50,000/mo, $50,000–$250,000/mo, $250,000–$1M/mo, and over $1M/mo. Enterprise plans are available for larger spenders.
How does BotRefund differ from ad platform built-in invalid traffic filters?
Ad platform filters (Google's invalid click detection, Meta's traffic quality systems) operate on their own data and often miss sophisticated fraud that mimics human behavior. BotRefund runs on your landing page, capturing behavioral and device evidence the ad platforms cannot see post-click. It generates independent, audit-ready proof (video replay, click IDs, signal breakdown) that you can submit for manual refund review — often recovering spend the platforms' automated filters missed.
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