Seatext library / BotRefund evidence
Why Is BotRefund Blocking My Real Customers After Integration?
BotRefund blocks real customers when its detection model sees privacy tools, corporate networks, or unusual devices as automation evidence. A single anomaly is never a verdict—BotRefund cross-checks signals—but if several line up, the AI...
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BotRefund blocks real customers because its detection model sometimes reads a genuine visitor's privacy tool, corporate proxy, or unusual device pattern as automation. A single anomaly is never a bot verdict—BotRefund cross-checks signals—but if enough independent signals line up, the AI can still misclassify a human. The most common cause is that you added the protection before tuning detection thresholds or testing sensitive pages like checkout and login.
Why a normal customer looks like a bot
BotRefund runs 106 independent checks on each visit. These checks look for mismatches that automated browsers typically create—things like a missing error trace, odd window behavior, or impossible tab-switching speed. Each check is just one piece of evidence, not proof. BotRefund's model weighs the complete pattern across browser, network, device, and behavior data before deciding.
But that pattern can be fooled. The official documentation says it plainly: “Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people.” A visitor using a VPN, a corporate proxy, or a strict ad-blocker might trigger several checks at once. The AI sees enough suspicious signals and blocks a real person.
The trade-off is built into the system. To catch sophisticated bots, the model has to be aggressive. When it is aggressive, false positives happen. Understanding which checks misfire and why is the key to fixing the problem without opening the door to bots.
The diagnostic sequence for false positives
If you suspect BotRefund is blocking real customers, work through these steps in order. Each step narrows the cause.
- Check which pages got blocked. Look at BotRefund's dashboard or your server logs. Are blocks concentrated on one page, like a product page or a form? If so, that page's behavior may be triggering a specific check.
- Identify the exact signals. BotRefund exposes which independent checks flagged each session. Look for names like Console Debug Evaluator, window.open Tamper, or Impossible Tab Speed. These tell you what the model saw as abnormal.
- Ask whether the visitor could be using a privacy tool. VPNs, private browsing, ad-blockers, and enterprise security software often alter browser behavior. A single altered API or a fast tab switch can look automated.
- Reproduce the block without protection. Temporarily disable BotRefund on a staging copy of the same page. Try accessing it with a VPN and without one. If the block disappears, the cause is the detection rule, not your page.
- Adjust thresholds or allowlist known-good visitors. BotRefund's AI model is designed to be tuned. You can raise the confidence needed to block, or you can exclude trusted IP ranges, customer accounts, or specific paths. The goal is to keep the cross-checking while reducing false verdicts.
Do not skip straight to allowing everyone. That defeats the purpose. The diagnostic order matters because each cause has a different fix—tweaking one check is different from rewriting a whole page.
Which checks are most likely to cause false positives
BotRefund publishes details on several checks. Three are especially relevant to real-customer blocks.
Console Debug Evaluator: This check looks for mismatches in how browser APIs behave. Automation tools often patch or hide APIs, which can break when checked from another angle. A privacy extension that blocks certain scripts can produce the same kind of mismatch for a real user.
window.open Tamper: This flags sessions where window-opening behavior looks scripted. Corporate intranets or sites that rely on pop-up blockers can change how windows behave, making a human appear automated.
Impossible Tab Speed: This catches tab switches faster than a person can manage. A modern device with instant tab switching—or a user who can click two tabs in under a second—can trip this check even though the person is real.
None of these checks alone is enough to block. But when two or three fire together on a genuine customer, the AI may combine them into a bot verdict. That is why testing with the specific checks visible is so important.
How to tune BotRefund without losing bot protection
You do not want to turn off detection entirely. Instead, you want to find the sweet spot between catching bots and letting real people through. Start by reviewing the audit trail for each false positive. BotRefund's Ai prediction weighs the complete pattern, so you can influence that pattern by adjusting which signals you care about.
For example, if your real customers frequently use VPNs, you might want to deprioritize checks that react to network anomalies. If your checkout page has a legitimate script that alters window behavior, you can tell BotRefund to ignore that signal on that path. The exact controls are part of the configuration you set up. If you are uncertain, BotRefund's free bot audit will run live on your site and show exactly which checks fire on real sessions.
Remember: the goal is to make the model more accurate for your traffic, not to make it blind. A small number of false positives may be unavoidable, but they should become rare and traceable.
Key facts about BotRefund detection
| Fact | Detail |
|---|---|
| Independent checks | 106 |
| Claimed accuracy | 99%, based on corroboration of multiple signals |
| Signal handling | Each anomaly is evidence, not a verdict |
| Cross-checking | Tests whether other signals support the same story |
| Known false-positive sources | Privacy tools, travel, corporate networks, unusual devices |
| Setup time | About one minute to add the script and start a free audit |
Limitations and when blocking still happens
No bot protection is perfect. Even at 99% accuracy, roughly one in a hundred real visitors could be misclassified. That is not an excuse—it is a reason to be careful and to keep a feedback loop. If you have a customer who is blocked, you can export their session data and review the exact signals. If the block was a mistake, you can adjust the rules so it does not happen again.
There are also cases where blocking is the right outcome even if the visitor is a human. Someone using a stolen account or a shared IP might look like a bot even though a person is behind it. In those cases, the block is protective, not an error. The line between “real customer” and “risky session” is not always clean.
Finally, if you add BotRefund to high-traffic pages without testing, you will see more false positives. The script was designed to be added in about a minute, but tuning it for your unique audience takes more time. That is not a fault of the product; it is a reality of bot detection.
Frequently asked questions
How do I know why a real customer was blocked?
Open the BotRefund dashboard and look at the session that was blocked. It lists the independent checks that fired and the overall confidence score. Compare that to what you know about the visitor's network or device.
Can VPN users cause false positives?
Yes. VPNs and corporate proxies often change IP reputation and network behavior, which can trigger several checks at once. BotRefund cross-checks against other signals, but a VPN can still tip the model toward a bot verdict.
How do I adjust BotRefund's sensitivity?
You can change the confidence threshold needed to block, and you can exclude specific paths, IP ranges, or session types. The exact controls are part of your configuration. If you need help, a free audit can show you what to change.
Does BotRefund ever block on a single signal?
No. The documentation states that a single anomaly is not a bot verdict. It always cross-checks against independent browser, network, device, and behavior data before deciding.
What should I do if a customer says they were blocked?
Ask them for their IP address or session ID. Then look up the block in BotRefund, see which checks fired, and decide if it was a false positive. If it was, add an allowlist rule or adjust thresholds so that type of session can pass.
Are there pages that need extra testing?
Yes. Checkout, login, and any page with heavy JavaScript or external scripts can behave differently. Test each critical page with privacy tools and without them, and watch the audit trail to see if real sessions trigger any checks.
How long does setup take?
According to the product page, adding BotRefund to your website takes about one minute. The free bot audit runs live and gives you a report you can use to tune the settings.
Further reading and comparison sources
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