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If your website is blocking real users as bots, the cause may be a rule that treats one clue as conclusive: a bot-like User-Agent, a high request count from one IP address, or a low bot score. Each can be useful, but none identifies a person or request with certainty. False positives depend on the site, traffic source, endpoint, and rule configuration.
To stop bots without blocking legitimate traffic, make rules specific to the operation they protect, choose a counter suited to the activity, and monitor results before turning uncertain signals into hard blocks.
Why can bot filters block legitimate users?
Bot controls make decisions from signals: headers, request frequency, IP addresses, fingerprints, and scores. Those signals can overlap between automated and legitimate traffic. An IP may represent many users; a request header can be imitated or shared by a service; and a score summarizes a detection method rather than the full context of a request.
The practical question is not whether a signal is useful, but whether it is specific enough for the decision attached to it. Compare rules by what they inspect, which endpoint and action they protect, how they count requests, what happens when a request matches, and whether you can review outcomes and adjust the rule.
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1. Treating a bot-like User-Agent as proof
A User-Agent is a request header that identifies the client software. A request claiming to come from Googlebot or Bingbot does not, by itself, prove that the request came from that crawler. Cloudflare’s fake-bot rules compare bot-like User-Agent patterns with verification such as reverse DNS or IP validation. Its documentation also describes false positives when a legitimate service uses a bot-like header pattern but connects from a different IP range: Cloudflare: Fake bot detection blocking legitimate requests.
Examples Cloudflare identifies include Google Cloud Workflows or Cloud Functions, Bing Webmaster Tools Site Scan, and monitoring or testing tools. A rule that blocks every matching header without checking context can therefore deny a legitimate service request.
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What to change
- Check the rule’s matching conditions and determine whether it relies on the header alone or also verifies the source.
- If a legitimate service is affected, make an exception as narrow as possible: for example, by a known source IP or range, URI path, or ASN, where those details are reliable for that service.
- Avoid broadly disabling the bot rule to fix one conflict. A broad exception can also remove protection from unrelated requests.
2. Treating an IP request count as a person or bot identity
Counting requests by IP address is convenient, but an IP address is not necessarily one user. Multiple legitimate users can share an address, while one user or automated client may appear through changing addresses. A request-wide limit can also catch harmless activity when the risk is confined to one action or endpoint.
Cloudflare’s rate-limiting guidance recommends matching the exact URI path for the protected operation. For OTP validation, it describes counting error responses so valid submissions do not consume the same limit. Its examples use different thresholds and actions for particular configurations; they are illustrations, not universal limits. The guidance also describes using a session cookie to group requests when IP addresses change: Cloudflare: Rate limiting best practices.
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Choose the counter to fit the activity
For login protection, OWASP warns against relying on a single combined IP-plus-username bucket: attempts spread across many usernames may avoid triggering the intended limit. Depending on the operation, multiple keys may be appropriate. OWASP also describes bot defense across edge, application, and backend layers rather than relying on one brittle control: OWASP: Bot Management and Anti-Automation Cheat Sheet.
- Protect the specific route and action that create risk instead of applying a broad limit to all requests.
- Decide which responses count. For example, a failed validation may matter more than a successful one.
- Select counting keys that reflect the activity, such as IP and session cookie where appropriate; consider how shared addresses or changing identities affect the rule.
- Use example thresholds only as a starting point for your own traffic and endpoint, not as a general standard.
3. Treating a low bot score as a command to block
A bot score is a product-specific signal, not a universal measure of whether a request is malicious. Cloudflare says its heuristics engine assigns a score of 1 when the User-Agent header is missing or empty. It identifies corporate proxies and WARP environments that strip the header as a common false-positive trigger: Cloudflare: Bot scores.
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Cloudflare’s documentation describes score categories including 2–29 for likely automated requests. These are descriptions of Cloudflare’s product, not a general scoring standard. A rule that hard-blocks every request below a threshold may turn an uncertain signal into a denial for legitimate traffic.
Use graduated enforcement
Cloudflare recommends observing traffic patterns before deploying rules, starting with small thresholds, and tuning with analytics and security events. Its example distinguishes blocking definitely automated traffic from challenging likely automated traffic; a challenge can allow legitimate users to proceed. The appropriate balance depends on the site’s tolerance for false positives: Cloudflare: Challenge bad bots.
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- Begin with observation or a less disruptive action where the signal is uncertain.
- Use a challenge instead of an immediate block when it is important to distinguish legitimate users from likely automation.
- Review analytics and security events, then adjust the threshold and action based on what is actually affected.
How to tune bot rules without locking out users
- Identify the protected operation. Name the specific endpoint and action at risk, such as OTP validation or a price lookup, rather than writing a broad request-wide rule.
- Inspect the evidence behind a match. Review the header, source, score, route, and other available signals. Check whether the apparent bot indicator can be shared with a legitimate service or users.
- Choose a suitable counting context. Use keys that fit the activity and account for shared or changing identities. A single IP, fingerprint, or combined key may not represent the actor you intend to limit.
- Start with proportionate enforcement. Observe, log, or challenge uncertain traffic before using a hard block, particularly while learning normal patterns.
- Review outcomes and revise narrowly. Cloudflare’s Bot Feedback Loop guidance advises checking whether a fingerprint overlaps with legitimate traffic before blocking on it; shared or frequently changing IPs can also make IP allowlisting inappropriate: Cloudflare: Bot Feedback Loop.
For diagnosis, OWASP suggests retaining request details such as time, request ID, route, status code, IP, ASN, country, fingerprint, and User-Agent. Those details help connect a blocked request to the rule and distinguish a noisy signal from an actual abusive pattern (OWASP: Bot Management and Anti-Automation Cheat Sheet).
Quick Recap
What to compare before enabling a rule
| Control | Question to ask | False-positive risk to check |
|---|---|---|
| IP rule or rate limit | Does the counter protect the exact route and operation, and is IP the right counting key? | Shared addresses can combine legitimate users; changing addresses can split one actor’s activity. |
| User-Agent rule | Is the header verified against source context, or treated as decisive on its own? | A legitimate service may share a bot-like header pattern. |
| Score-based rule | What does the score mean in this product, and what evidence or missing signal affects it? | A low score may reflect missing information, such as a stripped User-Agent, rather than harmful intent. |
| Challenge or block | How much user friction is acceptable for this endpoint and level of confidence? | A hard block denies uncertain legitimate requests; a challenge adds friction but can let legitimate users through. |
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