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Conflicting IP reputation scores do not, by themselves, prove that a proxy is unusable. Providers may be reporting different things—such as threat likelihood, trust, anonymizer confidence, or a composite fraud score—on scales that are not calibrated to one another. Check the field definitions, supporting signals, confidence, and the proxy’s behavior in the workflow you actually need before deciding to replace it.
Why can IP reputation providers disagree?
There is no single shared IP reputation scale in the provider documentation. One service may estimate the likelihood that an address is risky, another may report confidence that it belongs to an actively used proxy, and another may return a composite fraud score. These fields answer different questions, so their raw numbers should not be compared as if they were measurements on one common scale.
Providers may also use different evidence. Spamhaus describes IP reputation intelligence as informed by who, what, where, and when activity, using signal intelligence, open-source intelligence, machine learning, heuristics, and manual investigation. That approach illustrates why results can differ without establishing a universal score or ranking across providers. Spamhaus: What is IP reputation?
What each result actually means
IPdata: threat, trust, proxy, and VPN fields
IPdata distinguishes several fields. Its vpn_score and proxy_score are probabilities; its threat_score estimates the probability that an IP is likely to be a threat; and its trust_score is a 0–100 value. IPdata defines trust scores of 60–100 as low risk, 40–60 as moderate risk, and below 40 as high risk. These bands apply to IPdata’s trust score, not to other providers’ scores. IPdata: Threat and abuse detection
A score and a binary flag can also differ without contradicting each other. IPdata says is_threat reflects whether the address is currently present in tracked blocklists, while threat_score estimates likelihood based on reputation and behavior. Its documentation explains that a high threat score can occur while is_threat is false. The same distinction applies to proxy and VPN flags versus their scores.
MaxMind: anonymizer confidence
MaxMind describes its anonymizer confidence as a 1–99 measure of confidence that a network is part of an actively used proxy service. The score is computed differently for different anonymizer types, so it is not directly interchangeable with a threat, trust, or fraud score. MaxMind recommends additional checks for medium-confidence results to reduce false positives. It also cautions against blocking residential-proxy-feed results with confidence 1 because the address may belong to shared infrastructure such as carrier-grade NAT (CGNAT) or a mobile network. MaxMind: Anonymizer and Proxy Data
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IPQualityScore: a composite fraud score and context
IPQualityScore documents an overall fraud score that uses the IP address, user agent, language, and optional variables. Its response can include additional context, such as proxy or VPN status, connection type, recent abuse, and abuse velocity. A composite score is not the same thing as a proxy-only classification. The service describes frequent abusive behavior over a 24–72-hour period in its field documentation; this is part of its field definition, not a general rule for how long an IP reputation lasts. IPQualityScore: Proxy detection response documentation
IPQualityScore also notes that higher strictness settings can take longer and may increase false positives. Its Microsoft Learn connector documentation recommends beginning at the lowest setting and increasing it based on observed fraud levels. Treat that as guidance for its own setting, not a threshold to apply to another provider. Microsoft Learn: IPQualityScore Fraud and Risk connector
What to check before replacing the proxy
- Confirm the address and lookup time. Make sure both services evaluated the same exact IP, not a different exit address from the same proxy pool. Record when each lookup ran; reputation and classifications can change over time.
- Read the exact field name and scale. Note whether each result is a trust value, threat estimate, proxy/VPN probability, anonymizer confidence, fraud score, or list-match flag. Check which direction means greater risk for that particular field.
- Separate list membership from estimated risk. A false blocklist flag does not necessarily mean a provider estimates low risk, and a high estimated score does not necessarily mean the IP currently appears on a tracked list.
- Inspect the supporting response fields. Compare proxy, VPN, or Tor indicators; connection type; recent abuse; abuse velocity; and confidence where the provider supplies them. Avoid reducing a multi-field response to its headline number.
- Consider shared or reassigned infrastructure. Mobile networks, CGNAT, residential proxy ranges, and other shared addresses can carry signals generated by other users. A classification may describe the network context, not just your own use.
- Check the actual target workflow. Test the same proxy with the account, region, and action that matter. General-purpose reputation documentation does not establish how a particular website will treat an address.
How to decide whether replacement is warranted
Use provider scores as diagnostic evidence, not as a universal acceptance verdict. A disagreement is a prompt to inspect what each provider measured. Replacement becomes more reasonable when the relevant target workflow repeatedly fails and the surrounding evidence points to the IP itself, or when its classification conflicts with a documented requirement you must meet.
If the workflow succeeds and the disagreement is only between differently defined scores, replacing the proxy may not solve anything. A new address can receive different classifications, but the reviewed scoring documentation does not guarantee that a replacement will be accepted by a specific site.
Compare providers on the right dimensions
- Meaning: What behavior or risk does the field claim to represent?
- Scale and direction: What are the valid values, and does a higher number mean more trust or more risk?
- Evidence type: Is the result a current list match, an estimated probability, a confidence measure, or a composite score?
- Confidence and context: Does the response expose confidence, connection type, or other signals that help explain the result?
- Recency and network coverage: When was the address checked, and could shared or mobile infrastructure affect its classification?
- Practical outcome: Does the address work in the specific target workflow you need?
These dimensions make disagreements interpretable. They do not establish that one provider is universally more accurate: the available documentation does not compare providers on a common test set or determine which score predicts acceptance by a particular service.
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