Conflicting IP reputation scores do not, by themselves, mean a proxy is unusable. Providers may be reporting different things: a list match, an estimated threat likelihood, a proxy classification, a confidence level, or a composite fraud score. First confirm the exact IP and lookup time, interpret each field using that provider’s definitions, compare the supporting signals, and test the address in the workflow that matters.
Why two IP reputation results can disagree
There is no universal IP reputation scale. A score of 70 from one provider is not necessarily equivalent to 70 from another, and even fields with similar names may represent different evidence. One service may estimate risk; another may report confidence that an address belongs to an actively used proxy network; a third may combine IP and request context into a fraud score.
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Results can also differ because providers use different data, methods, coverage, and time windows. Spamhaus describes IP reputation as intelligence about an IP or range that considers who, what, where, and when activity, drawing on signal and open-source intelligence, machine learning, heuristics, and manual investigation. Its description illustrates why results may vary; it does not establish a shared scale with other providers. Spamhaus’s explanation of IP reputation.
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IPdata: probability, trust, and list flags
IPdata distinguishes several measures. Its vpn_score and proxy_score are probabilities, its threat_score estimates the likelihood that an IP is a threat, and its trust_score is a 0–100 value where higher means more trust. IPdata’s documented trust bands are 60–100 for low risk, 40–60 for moderate risk, and below 40 for high risk. These bands apply to IPdata’s trust score, not to other providers’ scores. IPdata’s field definitions and trust-score bands.
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A score and a list flag can differ without contradicting each other. IPdata says is_threat reflects whether an IP is present on tracked blocklists, while threat_score estimates likelihood. As IPdata explains, “If is_threat is false but the threat_score is high, it means that while the IP address is not currently known to be a threat, the scoring system has determined that it has a high likelihood of being a threat based on its reputation and behavior.” The same distinction applies to proxy and VPN flags versus their scores.
MaxMind: anonymizer confidence
MaxMind’s anonymizer confidence is a 1–99 measure of confidence that a network is part of an actively used proxy service. MaxMind says it calculates confidence differently for different anonymizer types, so the number should be read in that context rather than treated as a universal risk percentage. For medium-confidence results it recommends additional checks to reduce false positives. It also cautions against blocking residential-proxy-feed results with confidence 1, which may reflect shared infrastructure such as carrier-grade NAT (CGNAT) or mobile networks. MaxMind’s anonymizer and residential-proxy guidance.
IPQualityScore: a composite score plus context
IPQualityScore documents an overall fraud score that uses the IP address, user agent, language, and optional variables. Its response can also include proxy or VPN status, connection type, recent abuse, and abuse velocity. Its documentation refers to frequent abusive behavior over 24–72 hours; that is a description of a vendor field, not a general rule for how long an IP reputation lasts. IPQualityScore’s response-field definitions.
IPQualityScore’s Microsoft Learn connector documentation warns that higher strictness can take longer and may increase false positives. It recommends starting at the lowest setting and increasing it based on observed fraud levels. That setting and guidance are specific to IPQualityScore; do not apply its thresholds or behavior to another provider. Microsoft Learn’s IPQualityScore connector documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check these details before changing the proxy
- Confirm the address and lookup time. Make sure both providers assessed the same IP, not a different exit address or a proxy whose assigned IP changed between checks. Record the test time and, where relevant, the API version or plan. Reputation data and classifications can change, so compare lookups made close together.
- Write down the exact field names and directions. Note whether each number is a trust value, risk estimate, probability, confidence measure, or composite fraud score. Establish whether a higher value means more risk or more trust before comparing anything.
- Separate list membership from an estimate. A false blocklist or threat flag does not necessarily mean a provider considers the address low risk; the score may be based on a separate estimate or evidence threshold.
- Read the surrounding fields. Check proxy, VPN, or Tor indicators; connection type; recent abuse; abuse velocity; and confidence where available. A headline score alone hides the context that may explain the disagreement.
- Consider shared or reassigned infrastructure. A mobile, residential, or carrier-grade NAT address may be shared by many users. Signals from other users can affect a shared IP’s reputation, and MaxMind specifically identifies shared infrastructure as a false-positive concern.
- Test the real workflow. Use the same target service, account conditions, region, and action that matter to you. A general reputation result is not an acceptance test for a particular site, and no provider score guarantees that a target will accept the connection.
Decide whether replacement is justified
Treat a one-off disagreement as a prompt to investigate, not proof that replacing the proxy will fix a problem. Replacement is more defensible when the relevant workflow fails consistently and the evidence points to the IP itself, or when its classification conflicts with a documented requirement you must meet. If the workflow succeeds and no applicable requirement is violated, a provider’s isolated score is not enough on its own to establish that the proxy is unusable.
When you do compare reputation providers, compare the meaning and direction of their fields, the evidence type, confidence, data recency, and network context—not just the displayed numbers. Keep target-workflow results separate: they answer whether the connection works for your use case, not whether providers share a common definition of reputation.
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