IP blocking fails against residential proxies because the address is the one thing an attacker can cheaply swap, and the one thing a legitimate user may unknowingly share. A residential proxy network sends abusive requests out through real home connections, so each address looks like an ordinary subscriber. Block it and you may stop an attacker for a moment. You may also block a household whose device sits on the same address. Rotate to the next address and the attack continues. Carrier-grade NAT (CGNAT) adds a related problem: one public address can stand for many subscribers, so an address is not a dependable stand-in for one person.
The more reliable design treats the IP as one weak signal and decides per request or per client, using fingerprints, behaviour, timing and broader traffic trends. This article explains why address-level identity is noisy, how residential proxies exploit that, and how to lay out layered detection. Figures and product behaviour come from Cloudflare’s own published material and are attributed to it. The sources used here do not compare vendors.
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What an IP address actually identifies
An IPv4 address on the public internet is often not tied to one customer. RFC 6888 (IETF, April 2013) describes carrier-grade NAT as a way for an ISP to share public IPv4 addresses among its subscribers. Subscribers get private addresses, and the ISP’s NAT translates their traffic to a pool of shared public addresses. A website therefore sees the translated address, not the subscriber behind it.
Attribution is possible, but only for the operator. RFC 6888 says that tying abuse to a subscriber can require the external IPv4 address, the port and a timestamp, plus mapping records that link those values to the subscriber. A destination site normally has none of that mapping data. Its view is a shared egress point.
RFC 6967 (IETF, June 2013) surveys ways to reveal a host identifier when a CGN or application proxy sits in the path. It is informational and does not recommend a single solution. It supports a narrow point: shared-address environments make identifying the actual host hard, and no identifier is guaranteed to be available. RFC 7648 (IETF, September 2015) gives PCP examples in which ISP translation and a home NAT are cascaded. That is useful context, but it does not mean every connection passes through multiple NATs.
CGNAT and residential proxies are different things
The two are often lumped together because a destination can see similar-looking traffic from either. They operate at different layers:
| CGNAT | Residential proxy network | |
|---|---|---|
| What it is | An ISP address-sharing mechanism | A service that routes a client’s traffic out through residential network connections |
| Who decides to use it | The ISP, for subscribers generally | The proxy customer, who is often an attacker or scraper |
| Why IP blocks suffer | Many unrelated subscribers share one public address | Abusive and benign traffic can share one residential address, and the abuser can move to other addresses |
| Who can attribute traffic | The ISP, using address, port, timestamp and mapping logs (RFC 6888) | Not established by the sources used here, beyond what request-level analysis can infer |
They can overlap. A residential proxy exit might sit behind CGNAT, which makes the address even less specific. But neither implies the other, and a defender should not assume one from the other.
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Cloudflare’s June 24, 2024 technical post, by Bob AminAzad, Santiago Vargas and Adam Martinetti, describes attackers using residential proxy networks to spread requests across residential IP space. The aim is to get around controls built on country, ASN and rate limits. If a country is blocked, they use an exit in another. If an ASN is penalised, they move to new address space. If one address is rate-limited, requests spread across many.
Traditional hosting-provider blocklists work because datacenter ranges are easy to label and are rarely used by ordinary people. Residential address space reverses that. The range looks like homes, and the people in those homes are real customers.
The collateral-damage problem
The same Cloudflare post makes the false-positive point directly. In a 24-hour sample of active residential proxy IPs that Cloudflare observed, 4 out of 5 requests were direct, benign connections from residential devices. That is Cloudflare’s measurement from its own traffic over that window. It is not a universal rate, and other networks, regions and time periods could differ.
The implication holds regardless of the exact number. An address can carry both proxied abuse and ordinary traffic. A static blocklist, or an IP-level punishment window that blocks everything from an address for a set period, punishes the people who did nothing. Shared CGNAT addresses create the same exposure through a different mechanism.
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What request-level detection looks at
Cloudflare describes its bot model as combining request fingerprints, behavioural signals, and global statistics and trends. It says its v8 work added behavioural and latency-based features to identify residential proxy traffic on a per-request basis. It also says the model analyses an average of over 46 million HTTP requests per second in real time. That is the scale of Cloudflare’s own system, not an industry-wide total.
The post states the design principle this way:
“Effective defense against residential proxy attacks should be able to detect this type of bot traffic either based on single request features to stop the attack immediately, or identify unique fingerprints from the browsing agent to track and mitigate the bot traffic regardless of the IP source.”
That is the authors’ recommendation in a vendor blog, not a neutral standard. It does clarify the two routes that do not depend on the address:
- Single-request features: properties of one request that look wrong enough to act on immediately.
- Client fingerprints: characteristics of the browsing agent that persist even as the source IP changes, so the same client can be recognised across addresses.
What the sources do not establish
These sources do not independently evaluate Cloudflare’s model or compare its effectiveness with other vendors. They do not show that any given fingerprint is stable, unique or impossible to imitate. Fingerprinting also raises privacy considerations. Treat it as a probabilistic signal that will produce errors, not as proof of identity.
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Reading a bot score correctly
A bot score is one vendor’s output, not a standard. Cloudflare’s bot documentation lists a residential-proxy detection with ID 50331651. When it matches, Bot Management sets a bot score of 29 and records anomaly detection as the score source. The page was shown as updated in 2026, and detection IDs and scoring behaviour are versioned product details, so check the live documentation before relying on the numbers.
Two points about use. First, this behaviour is specific to Cloudflare and should not be assumed for other providers. Second, a score is an input to policy. A score of 29 does not define malicious traffic; it tells your rules how suspicious the platform thinks a request is. What you do with it is a decision about risk and cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A layered design that does not rely on the IP alone
The following is a design framework built from the signals above and the comparison axes they imply. It is guidance, not a measured benchmark. The key shift is changing the decision unit from the address to the request or client.
| Axis | IP-centric approach | Layered request-level approach |
|---|---|---|
| Decision unit | IP or subnet | Individual request or session, with client-level tracking where a fingerprint supports it |
| Signals | Address reputation, ASN, country | Those as context, plus request fingerprints, behaviour, timing and latency, and broader traffic trends |
| Collateral impact | High: benign users behind CGNAT or on a shared residential egress inherit the block | Lower, because the action targets what the request or client did |
| Evasion by rotation | Easy: attacker moves to a new address | Harder where a client-level signal persists across addresses; not guaranteed |
| Attribution and review | Limited to address and time | Can retain the evidence behind each decision, if you log it |
Step 1: Use the IP as a prior, not a verdict
Keep address reputation, ASN and country as context that nudges a decision. Do not let any of them alone trigger a long block on a residential or shared address.
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Combine request fingerprints, behavioural patterns, and timing or latency features. Where your tooling supports it, track a client across address changes so you can act on it regardless of the IP source.
Step 3: Match the response to your confidence
Use a graduated ladder: observe and log, then rate limit, then challenge, then block. Reserve outright blocks for high-confidence matches. Challenges and rate limits cost a legitimate user little when you are wrong. A multi-day block on a shared address costs a lot.
Step 4: Keep address-level actions short and narrow
If you do penalise an address, make it brief and scoped to the offending behaviour. A long punishment window is where the collateral damage described above accumulates.
Step 5: Preserve evidence for review
Record which signals fired, the score or confidence, the action taken, and the address, port and timestamp. That lets you distinguish an address-level event from a particular request pattern, investigate complaints, and tune rules. The address, port and timestamp triple matters because it is what an ISP would need to attribute a CGNAT-shared flow.
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If you run a service that attackers target, a managed bot-management or request-level bot-detection product is one way to get these signals without building them. Evaluate any such product on the axes in the table above, and ask what it logs about each decision. The sources used here show Cloudflare’s account of its own approach, not a cross-vendor comparison, so they cannot rank products. Do not assume a residential address is human, and do not assume it is hostile. Both are wrong often enough that the decision should rest on what the request does.
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