Why Am I Seeing So Many Residential Proxies Flagged in My Traffic?

I recently had a conversation with a customer who noticed that quite a large number of IPs in their traffic stream were being flagged as residential proxies based on our data.

In their case, something close to 30% of traffic carried some kind of residential proxy signal. At first glance, that can feel surprisingly high. Surely that doesn’t mean a third of your traffic is malicious, right?

I explained that residential proxy detection should not be interpreted as a blacklist. Instead, it should be looked at as infrastructure context, and understanding why these percentages can become so high requires understanding how residential proxy networks actually operate.

What a Residential Proxy Signal Actually Means

Our residential proxy product is fundamentally a zero-inference, observation-based dataset.

When IPinfo flags an IP as a residential proxy, we are not inferring that based on reputation scoring or suspicious behavior alone. We directly observe these IPs being offered through residential proxy services, and phone home to confirm these IPs are actually being served.

In practice, that means the signal itself is highly reliable: the IP was definitely available through a residential proxy network at some point. But that does not automatically mean every request coming from that IP is malicious, automated, or proxy-driven traffic.

Residential proxy infrastructure overlaps heavily with legitimate internet traffic by design. In fact, the entire value proposition of residential proxies depends on blending into normal residential and mobile traffic patterns.

Why Residential Proxy Saturation Can Become So High

Most residential proxy networks rely on shared infrastructure:

• Residential broadband connections
• Mobile carrier IPs
• CGNAT environments
• Shared or rotating IP pools
• Compromised consumer devices

That creates situations where a single compromised device can effectively “poison the well” for everyone else sharing that same IP address. (See how everyday users can unknowingly become part of residential proxy networks.)

For example, imagine a mobile carrier IP shared across thousands of devices. If just one device behind that shared connection becomes part of a residential proxy network, the IP itself may become available through those residential proxy pools and detected by us, even though most of the traffic associated with it will probably still be legitimate user activity given the sheer number of users.

This becomes especially pronounced in regions where IP sharing and carrier-grade NAT are more common. In some markets, we’ve observed ASNs where the percentage of IP space associated with residential proxy infrastructure over a 30-day period can reach 15-20%. 

That sounds extreme until you look at how heavily shared and rotated internet infrastructure has become in certain regions.

Why This Is Different From VPN Detection

During the conversation, we also talked about why residential proxy detection behaves very differently from VPN detection. VPN providers usually operate relatively centralized infrastructure. A known VPN exit node is generally expected to carry VPN traffic most or all of the time, and for many users. This is because the ratio of VPN users to the number of available VPN IPs is quite high. 

Residential proxy infrastructure, on the other hand, is much more distributed. The available IP pool is dramatically larger because it’s built on top of residential, mobile, and consumer internet infrastructure. Most traffic flowing through those IPs is still legitimate user traffic. The residential proxy activity exists alongside that legitimate activity. While residential proxies are a growing market in connection anonymization, their usage is still much lower than the totality of legitimate internet traffic, so anonymized activity gets diluted and masked.

This is why the usage of residential proxy detection data should be approached with nuance.

Why Blocking Every Residential Proxy IP Usually Fails

If you treat residential proxy data as a binary blocklist, you may end up blocking:

• Shared mobile carrier traffic
• Residential broadband users
• Large CGNAT pools
• Legitimate users sharing infrastructure with compromised devices

So the real challenge is interpreting what the signal means in context.

That’s where some of the additional fields in our residential proxy data become useful.

Get more details about how to interpret different risk signals.

How to Use last_seen and percent_days_seen

Two of the most useful fields for operationalizing residential proxy data are:

• last_seen
• percent_days_seen

These fields help distinguish between transient exposure and consistently active proxy infrastructure.

Recency Matters Most

Recency is often the most useful operational filter.

Residential proxy infrastructure changes quickly. IPs move in and out of proxy pools constantly. During the conversation, we discussed how many residential proxy IPs appear only briefly before disappearing again. My colleague recently reported, based on our data, that 60% of residential proxy IPs are only observed once in a 90-day window, and the average time an IP lasts in a pool is 4.5 days.

This means that older observations generally become less useful over time.

For teams trying to focus on the highest-confidence and highest-priority signals while reducing noise, filtering around recent activity, such as a last_seen value within the last 7 days, is usually a strong starting point.

An IP seen yesterday in an active proxy pool is usually more operationally relevant than one last observed several weeks ago.

High Activity Is Meaningful

percent_days_seen measures how consistently an IP appeared in residential proxy infrastructure during the rolling observation window. A high value is usually meaningful because it indicates a staying power that is unusual within residential proxy infrastructure.

If an IP has appeared in residential proxy pools across a large portion of the last 30 days, that suggests persistent availability and sustained activity inside those networks. Low values, however, require more nuance.

One of the important things we discussed is that a low percent_days_seen value does not necessarily mean an IP is low risk. Some residential proxy IPs are highly active but short-lived. An IP might only appear for a few days before disappearing entirely while still being heavily used during that time.

That’s why I generally recommend weighting recency more heavily than activity percentage alone, but high activity percentage should still be evaluated as an additional risk factor.

Residential Proxy Data Is Infrastructure Context

Ultimately, residential proxy detection works best when treated as one infrastructure signal among many. The key is in understanding:

• which infrastructure is available to anonymization networks
• how recently it was active
• how persistently it appears inside those ecosystems

That context becomes significantly more useful when combined with other data, like fraud signals, authentication workflows, behavioral analysis, etc. 

The internet has become increasingly dynamic, and thus difficult to interpret through static assumptions alone. Residential proxy infrastructure is one example of that shift.

And, as residential proxy ecosystems continue to grow, the teams that succeed will be the ones building systems capable of interpreting internet infrastructure with more precision and context, while avoiding “silver bullets” that are actually traps.