The Hidden Danger of IP Blocking: How to Navigate Security in a CGNAT World
You’ve identified malicious activity on your network—a DDoS attack, credential stuffing, or content scraping. Your first instinct is to block the offending IP address. It’s a simple, effective solution that has been a cornerstone of network security for decades. But what if that single IP address doesn’t represent a single bad actor? What if, by blocking it, you’ve just locked out hundreds, or even thousands, of legitimate customers?
This is the critical challenge posed by Carrier-Grade NAT (CGNAT), a technology widely used by Internet Service Providers (ISPs) that is quietly changing the landscape of online security. Understanding and adapting to it is no longer optional—it’s essential for anyone serious about protecting their services without alienating their user base.
What is Carrier-Grade NAT?
As the internet has grown, the pool of available IPv4 addresses has been exhausted. To solve this shortage, ISPs increasingly rely on CGNAT. In simple terms, CGNAT works like a router for an entire neighborhood or region. An ISP takes a single public IP address and shares it among multiple customers. Each customer has their own private IP address on the ISP’s internal network, but when they access the internet, their traffic all appears to come from that one shared public IP.
The fundamental security principle of “one IP, one user” is now broken. Instead, you have a “one IP, many users” scenario, which creates a significant risk of collateral damage. When you block an IP address involved in malicious activity, you are not just blocking one individual; you are blocking every innocent user who happens to be sharing that same IP address through their ISP’s CGNAT system. This can lead to frustrated customers, a flood of support tickets, and damage to your brand’s reputation.
How to Reliably Detect CGNAT Traffic
The first step to avoiding this collateral damage is to identify when an IP address is likely part of a CGNAT pool. A single detection method is rarely enough; a more reliable approach combines several technical clues.
Here are some of the most effective techniques for detecting CGNAT:
Traceroute Analysis: A traceroute command maps the journey of data packets from a source to a destination. When traffic is coming from behind a CGNAT, the traceroute will often reveal hops through private IP address ranges just before the final public IP. Look for addresses within the dedicated CGNAT space (100.64.0.0/10) or other private ranges like 10.0.0.0/8 or 192.168.0.0/16. The presence of these private hops is a strong indicator of a NAT layer.
Analyzing TCP/IP Header Data: Subtle differences in TCP/IP packets originating from the same IP can expose the presence of multiple devices behind it.
- Time-to-Live (TTL) Values: Different operating systems start with different default TTL values. If you receive packets from a single IP address with inconsistent initial TTLs, it suggests the traffic is coming from multiple, distinct devices.
- Passive OS Fingerprinting (p0f): Tools like p0f can analyze the nuances of TCP/IP packets (like window size and other options) to guess the operating system of the sender. Varying OS fingerprints (e.g., Windows, iOS, and Linux) from a single IP are a classic sign of CGNAT.
Inspecting HTTP Headers: Look for anomalies at the application layer. A single IP address sending requests with a wide variety of
User-Agentstrings in a short period (e.g., a mix of mobile Safari, desktop Chrome, and an Android browser) points to multiple users. Some providers also add specific headers, likeX-Forwarded-For, to indicate the original private IP, though this is not always reliable.
A Smarter Security Strategy: Mitigating Risk Without Harming Users
Detecting CGNAT is only half the battle. The next step is to evolve your security policies to be more precise and less reliant on broad, indiscriminate IP bans.
Here are actionable security tips for a CGNAT-aware world:
Shift from IP-Based Blocking to User-Centric Controls: The most important change is to move away from punishing an IP address and toward managing individual user accounts or sessions. Instead of a permanent IP ban, focus on actions like forcing a password reset, terminating a specific session, or temporarily suspending a user account.
Implement “Soft” Bans and Rate Limiting: If you must take action at the IP level, avoid permanent blocks. Instead, use more nuanced approaches.
- Rate-Limit the IP: Drastically slow down the number of requests allowed from the suspicious IP. This can thwart automated attacks without completely cutting off service for legitimate users.
- Introduce a CAPTCHA Challenge: For traffic from a suspected CGNAT IP, present a CAPTCHA to prove humanity. This effectively blocks bots while allowing real users to proceed.
- Use Temporary “Cool-Down” Periods: Block an IP for a short, escalating period (e.g., 5 minutes, then 15, then 60). This can disrupt an attack while minimizing the impact on innocent bystanders.
Leverage Application-Level Fingerprinting: Enhance your user identification beyond just an IP address. Use a combination of factors—such as browser characteristics, cookies, and device-specific data—to create a more robust “fingerprint” for each user. This allows you to block a single malicious actor’s device or browser, even if they share an IP with others.
Use Contextual Analysis: Combine your signals for better decision-making. For example, if an IP address belongs to a known mobile carrier’s network and exhibits multiple OS fingerprints, you can treat it with a high degree of confidence as a CGNAT IP and apply your “soft” ban policies accordingly.
As the internet continues to evolve, our security practices must evolve with it. Carrier-Grade NAT is a permanent fixture of modern network architecture. By learning to detect it and adapting our response strategies, we can build a more resilient and user-friendly security posture—one that effectively targets threats without causing unintended harm to the very customers we aim to protect.
Source: https://blog.cloudflare.com/detecting-cgn-to-reduce-collateral-damage/
