Firewall ban-sharing across machines

Posted on 2020-09-15

How can you block one IP simultaneously on multiple machines?

The goal

As described in My infrastructure as of 2019, my machines are located in three different sites and are loosely coupled. Nonetheless, I wanted to set things up so that if an IP address is acting maliciously toward one machine, all my machines block that IP at once so the meanie won't get to try one machine after another.

This isn't exactly new, with computers or even in nature. That's why I named this acacia.

One of multiple ways to achieve this goal would have used BGP and communities. The problem with this approach is that I wanted all exchanges between my machines to be encrypted. I could have met this requirement with a VPN, but I would have had to set up too many sessions for my taste. And I wanted to develop a REST API, just for its own sake.

The REST API

I wanted some boring technology so I went with Flask and PostgreSQL. This worked quite well and I enjoyed writing it. I also wrote a client in Python. The client reads the locally blocked IP addresses, sends them to the API, and then fetches the complete block list from the API and feeds it to pf(4)

At the beginning, my code was very unoptimized. I definitely didn't want to run the polling too frequently, so I set it to */5. The new problem was that a lot can happen in 5 minutes. I thought I could get closer to real-time with some Pubsub. And I wanted to play with Pubsub.

Pubsub

I initially tried to use a light MQTT implementation. However I was only left with an itch to scratch, as I couldn't get the examples to work.

While reviewing tb@'s work to update our Redis port to the 6.x branch, I noticed they had finally added TLS support. Well, that was great: I already had a Redis instance running and I like the software, so much that I went through their RU101 online course (and also, I had a lot of free time).

I got a proof of concept to work really easily (in Python as well). I made it evolve into a simple daemon. It worked well... when it was correctly connected to Redis.

The problem is that I often need to reboot my infra to upgrade it. That daemon didn't always detect it had lost the connection to the Redis server. To make the problem worse, I couldn't check at any given time whether the script was correctly connected to Redis. Redis can tell me how many listeners a channel has, but that may not reflect reality.

Only currently connected clients receive a Pubsub message. Therefore, I couldn't ditch my API. Instead, I use the Pubsub system as a light and near real-time system, and use the REST API to be confident no server misses any data.

Rewriting the pubsub daemon in go

Initially, what motivated me the most was to have a web status page so I could check whether it was connected to Redis with any http client. Doing that in Python would not have been simple. Based on what I had heard about Golang, I thought that would be more accessible.

Indeed, I painlessly achieved what I wanted. The new daemon is much more reliable (I haven't been able to disconnect it while it thinks it's connected) and I have a /status http endpoint to monitor it anyway.

I thought maybe people might want to do more stuff or other stuff with this daemon so I made it generic. You can specify any number of channels, each with an associated command you want it to execute.

How do I use it

Infrastructure

I recently published on Github one repository for the API (+ the client) and another one for the Pubsub daemon. Each has a README, but here's how I use the whole:

I run the flask API under docker because I didn't find a way I liked under OpenBSD. While Redis uses an internal PKI, for the API I just use Let's Encrypt. For Redis' PKI, I use a basic shell wrapper based on shellpki.

Here's an excerpt of my pf.conf:

table <api_bans> persist file "/etc/pf.api"
table <bruteweb> persist
[...]
block drop in quick from <api_bans>
block drop in quick from <bruteweb>
[...]
pass in on vio0 proto tcp to port { www, https } keep state \
  (max-src-conn XXX, max-src-conn-rate YYY, \
  overload <bruteweb> flush global)

I use a table per protocol (e.g. bruteweb, brutessh and so on). This allows me to identify why an IP has been banned.

I also have on each machine these two cron jobs:

#Ansible: acacia_client cron api
*/5 * * * * /usr/share/scripts/acacia_client -q cron --only-api
#Ansible: acacia_client cron pubsub
* * * * * /usr/share/scripts/acacia_client -q cron --only-pubsub

Let's dive into each of these two modes!

The REST API mode

In API mode, the script gets the IP addresses from the brute* tables and sends them to the API. Then it fetches the whole list of IP addresses and loads it into pf. Finally, the script expires the IP addresses in brute* so it doesn't process them eternally.

Pubsub Mode

In pubsub mode, the script gets the IP addresses from the brute* tables and shares them over Pubsub. Nothing more, to keep it light.

On each machine I also have a simple script that will ban a given IP address:

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#!/usr/bin/env python3

import ipaddress
import subprocess
import sys


def main():
    ip = sys.argv[1]
    try:
        ipaddress.ip_address(ip)
    except ValueError:
        print(f"{ip} is not valid", file=sys.stderr)
        sys.exit(1)
    subprocess.run(
        ["/sbin/pfctl", "-t", "api_bans", "-T", "add", ip],
        stdout=subprocess.PIPE,
        stderr=subprocess.STDOUT,
        encoding="utf-8",
    )


if __name__ == "__main__":
    main()

Since acacia_pubsub is dropping privileges, I need a doas(1) rule:

permit nopass _acacia as root cmd /usr/share/scripts/acacia_ban

And then acacia_pubsub can call the script to ban it.

Cron job execution frequency

Pubsub is lighter than doing the REST dance so it runs each minute. It's not perfect but there is no way to get a "notification" from pf when an IP address is banned. (Even using OpenBGPD, AFAIK, you can automatically fill a pf table from a community but you can't get it to automatically update a community from a pf table).

Conclusion

With these different bricks, any IP that gets blocked by one machine will automatically get blocked by my other machines, in near real-time. And all traffic still benefits from TLS.