Skip to content

Forum

AI Assistant
Notifications
Clear all

My method: I rate limit all agent outbound traffic and look for spikes.

1 Posts
1 Users
0 Reactions
0 Views
(@newbie_cautious_tom)
Eminent Member
Joined: 1 week ago
Posts: 14
Topic starter
Translate
English
Spanish
French
German
Italian
Portuguese
Russian
Chinese
Japanese
Korean
Arabic
Hindi
Dutch
Polish
Turkish
Vietnamese
Thai
Swedish
Danish
Finnish
Norwegian
Czech
Hungarian
Romanian
Greek
Hebrew
Indonesian
Malay
Ukrainian
Bulgarian
Croatian
Slovak
Slovenian
Serbian
Lithuanian
Latvian
Estonian
  [#506]

Hi everyone, I've been hosting a couple of OpenClaw agents on my home server for a few months now. I'm still pretty new to this, so I'm probably overthinking things, but I got really worried about an agent somehow being compromised and sending out all my data.

Since I'm more comfortable with networking than advanced log analysis, I decided to focus on controlling the *volume* of traffic as a first signal. My method is pretty straightforward: I rate limit all agent outbound traffic and then watch for any spikes.

I run each agent in its own Docker container. I use `--memory` and `--cpus` flags to limit resources, but for network, I apply rate limiting directly at the firewall. I'm using a simple `tc` (traffic control) rule on the host to shape the traffic coming from the agent's virtual interface. I set a conservative average rate and a small burst allowance.

The idea is that a normal agent operation—sending processed results or status updates—should be pretty low and consistent. If it suddenly tries to send a large, sustained burst of data, that trips the limit and gets logged. I also graph the traffic with a simple Prometheus/`node-exporter` setup, so I can see the history.

So far, it's been quiet (which is good!). But I'm wondering if this is too simplistic? Has anyone else tried a similar network-level approach? I know I should probably also look at connection patterns and destinations, but starting with the volume felt like a manageable first step for me.


Learning by doing, sometimes losing data.


   
Quote