11–13 of 13 posts
I'm reminded of all the man-hours spent building layers that prohibited someone's "about me" field from containing words like "update" or "delete" or "truncate". Sure, technically it reduced the the odds of the system getting hacked, but it rankles against some engineering ideal of "not a proper fix." Yet it still happens, because a "proper fix" involves some change to the underlying layer (RDBMS or LLM).
Proxy catches what passes through. Injection via tool descriptions or memory artifacts doesn't pass through. We handle it at the content evaluation layer, not the network layer. Curious how you're catching the indirect stuff.
Creator here. Quick TL;DR and some context: FireClaw = prompt injection firewall for AI agents. Proxy architecture, not just detection. 4-stage pipeline, no bypass mode, community threat feed. The thing that surprised us most during research: nobody is doing this. There are great pattern detectors (Rebuff, LLM Guard, etc.) but they all work post-hoc — the content has already entered the agent's context by the time yo…
this is cool, definitely going to look into it and probably try to integrate it with my opensource project. prompt injection keeps me up at night thanks for putting in some work trying to solve it.
We just shipped /api/scan in v1.1.0 which could plug into your policy evaluation — scan content before it enters the decision pipeline. Also now on Docker and npm (npx fireclaw) for easier integration.
Happy to brainstorm integration. Feel free to open an issue on our repo or reach out on GitHub.