Multi-tier runtime defence for AI agents: architecture, escalation logic, and a closed test-to-defence training loop (Apache-2.0)

humanbound-firewall is on PyPI and GitHub today, released under the Apache-2.0 license. It is a multi-tier defense layer for AI agents that catches prompt injection, scope violations, and adversarial inputs before they reach your model. For context, this is the second piece of Humanbound running in the open. The local engine, which is the CLI and SDK we use to run security tests against agents, is also Apache-2.0 and already shipped on PyPI as the humanbound package. The two projects are siblings, and together they make the full test-and-defend loop runnable locally without any dependency on Humanbound’s infrastructure. This article is about the firewall specifically. It explains why we built it the way we did, why we chose to put it in the open, and how it relates to the broader question of how AI agent defense should actually be structured. submitted by /u/Humanbound_AI [link] [comments]Technical Information Security Content & DiscussionRead More