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AI agents / Security

AI Guardian Lab

Python middleware that checks shell commands proposed by agents, with a dashboard to follow the decisions. Deterministic rules come before any LLM.

Research prototype

AI Guardian Lab dashboard: signed decisions, source badges and audit summary
Image from the project’s existing public presentation.

The problem

An agent with shell access is not safe just because it has been told to behave. I want to check actions before they reach execution.

My contribution

I built the checks between the agent and the shell: binary allowlist, regexes and deterministic task–command intent rules, then L4 semantic review for ambiguous cases. Around them I built a dashboard with search and filters, a demo with five fixed cases and a first-run CLI: setup, doctor, demo.

Available evidence

The demo validates five cases, writes five signed records and never executes a command. The repository contains the tests, CI and the read-only status page.

Usage and limits

Demo and research, not production-ready protection. Docker is not a strong sandbox here, and asking the agent to call Guardian is not enforcement: the executor needs a hook. The local model that observes commands did not pass qualification and is quarantined. Commands outside the pipeline are not covered; a shell string alone cannot establish what an external script will do.