Break your AI agent before attackers do
Every agent that can act is a new attack surface. Automated scans grade the easy stuff A — the real risk (prompt injection, tool abuse, sandbox escape, over-broad actions) needs an adversary. We bring an AI one, and turn what breaks into evidence you can hand a customer or an insurer.
Catch the obvious
A zero-dependency scanner grades the commodity layer A–D on the spot — exposed ports, cross-origin hijack, plaintext credentials, unauthenticated endpoints. This is the free front door.
Attack the layer scans can’t reach
An AI adversary probes what actually gets agents owned: prompt injection, tool/sandbox escape, over-broad autonomous actions, channel injection, supply chain. Thousands of adaptive attempts, not a checklist.
Turn breaks into evidence
You get a report and a defensible grade — proof for an enterprise buyer, a government tender, or an insurer deciding whether to cover the agent. Audit produces the evidence trust runs on.
A scored A. Ten minutes later it was owned.
We ran the automated baseline against a genuinely security-first open-source agent. It earned an A: admin bound to localhost, random gateway token, sensitive APIs returning 401. Then we red-teamed it by hand — and it obeyed an instruction hidden inside content it was asked to summarize, on the first try. Its tool sandbox was silently off, too.
Run the free scan on your own box
Scan a local agent with the CLI (browsers and hosted backends can’t reach your localhost). This is the funnel; the red-team is the depth.
python3 scan.py --target http://127.0.0.1:3000 --path /path/to/agent python3 scan.py --target http://127.0.0.1:3000 --json