ai penetration testing
AI agents that pentest
like real attackers.
Not a scanner with a chatbot bolted on. Muster's agents run the attacker's loop against your live app: recon, attack, prove, retest. Every finding is a replayed exploit with the exact request and the fix.
free · no card · first exploits in ~10 min
Order readable as another user
GET /api/orders/4012 → 200 OK
Webhook fetches cloud metadata
url=169.254.169.254 → 200
Admin API answers with no session
GET /api/admin/users → 200
Reset token never expires
reused 9-day-old token → 200
how the agents work
The attacker's loop, at machine speed.
Recon
The agents map your shipped surface: routes, APIs, auth flows, roles, and the parameters worth attacking.
GET /api/* · sitemap · JS bundles · roles
Attack
They probe like real attackers: auth bypass, IDOR, injection, SSRF, logic abuse. Scoped, rate-limited, non-destructive.
hypothesis → payload → observe → iterate
Prove
A finding only exists if the agent replays it successfully. You get the exact request it sent and what came back.
GET /api/orders/4012 → 200 OK (wrong user)
Retest
Ship the fix and the agent re-runs the same exploit. Closed means proven closed, on every deploy after.
replay after fix → 403 ✓
agents vs scanners
Scanners guess. Agents prove.
| Traditional scanner | Muster. | |
|---|---|---|
| How it finds issues | Matches known signatures and CVEs | Forms hypotheses and attacks, like a human tester |
| What a finding means | Something might be vulnerable | Something is exploitable, with the replayed proof |
| Business-logic flaws | Out of reach for signature checks | A core target: roles, workflows, tenant isolation |
| Noise | Hundreds of maybes to triage | If it can't prove it, it doesn't page you |
| Output | A severity score and a CVE link | The exact request, the impact, and the one-line fix |
control & safety
Autonomous, not unsupervised.
Letting agents attack your app should feel safe, because it is designed to be.
✓You authorize every target
Nothing is tested until you designate it and confirm you own it or have permission to test it.
✓Non-destructive by default
Scoped, rate-limited, read-only where possible. No data-changing or destructive payloads.
✓Every action is logged
Full transcript of what the agents sent and saw. Nothing hidden, everything reviewable.
✓Point it at staging first
Production-safe by design, but you choose the environment. Many teams start on staging.
“In fintech, ‘you might be vulnerable’ isn't good enough. Muster proved exactly what an attacker could reach and gave us the fix before it ever shipped.”
The questions engineers ask.
+How is this different from a vulnerability scanner?
A scanner pattern-matches known signatures and hands you a list of maybes. Muster's agents behave like an attacker: they form hypotheses about your specific app, try them, and only report what they actually exploited. The deliverable is proof, not probability.
+Do the agents hallucinate findings?
No finding ships without a successful replay. The agent must demonstrate the exploit with a real request and response before it appears in your results, and that evidence is attached to the finding. If it can't be proven, it isn't reported.
+Can I see exactly what the agents did?
Yes. Every finding includes the exact request the agent sent and the response that proved the issue, and runs are fully logged. It is the opposite of a black box.
+Is it safe to run against a live app?
Yes. Runs are scoped to targets you authorize, rate-limited, and non-destructive by default: no destructive payloads, read-only where possible. You can point it at staging first if you prefer.
+Is this pentesting of AI, or pentesting by AI?
By AI. Muster's agents attack your web app, APIs, and shipped surface. If you are looking for LLM red-teaming or prompt-injection testing of your own AI features, that is a different category of tool.
+Where do humans fit in?
The agents do the attacking at machine speed, which is what makes it fast and affordable. Humans stay in the loop for attested reports and for anything that needs judgment.