ai generated code security
AI generated code is
exploitable. Prove it.
AI generated code ships features fast and security flaws faster. Muster attacks your app the way a real intruder would, proves exactly what is exploitable, and hands you the fix. From $49 a month.
free to start · no sales call · first exploits in ~10 min
the request that worked
GET /api/orgs/42/invoices Authorization: Bearer <user in org 7> → 200 OK reads another tenant's invoices
Broken tenant isolation (IDOR). Any user can read any org's billing data.
Scope the query to the caller's org in withOrg() before the invoice lookup.
the patterns we prove
Five flaws AI generated code ships, over and over.
AI generated code makes the same mistakes. It does not check ownership, it concatenates strings, it leaks keys into client-visible files, it ships debug scaffolding, and it inherits the starter template's own flaws.
Broken auth and access control
The model writes a login check but forgets to scope data ownership. Any logged-in user reads anything.
Injection
String concatenation in queries, ORM misuse, template injection. The AI wrote it, and it runs.
Exposed secrets in the bundle
API keys, tokens, and passwords baked into client-visible JavaScript because the model put them in the wrong file.
Insecure defaults
Debug routes, open admin panels, permissive CORS, missing rate limits. The scaffold left them behind.
Dependency and scaffold flaws
The starter template's own vulnerable version pinned and shipped untouched.
why scanners miss it
A scanner lists maybes. Muster proves the exploit.
The bugs that matter in AI generated code are not in a CVE database. They are in how your specific ownership checks, query scopes, and file visibility line up. Signature matching cannot see that. An attacker automated to act like one can.
AI generated code security, answered.
+Is AI generated code secure?
Rarely by default. The models optimize for a working app, not a hardened one. The flaws we prove most often are broken access control, injection, and exposed secrets. Those are exploitable, not theoretical.
+What does AI generated code usually get wrong?
Broken auth first. The model writes a login check but forgets to scope data ownership. Injection second. Secrets exposed in the client bundle third. The order is consistent enough that we test for those classes before anything else.
+Can a vulnerability scanner catch these?
No. A scanner compares the app to known signatures. The bugs AI generated code ships are usually custom: a broken ownership check, a mis-scoped query, a leaked key. Those need a human-attacker approach, automated.
+How does Muster test AI generated code?
You point it at your app. AI agents map the shipped surface, attack it like an intruder, and only report what they proven exploited with an exact request. If they cannot prove it, they do not report it.
+What does a finding include?
The exact HTTP request that worked, what it exposed, and the fix. Plus a compliance-ready evidence trail, included at no extra charge. Retests are unlimited on every deploy.