Paste a public GitHub repo and get the exact file, line, and fix in seconds. Free, no signup. Prbl catches the leaked keys, disabled checks, and missing auth that Cursor, Copilot, and Claude leave behind.
create_blog_posts.py:22password='password123'auth.js:12WHERE user='' + username + ''jwt.js:3process.env.JWT_SECRET || 'default_secret'The problem
The seam finding
89.2% human-written. The bug was in the other 10.8%.
create_blog_posts.py:22 — password='password123'A repo that looked like clean, senior Python. One Cursor-generated migration file. That file had a hardcoded credential that would have shipped to production.
SQL injection
A booking platform's auth controller. Login bypass. Real users.
auth.js:12 — SQL injection via string concatenationThe AI wrote a fast login endpoint. It also wrote a classic 1990s-era SQL injection. The rest of the codebase used parameterized queries correctly.
The fallback secret
A payment API. JWT secret falls back in production.
jwt.js:3 — process.env.JWT_SECRET || 'default_secret'Every deployment where the env var is missing signs tokens with a public, predictable key. Every token ever issued can be forged.
How it works
Scan
Prbl detects AI-generated files and scans them for the vulnerability classes AI tools produce systematically — hardcoded secrets, injection, missing auth, weak crypto.
Baseline
Before touching anything, Prbl locks in what your functions actually do — so every fix can be proven not to break what was already working.
Fix
The rewriter applies the minimal fix, validates that behavior is preserved against the baseline, then shows you a clean diff. You approve before anything merges.
Verify
Rescan confirms zero findings. The baseline tests still pass. Nothing ships broken.
Built for AI-generated code
General-purpose security scanners weren’t built to ask one specific question: did an AI tool generate this code, and does it have the gaps AI tools leave behind?
Most of them — including the newest wave of AI-focused scanners — are built to catch known CVEs in your dependencies. Prbl checks something different: the vulnerabilities AI coding tools introduce in the code they write themselves.
Three things Prbl checks that general scanners typically don’t:
Missing access control on AI-scaffolded routes
// AI-generated — no auth check
app.delete('/api/users/:id', async (req, res) => {
await db.users.delete(req.params.id)
res.json({ deleted: true })
})AI coding tools are excellent at generating working CRUD endpoints and often forget the authentication check entirely. General SAST tools check for injection and crypto issues — most don't check whether your routes require auth at all.
Fallback secrets in environment variable lookups
// Becomes 'dev-secret' in production
// if JWT_SECRET was never set
const secret = process.env.JWT_SECRET || 'dev-secret'process.env.JWT_SECRET || 'dev-secret' works perfectly in development and becomes a public, predictable production secret the moment someone deploys without setting the variable. This pattern is extremely common in AI-generated config code.
Timing-unsafe webhook signature checks
// Vulnerable to timing attacks
if (req.headers['x-signature'] === computed) {
processWebhook(req.body)
}AI tools frequently generate signature === computed instead of a constant-time comparison for webhook verification — a subtle but real authentication bypass vector.
Run Prbl alongside your existing tools. It’s not a replacement — it’s the layer that looks specifically for what AI tools get wrong.
Pricing
Free
Pro
Team
Enterprise
Starting at $500/month — scales with usage
FAQ
Start by scanning every file an AI tool generated or touched — that's where systematic issues like hardcoded secrets, missing auth checks, and injection bugs cluster. Run a dedicated AI-code scanner alongside your usual review, since most vibe coded apps ship without anyone reading every line.
Learn more →AI coding tools systematically produce hardcoded credentials, SQL/NoSQL injection from string concatenation, missing authentication on generated CRUD routes, fallback secrets in environment variable lookups, and timing-unsafe comparisons. These aren't random — they're patterns the models repeat across unrelated codebases.
Learn more →Not by default — Cursor writes functionally correct code fast, but it doesn't reason about security unless explicitly prompted to. The risk isn't Cursor being bad at coding; it's that a single AI-generated file can carry a critical flaw inside an otherwise clean, senior-quality codebase.
Learn more →It means a JWT's payload is being read with a decode-only function instead of one that checks the cryptographic signature — so anyone can forge a token with any claims they want. This is one of the most common AI-generated auth bugs because decode and verify often have nearly identical function names.
Learn more →Run a secret-scanning tool across your full git history, not just the current branch, since secrets often remain in old commits even after being "removed." Pair that with a scanner that specifically flags AI-generated files, since hardcoded credentials show up disproportionately in code a model wrote and nobody re-read.
Learn more →It's a pattern like `process.env.JWT_SECRET || 'default_secret'` — code that works fine in development but silently falls back to a public, predictable value in production if the real environment variable was never set. Every token signed with that fallback can be forged by anyone who reads the open-source code.
Learn more →Run a baseline scan to find and fix the obvious issues (secrets, injection, missing auth) before an auditor does, then document your scan history and remediation as evidence. Auditors and SOC 2 reviewers want to see a repeatable process, not just a clean snapshot.
Learn more →BOLA (Broken Object Level Authorization) is when an API checks that a user is logged in but not that they actually own the specific object they're requesting — so changing an ID in the URL lets you read or modify someone else's data. It's the #1 issue in the OWASP API Security Top 10, and AI-generated CRUD endpoints are especially prone to it.
Learn more →Get started today