AI-generated code ships with security holes. Find yours first.
The pattern is now well documented: AI coding tools produce working apps that quietly ship insecure — exposed data, broken access control, secrets in the frontend. Some of 2025’s most-covered breaches came from exactly this. We audit your AI-generated app for the specific failure modes these tools produce, and close them before someone else finds them.
In short: A focused security review of your AI-generated or vibe-coded app, targeting the failure modes these tools are known to produce — broken authorization, exposed secrets, missing input validation. We find them, rank them by blast radius, and fix them. ISO 27001 certified.
The risk is specific, and it’s common
The things founders tell us — in their own words.
"Could someone access data they shouldn’t?" Broken access control is the #1 AI-code flaw.
"Are our API keys exposed?" Secrets in the frontend are routine in AI output.
"We had a scare." A near-miss made security suddenly real.
"We’re about to raise / sell." Security due diligence is coming and you’re not ready.
"We handle user data." And you can’t currently prove it’s safe.
From fragile to production-grade
The same product — rebuilt underneath so it’s secure, stable, and ready to scale.
- Authorization checked on the client, or not at all
- API keys and secrets shipped to the browser
- No input validation — open to injection & XSS
- Database access rules missing or wide open
- No logging — a breach could go unnoticed
- Server-side authorization on every sensitive path
- Secrets moved server-side and rotated
- Validated, sanitized inputs across the app
- Least-privilege data-access rules enforced
- Audit logging so nothing happens silently
Why this keeps happening
Documented, named incidents
The AI code editor Lovable had an access-control flaw (CVE-2025-48757) that publicly exposed data across a large number of apps; an AI agent on Replit deleted a production database during a code freeze; and Base44 had a platform-wide authentication bypass that let unauthenticated users register and bypass SSO across apps built on it. These are public examples of AI-built software shipping insecure.
The research agrees
Independent studies have repeatedly found that a large share of AI-generated code ships with security flaws — the security work simply doesn’t get done unless someone does it deliberately. We audit for the specific patterns, not a generic scan.
AI optimizes for "it works," not "it’s safe"
These tools generate code that passes a demo. Authorization, validation, secret management and rate-limiting are exactly the invisible work they skip — because nothing in a working demo reveals the gap.
What we do about it
We audit against the specific patterns AI tools produce, rank findings by real-world blast radius, and fix the dangerous ones first — under ISO 27001 practices, with an NDA if you want one.
How the security audit runs
Scope & NDA
We agree scope, sign an NDA if you want, and take confidential read access.
Targeted review
A senior engineer reviews for the known AI-code failure modes — auth, secrets, validation, data access.
Ranked findings
You get a severity-ranked report by real-world impact, with a fixed-price plan to remediate.
Harden
We close the urgent risks first, then the rest — verifying each fix.
Proof, not promises
Your security review is led by senior engineers who close these gaps on real production systems, working from the specific failure-mode checklist AI tools produce — not a generic scanner. SynchSoft is ISO 27001 certified and we’ll sign an NDA before you share anything. Note: the named incidents above are public, third-party examples of AI-generated software failing, not SynchSoft work; we reference them because they show the exact patterns we look for in your app.
Don't guess how bad it is. Get a real audit first.
A senior engineer reads your codebase and gives you a plain-English report: what's fragile, what's a security risk, what breaks when you scale — ranked by severity, with the effort to fix each one. You see the full diagnosis before anyone touches a line of your code. No surprises, no scope creep.
- A prioritized, plain-English findings report
- The real risks: auth, secrets, data, scale
- Effort + cost to fix each, ranked
- A rescue roadmap — no lock-in, you own it
Credited in full toward your rescue.
A senior read for a fraction of a $50K rebuild you might not even need.
Turnaround typically 3–5 business days. You own the report either way.
Common questions
Is AI-generated code really less secure?
The evidence points that way. Multiple independent studies have found a large share of AI-generated code ships with security flaws, and several high-profile 2025 breaches traced back to AI-built apps with broken access control. The tools optimize for working code, not safe code — so the security work simply doesn’t get done unless someone does it deliberately.
What’s the most common flaw you find?
Broken access control — authorization checked on the client side or missing entirely, so users can reach data and actions they shouldn’t. It’s the pattern behind most of the public AI-app breaches, and it’s the first thing we check.
Do you fix the issues or just report them?
Both — your choice. The audit is diagnosis, and you can take the report anywhere. If you want us to harden the app, you get a fixed-price plan and we close the urgent risks first.
Will you keep our code confidential?
Yes — ISO 27001 practices, and we’ll sign an NDA before you share anything.
Let’s make it production-grade.
Tell us what you built and where it hurts. You get a senior read, a fixed-price plan, and full code ownership — no hourly billing, no lock-in.
Book a free call