You shipped it.The hard part starts now.
Forty-five percent of AI-generated code introduces OWASP Top 10 vulnerabilities (Veracode, 100+ LLMs tested). Georgia Tech’s Vibe Security Radar tracked 74 CVEs directly attributable to AI coding tools by March 2026 — up from six in January. Amazon’s own AI coding tools were linked to a 13-hour AWS outage in December 2025 and a separate March 2026 outage that cost an estimated 6.3 million orders.
The Vibe-Coded App Hardening Kit is the bridge from “I shipped it” to “it can take real traffic.” A production-readiness audit, a 10-antipattern catalog with exact fixes, a 6-layer pre-launch checklist, and a monitoring template. Built for apps shipped with Blazr, Lovable, Bolt, v0, Replit — or any AI-assisted stack.
The code works. That’s the entire reason it’s dangerous.
AI-generated code is syntactically clean. It compiles. It passes linting. It follows naming conventions. The vulnerabilities are in the logic, not the syntax — which is exactly why traditional static analysis tools, built to catch patterns that human developers commonly produce, often miss them. AI models generate code that works; making it safe requires understanding deployment contexts the model doesn’t have.
When Wiz Research analyzed apps built on Lovable in early 2026, they found OpenAI API keys and Supabase service role keys hardcoded in browser-accessible client-side JavaScript. The model put them there because putting them there made the code work. Moving them to environment variables would have required context the model never had. Veracode tested 100+ LLMs across 80 coding tasks and found 45% of generated code introduces OWASP Top 10 vulnerabilities — concentrated in SQL injection, XSS, log injection, and insecure cryptographic algorithms. The pass rate hasn’t improved across multiple testing cycles from 2025 through early 2026, despite vendor claims of improvement.
CVEs directly attributable to AI coding tools as of March 2026 — up from 6 in January, 15 in February, 35 in March alone. The trajectory is accelerating, not improving. (Georgia Tech Vibe Security Radar)
Of AI-generated backend code includes overly broad permission settings. AI tools frequently generate default admin-level access controls without role restriction. (CrowdStrike 2026 threat reporting)
Orders lost by Amazon in a six-hour outage in March 2026 attributed to AI-coding-tool incidents. If Amazon's safeguards aren't fully established (their internal memo's exact phrasing), yours probably aren't either.
Sources: Veracode 2025-2026 LLM security testing, Georgia Tech Vibe Security Radar (March 2026 update), CrowdStrike 2026 threat reporting, Wiz Research findings on Lovable applications, Cloud Security Alliance research note on AI-generated code (April 2026).
Clear about the lane. No inflated promises.
- A founder-grade production-readiness audit — opinionated, deployable, written for non-security-experts.
- A 10-antipattern catalog with prompt-the-AI fixes and manual code fixes for each.
- A 6-layer pre-launch checklist: auth, rate limits, error handling, observability, secrets, cost ceilings.
- A monitoring template for Vercel, Cloudflare, AWS, and Supabase deployments.
- Tool-agnostic — works for apps built with Blazr, Lovable, Bolt, v0, Replit, Cursor, Windsurf, or hand-written.
- The bridge between “I shipped” and “I'm ready for Snyk / Semgrep / a real security audit.”
- A replacement for Snyk Code, Semgrep, SonarQube, CodeQL, or GitHub Advanced Security.
- A penetration test. Beagle Security and human pentesters do that work.
- A guarantee of safety. The kit gets you to ~80% safe; the last 20% needs professional tools.
- An auto-fixer. We give you the prompt and the code; you apply the fix.
- A subscription. One-time $79 with 12 months of catalog updates.
- Legal, regulatory, or compliance advice. Talk to counsel for GDPR / SOC 2 / HIPAA contexts.
Four artifacts. One hardening pass.
A 40-question structured audit organized by severity. Run it against your live app in 90 minutes. Outputs a prioritized findings list with severity scores, the exact prompt to feed back into your vibe-coding tool, and the manual code change if needed.
The 10 most common, most catastrophic patterns AI generates — sourced from Wiz Research, Veracode, Georgia Tech's Vibe Security Radar, and OWASP. Each one ships with the pattern, the risk, the prompt-back-to-AI fix, and the manual code fix.
The non-negotiable pre-launch pass: authentication, rate limits, error handling, observability, secret management, cost ceilings. Each layer includes the minimum-viable implementation and the “is this enough” question for your specific traffic profile.
Configuration files for Vercel, Cloudflare Workers, AWS (Lambda + API Gateway), and Supabase. Structured logging, error tracking (Sentry-compatible), uptime monitoring, cost alerts, and the specific metrics that catch the antipattern catalog's failure modes.
A 36-page operator manual: how to run the audit weekly post-launch, how to interpret findings, when to bring in Semgrep / Snyk vs. when the kit's checklist is enough, and the escalation criteria for getting a real security review.
As the antipattern landscape evolves (it's moving fast — Vibe Security Radar tracked more CVEs in March 2026 than all of 2025), the catalog and fixes update. Diffs delivered.
Ten patterns. Documented sources. Exact fixes.
The catalog below shows the ten antipatterns in their working form. Each one in the full kit ships with code samples, prompt-back-to-AI fixes, manual code fixes, and the specific tool reference (Semgrep / Snyk / Sentry / Cloudflare WAF) you graduate to once you’ve handled the basics.
API keys (OpenAI, Anthropic, Supabase service role) hardcoded in code that gets shipped to the browser.
AI models put secrets where the code works first. The browser doesn't have access to environment variables; the model puts the key in the file.
Wiz Research confirmed this pattern across Lovable apps in early 2026. Anyone with browser dev tools extracts the key in 60 seconds; your account is drained within days.
Supabase RLS turned off during development to unblock a feature, never re-enabled before launch. The anon key in the frontend allows direct database queries with no authorization.
AI tools rarely flag that disabled RLS persists. The developer remembers to re-enable until launch day pressure hits.
Real case: 50,000-user launch, viral on Twitter, full user database publicly accessible within 72 hours of going live. Security researcher disclosure within a week.
Auth check happens in JavaScript before showing protected routes. The server returns the data regardless — the only barrier is the client-side conditional.
The model treats “the user can’t see the route” as equivalent to “the user can’t access the data.” They are not equivalent.
Anyone with curl, Postman, or Chrome dev tools accesses admin endpoints directly. Compounds when admin endpoints have no rate limiting.
App calls OpenAI / Anthropic / Gemini APIs without per-user limits, per-tenant ceilings, or daily caps. Recursive call patterns can cascade.
The model writes the simplest implementation that calls the API. Adding cost controls is a deployment concern the model doesn't think about.
Documented: viral HackerNews post triggered 10 million requests in 4 hours. AI-generated code had no rate limiting, no request throttling, no cost monitoring, and no AWS billing alerts configured. Cost outcomes in this category of incident routinely land in the five-figure-bill range before the team notices.
Every endpoint accepts unlimited requests. No express-rate-limit, no Cloudflare WAF rule, no API Gateway throttle.
The model implements the happy path. Rate limiting is a defensive layer the user has to explicitly request.
Compounds with antipattern #04 (cost ceilings) and creates DOS risk independent of cost. A single bad actor can take your app offline with a basic script.
Premium feature gating checked via a JavaScript variable. API endpoints don't check payment state server-side.
The model builds the feature visibility logic in the same place as the UI logic — the frontend.
Documented: thousands of users on Reddit shared the JavaScript modification needed to bypass paywall. Six months of free premium access before the team noticed.
Service roles, database users, and deployment credentials granted admin-level access. The principle of least privilege is not applied.
The model grants permissions broad enough to make every code path work, rather than minimum permissions for each path.
41% of AI-generated backend code includes overly broad permissions (CrowdStrike 2026). Compromise of one credential becomes compromise of everything.
Access-Control-Allow-Origin set to *. State-changing routes have no CSRF token check.
The model removes CORS errors by setting the most permissive policy. CSRF protection is a separate framework concern the model doesn't surface.
Cross-origin attacks become trivial. Any site can call your authenticated API endpoints on behalf of a logged-in user.
Errors return stack traces, database schema details, or internal API paths in the response body.
The model uses verbose errors for development clarity and doesn't switch to sanitized errors for production by default.
Compounds with system-prompt leakage — Cycode March 2026 research found this is one of the most exploitable categories. An attacker maps your internal architecture in 20 minutes of probing.
AI agents add npm / pip packages liberally to solve problems. The package.json grows; the audit doesn't run; CVEs accumulate.
The model resolves “I need X functionality” by installing a package, not by checking if the package is maintained or secure.
Compounds across the dependency tree. A single vulnerable transitive dependency exposes the whole app. npm audit / pip audit catches most; nobody runs them.
Six non-negotiable layers. One pre-launch pass.
The checklist below maps to the audit and the antipattern catalog. Each layer has a minimum-viable implementation and an “is this enough” question for your specific traffic profile.
Server-side check on every protected route. No frontend-only gates. Sessions or JWTs verified per request.
Can a malicious user bypass your auth by removing the JavaScript or sending a direct API call? If yes — you're not done.
Per-endpoint and per-user limits via Cloudflare WAF, express-rate-limit, or platform-native throttling.
Can a single bad actor with a 10-line script take down your app or empty your AI API budget? If yes — you're not done.
Sanitized error responses in production. No stack traces, no schema details, no internal API paths in client-facing errors.
If a user triggers an error, do they learn anything about your internal architecture? If yes — you're not done.
Structured logging, error tracking (Sentry or equivalent), uptime monitoring, and a single dashboard showing the four key metrics.
When something breaks at 3am, will you know within 10 minutes or 10 hours? If 10 hours — you're not done.
No secrets in code. No secrets in client-side bundles. All credentials in environment variables or a secrets manager.
If you ran a string search for 'sk-' or 'api_key' across your repo and your built bundle, would anything come up? If yes — you're not done.
Per-user daily limits on AI API calls. Per-tenant monthly caps. Billing alerts at 50%, 80%, and 100% of expected burn.
If your app went viral tomorrow, what's the worst-case AWS / OpenAI / Anthropic bill? If you don't know — you're not done.
Tool-agnostic catalog. Tool-specific deployment notes.
The antipatterns are model-agnostic — they appear across every vibe-coding output regardless of the tool that generated them. The deployment notes are tool-specific, walking through how to apply each fix in the environment you’re actually using.
Native companion product. The kit's prompt-back-to-AI fixes are calibrated for Blazr's prompting model first; the same fixes work across the other tools below.
Wiz Research's findings on hardcoded secrets came from Lovable apps specifically. The kit's secret-management layer is the highest-leverage fix for this stack.
Common patterns: client-side auth, no rate limits, Supabase RLS forgotten in development. The kit's audit catches all three on a first pass.
v0 outputs typically deploy to Vercel. Cost-ceilings layer ships with Vercel-specific configuration for spend alerts and request budgets.
Replit apps often have direct database access through Replit's own secrets system. The kit covers the gap between Replit's defaults and production security posture.
The antipattern catalog is tool-agnostic. If your codebase has AI-generated code in it, the patterns apply regardless of which assistant wrote them.
The integrity moat.
Exactly what you get for $79, and what you don’t.
- 40-question production-readiness audit.
- 10-antipattern catalog with prompt fixes and manual code fixes.
- 6-layer pre-launch checklist with testable “is this enough” questions.
- Monitoring & cost-ceiling templates for Vercel, Cloudflare, AWS, Supabase.
- 36-page field manual.
- 12 months of catalog updates as the antipattern landscape evolves.
- Replacement for Snyk Code / Semgrep / SonarQube. Use them after the kit.
- Penetration testing. Beagle Security and human pentesters handle that.
- Compliance certification (SOC 2, HIPAA, PCI-DSS, GDPR). Talk to auditors.
- Custom security review for regulated workloads. Quote separately.
- Auto-application of fixes. We give you the prompt and code; you apply it.
- 100% safety. Nothing delivers that. The kit gets you to ~80%.
Building AI features into your vibe-coded app and worried about margin death from runaway token costs? Pair this with the Token Economics Workbook ($59). The Hardening Kit handles cost ceilings; the Workbook handles cost structure.
The questions vibe-coding founders actually ask before hardening their first ship.
No, and that matters. Snyk, Semgrep, SonarQube, CodeQL, and GitHub Advanced Security are professional SAST scanners — they're excellent, they're built for dev teams who already understand security findings, and the open-source options (Semgrep, SonarQube) are free. The Hardening Kit is the founder-grade audit pass that lives upstream of those tools. It tells you WHAT to look for, WHY it matters, the exact fix for the top 10 antipatterns, and WHEN to graduate to Snyk or Semgrep. Most vibe-coding founders should be using this kit AND open-source SAST scanners — the kit explains how to set them up correctly. Honest framing: at $79, this is the bridge to those tools, not a replacement for them.
Yes — all five, plus Cursor, Windsurf, Claude Code-built apps, and any AI-assisted stack. The antipattern catalog is tool-agnostic because the vulnerabilities are model-agnostic: hardcoded secrets, frontend-only auth, missing rate limits, overly broad permissions, and no cost ceilings appear across every vibe-coding output regardless of which tool generated them (Wiz Research's findings on Lovable apps mirror Georgia Tech's findings on Claude Code and GitHub Copilot output). The pre-launch checklist has tool-specific deployment notes for each platform.
Most of them, yes. The kit ships with two paths per antipattern: (1) the prompt to feed back into your vibe-coding tool (Blazr, Lovable, Bolt, etc.) to have it fix the issue itself — works for ~70% of the antipatterns, and (2) the exact code change required if your tool can't handle the fix or you want to apply it manually. Authentication and authorization fixes are flagged for human review or paid help because, per the established consensus, auth is where a single bug means game over. The kit is honest about which fixes are safe for non-developers to apply and which need a developer.
OWASP Top 10 is the canonical reference and the kit cites it explicitly — it's foundational and free. The free checklists online are usually generic web-app advice that doesn't account for the specific failure modes of AI-generated code. The Hardening Kit's IP is the *intersection*: which OWASP categories show up most often in AI-generated code specifically (Veracode found 45% of AI code introduces OWASP Top 10 issues, concentrated in SQL injection, XSS, log injection, and insecure crypto), with the exact prompt-back-to-Claude or code fix for each one. Plus the cost-ceiling and monitoring layers that OWASP doesn't cover because they're operational, not security categories.
Both. The cost-ceilings layer is specifically built for apps that hit paid LLM APIs — daily ceilings per user, per-tenant limits, recursive-call detection, prompt-injection guards (73% of AI systems were found exposed to prompt injection in 2026 audits per CrowdStrike-cited research), and system-prompt leakage prevention. If your vibe-coded app uses Claude, GPT, or Gemini API calls anywhere, this layer alone is worth the kit price the first time a viral spike doesn't blow up your bill.
The monitoring template ships with configurations for the four most common vibe-coding deployment targets: Vercel (where v0, Lovable, and many Bolt apps deploy), Cloudflare Workers, AWS (Lambda + API Gateway), and Supabase. Each variant covers structured logging, error tracking (Sentry-compatible), uptime monitoring, cost alerts, and the specific metrics that catch the failure modes documented in the antipattern catalog. You pick the variant matching your stack; the rest stay in the kit for future use.
The catalog ships with documented case studies — anonymized but real. The patterns: (1) Viral launch + no rate limiting = five-figure AWS bill in hours from a HackerNews post (10M requests in 4 hours is a documented case). (2) Supabase Row-Level Security disabled 'temporarily' in development = entire user database publicly accessible after a 50K-user launch. (3) Client-side payment verification = months of free premium access for any user who opens Chrome dev tools. (4) Hardcoded API keys in client-side JS = your OpenAI or Anthropic account drained in days. The honest framing: most vibe-coded apps that don't take significant traffic survive because they're below the radar. The catastrophic failures happen at the moment of success — which is usually the worst possible time.
30-day no-questions refund. Run the production-readiness audit against your live vibe-coded app. If it doesn't surface at least three issues you didn't already know about, email RedHub AI support and we refund. We can count refunds on one hand to date — the audit pass typically surfaces 8-15 findings on any vibe-coded app within the first hour. The patterns are consistent enough to be predictable.
Don’t learn the hard way.
Learn the eighty-dollar way.
Run the audit against your live app this afternoon. Apply the prompt fixes by tomorrow. Have the six layers in place by the weekend. The catastrophic failures happen at the moment of success — which is usually the worst possible time to be learning the hard way.
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