Security Questions & Answers

Yes — and for Lovable it isn't hypothetical. CVE-2025-48757: Lovable RLS Vulnerability demonstrated real exploitation in the wild. In January 2025, a critical RLS misconfiguration was discovered affecting 170+ Lovable apps, exposing emails, API keys, payment details, and personal data.

Lovable is secure enough for production *only after* you close the specific gaps that have already been exploited in the wild. CVE-2025-48757: Lovable RLS Vulnerability is the headline incident — apps built with Lovable and no security review have demonstrably shipped with critical data exposure.

Lovable apps surface a predictable distribution of issues: 2 critical-impact classes (complete database exposure via missing rls, data manipulation via open rls), 3 high-impact, 0 medium-or-below. The critical ones are what cause data breaches.

Lovable security best practices are dictated by Lovable's actual risk profile, not a generic checklist. The top three: enable rls on all tables and write policies that verify auth; move all secrets to server-side functions; enable email verification, enforce password requirements, add rate limiting.

Scans of Lovable apps surface a recurring set of findings: complete database exposure via missing rls, api key theft from js bundles, and account takeover via weak auth. 2 of these have documented real-world exploitation.

Lovable apps run in production — but CVE-2025-48757: Lovable RLS Vulnerability is the cautionary tale. The platform's suitability depends entirely on whether you've closed the same gap that caused real apps to leak data. Pre-scan, no; post-scan with findings fixed, yes.

A Lovable security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Lovable's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Lovable encrypts data at rest and in transit by default. But encryption doesn't protect against the breach class that actually happens to Lovable apps — CVE-2025-48757: Lovable RLS Vulnerability exposed data through Row Level Security (RLS) policies misconfiguration, not cryptographic weakness. Data protection in Lovable is 90% access control, 10% encryption.

The mistakes we see repeatedly in Lovable apps: complete database exposure via missing rls; api key theft from js bundles; account takeover via weak auth. Each one is a specific failure mode of Lovable's workflow — not generic programming mistakes.

Lovable sits in the same security posture class as Bolt.new, v0.dev, Replit. The differentiators are specific: Lovable has a documented CVE (Lovable RLS Vulnerability), its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Bolt.new app are exposed api keys and missing supabase rls — both routinely found by automated scanners within minutes of deployment.

Bolt.new gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Bolt.new breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Bolt.new apps are exposed api keys, missing supabase rls, no security headers. These aren't generic — they map to how Bolt.new deploys and what stack it leans on.

The best practices for Bolt.new apps track the attack vectors specific to Bolt.new's stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Bolt.new app scans surface the same cluster of vulnerabilities repeatedly: exposed api keys, missing supabase rls, no security headers. The pattern is stable across Bolt.new versions.

Bolt.new apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Bolt.new security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Bolt.new's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Bolt.new app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Bolt.new's infra handles it), but who can *read* the decrypted data is where production Bolt.new apps succeed or fail.

The mistakes we see repeatedly in Bolt.new apps: exposed api keys; missing supabase rls; no security headers. Each one is a specific failure mode of Bolt.new's workflow — not generic programming mistakes.

Bolt.new sits in the same security posture class as Lovable, v0.dev, Replit. The differentiators are specific: Bolt.new has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Replit app are credentials in public repls and ai agent database destruction — both routinely found by automated scanners within minutes of deployment.

Replit gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Replit breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Replit apps surface a predictable distribution of issues: 2 critical-impact classes (credentials in public repls, ai agent database destruction), 2 high-impact, 1 medium-or-below. The critical ones are what cause data breaches.

Replit security best practices are dictated by Replit's actual risk profile, not a generic checklist. The top three: use replit secrets feature; review all ai agent actions; migrate all secrets to replit secrets tab immediately.

Scans of Replit apps surface a recurring set of findings: credentials in public repls, ai agent database destruction, and secrets not using replit secrets. 2 of these have documented real-world exploitation.

Replit apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Replit security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Replit's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Replit app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Replit's infra handles it), but who can *read* the decrypted data is where production Replit apps succeed or fail.

The mistakes we see repeatedly in Replit apps: credentials in public repls; ai agent database destruction; secrets not using replit secrets. Each one is a specific failure mode of Replit's workflow — not generic programming mistakes.

Replit sits in the same security posture class as Bolt.new, Lovable, Cursor. The differentiators are specific: Replit has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a v0.dev app are xss via dangerouslysetinnerhtml and missing input validation — both routinely found by automated scanners within minutes of deployment.

v0.dev gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world v0.dev breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

v0.dev apps surface a predictable distribution of issues: 0 critical-impact classes (none), 3 high-impact, 2 medium-or-below. The critical ones are what cause data breaches.

v0.dev security best practices are dictated by v0.dev's actual risk profile, not a generic checklist. The top three: search for dangerouslysetinnerhtml; add server-side validation for all form inputs; review all fetch/api calls.

v0.dev app scans surface the same cluster of vulnerabilities repeatedly: xss via dangerouslysetinnerhtml, missing input validation, placeholder api calls. The pattern is stable across v0.dev versions.

v0.dev apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A v0.dev security audit is not a generic checklist — it's a targeted probe of the failure modes specific to v0.dev's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a v0.dev app rests on Row Level Security (RLS) policies. Encryption is table-stakes (v0.dev's infra handles it), but who can *read* the decrypted data is where production v0.dev apps succeed or fail.

The mistakes we see repeatedly in v0.dev apps: xss via dangerouslysetinnerhtml; missing input validation; placeholder api calls. Each one is a specific failure mode of v0.dev's workflow — not generic programming mistakes.

v0.dev sits in the same security posture class as Bolt.new, Lovable, Cursor. The differentiators are specific: v0.dev has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Cursor app are prompt injection in mcp servers and workspace trust exploitation — both routinely found by automated scanners within minutes of deployment.

Cursor gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Cursor breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Cursor apps surface a predictable distribution of issues: 2 critical-impact classes (prompt injection in mcp servers, workspace trust exploitation), 2 high-impact, 1 medium-or-below. The critical ones are what cause data breaches.

Cursor security best practices are dictated by Cursor's actual risk profile, not a generic checklist. The top three: review mcp server sources; enable workspace trust in settings; review all ai suggestions critically.

Scans of Cursor apps surface a recurring set of findings: prompt injection in mcp servers, workspace trust exploitation, and code suggestion security flaws. 4 of these have documented real-world exploitation.

Cursor apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Cursor security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Cursor's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Cursor app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Cursor's infra handles it), but who can *read* the decrypted data is where production Cursor apps succeed or fail.

The mistakes we see repeatedly in Cursor apps: prompt injection in mcp servers; workspace trust exploitation; code suggestion security flaws. Each one is a specific failure mode of Cursor's workflow — not generic programming mistakes.

Cursor sits in the same security posture class as Bolt.new, Lovable, Replit. The differentiators are specific: Cursor has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes — and for Windsurf it isn't hypothetical. 94+ Chromium CVEs Discovered in Windsurf demonstrated real exploitation in the wild. Security researchers have identified over 94 Chromium-based vulnerabilities in Windsurf IDE.

Windsurf is secure enough for production *only after* you close the specific gaps that have already been exploited in the wild. 94+ Chromium CVEs Discovered in Windsurf is the headline incident — apps built with Windsurf and no security review have demonstrably shipped with critical data exposure.

Windsurf apps surface a predictable distribution of issues: 3 critical-impact classes (chromium cve exposure, mcp server exploitation), 1 high-impact, 1 medium-or-below. The critical ones are what cause data breaches.

Windsurf security best practices are dictated by Windsurf's actual risk profile, not a generic checklist. The top three: keep windsurf updated to latest version; only install mcp servers from trusted sources; review all cascade suggestions.

Scans of Windsurf apps surface a recurring set of findings: chromium cve exposure, mcp server exploitation, and cascade code suggestions. 1 of these have documented real-world exploitation.

Windsurf apps run in production — but 94+ Chromium CVEs Discovered in Windsurf is the cautionary tale. The platform's suitability depends entirely on whether you've closed the same gap that caused real apps to leak data. Pre-scan, no; post-scan with findings fixed, yes.

A Windsurf security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Windsurf's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Windsurf app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Windsurf's infra handles it), but who can *read* the decrypted data is where production Windsurf apps succeed or fail.

The mistakes we see repeatedly in Windsurf apps: chromium cve exposure; mcp server exploitation; cascade code suggestions. Each one is a specific failure mode of Windsurf's workflow — not generic programming mistakes.

Windsurf sits in the same security posture class as Cursor, Bolt.new, Lovable. The differentiators are specific: Windsurf has a documented CVE (94+ Chromium CVEs Discovered in Windsurf), its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes — and for Supabase it isn't hypothetical. Most Supabase Security Issues: Missing RLS demonstrated real exploitation in the wild. The majority of Supabase security incidents stem from tables without Row Level Security enabled.

Supabase is secure enough for production *only after* you close the specific gaps that have already been exploited in the wild. Most Supabase Security Issues: Missing RLS is the headline incident — apps built with Supabase and no security review have demonstrably shipped with critical data exposure.

Supabase apps surface a predictable distribution of issues: 2 critical-impact classes (tables without rls, service role key exposure), 2 high-impact, 1 medium-or-below. The critical ones are what cause data breaches.

Supabase security best practices are dictated by Supabase's actual risk profile, not a generic checklist. The top three: alter table name enable row level security; on every table; never use service_role in frontend; use (select auth.

Scans of Supabase apps surface a recurring set of findings: tables without rls, service role key exposure, and permissive rls policies. 1 of these have documented real-world exploitation.

Supabase apps run in production — but Most Supabase Security Issues: Missing RLS is the cautionary tale. The platform's suitability depends entirely on whether you've closed the same gap that caused real apps to leak data. Pre-scan, no; post-scan with findings fixed, yes.

A Supabase security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Supabase's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Supabase encrypts data at rest and in transit by default. But encryption doesn't protect against the breach class that actually happens to Supabase apps — Most Supabase Security Issues: Missing RLS exposed data through Row Level Security (RLS) policies misconfiguration, not cryptographic weakness. Data protection in Supabase is 90% access control, 10% encryption.

The mistakes we see repeatedly in Supabase apps: tables without rls; service role key exposure; permissive rls policies. Each one is a specific failure mode of Supabase's workflow — not generic programming mistakes.

Supabase sits in the same security posture class as Firebase, Bolt.new, Lovable. The differentiators are specific: Supabase has a documented CVE (Most Supabase Security Issues: Missing RLS), its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Firebase app are test mode rules in production and auth without authorization — both routinely found by automated scanners within minutes of deployment.

Firebase gives you the primitives for a secure app (Firebase, managed auth, hosting), but every real-world Firebase breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Firebase apps surface a predictable distribution of issues: 2 critical-impact classes (test mode rules in production, admin sdk credential exposure), 2 high-impact, 1 medium-or-below. The critical ones are what cause data breaches.

Firebase security best practices are dictated by Firebase's actual risk profile, not a generic checklist. The top three: replace test rules immediately; add request; admin sdk is server-only.

Scans of Firebase apps surface a recurring set of findings: test mode rules in production, auth without authorization, and admin sdk credential exposure. 1 of these have documented real-world exploitation.

Firebase apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Firebase security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Firebase's stack (Firebase (Firestore + Security Rules) as the database). The audit order: fingerprint the deployment, test Firestore Security Rules, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Firebase app rests on Firestore Security Rules. Encryption is table-stakes (Firebase's infra handles it), but who can *read* the decrypted data is where production Firebase apps succeed or fail.

The mistakes we see repeatedly in Firebase apps: test mode rules in production; auth without authorization; admin sdk credential exposure. Each one is a specific failure mode of Firebase's workflow — not generic programming mistakes.

Firebase sits in the same security posture class as Supabase, Bolt.new, Lovable. The differentiators are specific: Firebase has no public critical CVE on file, its defaults around Firestore Security Rules differ, and its primary stack (Firebase (Firestore + Security Rules) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Vercel app are environment variable misconfiguration and preview deployment exposure — both routinely found by automated scanners within minutes of deployment.

Vercel gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Vercel breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Vercel apps surface a predictable distribution of issues: 0 critical-impact classes (none), 2 high-impact, 3 medium-or-below. The critical ones are what cause data breaches.

Vercel security best practices are dictated by Vercel's actual risk profile, not a generic checklist. The top three: audit variable scopes in vercel dashboard; enable vercel authentication for preview deployments; configure headers in next.

Vercel app scans surface the same cluster of vulnerabilities repeatedly: environment variable misconfiguration, preview deployment exposure, missing security headers. The pattern is stable across Vercel versions.

Vercel apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Vercel security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Vercel's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Vercel app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Vercel's infra handles it), but who can *read* the decrypted data is where production Vercel apps succeed or fail.

The mistakes we see repeatedly in Vercel apps: environment variable misconfiguration; preview deployment exposure; missing security headers. Each one is a specific failure mode of Vercel's workflow — not generic programming mistakes.

Vercel sits in the same security posture class as Netlify, Railway, Render. The differentiators are specific: Vercel has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Netlify app are build-time secret exposure and deploy preview exposure — both routinely found by automated scanners within minutes of deployment.

Netlify gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Netlify breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Netlify apps surface a predictable distribution of issues: 1 critical-impact class (build-time secret exposure), 0 high-impact, 4 medium-or-below. The critical ones are what cause data breaches.

Netlify security best practices are dictated by Netlify's actual risk profile, not a generic checklist. The top three: use netlify functions for runtime secrets; password-protect deploy previews in site settings; create _headers file with csp, x-frame-options, hsts.

Netlify app scans surface the same cluster of vulnerabilities repeatedly: build-time secret exposure, deploy preview exposure, missing _headers file. The pattern is stable across Netlify versions.

Netlify apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Netlify security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Netlify's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Netlify app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Netlify's infra handles it), but who can *read* the decrypted data is where production Netlify apps succeed or fail.

The mistakes we see repeatedly in Netlify apps: build-time secret exposure; deploy preview exposure; missing _headers file. Each one is a specific failure mode of Netlify's workflow — not generic programming mistakes.

Netlify sits in the same security posture class as Vercel, Railway, Render. The differentiators are specific: Netlify has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Bubble app are missing privacy rules and public api workflows — both routinely found by automated scanners within minutes of deployment.

Bubble's platform layer has no known systemic vulnerabilities, but Bubble apps regularly ship with misconfigurations that leave user data exposed. Platform security ≠ app security.

Bubble apps surface a predictable distribution of issues: 2 critical-impact classes (missing privacy rules, public api workflows), 1 high-impact, 2 medium-or-below. The critical ones are what cause data breaches.

Bubble security best practices are dictated by Bubble's actual risk profile, not a generic checklist. The top three: configure privacy rules for every data type in data → privacy; check 'this workflow requires authentication' on all api workflows; audit plugins.

Scans of Bubble apps surface a recurring set of findings: missing privacy rules, public api workflows, and plugin security risks. 1 of these have documented real-world exploitation.

Bubble apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Bubble security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Bubble's stack (a hosted backend). The audit order: fingerprint the deployment, test database access controls, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Bubble app rests on database access controls. Encryption is table-stakes (Bubble's infra handles it), but who can *read* the decrypted data is where production Bubble apps succeed or fail.

The mistakes we see repeatedly in Bubble apps: missing privacy rules; public api workflows; plugin security risks. Each one is a specific failure mode of Bubble's workflow — not generic programming mistakes.

Bubble sits in the same security posture class as Webflow, Retool, Lovable. The differentiators are specific: Bubble has no public critical CVE on file, its defaults around database access controls differ, and its primary stack (a hosted backend) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Webflow app are custom code xss and cms content visibility — both routinely found by automated scanners within minutes of deployment.

Webflow's platform layer has no known systemic vulnerabilities, but Webflow apps regularly ship with misconfigurations that leave user data exposed. Platform security ≠ app security.

Webflow apps surface a predictable distribution of issues: 0 critical-impact classes (none), 2 high-impact, 3 medium-or-below. The critical ones are what cause data breaches.

Webflow security best practices are dictated by Webflow's actual risk profile, not a generic checklist. The top three: audit all custom code embeds; configure collection visibility settings; review all third-party scripts.

Webflow app scans surface the same cluster of vulnerabilities repeatedly: custom code xss, cms content visibility, third-party script risks. The pattern is stable across Webflow versions.

Webflow apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Webflow security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Webflow's stack (a hosted backend). The audit order: fingerprint the deployment, test database access controls, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Webflow app rests on database access controls. Encryption is table-stakes (Webflow's infra handles it), but who can *read* the decrypted data is where production Webflow apps succeed or fail.

The mistakes we see repeatedly in Webflow apps: custom code xss; cms content visibility; third-party script risks. Each one is a specific failure mode of Webflow's workflow — not generic programming mistakes.

Webflow sits in the same security posture class as Framer, Bubble, Netlify. The differentiators are specific: Webflow has no public critical CVE on file, its defaults around database access controls differ, and its primary stack (a hosted backend) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Retool app are resource connection security and query parameter injection — both routinely found by automated scanners within minutes of deployment.

Retool gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Retool breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Retool apps surface a predictable distribution of issues: 1 critical-impact class (access control configuration), 1 high-impact, 2 medium-or-below. The critical ones are what cause data breaches.

Retool security best practices are dictated by Retool's actual risk profile, not a generic checklist. The top three: scan your deployed application with a security tool that understands this stack; use parameterized queries, sanitize all user input, and render dynamic content with framework escaping (react jsx, not dangerouslysetinnerhtml); enable row level security (supabase) or security rules (firebase) on every table.

Retool app scans surface the same cluster of vulnerabilities repeatedly: resource connection security, query parameter injection, access control configuration. The pattern is stable across Retool versions.

Retool apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Retool security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Retool's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Retool app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Retool's infra handles it), but who can *read* the decrypted data is where production Retool apps succeed or fail.

The mistakes we see repeatedly in Retool apps: resource connection security; query parameter injection; access control configuration. Each one is a specific failure mode of Retool's workflow — not generic programming mistakes.

Retool sits in the same security posture class as Bubble, Supabase, PostgreSQL. The differentiators are specific: Retool has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Framer app are cms collection visibility and code override security — both routinely found by automated scanners within minutes of deployment.

Framer's platform layer has no known systemic vulnerabilities, but Framer apps regularly ship with misconfigurations that leave user data exposed. Platform security ≠ app security.

Framer apps surface a predictable distribution of issues: 0 critical-impact classes (none), 0 high-impact, 4 medium-or-below. The critical ones are what cause data breaches.

Framer security best practices are dictated by Framer's actual risk profile, not a generic checklist. The top three: scan your deployed application with a security tool that understands this stack; scan your deployed application with a security tool that understands this stack; scan your deployed application with a security tool that understands this stack.

Framer app scans surface the same cluster of vulnerabilities repeatedly: cms collection visibility, code override security, third. The pattern is stable across Framer versions.

Framer apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Framer security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Framer's stack (a hosted backend). The audit order: fingerprint the deployment, test database access controls, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Framer app rests on database access controls. Encryption is table-stakes (Framer's infra handles it), but who can *read* the decrypted data is where production Framer apps succeed or fail.

The mistakes we see repeatedly in Framer apps: cms collection visibility; code override security; third. Each one is a specific failure mode of Framer's workflow — not generic programming mistakes.

Framer sits in the same security posture class as Webflow, Bubble, Vercel. The differentiators are specific: Framer has no public critical CVE on file, its defaults around database access controls differ, and its primary stack (a hosted backend) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Render app are auto-deploy to production and environment group over-sharing — both routinely found by automated scanners within minutes of deployment.

Render gives you the primitives for a secure app (Postgres, managed auth, hosting), but every real-world Render breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Render apps surface a predictable distribution of issues: 0 critical-impact classes (none), 2 high-impact, 3 medium-or-below. The critical ones are what cause data breaches.

Render security best practices are dictated by Render's actual risk profile, not a generic checklist. The top three: disable auto-deploy for production; create separate groups for prod/staging; configure separate env vars for preview environments.

Render app scans surface the same cluster of vulnerabilities repeatedly: auto-deploy to production, environment group over-sharing, preview environment leakage. The pattern is stable across Render versions.

Render apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Render security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Render's stack (Postgres as the database). The audit order: fingerprint the deployment, test row-level policies or server-side authorization middleware, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Render app rests on row-level policies or server-side authorization middleware. Encryption is table-stakes (Render's infra handles it), but who can *read* the decrypted data is where production Render apps succeed or fail.

The mistakes we see repeatedly in Render apps: auto-deploy to production; environment group over-sharing; preview environment leakage. Each one is a specific failure mode of Render's workflow — not generic programming mistakes.

Render sits in the same security posture class as Railway, Vercel, Netlify. The differentiators are specific: Render has no public critical CVE on file, its defaults around row-level policies or server-side authorization middleware differ, and its primary stack (Postgres as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Railway app are public database endpoints and connection string logging — both routinely found by automated scanners within minutes of deployment.

Railway gives you the primitives for a secure app (Postgres, managed auth, hosting), but every real-world Railway breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Railway apps surface a predictable distribution of issues: 1 critical-impact class (public database endpoints), 1 high-impact, 3 medium-or-below. The critical ones are what cause data breaches.

Railway security best practices are dictated by Railway's actual risk profile, not a generic checklist. The top three: enable private networking for all database connections; never console; use paid tier for production.

Railway app scans surface the same cluster of vulnerabilities repeatedly: public database endpoints, connection string logging, shared infrastructure risks. The pattern is stable across Railway versions.

Railway apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Railway security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Railway's stack (Postgres as the database). The audit order: fingerprint the deployment, test row-level policies or server-side authorization middleware, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Railway app rests on row-level policies or server-side authorization middleware. Encryption is table-stakes (Railway's infra handles it), but who can *read* the decrypted data is where production Railway apps succeed or fail.

The mistakes we see repeatedly in Railway apps: public database endpoints; connection string logging; shared infrastructure risks. Each one is a specific failure mode of Railway's workflow — not generic programming mistakes.

Railway sits in the same security posture class as Vercel, Netlify, Render. The differentiators are specific: Railway has no public critical CVE on file, its defaults around row-level policies or server-side authorization middleware differ, and its primary stack (Postgres as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Fly.io app are secrets synchronization across regions and edge deployment security — both routinely found by automated scanners within minutes of deployment.

Fly.io gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Fly.io breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

Fly.io apps surface a predictable distribution of issues: 0 critical-impact classes (none), 1 high-impact, 3 medium-or-below. The critical ones are what cause data breaches.

Fly.io security best practices are dictated by Fly.io's actual risk profile, not a generic checklist. The top three: move all secrets server-side (environment variables, serverless functions); scan your deployed application with a security tool that understands this stack; scan your deployed application with a security tool that understands this stack.

Fly.io app scans surface the same cluster of vulnerabilities repeatedly: secrets synchronization across regions, edge deployment security, private networking configuration. The pattern is stable across Fly.io versions.

Fly.io apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Fly.io security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Fly.io's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Fly.io app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Fly.io's infra handles it), but who can *read* the decrypted data is where production Fly.io apps succeed or fail.

The mistakes we see repeatedly in Fly.io apps: secrets synchronization across regions; edge deployment security; private networking configuration. Each one is a specific failure mode of Fly.io's workflow — not generic programming mistakes.

Fly.io sits in the same security posture class as Railway, Render, Vercel. The differentiators are specific: Fly.io has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Trae AI app are hardcoded secrets in generated code and missing database access controls — both routinely found by automated scanners within minutes of deployment.

Trae AI gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Trae AI breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Trae AI apps are hardcoded secrets in generated code, missing database access controls, data privacy — code sent to bytedance. These aren't generic — they map to how Trae AI deploys and what stack it leans on.

The best practices for Trae AI apps track the attack vectors specific to Trae AI's stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Trae AI app scans surface the same cluster of vulnerabilities repeatedly: hardcoded secrets in generated code, missing database access controls, data privacy — code sent to bytedance. The pattern is stable across Trae AI versions.

Trae AI apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Trae AI security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Trae AI's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Trae AI app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Trae AI's infra handles it), but who can *read* the decrypted data is where production Trae AI apps succeed or fail.

The mistakes we see repeatedly in Trae AI apps: hardcoded secrets in generated code; missing database access controls; data privacy — code sent to bytedance. Each one is a specific failure mode of Trae AI's workflow — not generic programming mistakes.

Trae AI sits in the same security posture class as Cursor, Windsurf, Bolt.new. The differentiators are specific: Trae AI has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Devin AI app are no human-in-the-loop for security decisions and exposed api endpoints — both routinely found by automated scanners within minutes of deployment.

Devin AI gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Devin AI breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Devin AI apps are no human-in-the-loop for security decisions, exposed api endpoints, insecure dependency choices. These aren't generic — they map to how Devin AI deploys and what stack it leans on.

The best practices for Devin AI apps track the attack vectors specific to Devin AI's stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Devin AI app scans surface the same cluster of vulnerabilities repeatedly: no human-in-the-loop for security decisions, exposed api endpoints, insecure dependency choices. The pattern is stable across Devin AI versions.

Devin AI apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Devin AI security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Devin AI's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Devin AI app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Devin AI's infra handles it), but who can *read* the decrypted data is where production Devin AI apps succeed or fail.

The mistakes we see repeatedly in Devin AI apps: no human-in-the-loop for security decisions; exposed api endpoints; insecure dependency choices. Each one is a specific failure mode of Devin AI's workflow — not generic programming mistakes.

Devin AI sits in the same security posture class as Cursor, Claude Code, GitHub Copilot. The differentiators are specific: Devin AI has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a OpenAI Codex app are test credentials in production and missing input validation — both routinely found by automated scanners within minutes of deployment.

OpenAI Codex gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world OpenAI Codex breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to OpenAI Codex apps are test credentials in production, missing input validation, weak auth defaults. These aren't generic — they map to how OpenAI Codex deploys and what stack it leans on.

The best practices for OpenAI Codex apps track the attack vectors specific to OpenAI Codex's stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

OpenAI Codex app scans surface the same cluster of vulnerabilities repeatedly: test credentials in production, missing input validation, weak auth defaults. The pattern is stable across OpenAI Codex versions.

OpenAI Codex apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A OpenAI Codex security audit is not a generic checklist — it's a targeted probe of the failure modes specific to OpenAI Codex's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a OpenAI Codex app rests on Row Level Security (RLS) policies. Encryption is table-stakes (OpenAI Codex's infra handles it), but who can *read* the decrypted data is where production OpenAI Codex apps succeed or fail.

The mistakes we see repeatedly in OpenAI Codex apps: test credentials in production; missing input validation; weak auth defaults. Each one is a specific failure mode of OpenAI Codex's workflow — not generic programming mistakes.

OpenAI Codex sits in the same security posture class as Devin AI, Claude Code, GitHub Copilot. The differentiators are specific: OpenAI Codex has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Augment Code app are bypassed security middleware and inherited insecure patterns — both routinely found by automated scanners within minutes of deployment.

Augment Code gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Augment Code breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Augment Code apps are bypassed security middleware, inherited insecure patterns, supply chain risk from ai suggestions. These aren't generic — they map to how Augment Code deploys and what stack it leans on.

The best practices for Augment Code apps track the attack vectors specific to Augment Code's stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Augment Code app scans surface the same cluster of vulnerabilities repeatedly: bypassed security middleware, inherited insecure patterns, supply chain risk from ai suggestions. The pattern is stable across Augment Code versions.

Augment Code apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Augment Code security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Augment Code's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Augment Code app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Augment Code's infra handles it), but who can *read* the decrypted data is where production Augment Code apps succeed or fail.

The mistakes we see repeatedly in Augment Code apps: bypassed security middleware; inherited insecure patterns; supply chain risk from ai suggestions. Each one is a specific failure mode of Augment Code's workflow — not generic programming mistakes.

Augment Code sits in the same security posture class as GitHub Copilot, Cursor, Claude Code. The differentiators are specific: Augment Code has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Emergent (emergent.sh) app are exposed supabase credentials with missing rls and client-side api key leakage — both routinely found by automated scanners within minutes of deployment.

Emergent (emergent.sh) gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Emergent (emergent.sh) breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Emergent (emergent.sh) apps are exposed supabase credentials with missing rls, client-side api key leakage, unprotected api endpoints. These aren't generic — they map to how Emergent (emergent.sh) deploys and what stack it leans on.

The best practices for Emergent (emergent.sh) apps track the attack vectors specific to Emergent (emergent.sh)'s stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Emergent (emergent.sh) app scans surface the same cluster of vulnerabilities repeatedly: exposed supabase credentials with missing rls, client-side api key leakage, unprotected api endpoints. The pattern is stable across Emergent (emergent.sh) versions.

Emergent (emergent.sh) apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Emergent (emergent.sh) security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Emergent (emergent.sh)'s stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Emergent (emergent.sh) app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Emergent (emergent.sh)'s infra handles it), but who can *read* the decrypted data is where production Emergent (emergent.sh) apps succeed or fail.

The mistakes we see repeatedly in Emergent (emergent.sh) apps: exposed supabase credentials with missing rls; client-side api key leakage; unprotected api endpoints. Each one is a specific failure mode of Emergent (emergent.sh)'s workflow — not generic programming mistakes.

Emergent (emergent.sh) sits in the same security posture class as Lovable, Bolt.new, Base44. The differentiators are specific: Emergent (emergent.sh) has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Hostinger Horizons app are default database credentials and misconfigured hosting security — both routinely found by automated scanners within minutes of deployment.

Hostinger Horizons gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Hostinger Horizons breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Hostinger Horizons apps are default database credentials, misconfigured hosting security, exposed environment variables. These aren't generic — they map to how Hostinger Horizons deploys and what stack it leans on.

The best practices for Hostinger Horizons apps track the attack vectors specific to Hostinger Horizons's stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Hostinger Horizons app scans surface the same cluster of vulnerabilities repeatedly: default database credentials, misconfigured hosting security, exposed environment variables. The pattern is stable across Hostinger Horizons versions.

Hostinger Horizons apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Hostinger Horizons security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Hostinger Horizons's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Hostinger Horizons app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Hostinger Horizons's infra handles it), but who can *read* the decrypted data is where production Hostinger Horizons apps succeed or fail.

The mistakes we see repeatedly in Hostinger Horizons apps: default database credentials; misconfigured hosting security; exposed environment variables. Each one is a specific failure mode of Hostinger Horizons's workflow — not generic programming mistakes.

Hostinger Horizons sits in the same security posture class as Lovable, Bolt.new, Replit. The differentiators are specific: Hostinger Horizons has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Firebase Studio app are overly permissive firebase security rules and unsecured cloud functions — both routinely found by automated scanners within minutes of deployment.

Firebase Studio gives you the primitives for a secure app (Firebase, managed auth, hosting), but every real-world Firebase Studio breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Firebase Studio apps are overly permissive firebase security rules, unsecured cloud functions, firebase storage exposure. These aren't generic — they map to how Firebase Studio deploys and what stack it leans on.

The best practices for Firebase Studio apps track the attack vectors specific to Firebase Studio's stack: configure Firestore Security Rules, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Firebase Studio app scans surface the same cluster of vulnerabilities repeatedly: overly permissive firebase security rules, unsecured cloud functions, firebase storage exposure. The pattern is stable across Firebase Studio versions.

Firebase Studio apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Firebase Studio security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Firebase Studio's stack (Firebase (Firestore + Security Rules) as the database). The audit order: fingerprint the deployment, test Firestore Security Rules, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Firebase Studio app rests on Firestore Security Rules. Encryption is table-stakes (Firebase Studio's infra handles it), but who can *read* the decrypted data is where production Firebase Studio apps succeed or fail.

The mistakes we see repeatedly in Firebase Studio apps: overly permissive firebase security rules; unsecured cloud functions; firebase storage exposure. Each one is a specific failure mode of Firebase Studio's workflow — not generic programming mistakes.

Firebase Studio sits in the same security posture class as Firebase, Cursor, Replit. The differentiators are specific: Firebase Studio has no public critical CVE on file, its defaults around Firestore Security Rules differ, and its primary stack (Firebase (Firestore + Security Rules) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Tempo Labs app are client-side data exposure and insecure api integration — both routinely found by automated scanners within minutes of deployment.

Tempo Labs gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Tempo Labs breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Tempo Labs apps are client-side data exposure, insecure api integration, missing input validation. These aren't generic — they map to how Tempo Labs deploys and what stack it leans on.

The best practices for Tempo Labs apps track the attack vectors specific to Tempo Labs's stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Tempo Labs app scans surface the same cluster of vulnerabilities repeatedly: client-side data exposure, insecure api integration, missing input validation. The pattern is stable across Tempo Labs versions.

Tempo Labs apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Tempo Labs security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Tempo Labs's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Tempo Labs app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Tempo Labs's infra handles it), but who can *read* the decrypted data is where production Tempo Labs apps succeed or fail.

The mistakes we see repeatedly in Tempo Labs apps: client-side data exposure; insecure api integration; missing input validation. Each one is a specific failure mode of Tempo Labs's workflow — not generic programming mistakes.

Tempo Labs sits in the same security posture class as Lovable, Bolt.new, v0.dev. The differentiators are specific: Tempo Labs has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Base44 app are exposed api keys and database exposure — both routinely found by automated scanners within minutes of deployment.

Base44 gives you the primitives for a secure app (Supabase, managed auth, hosting), but every real-world Base44 breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Base44 apps are exposed api keys, database exposure, missing security headers. These aren't generic — they map to how Base44 deploys and what stack it leans on.

The best practices for Base44 apps track the attack vectors specific to Base44's stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Base44 app scans surface the same cluster of vulnerabilities repeatedly: exposed api keys, database exposure, missing security headers. The pattern is stable across Base44 versions.

Base44 apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Base44 security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Base44's stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Base44 app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Base44's infra handles it), but who can *read* the decrypted data is where production Base44 apps succeed or fail.

The mistakes we see repeatedly in Base44 apps: exposed api keys; database exposure; missing security headers. Each one is a specific failure mode of Base44's workflow — not generic programming mistakes.

Base44 sits in the same security posture class as Bolt.new, Lovable, Replit. The differentiators are specific: Base44 has no public critical CVE on file, its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.

Yes. The realistic attack paths in a Wix Harmony app are exposed wix data collections and misconfigured wix permissions — both routinely found by automated scanners within minutes of deployment.

Wix Harmony gives you the primitives for a secure app (Firebase, managed auth, hosting), but every real-world Wix Harmony breach we track comes from missed configuration — not missing platform features. Secure-by-default it is not.

The security issues specific to Wix Harmony apps are exposed wix data collections, misconfigured wix permissions, insecure third-party integrations. These aren't generic — they map to how Wix Harmony deploys and what stack it leans on.

The best practices for Wix Harmony apps track the attack vectors specific to Wix Harmony's stack: configure Firestore Security Rules, keep secrets off the client, verify authorization server-side, and re-scan after every release.

Wix Harmony app scans surface the same cluster of vulnerabilities repeatedly: exposed wix data collections, misconfigured wix permissions, insecure third-party integrations. The pattern is stable across Wix Harmony versions.

Wix Harmony apps are production-capable, but "safe for production" is a binary dependent on verification: scanned and clean is safe, unscanned is not. The platform layer is fine; the application layer is where the question is actually decided.

A Wix Harmony security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Wix Harmony's stack (Firebase (Firestore + Security Rules) as the database). The audit order: fingerprint the deployment, test Firestore Security Rules, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Wix Harmony app rests on Firestore Security Rules. Encryption is table-stakes (Wix Harmony's infra handles it), but who can *read* the decrypted data is where production Wix Harmony apps succeed or fail.

The mistakes we see repeatedly in Wix Harmony apps: exposed wix data collections; misconfigured wix permissions; insecure third-party integrations. Each one is a specific failure mode of Wix Harmony's workflow — not generic programming mistakes.

Wix Harmony sits in the same security posture class as Webflow, Framer, Bubble. The differentiators are specific: Wix Harmony has no public critical CVE on file, its defaults around Firestore Security Rules differ, and its primary stack (Firebase (Firestore + Security Rules) as the database) changes which mistakes are easy to make.

Yes — and for Gemini Code (Google) it isn't hypothetical. CVE: Gemini Code Command Execution Vulnerability demonstrated real exploitation in the wild. A CVE was disclosed for Gemini Code involving a command execution vulnerability.

Gemini Code (Google) is secure enough for production *only after* you close the specific gaps that have already been exploited in the wild. CVE: Gemini Code Command Execution Vulnerability is the headline incident — apps built with Gemini Code (Google) and no security review have demonstrably shipped with critical data exposure.

The security issues specific to Gemini Code (Google) apps are command injection patterns, overly broad gcp permissions, hardcoded google cloud credentials. These aren't generic — they map to how Gemini Code (Google) deploys and what stack it leans on.

The best practices for Gemini Code (Google) apps track the attack vectors specific to Gemini Code (Google)'s stack: configure Row Level Security (RLS) policies, keep secrets off the client, verify authorization server-side, and re-scan after every release.

The CVE: Gemini Code Command Execution Vulnerability incident catalogued what scans find in Gemini Code (Google) apps: command injection patterns and overly broad gcp permissions are the most frequent. Others compound them.

Gemini Code (Google) apps run in production — but CVE: Gemini Code Command Execution Vulnerability is the cautionary tale. The platform's suitability depends entirely on whether you've closed the same gap that caused real apps to leak data. Pre-scan, no; post-scan with findings fixed, yes.

A Gemini Code (Google) security audit is not a generic checklist — it's a targeted probe of the failure modes specific to Gemini Code (Google)'s stack (Supabase (Postgres + RLS) as the database). The audit order: fingerprint the deployment, test Row Level Security (RLS) policies, scan bundles for secrets, probe auth endpoints, then verify remediation with a second pass.

Data protection in a Gemini Code (Google) app rests on Row Level Security (RLS) policies. Encryption is table-stakes (Gemini Code (Google)'s infra handles it), but who can *read* the decrypted data is where production Gemini Code (Google) apps succeed or fail.

The mistakes we see repeatedly in Gemini Code (Google) apps: command injection patterns; overly broad gcp permissions; hardcoded google cloud credentials. Each one is a specific failure mode of Gemini Code (Google)'s workflow — not generic programming mistakes.

Gemini Code (Google) sits in the same security posture class as GitHub Copilot, Cursor, Claude Code. The differentiators are specific: Gemini Code (Google) has a documented CVE (CVE: Gemini Code Command Execution Vulnerability), its defaults around Row Level Security (RLS) policies differ, and its primary stack (Supabase (Postgres + RLS) as the database) changes which mistakes are easy to make.