Vibe Coding Safety: How 7 AI Builders Compare

Is Vibe Coding Safe Enough for a Production App in 2026?

Not by default. Veracode's 2026 GenAI Code Security Report puts the average AI-code security pass rate at 56%, so roughly 44% of generation tasks introduce a risky vulnerability. The SusVibes benchmark found just 11.8% of one agent's solutions were secure despite 57% being functionally correct. It becomes safe once a human reviews access control and dependencies first.

That pass rate has barely moved. Veracode's own 2026 GenAI Code Security Report puts it plainly: the 56% average pass rate is barely changed from 55% in its first report, even as syntax correctness has become close to solved. Model choice widens the range inside that same 2026 report: GPT-5.5 leads the current snapshot at 68%, while more than half of tested models sit at 50-53%.

Before going further, here is how the seven builders this article compares actually handle security, on paper, according to each vendor's own documentation read on 28 September 2026:

That table is the shape of the whole answer: every one of these seven scans or hardens something, and none of them takes over responsibility for the app once it ships. This is the exact gap Joylo's AI Confidence Score is built to close: a security audit that runs on every build, on every plan, before a human ever needs to get involved.

What Security Risks Show Up Most in Vibe-Coded Apps?

The same three risk classes recur across vibe-coded apps: broken access control, exposed secrets, and vulnerable dependencies. Broken access control, missing or wrong row-level security, is OWASP's number one web risk for 2025 (A01 Broken Access Control), present in some form in 100% of the applications OWASP tested. Exposed API keys and unpatched dependencies round out the list.

Exposed secrets are the second recurring failure, and not a lab finding. Escape's scan of more than 5,600 publicly available vibe-coded apps found more than 2,000 vulnerabilities, 400+ exposed secrets, and 175 instances of personal data, including medical records, IBANs, phone numbers and emails. That dataset is skewed toward Lovable-built apps, so treat it as evidence the problem is real, not as a per-builder rate.

The mechanism behind the access-control failures is well documented outside any single vendor. Supabase's own docs state that a table in an exposed schema without row-level security is readable and writable by any role with a grant, and instruct enabling RLS on every table in an exposed schema (read 28 September 2026). That is the exact gap CVE-2025-48757 exploited: deployed Lovable-generated projects created on or before 15 April 2025 shipped with insufficient default row-level security, discovered 20 March 2025 and disclosed 29 May 2025, exposing personal data to unauthenticated attackers.

Joylo's AI Confidence Score runs a check across this same ground, access rules, secrets and dependencies, on every build, on every plan, by default, rather than waiting for a publish-time scan to catch it after the fact.

Why Do Vibe-Coded Apps Pass Functional Tests but Fail Security Tests?

Because passing a feature test and writing secure code are different skills, and current models are far better at the first. The SusVibes benchmark found that for SWE-Agent with Claude 4 Sonnet, 57% of solutions were functionally correct but only 11.8% were secure. Adding vulnerability hints to the prompt did not close that gap.

That figure comes from the SusVibes benchmark, arXiv v4, revised 21 September 2026 and accepted at ICML 2026, which evaluates 12 widely used coding agentic settings against 186 real-world feature-request tasks. The 57%/11.8% pair describes one agent setting, not every agent or every model, but the direction is the point: correctness and security diverge, and telling the agent to be careful does not fix it.

Model choice compounds the gap. Veracode's 2026 report puts GPT-5.5 at a 68% security pass rate while more than half of tested models sit at 50-53%, scored across four vulnerability categories: SQL injection, XSS, log injection and insecure cryptography. Those four categories do not even include vulnerable dependencies, which OWASP's A03 Software Supply Chain Failures separately names a top risk, ranked #1 by 50% of respondents in OWASP's own community survey. It is one reason Joylo's own AI Confidence Score scores five separate domains, scalability, security, reliability, integrations and code quality, rather than treating security as one pass or fail test.

Prompting a model to be secure is not a control. Reviewing what it produced is. That is the review Joylo's Expert Assist engineers run before code reaches production, not a line added to the prompt.

How Do Lovable, Replit, Bolt, Base44, v0, Cursor and Emergent Actually Handle Security?

Every vendor scans or hardens something, and every vendor leaves the shipped app's security to the customer. Lovable and Replit run automatic checks on every publish; Bolt and Base44 offer deeper scans on paid tiers; v0 sandboxes execution; Cursor secures only its own editor; and Emergent documents encryption and isolation but no security scan at all.

Lovable. Lovable's docs describe a Quick scan that runs automatically every time a project is published, checking database access rules including row-level security, running an npm dependency audit, and checking for exposed MCP servers. A separate Deep scan checks application code on demand rather than automatically. Blocking publish on critical findings is a workspace setting, on by default for new Enterprise workspaces, and scheduled scans are Enterprise only. Lovable's own docs say the tools do not replace a thorough security review and recommend a professional review for apps handling sensitive data.

Replit. Replit's Project Security Center runs a free automatic dependency scan against public CVE records across Node.js, Python, Go, Rust, PHP and Ruby, rechecking whenever a new CVE is disclosed. Its Auto-Protect feature can have the Agent prepare and test a patch, but both Auto-Protect settings are off by default, and a patch only reaches production after republishing. Agent scans of application code are reserved for paid builders. Replit's own shared-responsibility model states that code review of the Agent's output, including third-party dependencies it introduces, is the customer's job, and its production database docs state the Agent cannot modify the production database.

Bolt. Bolt's own support docs describe a project security audit that reviews code and the database, fixes what it can, and flags the rest, but it runs manually from the Publish menu on paid plans, up to 30 times a day. A lighter database security check runs on every plan. Bolt recommends running a check at least once before publishing; nothing runs on its own.

Base44. Base44's security docs describe a scan that checks data access, exposed secrets, dependencies and unauthenticated backend functions, with code-vulnerability analysis available from the Builder plan up. Its own docs put responsibility for security settings on the app owner. Auth tokens are stored in browser localStorage rather than HttpOnly cookies, and per-app CORS is not configurable. Base44 states it holds SOC 2 Type II and ISO 27001 certification for its own platform, a competitor fact worth naming accurately.

v0. Vercel's v0 docs say v0 treats all AI-generated code as potentially incorrect or adversarial by design, running it in a sandbox and analyzing NEXT_PUBLIC_ environment-variable usage to warn about secret exposure. What v0 does not document is a separate application-level security scan of the kind Lovable, Replit, Bolt and Base44 run.

Cursor. Cursor is a code editor, not a hosted app builder, so Cursor's security page describes securing its own platform rather than scanning the apps built inside it: at-least-annual third-party penetration testing, an upstream patch policy, and an acknowledgement of vulnerability reports within 5 business days. None of that reaches the security of the code a Cursor user ships; that review sits entirely with the developer.

Emergent. Emergent's own docs say Emergent isolates every workspace and encrypts data in transit and at rest, and separates preview from production secrets after the first publish. But secret values are masked in the panel by default and can be revealed and copied by anyone with publish access to the project, and a published app is public by URL: without login, all of its data is visible to anyone who finds the link. Emergent's docs describe a manual pre-launch checklist, not an automated security scan like the other six builders here.

None of the seven make the shipped app's security their own responsibility. Joylo's Expert Assist is built to be the accountable step these seven leave out: a named engineer who reviews the same access-control, secrets and dependency risks before real users arrive, not after.

Where this falls short: - None of the seven vendor scans reviewed here treats the shipped app's security as its own responsibility; each puts that on the customer. - Automatic checks catch known patterns, missing row-level security, exposed secrets, known CVEs, not a business-logic flaw or a wrong authorization rule that still runs without throwing an error. - Deeper or code-level scans sit behind a paid tier on Bolt, Replit and Base44, so a free or entry plan gets the lighter automatic check only.

This fits if: - The app has no login, no payment flow and no personal data yet, so a missed access-control bug costs little. - Someone on the team already knows how to read the vendor's scan output and fix what it flags. - A person still reviews the diff before every publish, even when the build stays on the vendor's automatic checks alone.

What Do Real Incidents Say About Vibe-Coded Apps Already in Production?

They say the risk is not theoretical. A disclosed Lovable vulnerability exposed user data through missing default database security, a platform-level flaw in Base44 let anyone register for private apps, and a scan of thousands of live vibe-coded apps found real exposed secrets and personal data, not just lab findings.

CVE-2025-48757, discovered 20 March 2025 and disclosed 29 May 2025, affects any Lovable project using a database created on or before 15 April 2025, though it may also affect later projects: insufficient default row-level security policies could expose personal data and credentials to unauthenticated attackers.

Wiz Research found a platform-level authentication flaw in Base44 that let an attacker register for private apps using only a non-secret app ID, bypassing SSO entirely. The vulnerability was fixed in less than 24 hours, and the vendor confirmed no evidence of past abuse. The broader point holds beyond this one fix: on a hosted builder, every app inherits the platform's own security posture, so a single platform flaw hits every app built on it at once. It is also why Joylo never lets one platform-wide scan stand in for a review of the specific app in front of it.

Escape's research team analyzed over 5,600 publicly available vibe-coded apps and identified more than 2,000 vulnerabilities, 400+ exposed secrets and 175 instances of personal data. Its dataset is heavily skewed toward Lovable deployments, so read it as evidence the problem is real, not a per-builder rate. Georgia Tech's Vibe Security Radar shows the trend accelerating: about 18 confirmed AI-linked CVEs in the second half of 2025, 56 in the first three months of 2026, and 35 of those in March 2026 alone, more than all of 2025 combined. The confirmed cases span the same failure classes seen elsewhere, authentication bypass and command injection among them, and the researchers advise reviewing AI-generated code the way a senior developer reviews a junior colleague's pull request, watching input handling and authentication closest.

Every one of these was caught by an outside researcher, not the platform's own review, and not before the app was already live. That gap between a publish-time scan and a pre-launch human review is exactly where Joylo places Expert Assist: before real traffic finds the hole, not after a researcher does.

Who Actually Reviews a Vibe-Coded App Before It Meets Real Users?

Usually nobody, until something breaks. Every vendor's documentation points the same way: the platform runs an automated check, and the builder is responsible for what ships. Builders describe the same failure points across independent threads: authentication, database and payment integrations breaking, security holes shipped to production, and apps that held up in a demo but failed at first real traffic.

That line comes from Joylo's own Rescue Economy research into the AI-app repair market, not from vendor marketing or a single case: it names a pattern across many independent builder accounts, not one client's story.

Every vendor's own docs describe the same shape: an automated check at publish or build time, then no answer for what happens after. Joylo is a strong fit for builders who want that second half built into the platform, it's backed by an in-house engineering team, scored in real time by an AI Confidence Score audit that runs on every build and every plan, and built on a conventional React, Node and Postgres stack that stays portable to any cloud instead of locking a project to one vendor's database.

One click connects a named in-house engineer, already familiar with the codebase, inside a 24-hour first-response window, covering the same ground a pre-launch review needs to check: sign-in and access control, data storage, load handling, and the launch setup itself.

This fits if: - The app already handles logins, payments or personal data before its first real user arrives. - Nobody on the team can read a dependency-scan or row-level-security report and know what to fix in it. - The vendor's own docs already say plainly that its scan does not replace a security review.

If your vibe-coded app is about to meet real users, see how Joylo reviews it first. Start free