Exposing Vibe Coding Security Risks with Bright: What AI App Builders Keep Getting Wrong
AI tools are changing how fast teams can build software.
With just a few prompts, you can spin up a working app and move on to the next feature.
The problem is that security checks don’t always keep up with that speed.
Some of these apps end up handling customer data, payments, and login flows without going through proper review.
When that happens, small logic gaps or missing controls can slip into production.
Attackers love those situations, and traditional scanning usually doesn’t catch them early enough.
To measure the real impact, we generated functional applications using several common AI app builders and evaluated them with Bright’s dynamic security platform.
The results revealed missing authorization controls, bypassable logic, and exploitable attack paths, highlighting a growing gap between rapid development and secure application design.
Table of Contents
- How We Ran the Tests
- Lovable – Beautiful UI, Broken Security
- Base44 – The Confident Pretender
- Anthropic Claude 4.5 – Smarter Code, Same Blind Spots
- Replit: The Zero Vulnerability Illusion
- Big Picture: AI Code Is Fast. Attacks Are Faster.
- How Bright Changes the Game
- Final Takeaway: Vibes Aren’t Security
Introduction
AI-generated applications are entering production faster than traditional security teams can evaluate them.
With only a prompt, development workflows now produce full-stack systems that process authentication, user data, and payment logic in minutes.
However, this acceleration introduces recurring weaknesses that bypass conventional testing.
Logic gaps, insecure defaults, and exposed API behaviors are increasingly visible in applications produced by automated tools.
Traditional security scanners are not designed to interpret how users interact with an application across several stages, which makes it difficult for them to detect logic-driven weaknesses.
As development teams increasingly introduce AI-assisted code into their products, it is becoming essential to embed behavior-aware security checks directly into the build pipeline.
This ensures that complex workflow flaws and authorization gaps are identified and remediated before they enter production environments, where the impact is significantly harder to control.
How We Ran the Tests
We asked each platform to generate a forum-style application with:
Login and authentication.User roles and content posting.
Database connections.Forms and interactive elements.API endpoints.
Instead of generating simple “Hello World” projects that have nothing worth hacking, we asked each platform to create an application with a real attack surface.
We requested authentication flows, user roles, form submissions, database interactions, and API endpoints.
These are the exact areas attackers probe first.
Once those apps were generated, we ran them through Bright DAST.
That means real exploit attempts, automated fix validation, and CI/CD patterns that mirror how an AppSec or DevSecOps team would test code before shipping.
It wasn’t theoretical – we tested them the way an attacker would.
Bright DAST – dynamic, real exploit testing.
Automated fix validation.
CI/CD patterns that match enterprise pipelines.
This mirrors what a CISO, AppSec engineer, or DevSecOps team would demand in a real development process.
Lovable – Beautiful UI, Broken Security
Lovable generated a visually appealing interface and functional components, but the underlying security posture was fragile.
Analysis identified authentication weaknesses that allowed impersonation, missing rate-limiting controls that exposed login endpoints to brute-force attempts, SQL injection paths across multiple flows, and permissions left unprotected on sensitive routes.
In total, 4 critical, 1 high, and 13 low vulnerabilities were identified.
These issues represent the exact categories that lead to account takeover, data leakage, or unauthorized access in production environments.
While easily presentable in a product demonstration, this application would expose real users the moment it launched. The pattern is consistent: accelerated generation does not equate to protection logic.
High-risk issues included:
- Broken Authentication (user impersonation possible)
- Lack of rate limiting (brute force paradise)
- SQL injection paths exposed in multiple data flows
- Missing access control on sensitive endpoints
This is the kind of app that would pass a demo – but fail a real user on day one.
Security summary: Lovable is great at code generation but terrible at protection logic.
Base44 – The Confident Pretender
Base44 includes an integrated security checker that should offer an advantage, yet its prioritization was inconsistent.
The scanner elevated harmless comment fields to high concern while overlooking SQL injection routes and horizontal access escalation.
Testing revealed plaintext storage of sensitive data and internal APIs reachable without authentication.
Findings totaled 4 critical, 3 high, 1 medium, and 14 low vulnerabilities. The platform’s confidence contrasted sharply with the application’s actual exposure surface.
This creates a particularly dangerous condition: development teams may deploy features under the assumption that automated scanning validates them, while silent compromise vectors remain unaddressed.
Key failures:
- SQLi in multiple query paths
- Horizontal privilege escalation
- Plaintext storage of user data
- Internal API exposure without auth
The irony:
it confidently said secure while attackers were practically invited inside.
Security summary: Base44 isn’t security-aware; it’s security-confused.
Anthropic Claude 4.5 – Smarter Code, Same Blind Spots
Claude generated the cleanest and most structured code among the evaluated platforms, which improved readability but did not eliminate risk.
The resulting application lacked input validation patterns, misconfigured authentication flows, and provided opportunities for insecure direct object reference (IDOR) attacks.
Cross-site request forgery defenses were also absent. The assessment surfaced 4 critical and 6 low issues.
Despite fewer features, the vulnerabilities that emerged demonstrate that structured code does not inherently produce secure behavior.
Instead, security must be explicitly modeled.
Issues that still showed up:
- Missing validation patterns
- No CSRF protection
- Misconfigured auth flows
- Direct Object Reference attack exposure
Security summary: Claude is the best of the worst still breachable.
Replit – The Zero Vulnerability Illusion
The application generated by Replit was initially assessed using its static scanning mechanism, which reported zero vulnerabilities.
Dynamic testing told a different story. Bright identified authentication bypass paths, IDOR exposures, weak session handling, and broken access control.
When Bright’s dynamic engine scanned the same application, it found authentication bypass paths, sensitive IDOR exposure, weak session handling, and broken access control.
The application contained 4 critical, 1 high, 1 medium, and 5 low issues. Static scanning evaluates code at rest; attackers interact with systems in motion.
The disparity underscores why organizations relying solely on structural analysis experience breach-class incidents despite “clean” reports.
Architectural flaws included:
- Authentication bypass paths
- Sensitive IDOR exposure
- Weak session controls
- Access control misconfigurations
Semgrep looked at the code.
Bright attacked the app like a real adversary and found the truth.
Big Picture: AI Code Is Fast. Attacks Are Faster.
The real problem isn’t AI, it’s the instructions humans give it.
We ask these tools to build apps quickly, but we never ask them to enforce compliance or threat modeling.
That leads to weak defaults, predictable patterns, shadow APIs, and endpoints that behave in unexpected ways.
Attackers don’t care how fast your feature shipped. They care about how easily it breaks.
AI is just doing what humans asked:
“Build this fast.”
Nobody said:
And make sure it’s secure under PCI-DSS, OWASP, SOC2, ISO 27001 compliance checks.
The core issue is not that AI generates insecure code intentionally; rather, these systems optimize for speed.
Developers are rewarded for rapid output, not for adhering to compliance frameworks such as PCI-DSS, SOC 2, ISO 27001, or OWASP recommendations.
Without explicit instruction, AI repeats insecure patterns and propagates logic weaknesses.
It does not understand the operational consequences of exposed shadow APIs, missing role checks, or incomplete threat models.
Attackers, however, take advantage of precisely these gaps. If organizations continue adopting vibe-driven development practices without security augmentation, breach volume will escalate accordingly.
So what do we get?
- Vulnerable design patterns repeated endlessly
- No threat modeling
- No secure defaults
- Shadow APIs everywhere
AI doesn’t understand consequences.
Attackers do.
If left unchecked – vibe-coded apps are tomorrow’s breach reports.
How Bright Changes the Game
Bright automatically:
Finds real, exploitable vulnerabilities.
Validates fixed dynamically in CI/CD.
Works on both human & AI-generated code.
Creates ready-to-merge fix PRs for developers.
That means:
- faster releases
- fewer incidents
- Security is finally keeping up with development
We aren’t here to slow you down.
We’re here to make sure your speed doesn’t blow up in your face.
Bright addresses this gap by validating vulnerabilities through live exploitation rather than relying on theoretical severity labels.
The platform identifies issues such as broken access control, logic manipulation, hidden entry points, and workflow bypasses, and then verifies whether developer patches resolve the issue.
Fix-validation prevents regression and eliminates reliance on manual interpretation.
Bright also supports applications generated by both human developers and AI systems, enabling cohesive remediation workflows regardless of the origin of the code.
When fixes are validated in CI/CD, releases move faster, incident volume decreases, and engineering teams regain confidence in their security posture.
Rather than slowing development, this approach enables velocity with guardrails.
Final Word: Vibes Aren’t a Security Strategy
AI is transforming how we code.
But it’s also transforming how we attack.
Teams that adopt AI-generated code without security automation are rolling the dice with their brand, compliance, and customer trust.
So yes – vibe code if you want.
Just make sure Bright is checking what the vibes missed.
Because somewhere out there, an attacker is building their exploit just as fast.
AI is changing how teams build software, but it’s also changing how attackers operate.
Organizations that rely on AI-generated code without security automation are rolling the dice with their brand, compliance posture, and customer trust.
Build with speed if you want just make sure Bright is checking what the vibes missed.
Because somewhere out there, someone is building an exploit just as fast.
Summary
AI-generated applications introduce risk patterns that traditional security tools are not capable of detecting.
Authentication gaps, shadow APIs, workflow manipulation, and authorization bypasses continue to appear in production environments when security is limited to static analysis or late-stage review.
Bright provides dynamic validation that identifies these issues within real user flows, confirming exploitability rather than generating theoretical alerts.
When combined with automated remediation and integration into CI/CD pipelines, security becomes measurable, enforceable, and repeatable.
Organizations that adopt this approach prevent logic flaws from reaching production, reduce remediation costs, and maintain compliance without slowing delivery.
