Security Audit for Vibe-Coded Apps: What We Find and How We Fix It

Your vibe-coded application works. Users are signing up, data is flowing, and the product is gaining traction. But there is a question nobody on the team can answer confidently: is it secure?

In most cases, the honest answer is no — or at least, not enough. AI-generated code inherits the security patterns (and anti-patterns) from its training data. Without expert review, vibe-coded applications consistently ship with vulnerabilities that range from embarrassing to catastrophic.

This is not a theoretical concern. In 2025-2026, multiple startups with vibe-coded products have experienced data exposures, account takeovers, and compliance failures that directly impacted their business. The pattern is consistent enough that security auditing vibe-coded applications has become a distinct discipline.

This post walks through what we find when we audit vibe-coded apps, why these vulnerabilities exist, and what the remediation process looks like.

Why AI-generated code has security blind spots

AI code generation models are trained on vast amounts of public code. That code includes both secure and insecure patterns. The model does not distinguish between them — it generates whatever pattern fits the prompt. Several factors make this worse:

Common patterns are not always secure patterns. The most frequently occurring authentication implementation in public repositories is not the most secure one. It is the quickest one to write. AI models optimize for "looks like typical code," not "meets OWASP Top 10 standards."

Security is about what is missing, not just what is present. A vibe-coded login endpoint might hash passwords and return a token — the positive functionality works. But it might miss rate limiting, account lockout after failed attempts, constant-time comparison, and secure token storage. Security lives in the gaps.

Context-dependent security is hard for AI. Whether a particular approach is secure depends on the deployment context — is this an internal tool or a public SaaS? Is the data regulated? Who are the likely attackers? AI models do not ask these questions. They generate code that works, regardless of threat model.

Feature-by-feature prompting misses cross-cutting concerns. When you prompt "add a user profile endpoint" and then "add an admin dashboard," the AI generates each independently. It does not consider whether the admin dashboard properly restricts which profiles an admin can see, or whether the profile endpoint leaks data that the admin dashboard assumes is private.

The 10 most common vulnerabilities in vibe-coded applications

After auditing dozens of vibe-coded applications across SaaS, e-commerce, FinTech, and internal tools, these are the vulnerabilities that appear most consistently:

1. Broken authentication

What we find:

• JWT tokens with no expiration — a stolen token works forever
• Refresh tokens that are never rotated — compromising one gives permanent access
• Password comparison using === instead of constant-time comparison (timing attack)
• No rate limiting on login endpoint — brute-force attacks succeed in minutes
• Password reset tokens that do not expire or can be reused
• Session tokens stored in localStorage, accessible to any XSS payload

Severity: Critical. Authentication is the front door. If it is broken, everything behind it is exposed.

Remediation: Replace token generation with a secure library, implement token expiration and rotation, add rate limiting and account lockout, move session tokens to httpOnly cookies with secure and SameSite flags.

2. Broken access control (IDOR)

What we find:

• API endpoints where changing the ID parameter returns another user's data: /api/users/123/invoices returns invoices for user 123 regardless of who is requesting
• Multi-tenant applications where tenant isolation exists in the frontend routing but not in the database queries
• Admin functions accessible to regular users because authorization is only checked in the UI
• File upload/download endpoints that serve any file by ID without ownership verification

Severity: Critical. This is the #1 vulnerability in the OWASP Top 10, and it is pervasive in AI-generated code because the AI does not model authorization intent — it just builds the data access path.

Remediation: Add server-side authorization checks on every endpoint. Implement a middleware or decorator pattern that verifies the requesting user has access to the requested resource. Never trust client-side authorization.

3. SQL injection and NoSQL injection

What we find:

• Raw SQL built with string concatenation or template literals: `SELECT * FROM users WHERE email = '${email}'`
• MongoDB queries built from unsanitized user input: { username: req.body.username } (NoSQL injection via $gt, $ne operators)
• ORM usage that bypasses parameterization: raw queries embedded in otherwise safe ORM code
• GraphQL resolvers that pass user input directly to database queries

Severity: Critical. Injection can lead to full database access, data exfiltration, data modification, and in some cases, remote code execution.

Remediation: Replace all raw SQL with parameterized queries or ORM methods. Validate and sanitize all user input before it reaches any query layer. Add automated SAST scanning that flags raw query patterns.

4. Cross-site scripting (XSS)

What we find:

• User-generated content rendered without sanitization — names, bios, comments, messages
• dangerouslySetInnerHTML in React components processing user input
• Server-side template rendering that does not escape output
• Missing Content Security Policy headers
• Markdown rendering without sanitization (allowing embedded HTML/JavaScript)

Severity: High. XSS can steal session tokens, redirect users, deface the application, and serve as a stepping stone for more sophisticated attacks.

Remediation: Ensure all user-generated content is escaped before rendering. Remove unnecessary dangerouslySetInnerHTML usage. Implement Content Security Policy headers. Use a sanitization library for any content that must support HTML or Markdown.

5. Sensitive data exposure

What we find:

• API responses that include fields not displayed in the UI — internal IDs, email addresses of other users, creation timestamps, soft-delete flags, database metadata
• Error responses that include stack traces, SQL queries, file paths, and environment variable names
• Debug logging that writes sensitive data (passwords, tokens, PII) to application logs
• API keys, database credentials, and third-party secrets hardcoded in source code or committed to git history

Severity: High. Data exposure enables further attacks and violates privacy regulations. Credentials in git history persist even after the code is changed.

Remediation: Implement explicit API response serialization — define exactly which fields each endpoint returns, rather than returning full database objects. Configure error handling to show generic messages to users while logging details server-side. Audit git history for secrets and rotate any that were exposed. Implement environment-based secrets management.

6. Missing rate limiting

What we find:

• No rate limiting on any endpoints — the entire API accepts unlimited requests
• Login endpoints vulnerable to credential stuffing and brute-force attacks
• Password reset endpoints that can be used for email bombing
• API endpoints that trigger expensive operations (report generation, email sending) without throttling
• Webhook endpoints that accept unlimited payloads

Severity: Medium-High. Missing rate limiting enables denial-of-service, brute-force attacks, resource exhaustion, and abuse of functionality.

Remediation: Implement rate limiting at the API gateway or application level. Apply stricter limits to authentication endpoints. Add per-user and per-IP throttling. Configure rate limit headers so clients can implement backoff.

7. Insecure file handling

What we find:

• File uploads with no type validation — users can upload executable files
• No file size limits — one upload can fill the disk
• Uploaded files served from the same domain without content-type restrictions (stored XSS via SVG or HTML files)
• File paths constructed from user input without sanitization (path traversal)
• No malware scanning on uploaded files

Severity: Medium-High. Insecure file handling can lead to remote code execution, stored XSS, denial of service, and data exfiltration.

Remediation: Validate file types by content (not just extension), enforce size limits, serve uploads from a separate domain or CDN with restrictive headers, sanitize file paths, and implement virus scanning for applications that accept file uploads from untrusted users.

8. Missing security headers

What we find:

• No Content Security Policy (CSP) header
• No X-Content-Type-Options header (MIME sniffing attacks)
• No Strict-Transport-Security header (HSTS bypass)
• No X-Frame-Options or frame-ancestors CSP directive (clickjacking)
• Permissive CORS configuration (Access-Control-Allow-Origin: * with credentials)

Severity: Medium. Each missing header is a defense-in-depth gap that makes other vulnerabilities easier to exploit.

Remediation: Configure security headers at the web server or application level. Start with a restrictive CSP and relax as needed. Enable HSTS with a reasonable max-age. Restrict CORS to specific trusted origins.

9. Inadequate logging and monitoring

What we find:

• No logging of authentication events (successful or failed logins)
• No logging of authorization failures (access denied events)
• No logging of data modification events (creates, updates, deletes)
• No alerting on suspicious patterns (multiple failed logins, unusual data access)
• Logs that contain PII without appropriate access controls

Severity: Medium. Without logging, you cannot detect attacks, investigate incidents, or satisfy compliance requirements. Without monitoring, breaches go undetected for months.

Remediation: Implement structured logging for all security-relevant events. Set up alerting for suspicious patterns. Ensure logs do not contain sensitive data (passwords, tokens) but do contain correlation IDs for tracing. Configure log retention to meet compliance requirements.

10. Dependency vulnerabilities

What we find:

• Outdated npm/composer packages with known CVEs
• No automated dependency scanning
• Lock files not committed (non-reproducible builds)
• Dev dependencies included in production builds
• Packages from unmaintained or low-reputation sources

Severity: Variable (depends on the specific vulnerability, but can be critical). Dependency vulnerabilities are the easiest attack vector because they are well-documented and often have public exploits.

Remediation: Run npm audit or equivalent, update vulnerable packages, add automated dependency scanning to CI/CD, remove unused dependencies, and evaluate the maintenance status of critical dependencies.

What a security audit engagement looks like

Phase 1: Automated scanning (Day 1)

• Static Application Security Testing (SAST) scan of the full codebase
• Dependency vulnerability scan (npm audit, Snyk, or equivalent)
• Secret detection scan (checking for hardcoded credentials, API keys, tokens)
• Infrastructure configuration review (if applicable)
• Output: Raw findings to be triaged in Phase 2

Phase 2: Manual review (Days 2-4)

• Authentication and authorization flow review
• Data access pattern analysis (who can access what, and is it enforced?)
• Input validation assessment across all endpoints
• API response review (checking for data leakage)
• Business logic security review (can users manipulate pricing, bypass limits, escalate privileges?)
• Output: Validated findings with severity ratings and exploitation scenarios

Phase 3: Report and remediation plan (Day 5)

• Prioritized vulnerability report with:
  • Description of each finding
  • Severity rating (Critical / High / Medium / Low)
  • Exploitation scenario (how an attacker would use it)
  • Specific remediation guidance (not "fix the vulnerability" but "change this code to this")
  • Estimated remediation effort
• Recommended fix order based on risk vs. effort
• Architecture recommendations for preventing recurrence

Phase 4: Remediation support (Week 2-3)

• Fix critical and high-severity vulnerabilities
• Implement security headers and CSP
• Set up automated security scanning in CI/CD
• Add security-focused logging and monitoring
• Verify fixes with re-testing
• Output: Clean scan results, documented security posture

After the audit: maintaining security

A security audit is a point-in-time assessment. To maintain security as your application evolves:

• Automated scanning in CI/CD: Every pull request gets a SAST scan and dependency check
• Security-focused code review: New features touching auth, data access, or user input get explicit security review
• Periodic re-assessment: Quarterly or after major feature launches, re-evaluate the security posture
• Dependency updates: Monthly review and update of third-party packages
• Incident response plan: Document what to do when (not if) a security issue is discovered

Key takeaways

• AI-generated code consistently ships with security vulnerabilities because AI models optimize for functionality, not security
• The 10 most common issues are well-understood and fixable — but they require expert assessment to find
• A structured security audit takes 1-2 weeks: automated scanning, manual review, prioritized report, and remediation
• Security gaps block enterprise sales, create legal risk under GDPR, and cause irreversible trust damage when exploited
• Post-audit, automated scanning and periodic re-assessment keep the application secure as it evolves

Secure your vibe-coded application before it is too late

Wolf-Tech provides security audits specifically designed for AI-generated and vibe-coded applications. We find the vulnerabilities, prioritize by real-world risk, and fix them — so your team can focus on growing the product with confidence. Get a free consultation to discuss your application's security posture.