Trae AI Security Scanner
Built something with Trae AI? ByteDance's free IDE moves fast — make sure your app is secure before you launch.
Our automated security scanner analyzes your Trae application for vulnerabilities, misconfigurations, and exposed secrets. Get a comprehensive security report in minutes, not days.
Top 4 Security Issues in Trae AI Apps
Hardcoded Secrets in Generated Code
Trae's code generation often includes placeholder API keys that make it to production.
Missing Database Access Controls
Generated database queries lack RLS or Security Rules by default.
Data Privacy — Code Sent to ByteDance
All code passes through ByteDance's cloud AI infrastructure for processing.
Weak Authentication Patterns
AI-generated auth may skip email verification and rate limiting.
Where Security Breaks in Trae AI Apps
Built on Supabase (Postgres + RLS), Trae AI applications share a recognizable fingerprint, which means attackers and automated scanners find them the same way every time. Based on real vulnerability patterns in Trae AI deployments, the breakdown is 1 critical-impact issue, 2 high-impact, and 1 medium-or-lower.
Hardcoded Secrets in Generated Code
Trae's code generation often includes placeholder API keys that make it to production.
Fix: Move all secrets server-side (environment variables, serverless functions). Rotate any keys previously in frontend code. Audit bundles for leftover credentials before each deploy.
Missing Database Access Controls
Generated database queries lack RLS or Security Rules by default.
Fix: Enable Row Level Security (Supabase) or Security Rules (Firebase) on every table. For custom backends, enforce authorization at the query layer — never client-side.
Data Privacy — Code Sent to ByteDance
All code passes through ByteDance's cloud AI infrastructure for processing.
Fix: Review vendor data processing agreements. Enable privacy/zero-data-retention modes where available. Use `.gitignore`/`.cursorignore` equivalents to keep sensitive files out of AI context.
Weak Authentication Patterns
AI-generated auth may skip email verification and rate limiting.
Fix: Enforce email verification, minimum password requirements, and rate limiting on auth endpoints. Test auth flows as unauthenticated and cross-user to verify access controls.
What We Check
Secrets Scan
Find API keys and credentials in generated code.
Database Security
Check RLS and access controls on data layer.
Auth Testing
Verify authentication and authorization flows.
Headers & Config
Test security headers and deployment config.
What You'll Get
Why Trae AI Apps Need Security Scanning
Trae AI is ByteDance's free AI-powered IDE that launched in January 2026. It generates complete applications quickly, but speed and security don't always go together.
vas scans your deployed Trae-built application for the security issues AI commonly introduces — exposed credentials, missing access controls, and authentication weaknesses.
How Trae AI Security Scanning Works
Submit Your URL
Enter your Trae application URL. Our scanner automatically detects your tech stack and configures the appropriate security checks for Trae AI.
Automated Analysis
We scan for exposed secrets, security headers, authentication issues, database misconfigurations, and Trae AI-specific vulnerabilities. A deep scan typically completes in 20-30 minutes.
Get Actionable Results
Receive a detailed report with prioritized vulnerabilities, severity ratings, and step-by-step remediation guidance with code examples specific to Trae AI.
Common Questions About Trae AI Security
What vulnerabilities are most common in Trae AI apps?
The top finding classes in Trae AI apps: hardcoded secrets in generated code; missing database access controls; data privacy — code sent to bytedance. Of those, missing database access controls is the most frequent critical-impact issue. It typically exposes the full dataset in a single query.
What does a vas scan of a Trae AI app check?
The scan probes your deployed app for the specific findings above: secrets scan, database security, auth testing, headers & config. It actually attempts each vulnerability class (not just header inspection) and reports results with severity + fix for each.
Is running a scan safe for production?
Yes. The scanner uses read-only probes against public endpoints: no data modification, no destructive tests. A deep scan typically finishes in 20-30 minutes and will not impact application availability.
Remediation Playbook for Trae AI
Priority-ordered fixes for the specific findings we see in Trae AI apps. Critical items close data-exposure gaps; high items prevent compromise; medium items reduce attack surface. Applies to apps using Supabase (Postgres + RLS), the dominant Trae AI stack.
1. Hardcoded Secrets in Generated Code
Why it matters: Trae's code generation often includes placeholder API keys that make it to production.
How to close it: Move all secrets server-side (environment variables, serverless functions). Rotate any keys previously in frontend code. Audit bundles for leftover credentials before each deploy.
2. Missing Database Access Controls
Why it matters: Generated database queries lack RLS or Security Rules by default.
How to close it: Enable Row Level Security (Supabase) or Security Rules (Firebase) on every table. For custom backends, enforce authorization at the query layer — never client-side.
3. Data Privacy — Code Sent to ByteDance
Why it matters: All code passes through ByteDance's cloud AI infrastructure for processing.
How to close it: Review vendor data processing agreements. Enable privacy/zero-data-retention modes where available. Use `.gitignore`/`.cursorignore` equivalents to keep sensitive files out of AI context.
4. Weak Authentication Patterns
Why it matters: AI-generated auth may skip email verification and rate limiting.
How to close it: Enforce email verification, minimum password requirements, and rate limiting on auth endpoints. Test auth flows as unauthenticated and cross-user to verify access controls.
Verify the fixes stuck
Run a vas scan after applying each fix to confirm the gap is actually closed. "I applied the fix" is not evidence. The fix may have been partial, reverted, or not deployed. Re-scanning gives you proof, and a record for compliance if you ever need it.
Secure Your Trae AI App
Don't let vulnerabilities compromise your hard work. Security issues in Trae AI applications can lead to data breaches, unauthorized access, and damaged user trust. The average data breach costs startups between $120,000 and $1.24 million.
Run your first scan free, in minutes. Scan before you launch and deploy with confidence knowing your application meets security best practices.
More on Trae AI Security
Every angle of Trae security, from the specific findings we detect to step-by-step fixes.
Trae AI Security Risks
Specific risks we find in Trae apps, with real-world examples.
Trae AI Security Issues
Issues grouped by severity with detection and fix steps.
Is Trae AI Safe?
Honest assessment of Trae's production readiness.
Trae AI Security Checklist
Pre-launch checklist covering every finding class for Trae.
How to Secure Trae AI Apps
Step-by-step hardening guide for Trae deployments.
Can Trae AI Apps Be Hacked?
Attack vectors specific to Trae and how they get exploited.
Trae AI with your database
The security gaps we find depend on which database sits behind Trae.