Trae AI Security Scanner
Built something with Trae AI? ByteDance's free IDE moves fast — make sure your app is secure before you launch.
Enter your deployed app's URL. Review the findings, take the suggested fixes to your coding tool, and retest after making changes.
First scan free: issue counts and one finding revealed in detail. No card required.
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 check the reachable app for exposed secrets, browser protections, authentication issues and database access problems. A standard scan typically takes a few minutes; blocked requests or incomplete coverage are reported.
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 does a VAS scan of a Trae AI app check?
VAS checks the deployed pages, scripts and endpoints it can reach for issues including secrets scan, database security, auth testing, headers & config. The report records findings and coverage limits; it does not certify the whole app as secure.
What will I receive in the report?
Findings include severity, supporting evidence and remediation guidance. Your first scan is free, with issue counts and one finding revealed in detail. Paid report access unlocks every finding. Use Export for AI to bring available findings into your coding tool, review the proposed changes and retest.
Can a scan verify every permission and private workflow?
No. Coverage depends on reachable pages, discovered endpoints and enabled checks. Configure a test login for supported authenticated checks. Review source code and business-specific permissions separately. Zero findings does not prove an app is secure.
Does VAS change my code or database policies?
VAS provides recommendations, not automatic fixes. Adapt any suggested policy or code change to your data model, test that permitted users still have access and that other users do not, then rescan. Read checks alone cannot confirm insert, update or delete permissions.
What should I know before scanning a production app?
Only scan apps you own or have permission to test. Scan requests can trigger firewall rules, logs and rate limits. Review enabled checks, and use staging or dedicated test accounts for sensitive workflows. Active tests, where enabled, need separate care because they can make changes.
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
Rescan after deploying a fix to check whether the original finding still appears. Compare the evidence and coverage with the previous report. For permissions and private workflows, also repeat the relevant tests with authorized and unauthorized test users.
Check your Trae AI app
Find observable security issues in your deployed app, review the evidence and take the next steps with your coding tool.
Start with a free scan. See issue counts and one finding in detail, then decide whether you need full report access.
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.