Remediation guide
AI-generated code ships fast but carries predictable security gaps: outdated dependencies, hardcoded secrets, missing authorization, and insecure patterns reproduced from older training data.
This guide walks through a five-step scanning workflow — npm audit, gitleaks, Semgrep, Trivy, and a website scan — that catches the highest-risk issues before deployment.
AI models reproduce the most common patterns from their training data, which includes older, less secure code.
The full workflow runs in under ten minutes and covers the most common risk categories.
Not inherently, but the risks are different. AI models reproduce the most common patterns in their training data, which includes older, less secure code. The main risks are outdated dependencies, hardcoded placeholders, missing authorization, and SQL injection — all fixable with a basic scanning workflow.
A secret scan (gitleaks or truffleHog) before the first push to a remote repository. Hardcoded credentials are the fastest path from AI-generated code to a real incident.
No. The core workflow — npm audit, gitleaks, Semgrep with the OWASP Top 10 ruleset, and Trivy — is entirely free and open source. CyberLens AI also offers a free no-signup website scan for the deployed application.
Yes. All four tools (npm audit, gitleaks, Semgrep, Trivy) have GitHub Actions integrations and run in sequence on every push and pull request with no paid plan required.
No. Automated scanning catches reproducible patterns but misses business-logic flaws and access control design issues. For anything handling user data or payments, combine automated scanning with a manual review of authentication and authorization flows.
CyberLens AI guidance is informed by established security standards and public vulnerability intelligence.