Security for AI-Generated Code

Scan your repositories for real vulnerabilities and high-risk flaws. Built for teams that ship with AI assistance.

Repo-wide analysis

Full repository context so the AI understands data flow and cross-file issues, not just single files.

Evidence-based findings

Taint analysis methodology and CWE/OWASP mapping. Fewer false positives, actionable remediation.

GitHub-native

Connect repos, run scans on demand, and review results in a single dashboard.

AI-generated code security is different. Copilot, ChatGPT, and other assistants produce code fast—but generic scanners often miss context, flag false positives, or ignore cross-file data flow. Vulnerability scanning for AI code needs repo-wide analysis and evidence-based findings so you catch real issues before they ship.

Why Aegis

Single-file or keyword-based tools struggle with AI-assisted code: they don't see the full picture. Aegis analyzes entire repositories with a security-engineer mindset. We use taint analysis to trace untrusted input to dangerous sinks, map findings to CWE and OWASP, and deliver clear remediation—so you get fewer false positives and more actionable results.

Who it's for

· Teams shipping with GitHub Copilot or other AI coding tools who want to catch vulnerabilities before merge
· Security-conscious developers who need evidence-based findings and CWE/OWASP mapping for audits
· Organizations that want dedicated AI-generated code security scanning without drowning in noise

How it works

Connect your GitHub repositories, choose a branch, and run a scan. Aegis analyzes the code and returns a prioritized list of findings with severity, evidence, and remediation. Review results in your dashboard and fix issues with confidence. See all features or view pricing to get started.