Every line your AI writes is a new attack surface.
CodeShield exists because the gap between how fast AI writes code and how fast we can review it keeps growing — and that gap is where vulnerabilities live.
The problem
Almost half of all new code is now written by AI assistants like Copilot, Cursor, and Claude. These tools are extraordinary at producing something that compiles — and surprisingly bad at producing something secure. Independent audits keep finding the same result: around 45% of AI-generated code contains an exploitable vulnerability. Most of that code ships anyway.
Traditional static analysis was built for hand-written code and a threat model from a decade ago. It misses the specific mistakes LLMs make: eval() on user input, hardcoded secrets in comments, weak default crypto, prompt injection in server-side code.
What we do differently
- →Patterns tuned for AI output. The 50+ rules we ship are based on real failures we’ve collected from Copilot, Claude, and Cursor transcripts — not a generic SAST rulebook from 2015.
- →Post-quantum crypto scanner. NIST has already set 2030 as the deprecation deadline for RSA and ECDSA. Our scanner finds every quantum-vulnerable algorithm in your codebase and generates a migration timeline.
- →Fixes, not just findings. When we flag a vulnerability, Claude writes the patch. You review the diff, not a 40-page report.
- →Built for the CI, not the war room. One GitHub Action. Every PR gets scanned. Criticals block the merge. That’s it.
What we won’t do
- ×We won’t train AI models on your code.
- ×We won’t sell your scan results or metadata.
- ×We won’t ship noisy scanners just to pad the finding count — false positives are worse than false negatives because they kill trust.
Who we are
Independent, founder-built. No VCs to please, no growth targets forcing bad trade-offs. If CodeShield disappoints you, reply to our welcome email — we read them all and we’ll try to make it right.