Security review for the era when AI writes the code

The inflection

AI fundamentally changed who writes software and how much of it gets written. A solo founder now ships what used to take a team — generated, skimmed, merged. The code compiles, passes tests, looks right in review.

Velocity went up. Attack surface went up with it. The missing ownership check, the query built with string interpolation, the secret that landed in a config file — at a three-person startup there is no AppSec team, so the security gate simply doesn't exist. Until the breach, usually at the worst possible moment. That gap is what Hypervex is built to close.

What makes Hypervex different

Security review as the primary job, not a rule pack bolted onto a code reviewer:

• The Trace. Critical findings get a second, deeper pass that follows the call chain through your codebase and confirms the exploit path — or dismisses the finding with the mitigation that blocks it.
• Codebase context. Hypervex indexes your repository and reviews every diff against it. Findings cite your files; fixes use the patterns you already have.
• Structured findings. Severity, confidence, OWASP, CWE, an exploit scenario in plain language, a suggested fix. Triageable in the ten seconds you have between deploys.
• Regression tracking. A vulnerability you fixed once should stay fixed. Hypervex flags it when a later PR quietly brings it back.

A short path from problem to fix

Hypervex is built with a deliberately short path between noticing a problem and shipping the fix: intentional product decisions instead of committee ones, direct accountability for every finding the product posts, and a feedback loop measured in hours rather than quarters.

It also means the thesis gets tested on itself daily. Every change to Hypervex is gated by its own security review before it ships. If that loop didn't hold, you'd be reading a different page.

Powered by Claude

Hypervex's reviews run on Claude, by Anthropic — the models several of the best reviewers in this market also chose to trust for the final call on whether a finding is real. AI writes more of the world's code every month, and the tools that check that code should be just as capable.