23 vulnerabilities found in under an hour. Fixed the same day.

The product team had moved quickly from prototype to launch candidate using AI-assisted development across the frontend, backend, and deployment scripts. That speed created exactly the kind of uncertainty most early-stage teams struggle to see clearly: the code looked plausible, the happy paths worked, and nobody had time to review every boundary the AI touched.

The founders were not asking for a long consulting engagement. They needed to know whether the codebase was safe enough for real users, real billing data, and real production traffic before launch day.

That meant checking for the failures AI coding tools introduce most often: hardcoded secrets, broken auth assumptions, unsafe query construction, and logging behavior that exposes more data than intended.

We ran our security agents across the full codebase, tuned for the failure patterns AI coding tools introduce most often. In under an hour they surfaced 23 vulnerabilities, including 4 hardcoded credentials, an authentication bypass in the admin dashboard, a SQL injection in search, and user data being logged without PII masking.

Speed was only half of it. Each finding came back with a proof-of-impact, a severity ranking, and a proposed fix, so nothing landed as a vague warning the team had to re-investigate. The findings that mattered most were the ones that looked normal: clean, readable code that was trivial to approve in a fast-moving startup workflow.

From there the agents drafted the remediations and our reviewer validated each one before it merged. Fixing was not handed back as a backlog. It happened in the same engagement, with a human signing off on every change that touched auth, secrets, or customer data.

• 23 vulnerabilities surfaced by security agents in under an hour
• Agent-drafted fixes, human-reviewed before every merge
• Each finding shipped with proof-of-impact and severity
• Critical issues remediated the same day, not deferred

Every critical issue was remediated the same day and the team still shipped on its original launch schedule. Secrets moved into managed environment configuration, access checks were tightened around privileged flows, and unsafe query logic was replaced before customer data entered the system.

Launch happened without incident, and the team gained something more durable than a one-off report: a repeatable, agent-driven security pass they could run on every release instead of hoping a manual review would catch the next regression.

The result was simple and important. Zero incidents post-launch, no slip in go-live timing, and a cleaner foundation for the next round of product work.