Vibescan

Solo founders and small engineering teams can generate substantial application code quickly with coding assistants while missing authorization boundaries, secret handling, request validation and abuse controls. The supplied research confirms an active open-source static-analysis engine and a broad commercial security platform that uses it. It reports a narrower gap for AI-assisted SaaS failure patterns, but the closest competitor has already launched stack-specific scanning in an adjacent area.

Vibescan would preserve organization, repository, scan authorization, branch or revision, inclusion and exclusion rules, dependency snapshot, build context, rule pack, rule identifier, rule version, rule source, file and line locator, matched syntax, suspected secret fingerprint, redaction result, finding category, severity candidate, confidence, exploitability unknown state, affected boundary, evidence excerpt, false-positive reason, developer triage, accepted finding, remediation proposal, patch reference, test evidence, rescan revision, rescan result, exception owner, risk-acceptance rationale, expiration, report export, delivery acknowledgment, correction and deletion as distinct records.

Static rules detect patterns, not exploitable vulnerabilities. A missing pattern may be implemented elsewhere, and a matching pattern may be unreachable or harmless. A clean scan proves only that the selected rules did not match the selected revision. The scanner must have explicit repository authority, avoid executing untrusted code by default, redact secret values and keep customer source isolated. It must not publish vulnerabilities, open public issues, patch code, merge changes or mark risk accepted without authorized owners. Severity and remediation remain review candidates until a qualified security practitioner evaluates context.

The pilot should use synthetic vulnerable and non-vulnerable fixture repositories with no real credentials. The likely buyer is a solo founder, technical lead, agency owner or security-conscious product team, but repository mix, language coverage, rule accuracy, integration friction, support burden, budget and willingness to buy a focused scanner alongside broad platforms remain unverified.

A solo founder, technical lead, agency owner or security-conscious product team responsible for reviewing recurring security mistakes in AI-assisted SaaS code.