Code passes, tests turn green, deployment succeeds. With all these in place, it looks complete. However, what we examine here are problems that occurred without failure messages appearing, or were reported publicly as issues.
In the attached case collection, regarding "vibe-coded" app investigations on Lovable, Replit, Base44, etc., it is recorded that "among approximately 5,000 apps, over 2,000 cases had corporate documents, conversation logs, medical and financial information in public state (causes and issues: no authentication, guessable URLs, public databases, access control not implemented)." The impact classification is "Vibe coding," and the evidence level is "investigation and aggregation report." The source material can be confirmed on WIRED.
This is a case based on investigation and aggregation reports. The confirmed scale and configuration issues do not necessarily match the actual range of misuse and damage that occurred. It is important not to directly replace the numbers with damage counts.
What can be said with certainty from this description is that phenomena classified as "Vibe coding" are being reported and organized in public information. We do not fabricate execution environments not in the attached file, additional damage, vendor judgments, or correction status after reporting. The more limited the information, the more trustworthiness comes from leaving unclear parts unclear.
Even if database and cloud operations execute commands as specified, mistakes in the target become accidents. When AI inference and authority to modify production are directly connected, selection discrepancies become direct data changes. Separation of verification environments and recoverability are critical.
- Separate accounts, authentication information, and target names between production and verification
- Display the target and count before deletion or initialization
- Test not just backups but actual restoration procedures
- Make human approval mandatory for production changes
These are general confirmation methods derived from public cases. This report does not mean all countermeasure shortfalls are confirmed as causes. Check whether the same conditions exist in your configuration and implement only what is necessary.
Code Diagnosis Rakuda is not a service that directly detects accidents within this local/cloud interior or restores lost data. Its role is to review separate risks remaining in publicly available web services from URLs. A free simple diagnosis in about one minute requires no card or GitHub integration. Use it separately from local backups and permission checks.
Before moving forward based only on displays saying "it works" or "processing is complete," check the change scope, public scope, and how to revert once. That brief pause becomes the process for continuing to use AI's speed with confidence.
Can you specifically explain the stopping conditions and recovery points in your current environment to avoid producing the same result as "among approximately 5,000 apps, over 2,000 cases with corporate documents, conversation logs, medical and financial information in public state"?
