According to Nagli on Twitter, a public Firestore endpoint for project rentahuman-prod exposed full user records via a direct GET request to firestore.googleapis.com/v1/projects/rentahuman-prod/databases/(default)/documents/humans?pageSize=300. As reported by the tweet, the Firebase config was embedded in homepage JavaScript, enabling unauthenticated access. According to Google Firebase documentation cited by industry reports, improperly configured Firestore rules can allow read access to collections without auth, creating high-severity data exposure risks for AI-driven apps that store user data alongside model interaction logs. For AI product teams, the immediate business impact includes regulatory exposure, reputational damage, and model retraining data leakage; remediation should include tightening Firestore security rules to require auth, rotating API keys, auditing access logs, and implementing backend proxies for model and user data, as recommended by Firebase security guidance and standard OWASP API best practices.

According to @galnagli on X, an AI agent discovered two misconfigured databases—moltbook on Supabase exposing 35K emails and RentAHuman on Firestore exposing 187K emails—both shipped without security rules and fixed before reported harm. As reported by Wiz, the moltbook exposure additionally revealed millions of API keys due to public database access and lack of row-level security, underscoring how rapid prototyping with managed backends can create severe data leakage risks. According to Wiz, enforcing default deny rules, enabling Supabase RLS, and hardening Firebase security rules can reduce blast radius, while integrating automated AI security agents into CI/CD offers a scalable guardrail for startups shipping fast.