Lovable denies mass data breach

2026-04-20 · Source: Sifted · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Emerging Technologies & Innovation · Depth: Fundamental Awareness, quick

Summary

Swedish vibe-coding startup Lovable has denied a mass data breach despite a user's claims of accessing other customers' chat histories and personal information. On April 19, 2026, an anonymous user posted on X, stating they could view conversations, emails, names, and dates of birth from public projects, a bug reportedly unfixed for 48 days. Lovable, founded in 2024 and backed by over $500m from investors like Accel, acknowledged unclear documentation regarding "public" project visibility but denied a breach. The company stated that chat messages for public projects are no longer visible and that the ability to set new enterprise projects as public has been disabled since May 25, 2025. This incident follows Lovable's recent partnership with security firm Aikido for penetration testing.

Key takeaway

For CTOs and VPs of Engineering evaluating platform security, this incident highlights the critical need for explicit and unambiguous data visibility controls. Ensure your documentation clearly defines "public" and "private" data implications, and default all sensitive user data to private. Proactively engage third-party security firms for penetration testing to identify and remediate potential data exposure vectors before they become public incidents.

Key insights

Unclear data visibility settings can lead to perceived breaches, even without external system compromise.

Principles

• Transparency in data handling is paramount.
• Default to private for user data visibility.

In practice

• Audit default data visibility settings.
• Enhance documentation for data sharing.
• Implement regular penetration testing.

Topics

• Lovable
• Data Breach Denial
• User Data Exposure
• No-code Development
• Security Vulnerability

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Consultant, Investor, Tech Journalist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sifted.