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Analyze » Lovable » LOV1776717678

Incident Score: Analysis & Impact (LOV1776717678)

The details regarding individual company incidents & reports gives you full view from every side.

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-61
Company Score Before Incident766 / 1000
Company Score After Incident705 / 1000
INCIDENT NUMBERLOV1776717678
Type of Cyber IncidentBreach
ATTACK VECTORMisconfiguration
DATA EXPOSEDChat histories, emails, names, dates...
INCIDENT DATE18/04/2026
STATUSOngoing (company denies breach but acknowledges misconfiguration)

Key Highlights From The Incident Analysis

  • Timeline of Lovable's Breach and lateral movement inside company's environment.
  • Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
  • How Rankiteo’s incident engine converts technical details into a normalized incident score.
  • How this cyber incident impacts Lovable Rankiteo cyber scoring and cyber rating.
  • Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.

Full Incident Analysis Transcript

In this Rankiteo incident briefing, we review the Lovable breach identified under incident ID LOV1776717678.

The analysis begins with a detailed overview of Lovable's information like the linkedin page: https://www.linkedin.com/company/lovable-dev, the number of followers: 427012, the industry type: Software Development and the number of employees: 957 employees

After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 766 and after the incident was 705 with a difference of -61 which is could be a good indicator of the severity and impact of the incident.

In the next step of the video, we will analyze in more details the incident and the impact it had on Lovable and their customers.

Lovable recently reported "Lovable Denies Data Breach After User Exposes Security Flaw in AI Coding Platform", a noteworthy cybersecurity incident.

Swedish no-code startup Lovable has refuted claims of a mass data breach after an anonymous user alleged that sensitive user information including chat histories, emails, names, and dates of birth was accessible through a security flaw.

The disruption is felt across the environment, affecting Lovable AI coding platform, and exposing Chat histories, emails, names, dates of birth, project data, website source code, full chat logs.

In response, moved swiftly to contain the threat with measures like Disabled public project chat visibility for enterprise customers, and began remediation that includes Addressed misconfiguration for enterprise customers (May 25, 2025), and stakeholders are being briefed through Public denial of breach, acknowledgment of poor communication.

The case underscores how Ongoing (company denies breach but acknowledges misconfiguration), teams are taking away lessons such as Need for improved bug triage and communication regarding data visibility settings; importance of addressing reported vulnerabilities promptly, and recommending next steps like Conduct a full security audit, implement stricter access controls, improve incident response communication, and extend security fixes to all users (not just enterprise), with advisories going out to stakeholders covering Public statement denying breach but acknowledging data visibility issues.

Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.

The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.

MITRE ATT&CK® Correlation Analysis

Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Exploit Public-Facing Application (T1190) with moderate to high confidence (80%), with evidence including security flaw in AI coding platform, and user demonstrated view/download of other customers’ project data. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with moderate confidence (60%), supported by evidence indicating website source code accessible via misconfiguration. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating chat histories, emails, names, dates of birth, project data compromised and Data from Information Repositories (T1213) with moderate to high confidence (80%), supported by evidence indicating full chat logs and website source code accessible via free account. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating user demonstrated download capability of other customers’ data. Under the Defense Evasion tactic, the analysis identified Impair Defenses: Disable or Modify Tools (T1562.001) with moderate confidence (50%), supported by evidence indicating public project chat visibility disabled only for enterprise customers. Under the Impact tactic, the analysis identified Endpoint Denial of Service: Application or System Exploitation (T1499.004) with lower confidence (40%), supported by evidence indicating potential reputational damage due to public disclosure and Disk Wipe: Disk Structure Wipe (T1561.002) with lower confidence (30%), supported by evidence indicating misconfiguration left unresolved for 48 days. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.

Initial Access
Exploit Public-Facing Application (80%)
Credential Access
Unsecured Credentials: Credentials In Files (60%)
Collection
Data from Local System (90%)
Data from Information Repositories (80%)
Exfiltration
Exfiltration Over C2 Channel (70%)
Defense Evasion
Impair Defenses: Disable or Modify Tools (50%)
Impact
Endpoint Denial of Service: Application or System Exploitation (40%)
Disk Wipe: Disk Structure Wipe (30%)

Sources & References