X, The Moonshot Factory Breach Incident Score: Analysis & Impact (X19101619112425)

In this Rankiteo incident briefing, we review the X, The Moonshot Factory breach identified under incident ID X19101619112425.

The analysis begins with a detailed overview of X, The Moonshot Factory's information like the linkedin page: https://www.linkedin.com/company/google, the number of followers: 175235, the industry type: Research and the number of employees: 2221 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 735 and after the incident was 731 with a difference of -4 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 X, The Moonshot Factory and their customers.

X (formerly Twitter) recently reported "The Shadow Breach: X’s 2025 Data Catastrophe and the Erosion of Digital Trust", a noteworthy cybersecurity incident.

In 2025, X (formerly Twitter) experienced a massive data exposure due to an accidental configuration error in its backend systems, leaking sensitive user information via public APIs.

The disruption is felt across the environment, affecting Public APIs, Backend Developer Tools and AI-Driven Features (e.g., Grok AI), and exposing User IDs, Locations and Interaction Histories, with nearly 200 million (confirmed); up to 2.8 billion (alleged) records at risk, plus an estimated financial loss of $285,000 per hour during outages (November 2025); potential billions in GDPR fines.

In response, and began remediation that includes Public warnings (e.g., Musk’s hacker alerts) and User advisories for password changes/2FA, and stakeholders are being briefed through Limited transparency and Public posts by Musk and cybersecurity accounts.

The case underscores how Ongoing (EU GDPR and FTC investigations, internal reviews), teams are taking away lessons such as Legacy infrastructure and new AI features must be integrated with robust security controls, Insider threats during layoffs require stricter access revocation protocols and Public APIs and developer tools need rigorous privacy safeguards, and recommending next steps like Implement zero-trust architecture and regular security audits, Enhance data anonymization for AI-driven features and Strengthen insider threat detection and employee offboarding processes, with advisories going out to stakeholders covering Users advised to monitor for identity theft, change passwords, enable 2FA.

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.

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 high confidence (95%), with evidence including misconfigured backend systems enabled public API access to private data, and accidental Configuration Error in Public API Exposure and Valid Accounts: Cloud Accounts (T1078.004) with high confidence (90%), with evidence including disgruntled employees exploited the vulnerability during layoffs, and lack of Access Controls During Layoffs. Under the Persistence tactic, the analysis identified Account Manipulation: Additional Cloud Roles (T1098.003) with moderate to high confidence (85%), with evidence including legacy infrastructure clashing with new AI-driven features (e.g., Grok AI), bypassing privacy controls, and inadequate Data Anonymization in AI Features. Under the Privilege Escalation tactic, the analysis identified Valid Accounts: Cloud Accounts (T1078.004) with high confidence (90%), with evidence including disgruntled employees exploited the vulnerability with elevated access, and lack of Access Controls During Layoffs. Under the Defense Evasion tactic, the analysis identified Impair Defenses: Disable or Modify Tools (T1562.001) with moderate to high confidence (80%), with evidence including bypassing privacy controls and enabling public API access to private data, and lack of real-time monitoring for anomalous data flows and Indicator Removal: File Deletion (T1070.004) with moderate to high confidence (75%), with evidence including data was exposed in plaintext via APIs (no logging/encryption), and inadequate Data Anonymization in AI Features. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with high confidence (90%), with evidence including public APIs and backend developer tools exposed emails, user IDs, locations, and data was exposed in plaintext via APIs and Account Discovery: Cloud Account (T1087.004) with moderate to high confidence (85%), with evidence including 200 million user records (later expanded to 2.8 billion) exposed via APIs, and user IDs, emails, bios, follower counts harvested. Under the Discovery tactic, the analysis identified Account Discovery: Cloud Account (T1087.004) with moderate to high confidence (85%), with evidence including scrapers and disgruntled employees exploited the vulnerability to harvest data, and 2.8 billion records totaling 400GB exfiltrated and Network Service Discovery (T1046) with moderate to high confidence (80%), with evidence including public API Exposure allowed opportunistic scrapers to probe systems, and legacy Infrastructure Weaknesses enabled reconnaissance. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (95%), with evidence including 200 million user records (later 2.8 billion records) collected via APIs, and emails, bios, follower counts, user IDs, locations, interaction histories and Data from Cloud Storage: Cloud API (T1213.002) with high confidence (95%), with evidence including public API Exposure enabled harvesting by scrapers and insider(s), and aPI logs, User databases, Metadata exposed. Under the Exfiltration tactic, the analysis identified Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol (T1048.003) with high confidence (95%), with evidence including 400GB leak exfiltrated via public APIs (unencrypted), and data sold on black market (200M+ records traded) and Automated Exfiltration: Traffic Duplication (T1020.001) with high confidence (90%), with evidence including opportunistic Data Scrapers automated harvesting via APIs, and disgruntled employees exploited the vulnerability for bulk exfiltration. Under the Impact tactic, the analysis identified Data Destruction (T1485) with moderate to high confidence (70%), with evidence including intermittent outages reported (e.g., March 2025 DDoS-like incident), and reputational and legal costs projected in the billions, Network Denial of Service: Direct Network Flood (T1498.002) with moderate to high confidence (75%), with evidence including march 2025 DDoS-like incident during breach timeline, and $285,000/hour during outages, and Data Manipulation (T1659) with moderate to high confidence (80%), with evidence including aI training datasets compromised via inadequate data anonymization, and grok AI integration bypassed privacy controls. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.