Meta Breach Incident Score: Analysis & Impact (MET1032410112025)

In this Rankiteo incident briefing, we review the Meta breach identified under incident ID MET1032410112025.

The analysis begins with a detailed overview of Meta's information like the linkedin page: https://www.linkedin.com/company/instagram, the number of followers: 11513481, the industry type: Software Development and the number of employees: 140153 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 760 and after the incident was 760 with a difference of 0 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 Meta and their customers.

WhatsApp (Meta Platforms, Inc.) recently reported "Critical WhatsApp Metadata Exposure Vulnerability Affecting 3.5 Billion Users", a noteworthy cybersecurity incident.

Cybersecurity experts uncovered a critical vulnerability in WhatsApp that exposed metadata of over 3.5 billion users globally.

The disruption is felt across the environment, affecting WhatsApp servers and user metadata databases, and exposing metadata (phone numbers, locations, device/OS details, account ages) and contact lists (associated phone numbers), with nearly 3.5 billion records at risk.

In response, teams activated the incident response plan, moved swiftly to contain the threat with measures like Vulnerability patched at root level (per Meta), and stakeholders are being briefed through Public advisory (Meta Advisory 2025) and Media statements.

The case underscores how Ongoing (no evidence of malicious exploitation per Meta; independent research suggests potential prior abuse), teams are taking away lessons such as Critical importance of rate-limiting and request validation for metadata APIs; need for proactive vulnerability testing in global communication platforms with high-risk user bases (e.g., restricted-access countries), and recommending next steps like Implement stricter API rate-limiting and anomaly detection for metadata queries, Conduct third-party red-team exercises to identify similar flaws and Enhance transparency in disclosing vulnerabilities affecting high-risk regions, with advisories going out to stakeholders covering Meta Advisory 2025.

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%), supported by evidence indicating unauthorized API/data request abuse due to lack of rate-limiting on metadata queries. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating exposed metadata of over 3.5 billion users globally, including phone numbers, locations, device types and Data from Information Repositories: Contact Lists (T1213.003) with high confidence (90%), supported by evidence indicating contact lists (associated phone numbers) compromised. Under the Exfiltration tactic, the analysis identified Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol (T1048.003) with moderate to high confidence (85%), supported by evidence indicating unlimited unauthorized data requests enabled metadata correlation into detailed user profiles. Under the Impact tactic, the analysis identified Phishing for Information: Spearphishing via Service (T1598.003) with moderate to high confidence (80%), supported by evidence indicating metadata could enable targeted phishing or profiling and Malicious Activity via API (T1659) with high confidence (90%), supported by evidence indicating unauthorized API/data request abuse to exploit unrestricted metadata access. Under the Reconnaissance tactic, the analysis identified Gather Victim Host Information: Firmware (T1590.005) with moderate to high confidence (85%), supported by evidence indicating device types, OS details exposed in metadata leak and Gather Victim Identity Information (T1591) with high confidence (90%), supported by evidence indicating phone numbers, approximate locations, account ages exposed. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.