Meta Breach Incident Score: Analysis & Impact (MET2632026111425)

In this Rankiteo incident briefing, we review the Meta breach identified under incident ID MET2632026111425.

The analysis begins with a detailed overview of Meta's information like the linkedin page: https://www.linkedin.com/company/meta, 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 733 and after the incident was 732 with a difference of -1 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.

Meta recently reported "Critical Remote Code Execution (RCE) Vulnerabilities in AI Inference Server Frameworks", a noteworthy cybersecurity incident.

Cybersecurity researchers at Oligo Security uncovered a chain of critical remote code execution (RCE) vulnerabilities in major AI inference server frameworks, including those from Meta, Nvidia, Microsoft, and open-source projects such as vLLM and SGLang.

The disruption is felt across the environment, affecting AI inference servers (Meta, Nvidia, Microsoft, vLLM, SGLang).

Formal response steps have not been shared publicly yet.

The case underscores how Ongoing (vulnerabilities disclosed, patches likely in development), teams are taking away lessons such as Code reuse without security review can propagate vulnerabilities across ecosystems. Critical infrastructure (e.g., AI frameworks) requires stricter scrutiny of third-party dependencies and serialization practices, and recommending next steps like Avoid unsafe deserialization (e.g., Python pickle) in production systems, Audit copied code for security flaws before integration and Implement secure alternatives to ZeroMQ or enforce strict input validation.

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 remote Code Execution (RCE) vulnerabilities in Meta’s AI inference server frameworks, and unsafe use of ZeroMQ (ZMQ) and Python’s pickle deserialization. Under the Execution tactic, the analysis identified Command and Scripting Interpreter: Python (T1059.006) with high confidence (95%), supported by evidence indicating python’s pickle deserialization enabling arbitrary code execution and Command and Scripting Interpreter: Unix Shell (T1059.004) with moderate to high confidence (85%), supported by evidence indicating execute arbitrary code on AI servers via ZMQ/pickle flaws. Under the Persistence tactic, the analysis identified Server Software Component: Web Shell (T1505.003) with moderate to high confidence (70%), supported by evidence indicating potential unauthorized code execution on AI infrastructure could implant persistent access. Under the Privilege Escalation tactic, the analysis identified Exploitation for Privilege Escalation (T1068) with moderate to high confidence (80%), supported by evidence indicating compromising sensitive training data, proprietary algorithms suggests elevated access post-exploitation. Under the Defense Evasion tactic, the analysis identified Obfuscated Files or Information (T1027) with moderate to high confidence (75%), supported by evidence indicating supply-chain attacks, model poisoning may involve evading detection via obfuscation and Indicator Removal: File Deletion (T1070.004) with moderate confidence (65%), supported by evidence indicating unauthorized access to internal AI pipelines could involve cleaning logs/traces. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (70%), supported by evidence indicating compromising sensitive training data may include credentials embedded in AI config files. Under the Discovery tactic, the analysis identified File and Directory Discovery (T1083) with moderate to high confidence (80%), supported by evidence indicating access to internal AI pipelines implies enumeration of files/directories for proprietary data and Network Service Discovery (T1046) with moderate to high confidence (75%), supported by evidence indicating aI inference servers targeted; attackers likely scan for exposed services (e.g., ZMQ ports). Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating compromising sensitive training data, proprietary algorithms, or user interactions. Under the Exfiltration tactic, the analysis identified Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol (T1048.003) with moderate to high confidence (80%), supported by evidence indicating data leaks via exploited ZMQ channels or custom protocols in AI frameworks. Under the Impact tactic, the analysis identified Endpoint Denial of Service: Application or System Exploitation (T1499.004) with moderate to high confidence (70%), supported by evidence indicating model poisoning could disrupt AI service availability/integrity and Data Destruction (T1485) with moderate confidence (60%), supported by evidence indicating potential unauthorized code execution could delete/corrupt training data. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.