Rankiteo Logo
Rankiteo
Leader in Cyber Underwriting
Loading...
NEWRankiteo Cyber Underwriting Desktop - Score, price, and bind from your desktop
WindowsmacOSLinux
Download
Analyze » Fortinet » FORCERMOX1770408103

Incident Score: Analysis & Impact (FORCERMOX1770408103)

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

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-2
Company Score Before Incident305 / 1000
Company Score After Incident303 / 1000
INCIDENT NUMBERFORCERMOX1770408103
Type of Cyber IncidentVulnerability
ATTACK VECTORExploited internet-exposed FortiGate VPN devices without multi-factor authentication (MFA) using compromised credentials and stolen configurations
DATA EXPOSEDBusiness-critical data, industrial control system...
INCIDENT DATE28/12/2025
STATUSpublished

Key Highlights From The Incident Analysis

  • Timeline of Fortinet's Vulnerability 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 Fortinet 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 Fortinet breach identified under incident ID FORCERMOX1770408103.

The analysis begins with a detailed overview of Fortinet's information like the linkedin page: https://www.linkedin.com/company/fortinet, the number of followers: 1232151, the industry type: Computer and Network Security and the number of employees: 15789 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 305 and after the incident was 303 with a difference of -2 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 Fortinet and their customers.

On 29 December 2025, Renewable energy facilities (wind and solar farms) disclosed Destructive Cyberattack, Sabotage and Cyberespionage issues under the banner "Coordinated Cyberattacks Target Poland’s Critical Infrastructure in December 2025".

On 29 December 2025, a series of destructive cyberattacks struck Poland’s energy and industrial sectors, orchestrated by a Russia-linked threat actor tracked as Static Tundra (also known as Berserk Bear, Ghost Blizzard, and Dragonfly).

The disruption is felt across the environment, affecting RTU controllers, Protection relays and HMI computers, and exposing Business-critical data, industrial control system configurations.

Formal response steps have not been shared publicly yet.

Overall, the incident is a reminder of why proactive monitoring and strong governance matter.

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 External Remote Services (T1133) with high confidence (90%), supported by evidence indicating exploited internet-exposed FortiGate VPN devices without multi-factor authentication (MFA) and Valid Accounts: Cloud Accounts (T1078.004) with high confidence (90%), supported by evidence indicating compromised credentials allowed initial access. Under the Execution tactic, the analysis identified Command and Scripting Interpreter: PowerShell (T1059.001) with moderate to high confidence (80%), supported by evidence indicating lazyWiper, a PowerShell-based wiper distributed via GPOs and User Execution: Malicious File (T1204.002) with moderate to high confidence (70%), supported by evidence indicating dynoWiper malware deployed via Group Policy Objects (GPOs). Under the Persistence tactic, the analysis identified Account Manipulation (T1098) with moderate to high confidence (80%), supported by evidence indicating modified settings to maintain persistence despite credential changes and Valid Accounts: Domain Accounts (T1078.002) with high confidence (90%), supported by evidence indicating active Directory admin access obtained. Under the Privilege Escalation tactic, the analysis identified Valid Accounts: Domain Accounts (T1078.002) with high confidence (90%), supported by evidence indicating active Directory admin access obtained. Under the Defense Evasion tactic, the analysis identified Impair Defenses: Disable or Modify Tools (T1562.001) with moderate to high confidence (70%), supported by evidence indicating eDR platform blocked the wiper’s execution and Indicator Removal: File Deletion (T1070.004) with moderate to high confidence (80%), supported by evidence indicating file deletions and factory resets on industrial devices. Under the Credential Access tactic, the analysis identified Brute Force: Password Guessing (T1110.001) with moderate confidence (60%), supported by evidence indicating compromised credentials allowed initial access and Credentials from Password Stores (T1555) with moderate to high confidence (70%), supported by evidence indicating stolen configurations used for initial access. Under the Discovery tactic, the analysis identified Account Discovery: Domain Account (T1087.002) with moderate to high confidence (80%), supported by evidence indicating internal reconnaissance and credential theft and Network Service Discovery (T1046) with moderate to high confidence (70%), supported by evidence indicating lateral movement across servers and workstations. Under the Lateral Movement tactic, the analysis identified Remote Services: Remote Desktop Protocol (T1021.001) with moderate to high confidence (70%), supported by evidence indicating lateral movement across servers and workstations and Lateral Tool Transfer (T1570) with moderate to high confidence (80%), supported by evidence indicating dynoWiper malware deployed via Group Policy Objects (GPOs). Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (70%), supported by evidence indicating industrial control system configurations compromised. Under the Impact tactic, the analysis identified Data Destruction (T1485) with high confidence (90%), supported by evidence indicating dynoWiper and LazyWiper malware corrupted firmware and business-critical data and Data Manipulation: Stored Data Manipulation (T1565.001) with moderate to high confidence (80%), supported by evidence indicating corrupted firmware, file deletions, and factory resets on industrial devices. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.

Initial Access
External Remote Services (90%)
Valid Accounts: Cloud Accounts (90%)
Execution
Command and Scripting Interpreter: PowerShell (80%)
User Execution: Malicious File (70%)
Persistence
Account Manipulation (80%)
Valid Accounts: Domain Accounts (90%)
Privilege Escalation
Valid Accounts: Domain Accounts (90%)
Defense Evasion
Impair Defenses: Disable or Modify Tools (70%)
Indicator Removal: File Deletion (80%)
Credential Access
Brute Force: Password Guessing (60%)
Credentials from Password Stores (70%)
Discovery
Account Discovery: Domain Account (80%)
Network Service Discovery (70%)
Lateral Movement
Remote Services: Remote Desktop Protocol (70%)
Lateral Tool Transfer (80%)
Collection
Data from Local System (70%)
Impact
Data Destruction (90%)
Data Manipulation: Stored Data Manipulation (80%)