Incident Score: Analysis & Impact (IVASONCIS1776702475)
The details regarding individual company incidents & reports gives you full view from every side.
Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Ivanti'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 Ivanti 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 Ivanti breach identified under incident ID IVASONCIS1776702475.
The analysis begins with a detailed overview of Ivanti's information like the linkedin page: https://www.linkedin.com/company/ivanti, the number of followers: 165752, the industry type: Software Development and the number of employees: 2975 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 700 and after the incident was 638 with a difference of -62 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 Ivanti and their customers.
On 31 March 2026, Cisco disclosed Zero-day exploitation, Reconnaissance and Brute-force attack issues under the banner "Exploitation Surges Preceding Vulnerability Disclosures (Dec 2025 - Mar 2026)".
GreyNoise report reveals hackers frequently exploit software vulnerabilities weeks before vendors publicly disclose them.
Impact assessments are still underway, so the full scope is not yet clear.
Formal response steps have not been shared publicly yet.
The case underscores how Completed (Report Published), teams are taking away lessons such as Exploitation surges can serve as an early indicator of undisclosed vulnerabilities, particularly for critical infrastructure vendors. Organizations monitoring such activity may gain a critical window to mitigate risks before patches are available, and recommending next steps like Monitor scanning and exploitation surges for early warning of undisclosed vulnerabilities, Prioritize patching for high-severity flaws and Implement enhanced monitoring for critical systems.
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 Reconnaissance tactic, the analysis identified Active Scanning (T1595) with high confidence (90%), with evidence including scanning activity was widely dispersed, and broad reconnaissance and Active Scanning: Vulnerability Scanning (T1595.002) with moderate to high confidence (80%), supported by evidence indicating nearly half of all scanning and exploitation surges targeting specific products. Under the Initial Access tactic, the analysis identified Exploit Public-Facing Application (T1190) with moderate to high confidence (80%), supported by evidence indicating exploiting software vulnerabilities before vendors publicly disclose them and External Remote Services (T1133) with moderate to high confidence (70%), supported by evidence indicating remote Code Execution (RCE) probes detected. Under the Credential Access tactic, the analysis identified Brute Force (T1110) with moderate to high confidence (80%), supported by evidence indicating 56% of brute-force attempts preceded public CVEs and Brute Force: Password Guessing (T1110.001) with moderate to high confidence (70%), supported by evidence indicating brute-force attempts detected in attack patterns. Under the Execution tactic, the analysis identified Exploitation for Client Execution (T1203) with moderate to high confidence (80%), supported by evidence indicating remote Code Execution (RCE) probes detected. Under the Persistence tactic, the analysis identified External Remote Services (T1133) with moderate confidence (60%), supported by evidence indicating shift from reconnaissance to focused attacks suggests persistence. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating motivation includes data exfiltration. Under the Impact tactic, the analysis identified Endpoint Denial of Service (T1499) with moderate confidence (50%), supported by evidence indicating high-severity flaws generated the most probing activity. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Ivanti Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/ivanti/incident/IVASONCIS1776702475
- Ivanti CyberSecurity Rating page: https://www.rankiteo.com/company/ivanti
- Ivanti Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/ivasoncis1776702475-ivanti-sonicwall-cisco-vulnerability-december-2025/
- Ivanti CyberSecurity Score History: https://www.rankiteo.com/company/ivanti/history
- Ivanti CyberSecurity Incident Source: https://www.cybersecuritydive.com/news/vulnerability-disclosure-surges-warnings-greynoise/817952/
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf