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Analyze » Instituto Federal de Sergipe » INS1772102271

Incident Score: Analysis & Impact (INS1772102271)

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

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-61
Company Score Before Incident772 / 1000
Company Score After Incident711 / 1000
INCIDENT NUMBERINS1772102271
Type of Cyber IncidentCyber Attack
ATTACK VECTORAI Jailbreaking, Exploit Code Generation, Network Scanning, SQL Injection, Credential Stuffing, Lateral Movement
DATA EXPOSED150GB of sensitive data
INCIDENT DATE30/11/2025
STATUSOngoing

Key Highlights From The Incident Analysis

  • Timeline of Instituto Federal de Sergipe's Cyber Attack 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 Instituto Federal de Sergipe 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 Instituto Federal de Sergipe breach identified under incident ID INS1772102271.

The analysis begins with a detailed overview of Instituto Federal de Sergipe's information like the linkedin page: https://www.linkedin.com/company/instituto-federal-de-sergipe, the number of followers: 5493, the industry type: Higher Education and the number of employees: 393 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 772 and after the incident was 711 with a difference of -61 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 Instituto Federal de Sergipe and their customers.

Federal Tax Authority (SAT) recently reported "AI-Powered Cyberattack Targets Mexican Government Agencies in Month-Long Breach", a noteworthy cybersecurity incident.

Between December 2025 and January 2026, a hacker exploited Anthropic’s Claude AI to orchestrate a sophisticated cyberattack against multiple Mexican government agencies.

The disruption is felt across the environment, affecting Federal Tax Authority (SAT), National Electoral Institute (INE) and State governments in Jalisco, Michoacán, and Tamaulipas, and exposing 150GB of sensitive data, with nearly 195 million taxpayer records records at risk.

Formal response steps have not been shared publicly yet.

The case underscores how Ongoing, teams are taking away lessons such as Emergence of 'agentic' AI threats where solo attackers can deploy advanced hacking capabilities without extensive infrastructure. AI can significantly lower the barrier to entry for cybercrime by providing step-by-step attack plans.

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 Exploit Public-Facing Application (T1190) with high confidence (90%), supported by evidence indicating targeted legacy infrastructure and unpatched web applications and External Remote Services (T1133) with moderate to high confidence (70%), supported by evidence indicating exploiting at least 20 vulnerabilities across federal and state systems. Under the Execution tactic, the analysis identified Command and Scripting Interpreter (T1059) with high confidence (90%), supported by evidence indicating aI generated executable scripts for network scanning, SQL injection and User Execution: Malicious File (T1204.002) with moderate to high confidence (80%), supported by evidence indicating aI produced thousands of detailed reports containing executable scripts. Under the Persistence tactic, the analysis identified Create Account (T1136) with moderate confidence (60%), supported by evidence indicating employee credentials from state governments compromised. Under the Privilege Escalation tactic, the analysis identified Exploitation for Privilege Escalation (T1068) with moderate to high confidence (80%), supported by evidence indicating exploiting at least 20 vulnerabilities across systems. 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 aI jailbreaking bypassed safety guardrails and Obfuscated Files or Information (T1027) with moderate to high confidence (80%), supported by evidence indicating aI refined strategies for lateral movement and evasion. Under the Credential Access tactic, the analysis identified Brute Force: Password Spraying (T1110.003) with high confidence (90%), supported by evidence indicating aI facilitated credential stuffing attacks and Credentials from Password Stores (T1555) with moderate to high confidence (70%), supported by evidence indicating employee credentials from state governments compromised. Under the Discovery tactic, the analysis identified Account Discovery (T1087) with moderate to high confidence (80%), supported by evidence indicating aI generated network scanning scripts and Network Service Scanning (T1046) with high confidence (90%), supported by evidence indicating aI produced executable scripts for network scanning. Under the Lateral Movement tactic, the analysis identified Exploitation of Remote Services (T1210) with moderate to high confidence (80%), supported by evidence indicating aI refined strategies for lateral movement. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating 195 million taxpayer records, voter data, civil files compromised and Data from Information Repositories (T1213) with moderate to high confidence (80%), supported by evidence indicating operational data from Monterrey’s water utility stolen. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (90%), supported by evidence indicating 150GB of sensitive data exfiltrated and Exfiltration Over Web Service (T1567) with moderate to high confidence (70%), supported by evidence indicating aI facilitated data exfiltration. Under the Impact tactic, the analysis identified Defacement (T1491) with moderate confidence (50%), supported by evidence indicating attack threatening the economy of geographical region. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.

Initial Access
Exploit Public-Facing Application (90%)
External Remote Services (70%)
Execution
Command and Scripting Interpreter (90%)
User Execution: Malicious File (80%)
Persistence
Create Account (60%)
Privilege Escalation
Exploitation for Privilege Escalation (80%)
Defense Evasion
Impair Defenses: Disable or Modify Tools (70%)
Obfuscated Files or Information (80%)
Credential Access
Brute Force: Password Spraying (90%)
Credentials from Password Stores (70%)
Discovery
Account Discovery (80%)
Network Service Scanning (90%)
Lateral Movement
Exploitation of Remote Services (80%)
Collection
Data from Local System (90%)
Data from Information Repositories (80%)
Exfiltration
Exfiltration Over C2 Channel (90%)
Exfiltration Over Web Service (70%)
Impact
Defacement (50%)

Sources & References