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Analyze » Instructure » INS1778690819

Incident Score: Analysis & Impact (INS1778690819)

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

Rankiteo Score Impact Analysis

Rankiteo Incident Impact0
Company Score Before Incident100 / 1000
Company Score After Incident100 / 1000
INCIDENT NUMBERINS1778690819
Type of Cyber IncidentBreach
ATTACK VECTORCross-Site Scripting (XSS)
DATA EXPOSED3.6TB of data
INCIDENT DATE30/04/2026
STATUSpublished

Key Highlights From The Incident Analysis

  • Timeline of Instructure's Breach 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 Instructure 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 Instructure breach identified under incident ID INS1778690819.

The analysis begins with a detailed overview of Instructure's information like the linkedin page: https://www.linkedin.com/company/instructure-inc-, the number of followers: 79694, the industry type: E-Learning Providers and the number of employees: 2172 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 100 and after the incident was 100 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 Instructure and their customers.

Instructure recently reported "Instructure Strikes Deal with ShinyHunters to Prevent Leak of 3.6TB Stolen Data", a noteworthy cybersecurity incident.

Instructure, the provider of the Canvas learning management system, has negotiated with the ShinyHunters extortion group to prevent the release of data stolen in a recent breach impacting over 30 million educators and students.

The disruption is felt across the environment, affecting Canvas learning management system, Free-for-Teacher environment, login portals, and exposing 3.6TB of data.

In response, moved swiftly to contain the threat with measures like Temporarily disabled Free-for-Teacher accounts, and began remediation that includes Addressing security flaws in Free-for-Teacher environment, and stakeholders are being briefed through Hosting a webinar on May 13 to discuss the breach and mitigation efforts.

The case underscores how with advisories going out to stakeholders covering Assurance that no further extortion demands will be met.

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 exploiting cross-site scripting (XSS) vulnerabilities in Instructure’s Free-for-Teacher environment. Under the Execution tactic, the analysis identified Exploitation for Client Execution (T1203) with moderate to high confidence (80%), supported by evidence indicating xSS vulnerabilities allowed attackers to gain administrative access. Under the Privilege Escalation tactic, the analysis identified Valid Accounts (T1078) with high confidence (90%), supported by evidence indicating vulnerabilities allowed attackers to gain administrative access. Under the Defense Evasion tactic, the analysis identified Masquerading (T1036) with moderate to high confidence (70%), supported by evidence indicating defacing Canvas login portals with an extortion message. Under the Credential Access tactic, the analysis identified Credentials in Files (T1552.006) with moderate confidence (60%), supported by evidence indicating 3.6TB of data exfiltrated, likely containing credentials or PII. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating 3.6TB of data exfiltrated from Canvas learning management system and Data from Information Repositories (T1213) with moderate to high confidence (80%), supported by evidence indicating data stolen from Free-for-Teacher environment. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (90%), supported by evidence indicating 3.6TB of data exfiltrated by ShinyHunters. Under the Impact tactic, the analysis identified Defacement (T1491) with moderate to high confidence (80%), supported by evidence indicating defacing Canvas login portals with an extortion message and Stored Data Manipulation (T1565.001) with moderate confidence (60%), supported by evidence indicating extortion message displayed on login portals. 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%)
Execution
Exploitation for Client Execution (80%)
Privilege Escalation
Valid Accounts (90%)
Defense Evasion
Masquerading (70%)
Credential Access
Credentials in Files (60%)
Collection
Data from Local System (90%)
Data from Information Repositories (80%)
Exfiltration
Exfiltration Over C2 Channel (90%)
Impact
Defacement (80%)
Stored Data Manipulation (60%)

Sources & References