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Analyze » Grist Labs » GRICAM1769683332

Incident Score: Analysis & Impact (GRICAM1769683332)

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

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-3
Company Score Before Incident750 / 1000
Company Score After Incident747 / 1000
INCIDENT NUMBERGRICAM1769683332
Type of Cyber IncidentVulnerability
ATTACK VECTORMalicious spreadsheet formulas
DATA EXPOSEDSensitive data, customer records, operational...
INCIDENT DATE19/01/2026
STATUSResolved

Key Highlights From The Incident Analysis

  • Timeline of Grist Labs'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 Grist Labs 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 Grist Labs breach identified under incident ID GRICAM1769683332.

The analysis begins with a detailed overview of Grist Labs's information like the linkedin page: https://www.linkedin.com/company/grist-labs, the number of followers: 1217, the industry type: Software Development and the number of employees: 12 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 750 and after the incident was 747 with a difference of -3 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 Grist Labs and their customers.

On 20 January 2026, Grist-Core disclosed Sandbox Escape Vulnerability issues under the banner "Critical Sandbox Escape Flaw in Grist-Core Patched After Disclosure by Cyera Research Labs".

A high-severity sandbox escape vulnerability in Grist-Core, tracked as GHSA-7xvx-8pf2-pv5g (CVSS 9.1), has been patched following responsible disclosure by Cyera Research Labs.

The disruption is felt across the environment, affecting Grist-Core SaaS and self-hosted deployments, and exposing Sensitive data, customer records, operational metrics, integration credentials.

In response, moved swiftly to contain the threat with measures like Patch released (version 1.7.9), migration of Pyodide formula execution under Deno by default, and began remediation that includes Disabled GRIST_PYODIDE_SKIP_DENO flag, introduced permission-based mediation layer.

The case underscores how Resolved, teams are taking away lessons such as Shift from blocklist-based sandbox to capability-based security model reduces risk of formula-based exploitation, and recommending next steps like Ensure GRIST_PYODIDE_SKIP_DENO flag is disabled to prevent reintroduction of the vulnerability. Migrate to Deno-based execution for enhanced security.

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 high-severity sandbox escape vulnerability in Grist-Core (CVSS 9.1) and Exploitation for Client Execution (T1203) with moderate to high confidence (80%), supported by evidence indicating malicious spreadsheet formulas that bypassed the platform’s Pyodide WebAssembly sandbox. Under the Execution tactic, the analysis identified Command and Scripting Interpreter: Python (T1059.006) with high confidence (90%), supported by evidence indicating python Class Hierarchy Traversal...enabling direct imports of the os module, Native API (T1106) with moderate to high confidence (80%), supported by evidence indicating ctypes.CDLL(None).system() loading system() from libc via Emscripten, and Command and Scripting Interpreter: JavaScript (T1059.007) with moderate to high confidence (80%), supported by evidence indicating emscripten_run_script_string() to execute JavaScript in host runtime. Under the Privilege Escalation tactic, the analysis identified Escape to Host (T1611) with high confidence (90%), supported by evidence indicating sandbox escape vulnerability...achieve remote code execution (RCE) and Abuse Elevation Control Mechanism: Bypass User Account Control (T1548.002) with moderate to high confidence (70%), supported by evidence indicating bypassed the platform’s Pyodide WebAssembly sandbox. 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 integration credentials exposed via RCE in SaaS deployments. Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating customer records, operational metrics compromised via RCE. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate confidence (60%), supported by evidence indicating potential compromise of multi-tenant workflows and connected systems. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with moderate to high confidence (70%), supported by evidence indicating rCE within vendor’s control plane, multi-tenant workflows at risk. 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%)
Exploitation for Client Execution (80%)
Execution
Command and Scripting Interpreter: Python (90%)
Native API (80%)
Command and Scripting Interpreter: JavaScript (80%)
Privilege Escalation
Escape to Host (90%)
Abuse Elevation Control Mechanism: Bypass User Account Control (70%)
Credential Access
Unsecured Credentials: Credentials In Files (70%)
Collection
Data from Local System (80%)
Exfiltration
Exfiltration Over C2 Channel (60%)
Impact
Resource Hijacking (70%)

Sources & References