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GDIT is a global technology and professional services company that delivers solutions, technology and mission services to every major agency across the U.S. government, defense and intelligence community. Our 30,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50+ countries worldwide, offering leading capabilities in digital modernization, AI/ML, Cloud, Cyber and application development. GDIT is part of General Dynamics, a global aerospace and defense company. We have shared our clients’ sense of purpose for over half a century and have a unique understanding of their missions, complex environments, and a rapidly changing world. Together with our clients, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology.

General Dynamics Information Technology A.I CyberSecurity Scoring

GDIT

Company Details

Linkedin ID:

gdit

Employees number:

26,089

Number of followers:

304,794

NAICS:

5415

Industry Type:

IT Services and IT Consulting

Homepage:

gdit.com

IP Addresses:

0

Company ID:

GEN_2314700

Scan Status:

In-progress

AI scoreGDIT Risk Score (AI oriented)

Between 750 and 799

https://images.rankiteo.com/companyimages/gdit.jpeg
GDIT IT Services and IT Consulting
Updated:
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globalscoreGDIT Global Score (TPRM)

XXXX

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GDIT IT Services and IT Consulting
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  • Vulnerabilities
  • Findings

GDIT Company CyberSecurity News & History

Past Incidents
1
Attack Types
1
EntityTypeSeverityImpactSeenBlog DetailsSupply Chain SourceIncident DetailsView
General Dynamics Information TechnologyVulnerability85412/2025NA
Rankiteo Explanation :
Attack with significant impact with customers data leaks

Description: The Critical Gap in Data Security: Governing Data in Motion Organizations have made significant progress in mapping their data landscapes, leveraging Data Security Posture Management (DSPM) tools to identify sensitive information, regulated records, and high-risk data concentrations. While visibility into data at rest has improved, a persistent blind spot remains: data in motion. Once information leaves secure repositories via email, file-sharing platforms, APIs, or web forms governance often becomes fragmented. This disconnect stems from legacy architectures where storage and transmission systems evolved independently, each with distinct security models and workflows. ### The Core Challenge: Decentralized Movement and Fragmented Policies Three key factors exacerbate this gap: 1. Decentralized Movement – Data flows through disparate channels (email, collaboration tools, automated workflows) without a unified control layer. 2. System-Centric Policies – Organizations enforce separate rules for email, file transfers, and partner access, but sensitive data doesn’t adhere to these boundaries. 3. Fractured Auditability – Tracking data movement requires piecing together logs from multiple systems, each with varying retention and detail levels. ### A Shift Toward Data-Centric Governance A promising solution lies in treating data labels as actionable policy signals. Traditionally, classification (via MIP labels, custom taxonomies, or DSPM insights) has been confined to storage systems. However, for labels to mitigate risk, they must travel with the data and influence decisions across transmission platforms. Recent integrations, such as the collaboration between BigID and Kiteworks, exemplify this shift. By connecting DSPM-driven classification with enforcement frameworks spanning email, file transfers, APIs, and web forms, organizations can enforce consistent policies regardless of how data moves. ### Impact on Managed Security Service Providers (MSSPs) For MSSPs, this evolution presents opportunities to: - Transform assessments into continuous programs by leveraging classification-driven enforcement for ongoing policy orchestration. - Reduce policy sprawl by defining data-centric rules (e.g., "encryption required for external sharing of sensitive data") that apply uniformly across channels. - Enhance third-party oversight with controls that persist beyond enterprise boundaries, improving supply-chain security. - Accelerate incident response by providing immutable logs tied to data classifications, reducing investigation time and regulatory uncertainty. ### Real-World Applications Connecting classification with enforcement addresses critical scenarios: - Outbound sharing of regulated data – Applying consistent controls (encryption, watermarking, or blocking) when sensitive data leaves via email or file-sharing. - Secure collaboration with partners – Retaining predictable controls for intellectual property, legal documents, or engineering files crossing organizational boundaries. - High-risk data intake – Routing web form submissions through governed channels to enforce access, encryption, and audit requirements. - Post-incident reconstruction – Using immutable logs to clarify data movement, reducing notification costs and regulatory friction. ### The Path Forward Data governance is transitioning from a system-centric model ("protect the repository") to a data-centric approach ("protect the information wherever it goes"). While DSPM has advanced visibility, the next phase involves integrating classification with enforcement across communication, transfer, and collaboration channels. The BigID-Kiteworks partnership reflects this broader industry trend, demonstrating how discovery and enforcement can work together to create a more coherent, auditable, and scalable approach to data movement governance.

General Dynamics Information Technology: Beyond DSPM Dashboards: Why Data Movement Remains an Underrated Risk
Vulnerability
Severity: 85
Impact: 4
Seen: 12/2025
Blog:
Supply Chain Source: NA
Rankiteo Explanation
Attack with significant impact with customers data leaks

Description: The Critical Gap in Data Security: Governing Data in Motion Organizations have made significant progress in mapping their data landscapes, leveraging Data Security Posture Management (DSPM) tools to identify sensitive information, regulated records, and high-risk data concentrations. While visibility into data at rest has improved, a persistent blind spot remains: data in motion. Once information leaves secure repositories via email, file-sharing platforms, APIs, or web forms governance often becomes fragmented. This disconnect stems from legacy architectures where storage and transmission systems evolved independently, each with distinct security models and workflows. ### The Core Challenge: Decentralized Movement and Fragmented Policies Three key factors exacerbate this gap: 1. Decentralized Movement – Data flows through disparate channels (email, collaboration tools, automated workflows) without a unified control layer. 2. System-Centric Policies – Organizations enforce separate rules for email, file transfers, and partner access, but sensitive data doesn’t adhere to these boundaries. 3. Fractured Auditability – Tracking data movement requires piecing together logs from multiple systems, each with varying retention and detail levels. ### A Shift Toward Data-Centric Governance A promising solution lies in treating data labels as actionable policy signals. Traditionally, classification (via MIP labels, custom taxonomies, or DSPM insights) has been confined to storage systems. However, for labels to mitigate risk, they must travel with the data and influence decisions across transmission platforms. Recent integrations, such as the collaboration between BigID and Kiteworks, exemplify this shift. By connecting DSPM-driven classification with enforcement frameworks spanning email, file transfers, APIs, and web forms, organizations can enforce consistent policies regardless of how data moves. ### Impact on Managed Security Service Providers (MSSPs) For MSSPs, this evolution presents opportunities to: - Transform assessments into continuous programs by leveraging classification-driven enforcement for ongoing policy orchestration. - Reduce policy sprawl by defining data-centric rules (e.g., "encryption required for external sharing of sensitive data") that apply uniformly across channels. - Enhance third-party oversight with controls that persist beyond enterprise boundaries, improving supply-chain security. - Accelerate incident response by providing immutable logs tied to data classifications, reducing investigation time and regulatory uncertainty. ### Real-World Applications Connecting classification with enforcement addresses critical scenarios: - Outbound sharing of regulated data – Applying consistent controls (encryption, watermarking, or blocking) when sensitive data leaves via email or file-sharing. - Secure collaboration with partners – Retaining predictable controls for intellectual property, legal documents, or engineering files crossing organizational boundaries. - High-risk data intake – Routing web form submissions through governed channels to enforce access, encryption, and audit requirements. - Post-incident reconstruction – Using immutable logs to clarify data movement, reducing notification costs and regulatory friction. ### The Path Forward Data governance is transitioning from a system-centric model ("protect the repository") to a data-centric approach ("protect the information wherever it goes"). While DSPM has advanced visibility, the next phase involves integrating classification with enforcement across communication, transfer, and collaboration channels. The BigID-Kiteworks partnership reflects this broader industry trend, demonstrating how discovery and enforcement can work together to create a more coherent, auditable, and scalable approach to data movement governance.

Ailogo

GDIT Company Scoring based on AI Models

Cyber Incidents Likelihood 3 - 6 - 9 months

🔒
Incident Predictions locked
Access Monitoring Plan

A.I Risk Score Likelihood 3 - 6 - 9 months

🔒
A.I. Risk Score Predictions locked
Access Monitoring Plan
statics

Underwriter Stats for GDIT

Incidents vs IT Services and IT Consulting Industry Average (This Year)

No incidents recorded for General Dynamics Information Technology in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for General Dynamics Information Technology in 2026.

Incident Types GDIT vs IT Services and IT Consulting Industry Avg (This Year)

No incidents recorded for General Dynamics Information Technology in 2026.

Incident History — GDIT (X = Date, Y = Severity)

GDIT cyber incidents detection timeline including parent company and subsidiaries

GDIT Company Subsidiaries

SubsidiaryImage

GDIT is a global technology and professional services company that delivers solutions, technology and mission services to every major agency across the U.S. government, defense and intelligence community. Our 30,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50+ countries worldwide, offering leading capabilities in digital modernization, AI/ML, Cloud, Cyber and application development. GDIT is part of General Dynamics, a global aerospace and defense company. We have shared our clients’ sense of purpose for over half a century and have a unique understanding of their missions, complex environments, and a rapidly changing world. Together with our clients, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology.

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newsone

GDIT CyberSecurity News

January 19, 2026 11:03 AM
General Dynamics to Deploy AI Cybersecurity Across 187 US Air Force Bases

General Dynamics Information Technology (GDIT) will deploy a new AI-powered cybersecurity system across 187 US Air Force bases worldwide.

January 16, 2026 04:58 PM
USAF selects GDIT for zero trust cybersecurity implementation

GDIT has received a task order to provide a zero trust cybersecurity solution for the US Air Force's global operations.

January 16, 2026 03:27 PM
General Dynamics Secures a $988M Deal to Modernize C5ISR Networks

General Dynamics' GD business unit, General Dynamics Information Technology, recently secured a $988 million contract to support the...

January 15, 2026 10:22 PM
GDIT tapped to deliver zero-trust security solution at nearly 200 Air Force bases

The $120 million task order will enable GDIT to implement zero-trust cybersecurity controls at facilities located across the world.

January 15, 2026 11:00 AM
GDIT Books $120M Air Force Task Order for Zero Trust Delivery

GDIT secured a $120 million Air Force task order to deliver an AI-enabled, zero trust cybersecurity platform to protect bases worldwide.

January 15, 2026 06:42 AM
GDIT secures $120 million task order to deliver zero trust for global US Air Force bases

General Dynamics Information Technology (GDIT), a business unit of General Dynamics, announced on Wednesday that it was awarded a US$120...

January 14, 2026 06:10 PM
GDIT Wins $120M Air Force Task Order to Deliver Zero Trust Cybersecurity

Brian Sheridan, GDIT General Dynamics Information Technology has secured a $120 million task order under the U.S. Air Force's Next...

January 14, 2026 08:00 AM
GDIT Selected to Implement Zero-Trust Security Solutions Across Nearly 200 Air Force Bases

General Dynamics will upgrade 187 Air Force bases with a $120 million zero trust cybersecurity solution for over one million users.

January 08, 2026 08:00 AM
GDIT wins $131m task order for USAF network upgrades

General Dynamics Information Technology (GDIT) has secured the first task order under the US Air Force's (USAF) Base Infrastructure...

faq

Frequently Asked Questions

Explore insights on cybersecurity incidents, risk posture, and Rankiteo's assessments.

GDIT CyberSecurity History Information

Official Website of General Dynamics Information Technology

The official website of General Dynamics Information Technology is https://www.gdit.com.

General Dynamics Information Technology’s AI-Generated Cybersecurity Score

According to Rankiteo, General Dynamics Information Technology’s AI-generated cybersecurity score is 785, reflecting their Fair security posture.

How many security badges does General Dynamics Information Technology’ have ?

According to Rankiteo, General Dynamics Information Technology currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has General Dynamics Information Technology been affected by any supply chain cyber incidents ?

According to Rankiteo, General Dynamics Information Technology has not been affected by any supply chain cyber incidents, and no incident IDs are currently listed for the organization.

Does General Dynamics Information Technology have SOC 2 Type 1 certification ?

According to Rankiteo, General Dynamics Information Technology is not certified under SOC 2 Type 1.

Does General Dynamics Information Technology have SOC 2 Type 2 certification ?

According to Rankiteo, General Dynamics Information Technology does not hold a SOC 2 Type 2 certification.

Does General Dynamics Information Technology comply with GDPR ?

According to Rankiteo, General Dynamics Information Technology is not listed as GDPR compliant.

Does General Dynamics Information Technology have PCI DSS certification ?

According to Rankiteo, General Dynamics Information Technology does not currently maintain PCI DSS compliance.

Does General Dynamics Information Technology comply with HIPAA ?

According to Rankiteo, General Dynamics Information Technology is not compliant with HIPAA regulations.

Does General Dynamics Information Technology have ISO 27001 certification ?

According to Rankiteo,General Dynamics Information Technology is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Industry Classification of General Dynamics Information Technology

General Dynamics Information Technology operates primarily in the IT Services and IT Consulting industry.

Number of Employees at General Dynamics Information Technology

General Dynamics Information Technology employs approximately 26,089 people worldwide.

Subsidiaries Owned by General Dynamics Information Technology

General Dynamics Information Technology presently has no subsidiaries across any sectors.

General Dynamics Information Technology’s LinkedIn Followers

General Dynamics Information Technology’s official LinkedIn profile has approximately 304,794 followers.

NAICS Classification of General Dynamics Information Technology

General Dynamics Information Technology is classified under the NAICS code 5415, which corresponds to Computer Systems Design and Related Services.

General Dynamics Information Technology’s Presence on Crunchbase

No, General Dynamics Information Technology does not have a profile on Crunchbase.

General Dynamics Information Technology’s Presence on LinkedIn

Yes, General Dynamics Information Technology maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/gdit.

Cybersecurity Incidents Involving General Dynamics Information Technology

As of January 21, 2026, Rankiteo reports that General Dynamics Information Technology has experienced 1 cybersecurity incidents.

Number of Peer and Competitor Companies

General Dynamics Information Technology has an estimated 38,451 peer or competitor companies worldwide.

What types of cybersecurity incidents have occurred at General Dynamics Information Technology ?

Incident Types: The types of cybersecurity incidents that have occurred include Vulnerability.

How does General Dynamics Information Technology detect and respond to cybersecurity incidents ?

Detection and Response: The company detects and responds to cybersecurity incidents through an third party assistance with integration of dspm tools (e.g., bigid) with enforcement frameworks (e.g., kiteworks), and containment measures with connecting classification engines with transmission platforms, containment measures with applying consistent controls across email, file transfer, apis, and forms, and remediation measures with unified data-centric policies for data in motion, remediation measures with enhanced auditability of data movement, remediation measures with persistent controls beyond enterprise boundaries, and enhanced monitoring with immutable logs tied to data classifications for post-incident reconstruction..

Incident Details

Can you provide details on each incident ?

Incident : Data Governance Blind Spot

Title: None

Description: Organizations face a structural gap in data governance where visibility into data at rest outpaces governance of data in motion. This blind spot arises from decentralized data movement systems, fragmented policies, and fractured auditability, leading to risks in email, file sharing, APIs, and web forms. The incident highlights the need for integrating data classification with enforcement frameworks to govern data movement consistently.

Type: Data Governance Blind Spot

Vulnerability Exploited: Decentralized data movement systemsFragmented policies for data in motionFractured auditability across communication channels

What are the most common types of attacks the company has faced ?

Common Attack Types: The most common types of attacks the company has faced is Vulnerability.

Impact of the Incidents

What was the impact of each incident ?

Incident : Data Governance Blind Spot GDI1765641604

Data Compromised: Sensitive, regulated, or personal/financial data

Systems Affected: EmailFile sharing platformsManaged file transfer systemsAPIsWeb forms

Operational Impact: Increased risk of data breaches, regulatory violations, and incident response challenges

Brand Reputation Impact: Potential erosion due to regulatory scrutiny or data breaches

Legal Liabilities: Increased risk of fines and legal actions due to non-compliance

Identity Theft Risk: Elevated due to exposure of personally identifiable information

Payment Information Risk: Elevated due to exposure of financial data

What types of data are most commonly compromised in incidents ?

Commonly Compromised Data Types: The types of data most commonly compromised in incidents are Regulated Data (E.G., Financial, Health Records), Personal Data, Intellectual Property, Engineering Files and .

Which entities were affected by each incident ?

Incident : Data Governance Blind Spot GDI1765641604

Entity Type: Organizations with fragmented data governance

Response to the Incidents

What measures were taken in response to each incident ?

Incident : Data Governance Blind Spot GDI1765641604

Third Party Assistance: Integration of DSPM tools (e.g., BigID) with enforcement frameworks (e.g., Kiteworks)

Containment Measures: Connecting classification engines with transmission platformsApplying consistent controls across email, file transfer, APIs, and forms

Remediation Measures: Unified data-centric policies for data in motionEnhanced auditability of data movementPersistent controls beyond enterprise boundaries

Enhanced Monitoring: Immutable logs tied to data classifications for post-incident reconstruction

How does the company involve third-party assistance in incident response ?

Third-Party Assistance: The company involves third-party assistance in incident response through Integration of DSPM tools (e.g., BigID) with enforcement frameworks (e.g., Kiteworks).

Data Breach Information

What type of data was compromised in each breach ?

Incident : Data Governance Blind Spot GDI1765641604

Type of Data Compromised: Regulated data (e.g., financial, health records), Personal data, Intellectual property, Engineering files

Sensitivity of Data: High

Data Exfiltration: Potential via email, file sharing, or APIs

Data Encryption: Recommended but not consistently applied

Personally Identifiable Information: Yes

What measures does the company take to prevent data exfiltration ?

Prevention of Data Exfiltration: The company takes the following measures to prevent data exfiltration: Unified data-centric policies for data in motion, Enhanced auditability of data movement, Persistent controls beyond enterprise boundaries, .

How does the company handle incidents involving personally identifiable information (PII) ?

Handling of PII Incidents: The company handles incidents involving personally identifiable information (PII) through by connecting classification engines with transmission platforms, applying consistent controls across email, file transfer, apis, and forms and .

Regulatory Compliance

Were there any regulatory violations and fines imposed for each incident ?

Incident : Data Governance Blind Spot GDI1765641604

Regulations Violated: Potential violations of privacy regulations (e.g., GDPR, CCPA, HIPAA),

Lessons Learned and Recommendations

What lessons were learned from each incident ?

Incident : Data Governance Blind Spot GDI1765641604

Lessons Learned: Data governance must extend beyond storage to include data in motion, Fragmented policies increase risk and complicate compliance, Auditability of data movement is critical for incident response and regulatory disclosures, Labels and classifications should be actionable signals for enforcement

What recommendations were made to prevent future incidents ?

Incident : Data Governance Blind Spot GDI1765641604

Recommendations: Integrate DSPM insights with enforcement frameworks for data movement, Define data-centric policies that apply consistently across communication channels, Improve third-party oversight with persistent controls beyond enterprise boundaries, Enhance incident response with immutable logs tied to data classificationsIntegrate DSPM insights with enforcement frameworks for data movement, Define data-centric policies that apply consistently across communication channels, Improve third-party oversight with persistent controls beyond enterprise boundaries, Enhance incident response with immutable logs tied to data classificationsIntegrate DSPM insights with enforcement frameworks for data movement, Define data-centric policies that apply consistently across communication channels, Improve third-party oversight with persistent controls beyond enterprise boundaries, Enhance incident response with immutable logs tied to data classificationsIntegrate DSPM insights with enforcement frameworks for data movement, Define data-centric policies that apply consistently across communication channels, Improve third-party oversight with persistent controls beyond enterprise boundaries, Enhance incident response with immutable logs tied to data classifications

What are the key lessons learned from past incidents ?

Key Lessons Learned: The key lessons learned from past incidents are Data governance must extend beyond storage to include data in motion,Fragmented policies increase risk and complicate compliance,Auditability of data movement is critical for incident response and regulatory disclosures,Labels and classifications should be actionable signals for enforcement.

What recommendations has the company implemented to improve cybersecurity ?

Implemented Recommendations: The company has implemented the following recommendations to improve cybersecurity: Improve third-party oversight with persistent controls beyond enterprise boundaries, Enhance incident response with immutable logs tied to data classifications, Define data-centric policies that apply consistently across communication channels and Integrate DSPM insights with enforcement frameworks for data movement.

References

Where can I find more information about each incident ?

Incident : Data Governance Blind Spot GDI1765641604

Source: BigID and Kiteworks Integration

Where can stakeholders find additional resources on cybersecurity best practices ?

Additional Resources: Stakeholders can find additional resources on cybersecurity best practices at and Source: BigID and Kiteworks Integration.

Post-Incident Analysis

What were the root causes and corrective actions taken for each incident ?

Incident : Data Governance Blind Spot GDI1765641604

Root Causes: Decentralized Data Movement Systems, Policies Written For Systems Rather Than Information, Fractured Auditability Across Platforms,

Corrective Actions: Unified Data Movement Governance, Consistent Enforcement Of Data-Centric Policies, Integration Of Classification And Enforcement Frameworks,

What is the company's process for conducting post-incident analysis ?

Post-Incident Analysis Process: The company's process for conducting post-incident analysis is described as Integration of DSPM tools (e.g., BigID) with enforcement frameworks (e.g., Kiteworks), Immutable logs tied to data classifications for post-incident reconstruction.

What corrective actions has the company taken based on post-incident analysis ?

Corrective Actions Taken: The company has taken the following corrective actions based on post-incident analysis: Unified Data Movement Governance, Consistent Enforcement Of Data-Centric Policies, Integration Of Classification And Enforcement Frameworks, .

Additional Questions

Impact of the Incidents

What was the most significant data compromised in an incident ?

Most Significant Data Compromised: The most significant data compromised in an incident were Sensitive, regulated and or personal/financial data.

What was the most significant system affected in an incident ?

Most Significant System Affected: The most significant system affected in an incident was EmailFile sharing platformsManaged file transfer systemsAPIsWeb forms.

Response to the Incidents

What third-party assistance was involved in the most recent incident ?

Third-Party Assistance in Most Recent Incident: The third-party assistance involved in the most recent incident was Integration of DSPM tools (e.g., BigID) with enforcement frameworks (e.g., Kiteworks).

What containment measures were taken in the most recent incident ?

Containment Measures in Most Recent Incident: The containment measures taken in the most recent incident were Connecting classification engines with transmission platformsApplying consistent controls across email, file transfer, APIs and and forms.

Data Breach Information

What was the most sensitive data compromised in a breach ?

Most Sensitive Data Compromised: The most sensitive data compromised in a breach were Sensitive, regulated and or personal/financial data.

Lessons Learned and Recommendations

What was the most significant lesson learned from past incidents ?

Most Significant Lesson Learned: The most significant lesson learned from past incidents was Labels and classifications should be actionable signals for enforcement.

What was the most significant recommendation implemented to improve cybersecurity ?

Most Significant Recommendation Implemented: The most significant recommendation implemented to improve cybersecurity was Improve third-party oversight with persistent controls beyond enterprise boundaries, Enhance incident response with immutable logs tied to data classifications, Define data-centric policies that apply consistently across communication channels and Integrate DSPM insights with enforcement frameworks for data movement.

References

What is the most recent source of information about an incident ?

Most Recent Source: The most recent source of information about an incident is BigID and Kiteworks Integration.

cve

Latest Global CVEs (Not Company-Specific)

Description

SummaryA command injection vulnerability (CWE-78) has been found to exist in the `wrangler pages deploy` command. The issue occurs because the `--commit-hash` parameter is passed directly to a shell command without proper validation or sanitization, allowing an attacker with control of `--commit-hash` to execute arbitrary commands on the system running Wrangler. Root causeThe commitHash variable, derived from user input via the --commit-hash CLI argument, is interpolated directly into a shell command using template literals (e.g.,  execSync(`git show -s --format=%B ${commitHash}`)). Shell metacharacters are interpreted by the shell, enabling command execution. ImpactThis vulnerability is generally hard to exploit, as it requires --commit-hash to be attacker controlled. The vulnerability primarily affects CI/CD environments where `wrangler pages deploy` is used in automated pipelines and the --commit-hash parameter is populated from external, potentially untrusted sources. An attacker could exploit this to: * Run any shell command. * Exfiltrate environment variables. * Compromise the CI runner to install backdoors or modify build artifacts. Credits Disclosed responsibly by kny4hacker. Mitigation * Wrangler v4 users are requested to upgrade to Wrangler v4.59.1 or higher. * Wrangler v3 users are requested to upgrade to Wrangler v3.114.17 or higher. * Users on Wrangler v2 (EOL) should upgrade to a supported major version.

Risk Information
cvss4
Base: 7.7
Severity: LOW
CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:N/VC:H/VI:H/VA:H/SC:L/SI:L/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Description

Vulnerability in the Oracle VM VirtualBox product of Oracle Virtualization (component: Core). Supported versions that are affected are 7.1.14 and 7.2.4. Easily exploitable vulnerability allows high privileged attacker with logon to the infrastructure where Oracle VM VirtualBox executes to compromise Oracle VM VirtualBox. While the vulnerability is in Oracle VM VirtualBox, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of Oracle VM VirtualBox. CVSS 3.1 Base Score 8.2 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H).

Risk Information
cvss3
Base: 8.2
Severity: LOW
CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H
Description

Vulnerability in the Oracle VM VirtualBox product of Oracle Virtualization (component: Core). Supported versions that are affected are 7.1.14 and 7.2.4. Easily exploitable vulnerability allows high privileged attacker with logon to the infrastructure where Oracle VM VirtualBox executes to compromise Oracle VM VirtualBox. While the vulnerability is in Oracle VM VirtualBox, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in unauthorized creation, deletion or modification access to critical data or all Oracle VM VirtualBox accessible data as well as unauthorized access to critical data or complete access to all Oracle VM VirtualBox accessible data and unauthorized ability to cause a partial denial of service (partial DOS) of Oracle VM VirtualBox. CVSS 3.1 Base Score 8.1 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:L).

Risk Information
cvss3
Base: 8.1
Severity: LOW
CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:L
Description

Vulnerability in the Oracle VM VirtualBox product of Oracle Virtualization (component: Core). Supported versions that are affected are 7.1.14 and 7.2.4. Easily exploitable vulnerability allows high privileged attacker with logon to the infrastructure where Oracle VM VirtualBox executes to compromise Oracle VM VirtualBox. While the vulnerability is in Oracle VM VirtualBox, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of Oracle VM VirtualBox. CVSS 3.1 Base Score 8.2 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H).

Risk Information
cvss3
Base: 8.2
Severity: LOW
CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H
Description

Vulnerability in the Oracle VM VirtualBox product of Oracle Virtualization (component: Core). Supported versions that are affected are 7.1.14 and 7.2.4. Easily exploitable vulnerability allows high privileged attacker with logon to the infrastructure where Oracle VM VirtualBox executes to compromise Oracle VM VirtualBox. While the vulnerability is in Oracle VM VirtualBox, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of Oracle VM VirtualBox. CVSS 3.1 Base Score 8.2 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H).

Risk Information
cvss3
Base: 8.2
Severity: LOW
CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H

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