Invariant Labs
Company Details
Linkedin ID:
invariant-labs
Website:
Employees number:
9
Number of followers:
248
NAICS:
5415
Industry Type:
Homepage:
invariant.app
IP Addresses:
0
Company ID:
INV_2147422
Scan Status:
In-progress
Invariant Labs Company CyberSecurity Posture
invariant.app
Invariant Labs is a blockchain-focused software house with deep expertise in DeFi and RWA infrastructure. For years, we have delivered secure, high-performance on-chain systems for clients worldwide, combining advanced engineering with rigorous research to help teams launch reliable products that perform in real market conditions. We also have experience pioneering completely new architectures and developing features built on innovative, frontier-level designs. If you plan to introduce Web3 solutions into your business, we’re ready to support you. We manage the full technical stack and provide strategic guidance shaped by extensive industry experience.
Invariant Labs A.I CyberSecurity Scoring
Invariant Labs Risk Score (AI oriented)Between 750 and 799
Invariant Labs
Updated:
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Invariant Labs Global Score (TPRM)XXXX
Invariant Labs
- Instant access to detailed risk factors
- Benchmark vs. industry & size peers
- Vulnerabilities
- Findings
Invariant Labs Company CyberSecurity News & History
Past Incidents
1
Attack Types
1
GitHub and Invariant Labs: Researchers warn of prompt injection vulnerability in GitHub MCP with no obvious fix • DEVCLASS
Vulnerability
Rankiteo Explanation
Attack with significant impact with customers data leaks
Description: GitHub MCP Server Vulnerable to Prompt Injection Attacks, Researchers Warn Researchers at Zurich-based Invariant Labs have identified a prompt injection vulnerability in GitHub’s Model Context Protocol (MCP) server, which could expose sensitive code from private repositories. The issue stems from an architectural flaw rather than a coding error, allowing attackers to manipulate AI agents into leaking confidential data. The attack scenario involves a developer working across both public and private repositories, with an AI agent granted access to the private ones. An attacker posts a malicious issue in a public repository containing hidden prompts instructing the AI to extract and publish private repository data. When the developer tasks the AI with reviewing the public repository, the agent unknowingly executes the malicious instructions, exposing private code. While the MCP server operates as designed, the attack is low-complexity and high-impact, with no straightforward fix. Researchers suggest mitigations, such as limiting AI agents to one repository per session and enforcing least-privilege access tokens, but these are not foolproof. Open-source developer Simon Willison described the flaw as a "lethal trifecta" for prompt injection, combining private data access, malicious instruction execution, and exfiltration capabilities. Prompt injection where malicious instructions are embedded in seemingly benign data remains difficult to prevent due to the unstructured nature of AI interactions. Despite warnings dating back over two years, effective defenses are still lacking. A proposed MCP server update would filter contributions to only those from users with push access, but this could block legitimate input. GitHub’s MCP server, currently in preview (v0.4.0), is open-source, and the vulnerability highlights broader challenges in securing AI-driven development tools. The incident underscores the need for stricter access controls and better prompt injection defenses as AI integration in software development expands.
Invariant Labs Company Scoring based on AI Models
Cyber Incidents Likelihood 3 - 6 - 9 months
A.I Risk Score Likelihood 3 - 6 - 9 months
Underwriter Stats for Invariant Labs
Incidents vs IT Services and IT Consulting Industry Average (This Year)
No incidents recorded for Invariant Labs in 2026.
Incidents vs All-Companies Average (This Year)
No incidents recorded for Invariant Labs in 2026.
Incident Types Invariant Labs vs IT Services and IT Consulting Industry Avg (This Year)
No incidents recorded for Invariant Labs in 2026.
Incident History — Invariant Labs (X = Date, Y = Severity)
Invariant Labs cyber incidents detection timeline including parent company and subsidiaries
Invariant Labs Company Subsidiaries
Invariant Labs is a blockchain-focused software house with deep expertise in DeFi and RWA infrastructure. For years, we have delivered secure, high-performance on-chain systems for clients worldwide, combining advanced engineering with rigorous research to help teams launch reliable products that perform in real market conditions. We also have experience pioneering completely new architectures and developing features built on innovative, frontier-level designs. If you plan to introduce Web3 solutions into your business, we’re ready to support you. We manage the full technical stack and provide strategic guidance shaped by extensive industry experience.
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Invariant Labs CyberSecurity News
October 20, 2025 07:00 AM
Insights around how to bridge the AI security chasm
Snyk has sought to expand its focus on securing AI-driven software development amid the growing convergence of AI innovation and...
October 08, 2025 07:00 AM
AI Security Goes Mainstream as Vendors Spend Heavily on M&A
Artificial intelligence security acquisitions have skyrocketed in recent months as major security vendors look to establish dominance in...
August 14, 2025 07:00 AM
Cybersecurity Software Sector Update - Summer 2025
Cybersecurity deal activity continued to accelerate in Q2 2025, building on the strong start to the year. At the mid-year point,...
August 05, 2025 07:00 AM
SentinelOne acquires AI security startup Prompt Security
SentinelOne Inc. today announced plans to buy Prompt Security Ltd., a startup with a platform for protecting workers from risky artificial...
August 01, 2025 07:00 AM
Storage and Data Protection News for the Week of August 1; Updates from Hitachi Vantara, IBM, Scality & More
Solutions Review Executive Editor Tim King curated this list of notable storage and data protection news for the week of August 1, 2025.
July 29, 2025 07:00 AM
Wiz Uncovers Critical Access Bypass Flaw in AI-Powered Vibe Coding Platform Base44
Cybersecurity researchers have disclosed a now-patched critical security flaw in a popular vibe coding platform called Base44 that could...
July 29, 2025 07:00 AM
Security for Agents and Agents for Security: The Next Cybersecurity Frontier
Securing agentic systems requires rethinking everything from authentication to observability. A new playbook is emerging: security for...
July 11, 2025 07:00 AM
Cybersecurity startup Snyk undergoes fresh wave of job cuts
Snyk, the London and Tel Aviv-founded cybersecurity firm, is understood to have undergone a fresh wave of job cuts, with sources saying over 100 workers have...
July 02, 2025 07:00 AM
Cybersecurity M&A Roundup: 41 Deals Announced in June 2025
Forty-one cybersecurity merger and acquisition (M&A) deals were announced in June 2025 and added to SecurityWeek tracker.
Frequently Asked Questions
Explore insights on cybersecurity incidents, risk posture, and Rankiteo's assessments.
Invariant Labs CyberSecurity History Information
Official Website of Invariant Labs
The official website of Invariant Labs is https://invariant.app/.
Invariant Labs’s AI-Generated Cybersecurity Score
According to Rankiteo, Invariant Labs’s AI-generated cybersecurity score is 750, reflecting their Fair security posture.
How many security badges does Invariant Labs’ have ?
According to Rankiteo, Invariant Labs currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.
Has Invariant Labs been affected by any supply chain cyber incidents ?
According to Rankiteo, Invariant Labs has been affected by a supply chain cyber incident involving Invariant Labs, with the incident ID GITINV1766037664.
Does Invariant Labs have SOC 2 Type 1 certification ?
According to Rankiteo, Invariant Labs is not certified under SOC 2 Type 1.
Does Invariant Labs have SOC 2 Type 2 certification ?
According to Rankiteo, Invariant Labs does not hold a SOC 2 Type 2 certification.
Does Invariant Labs comply with GDPR ?
According to Rankiteo, Invariant Labs is not listed as GDPR compliant.
Does Invariant Labs have PCI DSS certification ?
According to Rankiteo, Invariant Labs does not currently maintain PCI DSS compliance.
Does Invariant Labs comply with HIPAA ?
According to Rankiteo, Invariant Labs is not compliant with HIPAA regulations.
Does Invariant Labs have ISO 27001 certification ?
According to Rankiteo,Invariant Labs is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.
Industry Classification of Invariant Labs
Invariant Labs operates primarily in the IT Services and IT Consulting industry.
Number of Employees at Invariant Labs
Invariant Labs employs approximately 9 people worldwide.
Subsidiaries Owned by Invariant Labs
Invariant Labs presently has no subsidiaries across any sectors.
Invariant Labs’s LinkedIn Followers
Invariant Labs’s official LinkedIn profile has approximately 248 followers.
NAICS Classification of Invariant Labs
Invariant Labs is classified under the NAICS code 5415, which corresponds to Computer Systems Design and Related Services.
Invariant Labs’s Presence on Crunchbase
No, Invariant Labs does not have a profile on Crunchbase.
Invariant Labs’s Presence on LinkedIn
Yes, Invariant Labs maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/invariant-labs.
Cybersecurity Incidents Involving Invariant Labs
As of January 22, 2026, Rankiteo reports that Invariant Labs has experienced 1 cybersecurity incidents.
Number of Peer and Competitor Companies
Invariant Labs has an estimated 38,450 peer or competitor companies worldwide.
What types of cybersecurity incidents have occurred at Invariant Labs ?
Incident Types: The types of cybersecurity incidents that have occurred include Vulnerability.
How does Invariant Labs detect and respond to cybersecurity incidents ?
Detection and Response: The company detects and responds to cybersecurity incidents through an remediation measures with mitigation strategies include requiring ai agents to access only one repository per session, using least-privilege access tokens, and implementing additional controls like invariant labs' guardrails and mcp-scan product..
Incident Details
Can you provide details on each incident ?
Title: Prompt Injection Vulnerability in GitHub’s MCP Server Leading to Private Repository Code Leak
Description: Researchers at Invariant Labs discovered a prompt injection vulnerability in GitHub’s MCP (Model Context Protocol) server, which could result in code leaking from private repositories. The issue stems from an architectural flaw where an AI agent with access to both public and private repositories may follow malicious prompts in a public repository to exfiltrate private data. The attack has low complexity and high potential harm, with no easy architectural solution currently available.
Type: Prompt Injection
Attack Vector: Malicious issue posted in a public repository containing embedded prompts
Vulnerability Exploited: Architectural flaw in GitHub MCP server allowing AI agents to access and exfiltrate data from private repositories
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 : Prompt Injection GITINV1766037664
Data Compromised: Private repository code and information
Systems Affected: GitHub MCP server, AI agents configured with repository access
Operational Impact: Potential exposure of sensitive code and data from private repositories
Brand Reputation Impact: Potential reputational damage to GitHub and affected developers
What types of data are most commonly compromised in incidents ?
Commonly Compromised Data Types: The types of data most commonly compromised in incidents are Source code and repository information.
Which entities were affected by each incident ?
Incident : Prompt Injection GITINV1766037664
Entity Name: GitHub
Entity Type: Technology Platform
Industry: Software Development
Location: Global
Size: Large
Customers Affected: Developers using GitHub MCP server with AI agents configured for private repositories
Response to the Incidents
What measures were taken in response to each incident ?
Incident : Prompt Injection GITINV1766037664
Remediation Measures: Mitigation strategies include requiring AI agents to access only one repository per session, using least-privilege access tokens, and implementing additional controls like Invariant Labs' Guardrails and MCP-scan product
Data Breach Information
What type of data was compromised in each breach ?
Incident : Prompt Injection GITINV1766037664
Type of Data Compromised: Source code, repository information
Sensitivity of Data: High (private repository data)
Data Exfiltration: Yes (via malicious prompts in public repositories)
File Types Exposed: Code files, repository metadata
What measures does the company take to prevent data exfiltration ?
Prevention of Data Exfiltration: The company takes the following measures to prevent data exfiltration: Mitigation strategies include requiring AI agents to access only one repository per session, using least-privilege access tokens, and implementing additional controls like Invariant Labs' Guardrails and MCP-scan product.
Lessons Learned and Recommendations
What lessons were learned from each incident ?
Incident : Prompt Injection GITINV1766037664
Lessons Learned: Prompt injection vulnerabilities in AI systems are difficult to mitigate due to their unstructured nature. Developers must exercise caution when configuring AI agents with repository access, avoiding 'always allow' policies and implementing least-privilege principles. Confirmation fatigue can undermine security protections.
What recommendations were made to prevent future incidents ?
Incident : Prompt Injection GITINV1766037664
Recommendations: Restrict AI agents to one repository per session, Use least-privilege access tokens for AI agents, Implement additional auditing and control tools like Invariant Labs' Guardrails and MCP-scan, Avoid 'always allow' confirmation policies for AI agent actions, Carefully review and approve all AI agent tool invocations, Consider filtering AI agent access to contributions from users with push access to repositoriesRestrict AI agents to one repository per session, Use least-privilege access tokens for AI agents, Implement additional auditing and control tools like Invariant Labs' Guardrails and MCP-scan, Avoid 'always allow' confirmation policies for AI agent actions, Carefully review and approve all AI agent tool invocations, Consider filtering AI agent access to contributions from users with push access to repositoriesRestrict AI agents to one repository per session, Use least-privilege access tokens for AI agents, Implement additional auditing and control tools like Invariant Labs' Guardrails and MCP-scan, Avoid 'always allow' confirmation policies for AI agent actions, Carefully review and approve all AI agent tool invocations, Consider filtering AI agent access to contributions from users with push access to repositoriesRestrict AI agents to one repository per session, Use least-privilege access tokens for AI agents, Implement additional auditing and control tools like Invariant Labs' Guardrails and MCP-scan, Avoid 'always allow' confirmation policies for AI agent actions, Carefully review and approve all AI agent tool invocations, Consider filtering AI agent access to contributions from users with push access to repositoriesRestrict AI agents to one repository per session, Use least-privilege access tokens for AI agents, Implement additional auditing and control tools like Invariant Labs' Guardrails and MCP-scan, Avoid 'always allow' confirmation policies for AI agent actions, Carefully review and approve all AI agent tool invocations, Consider filtering AI agent access to contributions from users with push access to repositoriesRestrict AI agents to one repository per session, Use least-privilege access tokens for AI agents, Implement additional auditing and control tools like Invariant Labs' Guardrails and MCP-scan, Avoid 'always allow' confirmation policies for AI agent actions, Carefully review and approve all AI agent tool invocations, Consider filtering AI agent access to contributions from users with push access to repositories
What are the key lessons learned from past incidents ?
Key Lessons Learned: The key lessons learned from past incidents are Prompt injection vulnerabilities in AI systems are difficult to mitigate due to their unstructured nature. Developers must exercise caution when configuring AI agents with repository access, avoiding 'always allow' policies and implementing least-privilege principles. Confirmation fatigue can undermine security protections.
References
Where can I find more information about each incident ?
Incident : Prompt Injection GITINV1766037664
Source: Invariant Labs Research
Incident : Prompt Injection GITINV1766037664
Source: Simon Willison's Analysis
Incident : Prompt Injection GITINV1766037664
Source: GitHub MCP Server GitHub Repository
Where can stakeholders find additional resources on cybersecurity best practices ?
Additional Resources: Stakeholders can find additional resources on cybersecurity best practices at and Source: Invariant Labs Research, and Source: Simon Willison's Analysis, and Source: GitHub MCP Server GitHub Repository.
Investigation Status
What is the current status of the investigation for each incident ?
Incident : Prompt Injection GITINV1766037664
Investigation Status: Ongoing
Stakeholder and Customer Advisories
Were there any advisories issued to stakeholders or customers for each incident ?
Incident : Prompt Injection GITINV1766037664
Stakeholder Advisories: Developers using GitHub MCP server should review their AI agent configurations and implement recommended mitigations to prevent private data exposure.
Customer Advisories: Developers are advised to avoid 'always allow' policies for AI agent actions and to restrict agent access to one repository per session. Additional tools like Guardrails and MCP-scan can provide extra protection.
What advisories does the company provide to stakeholders and customers following an incident ?
Advisories Provided: The company provides the following advisories to stakeholders and customers following an incident: were Developers using GitHub MCP server should review their AI agent configurations and implement recommended mitigations to prevent private data exposure. and Developers are advised to avoid 'always allow' policies for AI agent actions and to restrict agent access to one repository per session. Additional tools like Guardrails and MCP-scan can provide extra protection..
Post-Incident Analysis
What were the root causes and corrective actions taken for each incident ?
Incident : Prompt Injection GITINV1766037664
Root Causes: Architectural flaw in GitHub MCP server allowing AI agents to access and exfiltrate data from private repositories via malicious prompts in public repositories. Lack of strict access controls and confirmation fatigue among developers.
Corrective Actions: GitHub could implement stricter access controls, such as limiting AI agents to one repository per session and filtering contributions based on user permissions. Developers should adopt least-privilege principles and avoid 'always allow' policies.
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: GitHub could implement stricter access controls, such as limiting AI agents to one repository per session and filtering contributions based on user permissions. Developers should adopt least-privilege principles and avoid 'always allow' policies..
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 was Private repository code and information.
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 was Private repository code and information.
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 Prompt injection vulnerabilities in AI systems are difficult to mitigate due to their unstructured nature. Developers must exercise caution when configuring AI agents with repository access, avoiding 'always allow' policies and implementing least-privilege principles. Confirmation fatigue can undermine security protections.
What was the most significant recommendation implemented to improve cybersecurity ?
Most Significant Recommendation Implemented: The most significant recommendation implemented to improve cybersecurity was Consider filtering AI agent access to contributions from users with push access to repositories, Use least-privilege access tokens for AI agents, Implement additional auditing and control tools like Invariant Labs' Guardrails and MCP-scan, Restrict AI agents to one repository per session, Carefully review and approve all AI agent tool invocations and Avoid 'always allow' confirmation policies for AI agent actions.
References
What is the most recent source of information about an incident ?
Most Recent Source: The most recent source of information about an incident are Invariant Labs Research, GitHub MCP Server GitHub Repository and Simon Willison's Analysis.
Investigation Status
What is the current status of the most recent investigation ?
Current Status of Most Recent Investigation: The current status of the most recent investigation is Ongoing.
Stakeholder and Customer Advisories
What was the most recent stakeholder advisory issued ?
Most Recent Stakeholder Advisory: The most recent stakeholder advisory issued was Developers using GitHub MCP server should review their AI agent configurations and implement recommended mitigations to prevent private data exposure., .
What was the most recent customer advisory issued ?
Most Recent Customer Advisory: The most recent customer advisory issued was an Developers are advised to avoid 'always allow' policies for AI agent actions and to restrict agent access to one repository per session. Additional tools like Guardrails and MCP-scan can provide extra protection.
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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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