Description
lunary-ai/lunary version v1.4.25 contains an improper access control vulnerability in the POST /api/v1/data-warehouse/bigquery endpoint. This vulnerability allows any user to export the entire database data by creating a stream to Google BigQuery without proper authentication or authorization. The issue is fixed in version 1.4.26.
EPSS Score:
0%
Comprehensive Technical Analysis of EUVD-2025-6876
1. Vulnerability Assessment and Severity Evaluation
Vulnerability Description: The vulnerability in lunary-ai/lunary version v1.4.25 involves an improper access control issue in the POST /api/v1/data-warehouse/bigquery endpoint. This flaw allows any user to export the entire database data to Google BigQuery without proper authentication or authorization.
Severity Evaluation: The vulnerability has a CVSS Base Score of 9.8, which is classified as Critical. The CVSS vector (CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H) indicates the following:
- Attack Vector (AV): Network (N) - The vulnerability is exploitable over the network.
- Attack Complexity (AC): Low (L) - The attack requires minimal skill or resources.
- Privileges Required (PR): None (N) - No privileges are required to exploit the vulnerability.
- User Interaction (UI): None (N) - No user interaction is required.
- Scope (S): Unchanged (U) - The vulnerability does not change the security scope.
- Confidentiality (C): High (H) - There is a high impact on confidentiality.
- Integrity (I): High (H) - There is a high impact on integrity.
- Availability (A): High (H) - There is a high impact on availability.
2. Potential Attack Vectors and Exploitation Methods
Attack Vectors:
- Unauthenticated Access: An attacker can exploit the vulnerability by sending a POST request to the /api/v1/data-warehouse/bigquery endpoint without any authentication.
- Data Exfiltration: The attacker can create a stream to Google BigQuery, exporting the entire database data.
Exploitation Methods:
- Automated Scripts: Attackers can use automated scripts to send POST requests to the vulnerable endpoint, facilitating large-scale data exfiltration.
- Man-in-the-Middle (MitM) Attacks: If the communication is not encrypted, an attacker could intercept and manipulate the data stream.
3. Affected Systems and Software Versions
Affected Systems:
- All systems running lunary-ai/lunary version v1.4.25.
Software Versions:
- lunary-ai/lunary version v1.4.25 and earlier versions.
Fixed Version:
- The issue is resolved in version 1.4.26.
4. Recommended Mitigation Strategies
Immediate Actions:
- Upgrade Software: Upgrade to lunary-ai/lunary version 1.4.26 or later.
- Access Controls: Implement strict access controls and authentication mechanisms for the /api/v1/data-warehouse/bigquery endpoint.
- Network Segmentation: Segment the network to limit access to the vulnerable endpoint.
- Monitoring: Implement monitoring and logging to detect any unauthorized access attempts.
Long-Term Strategies:
- Regular Audits: Conduct regular security audits and vulnerability assessments.
- Patch Management: Establish a robust patch management process to ensure timely updates.
- Security Training: Provide security training for developers and administrators to prevent similar vulnerabilities in the future.
5. Impact on European Cybersecurity Landscape
Regulatory Compliance:
- GDPR: The vulnerability poses a significant risk to data privacy, potentially leading to GDPR violations and hefty fines.
- NIS Directive: Organizations in critical sectors must comply with the NIS Directive, which mandates robust cybersecurity measures.
Economic Impact:
- Data Breaches: Unauthorized data exfiltration can result in financial losses, reputational damage, and legal consequences.
- Operational Disruption: The vulnerability can lead to operational disruptions, affecting business continuity.
Public Trust:
- Consumer Confidence: Data breaches can erode consumer confidence and trust in digital services.
6. Technical Details for Security Professionals
Vulnerability Details:
- Endpoint: POST /api/v1/data-warehouse/bigquery
- Impact: Allows unauthenticated users to export the entire database data to Google BigQuery.
Detection Methods:
- Log Analysis: Analyze logs for unauthorized access attempts to the /api/v1/data-warehouse/bigquery endpoint.
- Intrusion Detection Systems (IDS): Deploy IDS to detect and alert on suspicious activities.
Mitigation Steps:
- Upgrade to Version 1.4.26: Ensure all instances of lunary-ai/lunary are upgraded to version 1.4.26 or later.
- Implement Authentication: Enforce proper authentication and authorization mechanisms for the vulnerable endpoint.
- Network Security: Use firewalls and network segmentation to restrict access to the endpoint.
- Monitoring and Alerts: Set up monitoring and alerting systems to detect any unauthorized access attempts.
References:
- NVD: CVE-2024-8999
- GitHub Commit: Fix Commit
- Huntr Bounty: Bounty Details
By addressing this vulnerability promptly and effectively, organizations can mitigate the risk of data breaches and ensure compliance with regulatory requirements.