CVE-2024-24779 in Superset
Summary
by MITRE • 02/28/2024
Apache Superset with custom roles that include `can write on dataset` and without all data access permissions, allows for users to create virtual datasets to data they don't have access to. These users could then use those virtual datasets to get access to unauthorized data. This issue affects Apache Superset: before 3.0.4, from 3.1.0 before 3.1.1.
Users are recommended to upgrade to version 3.1.1 or 3.0.4, which fixes the issue.
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Analysis
by VulDB Data Team • 12/31/2024
The vulnerability described in CVE-2024-24779 represents a critical access control flaw within Apache Superset that undermines the security model designed to protect sensitive data through role-based permissions. This issue specifically targets environments where custom roles are implemented with granular permissions including the ability to create datasets while simultaneously lacking comprehensive data access privileges. The flaw exists in the virtual dataset creation mechanism that allows users with limited access rights to construct new data representations that bypass existing permission boundaries, effectively creating pathways to unauthorized data consumption.
The technical implementation of this vulnerability stems from insufficient validation during virtual dataset creation processes within Apache Superset's data access control framework. When users possess the specific permission combination of `can write on dataset` without proper all-data access rights, the system fails to enforce proper data source validation checks. This creates a privilege escalation scenario where users can manipulate the system to generate virtual datasets that reference underlying data sources they should not be able to access directly. The vulnerability operates at the intersection of data virtualization and access control enforcement, exploiting a gap in the permission validation logic that should prevent unauthorized data aggregation.
From an operational impact perspective, this vulnerability fundamentally compromises the integrity of data access controls in Apache Superset deployments. Attackers leveraging this flaw can bypass traditional data access restrictions to aggregate and analyze information that should remain protected within restricted datasets. The implications extend beyond simple data exposure to include potential data exfiltration, unauthorized analysis, and the creation of misleading data representations that could impact business decisions. Organizations relying on Apache Superset for data visualization and analysis face significant risk of unauthorized data access, particularly in environments where sensitive information is compartmentalized through role-based access controls.
The security implications of CVE-2024-24779 align with CWE-284 (Improper Access Control) and represent a classic case of privilege escalation through insufficient input validation. This vulnerability also maps to ATT&CK technique T1078.004 (Valid Accounts: Cloud Accounts) and T1566.001 (Phishing: Spearphishing Attachment) as attackers could potentially exploit this flaw after gaining initial access to create unauthorized data access paths. Organizations should implement immediate mitigations including upgrading to the patched versions 3.1.1 or 3.0.4, reviewing existing custom role configurations to ensure proper permission boundaries, and conducting comprehensive audits of dataset access controls. The fix addresses the core validation issue by implementing stricter checks during virtual dataset creation to ensure that users cannot reference data sources beyond their granted permissions, thereby restoring the intended security model of the platform.
This vulnerability demonstrates the complexity of access control enforcement in modern data platforms where virtualization capabilities can inadvertently create security gaps. The issue highlights the importance of comprehensive security testing for data access control mechanisms and the need for proper validation of all user actions within data platforms. Organizations should consider implementing additional monitoring and alerting for unusual dataset creation patterns, as well as regular security assessments of their data access control configurations to prevent similar issues from arising in other components of their data infrastructure.