CVE-2018-11760 in PySpark
Summary
by MITRE
When using PySpark , it's possible for a different local user to connect to the Spark application and impersonate the user running the Spark application. This affects versions 1.x, 2.0.x, 2.1.x, 2.2.0 to 2.2.2, and 2.3.0 to 2.3.1.
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Analysis
by VulDB Data Team • 07/04/2023
The vulnerability identified as CVE-2018-11760 represents a critical security flaw in Apache Spark's authentication and authorization mechanisms, specifically affecting PySpark implementations across multiple version ranges including 1.x, 2.0.x, 2.1.x, 2.2.0 to 2.2.2, and 2.3.0 to 2.3.1. This issue stems from insufficient user isolation and authentication controls that allow unauthorized local users to establish connections to running Spark applications and potentially impersonate the user account under which the Spark application is executing. The flaw creates a significant attack surface where malicious actors with local system access can exploit this weakness to gain elevated privileges and potentially access sensitive data processed by the Spark application. The vulnerability is particularly concerning because it operates at the operating system level rather than within the application layer, making it difficult to detect and mitigate through traditional application security measures.
The technical root cause of this vulnerability lies in the improper handling of user context and authentication within the Spark runtime environment. When Spark applications are executed, they typically run under specific user privileges, but the flaw allows local users to establish connections to the Spark application's endpoints without proper authentication checks. This creates a scenario where an attacker can leverage the existing Spark processes to perform operations as the user who initiated the Spark application, effectively bypassing normal access controls. The vulnerability is classified under CWE-284 which deals with improper access control, specifically focusing on inadequate privileges and authentication mechanisms. This weakness enables privilege escalation attacks where local users can potentially access data and perform operations that should be restricted to the legitimate user account.
The operational impact of CVE-2018-11760 extends beyond simple unauthorized access, creating potential data breaches and system compromise scenarios that can affect organizations processing sensitive information through Spark applications. Attackers can exploit this vulnerability to read, modify, or delete data processed by Spark applications, potentially accessing confidential datasets, personal information, or proprietary business data. The vulnerability also enables lateral movement within a network environment where Spark applications are deployed, as attackers can use the impersonation capabilities to gain access to other systems or resources that the legitimate user account might have access to. This vulnerability aligns with ATT&CK technique T1078 which covers valid accounts and privilege escalation, making it particularly dangerous in enterprise environments where Spark is used for data processing and analytics. Organizations utilizing Spark for big data processing and analytics may face significant compliance and regulatory risks if this vulnerability is exploited, as it can lead to unauthorized data access and potential data loss incidents.
Mitigation strategies for CVE-2018-11760 should focus on both immediate patching and operational security improvements. Organizations must prioritize upgrading to Spark versions that have addressed this vulnerability, specifically versions beyond the affected ranges mentioned in the CVE description. The patching process should include comprehensive testing to ensure that the upgrade does not disrupt existing Spark applications or workflows. Additionally, implementing network segmentation and firewall rules to restrict access to Spark application endpoints can provide an additional layer of protection. System administrators should also enforce strict user access controls and monitor for unauthorized local access to systems running Spark applications. The implementation of proper authentication mechanisms, including Kerberos authentication, should be considered as a long-term solution to address the underlying authentication weaknesses that enable this vulnerability. Regular security audits and vulnerability assessments should be conducted to identify and remediate similar issues in other big data frameworks and distributed computing environments that may present similar security challenges.