CVE-2018-1334 in Spark
Summary
by MITRE
In Apache Spark 1.0.0 to 2.1.2, 2.2.0 to 2.2.1, and 2.3.0, when using PySpark or SparkR, it's possible for a different local user to connect to the Spark application and impersonate the user running the Spark application.
If you want to get the best quality for vulnerability data then you always have to consider VulDB.
Analysis
by VulDB Data Team • 03/04/2020
The vulnerability described in CVE-2018-1334 represents a critical authorization flaw in Apache Spark that affects multiple versions from 1.0.0 through 2.3.0. This issue specifically impacts users utilizing PySpark or SparkR interfaces where the Spark application operates in an environment where multiple local users share the same system resources. The vulnerability stems from insufficient user authentication mechanisms that allow unauthorized local users to establish connections to running Spark applications and effectively impersonate the legitimate user account under which the Spark process is executing.
The technical flaw manifests through a lack of proper access control validation within the Spark application's communication protocols. When Spark applications are launched, they typically bind to local network interfaces and accept connections from clients. In vulnerable versions, these connections do not properly verify the identity of connecting users, particularly when using PySpark or SparkR which are client interfaces that communicate with the Spark master process. This weakness creates a scenario where any local user with network access to the Spark application can establish a connection and potentially execute commands or access data as if they were the original user who started the Spark process.
The operational impact of this vulnerability is significant and multifaceted. An attacker with local system access can exploit this flaw to gain elevated privileges and execute arbitrary code within the context of the Spark application process. This could lead to data exfiltration, unauthorized data manipulation, or complete system compromise depending on the privileges of the Spark application user. The vulnerability is particularly dangerous in multi-tenant environments or shared computing systems where multiple users operate different Spark applications. The impersonation capability means that an attacker could potentially access sensitive data processed by Spark applications, manipulate results, or even gain access to other system resources that the legitimate Spark user might have access to.
From a cybersecurity perspective, this vulnerability aligns with CWE-284, which addresses improper access control, and can be mapped to ATT&CK technique T1078 for valid accounts and privilege escalation. The flaw essentially creates a backdoor mechanism where unauthorized local users can bypass normal authentication procedures. Organizations using Apache Spark in production environments are particularly at risk since this vulnerability does not require network-level access or sophisticated attack vectors - simply having local access to the system where Spark is running is sufficient to exploit the flaw. The impact extends beyond immediate data compromise to include potential long-term persistence mechanisms and lateral movement opportunities within the affected systems.
Mitigation strategies for CVE-2018-1334 should focus on implementing proper network isolation and access controls around Spark applications. System administrators should ensure that Spark applications are configured with appropriate firewall rules to restrict access to Spark ports and interfaces. The recommended approach includes running Spark applications with least privilege accounts, implementing proper network segmentation, and using containerization or virtualization technologies to isolate Spark processes. Additionally, upgrading to patched versions of Apache Spark is essential since subsequent releases have addressed this authorization flaw through enhanced authentication mechanisms and improved access control validation. Organizations should also implement monitoring solutions to detect unauthorized connections to Spark processes and establish regular security audits to ensure proper configuration of Spark applications in their environments.