CVE-2017-12612 in Spark
Summary
by MITRE
In Apache Spark 1.6.0 until 2.1.1, the launcher API performs unsafe deserialization of data received by its socket. This makes applications launched programmatically using the launcher API potentially vulnerable to arbitrary code execution by an attacker with access to any user account on the local machine. It does not affect apps run by spark-submit or spark-shell. The attacker would be able to execute code as the user that ran the Spark application. Users are encouraged to update to version 2.2.0 or later.
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Analysis
by VulDB Data Team • 01/12/2021
The vulnerability identified as CVE-2017-12612 represents a critical security flaw in Apache Spark versions ranging from 1.6.0 through 2.1.1, specifically within the launcher API component that handles socket-based communication. This vulnerability stems from the improper handling of serialized data received through network connections, creating an avenue for malicious actors to exploit the system through unsafe deserialization practices. The flaw is particularly concerning because it allows local privilege escalation through a remote code execution vector, as the vulnerable API processes data from sockets without adequate validation or sanitization measures. The security implications extend beyond simple data corruption, as successful exploitation can result in complete system compromise with the privileges of the user running the Spark application. This vulnerability specifically affects applications launched programmatically through the launcher API rather than those initiated via standard spark-submit or spark-shell mechanisms, creating a nuanced attack surface that requires careful consideration of application deployment patterns.
The technical exploitation of this vulnerability occurs through the launcher API's handling of serialized objects received over socket connections, which directly maps to CWE-502 - Deserialization of Untrusted Data, a well-documented weakness in software security that frequently leads to remote code execution. The unsafe deserialization process allows attackers to craft malicious serialized objects that, when processed by the vulnerable Spark launcher, execute arbitrary code on the target system. This attack vector operates at the application layer and requires only local access to the machine where the Spark application is running, making it particularly dangerous in multi-user environments where attackers might gain foothold through legitimate user accounts. The vulnerability's impact is amplified by the fact that it operates with the privileges of the user who initiated the Spark application, potentially allowing attackers to escalate their access level depending on the application's execution context and the underlying system permissions. The attack requires minimal network interaction beyond establishing the socket connection, making it both stealthy and effective in compromising systems.
The operational impact of CVE-2017-12612 extends significantly beyond immediate code execution capabilities, as it fundamentally undermines the security boundaries of Spark applications deployed in enterprise environments. Organizations running affected Spark versions face potential data breaches, system compromise, and unauthorized access to sensitive information processed through their Spark applications. The vulnerability's exploitation does not require network-level access or sophisticated attack infrastructure, as local user accounts provide sufficient access to trigger the malicious deserialization process. This makes the vulnerability particularly dangerous in shared computing environments, cloud deployments, or containerized applications where multiple users or processes might be running on the same host system. The attack's success rate is high given the nature of the flaw, and the potential for cascading effects increases when considering that compromised Spark applications might be part of larger data processing pipelines or distributed computing frameworks where the impact of a single compromised node can affect entire clusters or distributed systems.
Mitigation strategies for this vulnerability primarily focus on immediate version updates to Apache Spark 2.2.0 or later, which contain the necessary patches to address the unsafe deserialization flaw in the launcher API. Organizations should prioritize updating their Spark installations across all environments, including development, testing, and production systems, to ensure comprehensive protection against exploitation. Additional defensive measures include implementing network segmentation to limit local access to Spark application hosts, configuring proper access controls and user privilege management, and monitoring for unusual socket activity or unauthorized connections to Spark launcher components. Security teams should also consider implementing application whitelisting policies to restrict which applications can communicate with Spark launcher APIs and establish logging mechanisms to detect potential exploitation attempts. The vulnerability's classification under ATT&CK technique T1059.007 - Command and Scripting Interpreter: PowerShell and T1203 - Exploitation for Client Execution highlights the need for comprehensive security monitoring and incident response procedures. Organizations should also review their current Spark deployment configurations to ensure that launcher APIs are not unnecessarily exposed or accessible to untrusted users, and implement regular security assessments to identify and remediate similar vulnerabilities in other components of their big data processing infrastructure.