CVE-2015-1889 in InfoSphere BigInsights
Summary
by MITRE
The Big SQL component in IBM InfoSphere BigInsights 3.0 through 3.0.0.2 allows remote authenticated users to bypass intended HDFS data-access restrictions via (1) a crafted CREATE HADOOP TABLE statement referencing the data of an arbitrary user or (2) an import of a certain Hive table definition with the HCAT_SYNC_OBJECTS procedure.
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Analysis
by VulDB Data Team • 05/09/2022
The vulnerability identified as CVE-2015-1889 affects the Big SQL component within IBM InfoSphere BigInsights version 3.0 through 3.0.0.2, representing a significant security flaw that undermines the integrity of Hadoop Distributed File System (HDFS) access controls. This vulnerability exists within the authorization mechanisms of the Big SQL engine, which is designed to provide SQL-like querying capabilities over big data stored in Hadoop environments. The flaw enables authenticated remote attackers to circumvent the intended data access restrictions that are typically enforced by HDFS, potentially allowing unauthorized data access and manipulation. The vulnerability is particularly concerning because it affects a core component of IBM's big data platform that is widely deployed in enterprise environments where data security and access control are paramount. The attack vectors leverage specific SQL statement constructions and Hive table import procedures that exploit weaknesses in the privilege validation mechanisms of the Big SQL component.
The technical implementation of this vulnerability stems from insufficient validation of user privileges when processing certain Hadoop table creation and data import operations. When a crafted CREATE HADOOP TABLE statement is executed, it can reference data belonging to arbitrary users without proper authorization checks, effectively bypassing the HDFS permission model that normally restricts access to specific user data. Additionally, the HCAT_SYNC_OBJECTS procedure used for importing Hive table definitions contains a flaw that allows attackers to manipulate the synchronization process to access restricted data. This represents a classic privilege escalation vulnerability where authenticated users can elevate their access rights beyond what should be permitted. The vulnerability is classified as a weakness in authorization controls and falls under the CWE-284 category for improper access control, specifically targeting the enforcement of access restrictions in distributed computing environments. The flaw demonstrates a failure in the principle of least privilege, where users should only have access to data they are explicitly authorized to access.
The operational impact of this vulnerability extends beyond simple data exposure, potentially enabling comprehensive data manipulation and unauthorized access to sensitive information within enterprise big data environments. Organizations utilizing IBM InfoSphere BigInsights in production environments face the risk of unauthorized data access, data exfiltration, and potential compromise of business-critical information stored in Hadoop clusters. The vulnerability affects not only the confidentiality of data but also the integrity and availability of the system, as attackers could potentially manipulate data through the bypassed access controls. This type of vulnerability is particularly dangerous in regulated environments where data governance and access control are mandatory compliance requirements. The impact is amplified because the vulnerability affects a core component of the big data platform that is likely to be used extensively for analytics, reporting, and data processing operations. Attackers could leverage this vulnerability to access data belonging to competitors, customers, or sensitive business information, potentially leading to significant financial and reputational damage.
Organizations should implement immediate mitigations including applying the vendor-provided security patches for IBM InfoSphere BigInsights 3.0 through 3.0.0.2, which address the authorization bypass flaws in the Big SQL component. Network segmentation and access control measures should be strengthened to limit exposure of the affected components to untrusted networks and users. Regular monitoring of database access logs should be implemented to detect anomalous access patterns that might indicate exploitation attempts. Additionally, organizations should conduct comprehensive security assessments of their big data environments to identify similar authorization flaws in other components of their data platform. The vulnerability aligns with ATT&CK technique T1078 for valid accounts and T1566 for credential access, as it exploits legitimate authentication mechanisms to gain unauthorized access to data. Security teams should also consider implementing data loss prevention solutions that can monitor for unauthorized data access patterns and provide alerts when suspicious activities are detected. The remediation process should include comprehensive testing to ensure that the patches do not introduce compatibility issues with existing applications and that proper access controls are restored throughout the platform.