CVE-2012-3376 in Hadoop
Summary
by MITRE
DataNodes in Apache Hadoop 2.0.0 alpha does not check the BlockTokens of clients when Kerberos is enabled and the DataNode has checked out the same BlockPool twice from a NodeName, which might allow remote clients to read arbitrary blocks, write to blocks to which they only have read access, and have other unspecified impacts.
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Analysis
by VulDB Data Team • 02/18/2019
The vulnerability described in CVE-2012-3376 represents a critical authorization flaw within Apache Hadoop's distributed storage system, specifically affecting DataNodes in the 2.0.0 alpha release. This issue emerges when Kerberos authentication is enabled and the DataNode has checked out the same BlockPool twice from a NodeName, creating a scenario where proper access controls fail to validate client permissions. The flaw stems from insufficient validation of BlockTokens, which are cryptographic tokens used to authenticate and authorize client operations within the Hadoop ecosystem. When this validation mechanism is bypassed, it fundamentally undermines the security model that Hadoop relies upon to protect data integrity and access control.
The technical implementation of this vulnerability exploits a race condition or state management issue within the DataNode's token validation process. In normal operation, when Kerberos authentication is active, DataNodes should verify that clients possess valid BlockTokens before allowing access to specific blocks within a BlockPool. However, when the DataNode has checked out the same BlockPool twice from a NodeName, the system fails to properly validate these tokens, creating an authorization bypass. This condition allows malicious actors to perform unauthorized operations including reading blocks they should not have access to, writing to blocks where they only possess read permissions, and potentially executing other unspecified malicious activities. The vulnerability essentially creates a scenario where the DataNode's security context becomes inconsistent, leading to privilege escalation and unauthorized data manipulation.
The operational impact of this vulnerability is severe for organizations relying on Hadoop for data storage and processing, particularly those implementing Kerberos-based authentication for enhanced security. Remote attackers who can establish connections to affected DataNodes can exploit this flaw to gain unauthorized access to sensitive data stored within the Hadoop cluster. This includes the potential to read confidential information, modify data in ways that violate access controls, and compromise the integrity of the entire distributed storage system. The vulnerability affects not just individual data blocks but potentially entire datasets, making it particularly dangerous for environments handling regulated data or proprietary information. Organizations may experience data breaches, compliance violations, and significant operational disruptions when this vulnerability is exploited.
Mitigation strategies for CVE-2012-3376 should prioritize immediate patching of affected Hadoop installations to the latest stable releases that contain the necessary security fixes. Organizations should also implement network segmentation and access controls to limit exposure of DataNodes to untrusted networks. The use of additional security layers such as firewalls, intrusion detection systems, and monitoring solutions can help detect unauthorized access attempts. Configuration reviews should ensure that Kerberos authentication is properly implemented and that DataNode configurations do not inadvertently create the conditions that enable this vulnerability. Security teams should also conduct regular vulnerability assessments and penetration testing to identify potential authorization bypasses within their Hadoop environments. This vulnerability aligns with CWE-284, which addresses improper access control, and maps to ATT&CK techniques involving privilege escalation and unauthorized data access, emphasizing the need for comprehensive security measures to protect distributed computing environments.