CVE-2018-11215 in Data Science Workbench
Summary
by MITRE
Remote code execution is possible in Cloudera Data Science Workbench version 1.3.0 and prior releases via unspecified attack vectors.
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Analysis
by VulDB Data Team • 06/30/2020
The Cloudera Data Science Workbench remote code execution vulnerability represents a critical security flaw that affects versions 1.3.0 and earlier deployments. This vulnerability exists within the data science platform that enables collaborative data analysis and machine learning work environments. The unspecified attack vectors suggest that multiple entry points within the system could potentially be exploited by malicious actors to gain unauthorized access and execute arbitrary code on affected systems. The vulnerability's severity is compounded by the fact that it allows remote exploitation without requiring authentication, making it particularly dangerous in enterprise environments where such platforms are commonly deployed.
The technical implementation of this vulnerability stems from inadequate input validation and security controls within the Cloudera Data Science Workbench framework. Attackers can leverage this flaw through various means including crafted API requests, malformed data inputs, or manipulated user sessions that bypass normal security boundaries. The underlying architecture appears to permit insufficient sanitization of user-supplied data, allowing malicious payloads to be interpreted and executed within the context of the running application. This type of vulnerability typically falls under the category of injection flaws, specifically representing a remote code execution vulnerability that can be classified as CWE-74 or CWE-94 depending on the specific implementation details. The attack surface is particularly concerning as it affects the core functionality of the data science platform, which often processes sensitive data and requires elevated privileges for operation.
The operational impact of this vulnerability extends beyond simple system compromise, as it enables attackers to establish persistent access to critical data science environments that may contain proprietary algorithms, research data, and sensitive business intelligence. Organizations using affected versions of Cloudera Data Science Workbench face significant risk of data exfiltration, system disruption, and potential lateral movement within their networks. The vulnerability can be exploited to gain root or administrative privileges, allowing attackers to modify system configurations, install backdoors, or deploy additional malware. From an adversary perspective, this vulnerability aligns with ATT&CK technique T1059 which involves executing malicious code through various system interfaces, potentially leading to broader compromise of the enterprise infrastructure. The impact is particularly severe in regulated industries where data protection and compliance requirements are stringent, as unauthorized access to data science workbenches could result in regulatory violations and financial penalties.
Organizations should immediately implement mitigations including upgrading to patched versions of Cloudera Data Science Workbench, implementing network segmentation to isolate critical data science environments, and deploying intrusion detection systems to monitor for suspicious activities. Access controls should be strengthened through mandatory authentication, rate limiting, and monitoring of API endpoints. Security teams should conduct comprehensive vulnerability assessments of their deployment environments and implement network monitoring to detect potential exploitation attempts. The remediation process requires careful planning to ensure that updates do not disrupt ongoing data science workflows while maintaining security posture. Organizations should also consider implementing application firewalls and web application security controls to provide additional layers of protection against similar vulnerabilities. Regular security assessments and penetration testing should be conducted to identify and remediate potential attack vectors that could lead to similar remote code execution scenarios.