CVE-2018-3823 in X-Pack Machine Learning
Summary
by MITRE
X-Pack Machine Learning versions before 6.2.4 and 5.6.9 had a cross-site scripting (XSS) vulnerability. Users with manage_ml permissions could create jobs containing malicious data as part of their configuration that could allow the attacker to obtain sensitive information from or perform destructive actions on behalf of other ML users viewing the results of the jobs.
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Analysis
by VulDB Data Team • 03/25/2020
The vulnerability identified as CVE-2018-3823 represents a critical cross-site scripting flaw within the X-Pack Machine Learning component of Elasticsearch systems. This security weakness affected versions prior to 6.2.4 and 5.6.9, creating a significant risk for organizations utilizing Elasticsearch's machine learning capabilities. The flaw specifically targeted users who possessed manage_ml permissions, which granted them the ability to configure and manage machine learning jobs within the system. The vulnerability stemmed from insufficient input validation and output sanitization mechanisms that failed to properly handle malicious data submitted as part of job configurations.
The technical nature of this vulnerability aligns with CWE-79, which categorizes cross-site scripting as a code injection flaw where malicious scripts are executed in the context of other users' browsers. Attackers exploiting this weakness could craft malicious data inputs that would be processed and displayed within the ML job results interface. When other users with appropriate permissions viewed these results, their browsers would execute the embedded malicious scripts, potentially enabling attackers to steal session cookies, access sensitive information, or perform unauthorized actions on behalf of the affected users. The vulnerability was particularly dangerous because it leveraged legitimate user permissions, making it difficult to distinguish between authorized and malicious activities.
The operational impact of this vulnerability extends beyond simple data theft, as it could enable attackers to perform destructive actions within the ML environment. Organizations relying on machine learning job results for critical business decisions faced potential compromise when malicious actors inserted harmful code into job configurations. The scope of damage was amplified by the fact that the vulnerability affected users with manage_ml permissions, which typically includes individuals with elevated privileges and access to sensitive data processing capabilities. This created a scenario where attackers could not only view results but potentially manipulate the entire machine learning workflow, undermining the integrity and security of the system's analytical processes.
Mitigation strategies for CVE-2018-3823 required immediate implementation of version updates to Elasticsearch 6.2.4 or 5.6.9, which contained the necessary patches to address the XSS vulnerability. Organizations should have implemented additional security controls including input validation for all user-supplied data within ML job configurations, output encoding for all displayed results, and regular security audits of ML job parameters. The remediation process needed to include comprehensive testing of updated systems to ensure that the patch did not introduce compatibility issues with existing ML workflows. Security teams should have also established monitoring protocols to detect anomalous behavior in ML job configurations and implemented network segmentation to limit access to ML components to only essential personnel with proper authorization. This vulnerability demonstrated the importance of securing all components within enterprise search and analytics platforms, as machine learning functionalities often process sensitive data and require robust security measures to prevent unauthorized access and data manipulation.