CVE-2018-3824 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. If an attacker is able to inject data into an index that has a ML job running against it, then when another user views the results of the ML job it could allow the attacker to obtain sensitive information from or perform destructive actions on behalf of that other ML user.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Analysis
by VulDB Data Team • 03/25/2020
The vulnerability identified as CVE-2018-3824 represents a critical cross-site scripting flaw within Elasticsearch's X-Pack Machine Learning component affecting versions prior to 6.2.4 and 5.6.9. This vulnerability arises from insufficient input validation and sanitization mechanisms when processing data within machine learning jobs, creating a pathway for malicious actors to inject malicious scripts into indexed data. The flaw specifically impacts environments where machine learning jobs are configured to analyze data from external sources or user-generated content, making it particularly dangerous in multi-tenant or collaborative environments where multiple users interact with shared data sets.
The technical nature of this vulnerability stems from the improper handling of user-supplied data within the machine learning job results display functionality. When an attacker successfully injects malicious content into an index that is being monitored by a machine learning job, the crafted data becomes part of the job's output. Upon subsequent viewing of these results by legitimate users, the malicious scripts execute within the context of the victim's browser session, leveraging the user's permissions and privileges. This behavior aligns with CWE-79, which defines cross-site scripting as the improper handling of input data that leads to arbitrary code execution in the victim's browser. The vulnerability operates at the intersection of data processing and user interface rendering, where machine learning job outputs are displayed without adequate sanitization of potentially malicious content.
The operational impact of this vulnerability extends beyond simple script execution to encompass significant security risks including privilege escalation, data exfiltration, and session hijacking. An attacker could potentially steal session cookies, obtain access to sensitive information processed by the machine learning jobs, or even perform destructive actions such as modifying or deleting data through the victim's authenticated session. The vulnerability is particularly concerning because it leverages legitimate machine learning functionality to deliver malicious payloads, making detection more difficult and potentially allowing attackers to maintain persistence within the system. This type of attack vector is categorized under ATT&CK technique T1566, which covers social engineering through malicious content delivery, and T1071, which addresses application layer protocol usage for command and control communications.
Organizations should implement immediate mitigations including updating to the patched versions of Elasticsearch that address this vulnerability, implementing strict input validation and sanitization policies for data entering machine learning jobs, and establishing network segmentation controls to limit access to machine learning job results. Additionally, security teams should conduct comprehensive assessments of existing machine learning jobs to identify potential data sources that may be vulnerable to injection attacks, while implementing monitoring solutions to detect anomalous behavior patterns in job execution results. The remediation process should also include user education regarding the risks of viewing untrusted data in machine learning contexts, as well as establishing privileged access controls that limit who can create or modify machine learning jobs within the system. Organizations should also consider implementing web application firewalls and content security policies to provide additional layers of protection against XSS attacks targeting machine learning interfaces.