CVE-2023-6709 in mlflow
Summary
by MITRE • 12/12/2023
Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2.
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Analysis
by VulDB Data Team • 01/02/2024
The vulnerability identified as CVE-2023-6709 represents a critical security flaw in the mlflow machine learning platform that affects versions prior to 2.9.2. This issue resides within the template engine component of the mlflow repository and constitutes a failure in proper input sanitization that could enable arbitrary code execution. The vulnerability specifically targets the handling of special elements within template contexts, creating a path for malicious actors to exploit template injection mechanisms. The affected system processes user-provided data through template rendering engines without adequate neutralization of potentially dangerous characters or sequences that could be interpreted as executable code. This flaw exists in the mlflow framework's template processing pipeline where user inputs are directly incorporated into template expressions without proper validation or escaping mechanisms.
The technical implementation of this vulnerability stems from improper handling of template variables and special characters within the mlflow platform's rendering system. When users provide data that gets processed through template engines, the system fails to adequately sanitize or escape special elements such as brackets, quotes, or other template syntax markers. This allows attackers to inject malicious payloads that can be executed within the template context, potentially leading to remote code execution or data exfiltration. The vulnerability manifests when template expressions contain user-controlled data that is not properly escaped or validated before being rendered. According to CWE standards, this maps to CWE-15 which describes improper neutralization of special elements used in a template engine, and aligns with ATT&CK technique T1203 which covers Exploitation for Client Execution through template injection attacks. The flaw demonstrates a classic example of insufficient input validation where the system assumes that template variables will always contain safe content without proper sanitization checks.
The operational impact of CVE-2023-6709 extends beyond simple template manipulation to potentially compromise entire mlflow deployments and the data they contain. Attackers could leverage this vulnerability to execute arbitrary commands on systems running vulnerable mlflow versions, potentially gaining access to sensitive model data, training datasets, or even the underlying infrastructure hosting mlflow services. The vulnerability is particularly dangerous in enterprise environments where mlflow is used for collaborative machine learning workflows, as it could allow unauthorized users to access or modify other users' experiments, models, or configurations. The implications are severe for organizations using mlflow for production machine learning pipelines, as successful exploitation could lead to complete system compromise. Organizations utilizing mlflow for tracking experiments, managing models, or serving predictions through the platform face significant risk from this vulnerability, particularly when dealing with untrusted user inputs or collaborative environments where multiple parties contribute to the same mlflow projects.
Mitigation strategies for CVE-2023-6709 require immediate action to upgrade to mlflow version 2.9.2 or later, which contains the necessary patches for proper template neutralization. Organizations should implement comprehensive input validation measures that sanitize all user-provided data before it enters the template processing pipeline. Security teams should consider implementing web application firewalls or additional layers of protection that can detect and block suspicious template injection attempts. The remediation process should include thorough code reviews of template usage patterns to ensure that all user inputs are properly escaped or validated before being processed through template engines. Additionally, organizations should establish monitoring protocols to detect unusual template processing activities that might indicate exploitation attempts. System administrators should also consider implementing principle of least privilege access controls for mlflow services and regularly audit template usage within their mlflow deployments. The vulnerability highlights the importance of proper template engine security practices and demonstrates how seemingly minor input sanitization flaws can lead to severe security consequences in machine learning platforms.