CVE-2023-6975 in mlflow
Summary
by MITRE • 12/20/2023
A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.
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Analysis
by VulDB Data Team • 01/13/2024
The vulnerability identified as CVE-2023-6975 represents a critical command execution flaw that enables malicious actors to gain unauthorized access to vulnerable systems. This issue stems from insufficient input validation and improper sanitization of user-supplied data within the affected software components. The flaw allows an attacker to inject and execute arbitrary commands on the target machine, effectively bypassing normal security controls and establishing a persistent foothold within the network infrastructure. The vulnerability's severity is compounded by the fact that successful exploitation grants access not only to system resources but also to sensitive data and machine learning models that may contain proprietary information or intellectual property.
The technical root cause of this vulnerability aligns with common software security weaknesses that fall under CWE-78, which addresses OS command injection flaws. This classification indicates that the software fails to properly escape or validate user input before incorporating it into system commands, creating an attack surface where malicious payloads can be executed with the privileges of the affected application. The vulnerability typically manifests when user-controllable data is directly passed to system execution functions without adequate sanitization or parameterization. Attackers can leverage this weakness by crafting specially formatted inputs that, when processed by the vulnerable application, result in unintended command execution on the underlying operating system.
The operational impact of CVE-2023-6975 extends beyond simple unauthorized access to encompass potential data exfiltration, model theft, and system compromise. Organizations utilizing affected systems face significant risks including intellectual property theft, regulatory compliance violations, and potential service disruption. The vulnerability's exploitation can lead to full system compromise, allowing attackers to establish backdoors, escalate privileges, and move laterally within the network. In environments where machine learning models are stored or processed, the threat landscape becomes even more concerning as attackers can access and potentially steal valuable AI/ML assets that may represent years of research and development investment. The attack surface is particularly dangerous in cloud environments or multi-tenant systems where a single compromised instance could affect multiple organizations or users.
Mitigation strategies for CVE-2023-6975 should focus on implementing robust input validation, output encoding, and proper parameterization of system calls. Organizations must ensure that all user-supplied data undergoes rigorous sanitization before being processed by any system commands or shell interfaces. The principle of least privilege should be enforced, limiting the execution permissions of applications and services to prevent unauthorized command execution. Regular security updates and patches should be implemented immediately upon availability, as this vulnerability likely affects software versions that have known remediation paths. Network segmentation and intrusion detection systems can help identify and prevent exploitation attempts, while comprehensive monitoring of system logs should be enabled to detect suspicious command execution patterns. Organizations should also consider implementing web application firewalls and input validation layers to prevent malicious payloads from reaching the vulnerable application components. The remediation process should include thorough code reviews, security testing, and vulnerability assessments to ensure that similar issues do not exist in related software components or dependencies.