CVE-2023-4033 in mlflow
Summary
by MITRE • 08/01/2023
OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0.
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Analysis
by VulDB Data Team • 08/23/2023
The vulnerability identified as OS command injection in the mlflow/mlflow repository represents a critical security flaw that enables attackers to execute arbitrary operating system commands through improperly sanitized user inputs. This issue affects versions prior to 2.6.0 and stems from inadequate input validation mechanisms within the mlflow framework's command processing pipelines. The vulnerability manifests when user-supplied data is directly incorporated into system commands without proper sanitization or escaping, creating opportunities for malicious actors to inject harmful commands that can be executed with the privileges of the affected application.
The technical implementation of this vulnerability occurs through multiple pathways within the mlflow codebase where external inputs are concatenated or interpolated into shell commands without appropriate validation. This flaw aligns with CWE-78 which specifically addresses OS command injection vulnerabilities, and represents a direct violation of secure coding principles that require proper input sanitization before command execution. The attack surface is particularly concerning as mlflow is widely used for machine learning experiment tracking, model management, and deployment workflows where user inputs are frequently processed through various system interfaces.
From an operational perspective, this vulnerability presents significant risk to organizations utilizing mlflow in production environments or development pipelines. Attackers could potentially execute commands with elevated privileges, access sensitive data stored within the mlflow environment, or even compromise entire underlying infrastructure. The impact extends beyond simple command execution as it can enable lateral movement within networks, data exfiltration, and persistence mechanisms that align with ATT&CK tactic T1059.003 for Command and Scripting Interpreter and T1078 for Valid Accounts when attackers leverage compromised mlflow instances to establish footholds.
The exploitation of this vulnerability requires minimal sophistication and can be achieved through simple input manipulation techniques such as command injection payloads that leverage shell metacharacters or separators. Organizations should immediately implement mitigations including upgrading to mlflow version 2.6.0 or later which contains the necessary fixes for input validation and sanitization. Additional protective measures include implementing strict input validation at all entry points, using parameterized commands instead of string concatenation, and employing principle of least privilege when running mlflow processes. Security monitoring should focus on detecting anomalous command execution patterns and unusual network connections that might indicate exploitation attempts.
The broader implications of this vulnerability highlight the importance of comprehensive security testing in machine learning frameworks where complex data processing pipelines can inadvertently create attack vectors. This issue demonstrates how seemingly benign functionality can become a critical security risk when proper sanitization controls are absent, emphasizing the need for security-aware development practices throughout the software lifecycle. Organizations should conduct thorough security assessments of their mlflow deployments and implement continuous monitoring to detect potential exploitation attempts that could compromise their machine learning infrastructure and associated data assets.