CVE-2024-34073 in sagemaker-python-sdk
Summary
by MITRE • 05/03/2024
sagemaker-python-sdk is a library for training and deploying machine learning models on Amazon SageMaker. In affected versions the capture_dependencies function in `sagemaker.serve.save_retrive.version_1_0_0.save.utils` module allows for potentially unsafe Operating System (OS) Command Injection if inappropriate command is passed as the “requirements_path” parameter. This consequently may allow an unprivileged third party to cause remote code execution, denial of service, affecting both confidentiality and integrity. This issue has been addressed in version 2.214.3. Users are advised to upgrade. Users unable to upgrade should not override the “requirements_path” parameter of capture_dependencies function in `sagemaker.serve.save_retrive.version_1_0_0.save.utils`, and instead use the default value.
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Analysis
by VulDB Data Team • 05/03/2024
The vulnerability CVE-2024-34073 affects the sagemaker-python-sdk library which serves as a critical interface for managing machine learning workflows on Amazon SageMaker platforms. This security flaw resides within the capture_dependencies function located in the sagemaker.serve.save_retrive.version_1_0_0.save.utils module where improper input validation allows for operating system command injection attacks. The specific weakness occurs when users pass inappropriate command strings as the requirements_path parameter, creating a dangerous execution path that bypasses normal security boundaries. This vulnerability represents a classic command injection flaw that falls under the CWE-77 category of Command Injection, where attacker-controlled data flows into operating system commands without proper sanitization or validation. The potential impact extends beyond simple privilege escalation as it enables unprivileged third parties to achieve remote code execution capabilities on affected systems.
The operational implications of this vulnerability are severe and multifaceted, affecting the confidentiality, integrity, and availability of machine learning environments. An attacker who successfully exploits this vulnerability could execute arbitrary commands on the target system with the privileges of the affected application, potentially leading to full system compromise. The attack surface is particularly concerning in cloud environments where SageMaker instances often process sensitive data and business-critical models. The vulnerability could enable attackers to access training datasets, steal intellectual property, modify model parameters, or disrupt services through denial of service attacks. Organizations using SageMaker for production workloads face significant risk as this flaw could be exploited to gain unauthorized access to their machine learning infrastructure and the valuable data it contains.
Security professionals should note that this vulnerability aligns with several ATT&CK techniques including T1059.001 for Command and Scripting Interpreter and T1021.004 for Remote Services. The attack chain typically involves an attacker identifying the vulnerable parameter in the capture_dependencies function and crafting malicious input that gets executed as system commands. The fix implemented in version 2.214.3 addresses the root cause by properly validating and sanitizing input parameters before they are processed as command arguments. Organizations should prioritize upgrading to this patched version immediately, as the mitigation strategy is straightforward and well-defined. For environments where immediate upgrade is not feasible, the recommended workaround involves avoiding explicit parameter setting for requirements_path and relying on the default implementation which does not expose the vulnerable code path. This vulnerability demonstrates the critical importance of input validation in cloud-based machine learning frameworks where the integration of multiple security layers is essential to protect against sophisticated attack vectors.