CVE-2024-34072 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. The sagemaker.base_deserializers.NumpyDeserializer module before v2.218.0 allows potentially unsafe deserialization when untrusted data is passed as pickled object arrays. This consequently may allow an unprivileged third party to cause remote code execution, denial of service, affecting both confidentiality and integrity. Users are advised to upgrade to version 2.218.0. Users unable to upgrade should not pass pickled numpy object arrays which originated from an untrusted source, or that could have been tampered with. Only pass pickled numpy object arrays from trusted sources.
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Analysis
by VulDB Data Team • 04/01/2025
The vulnerability identified as CVE-2024-34072 affects the sagemaker-python-sdk library, specifically within the sagemaker.base_deserializers.NumpyDeserializer module. This issue represents a critical security flaw that enables potentially unsafe deserialization of pickled object arrays, creating a pathway for remote code execution and denial of service attacks. The vulnerability exists in versions prior to v2.218.0, making any system utilizing these older versions susceptible to exploitation by unprivileged third parties who can manipulate the deserialization process.
The technical flaw stems from the improper handling of pickled data within the NumpyDeserializer module, which lacks adequate validation mechanisms for incoming serialized objects. When untrusted data is processed through this deserializer, it can execute arbitrary code on the target system, directly violating the principle of least privilege and enabling attackers to gain unauthorized access to system resources. This deserialization vulnerability aligns with CWE-502, which specifically addresses unsafe deserialization of untrusted data, and represents a significant risk to both confidentiality and integrity of the affected systems. The attack surface is particularly concerning in cloud environments where SageMaker is used for machine learning model training and deployment, as these systems often handle sensitive data and require robust security controls.
The operational impact of this vulnerability extends beyond simple code execution, as it can result in complete system compromise and data breaches. Attackers can leverage this vulnerability to establish persistent access, exfiltrate sensitive model data, or disrupt services through denial of service conditions. In enterprise environments using Amazon SageMaker, this vulnerability could lead to the exposure of proprietary machine learning models, training datasets, and other intellectual property. The risk is amplified by the fact that SageMaker is commonly used in regulated industries where data protection and integrity are paramount, making this vulnerability particularly dangerous in compliance-sensitive contexts.
Organizations should immediately upgrade to version 2.218.0 or later to address this vulnerability, as this release includes proper input validation and sanitization measures for the deserialization process. For environments where upgrading is not immediately feasible, administrators must implement strict data validation policies that prevent processing of pickled numpy object arrays from untrusted sources. This mitigation strategy aligns with the ATT&CK framework's defense evasion techniques, where adversaries often exploit deserialization vulnerabilities to execute malicious code. Security teams should also implement network segmentation and monitoring controls to detect suspicious deserialization activities, ensuring that any attempts to process untrusted pickled data are immediately flagged and investigated. The vulnerability underscores the importance of secure coding practices and input validation, particularly when handling serialized data in cloud-based machine learning platforms.