CVE-2024-34997 in joblib
Summary
by MITRE • 05/17/2024
joblib v1.4.2 was discovered to contain a deserialization vulnerability via the component joblib.numpy_pickle::NumpyArrayWrapper().read_array().
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Analysis
by VulDB Data Team • 08/28/2024
The vulnerability identified as CVE-2024-34997 affects joblib version 1.4.2 and represents a critical deserialization flaw within the joblib.numpy_pickle::NumpyArrayWrapper().read_array() component. This issue arises from the library's improper handling of serialized data during the numpy array deserialization process, creating a potential attack vector that could be exploited by malicious actors to execute arbitrary code on systems running vulnerable versions of joblib.
The technical flaw stems from insufficient input validation and sanitization within the numpy_pickle module, specifically in how the NumpyArrayWrapper processes serialized data structures. When the read_array() method encounters maliciously crafted serialized input, it fails to properly validate the data format and content, allowing attackers to inject and execute arbitrary Python code during the deserialization process. This vulnerability aligns with CWE-502 which categorizes deserialization of untrusted data as a critical security weakness, particularly when the deserialization occurs in a context where code execution privileges are available.
The operational impact of this vulnerability extends across various computing environments where joblib is utilized for machine learning model persistence, data processing workflows, and scientific computing applications. Attackers could exploit this flaw by crafting malicious serialized numpy arrays that, when loaded by vulnerable applications, would execute arbitrary commands on the target system. The vulnerability is particularly concerning in environments where joblib processes untrusted data from external sources, such as web applications that serialize user input or distributed computing frameworks that exchange serialized data between nodes.
This deserialization vulnerability enables several attack patterns that align with ATT&CK technique T1548.004, which covers abuse of service accounts and T1059.001 for command and scripting interpreter usage. The attack surface includes scenarios where joblib is used in web applications, data science pipelines, or automated processing systems that handle serialized data from multiple sources. Organizations utilizing joblib for model persistence in machine learning workflows face significant risk, as attackers could compromise entire data processing pipelines by exploiting this vulnerability in what appears to be a legitimate data serialization component.
Mitigation strategies should prioritize immediate version upgrades to joblib 1.4.3 or later, which contain patches addressing the deserialization vulnerability. Additionally, organizations should implement strict input validation for any serialized data processed by joblib components, particularly when handling external or untrusted inputs. Security measures should include network segmentation to limit access to systems processing sensitive data, implementation of automated monitoring for unusual deserialization activities, and regular security assessments of data processing pipelines. The vulnerability demonstrates the critical importance of validating serialized data inputs and implementing proper security controls around data serialization components, as highlighted in industry best practices for secure software development and the OWASP Top Ten security risks.