CVE-2024-0140 in RAPIDS cuDF and cuML
Summary
by MITRE • 01/28/2025
NVIDIA RAPIDS contains a vulnerability in cuDF and cuML, where a user could cause a deserialization of untrusted data issue. A successful exploit of this vulnerability might lead to code execution, data tampering, denial of service, and information disclosure.
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Analysis
by VulDB Data Team • 02/07/2025
The vulnerability identified as CVE-2024-0140 resides within NVIDIA RAPIDS framework, specifically affecting the cuDF and cuML components that are integral parts of the company's data science and machine learning libraries. This security weakness manifests as a deserialization of untrusted data issue, representing a critical flaw in how the software processes incoming data streams. The vulnerability operates at the intersection of software security and data processing integrity, where legitimate application functionality becomes a vector for malicious exploitation.
The technical flaw stems from insufficient validation and sanitization of data during the deserialization process within the cuDF and cuML modules. When these components receive data from external sources or untrusted inputs, they fail to properly verify the integrity and authenticity of the serialized objects before attempting to reconstruct them into usable application objects. This primitive failure creates a pathway for attackers to craft malicious serialized data that, when processed, triggers unintended behavior within the application context. The vulnerability aligns with CWE-502, which specifically addresses deserialization of untrusted data as a security weakness, and represents a direct threat to the principle of input validation in secure coding practices.
The operational impact of this vulnerability extends across multiple security dimensions, creating potential for severe consequences in affected environments. Successful exploitation could enable remote code execution, allowing attackers to run arbitrary commands on systems where the vulnerable RAPIDS libraries are deployed. This capability could lead to complete system compromise, particularly in data science environments where these libraries are commonly used for processing sensitive information. Additionally, the vulnerability enables data tampering operations that could corrupt analytical results, manipulate training datasets, or modify model outputs in ways that undermine the integrity of machine learning workflows. The potential for denial of service attacks exists through memory corruption or resource exhaustion techniques that leverage the deserialization flaw, while information disclosure capabilities could expose sensitive data processed through the affected libraries.
Organizations utilizing NVIDIA RAPIDS in production environments face significant risk from this vulnerability, particularly those handling sensitive datasets or operating in regulated industries where data integrity is paramount. The attack surface is broad given the widespread adoption of cuDF and cuML in data science workflows across various sectors including finance, healthcare, and cybersecurity analytics. The vulnerability's exploitation requires minimal privileges in many scenarios, making it particularly dangerous in multi-tenant environments or shared computing resources where isolation between different user processes may be inadequate. Attackers could leverage this weakness in supply chain attacks, where malicious actors compromise data sources or development environments to inject malicious serialized objects that would then be processed by vulnerable systems.
Mitigation strategies should focus on immediate patching of affected NVIDIA RAPIDS versions, implementation of strict input validation procedures, and deployment of network segmentation controls to limit exposure of vulnerable components. Organizations should implement monitoring solutions that can detect anomalous deserialization patterns and establish secure coding practices that enforce strict validation of all external data inputs. The remediation process should include comprehensive testing of patched environments to ensure that security updates do not introduce compatibility issues with existing data processing workflows. Additionally, security teams should conduct thorough risk assessments of their data science environments to identify all instances of vulnerable RAPIDS components and prioritize remediation efforts based on the sensitivity of data being processed. Implementation of the principle of least privilege and regular security audits of data processing pipelines will further reduce the potential impact of exploitation attempts.