NVIDIA is urging security teams to patch a critical vulnerability in its NVIDIA APEX code, a library used to accelerate deep learning training on Linux systems. The flaw (CVE-2025-33244) is a deserialization bug and carries a CVSS score of 9.0.
The vulnerability allows for “Scope Change”, according to the CVE record. This means an attacker who successfully exploits the flaw can break out of a specific application’s sandbox to seize control of the underlying Linux operating system.
The issue centers around how APEX code handles untrusted data. When a researcher or developer loads a model or a dataset, the library “deserializes” that data to make it usable. According to official documentation from the National Vulnerability Database (NVD), an unauthorized attacker can exploit this process to execute malicious code, trigger a denial of service, or escalate their privileges within a network.
The bug’s risk profile is highly dangerous yet geographically specific, according to the CVE record. While an attacker must be on an “adjacent network” (such as a corporate data center or a shared cloud environment), they require only low-level privileges and zero user interaction to trigger a full-scale compromise of confidentiality, integrity, and availability.
APEX is a staple for developers using PyTorch, a popular framework for machine learning research.
Specifically, the vulnerability affects environments running PyTorch versions earlier than 2.6. Because many enterprise-grade AI projects rely on stable, older versions of these frameworks to ensure consistency in model training, thousands of high-value servers in research labs and financial institutions may currently be exposed.
The “supply chain” risk is the primary concern for the market. Should a malicious actor upload a compromised model to a public repository, any researcher downloading and “unpacking” that model using a vulnerable version of APEX could inadvertently grant an attacker total access to their compute cluster.
NVIDIA advises administrators to upgrade to PyTorch version 2.6 or later to close the deserialization gap. It also suggests verifying data sources until patches are applied and avoid loading pre-trained models or configuration files from unverified or third-party repositories. Lastly, it advices network segmentation to ensure that AI training clusters are isolated from broader internal networks.
For full technical details and official patches, users should refer to the NVIDIA Security Advisory 5782.
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