CVE-2026-4538 in PyTorch
Summary
by MITRE • 03/22/2026
A vulnerability was identified in PyTorch 2.10.0. The affected element is an unknown function of the component pt2 Loading Handler. The manipulation leads to deserialization. The attack can only be performed from a local environment. The exploit is publicly available and might be used. The project was informed of the problem early through a pull request but has not reacted yet.
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Analysis
by VulDB Data Team • 05/24/2026
This vulnerability resides within PyTorch's pt2 Loading Handler component where an unknown function processes serialized data without adequate validation mechanisms. The flaw represents a classic deserialization vulnerability that allows maliciously crafted input to execute arbitrary code during the loading process. The vulnerability's impact is particularly concerning given that it affects the core loading handler functionality that processes various tensor and model serialization formats commonly used in machine learning workflows. The attack vector is limited to local environments, which suggests that the vulnerability requires physical access or execution privileges on the target system, but this restriction does not diminish its potential for significant damage in compromised environments. The fact that a public exploit exists increases the risk profile considerably, as it removes the barrier to exploitation for threat actors who may not require advanced technical skills to leverage this vulnerability.
The technical implementation of this vulnerability demonstrates a failure in input validation and sanitization within the deserialization pipeline. When PyTorch processes serialized data through the affected loading handler, it likely executes untrusted code or objects without proper sandboxing or type checking mechanisms. This behavior aligns with common deserialization attack patterns where attackers can manipulate serialized data to trigger unintended operations within the application context. The vulnerability's classification corresponds to CWE-502 which specifically addresses deserialization of untrusted data, making it a well-documented and dangerous class of vulnerability. The attack surface is further expanded by the fact that PyTorch is widely used in machine learning environments where serialized models and tensors are frequently exchanged between systems, creating numerous potential attack vectors.
From an operational perspective, this vulnerability poses significant risks to organizations utilizing PyTorch 2.10.0 in their machine learning pipelines. The local execution requirement means that attackers would need to have access to the system where PyTorch is installed, but this access could be gained through various means including compromised user accounts, insider threats, or lateral movement within network environments. The vulnerability's presence in a widely-used machine learning framework increases the potential impact across multiple industries including finance, healthcare, and technology sectors where model serialization and loading are critical components of their operations. The delayed response from the project maintainers, despite early notification through a pull request, indicates a potential gap in vulnerability triage processes and highlights the importance of timely security patch management in open source software ecosystems. This delay in addressing the vulnerability creates a window of opportunity for attackers to develop and deploy exploits without detection.
Organizations should implement immediate mitigations to reduce the risk associated with this vulnerability. The primary recommendation involves upgrading to a patched version of PyTorch as soon as available, which represents the most effective long-term solution. In the interim period, system administrators should implement strict access controls and monitor for unusual file access patterns that might indicate exploitation attempts. Network segmentation and privilege separation can help limit the potential impact if an attacker does gain local access to a system. The vulnerability's characteristics align with ATT&CK technique T1548.001 which covers abuse of cloud credentials, though the local execution requirement modifies the attack methodology. Security teams should also consider implementing runtime monitoring solutions that can detect anomalous deserialization behavior or unexpected code execution patterns. Additionally, organizations should review their software supply chain processes to ensure timely patch adoption and maintain awareness of security advisories from PyTorch maintainers and other open source security communities.