Deserialization of Untrusted Data Affecting torch package, versions [,2.10.0)
Threat Intelligence
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-PYTHON-TORCH-15123585
- published28 Jan 2026
- disclosed27 Jan 2026
- creditJi'an Zhou
Introduced: 27 Jan 2026
CVE-2026-24747 (opens in a new tab)How to fix?
Upgrade torch to version 2.10.0 or higher.
Overview
torch is a Tensors and Dynamic neural networks in Python with strong GPU acceleration
Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the load_state_dict() function, used during unpickling. An attacker can corrupt heap memory by convincing a user to load a malicious checkpoint (.pth) file with torch.load(), when weights_only=True. This can trigger a crash by corrupting opcodes, which may be exploitable to enable code execution.
Details
Serialization is a process of converting an object into a sequence of bytes which can be persisted to a disk or database or can be sent through streams. The reverse process of creating object from sequence of bytes is called deserialization. Serialization is commonly used for communication (sharing objects between multiple hosts) and persistence (store the object state in a file or a database). It is an integral part of popular protocols like Remote Method Invocation (RMI), Java Management Extension (JMX), Java Messaging System (JMS), Action Message Format (AMF), Java Server Faces (JSF) ViewState, etc.
Deserialization of untrusted data (CWE-502) is when the application deserializes untrusted data without sufficiently verifying that the resulting data will be valid, thus allowing the attacker to control the state or the flow of the execution.