CVE-2026-31214
TensorFlow PyTorch Insecure Deserialization Vulnerability
Description
The torch-checkpoint-shrink.py script in the ml-engineering project in commit 0099885db36a8f06556efe1faf552518852cb1e0 (2025-20-27) contains an insecure deserialization vulnerability (CWE-502). The script uses torch.load() to process PyTorch checkpoint files (.pt) without enabling the security-restrictive weights_only=True parameter. This oversight allows the deserialization of arbitrary Python objects via the pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution in the context of the user running the script.
INFO
Published Date :
May 12, 2026, 4:16 p.m.
Last Modified :
May 12, 2026, 4:16 p.m.
Remotely Exploit :
No
Source :
[email protected]
Affected Products
The following products are affected by CVE-2026-31214
vulnerability.
Even if cvefeed.io is aware of the exact versions of the
products
that
are
affected, the information is not represented in the table below.
No affected product recoded yet
Solution
- Use the weights_only=True parameter in torch.load().
- Ensure untrusted checkpoint files are not loaded.
- Update the script to use secure loading practices.
- Review all uses of torch.load() for security.
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2026-31214.
| URL | Resource |
|---|---|
| https://github.com/stas00/ml-engineering/blob/master/training/checkpoints/torch-checkpoint-shrink.py#L57 | |
| https://www.notion.so/CVE-2026-31214-35d1e1393188813fa40eef73c174cee5 |
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2026-31214 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2026-31214
weaknesses.
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2026-31214 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2026-31214 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
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New CVE Received by [email protected]
May. 12, 2026
Action Type Old Value New Value Added Description The torch-checkpoint-shrink.py script in the ml-engineering project in commit 0099885db36a8f06556efe1faf552518852cb1e0 (2025-20-27) contains an insecure deserialization vulnerability (CWE-502). The script uses torch.load() to process PyTorch checkpoint files (.pt) without enabling the security-restrictive weights_only=True parameter. This oversight allows the deserialization of arbitrary Python objects via the pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution in the context of the user running the script. Added Reference https://github.com/stas00/ml-engineering/blob/master/training/checkpoints/torch-checkpoint-shrink.py#L57 Added Reference https://www.notion.so/CVE-2026-31214-35d1e1393188813fa40eef73c174cee5