0.0
NA
CVE-2026-31218
Optimate Pickle Deserialization Remote Code Execution
Description

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When loading a model state dictionary from a state_dict.pt file via torch.load(), the function does not enable the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted state_dict.pt file within a directory specified via the --model argument, leading to arbitrary code execution during the deserialization process on the victim's system.

INFO

Published Date :

May 12, 2026, 4:16 p.m.

Last Modified :

May 12, 2026, 4:16 p.m.

Remotely Exploit :

No
Affected Products

The following products are affected by CVE-2026-31218 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
Update PyTorch to enable secure model loading and prevent arbitrary code execution.
  • Ensure PyTorch is updated to a secure version.
  • Enable the weights_only=True parameter when using torch.load().
  • Validate model files before loading them.
  • Restrict access to model loading functionality.
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-2026-31218.

URL Resource
https://github.com/nebuly-ai/optimate
https://www.notion.so/CVE-2026-31218-35d1e139318881839bc8cf6007be2c76
CWE - Common Weakness Enumeration

While CVE identifies specific instances of vulnerabilities, CWE categorizes the common flaws or weaknesses that can lead to vulnerabilities. CVE-2026-31218 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-2026-31218 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-2026-31218 vulnerability anywhere in the article.

The following table lists the changes that have been made to the CVE-2026-31218 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.

  • New CVE Received by [email protected]

    May. 12, 2026

    Action Type Old Value New Value
    Added Description The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When loading a model state dictionary from a state_dict.pt file via torch.load(), the function does not enable the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted state_dict.pt file within a directory specified via the --model argument, leading to arbitrary code execution during the deserialization process on the victim's system.
    Added Reference https://github.com/nebuly-ai/optimate
    Added Reference https://www.notion.so/CVE-2026-31218-35d1e139318881839bc8cf6007be2c76
EPSS is a daily estimate of the probability of exploitation activity being observed over the next 30 days. Following chart shows the EPSS score history of the vulnerability.