Is CVE-2026-109360 exploited in the wild?

No. Junglewise Threat Intelligence has no report of CVE-2026-109360 being exploited in the wild.

How severe is CVE-2026-109360?

High severity, with a CVSS score of 7.5, published 12 October 2026.

Which products and versions are affected?

Project-MONAI MONAI 0.4.0 through 1.6.0.

How is CVE-2026-109360 attacked?

It is attacked over the network, in a software library.

Am I affected by CVE-2026-109360?

Check whether you run Project-MONAI MONAI at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

CVE-2026-109360: MONAI remote code execution in SENet pretrained weight loading

CVE-2026-109360 · Severity: high · CVSS 7.5 · Published 2026-10-12

MONAI is a medical imaging deep learning framework that loads pre-trained neural network weights to accelerate model training. Versions 0.4.0 through 1.6.0 download SENet model weights over unencrypted HTTP and deserialize them without validation, allowing an attacker on the network path to inject malicious code that executes when a user constructs a pretrained model. This gives attackers complete control over the victim's system.

Technologies: Project-MONAI MONAI. Vendors: Project-MONAI.

Quick answers

Is CVE-2026-109360 exploited in the wild?: No. Junglewise Threat Intelligence has no report of CVE-2026-109360 being exploited in the wild.
How severe is CVE-2026-109360?: High severity, with a CVSS score of 7.5, published 12 October 2026.
Which products and versions are affected?: Project-MONAI MONAI 0.4.0 through 1.6.0.
Is there a fix for CVE-2026-109360?: Yes: fixed in 1.6.1. Until then: Avoid using pretrained=True when constructing SENet, EfficientNet, DenseNet, or text embedding models until upgrading to MONAI 1.6.1 or later. Restrict network access through HTTP-inspection proxies or network segmentation to prevent man-in-the-middle attacks on downloads from data.lip6.fr. Junglewise finds every place you run it, drafts the upgrade and applies it once you approve.
How is CVE-2026-109360 attacked?: It is attacked over the network, in a software library.
Am I affected by CVE-2026-109360?: Check whether you run Project-MONAI MONAI at an affected version. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

How to fix

Fixed in: 1.6.1

Avoid using pretrained=True when constructing SENet, EfficientNet, DenseNet, or text embedding models until upgrading to MONAI 1.6.1 or later. Restrict network access through HTTP-inspection proxies or network segmentation to prevent man-in-the-middle attacks on downloads from data.lip6.fr.

Technical details

The vulnerability exists in MONAI's SENet implementation (senet.py), which fetches pretrained weights from plaintext HTTP URLs and passes them to torch.hub.load_state_dict_from_url with the default weights_only=False parameter. This causes full pickle deserialization of untrusted data without integrity checking. An on-path attacker can perform a man-in-the-middle attack to substitute a crafted .pth file containing malicious pickle payloads that execute at model-construction time. Similar issues exist in EfficientNet, DenseNet, and text embedding modules but over HTTPS.

Affected products

Project-MONAI MONAI 0.4.0 through 1.6.0

Timeline

2026-09-27: disclosed
2026-10-12: advisory
2026-10-12: patched: Fixed in version 1.6.1

References

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