CVE-2026-118613: Ultralytics torch_load deserialization of untrusted data RCE

CVE-2026-118613 · Severity: high · CVSS 7.8 · Published 2026-10-28

Ultralytics YOLOv8 machine learning framework contains a remote code execution vulnerability in its torch_load function that deserializes untrusted data without proper validation. An attacker can trick a user into opening a malicious file or visiting a malicious page to execute arbitrary code with the privileges of the current user, potentially leading to complete system compromise.

Vendors: Ultralytics.

Quick answers

Is CVE-2026-118613 exploited in the wild?
No. Junglewise Threat Intelligence has no report of CVE-2026-118613 being exploited in the wild.
How severe is CVE-2026-118613?
High severity, with a CVSS score of 7.8, published 28 October 2026.
Which products and versions are affected?
Ultralytics YOLOv8.
Is there a fix for CVE-2026-118613?
No fixed version is recorded yet. Restrict user interaction with untrusted model files and malicious web pages, and avoid loading .pt model files from untrusted sources.</anigation> <parameter name="timeline">[{"kind": "disclosed", "at": "2026-06-18", "note": "Vulnerability reported to vendor"}, {"kind": "advisory", "at": "2026-10-28", "note": "Coordinated public release of advisory"}] If you run it, Junglewise drafts a mitigation that fits your setup and applies it once you approve, with senior security engineers behind it.
How is CVE-2026-118613 attacked?
It is attacked with local access, in a software library.
Am I affected by CVE-2026-118613?
Check whether you run Ultralytics YOLOv8. 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

Restrict user interaction with untrusted model files and malicious web pages, and avoid loading .pt model files from untrusted sources.</anigation> <parameter name="timeline">[{"kind": "disclosed", "at": "2026-06-18", "note": "Vulnerability reported to vendor"}, {"kind": "advisory", "at": "2026-10-28", "note": "Coordinated public release of advisory"}]

Technical details

The torch_load function in Ultralytics fails to validate user-supplied data before deserialization, allowing arbitrary code execution. The vulnerability requires local attack vector and user interaction (opening a malicious file or visiting a malicious page), but once triggered, an attacker gains code execution in the context of the current user. The flaw is a classic unsafe deserialization issue in Python's pickle-based model loading.

Affected products

  • Ultralytics YOLOv8

References

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