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VulnerabilityPublished Updated

CVE-2026-24747: torch · PyTorch Vulnerable to Remote Code Execution via Untrusted Checkpoint Files

Affects torchSource GCVE Database

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Not known to be exploited · CVSS 8.8 High : upgrade to 2.10.0 soon.

Data as of 4 Nov 2026, 04:28 UTC

CVSS 3.1 base score
8.8High
How severe the flaw is on the 0–10 CVSS scale.
Patch
Available – fixed in 2.10.0
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 27 Jan 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 3 Nov 2026.

What to do

Upgrade torch to 2.10.0.

Affected software

ProductAffectedFixed in
PyPItorchAffected
  • <2.10.0
Fixed in
  • 2.10.0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

A patch is available and users should upgrade to PyTorch version 2.10.0 or later to remediate this vulnerability. Red Hat has released security advisories and updated packages addressing this issue. Users should avoid loading untrusted checkpoint files with weights_only=True. No additional mitigations are specified by the vendor.

Description

A vulnerability in PyTorch's weights_only unpickler allows remote attackers to execute arbitrary code by loading a malicious checkpoint (.pth) file. The flaw involves improper validation of pickle opcodes and storage metadata, leading to heap memory corruption and storage size mismatches. This can be exploited if a user loads a crafted checkpoint file with torch.load(..., weights_only=True). The issue affects PyTorch versions prior to 2.10.0 and is fixed in version 2.10.0.

References

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