CVE-2026-31221
PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism.
Published May 12, 2026 · corrected/updated Jun 17, 2026 · lightningai pytorch lightning · evidence confidence medium
PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior 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 on the victim's system when the file is loaded.
Grounded briefings
Catalog interpretations for three audiences. Each brief cites ingested metadata and the original advisories below. They are not independent research and they do not invent exploit steps.
Executive
CVE-2026-31221 is a high public vulnerability record for lightningai pytorch lightning. It is not currently listed in the CISA Known Exploited catalog. Check the linked vendor or CERT advisories for patch status. This summary is a catalog interpretation of the ingested metadata and cites the original advisories below.
System administrator
Identify whether lightningai pytorch lightning is in your estate, then compare installed versions with the affected ranges. Affected packages or ranges on record: lightningai/pytorch lightning. Check the linked vendor or CERT advisories for patch status. Prefer the vendor or distribution advisory linked in Sources before changing production systems. This is a catalog interpretation, not a runbook.
Security analyst
Signals on file: CVSS 7.8. It is not currently listed in the CISA Known Exploited catalog. Weakness classes: CWE-502. Sources contributing to this record: cve_org, nvd. Public exploit references, when present, are outbound links only and are not proof of active exploitation. This is a catalog interpretation of stored metadata.
Exploitation and remediation
Exploitation evidence
Not listed as known exploited in CISA KEV. Not in the CISA KEV catalog.
Patch and remediation
No required-action text is stored yet. Use the vendor or CERT links in Sources for patch availability.
Safe detection guidance
Compare installed versions with the affected ranges below and verify the advisory from the original publisher. This site stores metadata and outbound links only; it does not ship exploit code or proof-of-concept payloads.
Code
The project this record applies to. Exploit listings stay in References.
Timeline
Detection, updates, and resolution for this record.
May 12, 2026 · NIST NVD
Detected
PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbi
Jun 17, 2026 · NIST NVD
Updated
PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbi
Affected
- cpe lightningai/pytorch lightningversion unspecified
Sources
- CVE.orgVulnerability recordCVE-2026-31221
Official CVE identifier
cve.org - NIST NVDVulnerability recordCVE-2026-31221
PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() withou
nvd.nist.gov