Security readout for executives and security teams
Plain-English summary
PyTorch before 2.10.0 can be compromised when an untrusted checkpoint file is loaded with the supposedly safer weights_only=True mode. A malicious .pth file could corrupt memory and potentially run attacker-controlled code. The victim still has to load the file, so risk is highest in ML workflows that ingest external models or checkpoints.
Executive priority
Treat this as high priority for teams running ML platforms, research environments, or automated model ingestion. The business risk is not broad internet wormability, but compromise through poisoned model artifacts in trusted workflows.
Technical view
The issue is in PyTorch's weights_only unpickler before 2.10.0. Crafted checkpoint files can trigger memory corruption during torch.load(..., weights_only=True), with potential arbitrary code execution. The CVSS 3.1 score is 8.8 and maps to CWE-502 and CWE-94. Sources name PyTorch 2.10.0 as the fixed version.
Likely exposure
Organizations using PyTorch versions below 2.10.0 are exposed when they load checkpoint files from third parties, model hubs, user uploads, research collaborators, or automated ML pipelines. Systems that never load untrusted .pth files have lower practical exposure, but should still upgrade.
Exploitation context
The source bundle does not state active exploitation, and KEV is false. Exploitation requires a user or workflow to load a malicious checkpoint file. The impact can be severe because successful exploitation may allow code execution in the context of the process loading the model.
Researcher notes
Focus review on checkpoint loading boundaries and assumptions around weights_only=True. The advisory indicates this safer mode was still vulnerable. Avoid treating weights_only=True as a complete trust boundary for untrusted files; validate version, provenance, sandboxing, and downstream package status.
Mitigation direction
- Upgrade PyTorch to version 2.10.0 or later.
- Do not load untrusted .pth checkpoint files.
- Restrict model ingestion to trusted, verified sources.
- Isolate model loading in least-privileged environments.
- Check Red Hat guidance for packaged dependency fixes.
Validation and detection
- Inventory deployed PyTorch versions across development and production.
- Find uses of torch.load with externally supplied checkpoint files.
- Review model and checkpoint provenance controls.
- Confirm PyTorch reports version 2.10.0 or later.
- Check vendor advisories for distribution-specific status.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-502: Code execution behavior lookup
Code execution and unsafe deserialization weaknesses often justify reviewing execution behavior and process telemetry. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCWE-94: Code execution behavior lookup
Code execution and unsafe deserialization weaknesses often justify reviewing execution behavior and process telemetry. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupExecution behavior lookup
The CVE wording references code or command execution, so execution technique review may help defensive triage. This is a Glexia inferred lookup path, not an official MITRE, ATT&CK, or CVE Program mapping.
Open ATT&CK lookupCVE-2026-24747 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
Open ATT&CK lookup- Severity
- High
- CVSS
- 8.8 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
SSVC decision data
CVSS vector scores
2 official scoresWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H2.85.9GitHub_MCVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H2.85.9redhat-SADPVulnerability scoring details
Base CVSS 3.1 score
8.8HighVector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Base CVSS 3.1 score
8.8HighVector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Vulnerability timeline
Timeline events are normalized from CVE metadata, CNA source timelines, ADP timelines, and KEV metadata when present.
- CVE reservedCVE Program
The CVE ID was reserved by the assigning CNA.
- ADP timelineredhat-SADP
Made public.
- CVE publishedCVE Program
The CVE record was published.
- ADP timelineredhat-SADP
Reported to Red Hat.
- CVE updatedCVE Program
The CVE record metadata indicates this as the latest update time.
ADP provider summaries
- 2026-01-27T22:01:52.649Z: Reported to Red Hat.
- 2026-01-27T21:13:46.878Z: Made public.
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/pytorch/pytorch/security/advisories/GHSA-63cw-57p8-fm3pCVE reference, GitHub_M · x_refsource_CONFIRM
- https://github.com/pytorch/pytorch/issues/163105CVE reference, GitHub_M · x_refsource_MISC
- https://github.com/pytorch/pytorch/163122/commit/954dc5183ee9205cbe79876ad05dd2d9ae752139CVE reference, GitHub_M · x_refsource_MISC
- https://github.com/pytorch/pytorch/releases/tag/v2.10.0CVE reference, GitHub_M · x_refsource_MISC
- https://access.redhat.com/security/cve/CVE-2026-24747CVE reference, redhat-SADP · vdb-entry, x_refsource_REDHAT
- RHBZ#2433612CVE reference, redhat-SADP · issue-tracking, x_refsource_REDHAT
- https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-24747.jsonCVE reference, redhat-SADP · x_sadp-csaf-vex
- https://access.redhat.com/errata/RHSA-2026:24977CVE reference, redhat-SADP · vendor-advisory, x_refsource_REDHAT
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
Deserialization of Untrusted Data
Deserialization of Untrusted Data represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.
Improper Control of Generation of Code ('Code Injection')
Improper Control of Generation of Code ('Code Injection') represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.