Deserialization of Untrusted DataWeakness · CWE-502

CVE-2026-31214

CRITICAL · 9.8 CVSS v3.1 Published 2026-05-12
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
100/100
Remediation priority · Urgent
Remotely reachable No privileges Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
The torch-checkpoint-shrink.py script in the ml-engineering project in commit 0099885db36a8f06556efe1faf552518852cb1e0 (2025-20-27) contains an insecure deserialization vulnerability (CWE-502). The script uses torch.load() to process PyTorch checkpoint files (.pt) without enabling the security-restrictive weights_only=True parameter. This oversight 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 in the context of the user running the script.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

The ml-engineering project's torch-checkpoint-shrink.py script uses torch.load() to deserialize PyTorch checkpoint files without the weights_only=True parameter, allowing arbitrary pickle deserialization and remote code execution via malicious checkpoint files.

MitigationAdd weights_only=True to torch.load() calls; if custom objects must be loaded, implement a proper allowlist or use torch.serialization.safe_globals() with explicit validation of expected classes.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Locate the torch-checkpoint-shrink.py script
    Search for the file named 'torch-checkpoint-shrink.py' in your ml-engineering project directory or Python environment
    Affected if The script exists in your environment and is used to load PyTorch checkpoint files
  2. Inspect torch.load() calls for weights_only parameter
    Open the script and search for torch.load() function calls. Examine each call's parameters to determine if weights_only=True is present
    Affected if The script contains torch.load() calls without weights_only=True or with weights_only=False
  3. Verify the script is executable or imported
    Check if the script has been executed, imported as a module, or is part of an automated pipeline that processes checkpoint files
    Affected if The script is actively used or accessible to users/systems that could supply malicious checkpoint files
  4. Confirm checkpoint file handling behavior
    Review whether the script accepts checkpoint file paths from user input, external sources, or untrusted locations
    Affected if The script loads checkpoint files from sources that could be controlled by untrusted parties

You are affected if the torch-checkpoint-shrink.py script exists in your environment and contains torch.load() calls without weights_only=True, especially when processing untrusted checkpoint files.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

From vendor data
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Add weights_only=True to torch.load() calls; if custom objects must be loaded, implement a proper allowlist or use torch.serialization.safe_globals() with explicit validation of expected classes.

Have this fixed Scoped from the published advisory
  • Consultation2.0 h
  • Implementation2.0 h
  • Testing4.0 h
  • Review / QA2.0 h
10.0 hours of engineering $1,720
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2026-31214 in production — separate from our analysis above.

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