PytorchApplication · Linuxfoundation

CVE-2025-3001

MEDIUM · 5.3 CVSS v3.1 Published 2025-03-31
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
55/100
Remediation priority · Elevated
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
A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstm_cell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used.

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 · moderate confidence

A memory corruption vulnerability exists in PyTorch 2.6.0's torch.lstm_cell function. The vulnerability has a local attack vector and publicly available exploit code, with a CVSS score of 5.3 indicating moderate severity.

MitigationApply vendor-supplied patches for PyTorch 2.6.0 when available; if immediate patching is not feasible, consider restricting access to the affected torch.lstm_cell function and monitoring for indicators of exploitation.

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

Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.

NVD · CPE data
PytorchApplication
Affected:= 2.6.0

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
Local
Complexity
Low
Privileges
Low
User interaction
None
Scope
Unchanged
Confidentiality
Low
Integrity
Low
Availability
Low

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

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. Identify installed PyTorch version
    Run 'python -c "import torch; print(torch.__version__)"' to retrieve the installed PyTorch version number
    Affected if The version shown is exactly 2.6.0
  2. Locate torch.lstm_cell usage in code
    Search Python source files, notebooks, and scripts for imports of torch.nn.LSTMCell or calls to torch.lstm_cell, or search for the string 'lstm_cell' using grep/grep -r
    Affected if Code explicitly invokes torch.lstm_cell or torch.nn.LSTMCell in any application running on the affected PyTorch version
  3. Check for dynamic usage in runtime
    Inspect running Python processes or imported modules in memory using 'python -c "import torch; print(hasattr(torch, 'lstm_cell'))"' to confirm the function exists in the installed build
    Affected if The function is present and accessible in the PyTorch 2.6.0 installation
  4. Assess local access context
    Review user permissions and access controls on the system where PyTorch runs; determine if untrusted users have local access to execute Python code or load malicious models
    Affected if The system running PyTorch 2.6.0 permits local access by users who could invoke the vulnerable function

You are affected if PyTorch version 2.6.0 is installed AND the torch.lstm_cell function is accessible in your environment where local users can trigger it.

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

Apply vendor-supplied patches for PyTorch 2.6.0 when available; if immediate patching is not feasible, consider restricting access to the affected torch.lstm_cell function and monitoring for indicators of exploitation.

Fix this in Pytorch Scoped from the published advisory
  • Consultation6.0 h
  • Implementation16.0 h
  • Testing8.0 h
  • Review / QA4.0 h
34.0 hours of engineering $6,000
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Scan for this in your stack

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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-2025-3001 in production — separate from our analysis above.

No notes yet

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
  • No weaponised exploit code, or anything meant to cause harm
  • No spam, self-promotion, credentials, or personal data