Integer OverflowWeakness · CWE-190

CVE-2025-30404

CRITICAL · 9.8 CVSS v3.1 Published 2025-08-07
Patch available
A vendor patch is available. No clean upgrade release — apply the published patch.
See remediation →
100/100
Remediation priority · Urgent
Remotely reachable No privileges Zero-click Patch available

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
An integer overflow vulnerability in the loading of ExecuTorch models can cause overlapping allocations, potentially resulting in code execution or other undesirable effects. This issue affects ExecuTorch prior to commit d158236b1dc84539c1b16843bc74054c9dcba006.

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

An integer overflow vulnerability exists in ExecuTorch's model loading functionality where calculation of allocation sizes can overflow, resulting in overlapping memory buffers. This memory corruption issue can be leveraged for arbitrary code execution. The vulnerability is in the model loading path prior to commit d158236b1dc84539c1b16843bc74054c9dcba006.

MitigationUpdate ExecuTorch to version containing commit d158236b1dc84539c1b16843bc74054c9dcba006 or later to resolve the integer overflow in model loading allocation calculations.

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. Identify ExecuTorch installation
    Locate the ExecuTorch library or source code in your environment. Check common paths like /usr/local/lib/libexecutorch.so, pip show executorch, or look for the executorch directory in your project dependencies.
    Affected if ExecuTorch is present in the environment
  2. Determine ExecuTorch version or commit hash
    Run 'git log --oneline -1' in the ExecuTorch source directory, or check the installed package version via 'pip show executorch' or the version header file. Compare against commit d158236b1dc84539c1b16843bc74054c9dcba006.
    Affected if The installed commit is earlier than d158236b1dc84539c1b16843bc74054c9dcba006 or the version is unknown/older
  3. Verify model loading functionality is used
    Inspect your code or application to determine if ExecuTorch's model loading APIs are called, such as loading .pte files via ExecuTorch's loader functions. Check if any ExecuTorch models are being loaded at runtime.
    Affected if The application loads ExecuTorch models (.pte files) using the affected code path
  4. Confirm allocation calculations occur during model loading
    Review whether the model loading process triggers memory allocation calculations for model structures. This is inherent to any ExecuTorch model loading operation prior to the fix.
    Affected if Models are being loaded without having updated to the patched version

A user is affected if ExecuTorch is installed and loads models using a version prior to commit d158236b1dc84539c1b16843bc74054c9dcba006, as the integer overflow occurs during allocation size calculations in the model loading path.

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
Patch available Apply the vendor patch
Vendor patch github.com →
Interim mitigation

Update ExecuTorch to version containing commit d158236b1dc84539c1b16843bc74054c9dcba006 or later to resolve the integer overflow in model loading allocation calculations.

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
Get the patch applied

An estimate, not a bill — we confirm scope with you before any work starts. Need it this week? Rush from $2,752.

Scan for this in your stack

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dbcve dependency scanner

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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-2025-30404 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
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