TensorFlow vulnerable to segfault when opening multiframe gif

Exploitation

No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.

CVSS

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

EPSS

• CVE-2023-25667: 0.31% estimated exploitation activity in the next 30 days; percentile 23.69 (FIRST EPSS, 2026-11-03). This is a model estimate, not proof of exploitation or customer exposure.

As published by the source; long descriptions are shortened.

Impact Integer overflow occurs when 2^31 <= numframes height width channels < 2^32, for example Full HD screencast of at least 346 frames. Patches We have patched the issue in GitHub commit 8dc723fcdd1a6127d6c970bd2ecb18b019a1a58d. The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1 For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Andrei

Published by the source

24 Mar 2023, 21:57 UTC

Last modified by the source

13 Jul 2026, 16:42 UTC

Source last checked

26 Oct 2026, 13:11 UTC

1. Revision 2 · amended · 26 Oct 2026

   Amended from ghsa record GHSA-fqm2-gh8w-gr68

2. Revision 1 · created · 24 Oct 2026

   Published from osv record GHSA-fqm2-gh8w-gr68

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# Patch Next advisory brief: CVE-2023-25667

Advisory: TensorFlow vulnerable to segfault when opening multiframe gif
IDs: CVE-2023-25667, GHSA-fqm2-gh8w-gr68
Record: https://patchnext.com/advisories/cve-2023-25667 (revision 2, source checked 26 Oct 2026, 13:11 UTC)
Inventory: none attached. This brief comes from a public page and does not say whether any deployment is affected.
Severity: Medium (CVSS 6.5)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: tensorflow 2.11.1; tensorflow-gpu 2.11.1; tensorflow-cpu 2.11.1. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI tensorflow: affected < 2.11.1; fixed in 2.11.1
- PyPI tensorflow-gpu: affected < 2.11.1; fixed in 2.11.1
- PyPI tensorflow-cpu: affected < 2.11.1; fixed in 2.11.1

## Suggested inspection (read-only)
- `python -m pip show tensorflow`: prints the installed version. Run with the environment that serves production active.
- `python -m pip show tensorflow-gpu`: prints the installed version. Run with the environment that serves production active.
- `python -m pip show tensorflow-cpu`: prints the installed version. Run with the environment that serves production active.

## Supported action
Upgrade each affected component to a fixed version on its release line. For a transitive dependency, upgrade the parent package or add an override or constraint, then regenerate the lockfile. Redeploy every environment that ran an affected version.

## Required regression checks
- Run the test suite and a production build.
- Exercise the features that depend on tensorflow, tensorflow-gpu, tensorflow-cpu.
- Repeat the inspection and confirm that only fixed versions remain installed.

## Rollback considerations
- Keep the previous lockfile and deployment artifact. Rolling back reinstates the vulnerable version, so apply any documented mitigation while you fix forward.

## Unresolved questions
- Which deployments run an affected version?
- Exploitation status can change. Check again before lowering the priority.
- Mark the work resolved only after the fixed version is verified in production.

## Sources
- Verified: https://osv.dev/vulnerability/GHSA-fqm2-gh8w-gr68 (OSV record GHSA-fqm2-gh8w-gr68 lists affected ranges: PyPI tensorflow >=0 =0 =0 <2.11.1 (fixed))
- Verified: https://github.com/advisories/GHSA-fqm2-gh8w-gr68 (GitHub-reviewed advisory GHSA-fqm2-gh8w-gr68 lists affected ranges: PyPI tensorflow >=0 =0 =0 <2.11.1)

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