Buffer overflow in CONV_3D_TRANSPOSE on TFLite

Exploitation

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

CVSS

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

EPSS

• CVE-2022-41894: 0.59% estimated exploitation activity in the next 30 days; percentile 48.06 (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 The reference kernel of the CONV3DTRANSPOSE TensorFlow Lite operator wrongly increments the dataptr when adding the bias to the result. Instead of dataptr += numchannels; it should be dataptr += outputnumchannels; as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if numchannels outputnumchannels. An attacker can craft a model with a specific number of input channels in a way similar to the attached example script. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter (i.e. experimentalopresolvertype=tf.lite.experimental.OpResolverType.BUILTINREF is used). Patches We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow…

Published by the source

21 Nov 2022, 20:44 UTC

Last modified by the source

7 Jul 2026, 11:56 UTC

Source last checked

26 Oct 2026, 13:11 UTC

1. Revision 2 · amended · 26 Oct 2026

   Amended from ghsa record GHSA-h6q3-vv32-2cq5

2. Revision 1 · created · 24 Oct 2026

   Published from osv record GHSA-h6q3-vv32-2cq5

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# Patch Next advisory brief: CVE-2022-41894

Advisory: Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite
IDs: CVE-2022-41894, GHSA-h6q3-vv32-2cq5
Record: https://patchnext.com/advisories/cve-2022-41894 (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: High (CVSS 7.1)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: tensorflow 2.10.1, 2.9.3, 2.8.4. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI tensorflow: affected < 2.8.4; >= 2.9.0, < 2.9.3; >= 2.10.0, < 2.10.1; fixed in 2.10.1, 2.9.3, 2.8.4

## Suggested inspection (read-only)
- `python -m pip show tensorflow`: 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.
- 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-h6q3-vv32-2cq5 (OSV record GHSA-h6q3-vv32-2cq5 lists affected ranges: PyPI tensorflow >=0 =2.9.0 =2.10.0 <2.10.1 (fixed))
- Verified: https://github.com/advisories/GHSA-h6q3-vv32-2cq5 (GitHub-reviewed advisory GHSA-h6q3-vv32-2cq5 lists affected ranges: PyPI tensorflow >=0 =2.9.0 =2.10.0 <2.10.1)

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