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High, CVSS 8.4amended

Heap OOB in nested tf.map_fn with RaggedTensors

CVE-2021-37679GHSA-g8wg-cjwc-xhhp

What to do

Vendor fix available. Upgrade each affected package to the fixed version on its release line (tensorflow 2.5.1, 2.4.3, 2.3.4; tensorflow-gpu 2.5.1, 2.4.3, 2.3.4; tensorflow-cpu 2.5.1, 2.4.3, 2.3.4), redeploy, then verify what is actually installed.

This page has no inventory attached, so it cannot say whether you are affected. A vendor fix is not proof that your deployment is fixed.

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Affected packages

  • PyPI tensorflow

    Affected
    < 2.3.4>= 2.4.0, < 2.4.3>= 2.5.0, <= 2.5.0
    Fixed in
    2.3.4, 2.4.3, 2.5.1

  • PyPI tensorflow-gpu

    Affected
    < 2.3.4>= 2.4.0, < 2.4.3>= 2.5.0, <= 2.5.0
    Fixed in
    2.3.4, 2.4.3, 2.5.1

  • PyPI tensorflow-cpu

    Affected
    < 2.3.4>= 2.4.0, < 2.4.3>= 2.5.0, <= 2.5.0
    Fixed in
    2.3.4, 2.4.3, 2.5.1

Sources

Severity and exploitation
Exploitation
No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.
CVSS 4.0
8.4 · CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N · score source
EPSS
  • CVE-2021-37679: 0.18% estimated exploitation activity in the next 30 days; percentile 7.74 (FIRST EPSS, 2026-11-03). This is a model estimate, not proof of exploitation or customer exposure.
Source description

As published by the source; long descriptions are shortened.

Impact It is possible to nest a tf.mapfn within another tf.mapfn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap: The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions in the above example. The same implementation can result in data loss, if input tensor is tweaked: Here, the output tensor will only have 2 elements for each inner dimension. Patches We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow…

Dates and revisions
Published by the source
Last modified by the source
Source last checked
  1. Revision 3 · amended ·

    Amended from ghsa record GHSA-g8wg-cjwc-xhhp

  2. Revision 2 · amended ·

    Reviewed OSV alias/range/CVSS4 correction; package source scopes and original publication preserved. No customer finding resolved.

  3. Revision 1 · created ·

    Published from osv record GHSA-g8wg-cjwc-xhhp

Agent brief

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# Patch Next advisory brief: CVE-2021-37679

Advisory: Heap OOB in nested `tf.map_fn` with `RaggedTensor`s
IDs: CVE-2021-37679, GHSA-g8wg-cjwc-xhhp
Record: https://patchnext.com/advisories/cve-2021-37679 (revision 3, source checked 26 Oct 2026, 13:26 UTC)
Inventory: none attached. This brief comes from a public page and does not say whether any deployment is affected.
Severity: High (CVSS 8.4)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: tensorflow 2.5.1, 2.4.3, 2.3.4; tensorflow-gpu 2.5.1, 2.4.3, 2.3.4; tensorflow-cpu 2.5.1, 2.4.3, 2.3.4. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI tensorflow: affected < 2.3.4; >= 2.4.0, < 2.4.3; >= 2.5.0, <= 2.5.0; fixed in 2.3.4, 2.4.3, 2.5.1
- PyPI tensorflow-gpu: affected < 2.3.4; >= 2.4.0, < 2.4.3; >= 2.5.0, <= 2.5.0; fixed in 2.3.4, 2.4.3, 2.5.1
- PyPI tensorflow-cpu: affected < 2.3.4; >= 2.4.0, < 2.4.3; >= 2.5.0, <= 2.5.0; fixed in 2.3.4, 2.4.3, 2.5.1

## 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-g8wg-cjwc-xhhp (OSV record GHSA-g8wg-cjwc-xhhp lists affected ranges: PyPI tensorflow >=0 =2.4.0 =2.5.0 =0 =2.4.0 =2.5.0 =0 =2.4.0 =2.5.0 <2.5.1 (fixed))
- Verified: https://github.com/advisories/GHSA-g8wg-cjwc-xhhp (GitHub-reviewed advisory GHSA-g8wg-cjwc-xhhp lists affected ranges: PyPI tensorflow >=0 =2.4.0 =2.5.0 =0 =2.4.0 =2.5.0 =0 =2.4.0 =2.5.0 <=2.5.0)

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