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Low, CVSS 2.0amended

Memory corruption in DrawBoundingBoxesV2

CVE-2021-29571GHSA-whr9-vfh2-7hm6

What to do

Vendor fix available. Upgrade each affected package to the fixed version on its release line (tensorflow 2.4.2, 2.3.3, 2.2.3, 2.1.4; tensorflow-gpu 2.4.2, 2.3.3, 2.2.3, 2.1.4; tensorflow-cpu 2.4.2, 2.3.3, 2.2.3, 2.1.4), 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
    < 2.1.4>= 2.2.0, < 2.2.3>= 2.3.0, < 2.3.3>= 2.4.0, < 2.4.2
    Fixed in
    2.4.2, 2.3.3, 2.2.3, 2.1.4

  • PyPI tensorflow-gpu

    Affected
    < 2.1.4>= 2.2.0, < 2.2.3>= 2.3.0, < 2.3.3>= 2.4.0, < 2.4.2
    Fixed in
    2.4.2, 2.3.3, 2.2.3, 2.1.4

  • PyPI tensorflow-cpu

    Affected
    < 2.1.4>= 2.2.0, < 2.2.3>= 2.3.0, < 2.3.3>= 2.4.0, < 2.4.2
    Fixed in
    2.4.2, 2.3.3, 2.2.3, 2.1.4

Sources

Severity and exploitation
Exploitation
No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.
CVSS 4.0
2.0 · CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N · score source
EPSS
  • CVE-2021-29571: 0.24% estimated exploitation activity in the next 30 days; percentile 15.17 (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 The implementation of tf.rawops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs: The implementation assumes that the last element of boxes input is 4, as required by the op. Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption: If the last dimension in boxes is less than 4, accesses similar to tboxes(b, bb, 3) will access data outside of bounds. Further during code execution there are also writes to these indices. Patches We have patched the issue in GitHub commit 79865b542f9ffdc9caeb255631f7c56f1d4b6517. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the…

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

    Amended from ghsa record GHSA-whr9-vfh2-7hm6

  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-whr9-vfh2-7hm6

Agent brief

Preview the exact text before you copy it. Commands are read-only suggestions; review them before running anything.

# Patch Next advisory brief: CVE-2021-29571

Advisory: Memory corruption in `DrawBoundingBoxesV2`
IDs: CVE-2021-29571, GHSA-whr9-vfh2-7hm6
Record: https://patchnext.com/advisories/cve-2021-29571 (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: Low (CVSS 2.0)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: tensorflow 2.4.2, 2.3.3, 2.2.3, 2.1.4; tensorflow-gpu 2.4.2, 2.3.3, 2.2.3, 2.1.4; tensorflow-cpu 2.4.2, 2.3.3, 2.2.3, 2.1.4. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI tensorflow: affected < 2.1.4; >= 2.2.0, < 2.2.3; >= 2.3.0, < 2.3.3; >= 2.4.0, < 2.4.2; fixed in 2.4.2, 2.3.3, 2.2.3, 2.1.4
- PyPI tensorflow-gpu: affected < 2.1.4; >= 2.2.0, < 2.2.3; >= 2.3.0, < 2.3.3; >= 2.4.0, < 2.4.2; fixed in 2.4.2, 2.3.3, 2.2.3, 2.1.4
- PyPI tensorflow-cpu: affected < 2.1.4; >= 2.2.0, < 2.2.3; >= 2.3.0, < 2.3.3; >= 2.4.0, < 2.4.2; fixed in 2.4.2, 2.3.3, 2.2.3, 2.1.4

## 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-whr9-vfh2-7hm6 (OSV record GHSA-whr9-vfh2-7hm6 lists affected ranges: PyPI tensorflow >=0 =2.2.0 =2.3.0 =2.4.0 =0 =2.2.0 =2.3.0 =2.4.0 =0 =2.2.0 =2.3.0 =2.4.0 <2.4.2 (fixed))
- Verified: https://github.com/advisories/GHSA-whr9-vfh2-7hm6 (GitHub-reviewed advisory GHSA-whr9-vfh2-7hm6 lists affected ranges: PyPI tensorflow >=0 =2.2.0 =2.3.0 =2.4.0 =0 =2.2.0 =2.3.0 =2.4.0 =0 =2.2.0 =2.3.0 =2.4.0 <2.4.2)

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