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High, CVSS 8.5amended

Incorrect validation of SaveV2 inputs

CVE-2021-37648GHSA-wp77-4gmm-7cq8

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
8.5 · CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N · score source
EPSS
  • CVE-2021-37648: 0.19% estimated exploitation activity in the next 30 days; percentile 8.31 (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 code for tf.rawops.SaveV2 does not properly validate the inputs and an attacker can trigger a null pointer dereference: The implementation uses ValidateInputs to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses OPREQUIRES which translates to setting the Status object of the current OpKernelContext to an error status, followed by an empty return statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in Compute that follows the call to ValidateInputs. This is equivalent to lacking the validation. Patches We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4,…

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

    Amended from ghsa record GHSA-wp77-4gmm-7cq8

  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-wp77-4gmm-7cq8

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-2021-37648

Advisory: Incorrect validation of `SaveV2` inputs
IDs: CVE-2021-37648, GHSA-wp77-4gmm-7cq8
Record: https://patchnext.com/advisories/cve-2021-37648 (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 8.5)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: tensorflow 2.5.1, 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-wp77-4gmm-7cq8 (OSV record GHSA-wp77-4gmm-7cq8 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-wp77-4gmm-7cq8 (GitHub-reviewed advisory GHSA-wp77-4gmm-7cq8 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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