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← Actionable
Medium, CVSS 6.9amended

Use after free and segfault in shape inference functions

CVE-2021-37690GHSA-3hxh-8cp2-g4hg

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
6.9 · CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N · score source
EPSS
  • CVE-2021-37690: 0.16% estimated exploitation activity in the next 30 days; percentile 5.51 (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 When running shape functions, some functions (such as MutableHashTableShape) produce extra output information in the form of a ShapeAndType struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. ShapeRefiner is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. Patches We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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, as these are also affected and still in supported range. For more information Please consult our security…

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

    Amended from ghsa record GHSA-3hxh-8cp2-g4hg

  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-3hxh-8cp2-g4hg

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-2021-37690

Advisory: Use after free and segfault in shape inference functions
IDs: CVE-2021-37690, GHSA-3hxh-8cp2-g4hg
Record: https://patchnext.com/advisories/cve-2021-37690 (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: Medium (CVSS 6.9)
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-3hxh-8cp2-g4hg (OSV record GHSA-3hxh-8cp2-g4hg 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-3hxh-8cp2-g4hg (GitHub-reviewed advisory GHSA-3hxh-8cp2-g4hg 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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