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High, CVSS 7.1amended

Integer overflows in Tensorflow

CVE-2022-23567GHSA-rrx2-r989-2c43

What to do

Vendor fix available. Upgrade each affected package to the fixed version on its release line (tensorflow 2.7.1, 2.6.3, 2.5.3; tensorflow-gpu 2.7.1, 2.6.3, 2.5.3; tensorflow-cpu 2.7.1, 2.6.3, 2.5.3), 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
    < 2.5.3>= 2.6.0, < 2.6.3>= 2.7.0, <= 2.7.0
    Fixed in
    2.5.3, 2.7.1, 2.6.3

  • PyPI tensorflow-gpu

    Affected
    < 2.5.3>= 2.6.0, < 2.6.3>= 2.7.0, <= 2.7.0
    Fixed in
    2.5.3, 2.7.1, 2.6.3

  • PyPI tensorflow-cpu

    Affected
    < 2.5.3>= 2.6.0, < 2.6.3>= 2.7.0, <= 2.7.0
    Fixed in
    2.5.3, 2.7.1, 2.6.3

Sources

Severity and exploitation
Exploitation
No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.
CVSS 4.0
7.1 · CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N · score source
EPSS
  • CVE-2022-23567: 1.11% estimated exploitation activity in the next 30 days; percentile 65.87 (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 implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or CHECK-fails when building new TensorShape objects (so, assert failures based denial of service): We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. The latter is an instance of TFSA-2021-198 (CVE-2021-41197) and is easily fixed by replacing a call to TensorShape constructor with a call to BuildTensorShape static helper factory. Patches We have patched the issue in GitHub commits 1b54cadd19391b60b6fcccd8d076426f7221d5e8 and e952a89b7026b98fe8cbe626514a93ed68b7c510. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. For more information Please consult our security guide for more…

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

    Amended from ghsa record GHSA-rrx2-r989-2c43

  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-rrx2-r989-2c43

Agent brief

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# Patch Next advisory brief: CVE-2022-23567

Advisory: Integer overflows in Tensorflow
IDs: CVE-2022-23567, GHSA-rrx2-r989-2c43
Record: https://patchnext.com/advisories/cve-2022-23567 (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 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 2.7.1, 2.6.3, 2.5.3; tensorflow-gpu 2.7.1, 2.6.3, 2.5.3; tensorflow-cpu 2.7.1, 2.6.3, 2.5.3. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI tensorflow: affected < 2.5.3; >= 2.6.0, < 2.6.3; >= 2.7.0, <= 2.7.0; fixed in 2.5.3, 2.7.1, 2.6.3
- PyPI tensorflow-gpu: affected < 2.5.3; >= 2.6.0, < 2.6.3; >= 2.7.0, <= 2.7.0; fixed in 2.5.3, 2.7.1, 2.6.3
- PyPI tensorflow-cpu: affected < 2.5.3; >= 2.6.0, < 2.6.3; >= 2.7.0, <= 2.7.0; fixed in 2.5.3, 2.7.1, 2.6.3

## 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-rrx2-r989-2c43 (OSV record GHSA-rrx2-r989-2c43 lists affected ranges: PyPI tensorflow >=0 =2.6.0 =2.7.0 =0 =2.6.0 =2.7.0 =0 =2.6.0 =2.7.0 <2.7.1 (fixed))
- Verified: https://github.com/advisories/GHSA-rrx2-r989-2c43 (GitHub-reviewed advisory GHSA-rrx2-r989-2c43 lists affected ranges: PyPI tensorflow >=0 =2.6.0 =2.7.0 =0 =2.6.0 =2.7.0 =0 =2.6.0 =2.7.0 <=2.7.0)

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