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GHSA-gq2j-cr96-gvqx tensorflow

MEDIUMFix: tensorflow/tensorflow@717ca98

GHSA-gq2j-cr96-gvqx is a medium-severity (CVSS 4.8) Out-of-bounds Read vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.

`MirrorPadGrad` heap out of bounds read

Also known asBIT-tensorflow-2022-41895CVE-2022-41895PYSEC-2026-1000PYSEC-2026-3192PYSEC-2026-3332
Published
Nov 21, 2022
Updated
Jul 13, 2026
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known
Exploitation data as of Sep 21, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
0.5%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs40th percentile — riskier than 40% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

How urgent is this, really

GHSA-gq2j-cr96-gvqx plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 377,333 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

Real-World Exposure

9 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-cpu+1 more

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Impact

If MirrorPadGrad is given outsize input paddings, TensorFlow will give a heap OOB error.

import tensorflow as tf
tf.raw_ops.MirrorPadGrad(input=[1],
             paddings=[[0x77f00000,0xa000000]],
             mode = 'REFLECT')

Patches

We have patched the issue in GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Vul AI.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.8.4pip install --upgrade 'tensorflow==2.8.4'
🐍PyPItensorflow2.9.0&&< 2.9.32.9.3pip install --upgrade 'tensorflow==2.9.3'
🐍PyPItensorflow2.10.0&&< 2.10.12.10.1pip install --upgrade 'tensorflow==2.10.1'
🐍PyPItensorflow-cpuall versions2.8.4pip install --upgrade 'tensorflow-cpu==2.8.4'
🐍PyPItensorflow-gpuall versions2.8.4pip install --upgrade 'tensorflow-gpu==2.8.4'
🐍PyPItensorflow-cpu2.9.0&&< 2.9.32.9.3pip install --upgrade 'tensorflow-cpu==2.9.3'
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update tensorflow to 2.8.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-gq2j-cr96-gvqx is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-gq2j-cr96-gvqx can be triaged on real exposure rather than presence alone.

Tailored to GHSA-gq2j-cr96-gvqx. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact If [`MirrorPadGrad`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/mirror_pad_op.cc) is given outsize input `paddings`, TensorFlow will give a heap OOB error. ```python import tensorflow as tf tf.raw_ops.MirrorPadGrad(input=[1], paddings=[[0x77f00000,0xa000000]], mode = 'REFLECT') ``` ### Patches We have patched the issue in GitHub commit [717ca98d8c3bba348ff62281fdf38dcb5ea1ec92](https://github.com/tensorflow/tensorflow/commit/717ca98d8c3bba348ff62281fdf38dcb5ea1ec92). The fix will be included in TensorFlow 2.11. We
O3 Security · Impact-Aware SCA

Is GHSA-gq2j-cr96-gvqx in your dependencies?

O3 Security finds GHSA-gq2j-cr96-gvqx across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

GHSA-gq2j-cr96-gvqx: tensorflow | O3 Security