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Undefined behavior in `MaxPool3DGradGrad`GHSA-828x-qc2p-wprq

LOWFix: tensorflow/tensorflow@a3d9f9b

GHSA-828x-qc2p-wprq is a low-severity (CVSS 2.5) NULL Pointer Dereference 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.

Also known asBIT-tensorflow-2021-29574CVE-2021-29574PYSEC-2021-211PYSEC-2021-502PYSEC-2021-700
Published
Updated
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known
Exploitation data as of Oct 10, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs9th percentile — riskier than 9% of all scored CVEsHighest risk
0.00%0.23%0.47%0.70%0.0%0.2%Apr 26Aug 26Oct 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

GHSA-828x-qc2p-wprq by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 385,738 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

12 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+4 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

The implementation of tf.raw_ops.MaxPool3DGradGrad exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors:

import tensorflow as tf

orig_input = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"

tf.raw_ops.MaxPool3DGradGrad(
    orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
    strides=strides, padding=padding)

The implementation fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer.

Patches

We have patched the issue in GitHub commit a3d9f9be9ac2296615644061b40cefcee341dcc4.

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 security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

Affected Packages

12 total 12 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.1.4pip install --upgrade 'tensorflow==2.1.4'
🐍PyPItensorflow≥ 2.2.0&&< 2.2.32.2.3pip install --upgrade 'tensorflow==2.2.3'
🐍PyPItensorflow≥ 2.3.0&&< 2.3.32.3.3pip install --upgrade 'tensorflow==2.3.3'
🐍PyPItensorflow≥ 2.4.0&&< 2.4.22.4.2pip install --upgrade 'tensorflow==2.4.2'
🐍PyPItensorflow-cpuall versions2.1.4pip install --upgrade 'tensorflow-cpu==2.1.4'
🐍PyPItensorflow-cpu≥ 2.2.0&&< 2.2.32.2.3pip install --upgrade 'tensorflow-cpu==2.2.3'

Affected Products

1 product · 4 configurations
Application
tensorflowgoogle
≥ 2.4.0 && < 2.4.2
range
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.

TensorFlow is an end-to-end open source platform for machine learning. T…

github.comNVDMay 2021

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 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-828x-qc2p-wprq 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.

Frequently Asked Questions

### Impact The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors: ```python import tensorflow as tf orig_input = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32) orig_output = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32) grad = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32) ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME" tf.raw_ops.MaxPool3DGradGrad( orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize, strides=str
O3 Security · Impact-Aware SCA

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Undefined behavior in `MaxPool3DGradGrad` (Low 2.5)