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LOW severity

GHSA-5gqf-456p-4836

LOWFix: tensorflow/tensorflow@f7cc875

GHSA-5gqf-456p-4836 is a low-severity (CVSS 2.5) NULL Pointer Dereference vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-5gqf-456p-4836 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Reference binding to nullptr in `SdcaOptimizer`

Also known asBIT-tensorflow-2021-29572CVE-2021-29572PYSEC-2021-209PYSEC-2021-500PYSEC-2021-698
Published
May 21, 2021
Updated
Jul 8, 2026
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known
Exploitation data as of Jul 8, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

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.SdcaOptimizer triggers undefined behavior due to dereferencing a null pointer:

import tensorflow as tf

sparse_example_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_feature_indices = [tf.constant([], shape=[0, 0, 0, 0], dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_feature_values = []

dense_features = []
dense_weights = []

example_weights = tf.constant((0.0), dtype=tf.float32)
example_labels = tf.constant((0.0), dtype=tf.float32)

sparse_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_weights = [tf.constant((0.0), dtype=tf.float32), tf.constant((0.0), dtype=tf.float32)]
  
example_state_data = tf.constant([0.0, 0.0, 0.0, 0.0], shape=[1, 4], dtype=tf.float32)
  
tf.raw_ops.SdcaOptimizer(
  sparse_example_indices=sparse_example_indices,
  sparse_feature_indices=sparse_feature_indices,
  sparse_feature_values=sparse_feature_values, dense_features=dense_features,
  example_weights=example_weights, example_labels=example_labels, 
  sparse_indices=sparse_indices, sparse_weights=sparse_weights, 
  dense_weights=dense_weights, example_state_data=example_state_data,
  loss_type="logistic_loss", l1=0.0, l2=0.0, num_loss_partitions=1,
  num_inner_iterations=1, adaptative=False)

The implementation does not validate that the user supplied arguments satisfy all constraints expected by the op.

Patches

We have patched the issue in GitHub commit f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb.

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 versions2.1.4
🐍PyPItensorflow≥ 2.2.0&&< 2.2.32.2.3
🐍PyPItensorflow≥ 2.3.0&&< 2.3.32.3.3
🐍PyPItensorflow≥ 2.4.0&&< 2.4.22.4.2
🐍PyPItensorflow-cpuall versions2.1.4
🐍PyPItensorflow-cpu≥ 2.2.0&&< 2.2.32.2.3
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.

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. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  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-5gqf-456p-4836 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 pinpoints whether GHSA-5gqf-456p-4836 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-5gqf-456p-4836. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer: ```python import tensorflow as tf sparse_example_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)] sparse_feature_indices = [tf.constant([], shape=[0, 0, 0, 0], dtype=tf.int64), tf.constant((0), dtype=tf.int64)] sparse_feature_values = [] dense_features = [] dense_weights = [] example_weights = tf.constant((0.0), dtype=tf.float32) example_labels = tf.constant((0.0), dtype=tf.float32) sparse_indices = [tf.constant((0), dtype=tf.int64), tf
O3 Security · Impact-Aware SCA

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