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GHSA-gv26-jpj9-c8gq

MEDIUM

Incomplete validation in `SparseSparseMinimum`

Also known asBIT-tensorflow-2021-29607CVE-2021-29607PYSEC-2021-244PYSEC-2021-535PYSEC-2021-733
Published
Mar 18, 2022
Updated
Mar 13, 2026
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk14th percentile+0.21%
0.00%0.24%0.49%0.73%0.0%0.2%Dec 25Apr 26Jun 26

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.

Blast Radius

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

Incomplete validation in SparseAdd results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data:

import tensorflow as tf

a_indices = tf.ones([45, 92], dtype=tf.int64)
a_values = tf.ones([45], dtype=tf.int64)
a_shape = tf.ones([1], dtype=tf.int64)
b_indices = tf.ones([1, 1], dtype=tf.int64)
b_values = tf.ones([1], dtype=tf.int64)
b_shape = tf.ones([1], dtype=tf.int64)
                    
tf.raw_ops.SparseSparseMinimum(a_indices=a_indices,
    a_values=a_values,
    a_shape=a_shape,
    b_indices=b_indices,
    b_values=b_values,
    b_shape=b_shape)

The implementation has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of *_indices matches the size of corresponding *_shape. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation.

Patches

We have patched the issue in GitHub commit ba6822bd7b7324ba201a28b2f278c29a98edbef2 followed by GitHub commit f6fde895ef9c77d848061c0517f19d0ec2682f3a.

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. I…

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-gv26-jpj9-c8gq 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-gv26-jpj9-c8gq 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-gv26-jpj9-c8gq. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data: ```python import tensorflow as tf a_indices = tf.ones([45, 92], dtype=tf.int64) a_values = tf.ones([45], dtype=tf.int64) a_shape = tf.ones([1], dtype=tf.int64) b_indices = tf.ones([1, 1], dtype=tf.int64) b_values = tf.ones([1], dtype=tf.int64) b_shape = tf.ones([1], dtype=tf.int64) tf.raw_ops.SparseSparseMinimum(a_indices=a_indices, a_values=a_values, a_shape=a_shape,
O3 Security · Impact-Aware SCA

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