How severe is GHSA-43q8-3fv7-pr5x?

GHSA-43q8-3fv7-pr5x has a CVSS score of 7/10, rated HIGH. Immediate patching is strongly recommended.

Which packages are affected by GHSA-43q8-3fv7-pr5x?

GHSA-43q8-3fv7-pr5x affects the following packages: tensorflow (PyPI), tensorflow (PyPI), tensorflow (PyPI), tensorflow-cpu (PyPI), tensorflow-cpu (PyPI), tensorflow-cpu (PyPI), tensorflow-gpu (PyPI), tensorflow-gpu (PyPI), and 1 more. Ecosystems affected: PyPI.

How do I fix GHSA-43q8-3fv7-pr5x?

Update tensorflow to 2.5.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-43q8-3fv7-pr5x is resolved across your whole dependency graph.

How do I detect GHSA-43q8-3fv7-pr5x in my PyPI dependencies?

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.

How do I mitigate GHSA-43q8-3fv7-pr5x if there is no patch (or I can't update yet)?

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.

How does O3 Security protect against GHSA-43q8-3fv7-pr5x?

O3 pinpoints whether GHSA-43q8-3fv7-pr5x is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Is GHSA-43q8-3fv7-pr5x actively exploited in the wild?

No public exploit code has been indexed for GHSA-43q8-3fv7-pr5x yet. This does not mean the vulnerability cannot be exploited — absence of public exploits does not imply safety. Apply the recommended fix and use O3 Security to monitor your exposure.

When was GHSA-43q8-3fv7-pr5x published, and has it been updated?

GHSA-43q8-3fv7-pr5x was published on February 9, 2022 and was last updated on December 5, 2024. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

🐍 PyPI

GHSA-43q8-3fv7-pr5x

HIGH

Improper Validation of Integrity Check Value in TensorFlow

Published

Feb 9, 2022

Updated

Dec 5, 2024

Affected

9 pkgs

Patched

9 / 9

Exploits

None indexed

Blast Radius

9 pkgs affected

🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+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

The implementation of tf.sparse.split does not fully validate the input arguments. Hence, a malicious user can trigger a denial of service via a segfault or a heap OOB read:

import tensorflow as tf
data = tf.random.uniform([1, 32, 32], dtype=tf.float32)
axis = [1, 2]
x = tf.sparse.from_dense(data)
result = tf.sparse.split(x,3, axis=axis)

The code assumes axis is a scalar. This is another instance of TFSA-2021-190 (CVE-2021-41206).

Patches

We have patched the issue in GitHub commit 61bf91e768173b001d56923600b40d9a95a04ad5 (merging #53695).

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 information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported externally via a GitHub issue.

Affected Packages

9 total 9 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`tensorflow`	all versions	2.5.3
🐍PyPI	`tensorflow`	≥ 2.6.0&&< 2.6.3	2.6.3
🐍PyPI	`tensorflow`	≥ 2.7.0&&< 2.7.1	2.7.1
🐍PyPI	`tensorflow-cpu`	all versions	2.5.3
🐍PyPI	`tensorflow-cpu`	≥ 2.6.0&&< 2.6.3	2.6.3
🐍PyPI	`tensorflow-cpu`	≥ 2.7.0&&< 2.7.1	2.7.1

Detection & mitigation playbook

Open-source dependency

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.
Fix
Update tensorflow to 2.5.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-43q8-3fv7-pr5x is resolved across your whole dependency graph.
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.
How O3 protects you
O3 pinpoints whether GHSA-43q8-3fv7-pr5x 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-43q8-3fv7-pr5x. 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.sparse.split`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_split_op.cc#L26-L102) does not fully validate the input arguments. Hence, a malicious user can trigger a denial of service via a segfault or a heap OOB read: ```python import tensorflow as tf data = tf.random.uniform([1, 32, 32], dtype=tf.float32) axis = [1, 2] x = tf.sparse.from_dense(data) result = tf.sparse.split(x,3, axis=axis) ``` The code assumes `axis` is a scalar. This is another instance of [TFSA-2021-190](https://g

O3 Security · Impact-Aware SCA

Is GHSA-43q8-3fv7-pr5x in your dependencies?

O3 detects GHSA-43q8-3fv7-pr5x across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

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