How severe is GHSA-jq6x-99hj-q636?

GHSA-jq6x-99hj-q636 has a CVSS score of 4.8/10, rated MEDIUM. Review your exposure and patch according to your risk tolerance.

Which packages are affected by GHSA-jq6x-99hj-q636?

GHSA-jq6x-99hj-q636 affects the following packages: tensorflow (PyPI), tensorflow (PyPI), tensorflow (PyPI), tensorflow-cpu (PyPI), tensorflow-gpu (PyPI), tensorflow-cpu (PyPI), tensorflow-gpu (PyPI), tensorflow-cpu (PyPI), and 1 more. Ecosystems affected: PyPI.

How do I fix GHSA-jq6x-99hj-q636?

Update tensorflow to 2.8.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-jq6x-99hj-q636 is resolved across your whole dependency graph.

How do I detect GHSA-jq6x-99hj-q636 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-jq6x-99hj-q636 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-jq6x-99hj-q636?

O3 pinpoints whether GHSA-jq6x-99hj-q636 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-jq6x-99hj-q636 actively exploited in the wild?

Yes. There are 1 known exploit references for GHSA-jq6x-99hj-q636, including 1 in-the-wild exploitation observed on GitHub and other sources. Treat this as actively exploitable and prioritize patching immediately. All exploit code should only be run in an isolated sandbox environment for research or authorized testing — never against production systems without explicit written authorization.

What is the EPSS score for GHSA-jq6x-99hj-q636?

GHSA-jq6x-99hj-q636 has an EPSS (Exploit Prediction Scoring System) score of 0.3%, placing it in the 25th percentile of all CVEs. EPSS is maintained by FIRST.org and estimates the probability that a vulnerability will be exploited in the wild within the next 30 days. This score indicates relatively lower exploitation probability, though the CVSS severity should still guide your patching priority.

When was GHSA-jq6x-99hj-q636 published, and has it been updated?

GHSA-jq6x-99hj-q636 was published on November 21, 2022 and was last updated on December 6, 2023. 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-jq6x-99hj-q636

MEDIUM

Seg fault in `ndarray_tensor_bridge` due to zero and large inputs

Also known asBIT-tensorflow-2022-41884CVE-2022-41884

Published

Nov 21, 2022

Updated

Dec 6, 2023

Affected

9 pkgs

Patched

9 / 9

Exploits

1 known

EPSS Exploitation Probability

via FIRST.org ↗

0.3%probability of exploitation in next 30 days

Lower Risk25th percentile+0.20%

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

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 a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. E.g. the following raises an error:

np.ones((0, 2**31, 2**31))

An example of a proof of concept:

import numpy as np
import tensorflow as tf

input_val = tf.constant([1])
shape_val = np.array([i for i in range(21)])

tf.broadcast_to(input=input_val,shape=shape_val)

The return value of PyArray_SimpleNewFromData, which returns null on such shapes, is not checked.

Patches

We have patched the issue in GitHub commit 2b56169c16e375c521a3bc8ea658811cc0793784.

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 Pattarakrit Rattanukul.

Affected Packages

9 total 9 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`tensorflow`	all versions	2.8.4
🐍PyPI	`tensorflow`	≥ 2.9.0&&< 2.9.3	2.9.3
🐍PyPI	`tensorflow`	≥ 2.10.0&&< 2.10.1	2.10.1
🐍PyPI	`tensorflow-cpu`	all versions	2.8.4
🐍PyPI	`tensorflow-gpu`	all versions	2.8.4
🐍PyPI	`tensorflow-cpu`	≥ 2.9.0&&< 2.9.3	2.9.3

Exploits & PoCs

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 open source platform for machine learning. If a numpy a…

github.comNVDNov 2022

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 2.8.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-jq6x-99hj-q636 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-jq6x-99hj-q636 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-jq6x-99hj-q636. 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 a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. E.g. the following raises an error: ```python np.ones((0, 2**31, 2**31)) ``` An example of a proof of concept: ```python import numpy as np import tensorflow as tf input_val = tf.constant([1]) shape_val = np.array([i for i in range(21)]) tf.broadcast_to(input=input_val,shape=shape_val) ``` The return value of `PyArray_SimpleNewFromData`, which returns null on such shapes, is not checked. ### Patches We have patched the issue in GitHub commit [2b561

O3 Security · Impact-Aware SCA

Is GHSA-jq6x-99hj-q636 in your dependencies?

O3 detects GHSA-jq6x-99hj-q636 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

Scan my dependencies How O3 SCA works