How severe is GHSA-xf83-q765-xm6m?

No CVSS score has been assigned to GHSA-xf83-q765-xm6m yet. Review the advisory details and affected package list to assess your exposure.

Which packages are affected by GHSA-xf83-q765-xm6m?

GHSA-xf83-q765-xm6m 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-xf83-q765-xm6m?

Update tensorflow to 2.8.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-xf83-q765-xm6m is resolved across your whole dependency graph.

How do I detect GHSA-xf83-q765-xm6m in my PyPI dependencies?

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.

How do I mitigate GHSA-xf83-q765-xm6m 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.

Is GHSA-xf83-q765-xm6m actively exploited in the wild?

No public exploit code has been indexed for GHSA-xf83-q765-xm6m 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-xf83-q765-xm6m published, and has it been updated?

GHSA-xf83-q765-xm6m was published on November 21, 2022 and was last updated on December 4, 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

Not in CISA KEV

`CHECK` fail in `TensorListScatter` and `TensorListScatterV2` in eager modeGHSA-xf83-q765-xm6m

GHSA-xf83-q765-xm6m is a security vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.

Published

Nov 21, 2022

Updated

Dec 4, 2024

Affected

9 pkgs

Patched

9 / 9

Exploits

None indexed

Exploitation data as of Dec 4, 2024 · OSV.dev, FIRST.org (EPSS)

Real-World Exposure

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

Another instance of CVE-2022-35991, where TensorListScatter and TensorListScatterV2 crash via non scalar inputs inelement_shape, was found in eager mode and fixed.

import tensorflow as tf
arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None)
arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)
arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)
arg_3=''
tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1, 
element_shape=arg_2, name=arg_3)

Patches

We have patched the issue in GitHub commit bf9932fc907aff0e9e8cccf769e8b00d30fd81a1.

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 Rattankul

Affected Packages

9 total 9 fixed

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

Detection & mitigation playbook

Open-source dependency

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.
Fix
Update tensorflow to 2.8.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-xf83-q765-xm6m 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.

Frequently Asked Questions

### Impact Another instance of CVE-2022-35991, where `TensorListScatter` and `TensorListScatterV2` crash via non scalar inputs in`element_shape`, was found in eager mode and fixed. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None) arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) arg_3='' tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1, element_shape=arg_2, name=arg_3) ``` ### Patches We have patched the issue in GitHub commit [bf9932fc9

O3 Security · Impact-Aware SCA

Is GHSA-xf83-q765-xm6m in your dependencies?

Find it across PyPI, including transitive dependencies.

Scan my dependencies How O3 SCA works