GHSA-q5jv-m6qw-5g37 is a medium-severity (CVSS 5.9) CWE-369 vulnerability in tensorflow. O3 Security confirms whether GHSA-q5jv-m6qw-5g37 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
TensorFlow vulnerable to floating point exception in `Conv2D`
Real-World Exposure
tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 moreReal-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 Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack.
import tensorflow as tf
import numpy as np
with tf.device("CPU"): # also can be triggerred on GPU
input = np.ones([1, 0, 2, 1])
filter = np.ones([1, 1, 1, 1])
strides = ([1, 1, 1, 1])
padding = "EXPLICIT"
explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]
data_format = "NHWC"
res = tf.raw_ops.Conv2D(
input=input,
filter=filter,
strides=strides,
padding=padding,
explicit_paddings=explicit_paddings,
data_format=data_format,
)
Patches
We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9.
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 Jingyi Shi.
Affected Packages
| 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 dependencyDetect
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-q5jv-m6qw-5g37 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-q5jv-m6qw-5g37 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-q5jv-m6qw-5g37. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-q5jv-m6qw-5g37 in your dependencies?
O3 detects GHSA-q5jv-m6qw-5g37 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.