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GHSA-mw6j-hh29-h379 — tensorflow

Fix: tensorflow/tensorflow@3796cc4

GHSA-mw6j-hh29-h379 is a remote code execution vulnerability in tensorflow. A fix is available for tensorflow — see the affected versions and patch details below.

`CHECK` failure in depthwise ops via overflows

Published
May 25, 2022
Updated
Dec 7, 2024
Affected
9 pkgs
Patched
9 / 9
Exploits
None indexed
Exploitation data as of Dec 7, 2024 · OSV.dev, FIRST.org (EPSS)

Real-World Exposure

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 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

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.6.4pip install --upgrade 'tensorflow==2.6.4'
🐍PyPItensorflow≥ 2.7.0&&< 2.7.22.7.2pip install --upgrade 'tensorflow==2.7.2'
🐍PyPItensorflow≥ 2.8.0&&< 2.8.12.8.1pip install --upgrade 'tensorflow==2.8.1'
🐍PyPItensorflow-cpuall versions2.6.4pip install --upgrade 'tensorflow-cpu==2.6.4'
🐍PyPItensorflow-cpu≥ 2.7.0&&< 2.7.22.7.2pip install --upgrade 'tensorflow-cpu==2.7.2'
🐍PyPItensorflow-cpu≥ 2.8.0&&< 2.8.12.8.1pip install --upgrade 'tensorflow-cpu==2.8.1'

Detection & mitigation playbook

Open-source dependency

  1. 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.

  2. 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.

  3. 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.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-mw6j-hh29-h379 can be triaged on real exposure rather than presence alone.

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

### 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: ```python 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
O3 Security · Impact-Aware SCA

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GHSA-mw6j-hh29-h379: tensorflow DoS | O3 Security