GHSA-MW6J-HH29-H379

CHECK failure in depthwise ops via overflows

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Attribution

This vulnerability has been reported by Neophytos Christou from Secure Systems Lab at Brown University.

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).

An arithmetic operation produces a value that exceeds the integer type's maximum, causing it to wrap to an unexpected small value. Typical impact: incorrect size calculations leading to heap overflows or logic errors.

Kodem intelligence

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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.

1. What is GHSA-MW6J-HH29-H379? GHSA-MW6J-HH29-H379 is a medium-severity integer overflow or wraparound vulnerability in tensorflow (pip), affecting versions < 2.6.4. It is fixed in 2.6.4, 2.7.2, 2.8.1. An arithmetic operation produces a value that exceeds the integer type's maximum, causing it to wrap to an unexpected small value.
2. Which packages are affected by GHSA-MW6J-HH29-H379?
   • tensorflow (pip) (versions < 2.6.4)
   • tensorflow-cpu (pip) (versions < 2.6.4)
   • tensorflow-gpu (pip) (versions < 2.6.4)
3. Is there a fix for GHSA-MW6J-HH29-H379? Yes. GHSA-MW6J-HH29-H379 is fixed in 2.6.4, 2.7.2, 2.8.1. Upgrade to this version or later.
4. Is GHSA-MW6J-HH29-H379 exploitable, and should I be worried? Whether GHSA-MW6J-HH29-H379 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
5. What actually determines whether GHSA-MW6J-HH29-H379 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
6. How do I fix GHSA-MW6J-HH29-H379?
   • Upgrade tensorflow to 2.6.4 or later
   • Upgrade tensorflow to 2.7.2 or later
   • Upgrade tensorflow to 2.8.1 or later
   • Upgrade tensorflow-cpu to 2.6.4 or later
   • Upgrade tensorflow-cpu to 2.7.2 or later
   • Upgrade tensorflow-cpu to 2.8.1 or later
   • Upgrade tensorflow-gpu to 2.6.4 or later
   • Upgrade tensorflow-gpu to 2.7.2 or later
   • Upgrade tensorflow-gpu to 2.8.1 or later

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