CVE-2021-37674Medium· 5.5▾ SunlitIncomplete validation in `MaxPoolGrad`
▾ Sunlit zone — Low / medium · no exploitation signal
impact 30.3 · likelihood 0 · exploitation 0
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Exploit-prediction probability, daily snapshots since Jul 8.
Disclosure to exploitation, from the record and what we observed since indexing it.
Disclosed via OSV
Last analysed / modified upstream
0.2%
An attacker can trigger a denial of service via a segmentation fault in tf.raw_ops.MaxPoolGrad caused by missing validation:
import tensorflow as tf
tf.raw_ops.MaxPoolGrad(
orig_input = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
orig_output = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
grad = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
ksize = [1, 16, 16, 1],
strides = [1, 16, 18, 1],
padding = "EXPLICIT",
explicit_paddings = [0, 0, 14, 3, 15, 5, 0, 0])
The implementation misses some validation for the orig_input and orig_output tensors.
The fixes for CVE-2021-29579 were incomplete.
We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
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This vulnerability has been reported by Yakun Zhang of Baidu Security.
tensorflow < 2.3.4tensorflow >= 2.4.0, < 2.4.3tensorflow >= 2.5.0, < 2.5.1tensorflow-cpu < 2.3.4tensorflow-cpu >= 2.4.0, < 2.4.3tensorflow-cpu >= 2.5.0, < 2.5.1tensorflow-gpu < 2.3.4tensorflow-gpu >= 2.4.0, < 2.4.3tensorflow-gpu >= 2.5.0, < 2.5.1Upgrade to a patched release:
tensorflow 2.3.4tensorflow 2.4.3tensorflow 2.5.1tensorflow-cpu 2.3.4tensorflow-cpu 2.4.3tensorflow-cpu 2.5.1tensorflow-gpu 2.3.4tensorflow-gpu 2.4.3tensorflow-gpu 2.5.1Connected by shared product, vendor, weakness, or advisory.
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