CVE-2021-37635High· 7.3▾ TwilightHeap out of bounds access in sparse reduction operations
▾ Twilight zone — High severity, or a signal on a lesser flaw
impact 40.2 · 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%
The implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data:
import tensorflow as tf
x = tf.SparseTensor(
indices=[[773, 773, 773], [773, 773, 773]],
values=[1, 1],
dense_shape=[337, 337, 337])
tf.sparse.reduce_sum(x, 1)
The implementation fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor.
We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750.
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 members of the Aivul Team from Qihoo 360.
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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