CVE-2021-37663High· 7.8▾ TwilightIncomplete validation in `QuantizeV2`
▾ Twilight zone — High severity, or a signal on a lesser flaw
impact 42.9 · 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%
Due to incomplete validation in tf.raw_ops.QuantizeV2, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:
import tensorflow as tf
tf.raw_ops.QuantizeV2(
input=[1,2,3],
min_range=[1,2],
max_range=[],
T=tf.qint32,
mode='SCALED',
round_mode='HALF_AWAY_FROM_ZERO',
narrow_range=False,
axis=1,
ensure_minimum_range=3)
The implementation has some validation but does not check that min_range and max_range both have the same non-zero number of elements. If axis is provided (i.e., not -1), then validation should check that it is a value in range for the rank of input tensor and then the lengths of min_range and max_range inputs match the axis dimension of the input tensor.
We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708.
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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