CVE-2021-41199Medium· 5.5▾ SunlitOverflow/crash in `tf.image.resize` when size is large
▾ 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%
0.2% → 0.2%
If tf.image.resize is called with a large input argument then the TensorFlow process will crash due to a CHECK-failure caused by an overflow.
import tensorflow as tf
import numpy as np
tf.keras.layers.UpSampling2D(
size=1610637938,
data_format='channels_first',
interpolation='bilinear')(np.ones((5,1,1,1)))
The number of elements in the output tensor is too much for the int64_t type and the overflow is detected via a CHECK statement. This aborts the process.
We have patched the issue in GitHub commit e5272d4204ff5b46136a1ef1204fc00597e21837 (merging #51497).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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This vulnerability has been reported externally via a GitHub issue.
tensorflow >= 2.6.0, < 2.6.1tensorflow >= 2.5.0, < 2.5.2tensorflow < 2.4.4tensorflow-cpu >= 2.6.0, < 2.6.1tensorflow-cpu >= 2.5.0, < 2.5.2tensorflow-cpu < 2.4.4tensorflow-gpu >= 2.6.0, < 2.6.1tensorflow-gpu >= 2.5.0, < 2.5.2tensorflow-gpu < 2.4.4Upgrade to a patched release:
tensorflow 2.6.1tensorflow 2.5.2tensorflow 2.4.4tensorflow-cpu 2.6.1tensorflow-cpu 2.5.2tensorflow-cpu 2.4.4tensorflow-gpu 2.6.1tensorflow-gpu 2.5.2tensorflow-gpu 2.4.4Connected by shared product, vendor, weakness, or advisory.
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