CVE-2023-25669High· 7.5▾ TwilightTensorFlow has Floating Point Exception in AvgPoolGrad with XLA
▾ Twilight zone — High severity, or a signal on a lesser flaw
impact 41.3 · 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
0.4%
0.4% → 0.4%
Last analysed / modified upstream
If the stride and window size are not positive for tf.raw_ops.AvgPoolGrad, it can give an FPE.
import tensorflow as tf
import numpy as np
@tf.function(jit_compile=True)
def test():
y = tf.raw_ops.AvgPoolGrad(orig_input_shape=[1,0,0,0], grad=[[[[0.39117979]]]], ksize=[1,0,0,0], strides=[1,0,0,0], padding="SAME", data_format="NCHW")
return y
print(test())
We have patched the issue in GitHub commit 1295ae4dbb52fe06b19733b0257e2340d7b63b8d.
The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.
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This vulnerability has been reported by r3pwnx of 360 AIVul Team
tensorflow < 2.11.1tensorflow-cpu < 2.11.1tensorflow-gpu < 2.11.1Upgrade to a patched release:
tensorflow 2.11.1tensorflow-cpu 2.11.1tensorflow-gpu 2.11.1Connected by shared product, vendor, weakness, or advisory.
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