GHSA-rcf8-g8jv-vg6p
TensorFlow has Floating Point Exception in AvgPoolGrad with XLA
Packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| PyPI | tensorflow | < 2.11.1 | 2.11.1 |
| PyPI | tensorflow-cpu | < 2.11.1 | 2.11.1 |
| PyPI | tensorflow-gpu | < 2.11.1 | 2.11.1 |
Details
As written in the advisory (Markdown source, shown as plain text).
### Impact
If the stride and window size are not positive for `tf.raw_ops.AvgPoolGrad`, it can give an FPE.
```python
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())
```
### Patches
We have patched the issue in GitHub commit [1295ae4dbb52fe06b19733b0257e2340d7b63b8d](https://github.com/tensorflow/tensorflow/commit/1295ae4dbb52fe06b19733b0257e2340d7b63b8d).
The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by r3pwnx of 360 AIVul Team
References
External links from the advisory. They lead to third-party sites.