Headline
GHSA-gq2j-cr96-gvqx: `MirrorPadGrad` heap out of bounds read
Impact
If MirrorPadGrad is given outsize input paddings, TensorFlow will give a heap OOB error.
import tensorflow as tf
tf.raw_ops.MirrorPadGrad(input=[1],
paddings=[[0x77f00000,0xa000000]],
mode = 'REFLECT')
Patches
We have patched the issue in GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92.
The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Vul AI.
Impact
If MirrorPadGrad is given outsize input paddings, TensorFlow will give a heap OOB error.
import tensorflow as tf tf.raw_ops.MirrorPadGrad(input=[1], paddings=[[0x77f00000,0xa000000]], mode = ‘REFLECT’)
Patches
We have patched the issue in GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92.
The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Vul AI.
References
- GHSA-gq2j-cr96-gvqx
- https://nvd.nist.gov/vuln/detail/CVE-2022-41895
- tensorflow/tensorflow@717ca98
- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/mirror_pad_op.cc
Related news
TensorFlow is an open source platform for machine learning. If `MirrorPadGrad` is given outsize input `paddings`, TensorFlow will give a heap OOB error. We have patched the issue in GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.