Headline
GHSA-jjm6-4vf7-cjh4: Integer overflow in `SpaceToBatchND`
Impact
The implementation of tf.raw_ops.SpaceToBatchND (in all backends such as XLA and handwritten kernels) is vulnerable to an integer overflow:
import tensorflow as tf
input = tf.constant(-3.5e+35, shape=[10,19,22], dtype=tf.float32)
block_shape = tf.constant(-1879048192, shape=[2], dtype=tf.int64)
paddings = tf.constant(0, shape=[2,2], dtype=tf.int32)
tf.raw_ops.SpaceToBatchND(input=input, block_shape=block_shape, paddings=paddings)
The result of this integer overflow is used to allocate the output tensor, hence we get a denial of service via a CHECK-failure (assertion failure), as in TFSA-2021-198.
Patches
We have patched the issue in GitHub commit acd56b8bcb72b163c834ae4f18469047b001fadf.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.
Impact
The implementation of tf.raw_ops.SpaceToBatchND (in all backends such as XLA and handwritten kernels) is vulnerable to an integer overflow:
import tensorflow as tf
input = tf.constant(-3.5e+35, shape=[10,19,22], dtype=tf.float32) block_shape = tf.constant(-1879048192, shape=[2], dtype=tf.int64) paddings = tf.constant(0, shape=[2,2], dtype=tf.int32) tf.raw_ops.SpaceToBatchND(input=input, block_shape=block_shape, paddings=paddings)
The result of this integer overflow is used to allocate the output tensor, hence we get a denial of service via a CHECK-failure (assertion failure), as in TFSA-2021-198.
Patches
We have patched the issue in GitHub commit acd56b8bcb72b163c834ae4f18469047b001fadf.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Neophytos Christou from Secure Systems Lab at Brown University.
References
- GHSA-jjm6-4vf7-cjh4
- https://nvd.nist.gov/vuln/detail/CVE-2022-29203
- tensorflow/tensorflow@acd56b8
- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md
- https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4
- https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2
- https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1
- https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0
Related news
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SpaceToBatchND` (in all backends such as XLA and handwritten kernels) is vulnerable to an integer overflow: The result of this integer overflow is used to allocate the output tensor, hence we get a denial of service via a `CHECK`-failure (assertion failure), as in TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid. In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.