Headline
GHSA-hx9q-2mx4-m4pg: Missing validation causes denial of service via `Conv3DBackpropFilterV2`
Impact
The implementation of tf.raw_ops.UnsortedSegmentJoin 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.strings.unsorted_segment_join(
inputs=['123'],
segment_ids=[0],
num_segments=-1)
The code assumes num_segments is a positive scalar but there is no validation:
const Tensor& num_segments_tensor = context->input(2);
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();
// ...
Tensor* output_tensor = nullptr;
TensorShape output_shape =
GetOutputShape(input_shape, segment_id_shape, num_segments);
Since this value is used to allocate the output tensor, a negative value would result in a CHECK-failure (assertion failure), as per TFSA-2021-198.
Patches
We have patched the issue in GitHub commit 84563f265f28b3c36a15335c8b005d405260e943 and GitHub commit 20cb18724b0bf6c09071a3f53434c4eec53cc147.
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 externally via a GitHub issue.
Impact
The implementation of tf.raw_ops.UnsortedSegmentJoin 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.strings.unsorted_segment_join( inputs=[‘123’], segment_ids=[0], num_segments=-1)
The code assumes num_segments is a positive scalar but there is no validation:
const Tensor& num_segments_tensor = context->input(2); auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()(); // … Tensor* output_tensor = nullptr; TensorShape output_shape = GetOutputShape(input_shape, segment_id_shape, num_segments);
Since this value is used to allocate the output tensor, a negative value would result in a CHECK-failure (assertion failure), as per TFSA-2021-198.
Patches
We have patched the issue in GitHub commit 84563f265f28b3c36a15335c8b005d405260e943 and GitHub commit 20cb18724b0bf6c09071a3f53434c4eec53cc147.
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 externally via a GitHub issue.
References
- GHSA-hx9q-2mx4-m4pg
- https://nvd.nist.gov/vuln/detail/CVE-2022-29204
- tensorflow/tensorflow@20cb187
- tensorflow/tensorflow@84563f2
- https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/unsorted_segment_join_op.cc#L83-L14
- 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.UnsortedSegmentJoin` 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 assumes `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per 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.