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GHSA-hx9q-2mx4-m4pg

Missing validation causes denial of service via `Conv3DBackpropFilterV2`

Source: GitHub Advisory Database (reviewed) Moderate CVE-2022-29204

Packages

EcosystemPackageAffectedFixed in
PyPI tensorflow < 2.6.4 2.6.4
PyPI tensorflow >= 2.7.0, < 2.7.2 2.7.2
PyPI tensorflow >= 2.8.0, < 2.8.1 2.8.1
PyPI tensorflow-cpu < 2.6.4 2.6.4
PyPI tensorflow-cpu >= 2.7.0, < 2.7.2 2.7.2
PyPI tensorflow-cpu >= 2.8.0, < 2.8.1 2.8.1
PyPI tensorflow-gpu < 2.6.4 2.6.4
PyPI tensorflow-gpu >= 2.7.0, < 2.7.2 2.7.2
PyPI tensorflow-gpu >= 2.8.0, < 2.8.1 2.8.1

Details

As written in the advisory (Markdown source, shown as plain text).

### Impact The implementation of [`tf.raw_ops.UnsortedSegmentJoin`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/unsorted_segment_join_op.cc#L83-L148) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python 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: ```cc 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](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md). ### Patches We have patched the issue in GitHub commit [84563f265f28b3c36a15335c8b005d405260e943](https://github.com/tensorflow/tensorflow/commit/84563f265f28b3c36a15335c8b005d405260e943) and GitHub commit [20cb18724b0bf6c09071a3f53434c4eec53cc147](https://github.com/tensorflow/tensorflow/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](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 externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/55305).

References

External links from the advisory. They lead to third-party sites.