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Advisory Weakness Curation

CVE-2022-29204

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Advisory Summaries

github_osv/GHSA-hx9q-2mx4-m4pg

Missing validation causes denial of service via `Conv3DBackpropFilterV2` ### 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.

gitlab/pypi/tensorflow-cpu/CVE-2022-29204

Missing validation causes denial of service via `Conv3DBackpropFilterV2` 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.

gitlab/pypi/tensorflow/CVE-2022-29204

Missing validation causes denial of service via `Conv3DBackpropFilterV2` 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.

gitlab/pypi/tensorflow-gpu/CVE-2022-29204

Missing validation causes denial of service via `Conv3DBackpropFilterV2` 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.