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CVE-2022-35934: Fix failed check in tf.reshape. · tensorflow/tensorflow@61f0f9b

TensorFlow is an open source platform for machine learning. The implementation of tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This issue has been patched in GitHub commit 61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

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#mac#dos#git

@@ -45,6 +45,11 @@ class ReshapeOp : public OpKernel { TensorShapeUtils::IsScalar(sizes.shape())), errors::InvalidArgument("sizes input must be 1-D, not ", sizes.shape().DebugString())); OP_REQUIRES( context, sizes.NumElements() < TensorShape::MaxDimensions(), errors::InvalidArgument("too many dimensions: must be < ", TensorShape::MaxDimensions(), ", but received ", sizes.NumElements()));
// Compute the output shape. Determine product of specified // dimensions, and find the index of the unspecified one.

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GHSA-f4w6-h4f5-wx45: TensorFlow vulnerable to `CHECK` failure in tf.reshape via overflows

### Impact The implementation of tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor: ```python import tensorflow as tf tf.reshape(tensor=[[1]],shape=tf.constant([0 for i in range(255)], dtype=tf.int64)) ``` This is another instance of [TFSA-2021-198](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md) (https://github.com/advisories/GHSA-prcg-wp5q-rv7p). ### Patches We have patched the issue in GitHub commit [61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555](https://github.com/tensorflow/tensorflow/commit/61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/te...

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