GHSA-2r2f-g8mw-9gvr Segfault and OOB write due to incomplete validation in `EditDistance` in TensorFlow

• PyPI tensorflow: < 2.6.4 → fixed in 2.6.4 • PyPI tensorflow: >= 2.7.0, < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow-cpu: < 2.6.4 → fixed in 2.6.4 • PyPI tensorflow-cpu: >= 2.7.0, < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow-cpu: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow-gpu: < 2.6.4 → fixed in 2.6.4 • PyPI tensorflow-gpu: >= 2.7.0, < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow-gpu: >= 2.8.0, < 2.8.1 → fixed in 2.8.1

Impact The implementation of `tf.raw_ops.EditDistance` has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service: ```python import tensorflow as tf hypothesis_indices = tf.constant(-1250999896764, shape=[3, 3], dtype=tf.int64) hypothesis_values = tf.constant(0, shape=[3], dtype=tf.int64) hypothesis_shape = tf.constant(0, shape=[3], dtype=tf.int64) truth_indices = tf.constant(-1250999896764, shape=[3, 3], dtype=tf.int64) truth_values = tf.constant(2, shape=[3], dtype=tf.int64) truth_shape = tf.constant(2, shape=[3], dtype=tf.int64) tf.raw_ops.EditDistance( hypothesis_indices=hypothesis_indices, hypothesis_values=hypothesis_values, hypothesis_shape=hypothesis_shape, truth_indices=truth_indices, truth_values=truth_values, truth_shape=truth_shape) ``` In multiple places throughout the code, we are computing an index for a write operation: ```cc if (g_truth == g_hypothesis) { auto loc = std::inner_product(g_truth.begin(), g_truth.end(), output_strides.begin(), int64_t{0}); OP_REQUIRES( ctx, loc (truth_seq, hypothesis_seq, cmp); // ... } ``` However, the existing validation only checks against the upper bound of the array. Hence, it is possible to write before the array by massaging the input to generate negative values for `loc`. Patches We have patched the issue in GitHub commit 30721cf564cb029d34535446d6a5a6357bebc8e7. 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.