Impact
An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.DenseCountSparseOutput:
import tensorflow as tf
values = tf.constant([], shape=[0, 0], dtype=tf.int64)
weights = tf.constant([])
tf.raw_ops.DenseCountSparseOutput(
values=values, weights=weights,
minlength=-1, maxlength=58, binary_output=True)
This is because the implementation computes a divisor value from user data but does not check that the result is 0 before doing the division:
int num_batch_elements = 1;
for (int i = 0; i < num_batch_dimensions; ++i) {
num_batch_elements *= data.shape().dim_size(i);
}
int num_value_elements = data.shape().num_elements() / num_batch_elements;
Since data is given by the values argument, num_batch_elements is 0.
Patches
We have patched the issue in GitHub commit da5ff2daf618591f64b2b62d9d9803951b945e9f.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected.
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.
Impact
An attacker can cause a denial of service via a FPE runtime error in
tf.raw_ops.DenseCountSparseOutput:This is because the implementation computes a divisor value from user data but does not check that the result is 0 before doing the division:
Since
datais given by thevaluesargument,num_batch_elementsis 0.Patches
We have patched the issue in GitHub commit da5ff2daf618591f64b2b62d9d9803951b945e9f.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected.
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.