title: Memory exhaustion in Tensorflow
description: Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger a denial of service attack by allocating too much memory. This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
metrics:
cvssV3_1: [
attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: LOW
baseScore: 4.3
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L
version: 3.1
]