CVE-2022-23567: Integer overflows in Tensorflow
Impact The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or CHECK-fails when building new TensorShape objects (so, assert failures based denial of service):
python import tensorflow as tf import numpy as np
tf.rawops.SparseDenseCwiseDiv( spindices=np.array([[9]]), spvalues=np.array([5]), spshape=np.array([92233720368., 92233720368]), dense=np.array([4]))
We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. The latter is an instance of TFSA-2021-198 (CVE-2021-41197) and is easily fixed by replacing a call to TensorShape constructor with a call to BuildTensorShape static helper factory.
Patches We have patched the issue in GitHub commits 1b54cadd19391b60b6fcccd8d076426f7221d5e8 and e952a89b7026b98fe8cbe626514a93ed68b7c510.
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.
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 Faysal Hossain Shezan from University of Virginia.
Other sources
Tensorflow is an Open Source Machine Learning Framework. The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or CHECK-fails when building new TensorShape objects (so, assert failures based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large TensorShape with user-provided dimensions. 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.
Affected Software
Remediation
Event History
Frequently Asked Questions
What is the severity of CVE-2022-23567?
The severity of CVE-2022-23567 is considered high due to potential integer overflows that can lead to large allocation issues.
How do I fix CVE-2022-23567?
To fix CVE-2022-23567, update affected TensorFlow packages to version 2.7.1 or later for both CPU and GPU versions.
Which versions of TensorFlow are affected by CVE-2022-23567?
Affected versions of TensorFlow include 2.5.0 to 2.7.0, specifically 2.5.0, 2.6.0, and 2.7.0.
What types of TensorFlow packages are impacted by CVE-2022-23567?
CVE-2022-23567 impacts tensorflow-gpu, tensorflow-cpu, and tensorflow packages.
Can CVE-2022-23567 lead to denial of service attacks?
Yes, CVE-2022-23567 can lead to denial of service conditions due to large memory allocations triggered by integer overflows.