Incomplete validation in MKL requantization in TensorFlow

Summary

TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The implementation does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Timeline

1. Aug 12, 2021, 10:40 PM UTCCVE published
2. Aug 4, 2024, 1:23 AM UTCRecord last updated

Affected versions

• tensorflow tensorflow

  From 2.5.0 · fixed in 2.5.1; From 2.4.0 · fixed in 2.4.3; fixed in 2.3.4

• tensorflow-cpuPyPI

  From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3

• tensorflow-gpuPyPI

  From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3

• tensorflowPyPI

  fixed in 2.3.4

• tensorflowPyPI

  From 2.4.0 · fixed in 2.4.3

• tensorflowPyPI

  From 2.5.0 · fixed in 2.5.1

• tensorflow-cpuPyPI

  fixed in 2.3.4

• tensorflow-cpuPyPI

  From 2.4.0 · fixed in 2.4.3

• tensorflow-cpuPyPI

  From 2.5.0 · fixed in 2.5.1

• tensorflow-gpuPyPI

  fixed in 2.3.4

• tensorflow-gpuPyPI

  From 2.4.0 · fixed in 2.4.3

• tensorflow-gpuPyPI

  From 2.5.0 · fixed in 2.5.1

• tensorflowPyPI

  From 2.3.0 · fixed in 2.3.4; From 2.4.0 · fixed in 2.4.3

References 3