Missing validation in shape inference for `Dequantize` in TensorFlow

Summary

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference implementation uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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, 10:35 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-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

• tensorflow-cpuPyPI

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

• tensorflowPyPI

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

References 2