Heap OOB in TFLite

Overview

Impact

TFLite's expand_dims.cc contains a vulnerability which allows reading one element outside of bounds of heap allocated data:

  if (axis < 0) { 
    axis = input_dims.size + 1 + axis;
  }   
  TF_LITE_ENSURE(context, axis <= input_dims.size);

  TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_dims.size + 1);
  for (int i = 0; i < output_dims->size; ++i) {
    if (i < axis) {
      output_dims->data[i] = input_dims.data[i];
    } else if (i == axis) {
      output_dims->data[i] = 1;
    } else {
      output_dims->data[i] = input_dims.data[i - 1];
    }
  }

If axis is a large negative value (e.g., -100000), then after the first if it would still be negative. The check following the if statement will pass and the for loop would read one element before the start of input_dims.data (when i = 0).

Patches

We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257.

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.

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 Yakun Zhang of Baidu Security.

Affected packages

• tensorflow < 2.3.4
• tensorflow >= 2.4.0, < 2.4.3
• tensorflow >= 2.5.0, < 2.5.1
• tensorflow-cpu < 2.3.4
• tensorflow-cpu >= 2.4.0, < 2.4.3
• tensorflow-cpu >= 2.5.0, < 2.5.1
• tensorflow-gpu < 2.3.4
• tensorflow-gpu >= 2.4.0, < 2.4.3
• tensorflow-gpu >= 2.5.0, < 2.5.1

Remediation

Upgrade to a patched release:

• tensorflow 2.3.4
• tensorflow 2.4.3
• tensorflow 2.5.1
• tensorflow-cpu 2.3.4
• tensorflow-cpu 2.4.3
• tensorflow-cpu 2.5.1
• tensorflow-gpu 2.3.4
• tensorflow-gpu 2.4.3
• tensorflow-gpu 2.5.1