CVE-2020-15211 (C++, published 2020-09-25T19:15Z)
Description: value: In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.
Severity: MEDIUM (CVSS v3 base score: 4.8)
Weakness: CWE-125 \u2014 Out-of-bounds Read
The product reads data past the end, or before the beginning, of the intended buffer.
Repository: https://github.com/tensorflow/tensorflow
Files changed:
tensorflow/lite/micro/kernels/activations.cc
tensorflow/lite/micro/kernels/add.cc
tensorflow/lite/micro/kernels/ceil.cc
tensorflow/lite/micro/kernels/circular_buffer.cc
tensorflow/lite/micro/kernels/comparisons.cc
tensorflow/lite/micro/kernels/concatenation.cc
tensorflow/lite/micro/kernels/conv.cc
tensorflow/lite/micro/kernels/depthwise_conv.cc
tensorflow/lite/micro/kernels/dequantize.cc
tensorflow/lite/micro/kernels/elementwise.cc
tensorflow/lite/micro/kernels/fully_connected.cc
tensorflow/lite/micro/kernels/hard_swish.cc
tensorflow/lite/micro/kernels/l2norm.cc
tensorflow/lite/micro/kernels/logistic.cc
tensorflow/lite/micro/kernels/mul.cc
tensorflow/lite/micro/kernels/pad.cc
tensorflow/lite/micro/kernels/pooling.cc
tensorflow/lite/micro/kernels/prelu.cc
tensorflow/lite/micro/kernels/quantize.cc
tensorflow/lite/micro/kernels/reduce.cc
tensorflow/lite/micro/kernels/reshape.cc
tensorflow/lite/micro/kernels/round.cc
tensorflow/lite/micro/kernels/softmax.cc
tensorflow/lite/micro/kernels/split.cc
tensorflow/lite/micro/kernels/sub.cc
tensorflow/lite/micro/kernels/svdf.cc
tensorflow/lite/micro/kernels/tanh.cc
Commit message: [tflite]: Insert `nullptr` checks when obtaining tensors.

As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages.

We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`).

PiperOrigin-RevId: 332520146
Change-Id: I405d986cfc653aaafcfdf4162c0acbd46220b921


Vulnerable code:
  TfLiteType input_type = GetInput(context, node, 0)->type;
  TfLiteType output_type = GetOutput(context, node, kOutputTensor)->type;
  TfLiteType output_type = GetOutput(context, node, kOutputTensor)->type;

Fixed code:
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, input1 != nullptr);
  TF_LITE_ENSURE(context, input2 != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input1 != nullptr);
  TF_LITE_ENSURE(context, input2 != nullptr);
  const TfLiteTensor* input_tensor = GetInput(context, node, 0);
  TF_LITE_ENSURE(context, input_tensor != nullptr);
  TfLiteType input_type = input_tensor->type;
  const TfLiteTensor* output_tensor = GetOutput(context, node, kOutputTensor);
  TF_LITE_ENSURE(context, output_tensor != nullptr);
  TfLiteType output_type = output_tensor->type;
    TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
        TF_LITE_ENSURE(context, t != nullptr);
  const TfLiteTensor* output_tensor = GetOutput(context, node, kOutputTensor);
  TF_LITE_ENSURE(context, output_tensor != nullptr);
  TfLiteType output_type = output_tensor->type;
    TF_LITE_ENSURE(context, input != nullptr);
    TF_LITE_ENSURE(context, filter != nullptr);
    TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, filter != nullptr);
    TF_LITE_ENSURE(context, input != nullptr);
    TF_LITE_ENSURE(context, filter != nullptr);
    TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, filter != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, filter != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input1 != nullptr);
  TF_LITE_ENSURE(context, input2 != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, paddings != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, alpha != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, axis != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, axis != nullptr);
  TF_LITE_ENSURE(context, input1 != nullptr);
  TF_LITE_ENSURE(context, input2 != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, weights_feature != nullptr);
  TF_LITE_ENSURE(context, weights_time != nullptr);
  TF_LITE_ENSURE(context, activation_state != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);
  TF_LITE_ENSURE(context, output != nullptr);
  TF_LITE_ENSURE(context, input != nullptr);