Commit message: Prevent an OOB read in `expand_dims.cc`

The for loop that follows this check assumes that `axis` is between `0` and `input_dims.size`. If user supplied `axis` is negative, the if code before this check is supposed to bring it back to positive (similar to how in Python one can do `l[-3]` to mean `l[-3 + len(l)]`).

PiperOrigin-RevId: 387200206
Change-Id: I162f4feba12d547c3a4340833ae682016a2ebfab
CVE: CVE-2021-37685
CWE: CWE-125
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. 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`). 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.

Project: tensorflow
File: expand_dims.cc

TfLiteStatus ExpandTensorDim(TfLiteContext* context, const TfLiteTensor& input,
                             int axis, TfLiteTensor* output) {
  const TfLiteIntArray& input_dims = *input.dims;
  if (axis < 0) {
    axis = input_dims.size + 1 + axis;
  }
  TF_LITE_ENSURE(context, axis <= input_dims.size);
  TF_LITE_ENSURE(context, axis >= 0);

  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];
    }
  }

  return context->ResizeTensor(context, output, output_dims);
}

Project: tensorflow
File: expand_dims.cc

TfLiteStatus GetAxisValueFromTensor(TfLiteContext* context,
                                    const TfLiteTensor& axis, int* axis_value) {
  TF_LITE_ENSURE_EQ(context, NumElements(&axis), 1);
  switch (axis.type) {
    case kTfLiteInt32:
      *axis_value = *GetTensorData<int32_t>(&axis);
      return kTfLiteOk;
    case kTfLiteInt64:
      *axis_value = *GetTensorData<int64_t>(&axis);
      return kTfLiteOk;
    default:
      return kTfLiteError;
  }
}

Project: tensorflow
File: expand_dims.cc

TfLiteRegistration* Register_EXPAND_DIMS() {
  static TfLiteRegistration r = {nullptr, nullptr, expand_dims::Prepare,
                                 expand_dims::Eval};
  return &r;
}

Project: tensorflow
File: expand_dims.cc

TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  const TfLiteTensor* input;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInput, &input));
  const TfLiteTensor* axis;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kAxis, &axis));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context, GetOutputSafe(context, node, 0, &output));
  output->type = input->type;
  if (IsConstantTensor(axis)) {
    int axis_value;
    TF_LITE_ENSURE_OK(context,
                      GetAxisValueFromTensor(context, *axis, &axis_value));
    return ExpandTensorDim(context, *input, axis_value, output);
  }
  SetTensorToDynamic(output);
  return kTfLiteOk;
}

Project: tensorflow
File: expand_dims.cc

TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  // Just copy input to output.
  const TfLiteTensor* input;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInput, &input));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context, GetOutputSafe(context, node, 0, &output));
  const TfLiteTensor* axis;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kAxis, &axis));
  if (IsDynamicTensor(output)) {
    int axis_value;
    TF_LITE_ENSURE_OK(context,
                      GetAxisValueFromTensor(context, *axis, &axis_value));
    TF_LITE_ENSURE_OK(context,
                      ExpandTensorDim(context, *input, axis_value, output));
  }
  if (output->type == kTfLiteString) {
    TfLiteTensorRealloc(input->bytes, output);
  }
  memcpy(output->data.raw, input->data.raw, input->bytes);
  return kTfLiteOk;
}