CVE-2021-29590 (C++, published 2021-05-14T20:15Z)
Description: value: TensorFlow is an end-to-end open source platform for machine learning. The implementations of the `Minimum` and `Maximum` TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty. This is because the broadcasting implementation(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/maximum_minimum.h#L52-L56) indexes in both tensors with the same index but does not validate that the index is within bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Severity: LOW (CVSS v3 base score: 7.1)
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/kernels/maximum_minimum.cc
Commit message: Prevent a null pointer exception in TFLite

PiperOrigin-RevId: 370800206
Change-Id: Idd437ebce4ff224120d8eefc1c14c062173b71d6


Vulnerable code:
    switch (op_context.output->type) {
      case kTfLiteFloat32:
        TFLiteOperation<kernel_type, float, OpType>(context, node, op_context);
        break;
      case kTfLiteUInt8:
        TFLiteOperation<kernel_type, uint8_t, OpType>(context, node,
                                                      op_context);
        break;
      case kTfLiteInt8:
        TFLiteOperation<kernel_type, int8_t, OpType>(context, node, op_context);
        break;
      case kTfLiteInt32:
        TFLiteOperation<kernel_type, int32_t, OpType>(context, node,
                                                      op_context);
        break;
      case kTfLiteInt64:
        TFLiteOperation<kernel_type, int64_t, OpType>(context, node,
                                                      op_context);
        break;
      case kTfLiteInt16:
        TFLiteOperation<kernel_type, int16_t, OpType>(context, node,
                                                      op_context);
        break;
      default:
        context->ReportError(context,
                             "Type %d is currently not supported by Maximum.",
                             op_context.output->type);
        return kTfLiteError;
    }

Fixed code:
  // If inputs have no element, shortcircuit.
  if (NumElements(op_context.input1) == 0 ||
      NumElements(op_context.input2) == 0) {
    return kTfLiteOk;
  }

  switch (op_context.output->type) {
    case kTfLiteFloat32:
      TFLiteOperation<kernel_type, float, OpType>(context, node, op_context);
      break;
    case kTfLiteUInt8:
      TFLiteOperation<kernel_type, uint8_t, OpType>(context, node, op_context);
      break;
    case kTfLiteInt8:
      TFLiteOperation<kernel_type, int8_t, OpType>(context, node, op_context);
      break;
    case kTfLiteInt32:
      TFLiteOperation<kernel_type, int32_t, OpType>(context, node, op_context);
      break;
    case kTfLiteInt64:
      TFLiteOperation<kernel_type, int64_t, OpType>(context, node, op_context);
      break;
    case kTfLiteInt16:
      TFLiteOperation<kernel_type, int16_t, OpType>(context, node, op_context);
      break;
    default:
      context->ReportError(context,
                           "Type %d is currently not supported by Maximum.",
                           op_context.output->type);
      return kTfLiteError;
  }