Commit message: Fix null CHECK issue with `tf.raw_ops.EncodePng`.

PiperOrigin-RevId: 369717714
Change-Id: I24136cd99c20b8466671f4f93b670ef6f6dd1250
CVE: CVE-2021-29531
CWE: CWE-787
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.

Project: tensorflow
File: encode_png_op.cc

  void Compute(OpKernelContext* context) override {
    const Tensor& image = context->input(0);
    OP_REQUIRES(context, image.dims() == 3,
                errors::InvalidArgument("image must be 3-dimensional",
                                        image.shape().DebugString()));
    OP_REQUIRES(
        context,
        FastBoundsCheck(image.NumElements(), std::numeric_limits<int32>::max()),
        errors::InvalidArgument("image cannot have >= int32 max elements"));
    const int32 height = static_cast<int32>(image.dim_size(0));
    const int32 width = static_cast<int32>(image.dim_size(1));
    const int32 channels = static_cast<int32>(image.dim_size(2));

    // In some cases, we pass width*channels*2 to png.
    const int32 max_row_width = std::numeric_limits<int32>::max() / 2;

    OP_REQUIRES(context, FastBoundsCheck(width * channels, max_row_width),
                errors::InvalidArgument("image too wide to encode"));

    OP_REQUIRES(context, channels >= 1 && channels <= 4,
                errors::InvalidArgument(
                    "image must have 1, 2, 3, or 4 channels, got ", channels));

    // Encode image to png string
    Tensor* output = nullptr;
    OP_REQUIRES_OK(context,
                   context->allocate_output(0, TensorShape({}), &output));
    if (desired_channel_bits_ == 8) {
      OP_REQUIRES(context,
                  png::WriteImageToBuffer(
                      image.flat<uint8>().data(), width, height,
                      width * channels, channels, desired_channel_bits_,
                      compression_, &output->scalar<tstring>()(), nullptr),
                  errors::Internal("PNG encoding failed"));
    } else {
      OP_REQUIRES(context,
                  png::WriteImageToBuffer(
                      image.flat<uint16>().data(), width, height,
                      width * channels * 2, channels, desired_channel_bits_,
                      compression_, &output->scalar<tstring>()(), nullptr),
                  errors::Internal("PNG encoding failed"));
    }
  }