diff --git a/include/onnxruntime/core/session/onnxruntime_c_api.h b/include/onnxruntime/core/session/onnxruntime_c_api.h
index cbf9fe232d..6dab20a055 100644
--- a/include/onnxruntime/core/session/onnxruntime_c_api.h
+++ b/include/onnxruntime/core/session/onnxruntime_c_api.h
@@ -1009,7 +1009,7 @@ struct OrtApi {
    * This function only works for numeric tensors.
    * This is a no-copy method whose pointer is only valid until the backing OrtValue is free'd.
    */
-  ORT_API2_STATUS(TensorAt, _Inout_ OrtValue* value, size_t* location_values, size_t location_values_count, _Outptr_ void** out);
+  ORT_API2_STATUS(TensorAt, _Inout_ OrtValue* value, const int64_t* location_values, size_t location_values_count, _Outptr_ void** out);
 
   /**
    * Creates an allocator instance and registers it with the env to enable
diff --git a/include/onnxruntime/core/session/onnxruntime_cxx_api.h b/include/onnxruntime/core/session/onnxruntime_cxx_api.h
index ff2ca58696..9e8f365adc 100644
--- a/include/onnxruntime/core/session/onnxruntime_cxx_api.h
+++ b/include/onnxruntime/core/session/onnxruntime_cxx_api.h
@@ -371,7 +371,7 @@ struct Value : Base<OrtValue> {
   const T* GetTensorData() const;
 
   template <typename T>
-  T At(const std::initializer_list<size_t>& location);
+  T& At(const std::vector<int64_t>& location);
 
   TypeInfo GetTypeInfo() const;
   TensorTypeAndShapeInfo GetTensorTypeAndShapeInfo() const;
diff --git a/include/onnxruntime/core/session/onnxruntime_cxx_inline.h b/include/onnxruntime/core/session/onnxruntime_cxx_inline.h
index ffe927d457..104ae566d5 100644
--- a/include/onnxruntime/core/session/onnxruntime_cxx_inline.h
+++ b/include/onnxruntime/core/session/onnxruntime_cxx_inline.h
@@ -777,10 +777,10 @@ const T* Value::GetTensorData() const {
 }
 
 template <typename T>
-inline T Value::At(const std::initializer_list<size_t>& location) {
+inline T& Value::At(const std::vector<int64_t>& location) {
+  static_assert(!std::is_same<T, std::string>::value, "this api does not support std::string");
   T* out;
-  std::vector<size_t> location_ = location;
-  ThrowOnError(GetApi().TensorAt(p_, location_.data(), location_.size(), (void**)&out));
+  ThrowOnError(GetApi().TensorAt(p_, location.data(), location.size(), (void**)&out));
   return *out;
 }
 
diff --git a/onnxruntime/core/session/onnxruntime_c_api.cc b/onnxruntime/core/session/onnxruntime_c_api.cc
index 424873ac5e..dbec53ec89 100644
--- a/onnxruntime/core/session/onnxruntime_c_api.cc
+++ b/onnxruntime/core/session/onnxruntime_c_api.cc
@@ -1727,27 +1727,45 @@ ORT_API_STATUS_IMPL(OrtApis::ReleaseAvailableProviders, _In_ char** ptr,
   return NULL;
 }
 
-ORT_API_STATUS_IMPL(OrtApis::TensorAt, _Inout_ OrtValue* value, size_t* location_values, size_t location_values_count,
+ORT_API_STATUS_IMPL(OrtApis::TensorAt, _Inout_ OrtValue* value, const int64_t* location_values, size_t location_values_count,
                     _Outptr_ void** out) {
   TENSOR_READWRITE_API_BEGIN
-  //TODO: test if it's a string tensor
-  if (location_values_count != tensor->Shape().NumDimensions())
+
+  if(tensor->IsDataTypeString()) {
+    return OrtApis::CreateStatus(ORT_INVALID_ARGUMENT, "this API does not support strings");
+  }
+
+  const auto& tensor_shape = tensor->Shape();
+  const auto num_dimensions = tensor_shape.NumDimensions();
+  if (location_values_count != num_dimensions) {
     return OrtApis::CreateStatus(ORT_INVALID_ARGUMENT, "location dimensions do not match shape size");
-  std::vector<size_t> location(location_values_count);
+  }
+
   for (size_t i = 0; i < location_values_count; i++) {
-    if (location_values[i] >= (size_t)tensor->Shape()[i])
+    if (location_values[i] >= tensor_shape[i] || location_values[i] < 0) {
       return OrtApis::CreateStatus(ORT_INVALID_ARGUMENT, "invalid location range");
-    location[i] = location_values[i];
+    }
   }
-  // data has row-major format
-  size_t offset = 0;
-  for (size_t i = 1; i <= tensor->Shape().NumDimensions(); i++) {
-    size_t sum = 1;
-    for (size_t j = i + 1; j <= tensor->Shape().NumDimensions(); j++) sum *= (size_t)tensor->Shape()[j - 1];
-    offset += location[i - 1] * sum;
+
+  // compute strides
+  // TensorPitches p;
+  std::vector<int64_t> strides(num_dimensions);
+  {
+    int64_t stride = 1;
+    for (size_t dim = num_dimensions; dim > 0; --dim) {
+      strides[dim - 1] = stride;
+      stride *= tensor_shape[dim - 1];
+    }
   }
-  auto data = ((char*)tensor->MutableDataRaw()) + (tensor->DataType()->Size() * offset);
-  *out = (void*)data;
+
+  // For Scalers the offset would always be zero
+  int64_t offset = 0;
+  for (size_t i = 0; i < num_dimensions; i++) {
+    offset += location_values[i] * strides[i];
+  }
+
+  auto data = reinterpret_cast<char*>(tensor->MutableDataRaw()) + tensor->DataType()->Size() * offset;
+  *out = data;
   return nullptr;
   API_IMPL_END
 }
diff --git a/onnxruntime/core/session/ort_apis.h b/onnxruntime/core/session/ort_apis.h
index 21b4aca34d..2d86ae39d4 100644
--- a/onnxruntime/core/session/ort_apis.h
+++ b/onnxruntime/core/session/ort_apis.h
@@ -228,7 +228,7 @@ ORT_API_STATUS_IMPL(ReleaseAvailableProviders, _In_ char** ptr,
 ORT_API_STATUS_IMPL(AddSessionConfigEntry, _Inout_ OrtSessionOptions* options,
                     _In_z_ const char* config_key, _In_z_ const char* config_value);
 
-ORT_API_STATUS_IMPL(TensorAt, _Inout_ OrtValue* value, size_t* location_values, size_t location_values_count, _Outptr_ void** out);
+ORT_API_STATUS_IMPL(TensorAt, _Inout_ OrtValue* value, const int64_t* location_values, size_t location_values_count, _Outptr_ void** out);
 
 ORT_API_STATUS_IMPL(CreateAndRegisterAllocator, _Inout_ OrtEnv* env, _In_ const OrtMemoryInfo* mem_info, _In_ const OrtArenaCfg* arena_cfg);
 
diff --git a/onnxruntime/test/shared_lib/test_inference.cc b/onnxruntime/test/shared_lib/test_inference.cc
index 2cae2269d3..04169d129c 100644
--- a/onnxruntime/test/shared_lib/test_inference.cc
+++ b/onnxruntime/test/shared_lib/test_inference.cc
@@ -680,8 +680,8 @@ TEST(CApiTest, access_tensor_data_elements) {
   Ort::Value tensor = Ort::Value::CreateTensor<float>(info, values.data(), values.size(), shape.data(), shape.size());
 
   float expected_value = 0;
-  for (size_t row = 0; row < (size_t)shape[0]; row++) {
-    for (size_t col = 0; col < (size_t)shape[1]; col++) {
+  for (int64_t row = 0; row < shape[0]; row++) {
+    for (int64_t col = 0; col < shape[1]; col++) {
       ASSERT_EQ(expected_value++, tensor.At<float>({row, col}));
     }
   }