diff --git a/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h b/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h
index e260774570f..b4087f3b87b 100644
--- a/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h
+++ b/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h
@@ -33,7 +33,7 @@ class MliTensorInterface {
  public:
   // Make sure that lifetime of MliTensorInterface instance isn't bigger than
   // related mli_tensor.
-  MliTensorInterface(mli_tensor* tensor) : tensor_(tensor) {};
+  MliTensorInterface(mli_tensor* tensor) : tensor_(tensor){};
   MliTensorInterface() = default;
   ~MliTensorInterface() = default;
 
diff --git a/tensorflow/lite/micro/kernels/reduce_common.cc b/tensorflow/lite/micro/kernels/reduce_common.cc
index cdc95d89424..8aeb529d757 100644
--- a/tensorflow/lite/micro/kernels/reduce_common.cc
+++ b/tensorflow/lite/micro/kernels/reduce_common.cc
@@ -120,7 +120,7 @@ enum MinMaxEvalType { kEvalMin, kEvalMax };
 template <typename T>
 struct MinMaxReducerCompare {
   MinMaxReducerCompare() = delete;
-  MinMaxReducerCompare(MinMaxEvalType evalType) : type_(evalType) {};
+  MinMaxReducerCompare(MinMaxEvalType evalType) : type_(evalType){};
 
   constexpr T initialValue() const {
     return (type_ == kEvalMin) ? std::numeric_limits<T>::max()
diff --git a/tensorflow/lite/micro/micro_allocator.cc b/tensorflow/lite/micro/micro_allocator.cc
index 8c8df8cd067..b6a482863e5 100644
--- a/tensorflow/lite/micro/micro_allocator.cc
+++ b/tensorflow/lite/micro/micro_allocator.cc
@@ -230,6 +230,19 @@ TfLiteStatus InitializeTfLiteTensorFromFlatbuffer(
     TfLiteTensor* result) {
   TFLITE_DCHECK(result != nullptr);
 
+  // Validate buffer index before any dereference of the buffers vector.
+  if (buffers == nullptr) {
+    MicroPrintf("Model buffers vector is null\n");
+    return kTfLiteError;
+  }
+  const uint32_t buffer_index = flatbuffer_tensor.buffer();
+  if (buffer_index >= buffers->size()) {
+    MicroPrintf(
+        "Tensor references invalid buffer index %u, model has only %d buffers\n",
+        buffer_index, buffers->size());
+    return kTfLiteError;
+  }
+
   *result = {};
   // Make sure the serialized type is one we know how to deal with, and convert
   // it from a flatbuffer enum into a constant used by the kernel C API.
@@ -347,6 +360,20 @@ TfLiteStatus InitializeTfLiteEvalTensorFromFlatbuffer(
     const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
     TfLiteEvalTensor* result) {
   *result = {};
+
+  // Validate buffer index before any dereference of the buffers vector.
+  if (buffers == nullptr) {
+    MicroPrintf("Model buffers vector is null\n");
+    return kTfLiteError;
+  }
+  const uint32_t buffer_index = flatbuffer_tensor.buffer();
+  if (buffer_index >= buffers->size()) {
+    MicroPrintf(
+        "Tensor references invalid buffer index %u, model has only %d buffers\n",
+        buffer_index, buffers->size());
+    return kTfLiteError;
+  }
+
   // Make sure the serialized type is one we know how to deal with, and convert
   // it from a flatbuffer enum into a constant used by the kernel C API.
   TF_LITE_ENSURE_STATUS(
@@ -1107,11 +1134,43 @@ TfLiteStatus MicroAllocator::PopulateTfLiteTensorFromFlatbuffer(
   // TODO(b/162311891): This method serves as a stub to ensure quantized
   // allocations in the tail can be recorded. Once the interpreter has APIs for
   // accessing buffers on TfLiteEvalTensor this method can be dropped.
+
+  // Validate subgraph and tensor indices before dereferencing FlatBuffer
+  // vectors to avoid out-of-bounds access.
+  if (subgraph_idx < 0 ||
+      static_cast<size_t>(subgraph_idx) >= model->subgraphs()->size()) {
+    MicroPrintf("Invalid subgraph index %d, model has only %d subgraphs\n",
+                subgraph_idx, model->subgraphs()->size());
+    return kTfLiteError;
+  }
+  const SubGraph* subgraph = model->subgraphs()->Get(subgraph_idx);
+  if (subgraph == nullptr || subgraph->tensors() == nullptr) {
+    MicroPrintf("Subgraph %d has no tensors vector\n", subgraph_idx);
+    return kTfLiteError;
+  }
+  if (tensor_index < 0 ||
+      static_cast<size_t>(tensor_index) >= subgraph->tensors()->size()) {
+    MicroPrintf(
+        "Invalid tensor index %d for subgraph %d, subgraph has only %d tensors\n",
+        tensor_index, subgraph_idx, subgraph->tensors()->size());
+    return kTfLiteError;
+  }
+
+  const tflite::Tensor* flatbuffer_tensor =
+      subgraph->tensors()->Get(tensor_index);
+
+  // Buffer index validation is performed in
+  // InitializeTfLiteTensorFromFlatbuffer but we also ensure model->buffers() is
+  // non-null here for completeness.
+  if (model->buffers() == nullptr) {
+    MicroPrintf("Model buffers vector is null\n");
+    return kTfLiteError;
+  }
+
+  // Populate the TfLiteTensor fields from the flatbuffer.
   return internal::InitializeTfLiteTensorFromFlatbuffer(
       persistent_buffer_allocator_, non_persistent_buffer_allocator_,
-      allocate_temp,
-      *model->subgraphs()->Get(subgraph_idx)->tensors()->Get(tensor_index),
-      model->buffers(), tensor);
+      allocate_temp, *flatbuffer_tensor, model->buffers(), tensor);
 }
 
 TfLiteStatus MicroAllocator::CommitStaticMemoryPlan(
diff --git a/tensorflow/lite/micro/micro_allocator.h b/tensorflow/lite/micro/micro_allocator.h
index 215bffc6a8c..af688e865dc 100644
--- a/tensorflow/lite/micro/micro_allocator.h
+++ b/tensorflow/lite/micro/micro_allocator.h
@@ -49,6 +49,13 @@ TfLiteStatus InitializeTfLiteTensorFromFlatbuffer(
     const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
     TfLiteTensor* result);
 
+// Initializes a TfLiteEvalTensor from a flatbuffer tensor and buffers vector.
+// This is the eval-phase counterpart to InitializeTfLiteTensorFromFlatbuffer.
+TfLiteStatus InitializeTfLiteEvalTensorFromFlatbuffer(
+    const tflite::Tensor& flatbuffer_tensor,
+    const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
+    TfLiteEvalTensor* result);
+
 // Holds placeholder information for a scratch buffer request from a kernel.
 // This struct is only used during the model prepare stage. Each request from a
 // kernel is stored in the head section. During the prepare stage, the head
diff --git a/tensorflow/lite/micro/micro_allocator_test.cc b/tensorflow/lite/micro/micro_allocator_test.cc
index 9e19e271daf..2ed08f98ad0 100644
--- a/tensorflow/lite/micro/micro_allocator_test.cc
+++ b/tensorflow/lite/micro/micro_allocator_test.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h"
 #include "tensorflow/lite/micro/micro_allocator.h"
 #include "tensorflow/lite/micro/micro_arena_constants.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
 #include "tensorflow/lite/micro/test_helpers.h"
 #include "tensorflow/lite/micro/testing/micro_test.h"
 #include "tensorflow/lite/micro/testing/test_conv_model.h"
@@ -1384,4 +1385,101 @@ TF_LITE_MICRO_TEST(TestMultiSubgraphNumScratchAllocations) {
                           used_bytes + sizeof(tflite::ScratchBufferHandle) * 2);
 }
 
+// New tests validating invalid buffer index guards in tensor initialization
+// and model allocation paths.
+
+TF_LITE_MICRO_TEST(TestInitializeTensorInvalidBufferIndex) {
+  // Arena and allocator for temporary/persistent allocations used by init.
+  constexpr size_t arena_size = 1024;
+  uint8_t arena[arena_size];
+  tflite::SingleArenaBufferAllocator* simple_allocator =
+      tflite::SingleArenaBufferAllocator::Create(arena, arena_size);
+
+  // Build a flatbuffer Tensor that references a non-existent buffer index.
+  flatbuffers::FlatBufferBuilder builder;
+  const int32_t dims_data[] = {1};
+  auto dims_vec = builder.CreateVector(dims_data, 1);
+  // Use an invalid buffer index (>= buffers->size()).
+  const uint32_t kInvalidBufferIndex = 5;
+  auto name_str = builder.CreateString("invalid_buffer_tensor");
+  auto tensor_offset = tflite::CreateTensor(
+      builder, /*shape=*/dims_vec, tflite::TensorType_INT32,
+      /*buffer=*/kInvalidBufferIndex, /*name=*/name_str,
+      /*quantization=*/0, /*is_variable=*/false, /*sparsity=*/0);
+  builder.Finish(tensor_offset);
+  const tflite::Tensor* bad_tensor =
+      flatbuffers::GetRoot<tflite::Tensor>(builder.GetBufferPointer());
+
+  // Create a buffers vector with a single empty buffer.
+  const flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>>* buffers =
+      tflite::testing::CreateFlatbufferBuffers();
+
+  TfLiteTensor out_tensor;
+  TfLiteEvalTensor out_eval_tensor;
+
+  // Expect kTfLiteError due to invalid buffer index.
+  TF_LITE_MICRO_EXPECT_EQ(
+      kTfLiteError,
+      tflite::internal::InitializeTfLiteTensorFromFlatbuffer(
+          simple_allocator, simple_allocator, /*allocate_temp=*/false,
+          *bad_tensor, buffers, &out_tensor));
+  TF_LITE_MICRO_EXPECT_EQ(
+      kTfLiteError, tflite::internal::InitializeTfLiteEvalTensorFromFlatbuffer(
+                        *bad_tensor, buffers, &out_eval_tensor));
+}
+
+TF_LITE_MICRO_TEST(TestModelAllocationSubgraphInvalidBufferIndex) {
+  // Build a minimal model with a single tensor that references an invalid
+  // buffer index. Make it an output tensor to mimic output-table invalid case
+  // but the allocator will validate all tensors equally, so this also
+  // simulates intermediate cases.
+  flatbuffers::FlatBufferBuilder fbb;
+
+  // One empty buffer at index 0 in Model.buffers().
+  flatbuffers::Offset<tflite::Buffer> buffers_arr[1] = {
+      tflite::CreateBuffer(fbb)};
+  auto buffers_fb = fbb.CreateVector(buffers_arr, 1);
+
+  // Tensor with invalid buffer index.
+  const int32_t dims_data[] = {1};
+  auto dims_vec = fbb.CreateVector(dims_data, 1);
+  const uint32_t kInvalidBufferIndex = 3;  // >= buffers_vec.size()
+  auto t_name = fbb.CreateString("out_tensor_invalid_buf");
+  auto tensor = tflite::CreateTensor(
+      fbb, dims_vec, tflite::TensorType_INT32, kInvalidBufferIndex, t_name,
+      /*quantization=*/0, /*is_variable=*/false, /*sparsity=*/0);
+  auto tensors_vec = fbb.CreateVector(&tensor, 1);
+
+  // Subgraph with the tensor as an output; no operators required.
+  const int32_t outputs_idx[] = {0};
+  auto outputs = fbb.CreateVector(outputs_idx, 1);
+  auto inputs = fbb.CreateVector<int32_t>({});
+  auto ops = fbb.CreateVector<flatbuffers::Offset<tflite::Operator>>({});
+  auto subgraph = tflite::CreateSubGraph(fbb, tensors_vec, inputs, outputs, ops,
+                                         fbb.CreateString("sg0"));
+  auto subgraphs = fbb.CreateVector(&subgraph, 1);
+
+  // Minimal model (no operator codes needed as there are no operators).
+  auto model =
+      tflite::CreateModel(fbb, /*version=*/TFLITE_SCHEMA_VERSION,
+                          /*operator_codes=*/0, subgraphs,
+                          fbb.CreateString("invalid_buf_model"), buffers_fb);
+  tflite::FinishModelBuffer(fbb, model);
+  const tflite::Model* m =
+      flatbuffers::GetRoot<tflite::Model>(fbb.GetBufferPointer());
+
+  // Allocate an arena and create the allocator.
+  constexpr size_t arena_size = 2048;
+  uint8_t arena[arena_size];
+  tflite::MicroAllocator* allocator =
+      tflite::MicroAllocator::Create(arena, arena_size);
+  TF_LITE_MICRO_EXPECT(nullptr != allocator);
+
+  // StartModelAllocation should fail (return nullptr) because initializing
+  // any eval tensor with an invalid buffer index returns kTfLiteError.
+  tflite::SubgraphAllocations* subgraph_allocations =
+      allocator->StartModelAllocation(m);
+  TF_LITE_MICRO_EXPECT(nullptr == subgraph_allocations);
+}
+
 TF_LITE_MICRO_TESTS_END