diff --git a/onnxruntime/core/providers/cpu/tensor/gather_nd.cc b/onnxruntime/core/providers/cpu/tensor/gather_nd.cc
index ad3faa70ed6af..a0a848eef0dff 100644
--- a/onnxruntime/core/providers/cpu/tensor/gather_nd.cc
+++ b/onnxruntime/core/providers/cpu/tensor/gather_nd.cc
@@ -66,6 +66,18 @@ Status GatherNDBase::PrepareForCompute(const TensorShape& input_shape, const Ten
   const auto num_slices = indices_shape.SizeToDimension(indices_shape.NumDimensions() - 1);
   const auto slice_size = input_shape.SizeFromDimension(SafeInt<size_t>(batch_dims_) + num_slice_dims);
   const auto num_batches = input_shape.SizeToDimension(SafeInt<size_t>(batch_dims_));
+
+  // Validate batch dimensions to prevent division by zero
+  if (num_batches == 0) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "GatherND: input tensor batch dimensions cannot be zero");
+  }
+  if (num_slices % num_batches != 0) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "GatherND: indices batch size (", num_slices,
+                           ") is not divisible by input batch size (", num_batches, ")");
+  }
+
   const auto input_batch_stride = input_shape.SizeFromDimension(SafeInt<size_t>(batch_dims_));
   const auto num_slices_per_batch = num_slices / num_batches;
   std::vector<int64_t> sizes_from_slice_dims(onnxruntime::narrow<size_t>(num_slice_dims));
diff --git a/onnxruntime/test/providers/cpu/tensor/gather_nd_op_test.cc b/onnxruntime/test/providers/cpu/tensor/gather_nd_op_test.cc
index 081b4b484a73b..a8f3b99b2b3d3 100644
--- a/onnxruntime/test/providers/cpu/tensor/gather_nd_op_test.cc
+++ b/onnxruntime/test/providers/cpu/tensor/gather_nd_op_test.cc
@@ -329,5 +329,48 @@ TEST(GatherNDOpTest, GatherND_slice_int64_t) {
   test.Run();
 }
 
+// Test for issue #23828: GatherND should return error instead of crashing
+// when batch dimensions mismatch between input and indices
+TEST(GatherNDOpTest, GatherND_batch_dims_mismatch_error) {
+  OpTester test("GatherND", 12, kOnnxDomain);
+  test.AddAttribute<int64_t>("batch_dims", 1);
+
+  // Input has 3 batches, but indices has 2 slices (indices batch size 2), which is not divisible by 3 - mismatch!
+  test.AddInput<float>("data", {3, 3}, {0.f, 1.f, 2.f, 10.f, 11.f, 12.f, 20.f, 21.f, 22.f});
+  test.AddInput<int64_t>("indices", {2, 1}, {1, 2});
+  test.AddOutput<float>("output", {2}, {0.f, 0.f});  // dummy output, won't be used
+
+  // Force execution only on CPU
+  std::vector<std::unique_ptr<onnxruntime::IExecutionProvider>> cpu_only_ep;
+  cpu_only_ep.push_back(DefaultCpuExecutionProvider());
+
+  test.Run(OpTester::ExpectResult::kExpectFailure,
+           "GatherND: indices batch size (2) is not divisible by input batch size (3)",
+           {},             // no excluded providers needed
+           nullptr,        // no RunOptions
+           &cpu_only_ep);  // force CPU
+}
+
+// Test for issue #23828: GatherND should return error when input batch dimension is zero
+TEST(GatherNDOpTest, GatherND_zero_batch_dims_error) {
+  OpTester test("GatherND", 12, kOnnxDomain);
+  test.AddAttribute<int64_t>("batch_dims", 1);
+
+  // Input has 0 batches - should fail with clear error instead of division by zero
+  test.AddInput<float>("data", {0, 3}, {});
+  test.AddInput<int64_t>("indices", {2, 1}, {1, 2});
+  test.AddOutput<float>("output", {2}, {0.f, 0.f});  // dummy output, won't be used
+
+  // Force execution only on CPU
+  std::vector<std::unique_ptr<onnxruntime::IExecutionProvider>> cpu_only_ep;
+  cpu_only_ep.push_back(DefaultCpuExecutionProvider());
+
+  test.Run(OpTester::ExpectResult::kExpectFailure,
+           "GatherND: input tensor batch dimensions cannot be zero",
+           {},
+           nullptr,
+           &cpu_only_ep);  // force CPU
+}
+
 }  // namespace test
 }  // namespace onnxruntime