diff --git a/include/LightGBM/c_api.h b/include/LightGBM/c_api.h
index e0df38eeb82e..ed639b7f298c 100644
--- a/include/LightGBM/c_api.h
+++ b/include/LightGBM/c_api.h
@@ -572,6 +572,9 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle,
 /*!
  * \brief Update the model by specifying gradient and Hessian directly
  *        (this can be used to support customized loss functions).
+ * \note
+ * The length of the arrays referenced by ``grad`` and ``hess`` must be equal to
+ * ``num_class * num_train_data``, this is not verified by the library, the caller must ensure this.
  * \param handle Handle of booster
  * \param grad The first order derivative (gradient) statistics
  * \param hess The second order derivative (Hessian) statistics
diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py
index 64f1cb31edaa..c6aa2a69e88c 100644
--- a/python-package/lightgbm/basic.py
+++ b/python-package/lightgbm/basic.py
@@ -3031,7 +3031,15 @@ def __boost(self, grad, hess):
         assert grad.flags.c_contiguous
         assert hess.flags.c_contiguous
         if len(grad) != len(hess):
-            raise ValueError(f"Lengths of gradient({len(grad)}) and hessian({len(hess)}) don't match")
+            raise ValueError(f"Lengths of gradient ({len(grad)}) and Hessian ({len(hess)}) don't match")
+        num_train_data = self.train_set.num_data()
+        num_models = self.__num_class
+        if len(grad) != num_train_data * num_models:
+            raise ValueError(
+                f"Lengths of gradient ({len(grad)}) and Hessian ({len(hess)}) "
+                f"don't match training data length ({num_train_data}) * "
+                f"number of models per one iteration ({num_models})"
+            )
         is_finished = ctypes.c_int(0)
         _safe_call(_LIB.LGBM_BoosterUpdateOneIterCustom(
             self.handle,
diff --git a/tests/python_package_test/test_basic.py b/tests/python_package_test/test_basic.py
index 18a8403eba85..99f581775199 100644
--- a/tests/python_package_test/test_basic.py
+++ b/tests/python_package_test/test_basic.py
@@ -1,6 +1,7 @@
 # coding: utf-8
 import filecmp
 import numbers
+import re
 from pathlib import Path
 
 import numpy as np
@@ -579,3 +580,32 @@ def test_param_aliases():
     assert all(len(i) >= 1 for i in aliases.values())
     assert all(k in v for k, v in aliases.items())
     assert lgb.basic._ConfigAliases.get('config', 'task') == {'config', 'config_file', 'task', 'task_type'}
+
+
+def _bad_gradients(preds, _):
+    return np.random.randn(len(preds) + 1), np.random.rand(len(preds) + 1)
+
+
+def _good_gradients(preds, _):
+    return np.random.randn(len(preds)), np.random.rand(len(preds))
+
+
+def test_custom_objective_safety():
+    nrows = 100
+    X = np.random.randn(nrows, 5)
+    y_binary = np.arange(nrows) % 2
+    classes = [0, 1, 2]
+    nclass = len(classes)
+    y_multiclass = np.arange(nrows) % nclass
+    ds_binary = lgb.Dataset(X, y_binary).construct()
+    ds_multiclass = lgb.Dataset(X, y_multiclass).construct()
+    bad_bst_binary = lgb.Booster({'objective': "none"}, ds_binary)
+    good_bst_binary = lgb.Booster({'objective': "none"}, ds_binary)
+    bad_bst_multi = lgb.Booster({'objective': "none", "num_class": nclass}, ds_multiclass)
+    good_bst_multi = lgb.Booster({'objective': "none", "num_class": nclass}, ds_multiclass)
+    good_bst_binary.update(fobj=_good_gradients)
+    with pytest.raises(ValueError, match=re.escape("number of models per one iteration (1)")):
+        bad_bst_binary.update(fobj=_bad_gradients)
+    good_bst_multi.update(fobj=_good_gradients)
+    with pytest.raises(ValueError, match=re.escape(f"number of models per one iteration ({nclass})")):
+        bad_bst_multi.update(fobj=_bad_gradients)