diff --git a/numpy/core/src/multiarray/mapping.c b/numpy/core/src/multiarray/mapping.c
index d64962f87150..8b9b67387181 100644
--- a/numpy/core/src/multiarray/mapping.c
+++ b/numpy/core/src/multiarray/mapping.c
@@ -2328,7 +2328,7 @@ PyArray_MapIterNext(PyArrayMapIterObject *mit)
  * @param Number of indices
  * @param The array that is being iterated
  *
- * @return 0 on success -1 on failure
+ * @return 0 on success -1 on failure (broadcasting or too many fancy indices)
  */
 static int
 mapiter_fill_info(PyArrayMapIterObject *mit, npy_index_info *indices,
@@ -2369,6 +2369,17 @@ mapiter_fill_info(PyArrayMapIterObject *mit, npy_index_info *indices,
             }
         }
 
+        /* Before contunuing, ensure that there are not too fancy indices */
+        if (indices[i].type & HAS_FANCY) {
+            if (NPY_UNLIKELY(j >= NPY_MAXDIMS)) {
+                PyErr_Format(PyExc_IndexError,
+                        "too many advanced (array) indices. This probably "
+                        "means you are indexing with too many booleans. "
+                        "(more than %d found)", NPY_MAXDIMS);
+                return -1;
+            }
+        }
+
         /* (iterating) fancy index, store the iterator */
         if (indices[i].type == HAS_FANCY) {
             mit->fancy_strides[j] = PyArray_STRIDE(arr, curr_dim);
@@ -2655,6 +2666,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type,
     /* For shape reporting on error */
     PyArrayObject *original_extra_op = extra_op;
 
+    /* NOTE: MAXARGS is the actual limit (2*NPY_MAXDIMS is index number one) */
     PyArrayObject *index_arrays[NPY_MAXDIMS];
     PyArray_Descr *intp_descr;
     PyArray_Descr *dtypes[NPY_MAXDIMS];  /* borrowed references */
diff --git a/numpy/core/tests/test_indexing.py b/numpy/core/tests/test_indexing.py
index 667c49240e4a..73dbc429c89c 100644
--- a/numpy/core/tests/test_indexing.py
+++ b/numpy/core/tests/test_indexing.py
@@ -3,6 +3,8 @@
 import functools
 import operator
 
+import pytest
+
 import numpy as np
 from numpy.core._multiarray_tests import array_indexing
 from itertools import product
@@ -547,6 +549,21 @@ def test_character_assignment(self):
         assert_array_equal(arr[0], np.array("asdfg", dtype="c"))
         assert arr[0, 1] == b"s"  # make sure not all were set to "a" for both
 
+    @pytest.mark.parametrize("index",
+            [True, False, np.array([0])])
+    @pytest.mark.parametrize("num", [32, 40])
+    @pytest.mark.parametrize("original_ndim", [1, 32])
+    def test_too_many_advanced_indices(self, index, num, original_ndim):
+        # These are limitations based on the number of arguments we can process.
+        # For `num=32` (and all boolean cases), the result is actually define;
+        # but the use of NpyIter (NPY_MAXARGS) limits it for technical reasons.
+        arr = np.ones((1,) * original_ndim)
+        with pytest.raises(IndexError):
+            arr[(index,) * num]
+        with pytest.raises(IndexError):
+            arr[(index,) * num] = 1.
+
+
 class TestFieldIndexing:
     def test_scalar_return_type(self):
         # Field access on an array should return an array, even if it