diff --git a/‎numpy/_core/numeric.py‎
Lines changed: 14 additions & 1 deletion b/‎numpy/_core/numeric.py‎
Lines changed: 14 additions & 1 deletion
diff --git a/‎numpy/_core/tests/test_numeric.py‎
Lines changed: 6 additions & 0 deletions b/‎numpy/_core/tests/test_numeric.py‎
Lines changed: 6 additions & 0 deletions
@@ -1022,7 +1022,8 @@ def tensordot(a, b, axes=2):
         * (2,) array_like
           Or, a list of axes to be summed over, first sequence applying to `a`,
           second to `b`. Both elements array_like must be of the same length.
-
+          Each axis may appear at most once; repeated axes are not allowed.
+          For example, ``axes=([1, 1], [0, 0])`` is invalid.
     Returns
     -------
     output : ndarray
@@ -1053,6 +1054,13 @@ def tensordot(a, b, axes=2):
     first in both sequences, the second axis second, and so forth.
     The calculation can be referred to ``numpy.einsum``.
 
+    For example, if ``a.shape == (2, 3, 4)`` and ``b.shape == (3, 4, 5)``,
+    then ``axes=([1, 2], [0, 1])`` sums over the ``(3, 4)`` dimensions of
+    both arrays and produces an output of shape ``(2, 5)``.
+
+    Each summation axis corresponds to a distinct contraction index; repeating
+    an axis (for example ``axes=([1, 1], [0, 0])``) is invalid.
+
     The shape of the result consists of the non-contracted axes of the
     first tensor, followed by the non-contracted axes of the second.
 
@@ -1170,6 +1178,11 @@ def tensordot(a, b, axes=2):
         axes_b = [axes_b]
         nb = 1
 
+    if len(set(axes_a)) != len(axes_a):
+        raise ValueError("duplicate axes are not allowed in tensordot")
+    if len(set(axes_b)) != len(axes_b):
+        raise ValueError("duplicate axes are not allowed in tensordot")
+
     a, b = asarray(a), asarray(b)
     as_ = a.shape
     nda = a.ndim
 
@@ -4254,6 +4254,12 @@ def test_raise(self):
 
 class TestTensordot:
 
+    def test_rejects_duplicate_axes(self):
+        a = np.ones((2, 3, 3))
+        b = np.ones((3, 3, 4))
+        with pytest.raises(ValueError):
+            np.tensordot(a, b, axes=([1, 1], [0, 0]))
+
     def test_zero_dimension(self):
         # Test resolution to issue #5663
         a = np.ndarray((3, 0))