diff --git a/doc/whats_new/v1.4.rst b/doc/whats_new/v1.4.rst
index 44d5cfa01c8bb..fdaa30f18f577 100644
--- a/doc/whats_new/v1.4.rst
+++ b/doc/whats_new/v1.4.rst
@@ -242,6 +242,11 @@ Changelog
   `kdtree` and `balltree` values will be removed in 1.6.
   :pr:`26744` by :user:`Shreesha Kumar Bhat <Shreesha3112>`.
 
+- |FIX| : Create copy of precomputed sparse matrix within the
+  `fit` method of `cluster.DBSCAN` to avoid in-place modification of
+  the sparse matrix.
+  :pr:`27651` by :user:`Ganesh Tata <tataganesh>`.
+
 - |API| The option `metric=None` in
   :class:`cluster.AggomerativeClustering` and :class:`cluster.FeatureAgglomeration`
   is deprecated in version 1.4 and will be removed in version 1.6. Use the default
diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py
index 0129138801973..98f524752a39a 100644
--- a/sklearn/cluster/_dbscan.py
+++ b/sklearn/cluster/_dbscan.py
@@ -391,9 +391,10 @@ def fit(self, X, y=None, sample_weight=None):
         if self.metric == "precomputed" and sparse.issparse(X):
             # set the diagonal to explicit values, as a point is its own
             # neighbor
+            X = X.copy()  # copy to avoid in-place modification
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore", sparse.SparseEfficiencyWarning)
-                X.setdiag(X.diagonal())  # XXX: modifies X's internals in-place
+                X.setdiag(X.diagonal())
 
         neighbors_model = NearestNeighbors(
             radius=self.eps,
diff --git a/sklearn/cluster/tests/test_dbscan.py b/sklearn/cluster/tests/test_dbscan.py
index 0b5a067b3f8b1..d42cc2b17d518 100644
--- a/sklearn/cluster/tests/test_dbscan.py
+++ b/sklearn/cluster/tests/test_dbscan.py
@@ -122,6 +122,28 @@ def test_dbscan_input_not_modified(metric, csr_container):
         assert_array_equal(X, X_copy)
 
 
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_dbscan_input_not_modified_precomputed_sparse_nodiag(csr_container):
+    """Check that we don't modify in-place the pre-computed sparse matrix.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/27508
+    """
+    X = np.random.RandomState(0).rand(10, 10)
+    # Add zeros on the diagonal that will be implicit when creating
+    # the sparse matrix. If `X` is modified in-place, the zeros from
+    # the diagonal will be made explicit.
+    np.fill_diagonal(X, 0)
+    X = csr_container(X)
+    assert all(row != col for row, col in zip(*X.nonzero()))
+    X_copy = X.copy()
+    dbscan(X, metric="precomputed")
+    # Make sure that we did not modify `X` in-place even by creating
+    # explicit 0s values.
+    assert X.nnz == X_copy.nnz
+    assert_array_equal(X.toarray(), X_copy.toarray())
+
+
 @pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
 def test_dbscan_no_core_samples(csr_container):
     rng = np.random.RandomState(0)