diff --git a/pymc/logprob/scan.py b/pymc/logprob/scan.py
index 8626c20c68f..8f6942458ed 100644
--- a/pymc/logprob/scan.py
+++ b/pymc/logprob/scan.py
@@ -463,7 +463,9 @@ def find_measurable_scans(fgraph, node):
     # We must also replace any lingering references to the old RVs by the new measurable RVS
     # For example if we had measurable out1 = exp(normal()) and out2 = out1 - x
     # We need to replace references of original out1 by the new MeasurableExp(normal())
-    inner_outs = node.op.inner_outputs.copy()
+    clone_fgraph = node.op.fgraph.clone()
+    inner_inps = clone_fgraph.inputs
+    inner_outs = clone_fgraph.outputs
     inner_rvs_replacements = []
     for idx, new_inner_rv in zip(valued_output_idxs, inner_rvs, strict=True):
         old_inner_rv = inner_outs[idx]
@@ -474,8 +476,7 @@ def find_measurable_scans(fgraph, node):
         clone=False,
     )
     toposort_replace(temp_fgraph, inner_rvs_replacements)
-    inner_outs = temp_fgraph.outputs[: len(inner_outs)]
-    op = MeasurableScan(node.op.inner_inputs, inner_outs, node.op.info, mode=copy(node.op.mode))
+    op = MeasurableScan(inner_inps, inner_outs, node.op.info, mode=copy(node.op.mode))
     new_outs = op.make_node(*node.inputs).outputs
 
     old_outs = node.outputs
diff --git a/tests/logprob/test_scan.py b/tests/logprob/test_scan.py
index 381eed221d1..17fb198ca21 100644
--- a/tests/logprob/test_scan.py
+++ b/tests/logprob/test_scan.py
@@ -550,3 +550,25 @@ def test_scan_multiple_output_types():
             test_value, [a + b for a, b in itertools.pairwise([1, 1, *test_value[:-1]])]
         ),
     )
+
+
+def test_generative_graph_unchanged():
+    # Regression test where creating the IR would overwrite the original Scan inner fgraph
+
+    def step(eps_tm1):
+        x = pt.random.normal(0, eps_tm1)
+        eps_t = x - 0
+        return (x, eps_t), {x.owner.inputs[0]: x.owner.outputs[0]}
+
+    [xs, _], update = pytensor.scan(step, outputs_info=[None, pt.ones(())], n_steps=5)
+
+    before = xs.dprint(file="str")
+
+    xs_value = np.ones(5)
+    expected_logp = stats.norm.logpdf(xs_value, 0, 1)
+    for i in range(2):
+        xs_logp = logp(xs, xs_value)
+        np.testing.assert_allclose(xs_logp.eval(), expected_logp)
+
+    after = xs.dprint(file="str")
+    assert before == after