diff --git a/nemo/collections/asr/parts/submodules/multitask_beam_decoding.py b/nemo/collections/asr/parts/submodules/multitask_beam_decoding.py
index 3c2a424dcd09..e6598f0edc97 100644
--- a/nemo/collections/asr/parts/submodules/multitask_beam_decoding.py
+++ b/nemo/collections/asr/parts/submodules/multitask_beam_decoding.py
@@ -308,6 +308,8 @@ def format_hypotheses(
                         break  # empty sequence
                 if pos < -1:
                     hyp.y_sequence = ids[: pos + 1]
+                    if hyp.xatt_scores is not None:
+                        hyp.xatt_scores = [xatt_layer[:, : pos + 1, :] for xatt_layer in hyp.xatt_scores]
 
 
 @dataclass
diff --git a/tests/collections/asr/decoding/test_multi_task_decoding.py b/tests/collections/asr/decoding/test_multi_task_decoding.py
index fa21b2f60328..256e012d9c3f 100644
--- a/tests/collections/asr/decoding/test_multi_task_decoding.py
+++ b/tests/collections/asr/decoding/test_multi_task_decoding.py
@@ -292,3 +292,51 @@ def test_transformer_aed_greedy_infer_strips_prompt(prompted_inputs, decoder_nm,
     torch.testing.assert_close(
         untrimmed[decoder_input_ids.shape[1] :], best_path
     )  # stripped the prompt from the beggining
+
+
+def test_transformer_aed_beam_infer_trims_xatt_scores(prompted_inputs, decoder_nm, nnet, tokenizer):
+    decoder_input_ids, encoder_hidden_states, encoder_input_mask = prompted_inputs
+    *_, classifier = nnet
+
+    # Run the actual top-level module used by MultiTask AED model for decoding.
+    # This module is expected to trim eos and pads in xatt from the end.
+    gen = TransformerAEDBeamInfer(decoder_nm, classifier, tokenizer, return_xattn_scores=True)
+    ans = gen(
+        encoder_hidden_states=encoder_hidden_states,
+        encoder_input_mask=encoder_input_mask,
+        decoder_input_ids=decoder_input_ids,
+    )
+    hyp = ans[0][0]
+
+    assert hyp.xatt_scores is not None
+    seq_len = hyp.y_sequence.shape[0]
+    decoder_input_ids_len = decoder_input_ids.shape[1]
+    total_expected_len = seq_len + decoder_input_ids_len - 1
+
+    # Check that the expected trimming has indeed been done.
+    for layer_scores in hyp.xatt_scores:
+        assert layer_scores.shape[1] == total_expected_len
+
+
+def test_transformer_aed_greedy_infer_trims_xatt_scores(prompted_inputs, decoder_nm, nnet, tokenizer):
+    decoder_input_ids, encoder_hidden_states, encoder_input_mask = prompted_inputs
+    *_, classifier = nnet
+
+    # Run the actual top-level module used by MultiTask AED model for decoding.
+    # This module is expected to trim eos and pads in xatt from the end.
+    gen = TransformerAEDGreedyInfer(decoder_nm, classifier, tokenizer, return_xattn_scores=True)
+    ans = gen(
+        encoder_hidden_states=encoder_hidden_states,
+        encoder_input_mask=encoder_input_mask,
+        decoder_input_ids=decoder_input_ids,
+    )
+    hyp = ans[0][0]
+
+    assert hyp.xatt_scores is not None
+    seq_len = hyp.y_sequence.shape[0]
+    decoder_input_ids_len = decoder_input_ids.shape[1]
+    total_expected_len = seq_len + decoder_input_ids_len - 1
+
+    # Check that the expected trimming has indeed been done.
+    for layer_scores in hyp.xatt_scores:
+        assert layer_scores.shape[1] == total_expected_len