diff --git a/vllm/v1/attention/backends/gdn_attn.py b/vllm/v1/attention/backends/gdn_attn.py
index 41109ff41..c7a41abe5 100644
--- a/vllm/v1/attention/backends/gdn_attn.py
+++ b/vllm/v1/attention/backends/gdn_attn.py
@@ -208,7 +208,9 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
 
             non_spec_query_lens = query_lens[~spec_sequence_masks]
             num_decodes = (non_spec_query_lens == 1).sum().item()
-            num_prefills = non_spec_query_lens.size(0) - num_decodes
+            # Exclude zero-length padded sequences from prefill count.
+            num_zero_len = (non_spec_query_lens == 0).sum().item()
+            num_prefills = non_spec_query_lens.size(0) - num_decodes - num_zero_len
             num_decode_tokens = num_decodes
             num_prefill_tokens = non_spec_query_lens.sum().item() - num_decode_tokens
             num_spec_decode_tokens = (
@@ -228,9 +230,15 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
                 non_spec_token_indx = torch.empty(
                     0, dtype=torch.int32, device=query_start_loc.device
                 )
-                spec_state_indices_tensor = block_table_tensor[:, : self.num_spec + 1]
+                # Filter by spec_sequence_masks to exclude padded sequences
+                spec_state_indices_tensor = block_table_tensor[
+                    spec_sequence_masks, : self.num_spec + 1
+                ]
                 non_spec_state_indices_tensor = None
-                spec_query_start_loc = query_start_loc
+                # Padded sequences are always at the back, so the first
+                # num_spec_decodes + 1 entries of query_start_loc already
+                # contain the correct cumulative token counts.
+                spec_query_start_loc = query_start_loc[: num_spec_decodes + 1]
                 non_spec_query_start_loc = None
                 non_spec_query_start_loc_cpu = None
             else:
@@ -294,6 +302,12 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
         else:
             has_initial_state = None
 
+        # Function code counted on either presency non-spec decode or spec decode,
+        # but not both.
+        assert not (num_decodes > 0 and num_spec_decodes > 0), (
+            f"num_decodes: {num_decodes}, num_spec_decodes: {num_spec_decodes}"
+        )
+
         # Prepare tensors for cudagraph
         # Note: m.num_actual_tokens is already padded by the model runner for CUDAGraph
         batch_size = m.num_actual_tokens
@@ -312,7 +326,7 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
             spec_state_indices_tensor[num_spec_decodes:].fill_(PAD_SLOT_ID)
 
             self.spec_sequence_masks[:num_spec_decodes].copy_(
-                spec_sequence_masks, non_blocking=True
+                spec_sequence_masks[:num_spec_decodes], non_blocking=True
             )
             spec_sequence_masks = self.spec_sequence_masks[:batch_size]
             spec_sequence_masks[num_spec_decodes:].fill_(False)