diff --git a/vllm_npu/attention/attention_v1.py b/vllm_npu/attention/attention_v1.py
index 1da282e..781a707 100644
--- a/vllm_npu/attention/attention_v1.py
+++ b/vllm_npu/attention/attention_v1.py
@@ -467,12 +467,14 @@ class AscendAttentionBackendImpl(AttentionImpl):
             k = key[start:end].unsqueeze(0)
             v = value[start:end].unsqueeze(0)
 
-            # Mask (lower triangular for causal)
-            # npu_fusion_attention requires mask dim to be 2 or 4.
-            # We use (B, 1, S, S) -> (1, 1, S, S)
-            attn_mask = torch.ones(
+            # Create additive mask (0 for keep, -inf for mask)
+            inf_value = float("-inf")
+            mask_bool = torch.ones(
                 q_len, q_len, dtype=torch.bool, device=query.device
-            ).triu_(diagonal=1).unsqueeze(0).unsqueeze(0)
+            ).triu_(diagonal=1)
+            attn_mask = torch.zeros(
+                q_len, q_len, dtype=query.dtype, device=query.device
+            ).masked_fill_(mask_bool, inf_value).unsqueeze(0).unsqueeze(0)
 
             # Run npu_fusion_attention (BSND)
             attn_out = torch_npu.npu_fusion_attention(
@@ -565,9 +567,14 @@ class AscendAttentionBackendImpl(AttentionImpl):
                         -1, self.num_kv_heads, self.head_size
                     )[:kv_len]
 
-                    causal_mask = torch.ones(
+                    inf_value = float("-inf")
+                    mask_bool = torch.ones(
                         q_len, kv_len, dtype=torch.bool, device=query.device
                     ).triu_(diagonal=kv_len - q_len + 1)
+                    
+                    causal_mask = torch.zeros(
+                        q_len, kv_len, dtype=query.dtype, device=query.device
+                    ).masked_fill_(mask_bool, inf_value)
 
                     attn_out = torch_npu.npu_fusion_attention(
                         q.unsqueeze(0),
@@ -586,9 +593,15 @@ class AscendAttentionBackendImpl(AttentionImpl):
                     # Full self-attention (no prior cache)
                     k = key[start:end]
                     v = value[start:end]
-                    causal_mask = torch.ones(
+                    
+                    inf_value = float("-inf")
+                    mask_bool = torch.ones(
                         q_len, q_len, dtype=torch.bool, device=query.device
                     ).triu_(diagonal=1)
+                    
+                    causal_mask = torch.zeros(
+                        q_len, q_len, dtype=query.dtype, device=query.device
+                    ).masked_fill_(mask_bool, inf_value)
 
                     attn_out = torch_npu.npu_fusion_attention(
                         q.unsqueeze(0),