diff --git a/python/sglang/srt/layers/attention/flashinfer_backend.py b/python/sglang/srt/layers/attention/flashinfer_backend.py
index 23ccf51a4..ed28fd019 100644
--- a/python/sglang/srt/layers/attention/flashinfer_backend.py
+++ b/python/sglang/srt/layers/attention/flashinfer_backend.py
@@ -864,12 +864,21 @@ class FlashInferAttnBackend(AttentionBackend):
                 )
 
             else:
+                swa_window_left = (
+                    layer.sliding_window_size
+                    if not (
+                        self.forward_metadata.multi_item_params
+                        and self.forward_metadata.multi_item_params.is_enabled()
+                    )
+                    else -1
+                )
                 o1, s1 = self.prefill_wrapper_ragged.forward_return_lse(
                     q.view(-1, layer.tp_q_head_num, layer.head_dim),
                     k.view(-1, layer.tp_k_head_num, layer.head_dim),
                     v.view(-1, layer.tp_v_head_num, layer.head_dim),
                     causal=causal,
                     sm_scale=layer.scaling,
+                    window_left=swa_window_left,
                     logits_soft_cap=logits_soft_cap,
                 )
                 o2, s2 = prefill_wrapper_paged.forward_return_lse(
@@ -877,6 +886,7 @@ class FlashInferAttnBackend(AttentionBackend):
                     self.token_to_kv_pool.get_kv_buffer(layer.layer_id),
                     causal=False,
                     sm_scale=layer.scaling,
+                    window_left=swa_window_left,
                     logits_soft_cap=logits_soft_cap,
                 )
 
@@ -1313,19 +1323,34 @@ class FlashInferIndicesUpdaterPrefill:
         cross_attention_custom_mask: Optional[torch.Tensor] = None,
     ):
         for wrapper_id in range(2):
+            swa_paged_custom_mask = None
             if wrapper_id == 0:
-                # window attention use paged only
-                paged_kernel_lens = torch.minimum(
-                    seq_lens,
-                    torch.tensor(self.sliding_window_size) + seq_lens - prefix_lens,
-                )
-                paged_kernel_lens_sum = paged_kernel_lens.sum().item()
+                if use_ragged:
+                    # K for extend tokens is written after the paged wrapper runs, so
+                    # the paged wrapper sees prefix-only. Trim to the last `window` tokens
+                    # (required for SWATokenToKVPoolAllocator; also keeps mask O(window)).
+                    effective_start = torch.clamp(
+                        prefix_lens - self.sliding_window_size, min=0
+                    )
+                    paged_kernel_lens = prefix_lens - effective_start
+                    paged_kernel_lens_sum = paged_kernel_lens.sum().item()
+                    kv_start_idx = effective_start
+                    swa_paged_custom_mask = self._build_swa_prefix_custom_mask(
+                        prefix_lens, seq_lens, effective_start
+                    )
+                else:
+                    # window attention use paged only
+                    paged_kernel_lens = torch.minimum(
+                        seq_lens,
+                        torch.tensor(self.sliding_window_size) + seq_lens - prefix_lens,
+                    )
+                    paged_kernel_lens_sum = paged_kernel_lens.sum().item()
+                    kv_start_idx = seq_lens - paged_kernel_lens
             else:
                 # full attention
                 paged_kernel_lens = seq_lens
                 paged_kernel_lens_sum = seq_lens_sum
-
-            kv_start_idx = seq_lens - paged_kernel_lens
+                kv_start_idx = seq_lens - paged_kernel_lens
             use_sliding_window_kv_pool = wrapper_id == 0 and isinstance(
                 self.token_to_kv_pool_allocator, SWATokenToKVPoolAllocator
             )
@@ -1346,8 +1371,50 @@ class FlashInferIndicesUpdaterPrefill:
                 use_sliding_window_kv_pool=use_sliding_window_kv_pool,
                 fixed_split_size=fixed_split_size,
                 multi_item_params=multi_item_params,
+                cross_attention_custom_mask=swa_paged_custom_mask,
             )
 
+    def _build_swa_prefix_custom_mask(
+        self,
+        prefix_lens: torch.Tensor,
+        seq_lens: torch.Tensor,
+        kv_start_idx: torch.Tensor,
+    ) -> Optional[torch.Tensor]:
+        """Custom SWA mask for the paged wrapper in the ragged merge_state EXTEND path.
+
+        Paged KV covers absolute positions [kv_start_idx[i], prefix_lens[i]).
+        Returns None when every key is in-window for every extend query.
+        """
+        window = self.sliding_window_size
+        if window is None or window < 0:
+            return None
+
+        prefix_lens_cpu = prefix_lens.detach().cpu().tolist()
+        extend_lens_cpu = (seq_lens - prefix_lens).detach().cpu().tolist()
+        kv_start_cpu = kv_start_idx.detach().cpu().tolist()
+        if all(p == 0 for p in prefix_lens_cpu):
+            return None
+
+        device = prefix_lens.device
+        mask_parts: List[torch.Tensor] = []
+        need_mask = False
+        for prefix_len, extend_len, kv_start in zip(
+            prefix_lens_cpu, extend_lens_cpu, kv_start_cpu
+        ):
+            paged_len = int(prefix_len - kv_start)  # = min(prefix_len, window)
+            if paged_len == 0 or extend_len == 0:
+                continue
+            q_abs = torch.arange(extend_len, device=device).view(-1, 1) + prefix_len
+            k_abs = torch.arange(paged_len, device=device).view(1, -1) + kv_start
+            block = (k_abs >= (q_abs - window)).to(torch.uint8)
+            if not bool(block.all()):
+                need_mask = True
+            mask_parts.append(block.view(-1))
+
+        if not need_mask or not mask_parts:
+            return None
+        return torch.cat(mask_parts)
+
     def update_cross_attention(
         self,
         req_pool_indices: torch.Tensor,