diff --git a/python/sglang/srt/layers/attention/flashattention_backend.py b/python/sglang/srt/layers/attention/flashattention_backend.py
index 60a46b146..e06bcb449 100644
--- a/python/sglang/srt/layers/attention/flashattention_backend.py
+++ b/python/sglang/srt/layers/attention/flashattention_backend.py
@@ -616,11 +616,14 @@ class FlashAttentionBackend(AttentionBackend):
             # upper bound) so captured graphs keep a valid address; each replay
             # refills a [:num_tokens] view.
             if self.use_sliding_window_kv_pool:
+                assert forward_batch.out_cache_loc is not None
                 m.swa_page_table = torch.zeros(
                     (bs, self.max_num_pages), dtype=torch.int32, device=device
                 )
                 self.full_cg_prefill_swa_out_cache_loc = torch.zeros(
-                    (self.max_context_len,), dtype=torch.int64, device=device
+                    (forward_batch.out_cache_loc.shape[0],),
+                    dtype=torch.int64,
+                    device=device,
                 )
             self.full_cg_prefill_metadata = m
         m = self.full_cg_prefill_metadata
@@ -657,6 +660,11 @@ class FlashAttentionBackend(AttentionBackend):
             # SWA write targets for the new tokens (KVWriteLoc.swa_loc), refilled
             # into the pointer-stable buffer and bound as a [:num_tokens] view.
             num_out = forward_batch.out_cache_loc.shape[0]
+            assert_buffer_fits(
+                num_out,
+                self.full_cg_prefill_swa_out_cache_loc.shape[0],
+                "full-CG prefill SWA write-location buffer",
+            )
             self.full_cg_prefill_swa_out_cache_loc[:num_out].copy_(
                 self.token_to_kv_pool.translate_loc_from_full_to_swa(
                     forward_batch.out_cache_loc
@@ -686,7 +694,7 @@ class FlashAttentionBackend(AttentionBackend):
         seq_lens_cpu = forward_batch.seq_lens_cpu
         eager_max_k = (
             seq_lens_cpu.max().item()
-            if seq_lens_cpu is not None
+            if seq_lens_cpu is not None and seq_lens_cpu.numel() > 0
             else self.max_context_len
         )
 
diff --git a/test/registered/unit/layers/attention/test_flashattention_graph_metadata.py b/test/registered/unit/layers/attention/test_flashattention_graph_metadata.py
new file mode 100644
index 000000000..e44db9903
--- /dev/null
+++ b/test/registered/unit/layers/attention/test_flashattention_graph_metadata.py
@@ -0,0 +1,72 @@
+import unittest
+from types import SimpleNamespace
+
+import torch
+
+from sglang.srt.layers.attention.flashattention_backend import FlashAttentionBackend
+from sglang.srt.model_executor.forward_batch_info import ForwardMode
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cuda_ci(est_time=2, stage="base-b", runner_config="1-gpu-small")
+
+
+class TestFlashAttentionGraphMetadata(CustomTestCase):
+    def test_full_prefill_swa_buffer_uses_capture_token_capacity(self):
+        backend = FlashAttentionBackend.__new__(FlashAttentionBackend)
+        backend.full_cg_prefill_metadata = None
+        backend.use_sliding_window_kv_pool = True
+        backend.max_num_pages = 4
+        backend.max_context_len = 4
+        backend.page_size = 1
+        backend.req_to_token = torch.zeros((1, 4), dtype=torch.int32)
+        backend.token_to_kv_pool = SimpleNamespace(
+            translate_loc_from_full_to_swa=lambda locations: locations
+        )
+        forward_batch = SimpleNamespace(
+            batch_size=1,
+            seq_lens=torch.zeros(1, dtype=torch.int64),
+            extend_seq_lens=torch.zeros(1, dtype=torch.int64),
+            seq_lens_cpu=torch.zeros(1, dtype=torch.int64),
+            req_pool_indices=torch.zeros(1, dtype=torch.int64),
+            out_cache_loc=torch.arange(8, dtype=torch.int64),
+            positions=torch.arange(8, dtype=torch.int64),
+        )
+
+        backend._init_full_cg_prefill_metadata(forward_batch, in_capture=True)
+
+        self.assertEqual(backend.full_cg_prefill_swa_out_cache_loc.shape, (8,))
+        forward_batch.out_cache_loc = torch.arange(9, dtype=torch.int64)
+        with self.assertRaisesRegex(AssertionError, "used 9 > capacity 8"):
+            backend._init_full_cg_prefill_metadata(forward_batch, in_capture=False)
+
+    def test_empty_cpu_sequence_lengths_use_static_bound(self):
+        backend = FlashAttentionBackend.__new__(FlashAttentionBackend)
+        backend.max_context_len = 16
+        backend.req_to_token_pool = SimpleNamespace(
+            req_to_token=torch.zeros((1, 16), dtype=torch.int32)
+        )
+        backend.is_prefill_aware_swa = False
+        backend.has_swa = False
+        backend.use_sliding_window_kv_pool = False
+        backend._unified_dense = False
+        backend.page_size = 1
+        backend._compute_scheduler_metadata = lambda *_: None
+        backend._maybe_init_local_attn_metadata = lambda *_: None
+        forward_batch = SimpleNamespace(
+            forward_mode=ForwardMode.DECODE,
+            seq_lens=torch.empty(0, dtype=torch.int64),
+            batch_size=0,
+            seq_lens_cpu=torch.empty(0, dtype=torch.int64),
+            spec_info=None,
+            req_pool_indices=torch.empty(0, dtype=torch.int64),
+            encoder_lens=None,
+        )
+
+        backend.init_forward_metadata(forward_batch)
+
+        self.assertEqual(backend.forward_metadata.max_seq_len_k, 16)
+
+
+if __name__ == "__main__":
+    unittest.main()