diff --git a/tensorrt_llm/_torch/attention_backend/sparse/deepseek_v4/deepseek_v4.py b/tensorrt_llm/_torch/attention_backend/sparse/deepseek_v4/deepseek_v4.py
index 1f76e5a73e51..c07d82cf9f6c 100644
--- a/tensorrt_llm/_torch/attention_backend/sparse/deepseek_v4/deepseek_v4.py
+++ b/tensorrt_llm/_torch/attention_backend/sparse/deepseek_v4/deepseek_v4.py
@@ -931,7 +931,11 @@ def _compute_token_positions(
         cu_seq_lens_buf: torch.Tensor,
         req_idx_per_token_buf: torch.Tensor,
     ) -> torch.Tensor:
-        """Compute cu_seq_lens, req_idx_per_token, and token_positions (eager)."""
+        """Compute cu_seq_lens and token_positions (eager).
+
+        req_idx_per_token_buf is read, not recomputed: super().on_update_kv_lens()
+        has already rebuilt it for the current seq_lens.
+        """
         device = seq_lens.device
 
         # cu_seq_lens
@@ -939,14 +943,10 @@ def _compute_token_positions(
             torch.cumsum(seq_lens.to(torch.int), dim=0), (1, 0)
         )
 
-        # req_idx_per_token via searchsorted
-        token_idx = torch.arange(num_tokens, dtype=torch.int32, device=device)
-        req_idx = torch.searchsorted(
-            cu_seq_lens_buf[1 : batch_size + 1].to(torch.int32), token_idx, right=True
-        )
-        req_idx_per_token_buf[:num_tokens] = req_idx
+        req_idx = req_idx_per_token_buf[:num_tokens].to(torch.int64)
 
         # token positions
+        token_idx = torch.arange(num_tokens, dtype=torch.int32, device=device)
         base_pos = cached_tokens[req_idx].to(torch.int32)
         offsets = token_idx - cu_seq_lens_buf[req_idx].to(torch.int32)
         return base_pos + offsets
diff --git a/tensorrt_llm/_torch/attention_backend/sparse/dsa.py b/tensorrt_llm/_torch/attention_backend/sparse/dsa.py
index a57fdadf0776..f1a45201e16e 100644
--- a/tensorrt_llm/_torch/attention_backend/sparse/dsa.py
+++ b/tensorrt_llm/_torch/attention_backend/sparse/dsa.py
@@ -263,6 +263,20 @@ def _pick_dsl_expand(
     return factor, eff
 
 
+def build_req_idx_per_token(seq_lens: torch.Tensor,
+                            num_tokens: int) -> torch.Tensor:
+    """Map each flattened-batch token to its request index.
+
+    Capture-safe device counterpart of prepare_for_indices_conversion()'s
+    host repeat_interleave; right=True keeps zero-length rows equivalent.
+    """
+    cu_seq_lens = torch.cumsum(seq_lens, dim=0, dtype=torch.int32)
+    token_idx = torch.arange(num_tokens,
+                             device=seq_lens.device,
+                             dtype=torch.int32)
+    return torch.searchsorted(cu_seq_lens, token_idx, right=True)
+
+
 def _compute_slot_mappings(
     global_positions: torch.Tensor,
     block_offsets: torch.Tensor,
@@ -829,13 +843,18 @@ def on_update_kv_lens(self):
         # boundary so a "shared" layer never reuses a stale top-k.
         self.shared_topk_indices = None
 
+        # prepare()'s map is stale once the draft loop rewrites seq_lens.
+        # Unconditional so subclasses (DeepSeek-V4) can reuse the buffer.
+        if self.num_tokens > 0:
+            self.req_idx_per_token[:self.num_tokens] = build_req_idx_per_token(
+                self.seq_lens_cuda[:self.num_seqs],
+                self.num_tokens).to(self.req_idx_per_token.dtype)
+
         if self.kv_cache_manager is not None and self.num_tokens > 0:
             seq_lens = self.seq_lens_cuda[:self.num_seqs]
             # Runtime cached lengths after overlap/spec-dec correction.
             start_positions = self.kv_lens_cuda[:self.num_seqs] - seq_lens
 
-            # Reuse request-per-token mapping prepared in metadata.prepare().
-            # This avoids repeat_interleave in graph-capture mode.
             req_indices = self.req_idx_per_token[:self.num_tokens].to(
                 dtype=torch.int64)
             seq_starts = torch.cumsum(
diff --git a/tests/unittest/_torch/attention/sparse/dsa/test_req_idx_per_token.py b/tests/unittest/_torch/attention/sparse/dsa/test_req_idx_per_token.py
new file mode 100644
index 000000000000..f4d2144bc151
--- /dev/null
+++ b/tests/unittest/_torch/attention/sparse/dsa/test_req_idx_per_token.py
@@ -0,0 +1,100 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Tests for the token->request map used by DSAtrtllmAttentionMetadata.
+
+1. build_req_idx_per_token must match the host repeat_interleave build for
+   every layout, so the device and host builders cannot drift.
+2. on_update_kv_lens() must rebuild the map after the MTP draft loop rewrites
+   seq_lens, or every draft token is misattributed to request 0
+   (https://nvbugs/6513132, https://nvbugs/6513093).
+"""
+
+from unittest.mock import Mock
+
+import pytest
+import torch
+
+from tensorrt_llm._torch.attention_backend.sparse.dsa import (
+    DSAtrtllmAttentionMetadata,
+    build_req_idx_per_token,
+)
+
+
+def _host_reference(seq_lens: torch.Tensor) -> torch.Tensor:
+    """The host build from prepare_for_indices_conversion()."""
+    return torch.repeat_interleave(
+        torch.arange(len(seq_lens), dtype=torch.int32, device=seq_lens.device),
+        seq_lens,
+        dim=0,
+    )
+
+
+@pytest.mark.parametrize(
+    "seq_lens",
+    [
+        pytest.param([4, 4, 4], id="target_forward_mtp3"),
+        pytest.param([1, 1, 1], id="draft_loop"),
+        pytest.param([37, 5, 1, 1], id="mixed_ctx_gen"),
+        pytest.param([2, 0, 3], id="zero_length_row"),
+        pytest.param([0, 4], id="leading_zero_row"),
+    ],
+)
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+def test_matches_host_repeat_interleave(seq_lens, device):
+    if device == "cuda" and not torch.cuda.is_available():
+        pytest.skip("CUDA not available")
+    seq_lens = torch.tensor(seq_lens, dtype=torch.int32, device=device)
+    num_tokens = int(seq_lens.sum())
+
+    result = build_req_idx_per_token(seq_lens, num_tokens)
+
+    assert result.to(torch.int32).tolist() == _host_reference(seq_lens).tolist()
+
+
+def test_on_update_kv_lens_rebuilds_stale_map():
+    """on_update_kv_lens() must replace prepare()'s stale map (fails pre-fix)."""
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA not available")
+    device = "cuda"
+    max_draft_len = 3
+    num_requests = 3
+
+    md = object.__new__(DSAtrtllmAttentionMetadata)
+    md.kv_cache_manager = None
+    md._num_generations = 0
+    # Stub collaborators unrelated to the map rebuild (test_dsa_indexer.py style).
+    md.kv_lens_cuda = torch.tensor([100, 200, 300], dtype=torch.int32, device=device)
+    md._compute_kv_lens_row_reorder = Mock()
+    md.prepare_dense_topk_indices = Mock()
+
+    # prepare()-time state: target forward, 1 + max_draft_len tokens/request.
+    target_seq_lens = torch.full((num_requests,), 1 + max_draft_len, dtype=torch.int32)
+    md._seq_lens = target_seq_lens
+    md._seq_lens_cuda = target_seq_lens.to(device)
+    md._num_tokens = int(target_seq_lens.sum())
+    md.req_idx_per_token = torch.empty(md._num_tokens, dtype=torch.int32, device=device)
+    md.req_idx_per_token[:] = _host_reference(md._seq_lens_cuda)
+
+    # Draft loop: one token per request; the stale prefix reads [0, 0, 0].
+    draft_seq_lens = torch.ones(num_requests, dtype=torch.int32)
+    md._seq_lens = draft_seq_lens
+    md._seq_lens_cuda = draft_seq_lens.to(device)
+    md._num_tokens = num_requests
+    assert md.req_idx_per_token[:num_requests].tolist() == [0, 0, 0]
+
+    md.on_update_kv_lens()
+
+    assert md.req_idx_per_token[:num_requests].tolist() == [0, 1, 2]