diff --git a/src/relax/ir/dataflow_block_rewriter.cc b/src/relax/ir/dataflow_block_rewriter.cc
index fb08dfe96a17..88efad86cfdc 100644
--- a/src/relax/ir/dataflow_block_rewriter.cc
+++ b/src/relax/ir/dataflow_block_rewriter.cc
@@ -49,7 +49,7 @@ class MatcherUseDefAnalysis : public relax::ExprVisitor {
   // caller -> callee table.
   std::map<const VarNode*, std::vector<const VarNode*>> caller2callees;
 
-  const VarNode* cur_user_;
+  const VarNode* cur_user_ = nullptr;
 
   void VisitBinding_(const VarBindingNode* binding) override {
     // init
diff --git a/tests/python/relax/test_dataflow_pattern.py b/tests/python/relax/test_dataflow_pattern.py
index f67b0530ca87..03a3beb2f27e 100644
--- a/tests/python/relax/test_dataflow_pattern.py
+++ b/tests/python/relax/test_dataflow_pattern.py
@@ -1053,9 +1053,17 @@ def main(
         assert ctx.match_dfb(dfb) is None
 
 
-def get_qkv_proj_rewriter(
-    inp_pat, Q_weight_pat, K_weight_pat, V_weight_pat, matmul1, matmul2, matmul3
-):
+def get_qkv_proj_rewriter():
+    with PatternContext() as ctx:
+        inp_pat = wildcard()
+        Q_weight_pat = wildcard()
+        K_weight_pat = wildcard()
+        V_weight_pat = wildcard()
+
+        matmul1 = is_op("relax.matmul")(inp_pat, Q_weight_pat)
+        matmul2 = is_op("relax.matmul")(inp_pat, K_weight_pat)
+        matmul3 = is_op("relax.matmul")(inp_pat, V_weight_pat)
+
     def qkv_proj_rewriter(matchings, _):
         inp = matchings[inp_pat]
         Q_weight = matchings[Q_weight_pat]
@@ -1071,7 +1079,7 @@ def qkv_proj_rewriter(matchings, _):
 
         return {matchings[matmul1]: Q, matchings[matmul2]: K, matchings[matmul3]: V}
 
-    return qkv_proj_rewriter
+    return ctx, qkv_proj_rewriter
 
 
 def test_combine_matmul_twice():
@@ -1123,21 +1131,63 @@ def expected(
             R.output(out)
         return out
 
-    with PatternContext() as ctx:
-        inp_pat = wildcard()
-        Q_weight_pat = wildcard()
-        K_weight_pat = wildcard()
-        V_weight_pat = wildcard()
+    ctx, rewriter = get_qkv_proj_rewriter()
+    rewritten = rewrite_bindings(ctx, rewriter, qkv_x2)
+    tvm.ir.assert_structural_equal(rewritten, expected)
 
-        matmul1 = is_op("relax.matmul")(inp_pat, Q_weight_pat)
-        matmul2 = is_op("relax.matmul")(inp_pat, K_weight_pat)
-        matmul3 = is_op("relax.matmul")(inp_pat, V_weight_pat)
 
-        rewriter = get_qkv_proj_rewriter(
-            inp_pat, Q_weight_pat, K_weight_pat, V_weight_pat, matmul1, matmul2, matmul3
-        )
-        rewritten = rewrite_bindings(ctx, rewriter, qkv_x2)
-        tvm.ir.assert_structural_equal(rewritten, expected)
+def test_dataflow_may_start_with_match_cast():
+    """Inputs to rewrite_bindings may contain R.match_cast
+
+    This is a regression test.  In previous implementations, applying
+    `rewrite_bindings` when `R.match_cast` is the first binding of a
+    `R.dataflow` block would cause a segfault.
+
+    """
+
+    @R.function(private=True)
+    def before(
+        x_untyped: R.Tensor,
+        w0_untyped: R.Tensor,
+        w1_untyped: R.Tensor,
+        w2_untyped: R.Tensor,
+    ):
+        with R.dataflow():
+            x = R.match_cast(x_untyped, R.Tensor((2, 1024, 640), "float32"))
+            w0 = R.match_cast(w0_untyped, R.Tensor((640, 640), "float32"))
+            w1 = R.match_cast(w1_untyped, R.Tensor((640, 640), "float32"))
+            w2 = R.match_cast(w2_untyped, R.Tensor((640, 640), "float32"))
+            out_0 = R.matmul(x, w0)
+            out_1 = R.matmul(x, w1)
+            out_2 = R.matmul(x, w2)
+            out = (out_0, out_1, out_2)
+            R.output(out)
+        return out
+
+    @R.function(private=True)
+    def expected(
+        x_untyped: R.Tensor,
+        w0_untyped: R.Tensor,
+        w1_untyped: R.Tensor,
+        w2_untyped: R.Tensor,
+    ):
+        with R.dataflow():
+            x = R.match_cast(x_untyped, R.Tensor((2, 1024, 640), "float32"))
+            w0 = R.match_cast(w0_untyped, R.Tensor((640, 640), "float32"))
+            w1 = R.match_cast(w1_untyped, R.Tensor((640, 640), "float32"))
+            w2 = R.match_cast(w2_untyped, R.Tensor((640, 640), "float32"))
+            w_concat = R.concat((w0, w1, w2), axis=1)
+            out_concat = R.matmul(x, w_concat)
+            out_0 = R.strided_slice(out_concat, axes=[2], begin=[0], end=[640])
+            out_1 = R.strided_slice(out_concat, axes=[2], begin=[640], end=[1280])
+            out_2 = R.strided_slice(out_concat, axes=[2], begin=[1280], end=[1920])
+            out = (out_0, out_1, out_2)
+            R.output(out)
+        return out
+
+    ctx, rewriter = get_qkv_proj_rewriter()
+    rewritten = rewrite_bindings(ctx, rewriter, before)
+    tvm.ir.assert_structural_equal(rewritten, expected)
 
 
 def test_combine_matmul_emit_order():
@@ -1181,27 +1231,16 @@ def expected(
             R.output(out)
         return out
 
-    with PatternContext() as ctx:
-        inp_pat = wildcard()
-        Q_weight_pat = wildcard()
-        K_weight_pat = wildcard()
-        V_weight_pat = wildcard()
+    ctx, rewriter = get_qkv_proj_rewriter()
 
-        matmul1 = is_op("relax.matmul")(inp_pat, Q_weight_pat)
-        matmul2 = is_op("relax.matmul")(inp_pat, K_weight_pat)
-        matmul3 = is_op("relax.matmul")(inp_pat, V_weight_pat)
+    rewritten = rewrite_bindings(ctx, rewriter, main)
+    tvm.ir.assert_structural_equal(rewritten, expected)
 
-        rewriter = get_qkv_proj_rewriter(
-            inp_pat, Q_weight_pat, K_weight_pat, V_weight_pat, matmul1, matmul2, matmul3
-        )
-        rewritten = rewrite_bindings(ctx, rewriter, main)
-        tvm.ir.assert_structural_equal(rewritten, expected)
-
-        # make sure it builds
-        mod = tvm.IRModule()
-        mod["main"] = rewritten
+    # make sure it builds
+    mod = tvm.IRModule()
+    mod["main"] = rewritten
 
-        rx.build(mod, target="llvm")
+    rx.build(mod, target="llvm")
 
 
 def test_combine_transposed_matmul_twice():