diff --git a/mlir/lib/Catalyst/Transforms/ScatterPatterns.cpp b/mlir/lib/Catalyst/Transforms/ScatterPatterns.cpp
index 013cdec2a3..d92fc10788 100644
--- a/mlir/lib/Catalyst/Transforms/ScatterPatterns.cpp
+++ b/mlir/lib/Catalyst/Transforms/ScatterPatterns.cpp
@@ -32,9 +32,33 @@ namespace catalyst {
 struct ScatterOpRewritePattern : public mlir::OpRewritePattern<mhlo::ScatterOp> {
     using mlir::OpRewritePattern<mhlo::ScatterOp>::OpRewritePattern;
 
+    mlir::LogicalResult onlyOneInputUpdateAndResult(mhlo::ScatterOp op) const
+    {
+        // Assumption 1: only one input, one update, and one result
+        // * size(inputs) == 1
+        // * size(updates) == 1
+        // * size(results) == 1
+        // All ScatterOp ops with N inputs, N updates, and N results can be split
+        // into N ScatterOp ops with 1 input, 1 update and 1 result.
+        // This simplifies the analysis of the update_computation.
+
+        // From:
+        // C5: 0 < size(inputs) = size(updates) = N
+        // C24: element_type(results[i]) = Ei for all i in [0,N).
+        // It follows that:
+        // 0 < size(inputs) = size(updates) = size(results) = N
+        return op.getResults().size() == 1 ? success() : failure();
+    }
+
     mlir::LogicalResult matchAndRewrite(mhlo::ScatterOp op,
                                         mlir::PatternRewriter &rewriter) const override
     {
+
+        if (failed(onlyOneInputUpdateAndResult(op))) {
+            // Otherwise it will segfault.
+            op.emitError() << "Only one input, update, and result";
+            return failure();
+        }
         // Compute operation hash in case they are more than one scatter and they have different
         // update function
         auto opHash = OperationEquivalence::computeHash(op);
diff --git a/mlir/test/Catalyst/ScatterTest.mlir b/mlir/test/Catalyst/ScatterTest.mlir
index b6e953ad05..217db6fcc1 100644
--- a/mlir/test/Catalyst/ScatterTest.mlir
+++ b/mlir/test/Catalyst/ScatterTest.mlir
@@ -247,3 +247,25 @@ func.func public @example_no_update_dim(%arg0: tensor<4xf64>) -> tensor<4xf64> {
 //   CHECK:      scf.yield [[INSERTED]] : tensor<4xf64>
 //   CHECK:    }
 //   CHECK:    return [[FORRES]] : tensor<4xf64>
+
+// -----
+
+module @test_multiple_inputs {
+      %inputs = "test.op"() : () -> (tensor<7x131072xf64>)
+      %scatter_indices = "test.op"() : () -> (tensor<1xi32>)
+      %updates = "test.op"() : () -> (tensor<131072xf64>)
+      // expected-error@+1 {{Only one input, update, and result}}
+      %results:2 = "mhlo.scatter"(%inputs, %inputs, %scatter_indices, %updates, %updates) <{
+          indices_are_sorted = true,
+          unique_indices = true,
+          scatter_dimension_numbers = #mhlo.scatter<
+              update_window_dims = [0],
+              inserted_window_dims = [0],
+              scatter_dims_to_operand_dims = [0]
+          >
+      }> ({
+      ^bb0(%arg3: tensor<f64>, %arg4: tensor<f64>, %arg5: tensor<f64>, %arg6: tensor<f64>):
+        mhlo.return %arg4, %arg6 : tensor<f64>, tensor<f64>
+      }) : (tensor<7x131072xf64>, tensor<7x131072xf64>, tensor<1xi32>, tensor<131072xf64>, tensor<131072xf64>) -> (tensor<7x131072xf64>, tensor<7x131072xf64>)
+      "test.op"(%results#0, %results#1) : (tensor<7x131072xf64>, tensor<7x131072xf64>) -> ()
+}