diff --git a/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp b/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
index ac5d6207259eb..62c015a85ee36 100644
--- a/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
+++ b/mlir/lib/Dialect/Tosa/Utils/ConversionUtils.cpp
@@ -216,22 +216,23 @@ mlir::tosa::convertFromIntAttr(const DenseElementsAttr &attr, const int rank) {
 
 bool mlir::tosa::hasUniqueConstantScatterIndices(
     ShapedType indicesType, DenseIntElementsAttr indicesAttr) {
-  llvm::ArrayRef<int64_t> const indicesShape = indicesType.getShape();
+  const llvm::ArrayRef<int64_t> indicesShape = indicesType.getShape();
   const unsigned int indicesRank = indicesShape.size();
   const unsigned int lastDimSize = indicesShape[indicesRank - 1];
 
   // check each batch of indices from the flat indicesAttr values
   // for duplicates
-  auto const indicesValues = indicesAttr.getValues<int32_t>();
+  auto const indicesValues = indicesAttr.getValues<APInt>();
   assert(
       (indicesValues.size() % lastDimSize == 0) &&
       "Constant indices data length should be a multiple of indicesShape[-1]");
 
-  std::vector<uint64_t> indices(lastDimSize);
+  std::vector<APInt> indices(lastDimSize);
   for (auto beg = indicesValues.begin(); beg < indicesValues.end();
        beg += lastDimSize) {
     std::copy(beg, beg + lastDimSize, indices.begin());
-    std::sort(indices.begin(), indices.end());
+    std::sort(indices.begin(), indices.end(),
+              [](const APInt &a, const APInt &b) { return a.slt(b); });
     if (std::adjacent_find(indices.begin(), indices.end()) != indices.end()) {
       // found duplicate values in indices in batch
       return false;
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index c9e03ca53a729..3d24928487ed2 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -4,7 +4,7 @@
 // validation flow.
 //--------------------------------------------------------------------------------------------------
 
-// RUN: mlir-opt %s -split-input-file -verify-diagnostics -tosa-attach-target="profiles=pro_int,pro_fp extensions=int16,int4,bf16,fp8e4m3,fp8e5m2,fft,variable,controlflow,doubleround,inexactround" -tosa-validate="strict-op-spec-alignment"
+// RUN: mlir-opt %s -split-input-file -verify-diagnostics -tosa-attach-target="specification_version=1.1.draft profiles=pro_int,pro_fp extensions=int16,int4,int64,bf16,fp8e4m3,fp8e5m2,fft,variable,controlflow,doubleround,inexactround" -tosa-validate="strict-op-spec-alignment"
 
 
 func.func @test_cast(%arg0: tensor<i1>) -> tensor<5xi32> {
@@ -2044,6 +2044,16 @@ func.func @test_scatter_duplicate_indices(%arg0: tensor<2x52x3xf32>, %arg2: tens
 
 // -----
 
+// CHECK-LABEL: test_scatter_duplicate_indices_int64
+func.func @test_scatter_duplicate_indices_int64(%arg0: tensor<2x52x3xf32>, %arg2: tensor<2x12x3xf32>) -> tensor<2x52x3xf32> {
+  %indices = "tosa.const"() { values = dense<[[1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 11, 12], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]> : tensor<2x12xi64> } : () -> tensor<2x12xi64>
+  // expected-error@+1 {{'tosa.scatter' op indices values contain duplicates}}
+  %0 = tosa.scatter %arg0, %indices, %arg2 : (tensor<2x52x3xf32>, tensor<2x12xi64>, tensor<2x12x3xf32>) -> tensor<2x52x3xf32>
+  return %0 : tensor<2x52x3xf32>
+}
+
+// -----
+
 func.func @test_reduce_all_unsupported_data_types(%arg0: tensor<2x12x11xf32>) -> tensor<1x12x11xf32> {
   // expected-error@+1 {{'tosa.reduce_all' op illegal: operation operand/result data types did not align with any profile or extension, got (f32,f32), did you mean (i1,i1)?}}
   %0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<2x12x11xf32>) -> tensor<1x12x11xf32>
diff --git a/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-valid.mlir b/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-valid.mlir
index acbff73b8b948..c285ae3cf44ee 100644
--- a/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-valid.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-validation-version-1p1-valid.mlir
@@ -2,6 +2,7 @@
 
 // -----
 
+// CHECK-LABEL: test_matmul_fp8_mixed_precision_operands
 func.func @test_matmul_fp8_mixed_precision_operands(%arg0: tensor<1x14x19xf8E4M3FN>, %arg1: tensor<1x19x28xf8E5M2>) -> tensor<1x14x28xf16> {
   %azp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E4M3FN>}> : () -> tensor<1xf8E4M3FN>
   %bzp0 = "tosa.const"() <{values = dense<0.0> : tensor<1xf8E5M2>}> : () -> tensor<1xf8E5M2>
@@ -146,3 +147,12 @@ func.func @test_argmax_bf16_i64(%arg0: tensor<12x8x16xbf16>) -> tensor<12x16xi64
   %0 = tosa.argmax %arg0 { axis = 1 : i32 } : (tensor<12x8x16xbf16>) -> tensor<12x16xi64>
   return %0 : tensor<12x16xi64>
 }
+
+// -----
+
+// CHECK-LABEL: test_scatter_const_indices_int64
+func.func @test_scatter_const_indices_int64(%arg0: tensor<2x52x3xf32>, %arg2: tensor<2x12x3xf32>) -> tensor<2x52x3xf32> {
+  %indices = "tosa.const"() { values = dense<[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]> : tensor<2x12xi64> } : () -> tensor<2x12xi64>
+  %0 = tosa.scatter %arg0, %indices, %arg2 : (tensor<2x52x3xf32>, tensor<2x12xi64>, tensor<2x12x3xf32>) -> tensor<2x52x3xf32>
+  return %0 : tensor<2x52x3xf32>
+}