diff --git a/js/web/lib/wasm/jsep/webgpu/ops/conv-transpose.ts b/js/web/lib/wasm/jsep/webgpu/ops/conv-transpose.ts
index 18bf30a325d83..994aeb83a0ed5 100644
--- a/js/web/lib/wasm/jsep/webgpu/ops/conv-transpose.ts
+++ b/js/web/lib/wasm/jsep/webgpu/ops/conv-transpose.ts
@@ -132,7 +132,7 @@ export const parseConvTransposeAttributes = (attributes: Record<string, unknown>
     typeof attributes.autoPad == 'undefined' ? 0 : (attributes.autoPad as number)
   ];
   const dilations = attributes.dilations as [number, number];
-  const group = attributes.group as number;
+  const group = (attributes.group as number) ?? 1; // default to 1 per ONNX spec
   const kernelShape = attributes.kernelShape as [number, number];
   const pads = attributes.pads as [number, number, number, number];
   const strides = attributes.strides as [number, number];
diff --git a/onnxruntime/core/providers/webgpu/nn/conv_transpose.cc b/onnxruntime/core/providers/webgpu/nn/conv_transpose.cc
index 84a0afd873d23..c3842a5c875e3 100644
--- a/onnxruntime/core/providers/webgpu/nn/conv_transpose.cc
+++ b/onnxruntime/core/providers/webgpu/nn/conv_transpose.cc
@@ -57,6 +57,11 @@ Status ConvTranspose<is_channels_last>::ComputeInternal(ComputeContext& context)
 
   bool has_bias = context.InputCount() > 2;
   const auto* bias = has_bias ? context.Input<Tensor>(2) : nullptr;
+  // Validate bias shape if provided
+  if (has_bias && (bias->Shape().NumDimensions() != 1 || bias->Shape()[0] != num_output_channels)) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "invalid bias");
+  }
+
   if (input_shape.NumDimensions() == 3 && filter_shape.NumDimensions() == 3) {
     // ConvTranspose1D
     TensorShapeVector input_shape_vector = input_shape.AsShapeVector();