diff --git a/modules/dnn/src/layers/tile_layer.cpp b/modules/dnn/src/layers/tile_layer.cpp
index 1357b9e89e..b78eacf35f 100644
--- a/modules/dnn/src/layers/tile_layer.cpp
+++ b/modules/dnn/src/layers/tile_layer.cpp
@@ -80,8 +80,10 @@ public:
             {
                 tmp = tmp.reshape(0, dims);
                 tmp = cv::repeat(tmp, 1, rep_i);
-                dims *= out_shape[i];
             }
+            // accumulate for every axis so a repeated non-leading axis tiles
+            // per-axis blocks instead of repeating the whole flattened tensor
+            dims *= out_shape[i];
         }
         tmp = tmp.reshape(0, out_shape);
 
diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
index 11fde4278a..867f7d7181 100644
--- a/modules/dnn/src/onnx/onnx_importer.cpp
+++ b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -3132,13 +3132,14 @@ void ONNXImporter::parseTile(LayerParams& layerParams, const opencv_onnx::NodePr
         // input2 in tile-1: axis, 1d tensor of shape [1]
         Mat input2_blob = getBlob(node_proto, 2);
         CV_CheckEQ(input2_blob.total(), 1ull, "ONNX/Tile: axis must be a 0D tensor or 1D tensor of shape [1].");
-        int axis = input2_blob.at<int>(0);
+        int axis = normalize_axis(input2_blob.at<int>(0), input0_dims);
         repeats_vec[axis] = tiles;
     }
     else
     {
         // input1 in tile>1: repeats
         CV_CheckEQ(input1_blob.dims, 2, "ONNX/Tile: repeats must be a 1D tensor."); // 1D tensor is represented as a 2D Mat
+        CV_CheckEQ((int)input1_blob.total(), input0_dims, "ONNX/Tile: repeats length must match the input rank.");
         for (int i = 0; i < input1_blob.total(); i++)
             repeats_vec[i] = input1_blob.at<int>(i);
     }
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index 86363f37b3..1868cb3708 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -3039,6 +3039,9 @@ TEST_P(Test_ONNX_nets, YOLOv5n)
 TEST_P(Test_ONNX_layers, Tile)
 {
     testONNXModels("tile", pb);
+    // tile-1 (opset 1) form with a negative axis; the parser must normalize it
+    // against the input rank instead of indexing the repeats buffer directly.
+    testONNXModels("tile_neg_axis");
 }
 
 TEST_P(Test_ONNX_layers, Gelu)