CVE-2026-63632

{"id": "CVE-2026-63632", "cveTags": [], "metrics": {"ssvcV203": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "ssvcData": {"id": "CVE-2026-63632", "role": "CISA Coordinator", "options": [{"exploitation": "poc"}, {"automatable": "no"}, {"technicalImpact": "partial"}], "version": "2.0.3", "timestamp": "2026-08-18T18:51:22.894530Z"}}], "cvssMetricV31": [{"type": "Secondary", "source": "security-advisories@github.com", "cvssData": {"scope": "UNCHANGED", "version": "3.1", "baseScore": 3.3, "attackVector": "LOCAL", "baseSeverity": "LOW", "vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L", "integrityImpact": "NONE", "userInteraction": "REQUIRED", "attackComplexity": "LOW", "availabilityImpact": "LOW", "privilegesRequired": "NONE", "confidentialityImpact": "NONE"}, "impactScore": 1.4, "exploitabilityScore": 1.8}]}, "affected": [{"source": "security-advisories@github.com", "affectedData": [{"vendor": "onnx", "product": "onnx", "versions": [{"status": "affected", "version": ">= 1.3.0, < 1.22.0"}]}]}], "published": "2026-08-18T15:16:56.463", "references": [{"url": "https://github.com/onnx/onnx/commit/e9c74f596eaa0250f89e52a54160a25bbcb25b66", "source": "security-advisories@github.com"}, {"url": "https://github.com/onnx/onnx/pull/7880", "source": "security-advisories@github.com"}, {"url": "https://github.com/onnx/onnx/releases/tag/v1.22.0", "source": "security-advisories@github.com"}, {"url": "https://github.com/onnx/onnx/security/advisories/GHSA-p893-rvq9-2xf9", "source": "security-advisories@github.com"}, {"url": "https://github.com/onnx/onnx/security/advisories/GHSA-p893-rvq9-2xf9", "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0"}], "vulnStatus": "Received", "weaknesses": [{"type": "Secondary", "source": "security-advisories@github.com", "description": [{"lang": "en", "value": "CWE-125"}]}], "descriptions": [{"lang": "en", "value": "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."}], "lastModified": "2026-08-18T19:16:59.787", "sourceIdentifier": "security-advisories@github.com"}