CVE-2026-63632 (GCVE-0-2026-63632)
Vulnerability from cvelistv5 – Published: 2026-08-18 14:55 – Updated: 2026-08-18 18:51
VLAI
EPSS
VEX
Title
ONNX: Heap-Buffer-Overflow READ in Gemm Version Converter Adapter via Undersized Input Shape
Summary
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
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-18 18:51 UTC
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/onnx/onnx/security/advisories/… | x_refsource_CONFIRM |
| https://github.com/onnx/onnx/pull/7880 | x_refsource_MISC |
| https://github.com/onnx/onnx/commit/e9c74f596eaa0… | x_refsource_MISC |
| https://github.com/onnx/onnx/releases/tag/v1.22.0 | x_refsource_MISC |
Impacted products
{
"containers": {
"adp": [
{
"metrics": [
{
"other": {
"content": {
"id": "CVE-2026-63632",
"options": [
{
"Exploitation": "poc"
},
{
"Automatable": "no"
},
{
"Technical Impact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-18T18:51:22.894530Z",
"version": "2.0.3"
},
"type": "ssvc"
}
}
],
"providerMetadata": {
"dateUpdated": "2026-08-18T18:51:29.068Z",
"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"references": [
{
"tags": [
"exploit"
],
"url": "https://github.com/onnx/onnx/security/advisories/GHSA-p893-rvq9-2xf9"
}
],
"title": "CISA ADP Vulnrichment"
}
],
"cna": {
"affected": [
{
"product": "onnx",
"vendor": "onnx",
"versions": [
{
"status": "affected",
"version": "\u003e= 1.3.0, \u003c 1.22.0"
}
]
}
],
"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."
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "LOW",
"baseScore": 3.3,
"baseSeverity": "LOW",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L",
"version": "3.1"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-125",
"description": "CWE-125: Out-of-bounds Read",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-08-18T14:55:15.923Z",
"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"shortName": "GitHub_M"
},
"references": [
{
"name": "https://github.com/onnx/onnx/security/advisories/GHSA-p893-rvq9-2xf9",
"tags": [
"x_refsource_CONFIRM"
],
"url": "https://github.com/onnx/onnx/security/advisories/GHSA-p893-rvq9-2xf9"
},
{
"name": "https://github.com/onnx/onnx/pull/7880",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/onnx/onnx/pull/7880"
},
{
"name": "https://github.com/onnx/onnx/commit/e9c74f596eaa0250f89e52a54160a25bbcb25b66",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/onnx/onnx/commit/e9c74f596eaa0250f89e52a54160a25bbcb25b66"
},
{
"name": "https://github.com/onnx/onnx/releases/tag/v1.22.0",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/onnx/onnx/releases/tag/v1.22.0"
}
],
"source": {
"advisory": "GHSA-p893-rvq9-2xf9",
"discovery": "UNKNOWN"
},
"title": "ONNX: Heap-Buffer-Overflow READ in Gemm Version Converter Adapter via Undersized Input Shape"
}
},
"cveMetadata": {
"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"assignerShortName": "GitHub_M",
"cveId": "CVE-2026-63632",
"datePublished": "2026-08-18T14:55:15.923Z",
"dateReserved": "2026-07-17T14:11:15.482Z",
"dateUpdated": "2026-08-18T18:51:29.068Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2",
"vulnerability-lookup:meta": {
"nvd": "{\"cve\":{\"id\":\"CVE-2026-63632\",\"sourceIdentifier\":\"security-advisories@github.com\",\"published\":\"2026-08-18T15:16:56.463\",\"lastModified\":\"2026-08-18T15:16:56.463\",\"vulnStatus\":\"Received\",\"cveTags\":[],\"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.\"}],\"affected\":[{\"source\":\"security-advisories@github.com\",\"affectedData\":[{\"vendor\":\"onnx\",\"product\":\"onnx\",\"versions\":[{\"version\":\"\u003e= 1.3.0, \u003c 1.22.0\",\"status\":\"affected\"}]}]}],\"metrics\":{\"cvssMetricV31\":[{\"source\":\"security-advisories@github.com\",\"type\":\"Secondary\",\"cvssData\":{\"version\":\"3.1\",\"vectorString\":\"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L\",\"baseScore\":3.3,\"baseSeverity\":\"LOW\",\"attackVector\":\"LOCAL\",\"attackComplexity\":\"LOW\",\"privilegesRequired\":\"NONE\",\"userInteraction\":\"REQUIRED\",\"scope\":\"UNCHANGED\",\"confidentialityImpact\":\"NONE\",\"integrityImpact\":\"NONE\",\"availabilityImpact\":\"LOW\"},\"exploitabilityScore\":1.8,\"impactScore\":1.4}]},\"weaknesses\":[{\"source\":\"security-advisories@github.com\",\"type\":\"Primary\",\"description\":[{\"lang\":\"en\",\"value\":\"CWE-125\"}]}],\"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\"}]}}"
}
}
Loading…
Loading…
Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Loading…
The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Loading…
Loading…