CVE-2026-14647 (GCVE-0-2026-14647)
Vulnerability from cvelistv5 – Published: 2026-07-04 19:00 – Updated: 2026-07-06 18:44 X_Open Source
VLAI
EPSS
VEX
Title
onnx onnxruntime old.cc convPoolShapeInference_opset19 out-of-bounds
Summary
A weakness has been identified in onnx up to 1.21.x. This vulnerability affects the function convPoolShapeInference_opset19 of the file onnx/defs/nn/old.cc of the component onnxruntime. This manipulation causes out-of-bounds read. It is possible to initiate the attack remotely. The exploit has been made available to the public and could be used for attacks. Patch name: a7bf3a0f1d18bb62575236ef6e4944980c40e045. It is recommended to apply a patch to fix this issue.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-06 18:44 UTC
Assigner
References
8 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/376160 | vdb-entrytechnical-description |
| https://vuldb.com/vuln/376160/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-14647 | third-party-advisory |
| https://vuldb.com/submit/846317 | third-party-advisory |
| https://github.com/onnx/onnx/issues/8036 | exploitissue-tracking |
| https://github.com/onnx/onnx/pull/8051 | issue-trackingpatch |
| https://github.com/onnx/onnx/commit/a7bf3a0f1d18b… | patch |
| https://github.com/onnx/onnx/ | product |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | onnx |
Affected:
1.0
Affected: 1.1 Affected: 1.2 Affected: 1.3 Affected: 1.4 Affected: 1.5 Affected: 1.6 Affected: 1.7 Affected: 1.8 Affected: 1.9 Affected: 1.10 Affected: 1.11 Affected: 1.12 Affected: 1.13 Affected: 1.14 Affected: 1.15 Affected: 1.16 Affected: 1.17 Affected: 1.18 Affected: 1.19 Affected: 1.20 Affected: 1.21 cpe:2.3:a:onnx:onnx:*:*:*:*:*:*:*:* |
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"baseScore": 4,
"vectorString": "AV:N/AC:L/Au:S/C:P/I:N/A:N/E:POC/RL:OF/RC:C",
"version": "2.0"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-125",
"description": "Out-of-Bounds Read",
"lang": "en",
"type": "CWE"
}
]
},
{
"descriptions": [
{
"cweId": "CWE-119",
"description": "Memory Corruption",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-07-04T19:00:11.367Z",
"orgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"shortName": "VulDB"
},
"references": [
{
"name": "VDB-376160 | onnx onnxruntime old.cc convPoolShapeInference_opset19 out-of-bounds",
"tags": [
"vdb-entry",
"technical-description"
],
"url": "https://vuldb.com/vuln/376160"
},
{
"name": "VDB-376160 | CTI Indicators (IOB, IOC, IOA)",
"tags": [
"signature",
"permissions-required"
],
"url": "https://vuldb.com/vuln/376160/cti"
},
{
"name": "CVE-2026-14647 | CVE Analysis and Report",
"tags": [
"third-party-advisory"
],
"url": "https://vuldb.com/cve/CVE-2026-14647"
},
{
"name": "Submit #846317 | onnx unpatched (HEAD); via onnxruntime 1.22.0 bundle Out-of-Bounds Read",
"tags": [
"third-party-advisory"
],
"url": "https://vuldb.com/submit/846317"
},
{
"tags": [
"exploit",
"issue-tracking"
],
"url": "https://github.com/onnx/onnx/issues/8036"
},
{
"tags": [
"issue-tracking",
"patch"
],
"url": "https://github.com/onnx/onnx/pull/8051"
},
{
"tags": [
"patch"
],
"url": "https://github.com/onnx/onnx/commit/a7bf3a0f1d18bb62575236ef6e4944980c40e045"
},
{
"tags": [
"product"
],
"url": "https://github.com/onnx/onnx/"
}
],
"tags": [
"x_open-source"
],
"timeline": [
{
"lang": "en",
"time": "2026-07-03T00:00:00.000Z",
"value": "Advisory disclosed"
},
{
"lang": "en",
"time": "2026-07-03T02:00:00.000Z",
"value": "VulDB entry created"
},
{
"lang": "en",
"time": "2026-07-03T20:34:34.000Z",
"value": "VulDB entry last update"
}
],
"title": "onnx onnxruntime old.cc convPoolShapeInference_opset19 out-of-bounds"
}
},
"cveMetadata": {
"assignerOrgId": "1af790b2-7ee1-4545-860a-a788eba489b5",
"assignerShortName": "VulDB",
"cveId": "CVE-2026-14647",
"datePublished": "2026-07-04T19:00:11.367Z",
"dateReserved": "2026-07-03T18:29:28.371Z",
"dateUpdated": "2026-07-06T18:44:46.173Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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.
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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