UBUNTU-CVE-2026-42627 (CVE-2026-42627)
Vulnerability from osv_ubuntu – Published: 2026-05-22 18:16 – Updated: 2026-05-27 19:09 – Source website
VLAI
Details
In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements() in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions using 32-bit unsigned arithmetic without overflow detection, causing GetNumBytes() to return an understated allocation size. During Optimize()->InferOutputShapes(), the BatchToSpaceNdLayer reads beyond the allocated buffer.
Severity
6.2 (Medium)
N/A (UNKNOWN)
References
{
"affected": [
{
"ecosystem_specific": {
"binaries": [
{
"binary_name": "libarmnn-cpuacc-backend22",
"binary_version": "20.08-10build1"
},
{
"binary_name": "libarmnn-cpuref-backend22",
"binary_version": "20.08-10build1"
},
{
"binary_name": "libarmnn-gpuacc-backend22",
"binary_version": "20.08-10build1"
},
{
"binary_name": "libarmnn22",
"binary_version": "20.08-10build1"
},
{
"binary_name": "libarmnnaclcommon22",
"binary_version": "20.08-10build1"
},
{
"binary_name": "libarmnntfliteparser22",
"binary_version": "20.08-10build1"
},
{
"binary_name": "python3-pyarmnn",
"binary_version": "20.08-10build1"
}
]
},
"package": {
"ecosystem": "Ubuntu:22.04:LTS",
"name": "armnn",
"purl": "pkg:deb/ubuntu/armnn@20.08-10build1?arch=source\u0026distro=jammy"
},
"ranges": [
{
"events": [
{
"introduced": "0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"20.08-9",
"20.08-10",
"20.08-10build1"
]
},
{
"ecosystem_specific": {
"binaries": [
{
"binary_name": "armnn-latest-all",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "armnn-latest-cpu",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "armnn-latest-cpu-gpu",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "armnn-latest-cpu-gpu-ref",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "armnn-latest-gpu",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "armnn-latest-ref",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "libarmnn-cpuacc-backend33",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "libarmnn-cpuref-backend33",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "libarmnn-gpuacc-backend33",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "libarmnn33t64",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "libarmnnaclcommon33t64",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "libarmnntfliteparser24t64",
"binary_version": "23.08-4.1build1"
},
{
"binary_name": "python3-pyarmnn",
"binary_version": "23.08-4.1build1"
}
]
},
"package": {
"ecosystem": "Ubuntu:24.04:LTS",
"name": "armnn",
"purl": "pkg:deb/ubuntu/armnn@23.08-4.1build1?arch=source\u0026distro=noble"
},
"ranges": [
{
"events": [
{
"introduced": "0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"23.08-3",
"23.08-4",
"23.08-4build1",
"23.08-4.1",
"23.08-4.1build1"
]
},
{
"ecosystem_specific": {
"binaries": [
{
"binary_name": "armnn-latest-all",
"binary_version": "23.08-5"
},
{
"binary_name": "armnn-latest-cpu",
"binary_version": "23.08-5"
},
{
"binary_name": "armnn-latest-cpu-gpu",
"binary_version": "23.08-5"
},
{
"binary_name": "armnn-latest-cpu-gpu-ref",
"binary_version": "23.08-5"
},
{
"binary_name": "armnn-latest-gpu",
"binary_version": "23.08-5"
},
{
"binary_name": "armnn-latest-ref",
"binary_version": "23.08-5"
},
{
"binary_name": "libarmnn-cpuacc-backend33",
"binary_version": "23.08-5"
},
{
"binary_name": "libarmnn-cpuref-backend33",
"binary_version": "23.08-5"
},
{
"binary_name": "libarmnn-gpuacc-backend33",
"binary_version": "23.08-5"
},
{
"binary_name": "libarmnn33t64",
"binary_version": "23.08-5"
},
{
"binary_name": "libarmnnaclcommon33t64",
"binary_version": "23.08-5"
},
{
"binary_name": "libarmnntfliteparser24t64",
"binary_version": "23.08-5"
},
{
"binary_name": "python3-pyarmnn",
"binary_version": "23.08-5"
}
]
},
"package": {
"ecosystem": "Ubuntu:25.10",
"name": "armnn",
"purl": "pkg:deb/ubuntu/armnn@23.08-5?arch=source\u0026distro=questing"
},
"ranges": [
{
"events": [
{
"introduced": "0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"23.08-5"
]
}
],
"aliases": [],
"details": "In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements() in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions using 32-bit unsigned arithmetic without overflow detection, causing GetNumBytes() to return an understated allocation size. During Optimize()-\u003eInferOutputShapes(), the BatchToSpaceNdLayer reads beyond the allocated buffer.",
"id": "UBUNTU-CVE-2026-42627",
"modified": "2026-05-27T19:09:51Z",
"published": "2026-05-22T18:16:00Z",
"references": [
{
"type": "REPORT",
"url": "https://ubuntu.com/security/CVE-2026-42627"
},
{
"type": "REPORT",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-42627"
},
{
"type": "REPORT",
"url": "https://github.com/ARM-software/armnn/blob/main/src/armnn/Tensor.cpp"
},
{
"type": "REPORT",
"url": "https://github.com/ARM-software/armnn/blob/main/src/armnnTfLiteParser/TfLiteParser.cpp"
}
],
"related": [],
"schema_version": "1.7.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
},
{
"score": "medium",
"type": "Ubuntu"
}
],
"upstream": [
"CVE-2026-42627"
]
}
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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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