What is the CVSS score of EUVD-2026-31476?

EUVD-2026-31476 has a CVSS 3.1 base score of 6.2 (Medium). CVSS vector: CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H. EPSS exploitation probability: 0.0%.

Arm ArmNN EUVD-2026-31476

| CVE-2026-42627 MEDIUM

Integer Overflow or Wraparound (CWE-190)

2026-05-22 mitre

GHSA-cf4x-r2x9-wwh2

Integer Overflow Buffer Overflow N A

6.2

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

6.2 MEDIUM

AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Attack Vector

Local

Attack Complexity

Low

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

None

Integrity

None

Availability

High

Lifecycle Timeline

Analysis Generated

Jun 08, 2026 - 12:08 vuln.today

CVSS changed

May 26, 2026 - 16:22 NVD

6.2 (MEDIUM)

CVE Published

May 22, 2026 - 00:00 nvd

UNKNOWN (no severity yet)

DescriptionCVE.org

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.

AnalysisAI

Integer overflow in Arm ArmNN's TensorShape::GetNumElements() enables a crafted TFLite model to bypass buffer size validation and trigger a heap-based buffer over-read, resulting in a denial of service. Affected systems are those that load externally-supplied or untrusted TFLite model files using any ArmNN build through 2026-03-27. An attacker who controls a model file processed by an ArmNN-backed application can crash the host process during the optimization pass via BatchToSpaceNdLayer's InferOutputShapes() routine. No public exploit code and no active exploitation has been identified at time of analysis; EPSS sits at 0.02% (5th percentile), consistent with low real-world threat.

Technical ContextAI

ArmNN is Arm's open-source neural network inference engine for Arm-based platforms (Cortex-A, Mali GPU, Ethos NPU). The defect resides in TensorShape::GetNumElements() in src/armnn/Tensor.cpp, which computes total element counts by multiplying per-dimension sizes using 32-bit unsigned integer arithmetic with no overflow guard. When the product of dimensions wraps around the 32-bit boundary, GetNumBytes() returns an understated allocation size, so the heap buffer allocated for the tensor is smaller than required. During the model optimization pipeline (Optimize() → InferOutputShapes()), the BatchToSpaceNdLayer reads tensor data beyond the actual allocation, constituting a CWE-190 (Integer Overflow or Wraparound) that cascades into a heap-based buffer over-read. The attack surface is the TFLite parser (src/armnnTfLiteParser/TfLiteParser.cpp), which translates TFLite flatbuffer models into ArmNN's internal graph; no additional runtime or hardware is required to reach the vulnerable code path.

RemediationAI

No vendor-released patch with a confirmed tagged version has been identified at time of analysis. The references point directly to source code files on GitHub (https://github.com/ARM-software/armnn/blob/main/src/armnn/Tensor.cpp and https://github.com/ARM-software/armnn/blob/main/src/armnnTfLiteParser/TfLiteParser.cpp) rather than to a patch advisory or tagged release, so consumers should monitor the ARM-software/armnn repository for a commit that adds overflow detection to TensorShape::GetNumElements(). As a specific compensating control, organizations should restrict ArmNN-based services to loading only models from trusted, integrity-verified sources (e.g., cryptographic signature validation on model files before passing them to the parser); this eliminates the attacker-controlled input prerequisite and neutralizes the vulnerability without requiring a code change. A second control is to sandbox the ArmNN optimization process (e.g., via seccomp, containers, or separate privilege domain) so that a triggered crash is contained and does not affect higher-privilege components; this limits the blast radius to the inference worker process. Do not expose model-upload or model-selection interfaces to unauthenticated or untrusted users until a patched release is available. VulDB advisory reference: https://vuldb.com/vuln/365257.