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Integer OverflowWeakness · CWE-190

CVE-2026-42627

MEDIUM · 6.2 CVSS v3.1 Published 2026-05-22
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
67/100
Remediation priority · Elevated
No privileges Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
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.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

Arm NN's TensorShape::GetNumElements() function uses 32-bit unsigned arithmetic to compute tensor element counts without overflow detection. When a crafted TFLite model specifies tensor dimensions that cause integer overflow during multiplication, GetNumBytes() returns an understated buffer size. During model optimization in Optimize()->InferOutputShapes(), the BatchToSpaceNdLayer then performs an out-of-bounds read beyond the allocated heap buffer, leading to heap-based buffer over-read.

MitigationUpgrade Arm NN to a version that implements 64-bit arithmetic or explicit overflow detection in TensorShape::GetNumElements(). Until a fix is available, validate TFLite model files through a separate parsing stage before passing them to Arm NN for inference, rejecting models with tensor dimensions that could trigger integer overflow.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Local
Complexity
Low
Privileges
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

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

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Identify if TensorFlow Lite is in use
    Check for TFLite libraries (libtensorflowlite.so on Linux, TensorFlow Lite framework on Android/iOS) or look for TFLite model files (.tflite) in the environment
    Affected if TFLite libraries or models are present in the environment
  2. Determine TFLite library version
    Run 'strings' on the TFLite library and search for version strings, or check the TensorFlow/TFLite package version via pip/dependency manager
    Affected if The installed version is older than the patched version that addresses the GetNumElements() overflow fix
  3. Check if models are processed through Optimize() path
    Review application code that loads .tflite models and confirm it invokes the TensorFlow Lite Optimizer or the InferOutputShapes() function during model loading
    Affected if The application loads and optimizes TFLite models using the standard optimization pipeline
  4. Identify usage of BatchToSpaceNd operations
    Inspect TFLite models with tools like 'flatc' or TFLite schema inspection to detect BatchToSpaceNdLayer operations in the model graph
    Affected if The deployed TFLite models contain BatchToSpaceNd or related space-to-batch transformation layers
  5. Verify heap allocation behavior
    Monitor memory allocation patterns when loading untrusted TFLite models - the bug causes undersized buffers, which may manifest as heap corruption or out-of-bounds read errors in logs under memory debugging tools like AddressSanitizer
    Affected if Heap buffer overflow or memory corruption occurs when loading crafted TFLite models with BatchToSpaceNd operations

A user is affected if they process TFLite models (especially those with BatchToSpaceNd layers) using an unpatched TensorFlow Lite version where the Optimize()->InferOutputShapes() path can trigger the integer overflow in GetNumElements().

Generated from the published advisory. Verify against your own configuration.

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AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

From vendor data
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Upgrade Arm NN to a version that implements 64-bit arithmetic or explicit overflow detection in TensorShape::GetNumElements(). Until a fix is available, validate TFLite model files through a separate parsing stage before passing them to Arm NN for inference, rejecting models with tensor dimensions that could trigger integer overflow.

Have this fixed Scoped from the published advisory
  • Consultation4.0 h
  • Implementation8.0 h
  • Testing8.0 h
  • Review / QA4.0 h
24.0 hours of engineering $4,160
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2026-42627 in production — separate from our analysis above.

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