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Response snapshot Oct 29, 2026, 02:31 UTC

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VulnerabilityCVE-2026-115920

ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank

ONNX ConvTranspose shape inference performed an out-of-bounds read when the weight tensor had fewer than three dimensions and the model did not provide a kernel_shape attribute.

At a glance

Exploited
Not known
Severity
6.2 Medium (CVSS 3.1)
Chance of exploitation
0.2% in the next 30 days
Sources
1 source

What to do

GitHub lists first patched versions: onnx: 1.22.0. Confirm the affected range and upgrade guidance in the exact advisory.

Read GitHub-reviewed advisory GHSA-6pwm-x34p-fvj2 (opens a new tab)

What happened

Summary

ONNX ConvTranspose shape inference performed an out-of-bounds read when the weight tensor had fewer than three dimensions and the model did not provide a kernel_shape attribute.

The inference function derived an empty effective_kernel_shape vector from the undersized weight tensor, then indexed that vector once for each spatial dimension of the input tensor. A crafted model passed onnx.checker.check_model() and caused SIGSEGV when passed to onnx.shape_inference.infer_shapes().

Technical details

Continue reading

For an input shape such as [1, 1, 5], shape inference computes one spatial input dimension. A weight shape such as [1, 3] has no spatial dimensions, so deriving kernel_shape from dimensions 2 onward produces an empty vector. The output loop nevertheless accesses effective_kernel_shape[0].

The defect affected both the older convTransposeShapeInference_opset11() path and the then-current ConvTranspose inference implementation.

Impact

An application that performs shape inference on an untrusted model can be terminated, causing denial of service. The observed result was a deterministic process crash. No confidentiality or integrity impact was demonstrated.

Affected versions

- Affected: ONNX 1.2.1 through 1.21.0 - Patched: ONNX 1.22.0

Remediation

Upgrade to ONNX 1.22.0 or later. The fix rejects input and weight tensors with fewer than three dimensions before deriving the spatial kernel shape.

- Fix: [onnx/onnx#7997](https://github.com/onnx/onnx/pull/7997) - Commit: [2ec288908](https://github.com/onnx/onnx/commit/2ec288908952eea9dcf48d4b197915e1f72c6abf)

The original malformed model was re-tested against the published ONNX 1.23.2 wheel and correctly raises InferenceError.

Severity

The vulnerable ONNX component processes the malicious model through a local library API or file boundary and is not itself bound to a network stack. Applications that expose ONNX model processing over a network should calculate an Environmental score with Modified Attack Vector set to Network.

Affected package ranges reported by GitHub: onnx: >= 1.2.1, <= 1.21.0

Scores and how they were measured

CVSS base severity

6.2

Version 3.1As of

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

Measurement source (opens a new tab)

How bad it is if exploited. Not a prediction of exploitation or proof you are exposed.

EPSS probability

0.2%

Scored Percentile 7.4%

Model and measurement source (opens a new tab)

A model's estimate of exploitation in the next 30 days, not certainty or your own risk.

Related technology

Sources

  1. Primary source

    GitHub-reviewed advisory GHSA-6pwm-x34p-fvj2 (opens a new tab)

    The public advisory supplies the description, affected package/version ranges, and any listed CVSS metric. GitHub review is not local review.

    Published Checked

  2. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-6pwm-x34p-fvj2, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  3. Primary source

    FIRST measured EPSS score (opens a new tab)

    Measured EPSS probability and percentile for CVE-2026-115920, scored 2026-10-28. This is a model estimate, not proof of exposure.

    Published Checked

Cite this record

For factual claims, cite the primary sources above; cite KEVWire for its compilation, dates, and classifications.

APA

KEVWire. (2026, October 28). ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank (CVE-2026-115920). https://kevwire.com/record/CVE-2026-115920

MLA

"ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank (CVE-2026-115920)." KEVWire, 28 Oct. 2026, kevwire.com/record/CVE-2026-115920.

Chicago

KEVWire. "ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank (CVE-2026-115920)." Last modified October 28, 2026. https://kevwire.com/record/CVE-2026-115920.

Evidence summary (plain text)

ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank (Oct 28, 2026).
ONNX ConvTranspose shape inference performed an out-of-bounds read when the weight tensor had fewer than three dimensions and the model did not provide a kernel_shape attribute.

The inference function derived an empty effective_kernel_shape vector from the undersized weight tensor, then indexed that vector once for each spatial dimension of the input tensor. A crafted model passed onnx.checker.check_model() and caused SIGSEGV when passed to onnx.shape_inference.infer_shapes().

For an input shape such as [1, 1, 5], shape inference computes one spatial input dimension.
CVSS 6.2 (version 3.1; evidence as of Oct 28, 2026; https://github.com/advisories/GHSA-6pwm-x34p-fvj2).
EPSS 0.2% for the following 30 days, scored Oct 28, 2026 (https://api.first.org/data/v1/epss?cve=CVE-2026-115920&date=2026-10-28).
Sources:
GitHub-reviewed advisory GHSA-6pwm-x34p-fvj2: https://github.com/advisories/GHSA-6pwm-x34p-fvj2
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc
FIRST measured EPSS score: https://api.first.org/data/v1/epss?cve=CVE-2026-115920&date=2026-10-28
Record ID: CVE-2026-115920.
Record origin: Ingested record.
AI evidence: Not assessed.
Known exploitation: Not assessed.
Zero-day status: Not assessed. KEV membership alone does not establish zero-day status.
About this data

This record was ingested from a data source. Retrieved: Oct 29, 2026, 02:16 UTC. Ingestion is not an independent editorial investigation.

A CISA KEV listing documents known exploitation, not necessarily a zero-day; absence from the catalog does not prove a vulnerability has never been exploited.

Technology tags indicate possible relevance, not verified exposure. Confirm the affected product, version, and configuration against your own inventory; KEVWire does not scan your systems.

A personal reading checklist on this device, not verified remediation.

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