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Response snapshot Oct 29, 2026, 01:26 UTC

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VulnerabilityCVE-2026-115921

ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute

ONNX shape inference did not safely validate the required Scan num_scan_inputs attribute. Two malformed values could terminate or disrupt the process: 1. If the attribute was missing, ctx.getAttribute("num_scan_inputs") returned null and was dereferenced. 2.

At a glance

Exploited
Not known
Severity
6.2 Medium (CVSS 3.1)
Chance of exploitation
Not estimated
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-m4pr-6v3c-6fgr (opens a new tab)

What happened

Summary

ONNX shape inference did not safely validate the required Scan num_scan_inputs attribute. Two malformed values could terminate or disrupt the process:

1. If the attribute was missing, ctx.getAttribute("num_scan_inputs") returned null and was dereferenced. 2. If the attribute was negative, conversion to size_t produced a very large value that reached a vector allocation and raised std::length_error.

The negative-value variant passed onnx.checker.check_model() before reaching shape inference.

Continue reading

Technical details

The vulnerable Scan inference implementations used the following pattern:

auto num_scan_inputs =
 narrow_cast (ctx.getAttribute("num_scan_inputs")->i());

This code neither checked the returned pointer nor rejected a negative signed value before converting it to an unsigned size. The pattern was present in the current Scan inference function and the older opset 8 and 9 paths.

The fix uses the required-attribute helper and checked narrowing:

auto num_scan_inputs =
 narrow (getRequiredAttributeInt(ctx, "num_scan_inputs"));

Impact

An application that performs shape inference on an untrusted model can be terminated or forced into an exceptional allocation path, causing denial of service. No confidentiality or integrity impact was demonstrated.

Affected versions

- Affected: ONNX 1.4.0 through 1.21.0 - Patched: ONNX 1.22.0

ONNX 1.4.0 is the first release containing the vulnerable Scan shape-inference implementation.

Remediation

Upgrade to ONNX 1.22.0 or later.

- Fix: [onnx/onnx#7916](https://github.com/onnx/onnx/pull/7916) - Commit: [dd6bc92ea](https://github.com/onnx/onnx/commit/dd6bc92ea39fd2274fd671f0734b0b8b558356f5)

Both reported models were re-tested against the published ONNX 1.23.2 wheel and correctly raise 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.

The two triggers remain in one advisory because they affect the same required attribute, share the same unsafe conversion path and remediation, and were fixed in the same release.

Affected package ranges reported by GitHub: onnx: >= 1.4.0, <= 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

Not available

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-m4pr-6v3c-6fgr (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-m4pr-6v3c-6fgr, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

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: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute (CVE-2026-115921). https://kevwire.com/record/CVE-2026-115921

MLA

"ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute (CVE-2026-115921)." KEVWire, 28 Oct. 2026, kevwire.com/record/CVE-2026-115921.

Chicago

KEVWire. "ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute (CVE-2026-115921)." Last modified October 28, 2026. https://kevwire.com/record/CVE-2026-115921.

Evidence summary (plain text)

ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute (Oct 28, 2026).
ONNX shape inference did not safely validate the required Scan num_scan_inputs attribute. Two malformed values could terminate or disrupt the process:

1. If the attribute was missing, ctx.getAttribute("num_scan_inputs") returned null and was dereferenced.
2. If the attribute was negative, conversion to size_t produced a very large value that reached a vector allocation and raised std::length_error.

The negative-value variant passed onnx.checker.check_model() before reaching shape inference.
CVSS 6.2 (version 3.1; evidence as of Oct 28, 2026; https://github.com/advisories/GHSA-m4pr-6v3c-6fgr).
Sources:
GitHub-reviewed advisory GHSA-m4pr-6v3c-6fgr: https://github.com/advisories/GHSA-m4pr-6v3c-6fgr
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc
Record ID: CVE-2026-115921.
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, 00:55 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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