KEVWire

Security intelligence, with context.

Response snapshot Oct 29, 2026, 02:31 UTC

Back to the wire
Permalink
VulnerabilityCVE-2026-115922

ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs

ONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.

At a glance

Exploited
Not known
Severity
6.2 Medium (CVSS 3.1)
Chance of exploitation
0.1% 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-q69g-f4f5-v8m7 (opens a new tab)

What happened

Summary

ONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.

A very small model could therefore cause a disproportionately large allocation when an application called onnx.numpy_helper.to_array() or constructed onnx.reference.ReferenceEvaluator with the model. The issue affected UINT2, INT2, UINT4, INT4, and FLOAT4E2M1 tensors stored in either raw_data or packed int32_data.

Technical details

Continue reading

The vulnerable _unpack_2bit() and _unpack_4bit() paths first allocated a small array based on the available packed bytes, then resized it to the shape declared in TensorProto.dims. They did not first confirm that the payload encoded enough logical elements for that shape.

For example, the reporter demonstrated a 66-byte model containing one packed byte and a declared dimension of 700,000,000. The model passed onnx.checker.check_model() and allocated approximately 700 MB while materializing the initializer, followed by another large allocation when executing the reference evaluator.

Loading, checking, or running shape inference on the serialized model alone did not trigger the allocation. The vulnerable tensor had to be materialized.

Impact

An application that materializes packed tensors from an untrusted model can exhaust available memory, causing denial of service. Memory corruption and code execution were not demonstrated.

Affected versions

- Affected: ONNX through 1.21.0 - Patched: ONNX 1.22.0

Remediation

Upgrade to ONNX 1.22.0 or later.

The primary fix validates the logical element count before resizing the unpacked NumPy array. A complementary checker change rejects an undersized packed payload before materialization.

- Materialization fix: [onnx/onnx#8033](https://github.com/onnx/onnx/pull/8033) - Checker defense-in-depth: [onnx/onnx#8038](https://github.com/onnx/onnx/pull/8038)

The five affected tensor types and both storage formats were re-tested against the published ONNX 1.23.2 wheel and are rejected before the oversized allocation.

To verify the behavior using a released vulnerable package in an isolated environment, install onnx==1.21.0 with python -m pip install "onnx==1.21.0". Run memory-exhaustion demonstrations only with an appropriate process memory limit.

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: <= 1.21.0

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

0.1%

Scored Percentile 2.7%

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-q69g-f4f5-v8m7 (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-q69g-f4f5-v8m7, 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-2026-115922, 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: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs (CVE-2026-115922). https://kevwire.com/record/CVE-2026-115922

MLA

"ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs (CVE-2026-115922)." KEVWire, 28 Oct. 2026, kevwire.com/record/CVE-2026-115922.

Chicago

KEVWire. "ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs (CVE-2026-115922)." Last modified October 28, 2026. https://kevwire.com/record/CVE-2026-115922.

Evidence summary (plain text)

ONNX: Denial of service via unchecked expansion of packed sub-byte tensors in ONNX Python APIs (Oct 28, 2026).
ONNX's Python tensor-materialization helpers could allocate memory according to attacker-controlled tensor dimensions without verifying that a packed sub-byte payload contained the declared number of elements.

A very small model could therefore cause a disproportionately large allocation when an application called onnx.numpy_helper.to_array() or constructed onnx.reference.ReferenceEvaluator with the model. The issue affected UINT2, INT2, UINT4, INT4, and FLOAT4E2M1 tensors stored in either raw_data or packed int32_data.
CVSS 6.2 (version 3.1; evidence as of Oct 28, 2026; https://github.com/advisories/GHSA-q69g-f4f5-v8m7).
EPSS 0.1% for the following 30 days, scored Oct 28, 2026 (https://api.first.org/data/v1/epss?cve=CVE-2026-115922&date=2026-10-28).
Sources:
GitHub-reviewed advisory GHSA-q69g-f4f5-v8m7: https://github.com/advisories/GHSA-q69g-f4f5-v8m7
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-2026-115922&date=2026-10-28
Record ID: CVE-2026-115922.
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.

Record JSON

Your account

Email alerts

Email alerts are coming as part of KEVWire Premium: join the waitlist. To follow your stack today, use its RSS feed, which honors your filters, or connect an AI agent through the API.

Suggest an improvement

Ideas, missing sources, corrections, and bugs are all welcome. No account needed. Suggestions are reviewed by a person before anything changes.

Record details

Record detail