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VulnerabilityPublished

CVE-2026-115921: ONNX · Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute

Affects onnxSource GCVE Database

0
Authoritative record:CVE-2026-115921· Medium 6.2

Not known to be exploited · CVSS 6.2 Medium : upgrade to 1.22.0 when convenient.

Data as of 28 Oct 2026, 22:12 UTC

CVSS 3.1 base score
6.2Medium
How severe the flaw is on the 0–10 CVSS scale.
Patch
Available – fixed in 1.22.0
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 26 Oct 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

What to do

Upgrade onnx to 1.22.0.

Affected software

ProductAffectedFixed in
PyPIonnxAffected
  • >=1.4.0 <1.22.0
Fixed in
  • 1.22.0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

Description

## 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. ## Technical details The vulnerable Scan inference implementations used the following pattern: ```cpp auto num_scan_inputs = narrow_cast<size_t>(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: ```cpp auto num_scan_inputs = narrow<size_t>(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.

References

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