ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute
Impact
ONNX versions 1.4.0 through 1.21.0 have a null pointer dereference vulnerability in the ScanInferenceFunction due to a missing required attribute, potentially causing denial of service. An application processing an untrusted model can be terminated or forced into an exceptional allocation path. The vulnerability requires local access to the ONNX library API or file boundary.
Summarized by @cf/meta/llama-3.3-70b-instruct-fp8-fast. Verify details against the original sources.Affected packages
Start with the recorded fix for your release branch. Check your installed version and test compatibility before upgrading. Fixing this advisory does not rule out other vulnerabilities in the release.
| Affected range | Recorded fix |
|---|---|
| From 1.4.0 before 1.22.0Ecosystem versions | 1.22.0 |
Version evidence: OSV · GHSA-m4pr-6v3c-6fgr
View original version evidence (38 explicit versions)
Affected products
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onnx · GitHub_M
onnx
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onnx · GitHub_M
onnx
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affected
>= 1.4.0, < 1.22.0
Other versions: unknown
Original source 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:
- If the attribute was missing,
ctx.getAttribute("num_scan_inputs")returned null and was dereferenced. - If the attribute was negative, conversion to
size_tproduced a very large value that reached a vector allocation and raisedstd::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:
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:
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
- Commit:
dd6bc92ea
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.
Inside the record
Evidence available
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Original records
CVE-2026-115921View the latest unmodified CVE JSON from the official source. It may be newer than this imported page.
cve.new JSON →Underlying weaknesses
The source’s classification of what caused the vulnerability.
Contributing organizations · 2
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What changed
Follow changes- CVE · CVE-2026-115921
Observed 2026-10-31T01:57:42.824Z · upstream updated 2026-10-31T01:40:08.161Z
Prioritization updated
before
[]
after
[ { "source": "CISA-ADP", "type": "SSVC", "role": "CISA Coordinator", "timestamp": "2026-10-31T01:39:56.289903Z", "decisions": [ { "name": "Exploitation", "value": "none" }, { "name": "Automatable", "value": "no" }, { "name": "Technical Impact", "value": "partial" } ] } ]
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