FKIE_CVE-2026-105043
Vulnerability from fkie_nvd - Published: 2026-10-02 22:16 - Updated: 2026-10-02 22:16
Severity
Summary
MathWorks Simulink before R2026b, when showing a crafted .slx file, can have blocks that are never visible in the Simulink Editor but will cause code execution.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"modules": [
"MATLAB Function block; Simulink Editor block geometry rendering (block Position / ShowName)"
],
"product": "Simulink",
"vendor": "MathWorks",
"versions": [
{
"lessThan": "R2026b",
"status": "affected",
"version": "0",
"versionType": "custom"
}
]
}
],
"source": "cve@mitre.org"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "MathWorks Simulink before R2026b, when showing a crafted .slx file, can have blocks that are never visible in the Simulink Editor but will cause code execution."
}
],
"id": "CVE-2026-105043",
"lastModified": "2026-10-02T22:16:54.227",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 3.6,
"baseSeverity": "LOW",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:H/PR:N/UI:R/S:U/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 1.0,
"impactScore": 2.5,
"source": "cve@mitre.org",
"type": "Secondary"
}
]
},
"published": "2026-10-02T22:16:54.227",
"references": [
{
"source": "cve@mitre.org",
"url": "https://alexanderboll.dev/disclosures/simulink-vulnerability-concealed-blocks/"
},
{
"source": "cve@mitre.org",
"url": "https://www.mathworks.com/help/simulink/release-notes.html#mw_81eb54b6-4dfa-42c1-8c3c-e998eff5a8cd"
},
{
"source": "cve@mitre.org",
"url": "https://www.mathworks.com/products/simulink.html"
}
],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-451"
}
],
"source": "cve@mitre.org",
"type": "Primary"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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