FKIE_CVE-2026-43980
Vulnerability from fkie_nvd - Published: 2026-08-21 22:16 - Updated: 2026-10-12 16:10
Severity
Summary
Malla is a web analyzer for Meshtastic networks based on MQTT data. Prior to commit 4086e2b5f61615a813b70b25bc76095083552135, code names (long_name, short_name) received via MQTT are stored in SQLite without sanitization and rendered into the DOM without escaping. Any participant on a public Meshtastic MQTT broker can set a malicious node name that executes JavaScript in the browser of every Malla dashboard visitor. Commit 4086e2b5f61615a813b70b25bc76095083552135 fixes the issue.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "malla",
"vendor": "zenitraM",
"versions": [
{
"status": "affected",
"version": "\u003c 4086e2b5f61615a813b70b25bc76095083552135"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Malla is a web analyzer for Meshtastic networks based on MQTT data. Prior to commit 4086e2b5f61615a813b70b25bc76095083552135, code names (long_name, short_name) received via MQTT are stored in SQLite without sanitization and rendered into the DOM without escaping. Any participant on a public Meshtastic MQTT broker can set a malicious node name that executes JavaScript in the browser of every Malla dashboard visitor. Commit 4086e2b5f61615a813b70b25bc76095083552135 fixes the issue."
},
{
"lang": "es",
"value": "Malla es un analizador web para redes Meshtastic basado en datos MQTT. Antes del commit 4086e2b5f61615a813b70b25bc76095083552135, los nombres de c\u00f3digo (long_name, short_name) recibidos a trav\u00e9s de MQTT se almacenan en SQLite sin sanitizaci\u00f3n y se renderizan en el DOM sin escape. Cualquier participante en un broker MQTT p\u00fablico de Meshtastic puede establecer un nombre de nodo malicioso que ejecuta JavaScript en el navegador de cada visitante del panel de control de Malla. El commit 4086e2b5f6161615a813b70b25bc76095083552135 soluciona el problema."
}
],
"id": "CVE-2026-43980",
"lastModified": "2026-10-12T16:10:00.220",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "LOW",
"baseScore": 6.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:L",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 3.4,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-43980",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-24T20:10:26.577504Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-08-21T22:16:37.563",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/malla/PYSEC-2026-2618.yaml"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/zenitraM/malla/commit/4086e2b5f61615a813b70b25bc76095083552135"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/zenitraM/malla/security/advisories/GHSA-ch57-39q2-4crm"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"url": "https://github.com/zenitraM/malla/security/advisories/GHSA-ch57-39q2-4crm"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-79"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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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