GHSA-JJ76-67XG-C9GJ
Vulnerability from github – Published: 2026-09-25 12:31 – Updated: 2026-09-25 12:31
VLAI
Details
An attacker with access via network to the Regesta Smart HD-PLC of the provider Teldat (in this case, registration action is required) who has the vulnerable firmware version could inject a specific payload via the parameter "cmdcookie" withing the /upgrade/index.html resulting in to a Cross-Site Scripting (XSS). This issue affects Regesta Smart HD-PLC - TLDPH16D2: 11.02.06.00.02
Severity
{
"affected": [],
"aliases": [
"CVE-2026-27867"
],
"database_specific": {
"cwe_ids": [
"CWE-79"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-09-25T11:17:01Z",
"severity": "MODERATE"
},
"details": "An attacker with access via network to the Regesta Smart HD-PLC of the provider Teldat (in this case, registration action is required) who has the vulnerable firmware version could inject\na specific payload\u00a0via the parameter \"cmdcookie\" withing the /upgrade/index.html resulting in to a Cross-Site Scripting (XSS).\u00a0This issue affects Regesta Smart HD-PLC - TLDPH16D2:\u00a0\n11.02.06.00.02",
"id": "GHSA-jj76-67xg-c9gj",
"modified": "2026-09-25T12:31:22Z",
"published": "2026-09-25T12:31:22Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-27867"
},
{
"type": "WEB",
"url": "https://support.teldat.com/images/content/docs/Teldat_dm1087_regesta_smart_nessum_series_installation(1).pdf"
},
{
"type": "WEB",
"url": "https://support.teldat.com/portal/supportcontent?page=cgs-customer-global-support\u0026none=true\u0026language=en-US"
},
{
"type": "WEB",
"url": "https://www.hackrtu.com/blog/CNA-CVE-2026-27867"
},
{
"type": "WEB",
"url": "https://www.hackrtu.com/blog/CNA-HRTU-0004"
},
{
"type": "WEB",
"url": "https://www.teldat.com/es"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:A/VC:L/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
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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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