CVE-2026-0853 (GCVE-0-2026-0853)
Vulnerability from cvelistv5 – Published: 2026-01-12 03:26 – Updated: 2026-01-12 15:54
VLAI
EPSS
VEX
Title
A-Plus Video Technologies｜NVR - Sensitive Data Exposure
Summary
Certain NVR models developed by A-Plus Video Technologies has a Sensitive Data Exposure vulnerability, allowing unauthenticated remote attackers to access the debug page and obtain device status information.
Severity
5.3 (Medium)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-01-12 15:54 UTC
CWE
- CWE-497 - Exposure of Sensitive System Information to an Unauthorized Control Sphere
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://www.twcert.org.tw/tw/cp-132-10620-527f1-1.html | third-party-advisory |
| https://www.twcert.org.tw/en/cp-139-10621-55584-2.html | third-party-advisory |
Impacted products
8 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| A-Plus Video Technologies | AP-RM864P |
Affected:
0 , ≤ 2.1.0
(custom)
|
guessed | |
| A-Plus Video Technologies | AP-RM864 |
Affected:
0 , ≤ 2.1.0
(custom)
|
guessed | |
| A-Plus Video Technologies | AP-RM832P |
Affected:
0 , ≤ 2.1.0
(custom)
|
guessed | |
| A-Plus Video Technologies | AP-RM832 |
Affected:
0 , ≤ 2.1.0
(custom)
|
guessed | |
| A-Plus Video Technologies | AP-RM816 |
Affected:
0 , ≤ 2.1.0
(custom)
|
guessed | |
| A-Plus Video Technologies | AP-BS416 |
Affected:
0 , ≤ 2.1.0
(custom)
|
guessed | |
| A-Plus Video Technologies | AP-BS408 |
Affected:
0 , ≤ 2.1.0
(custom)
|
guessed | |
| A-Plus Video Technologies | AP-BS404 |
Affected:
0 , ≤ 2.1.0
(custom)
|
guessed |
Date Public
2026-01-12 03:16
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"title": "A-Plus Video Technologies\uff5cNVR - Sensitive Data Exposure",
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"engine": "Vulnogram 0.5.0"
}
}
},
"cveMetadata": {
"assignerOrgId": "cded6c7f-6ce5-4948-8f87-aa7a3bbb6b0e",
"assignerShortName": "twcert",
"cveId": "CVE-2026-0853",
"datePublished": "2026-01-12T03:26:47.546Z",
"dateReserved": "2026-01-12T03:07:23.341Z",
"dateUpdated": "2026-01-12T15:54:38.969Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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