CVE-2021-28812 (GCVE-0-2021-28812)
Vulnerability from cvelistv5 – Published: 2021-06-03 02:45 – Updated: 2024-09-16 20:11
VLAI
EPSS
VEX
Title
Command Injection Vulnerability in Video Station
Summary
A command injection vulnerability has been reported to affect certain versions of Video Station. If exploited, this vulnerability allows remote attackers to execute arbitrary commands. This issue affects: QNAP Systems Inc. Video Station versions prior to 5.5.4 on QTS 4.5.2; versions prior to 5.5.4 on QuTS hero h4.5.2; versions prior to 5.5.4 on QuTScloud c4.5.4. This issue does not affect: QNAP Systems Inc. Video Station on QTS 4.3.6; on QTS 4.3.3.
Severity
8.8 (High)
CWE
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://www.qnap.com/zh-tw/security-advisory/qsa-21-21 | x_refsource_MISC |
Impacted products
3 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| QNAP Systems Inc. | Video Station |
Affected:
unspecified , < 5.5.4
(custom)
|
guessed | |
| QNAP Systems Inc. | Video Station |
Unaffected:
5.3.x
|
guessed | |
| QNAP Systems Inc. | Video Station |
Unaffected:
5.1.x
|
guessed |
Date Public
2021-06-03 00:00
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"url": "https://www.qnap.com/zh-tw/security-advisory/qsa-21-21"
}
],
"sourceIdentifier": "security@qnapsecurity.com.tw",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-77"
},
{
"lang": "en",
"value": "CWE-78"
},
{
"lang": "en",
"value": "CWE-1286"
}
],
"source": "security@qnapsecurity.com.tw",
"type": "Secondary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-77"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T14:20:21+00:00",
"cve": "CVE-2021-28812",
"id": "CVE-2021-28812",
"initial_release_date": "2021-06-03T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "Command Injection Vulnerability in Video Station",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2021/cve-2021-28812.json",
"version": "3"
}
}
}
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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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