CVE-2013-3613 (GCVE-0-2013-3613)
Vulnerability from cvelistv5 – Published: 2013-09-17 10:00 – Updated: 2024-09-16 18:33
VLAI
EPSS
VEX
Summary
Dahua DVR appliances do not properly restrict UPnP requests, which makes it easier for remote attackers to obtain access via vectors involving a replay attack against the TELNET port.
Severity
No CVSS data available.
CWE
- n/a
Assigner
References
1 reference
| URL | Tags |
|---|---|
| http://www.kb.cert.org/vuls/id/800094 | third-party-advisoryx_refsource_CERT-VN |
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"negate": false,
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]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Dahua DVR appliances do not properly restrict UPnP requests, which makes it easier for remote attackers to obtain access via vectors involving a replay attack against the TELNET port."
},
{
"lang": "es",
"value": "Los dispositivos Dahua DVR no restringen adecuadamente las peticiones UPnP, lo que hace que sea m\u00e1s f\u00e1cil para los atacantes remotos conseguir acceso a trav\u00e9s de vectores que involucran un ataque por repetici\u00f3n contra el puerto Telnet ."
}
],
"id": "CVE-2013-3613",
"lastModified": "2026-04-29T01:13:23.040",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "HIGH",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 7.8,
"confidentialityImpact": "COMPLETE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:C/I:N/A:N",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 6.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
]
},
"published": "2013-09-17T12:04:24.757",
"references": [
{
"source": "cret@cert.org",
"tags": [
"US Government Resource"
],
"url": "http://www.kb.cert.org/vuls/id/800094"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
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],
"url": "http://www.kb.cert.org/vuls/id/800094"
}
],
"sourceIdentifier": "cret@cert.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-287"
}
],
"source": "nvd@nist.gov",
"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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