CVE-2013-4709 (GCVE-0-2013-4709)
Vulnerability from cvelistv5 – Published: 2013-09-20 15:00 – Updated: 2024-09-16 17:58
VLAI
EPSS
VEX
Summary
Buffer overflow in the PPP Access Concentrator (PPPAC) on the SEIL/x86 with firmware before 2.82, SEIL/X1 with firmware before 4.32, SEIL/X2 with firmware before 4.32, SEIL/B1 with firmware before 4.32, SEIL/Turbo with firmware before 2.16, and SEIL/neu 2FE Plus with firmware before 2.16 allows remote attackers to execute arbitrary code via a crafted L2TP message.
Severity
No CVSS data available.
CWE
- n/a
Assigner
References
3 references
| URL | Tags |
|---|---|
| http://jvn.jp/en/jp/JVN43152129/index.html | third-party-advisoryx_refsource_JVN |
| http://www.seil.jp/support/security/a01396.html | x_refsource_CONFIRM |
| http://jvndb.jvn.jp/jvndb/JVNDB-2013-000092 | third-party-advisoryx_refsource_JVNDB |
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"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": true
}
]
},
"published": "2013-09-20T15:26:04.230",
"references": [
{
"source": "vultures@jpcert.or.jp",
"tags": [
"Vendor Advisory"
],
"url": "http://jvn.jp/en/jp/JVN43152129/index.html"
},
{
"source": "vultures@jpcert.or.jp",
"tags": [
"Vendor Advisory"
],
"url": "http://jvndb.jvn.jp/jvndb/JVNDB-2013-000092"
},
{
"source": "vultures@jpcert.or.jp",
"tags": [
"Vendor Advisory"
],
"url": "http://www.seil.jp/support/security/a01396.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "http://jvn.jp/en/jp/JVN43152129/index.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "http://jvndb.jvn.jp/jvndb/JVNDB-2013-000092"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "http://www.seil.jp/support/security/a01396.html"
}
],
"sourceIdentifier": "vultures@jpcert.or.jp",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-119"
}
],
"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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