UBUNTU-CVE-2011-2516 (CVE-2011-2516)
Vulnerability from osv_ubuntu – Published: 2011-07-11 20:55 – Updated: 2025-07-16 04:49 – Source website
Withdrawn 2025-07-18
VLAI
Details
Off-by-one error in the XML signature feature in Apache XML Security for C++ 1.6.0, as used in Shibboleth before 2.4.3 and possibly other products, allows remote attackers to cause a denial of service (crash) via a signature using a large RSA key, which triggers a buffer overflow.
Severity
N/A (UNKNOWN)
References
{
"affected": [
{
"ecosystem_specific": {
"availability": "No subscription required",
"binaries": [
{
"binary_name": "libapache2-mod-shib2",
"binary_version": "2.5.2+dfsg-2"
},
{
"binary_name": "libshibsp-dev",
"binary_version": "2.5.2+dfsg-2"
},
{
"binary_name": "libshibsp-doc",
"binary_version": "2.5.2+dfsg-2"
},
{
"binary_name": "libshibsp6",
"binary_version": "2.5.2+dfsg-2"
},
{
"binary_name": "shibboleth-sp2-schemas",
"binary_version": "2.5.2+dfsg-2"
}
]
},
"package": {
"ecosystem": "Ubuntu:14.04:LTS",
"name": "shibboleth-sp2",
"purl": "pkg:deb/ubuntu/shibboleth-sp2@2.5.2+dfsg-2?arch=source\u0026distro=trusty"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.5.2+dfsg-2"
}
],
"type": "ECOSYSTEM"
}
],
"versions": []
}
],
"aliases": [],
"details": "Off-by-one error in the XML signature feature in Apache XML Security for C++ 1.6.0, as used in Shibboleth before 2.4.3 and possibly other products, allows remote attackers to cause a denial of service (crash) via a signature using a large RSA key, which triggers a buffer overflow.",
"id": "UBUNTU-CVE-2011-2516",
"modified": "2025-07-16T04:49:59Z",
"published": "2011-07-11T20:55:00Z",
"references": [
{
"type": "REPORT",
"url": "https://ubuntu.com/security/CVE-2011-2516"
},
{
"type": "REPORT",
"url": "http://santuario.apache.org/secadv/CVE-2011-2516.txt"
},
{
"type": "REPORT",
"url": "https://www.cve.org/CVERecord?id=CVE-2011-2516"
}
],
"related": [],
"schema_version": "1.7.0",
"severity": [
{
"score": "medium",
"type": "Ubuntu"
}
],
"upstream": [
"CVE-2011-2516"
],
"withdrawn": "2025-07-18T16:42:43Z"
}
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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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