CVE-2008-4482 (GCVE-0-2008-4482)
Vulnerability from cvelistv5 – Published: 2008-10-08 01:00 – Updated: 2024-08-07 10:17
VLAI
EPSS
VEX
Summary
The XML parser in Xerces-C++ before 3.0.0 allows context-dependent attackers to cause a denial of service (stack consumption and crash) via an XML schema definition with a large maxOccurs value, which triggers excessive memory consumption during validation of an XML file.
Severity
No CVSS data available.
CWE
- n/a
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://exchange.xforce.ibmcloud.com/vulnerabilit… | vdb-entryx_refsource_XF |
| http://www.securityfocus.com/bid/31533 | vdb-entryx_refsource_BID |
| http://xerces.apache.org/xerces-c/releases.html | x_refsource_CONFIRM |
| http://secunia.com/advisories/32108 | third-party-advisoryx_refsource_SECUNIA |
| http://issues.apache.org/jira/browse/XERCESC-1051 | x_refsource_MISC |
Date Public
2008-10-02 00:00
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"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Patch"
],
"url": "http://www.securityfocus.com/bid/31533"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://xerces.apache.org/xerces-c/releases.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/45596"
}
],
"sourceIdentifier": "cve@mitre.org",
"vendorComments": [
{
"comment": "Not Vulnerable. Red Hat Enterprise MRG does not use Xerces-C++ in a manner that is vulnerable to this flaw.",
"lastModified": "2008-12-02T00:00:00",
"organization": "Red Hat"
}
],
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-20"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"suse_vex": {
"aggregate_severity": "moderate",
"current_release_date": "2026-05-01T02:29:46Z",
"cve": "CVE-2008-4482",
"id": "CVE-2008-4482",
"initial_release_date": "2023-02-15T06:06:55Z",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2008-4482",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2008-4482.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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