FKIE_CVE-2005-2708
Vulnerability from fkie_nvd - Published: 2005-10-25 18:02 - Updated: 2026-06-16 22:15
Severity
Summary
The search_binary_handler function in exec.c in Linux 2.4 kernel on 64-bit x86 architectures does not check a return code for a particular function call when virtual memory is low, which allows local users to cause a denial of service (panic), as demonstrated by running a process using the bash ulimit -v command.
References
Impacted products
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"lang": "en",
"value": "The search_binary_handler function in exec.c in Linux 2.4 kernel on 64-bit x86 architectures does not check a return code for a particular function call when virtual memory is low, which allows local users to cause a denial of service (panic), as demonstrated by running a process using the bash ulimit -v command."
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"id": "CVE-2005-2708",
"lastModified": "2026-06-16T22:15:31.500",
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"integrityImpact": "NONE",
"vectorString": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
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},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.redhat.com/support/errata/RHSA-2006-0140.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.redhat.com/support/errata/RHSA-2006-0190.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.securityfocus.com/archive/1/428028/100/0/threaded"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.securityfocus.com/archive/1/428058/100/0/threaded"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.securityfocus.com/bid/16320"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Exploit",
"Vendor Advisory"
],
"url": "https://bugzilla.redhat.com/bugzilla/show_bug.cgi?id=161925"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://oval.cisecurity.org/repository/search/definition/oval%3Aorg.mitre.oval%3Adef%3A10649"
}
],
"sourceIdentifier": "secalert@redhat.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-399"
}
],
"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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