CVE-2006-2093 (GCVE-0-2006-2093)
Vulnerability from cvelistv5 – Published: 2006-04-29 10:00 – Updated: 2024-08-07 17:35
VLAI
EPSS
VEX
Summary
Nessus before 2.2.8, and 3.x before 3.0.3, allows user-assisted attackers to cause a denial of service (memory consumption) via a NASL script that calls split with an invalid sep parameter. NOTE: a design goal of the NASL language is to facilitate sharing of security tests by guaranteeing that a script "can not do anything nasty." This issue is appropriate for CVE only if Nessus users have an expectation that a split statement will not use excessive memory.
Severity
No CVSS data available.
CWE
- n/a
Assigner
References
9 references
| URL | Tags |
|---|---|
| http://www.securityfocus.com/archive/1/431987/100… | mailing-listx_refsource_BUGTRAQ |
| https://usn.ubuntu.com/279-1/ | vendor-advisoryx_refsource_UBUNTU |
| http://www.securityfocus.com/archive/1/431993/100… | mailing-listx_refsource_BUGTRAQ |
| http://securityreason.com/securityalert/817 | third-party-advisoryx_refsource_SREASON |
| http://www.vupen.com/english/advisories/2006/1541 | vdb-entryx_refsource_VUPEN |
| http://www.securityfocus.com/archive/1/431994/100… | mailing-listx_refsource_BUGTRAQ |
| https://exchange.xforce.ibmcloud.com/vulnerabilit… | vdb-entryx_refsource_XF |
| http://securitytracker.com/id?1015996 | vdb-entryx_refsource_SECTRACK |
| http://www.osvdb.org/25084 | vdb-entryx_refsource_OSVDB |
Date Public
2006-04-25 00:00
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"url": "http://www.securityfocus.com/archive/1/431994/100/0/threaded"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "http://www.vupen.com/english/advisories/2006/1541"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/26034"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://usn.ubuntu.com/279-1/"
}
],
"sourceIdentifier": "cve@mitre.org",
"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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