GSD-2023-46847
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
Squid is vulnerable to a Denial of Service, where a remote attacker can perform buffer overflow attack by writing up to 2 MB of arbitrary data to heap memory when Squid is configured to accept HTTP Digest Authentication.
Aliases
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"Third Party Advisory"
],
"url": "https://access.redhat.com/errata/RHSA-2023:6810"
},
{
"source": "secalert@redhat.com",
"tags": [
"Third Party Advisory"
],
"url": "https://access.redhat.com/errata/RHSA-2023:6882"
},
{
"source": "secalert@redhat.com",
"tags": [
"Third Party Advisory"
],
"url": "https://access.redhat.com/errata/RHSA-2023:6884"
},
{
"source": "secalert@redhat.com",
"tags": [
"Third Party Advisory"
],
"url": "https://access.redhat.com/errata/RHSA-2023:7213"
},
{
"source": "secalert@redhat.com",
"tags": [
"Third Party Advisory"
],
"url": "https://access.redhat.com/errata/RHSA-2023:7576"
},
{
"source": "secalert@redhat.com",
"tags": [
"Third Party Advisory"
],
"url": "https://access.redhat.com/errata/RHSA-2023:7578"
},
{
"source": "secalert@redhat.com",
"tags": [
"Third Party Advisory"
],
"url": "https://access.redhat.com/security/cve/CVE-2023-46847"
},
{
"source": "secalert@redhat.com",
"tags": [
"Issue Tracking",
"Third Party Advisory"
],
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2245916"
},
{
"source": "secalert@redhat.com",
"tags": [
"Vendor Advisory"
],
"url": "https://github.com/squid-cache/squid/security/advisories/GHSA-phqj-m8gv-cq4g"
},
{
"source": "secalert@redhat.com",
"tags": [
"Mailing List",
"Third Party Advisory"
],
"url": "https://lists.debian.org/debian-lts-announce/2024/01/msg00003.html"
},
{
"source": "secalert@redhat.com",
"tags": [
"Third Party Advisory"
],
"url": "https://security.netapp.com/advisory/ntap-20231130-0002/"
}
],
"sourceIdentifier": "secalert@redhat.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-120"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-120"
}
],
"source": "secalert@redhat.com",
"type": "Secondary"
}
]
}
}
}
}
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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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