GHSA-2R7V-V86R-776V
Vulnerability from github – Published: 2022-05-17 04:53 – Updated: 2022-05-17 04:53
VLAI
Details
The ioapic_read_indirect function in virt/kvm/ioapic.c in the Linux kernel through 3.8.4 does not properly handle a certain combination of invalid IOAPIC_REG_SELECT and IOAPIC_REG_WINDOW operations, which allows guest OS users to obtain sensitive information from host OS memory or cause a denial of service (host OS OOPS) via a crafted application.
{
"affected": [],
"aliases": [
"CVE-2013-1798"
],
"database_specific": {
"cwe_ids": [
"CWE-20"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2013-03-22T11:59:00Z",
"severity": "MODERATE"
},
"details": "The ioapic_read_indirect function in virt/kvm/ioapic.c in the Linux kernel through 3.8.4 does not properly handle a certain combination of invalid IOAPIC_REG_SELECT and IOAPIC_REG_WINDOW operations, which allows guest OS users to obtain sensitive information from host OS memory or cause a denial of service (host OS OOPS) via a crafted application.",
"id": "GHSA-2r7v-v86r-776v",
"modified": "2022-05-17T04:53:44Z",
"published": "2022-05-17T04:53:44Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2013-1798"
},
{
"type": "WEB",
"url": "https://github.com/torvalds/linux/commit/a2c118bfab8bc6b8bb213abfc35201e441693d55"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=917017"
},
{
"type": "WEB",
"url": "http://git.kernel.org/?p=linux/kernel/git/torvalds/linux-2.6.git%3Ba=commit%3Bh=a2c118bfab8bc6b8bb213abfc35201e441693d55"
},
{
"type": "WEB",
"url": "http://git.kernel.org/?p=linux/kernel/git/torvalds/linux-2.6.git;a=commit;h=a2c118bfab8bc6b8bb213abfc35201e441693d55"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2013-05/msg00018.html"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2013-06/msg00005.html"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2013-07/msg00018.html"
},
{
"type": "WEB",
"url": "http://packetstormsecurity.com/files/157233/Kernel-Live-Patch-Security-Notice-LSN-0065-1.html"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2013-0727.html"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2013-0744.html"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2013-0746.html"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2013-0928.html"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2013-1026.html"
},
{
"type": "WEB",
"url": "http://www.mandriva.com/security/advisories?name=MDVSA-2013:176"
},
{
"type": "WEB",
"url": "http://www.openwall.com/lists/oss-security/2013/03/20/9"
},
{
"type": "WEB",
"url": "http://www.ubuntu.com/usn/USN-1809-1"
},
{
"type": "WEB",
"url": "http://www.ubuntu.com/usn/USN-1812-1"
},
{
"type": "WEB",
"url": "http://www.ubuntu.com/usn/USN-1813-1"
}
],
"schema_version": "1.4.0",
"severity": []
}
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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
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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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