GSD-2020-15669
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
When aborting an operation, such as a fetch, an abort signal may be deleted while alerting the objects to be notified. This results in a use-after-free and we presume that with enough effort it could have been exploited to run arbitrary code. This vulnerability affects Firefox ESR < 68.12 and Thunderbird < 68.12.
Aliases
Aliases
{
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"alias": "CVE-2020-15669",
"description": "When aborting an operation, such as a fetch, an abort signal may be deleted while alerting the objects to be notified. This results in a use-after-free and we presume that with enough effort it could have been exploited to run arbitrary code. This vulnerability affects Firefox ESR \u003c 68.12 and Thunderbird \u003c 68.12.",
"id": "GSD-2020-15669",
"references": [
"https://www.suse.com/security/cve/CVE-2020-15669.html",
"https://www.debian.org/security/2020/dsa-4754",
"https://www.debian.org/security/2020/dsa-4749",
"https://access.redhat.com/errata/RHSA-2020:3643",
"https://access.redhat.com/errata/RHSA-2020:3634",
"https://access.redhat.com/errata/RHSA-2020:3633",
"https://access.redhat.com/errata/RHSA-2020:3632",
"https://access.redhat.com/errata/RHSA-2020:3631",
"https://access.redhat.com/errata/RHSA-2020:3559",
"https://access.redhat.com/errata/RHSA-2020:3558",
"https://access.redhat.com/errata/RHSA-2020:3557",
"https://access.redhat.com/errata/RHSA-2020:3556",
"https://access.redhat.com/errata/RHSA-2020:3555",
"https://advisories.mageia.org/CVE-2020-15669.html",
"https://linux.oracle.com/cve/CVE-2020-15669.html"
]
},
"gsd": {
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"remediation": "unknown",
"reportConfidence": "confirmed",
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"id": "GSD-2020-15669",
"modified": "2023-12-13T01:21:43.350070Z",
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"impact": {
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"authentication": "NONE",
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"baseScore": 6.8,
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"vectorString": "AV:N/AC:M/Au:N/C:P/I:P/A:P",
"version": "2.0"
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"severity": "MEDIUM",
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"confidentialityImpact": "HIGH",
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"privilegesRequired": "NONE",
"scope": "UNCHANGED",
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"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
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"lastModifiedDate": "2020-10-02T19:02Z",
"publishedDate": "2020-10-01T19:15Z"
}
}
}
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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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