GSD-2017-7761
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
The Mozilla Maintenance Service "helper.exe" application creates a temporary directory writable by non-privileged users. When this is combined with creation of a junction (a form of symbolic link), protected files in the target directory of the junction can be deleted by the Mozilla Maintenance Service, which has privileged access. Note: This attack requires local system access and only affects Windows. Other operating systems are not affected. This vulnerability affects Firefox ESR < 52.2 and Firefox < 54.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2017-7761",
"description": "The Mozilla Maintenance Service \"helper.exe\" application creates a temporary directory writable by non-privileged users. When this is combined with creation of a junction (a form of symbolic link), protected files in the target directory of the junction can be deleted by the Mozilla Maintenance Service, which has privileged access. Note: This attack requires local system access and only affects Windows. Other operating systems are not affected. This vulnerability affects Firefox ESR \u003c 52.2 and Firefox \u003c 54.",
"id": "GSD-2017-7761",
"references": [
"https://www.suse.com/security/cve/CVE-2017-7761.html",
"https://advisories.mageia.org/CVE-2017-7761.html"
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"id": "GSD-2017-7761",
"modified": "2023-12-13T01:21:06.391955Z",
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"impact": {
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"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 3.6,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:L/AC:L/Au:N/C:N/I:P/A:P",
"version": "2.0"
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"impactScore": 4.9,
"obtainAllPrivilege": false,
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"severity": "LOW",
"userInteractionRequired": false
},
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"baseScore": 5.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:N",
"version": "3.0"
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"lastModifiedDate": "2019-10-03T00:03Z",
"publishedDate": "2018-06-11T21:29Z"
}
}
}
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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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