GSD-2016-1267
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
Race condition in the RPC functionality in Juniper Junos OS before 12.1X44-D55, 12.1X46 before 12.1X46-D40, 12.1X47 before 12.1X47-D25, 12.3 before 12.3R11, 12.3X48 before 12.3X48-D20, 13.2 before 13.2R8, 13.2X51 before 13.2X51-D39, 13.3 before 13.3R7, 14.1 before 14.1R6, 14.1X53 before 14.1X53-D30, 14.2 before 14.2R3-S4, 15.1 before 15.1F2, or 15.1R2, 15.1X49 before 15.1X49-D20, and 16.1 before 16.1R1 allows local users to read, delete, or modify arbitrary files via unspecified vectors.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2016-1267",
"description": "Race condition in the RPC functionality in Juniper Junos OS before 12.1X44-D55, 12.1X46 before 12.1X46-D40, 12.1X47 before 12.1X47-D25, 12.3 before 12.3R11, 12.3X48 before 12.3X48-D20, 13.2 before 13.2R8, 13.2X51 before 13.2X51-D39, 13.3 before 13.3R7, 14.1 before 14.1R6, 14.1X53 before 14.1X53-D30, 14.2 before 14.2R3-S4, 15.1 before 15.1F2, or 15.1R2, 15.1X49 before 15.1X49-D20, and 16.1 before 16.1R1 allows local users to read, delete, or modify arbitrary files via unspecified vectors.",
"id": "GSD-2016-1267"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
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],
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"id": "GSD-2016-1267",
"modified": "2023-12-13T01:21:24.343325Z",
"schema_version": "1.4.0"
}
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"value": "Race condition in the RPC functionality in Juniper Junos OS before 12.1X44-D55, 12.1X46 before 12.1X46-D40, 12.1X47 before 12.1X47-D25, 12.3 before 12.3R11, 12.3X48 before 12.3X48-D20, 13.2 before 13.2R8, 13.2X51 before 13.2X51-D39, 13.3 before 13.3R7, 14.1 before 14.1R6, 14.1X53 before 14.1X53-D30, 14.2 before 14.2R3-S4, 15.1 before 15.1F2, or 15.1R2, 15.1X49 before 15.1X49-D20, and 16.1 before 16.1R1 allows local users to read, delete, or modify arbitrary files via unspecified vectors."
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"scope": "UNCHANGED",
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"vectorString": "CVSS:3.0/AV:L/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H",
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},
"lastModifiedDate": "2016-12-03T03:20Z",
"publishedDate": "2016-04-15T14:59Z"
}
}
}
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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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