GSD-2020-1650
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
On Juniper Networks Junos MX Series with service card configured, receipt of a stream of specific packets may crash the MS-PIC component on MS-MIC or MS-MPC. By continuously sending these specific packets, an attacker can repeatedly bring down MS-PIC on MS-MIC/MS-MPC causing a prolonged Denial of Service. This issue affects MX Series devices using MS-PIC, MS-MIC or MS-MPC service cards with any service configured. This issue affects Juniper Networks Junos OS on MX Series: 17.2R2-S7; 17.3R3-S4, 17.3R3-S5; 17.4R2-S4 and the subsequent SRs (17.4R2-S5, 17.4R2-S6, etc.); 17.4R3; 18.1R3-S3, 18.1R3-S4, 18.1R3-S5, 18.1R3-S6, 18.1R3-S7, 18.1R3-S8; 18.2R3, 18.2R3-S1, 18.2R3-S2; 18.3R2 and the SRs based on 18.3R2; 18.4R2 and the SRs based on 18.4R2; 19.1R1 and the SRs based on 19.1R1; 19.2R1 and the SRs based on 19.2R1; 19.3R1 and the SRs based on 19.3R1.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-1650",
"description": "On Juniper Networks Junos MX Series with service card configured, receipt of a stream of specific packets may crash the MS-PIC component on MS-MIC or MS-MPC. By continuously sending these specific packets, an attacker can repeatedly bring down MS-PIC on MS-MIC/MS-MPC causing a prolonged Denial of Service. This issue affects MX Series devices using MS-PIC, MS-MIC or MS-MPC service cards with any service configured. This issue affects Juniper Networks Junos OS on MX Series: 17.2R2-S7; 17.3R3-S4, 17.3R3-S5; 17.4R2-S4 and the subsequent SRs (17.4R2-S5, 17.4R2-S6, etc.); 17.4R3; 18.1R3-S3, 18.1R3-S4, 18.1R3-S5, 18.1R3-S6, 18.1R3-S7, 18.1R3-S8; 18.2R3, 18.2R3-S1, 18.2R3-S2; 18.3R2 and the SRs based on 18.3R2; 18.4R2 and the SRs based on 18.4R2; 19.1R1 and the SRs based on 19.1R1; 19.2R1 and the SRs based on 19.2R1; 19.3R1 and the SRs based on 19.3R1.",
"id": "GSD-2020-1650"
},
"gsd": {
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"id": "GSD-2020-1650",
"modified": "2023-12-13T01:21:58.171466Z",
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"DATE_PUBLIC": "2020-07-08T07:00:00.000Z",
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},
"lastModifiedDate": "2021-07-21T11:39Z",
"publishedDate": "2020-07-17T19:15Z"
}
}
}
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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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