GSD-2020-1634
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
On High-End SRX Series devices, in specific configurations and when specific networking events or operator actions occur, an SPC receiving genuine multicast traffic may core. Subsequently, all FPCs in a chassis may reset causing a Denial of Service. This issue affects both IPv4 and IPv6. This issue affects: Juniper Networks Junos OS 12.3X48 version 12.3X48-D80 and later versions prior to 12.3X48-D95 on High-End SRX Series. This issue does not affect Branch SRX Series devices.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-1634",
"description": "On High-End SRX Series devices, in specific configurations and when specific networking events or operator actions occur, an SPC receiving genuine multicast traffic may core. Subsequently, all FPCs in a chassis may reset causing a Denial of Service. This issue affects both IPv4 and IPv6. This issue affects: Juniper Networks Junos OS 12.3X48 version 12.3X48-D80 and later versions prior to 12.3X48-D95 on High-End SRX Series. This issue does not affect Branch SRX Series devices.",
"id": "GSD-2020-1634"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "On High-End SRX Series devices, in specific configurations and when specific networking events or operator actions occur, an SPC receiving genuine multicast traffic may core. Subsequently, all FPCs in a chassis may reset causing a Denial of Service. This issue affects both IPv4 and IPv6. This issue affects: Juniper Networks Junos OS 12.3X48 version 12.3X48-D80 and later versions prior to 12.3X48-D95 on High-End SRX Series. This issue does not affect Branch SRX Series devices.",
"id": "GSD-2020-1634",
"modified": "2023-12-13T01:21:58.598509Z",
"schema_version": "1.4.0"
}
},
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"TITLE": "Junos OS: High-End SRX Series: Multicast traffic might cause all FPCs to reset."
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}
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"confidentialityImpact": "NONE",
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"lastModifiedDate": "2021-11-22T16:16Z",
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}
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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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