GSD-2020-1651
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
On Juniper Networks MX series, receipt of a stream of specific Layer 2 frames may cause a memory leak resulting in the packet forwarding engine (PFE) on the line card to crash and restart, causing traffic interruption. By continuously sending this stream of specific layer 2 frame, an attacker connected to the same broadcast domain can repeatedly crash the PFE, causing a prolonged Denial of Service (DoS). This issue affects Juniper Networks Junos OS on MX Series: 17.2 versions prior to 17.2R3-S4; 17.2X75 versions prior to 17.2X75-D105.19; 17.3 versions prior to 17.3R3-S7; 17.4 versions prior to 17.4R1-S3, 17.4R2; 18.1 versions prior to 18.1R2. This issue does not affect Juniper Networks Junos OS releases prior to 17.2R1.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-1651",
"description": "On Juniper Networks MX series, receipt of a stream of specific Layer 2 frames may cause a memory leak resulting in the packet forwarding engine (PFE) on the line card to crash and restart, causing traffic interruption. By continuously sending this stream of specific layer 2 frame, an attacker connected to the same broadcast domain can repeatedly crash the PFE, causing a prolonged Denial of Service (DoS). This issue affects Juniper Networks Junos OS on MX Series: 17.2 versions prior to 17.2R3-S4; 17.2X75 versions prior to 17.2X75-D105.19; 17.3 versions prior to 17.3R3-S7; 17.4 versions prior to 17.4R1-S3, 17.4R2; 18.1 versions prior to 18.1R2. This issue does not affect Juniper Networks Junos OS releases prior to 17.2R1.",
"id": "GSD-2020-1651"
},
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"remediation": "unknown",
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"type": "vulnerability"
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"modified": "2023-12-13T01:21:58.773378Z",
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"TITLE": "Junos OS: MX Series: PFE on the line card may crash due to memory leak."
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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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