VAR-201704-0502
Vulnerability from variot - Updated: 2025-04-20 23:26Huawei S5300 with software V200R003C00, V200R007C00, V200R008C00, V200R009C00; S5700 with software V200R001C00, V200R002C00, V200R003C00, V200R005C00, V200R005C03, V200R007C00, V200R008C00, V200R009C00; S6300 with software V200R003C00, V200R005C00, V200R008C00, V200R009C00; S6700 with software V200R001C00, V200R001C01, V200R002C00, V200R003C00, V200R005C00, V200R008C00, V200R009C00; S7700 with software V200R007C00, V200R008C00, V200R009C00; S9300 with software V200R007C00, V200R008C00, V200R009C00; S9700 with software V200R007C00, V200R008C00, V200R009C00; and S12700 with software V200R007C00, V200R007C01, V200R008C00, V200R009C00 allow the attacker to cause a denial of service condition by sending malformed MPLS packets. Multiple Huawei Product has a service disruption (DoS) There are vulnerabilities that are put into a state.An attacker could create a malformed MPLS Service interruption due to packet transmission (DoS) There is a possibility of being put into a state. Huawei S9700 is the S series switch of China Huawei. A denial of service vulnerability exists in several Huawei products due to the failure of the program to properly verify MultiprotocolLabelSwitching (MPLS) messages. Multiple Huawei products are prone to a remote denial-of-service vulnerability. Attackers can exploit this issue to crash the affected device, denying service to legitimate users. The following products and versions are affected: Huawei S9700 V200R009C00 , V200R008C00 , V200R007C00 ; Huawei S9300 V200R009C00 , V200R008C00 , V200R007C00 ; Huawei S7700 V200R009C00 , V200R008C00 , V200R007C00 ; Huawei S6700 V200R009C00 , V200R008C00 , V200R005C00 , V200R003C00 , V200R002C00 , V200R001C01 , V200R001C00 ; Huawei S6300 V200R009C00 , V200R008C00 , V200R005C00 , V200R003C00 ; Huawei S5700 V200R009C00 , V200R008C00 , V200R007C00 , V200R005C00 , V200R003C00 , V200R002C00 , V200R001C00 ; Huawei S5300 V200R009C00 , V200R008C00 , V200R007C00 , V200R003C00 ; Huawei S12700 V200R009C00 , V200R008C00 , V200R007C01 , V200R007C00
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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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