FKIE_CVE-2015-8676
Vulnerability from fkie_nvd - Published: 2016-04-14 15:59 - Updated: 2026-06-17 00:35
Severity
Summary
Memory leak in Huawei S5300EI, S5300SI, S5310HI, S6300EI/ S2350EI, and S5300LI Campus series switches with software V200R001C00 before V200R001SPH018, V200R002C00 before V200R003SPH011, and V200R003C00 before V200R003SPH011; S9300, S7700, and S9700 Campus series switches with software V200R001C00 before V200R001SPH023, V200R002C00 before V200R003SPH011, and V200R003C00 before V200R003SPH011; and S2300 and S3300 Campus series switches with software V100R006C05 before V100R006SPH022 allows remote attackers to cause a denial of service (memory consumption and reboot) via a large number of ICMPv6 packets.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| huawei | s2350ei_firmware | * | |
| huawei | s2350ei_firmware | * | |
| huawei | s2350ei | - | |
| huawei | s5300ei_firmware | * | |
| huawei | s5300ei_firmware | * | |
| huawei | s5300ei | - | |
| huawei | s5300si_firmware | * | |
| huawei | s5300si_firmware | * | |
| huawei | s5300si | - | |
| huawei | s5310hi_firmware | * | |
| huawei | s5310hi_firmware | * | |
| huawei | s5310hi | - | |
| huawei | s6300ei_firmware | * | |
| huawei | s6300ei_firmware | * | |
| huawei | s6300ei | - | |
| huawei | s5300li_firmware | * | |
| huawei | s5300li_firmware | * | |
| huawei | s5300li | - | |
| huawei | s9300_firmware | * | |
| huawei | s9300_firmware | * | |
| huawei | s9300_firmware | v200r003c00 | |
| huawei | s9300 | - | |
| huawei | s7700_firmware | * | |
| huawei | s7700_firmware | * | |
| huawei | s7700_firmware | v200r003c00 | |
| huawei | s7700 | - | |
| huawei | s9700_firmware | * | |
| huawei | s9700_firmware | * | |
| huawei | s9700_firmware | v200r003c00 | |
| huawei | s9700 | - | |
| huawei | s2300_firmware | * | |
| huawei | s2300 | - | |
| huawei | s3300_firmware | * | |
| huawei | s3300 | - |
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"id": "CVE-2015-8676",
"lastModified": "2026-06-17T00:35:01.407",
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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
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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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