CVE-2021-22312 (GCVE-0-2021-22312)
Vulnerability from cvelistv5 – Published: 2021-04-08 18:18 – Updated: 2024-08-03 18:37
VLAI
EPSS
VEX
Summary
There is a memory leak vulnerability in some Huawei products. An authenticated remote attacker may exploit this vulnerability by sending specific message to the affected product. Due to not release the allocated memory properly, successful exploit may cause some service abnormal. Affected product include some versions of IPS Module, NGFW Module, Secospace USG6300, Secospace USG6500, Secospace USG6600 and USG9500.
Severity
6.5 (Medium)
CWE
- Memory Leak
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://www.huawei.com/en/psirt/security-advisori… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | IPS Module;NGFW Module;Secospace USG6300;Secospace USG6500;Secospace USG6600;USG9500 |
Affected:
V500R005C00SPC100,V500R005C00SPC200
Affected: V500R001C30SPC200,V500R001C30SPC600,V500R001C60SPC500,V500R005C00SPC100,V500R005C00SPC200 |
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"references": [
{
"source": "psirt@huawei.com",
"tags": [
"Vendor Advisory"
],
"url": "https://www.huawei.com/en/psirt/security-advisories/huawei-sa-20210210-01-memoryleak-en"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "https://www.huawei.com/en/psirt/security-advisories/huawei-sa-20210210-01-memoryleak-en"
}
],
"sourceIdentifier": "psirt@huawei.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-401"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T14:51:54+00:00",
"cve": "CVE-2021-22312",
"id": "CVE-2021-22312",
"initial_release_date": "2021-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "CVE-2021-22312",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2021/cve-2021-22312.json",
"version": "3"
}
}
}
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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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